diff --git a/build.gradle b/build.gradle index de33f53a7..5ab63a88a 100644 --- a/build.gradle +++ b/build.gradle @@ -283,9 +283,9 @@ tasks.register('deploy') { dependsOn 'jar' dependsOn 'releaseJDocs' - dependsOn 'publishToSonatype' } // 3: +// execute dependsOn 'publishToSonatype' then // go to https://oss.sonatype.org/#stagingRepositories // close and release the repository diff --git a/docs/coverage/test/html/index.html b/docs/coverage/test/html/index.html index 9040a6907..8f713a3f7 100644 --- a/docs/coverage/test/html/index.html +++ b/docs/coverage/test/html/index.html @@ -1 +1 @@ -neureka

neureka

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total13,739 of 91,71385%1,795 of 7,59376%2,2288,3511,94416,2157524,54041537
neureka.backend.main.implementations.broadcast1,2752,27164%6014771%2063841956701642782768
neureka1,1478,08587%16059678%2039391571,54251560125
neureka.devices.file8132,33574%6512766%8221614459026120016
neureka.devices.opencl7603,38981%7928478%9136210469627178015
neureka.backend.main.operations.other7183,22081%9422070%9629010978624133112
neureka.common.utility6733,54184%12624666%1383815765441195010
neureka.autograd6462,24977%9624671%983149352723142012
neureka.devices.host.machine59782558%831918%618094182152804
neureka.backend.main.implementations.elementwise5443,22885%6427481%1023736671851204238
neureka.backend.main.operations.operator5263,36786%6325780%73261596722010107
neureka.devices.host4492,32283%6234284%562554745135303
neureka.backend.api.template.algorithms4462,04482%6520876%592266043928807
neureka.backend.api4231,92081%6213668%602454937614146020
neureka.backend.main.algorithms3181,98986%6516071%692204942213107012
neureka.dtype.custom2931,34782%238378%622407934848187011
neureka.backend.main.implementations.scalar27114134%16833%1623569351103
neureka.framing2641,24482%4910568%371274725635002
neureka.backend.main.operations.linear.internal.blas2535,48295%828097%15240231,19379607
neureka.backend.main.implementations.fun2481,36184%111557%453034029038290570
neureka.backend.api.template.operations23455470%364857%40911915674903
neureka.math23189579%1979%50159502334514705
neureka.devices.opencl.utility2121,59588%303251%32722928644004
neureka.backend.main.implementations.convolution2081,79689%4018682%361473639023407
neureka.math.parsing1551,84792%6326380%591871435902402
neureka.backend.main.operations.linear13754679%253759%24521613452105
neureka.backend.main.operations.other.internal1251,47492%1714589%161291327704503
neureka.backend.main.implementations.fun.api11714455%1016%16241142111803
neureka.view1072,16295%3824486%40223737628204
neureka.ndim.config1051,44093%4920380%501771125125105
neureka.backend.main.operations.indexer9648283%123675%10361110601202
neureka.optimization.implementations9563987%50%1743191151642010
neureka.backend.main.operations9323771%141450%1228186031402
neureka.math.implementations8890091%1512989%231301517495804
neureka.backend.main.operations.linear.internal.opencl8683590%163568%1747712222104
neureka.fluent.slicing7773090%115583%1353714222003
neureka.devices1,26994%189684%221389222481017
neureka.ndim26279%101458%114185732905
neureka.math.args24177%101661%1551754638215
neureka.backend.ocl1,06394%81869%71081018409502
neureka.ndim.config.types.permuted73392%121453%1581812686804
neureka.common.composition50489%195775%22691110153117
neureka.ndim.config.types.sliced1,08395%122870%12104415648405
neureka.fluent.building75394%147283%1266713202301
neureka.devices.host.concurrent21882%990%1027105292213
neureka.backend.main.implementations.matmul42090%73382%73057301002
neureka.backend.main.implementations.linear37689%101661%102386701002
neureka.ndim.iterator.types.sliced36889%81666%94787953504
neureka.dtype28787%84083%93865711402
neureka.backend.api.template.implementations45%50%251100201
neureka.ndim.iterator.types.virtual28%n/a71171171101
neureka.optimization65%n/a51031151013
neureka.ndim.config.types.views17689%1191%42343931701
neureka.ndim.iterator.types.permuted25693%2083%42845521602
neureka.fluent.building.states67%n/a14381402
neureka.backend.main.memory31396%3685%64125602002
neureka.backend.main.implementations33096%81463%72636401504
neureka.backend.api.ini83%n/a182101803
neureka.backend.cpu74798%83%282111817901
neureka.backend.main.operations.functions37398%1285%235091028024
neureka.ndim.config.types.views.virtual93%n/a21721721701
neureka.ndim.config.types.simple800100%22100%093012808205
neureka.ndim.iterator.types.simple319100%24100%03606402403
neureka.ndim.iterator120100%20100%0150170502
neureka.framing.fluent95100%n/a01402501402
neureka.ndim.config.types100%n/a03030303
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka

neureka

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total19,972 of 92,38878%2,112 of 7,63372%2,7428,4032,99516,3131,1244,57283539
neureka.devices.opencl3,52573217%23612935%2883645807021621791115
neureka.backend.main.implementations.broadcast1,7491,79750%7812962%2383842966701882784268
neureka1,2338,07786%18058476%2219511741,55957568126
neureka.devices.opencl.utility1,06875641%451727%4372138286124014
neureka.devices.file8092,34174%6412866%8121614459126120016
neureka.backend.main.implementations.elementwise7553,01779%6926979%115373112718642041138
neureka.backend.ocl73040035%260%10210866184899502
neureka.backend.main.operations.other7283,21081%9621869%9829011178624133112
neureka.common.utility6733,54384%12624666%1383815765541195010
neureka.devices.host.machine67175152%822019%6180102182162804
neureka.autograd6522,25677%9824471%1003149352723142012
neureka.backend.main.operations.operator5263,45486%6526980%75269596842010207
neureka.devices.host4732,29882%6533983%592555245145303
neureka.backend.api.template.algorithms4472,04382%6720675%612266043928807
neureka.backend.api4331,92981%6413467%642455137716146020
neureka.backend.main.algorithms3691,93783%6615970%702196142214106012
neureka.backend.main.implementations.fun3401,26978%111557%913038629084290570
neureka.backend.main.implementations3284%220%23266264121534
neureka.devices3041,15479%537157%4814655251984017
neureka.dtype.custom2931,34782%238378%622407934848187011
neureka.backend.main.implementations.convolution2891,71585%4218481%391475139053417
neureka.backend.main.implementations.scalar27713532%16833%1723599361113
neureka.framing2661,24282%5010467%381274725635002
neureka.backend.main.operations.linear.internal.blas2535,48295%828097%15240231,19379607
neureka.backend.api.template.operations24154769%364857%42912115694903
neureka.math23189579%1979%50159502334514705
neureka.backend.main.operations.linear20356573%293957%27572414752305
neureka.math.parsing1551,84792%6326380%591871435902402
neureka.backend.main.operations.linear.internal.opencl15477683%213058%24471012252104
neureka.ndim.config1431,48991%4920380%6619527269186906
neureka.backend.main.implementations.matmul13533070%112972%1230237331012
neureka.backend.main.operations.other.internal1251,48392%1714589%161291327704503
neureka.backend.main.implementations.fun.api10914456%1016%16241142111803
neureka.view1072,16295%3824486%40223737628204
neureka.math.implementations9789190%2212284%291301517495804
neureka.backend.main.operations.indexer9648283%123675%10361110601202
neureka.optimization.implementations9563987%50%1743191151642010
neureka.backend.main.operations9323771%141450%1228186031402
neureka.fluent.slicing73090%115583%1353714222003
neureka.ndim26279%101458%114185732905
neureka.math.args24177%101661%1551754638215
neureka.ndim.config.types.permuted73392%121453%1581812686804
neureka.common.composition52189%195775%22691110153117
neureka.backend.main.implementations.linear36586%101661%112396711002
neureka.ndim.config.types.sliced1,08395%122870%12104415648405
neureka.fluent.building75394%147283%1266713202301
neureka.devices.host.concurrent24784%990%1027105292213
neureka.ndim.iterator.types.sliced36889%81666%94787953504
neureka.dtype28787%84083%93865711402
neureka.backend.api.template.implementations45%50%251100201
neureka.ndim.iterator.types.virtual28%n/a71171171101
neureka.optimization65%n/a51031151013
neureka.ndim.config.types.views17689%1191%42343931701
neureka.ndim.iterator.types.permuted25693%2083%42845521602
neureka.fluent.building.states67%n/a14381402
neureka.backend.main.memory31396%3685%64125602002
neureka.backend.api.ini83%n/a182101803
neureka.backend.cpu74798%83%282111817901
neureka.backend.main.operations.functions37398%1285%235091028024
neureka.ndim.config.types.views.virtual93%n/a21721721701
neureka.ndim.config.types.simple800100%22100%093012808205
neureka.ndim.iterator.types.simple319100%24100%03606402403
neureka.ndim.iterator129100%20100%0150170502
neureka.framing.fluent95100%n/a01402501402
neureka.ndim.config.types100%n/a03030303
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/jacoco-sessions.html b/docs/coverage/test/html/jacoco-sessions.html index 55de35950..df7071f28 100644 --- a/docs/coverage/test/html/jacoco-sessions.html +++ b/docs/coverage/test/html/jacoco-sessions.html @@ -1 +1 @@ -Sessions

Sessions

This coverage report is based on execution data from the following sessions:

SessionStart TimeDump Time
Aotearoa-f28aa40829 Jun 2023, 16:30:1329 Jun 2023, 16:31:38

Execution data for the following classes is considered in this report:

ClassId
ComplexNumber4a8adf930d04b26e
Example_Spec85d2fc8c0164109f
Kotlin_Compatibility_Unit_Testinga4f1a239df40c4e8
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.1aae93a7e79fde227
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.10acbc2b8265b1e3e6
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.11eda6c1f08b83b12a
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.20255e80ca8699b15
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.32f32ea99c6fcdb85
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.49d56ca690f6da838
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.5876cfc0710a14c80
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.621a688451dfefe26
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.7a4df09935754207f
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.8dda895ef437a1c8a
Kotlin_Compatibility_Unit_Testing.convenience_methods_in_function_API_are_consistent.9d5d8afbc6fe8f37c
Kotlin_Compatibility_Unit_Testing.operator_overloading_works_for_scalars_in_kotlin.19eb98ada798ab6e3
Kotlin_Compatibility_Unit_Testing.operator_overloading_works_for_scalars_in_kotlin.2a9b0e4010d27e195
Kotlin_Compatibility_Unit_Testing.operator_overloading_works_for_scalars_in_kotlin.3e82a45a51744e134
Kotlin_Compatibility_Unit_Testing.operator_overloading_works_for_scalars_in_kotlin.4f0686ade65f731a0
Kotlin_Compatibility_Unit_Testing.optimization_is_being_called.187d82ce1eb12ef07
Kotlin_Compatibility_Unit_Testing.optimization_is_being_called.2f34d80a0191955c1
Kotlin_Compatibility_Unit_Testing.optimization_is_being_called.32d4b8b8f108a8a81
Kotlin_Compatibility_Unit_Testing.optimization_is_being_called.4f7aa9067099939ac
Kotlin_Compatibility_Unit_Testing.optimization_is_being_called.57ca734a6d719515e
Kotlin_Compatibility_Unit_Testing.optimization_is_being_called.6.199623e82602e28ff
Kotlin_Compatibility_Unit_Testing.settings_API_for_formatting_tensors_allows_us_to_configure_the_indent.1e5fb3ef565a7ceb4
Kotlin_Compatibility_Unit_Testing.settings_API_for_formatting_tensors_allows_us_to_configure_the_indent.t.15551e3ea4b2737eb
Kotlin_Compatibility_Unit_Testing.settings_API_for_formatting_tensors_is_convenient_in_kotlin.130822e153b0551b6
Kotlin_Compatibility_Unit_Testing.settings_API_for_formatting_tensors_is_convenient_in_kotlin.2e245a0685e73cc57
Kotlin_Compatibility_Unit_Testing.settings_API_for_formatting_tensors_is_convenient_in_kotlin.t.1881031bf60867924
Kotlin_Compatibility_Unit_Testing.setupSpec.19d15a57a2049a964
Kotlin_Compatibility_Unit_Testing.tensor_operations_translate_to_custom_ComplexNumber_type_written_in_kotlin.a.1a60cac6f04aefc68
Kotlin_Compatibility_Unit_Testing.tensor_operations_translate_to_custom_ComplexNumber_type_written_in_kotlin.b.1c9216b6019ceb731
Kotlin_Compatibility_Unit_Testing.we_can_create_nd_arrays_with_a_fluent_builder_api.nda.171b66eb0001dab94
Kotlin_Compatibility_Unit_Testing.we_can_use_the_subscription_operator_to_slice_nd_arrays.1a0c5c29d336d358f
Kotlin_Compatibility_Unit_Testing.we_can_use_the_subscription_operator_to_slice_nd_arrays.2a0ce325ff70a54ba
Kotlin_Compatibility_Unit_Testing.we_can_use_the_subscription_operator_to_slice_nd_arrays.319702c07c8e316d1
Kotlin_Compatibility_Unit_Testing.we_can_use_the_subscription_operator_to_slice_nd_arrays.48c382ef69ff56a30
Kotlin_Compatibility_Unit_Testing.we_can_use_the_subscription_operator_to_slice_nd_arrays.n.1179f39e0413c05f8
Kotlin_Compatibility_Unit_Testing.we_can_use_the_subscription_operator_to_slice_tensors.135390ea47bfacd5a
Kotlin_Compatibility_Unit_Testing.we_can_use_the_subscription_operator_to_slice_tensors.2d24daedab499a2fb
Kotlin_Compatibility_Unit_Testing.we_can_use_the_subscription_operator_to_slice_tensors.3d2bace9d1733ba7d
Kotlin_Compatibility_Unit_Testing.we_can_use_the_subscription_operator_to_slice_tensors.46b755ff0cddefa5f
Kotlin_Compatibility_Unit_Testing.we_can_use_the_subscription_operator_to_slice_tensors.t.18476849037210f00
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sessions

Sessions

This coverage report is based on execution data from the following sessions:

SessionStart TimeDump Time
danpad-7975620aDec 16, 2024 2:36:05 PMDec 16, 2024 2:38:28 PM

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ADAction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 8369%2 of 875%3871624
partialDerivative()160%20%224411
of(Tensor, ADAction)60%n/a111111
findCaptured()35294%6100%0421001
of(ADAction)6100%n/a010101
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ADAction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 8369%2 of 875%3871624
partialDerivative()160%20%224411
of(Tensor, ADAction)60%n/a111111
findCaptured()35294%6100%0421001
of(ADAction)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/ADAction.java.html b/docs/coverage/test/html/neureka.autograd/ADAction.java.html index b04469979..50cd67bd2 100644 --- a/docs/coverage/test/html/neureka.autograd/ADAction.java.html +++ b/docs/coverage/test/html/neureka.autograd/ADAction.java.html @@ -70,4 +70,4 @@ return Optional.empty(); } } -
Created with JaCoCo 0.8.9.202303310957
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\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/ADTarget.html b/docs/coverage/test/html/neureka.autograd/ADTarget.html index 657fcd1ca..3c950fc6f 100644 --- a/docs/coverage/test/html/neureka.autograd/ADTarget.html +++ b/docs/coverage/test/html/neureka.autograd/ADTarget.html @@ -1 +1 @@ -ADTarget

ADTarget

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2185%0 of 0n/a141814
node()30%n/a111111
ADTarget(int, GraphNode, Tensor)12100%n/a010501
inputIndex()3100%n/a010101
error()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
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ADTarget

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2185%0 of 0n/a141814
node()30%n/a111111
ADTarget(int, GraphNode, Tensor)12100%n/a010501
inputIndex()3100%n/a010101
error()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/ADTarget.java.html b/docs/coverage/test/html/neureka.autograd/ADTarget.java.html index de09ee92b..99df71c4f 100644 --- a/docs/coverage/test/html/neureka.autograd/ADTarget.java.html +++ b/docs/coverage/test/html/neureka.autograd/ADTarget.java.html @@ -35,4 +35,4 @@ public Tensor<V> error() { return _error; } } -
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\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector$Value.html b/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector$Value.html index ffba2169a..47ef388c0 100644 --- a/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector$Value.html +++ b/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector$Value.html @@ -1 +1 @@ -BackPropTargetCollector.Value

BackPropTargetCollector.Value

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 22100%0 of 0n/a030703
BackPropTargetCollector.Value(int, ADAction)16100%n/a010501
index()3100%n/a010101
agents()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BackPropTargetCollector.Value

BackPropTargetCollector.Value

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 22100%0 of 0n/a030703
BackPropTargetCollector.Value(int, ADAction)16100%n/a010501
index()3100%n/a010101
agents()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector.html b/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector.html index 64d2f18e3..e863babf2 100644 --- a/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector.html +++ b/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector.html @@ -1 +1 @@ -BackPropTargetCollector

BackPropTargetCollector

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 75100%0 of 6100%0801105
put(int, GraphNode, ADAction)36100%4100%030501
getTargets()15100%2100%020501
lambda$getTargets$1(Map.Entry)15100%n/a010101
lambda$put$0(GraphNode, GraphNode)6100%n/a010101
BackPropTargetCollector()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BackPropTargetCollector

BackPropTargetCollector

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 75100%0 of 6100%0801105
put(int, GraphNode, ADAction)36100%4100%030501
getTargets()15100%2100%020501
lambda$getTargets$1(Map.Entry)15100%n/a010101
lambda$put$0(GraphNode, GraphNode)6100%n/a010101
BackPropTargetCollector()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector.java.html b/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector.java.html index 1bfcaaa51..4bfdc1c0a 100644 --- a/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector.java.html +++ b/docs/coverage/test/html/neureka.autograd/BackPropTargetCollector.java.html @@ -48,4 +48,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/BackPropTargets.html b/docs/coverage/test/html/neureka.autograd/BackPropTargets.html index 8100ff5d0..27610b3b1 100644 --- a/docs/coverage/test/html/neureka.autograd/BackPropTargets.html +++ b/docs/coverage/test/html/neureka.autograd/BackPropTargets.html @@ -1 +1 @@ -BackPropTargets

BackPropTargets

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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BackPropTargets(int, GraphNode, List)13100%n/a010501
node()3100%n/a010101
index()3100%n/a010101
actions()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BackPropTargets

BackPropTargets

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 22100%0 of 0n/a040804
BackPropTargets(int, GraphNode, List)13100%n/a010501
node()3100%n/a010101
index()3100%n/a010101
actions()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/BackPropTargets.java.html b/docs/coverage/test/html/neureka.autograd/BackPropTargets.java.html index 1c6687477..7bcca28ed 100644 --- a/docs/coverage/test/html/neureka.autograd/BackPropTargets.java.html +++ b/docs/coverage/test/html/neureka.autograd/BackPropTargets.java.html @@ -21,4 +21,4 @@ public List<ADAction> actions() { return _actions; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/DefaultADAction.html b/docs/coverage/test/html/neureka.autograd/DefaultADAction.html index 81866407f..45cc36156 100644 --- a/docs/coverage/test/html/neureka.autograd/DefaultADAction.html +++ b/docs/coverage/test/html/neureka.autograd/DefaultADAction.html @@ -1 +1 @@ -DefaultADAction

DefaultADAction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 6863%3 of 862%3831304
act(ADTarget)15834%1150%122401
toString()6650%1150%120201
partialDerivative()42083%1375%131601
DefaultADAction(ADAction, Tensor)9100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DefaultADAction

DefaultADAction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 6863%3 of 862%3831304
act(ADTarget)15834%1150%122401
toString()6650%1150%120201
partialDerivative()42083%1375%131601
DefaultADAction(ADAction, Tensor)9100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/DefaultADAction.java.html b/docs/coverage/test/html/neureka.autograd/DefaultADAction.java.html index 4f77103ad..ad6926918 100644 --- a/docs/coverage/test/html/neureka.autograd/DefaultADAction.java.html +++ b/docs/coverage/test/html/neureka.autograd/DefaultADAction.java.html @@ -72,4 +72,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/GraphNode$Print.html b/docs/coverage/test/html/neureka.autograd/GraphNode$Print.html index 4e2d9be6f..00aaee6a4 100644 --- a/docs/coverage/test/html/neureka.autograd/GraphNode$Print.html +++ b/docs/coverage/test/html/neureka.autograd/GraphNode$Print.html @@ -1 +1 @@ -GraphNode.Print

GraphNode.Print

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 21100%0 of 0n/a010101
static {...}21100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +GraphNode.Print

GraphNode.Print

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 34100%0 of 0n/a010101
static {...}34100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/GraphNode.html b/docs/coverage/test/html/neureka.autograd/GraphNode.html index 10fcd0055..1d21c4fb3 100644 --- a/docs/coverage/test/html/neureka.autograd/GraphNode.html +++ b/docs/coverage/test/html/neureka.autograd/GraphNode.html @@ -1 +1 @@ -GraphNode

GraphNode

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total472 of 2,08577%67 of 25673%64221673731392
_verifyErrorAccumulation(Set)134128%7112%45243001
_findRootPathsRecursively(List, List, List, GraphNode)720%140%88151511
lambda$_verifyErrorAccumulation$10(GraphNode)520%n/a117711
lambda$_verifyErrorAccumulation$12(List, int)230%n/a111111
lambda$_verifyErrorAccumulation$11(List, int)230%n/a111111
_toString(String, boolean, GraphNode.Print, int)2114387%31583%21011401
_registerADActions(Result, Function, ExecutionCall)1818591%63083%61913201
_backward(Tensor, Set, boolean)144977%21083%2711401
lambda$_verifyErrorAccumulation$8(Function)120%n/a115511
lambda$_migrateAndOrApplyError$2(Tensor, Consumer, Tensor)112367%2675%154901
_backwardJIT(Tensor, GraphNode)82071%4233%342801
lambda$has$20(GraphNode, BackPropTargets)80%20%221111
lambda$_verifyErrorAccumulation$5(GraphNode)80%20%221111
has(GraphNode)70%n/a111111
lambda$_verifyErrorAccumulation$6(Function)70%20%221111
GraphNode(Function, ExecutionCall, Supplier)513296%11995%11102701
_checkConstructorArgValidity(Function, ExecutionCall)54990%11392%1811201
_attachChild(GraphNode)51676%1150%121501
lambda$new$0()50%n/a111111
isGraphLeave()42485%2466%241401
toString(GraphNode.Print)42184%1375%141601
getPayloadDataType()40%n/a111111
lambda$_verifyErrorAccumulation$9(WeakReference)40%n/a111111
lambda$_verifyErrorAccumulation$7(Function)40%n/a111111
canBeDeleted()32990%2675%250701
lambda$_carryPendingBackPropToGradients$15(Set, Tensor)32488%2466%241601
_deleteDerivativesRecursively()31986%2675%250401
_simpleToString()4897%1375%130401
forEachDerivative(BiConsumer)1392%1150%120301
_forEachTargetActionPair(BiConsumer)1392%1150%120401
hasDerivatives()787%1150%120101
lambda$_numberOfDerivativeUsages$30(Tensor, Tensor)685%1150%120101
_checkInputValidity(Tensor[], Function)62100%8100%050901
backward(Tensor)62100%8100%0501301
_forEachBackRef(Tensor, BiConsumer)52100%6100%040601
lambda$_registerADActions$1(Tensor, Result, Function, ExecutionCall, int, BackPropTargetCollector, BackPropTargets, ADAction)48100%n/a0101001
_compactToString()47100%2100%020701
type()43100%2100%020801
_numberOfReverseModeADChildren()35100%4866%470801
lambda$_compactToString$38(NDPrintSettings)29100%n/a0101401
lambda$_informPartialDerivative$19(Tensor)24100%2100%020401
lambda$_numberOfDerivativeUsages$31(Tensor, GraphNode)20100%n/a010801
lambda$_backwardJIT$16(GraphNode, Tensor)18100%1375%130501
_fancyToString(int)16100%n/a010101
lambda$_numberOfExistingAncestors$27(GraphNode)15100%n/a010201
_carryPendingBackPropToGradients(Set)14100%n/a010401
forEachTarget(Consumer)14100%2100%020301
_numberOfDerivativeUsages(Tensor)12100%n/a010601
lambda$_parentsToString$37(GraphNode[])12100%n/a010401
_numberOfExistingAncestors()11100%n/a010601
isLeave()10100%1375%130101
_parentsToString()10100%n/a010301
lambda$_parentsToString$35(Tensor)10100%n/a010101
getParents()9100%2100%020101
lambda$_parentsToString$36(Optional)9100%n/a010301
getAndRemovePendingError()8100%n/a010301
lambda$_numberOfExistingAncestors$26(Tensor)8100%2100%020101
_migrateAndOrApplyError(Tensor, Consumer)7100%n/a010201
usesAD()7100%2100%020101
usesForwardAD()7100%2100%020101
usesReverseAD()7100%2100%020101
backwardJIT(Tensor)7100%n/a010301
isUsedAsDerivative()7100%2100%020101
lambda$_parentsToString$34(Tensor)7100%2100%020101
lambda$type$33(Tensor)7100%2100%020101
lambda$type$32(Tensor)7100%2100%020101
lambda$_forEachTargetActionPair$25(BiConsumer, BackPropTargets)7100%n/a010101
lambda$forEachDerivative$22(BiConsumer, BackPropTargets)7100%n/a010101
lambda$_numberOfDerivativeUsages$29(ADAction)6100%n/a010101
lambda$forEachDerivative$21(BiConsumer, BackPropTargets, ADAction)6100%n/a010101
lambda$_backward$13(Set, GraphNode, Tensor)6100%n/a010101
getPayload()5100%n/a010101
update(Component.OwnerChangeRequest)5100%n/a010201
_informPartialDerivative(ADAction)5100%n/a010301
lambda$_forEachTargetActionPair$24(BiConsumer, BackPropTargets, ADAction)5100%n/a010101
lambda$forEachTarget$23(Consumer, BackPropTargets)5100%n/a010101
lambda$_backwardJIT$17(GraphNode, GraphNode, Tensor)5100%n/a010101
getPayloadShape()4100%n/a010101
size()4100%n/a010101
getPendingError()4100%n/a010101
getFunction()4100%n/a010101
getPayloadReferenceVersion()4100%n/a010101
getChildren()4100%n/a010101
toString()4100%n/a010101
lambda$_compactToString$39(Tensor)4100%n/a010101
lambda$_numberOfDerivativeUsages$28(BackPropTargets)4100%n/a010101
lambda$_carryPendingBackPropToGradients$14(Set, GraphNode)4100%n/a010101
lambda$backward$4(GraphNode)4100%n/a010101
lambda$backward$3(Set, GraphNode)4100%n/a010101
getMode()3100%n/a010101
isReliesOnJustInTimeProp()3100%n/a010101
lambda$_informPartialDerivative$18(Tensor)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +GraphNode

GraphNode

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total478 of 2,08577%69 of 25673%66221673731392
_verifyErrorAccumulation(Set)134128%7112%45243001
_findRootPathsRecursively(List, List, List, GraphNode)720%140%88151511
lambda$_verifyErrorAccumulation$10(GraphNode)520%n/a117711
lambda$_verifyErrorAccumulation$12(List, int)230%n/a111111
lambda$_verifyErrorAccumulation$11(List, int)230%n/a111111
_toString(String, boolean, GraphNode.Print, int)2114387%31583%21011401
_registerADActions(Result, Function, ExecutionCall)1818591%63083%61913201
lambda$_migrateAndOrApplyError$2(Tensor, Consumer, Tensor)161852%3562%254901
_backward(Tensor, Set, boolean)144977%21083%2711401
lambda$_verifyErrorAccumulation$8(Function)120%n/a115511
_backwardJIT(Tensor, GraphNode)82071%4233%342801
lambda$has$20(GraphNode, BackPropTargets)80%20%221111
lambda$_verifyErrorAccumulation$5(GraphNode)80%20%221111
has(GraphNode)70%n/a111111
lambda$_verifyErrorAccumulation$6(Function)70%20%221111
GraphNode(Function, ExecutionCall, Supplier)513296%11995%11102701
_checkConstructorArgValidity(Function, ExecutionCall)54990%11392%1811201
_attachChild(GraphNode)51676%1150%121501
lambda$new$0()50%n/a111111
isGraphLeave()42485%2466%241401
toString(GraphNode.Print)42184%1375%141601
getPayloadDataType()40%n/a111111
lambda$_verifyErrorAccumulation$9(WeakReference)40%n/a111111
lambda$_verifyErrorAccumulation$7(Function)40%n/a111111
canBeDeleted()32990%2675%250701
lambda$_carryPendingBackPropToGradients$15(Set, Tensor)32488%2466%241601
_deleteDerivativesRecursively()31986%2675%250401
_simpleToString()4897%1375%130401
forEachDerivative(BiConsumer)1392%1150%120301
_forEachTargetActionPair(BiConsumer)1392%1150%120401
hasDerivatives()787%1150%120101
lambda$type$32(Tensor)685%1150%120101
lambda$_numberOfDerivativeUsages$30(Tensor, Tensor)685%1150%120101
_checkInputValidity(Tensor[], Function)62100%8100%050901
backward(Tensor)62100%8100%0501301
_forEachBackRef(Tensor, BiConsumer)52100%6100%040601
lambda$_registerADActions$1(Tensor, Result, Function, ExecutionCall, int, BackPropTargetCollector, BackPropTargets, ADAction)48100%n/a0101001
_compactToString()47100%2100%020701
type()43100%2100%020801
_numberOfReverseModeADChildren()35100%4866%470801
lambda$_compactToString$38(NDPrintSettings)29100%n/a0101401
lambda$_informPartialDerivative$19(Tensor)24100%2100%020401
lambda$_numberOfDerivativeUsages$31(Tensor, GraphNode)20100%n/a010801
lambda$_backwardJIT$16(GraphNode, Tensor)18100%1375%130501
_fancyToString(int)16100%n/a010101
lambda$_numberOfExistingAncestors$27(GraphNode)15100%n/a010201
_carryPendingBackPropToGradients(Set)14100%n/a010401
forEachTarget(Consumer)14100%2100%020301
_numberOfDerivativeUsages(Tensor)12100%n/a010601
lambda$_parentsToString$37(GraphNode[])12100%n/a010401
_numberOfExistingAncestors()11100%n/a010601
isLeave()10100%1375%130101
_parentsToString()10100%n/a010301
lambda$_parentsToString$35(Tensor)10100%n/a010101
getParents()9100%2100%020101
lambda$_parentsToString$36(Optional)9100%n/a010301
getAndRemovePendingError()8100%n/a010301
lambda$_numberOfExistingAncestors$26(Tensor)8100%2100%020101
_migrateAndOrApplyError(Tensor, Consumer)7100%n/a010201
usesAD()7100%2100%020101
usesForwardAD()7100%2100%020101
usesReverseAD()7100%2100%020101
backwardJIT(Tensor)7100%n/a010301
isUsedAsDerivative()7100%2100%020101
lambda$_parentsToString$34(Tensor)7100%2100%020101
lambda$type$33(Tensor)7100%2100%020101
lambda$_forEachTargetActionPair$25(BiConsumer, BackPropTargets)7100%n/a010101
lambda$forEachDerivative$22(BiConsumer, BackPropTargets)7100%n/a010101
lambda$_numberOfDerivativeUsages$29(ADAction)6100%n/a010101
lambda$forEachDerivative$21(BiConsumer, BackPropTargets, ADAction)6100%n/a010101
lambda$_backward$13(Set, GraphNode, Tensor)6100%n/a010101
getPayload()5100%n/a010101
update(Component.OwnerChangeRequest)5100%n/a010201
_informPartialDerivative(ADAction)5100%n/a010301
lambda$_forEachTargetActionPair$24(BiConsumer, BackPropTargets, ADAction)5100%n/a010101
lambda$forEachTarget$23(Consumer, BackPropTargets)5100%n/a010101
lambda$_backwardJIT$17(GraphNode, GraphNode, Tensor)5100%n/a010101
getPayloadShape()4100%n/a010101
size()4100%n/a010101
getPendingError()4100%n/a010101
getFunction()4100%n/a010101
getPayloadReferenceVersion()4100%n/a010101
getChildren()4100%n/a010101
toString()4100%n/a010101
lambda$_compactToString$39(Tensor)4100%n/a010101
lambda$_numberOfDerivativeUsages$28(BackPropTargets)4100%n/a010101
lambda$_carryPendingBackPropToGradients$14(Set, GraphNode)4100%n/a010101
lambda$backward$4(GraphNode)4100%n/a010101
lambda$backward$3(Set, GraphNode)4100%n/a010101
getMode()3100%n/a010101
isReliesOnJustInTimeProp()3100%n/a010101
lambda$_informPartialDerivative$18(Tensor)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/GraphNode.java.html b/docs/coverage/test/html/neureka.autograd/GraphNode.java.html index 3f9c5a1aa..9bdf35e28 100644 --- a/docs/coverage/test/html/neureka.autograd/GraphNode.java.html +++ b/docs/coverage/test/html/neureka.autograd/GraphNode.java.html @@ -288,7 +288,7 @@ this.getPayload().ifPresent( payload -> { // It was not garbage collected: try { - if ( payload.isOutsourced() ) payload.getDevice().store( e ); + if ( payload.isOutsourced() ) payload.getDevice().store( e ); } catch ( Exception exception ) { if ( payload.isUndefined() ) throw new IllegalStateException( @@ -911,7 +911,7 @@ if ( this.isLeave() ) type += "LEAVE"; else type += "BRANCH"; type += this.getPayload() - .filter( p -> !p.isDeleted() ) + .filter( p -> !p.isDeleted() ) .map( p -> p.rqsGradient() ? " RQS GRADIENT" : "" ) .orElse(" DELETED"); return type; @@ -1022,4 +1022,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/GraphNodeUtility.html b/docs/coverage/test/html/neureka.autograd/GraphNodeUtility.html index e63646b8c..286c7d722 100644 --- a/docs/coverage/test/html/neureka.autograd/GraphNodeUtility.html +++ b/docs/coverage/test/html/neureka.autograd/GraphNodeUtility.html @@ -1 +1 @@ -GraphNodeUtility

GraphNodeUtility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 92100%0 of 18100%01001601
modeOf(AutoDiffMode, ExecutionCall)92100%18100%01001601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +GraphNodeUtility

GraphNodeUtility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 92100%0 of 18100%01001601
modeOf(AutoDiffMode, ExecutionCall)92100%18100%01001601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/GraphNodeUtility.java.html b/docs/coverage/test/html/neureka.autograd/GraphNodeUtility.java.html index ca8803473..9bcf4da80 100644 --- a/docs/coverage/test/html/neureka.autograd/GraphNodeUtility.java.html +++ b/docs/coverage/test/html/neureka.autograd/GraphNodeUtility.java.html @@ -49,4 +49,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/JITProp.html b/docs/coverage/test/html/neureka.autograd/JITProp.html index 8b5536444..4d4d701db 100644 --- a/docs/coverage/test/html/neureka.autograd/JITProp.html +++ b/docs/coverage/test/html/neureka.autograd/JITProp.html @@ -1 +1 @@ -JITProp

JITProp

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total55 of 21173%20 of 3441%182693729
addPending(Set)220%40%336611
lambda$execute$0(GraphNode)101458%4233%341601
finishedCount()90%20%221111
execute()61571%2250%231601
toString()54389%2250%230301
pendingCount()3666%1150%120101
noteFinished(GraphNode)55100%4450%450901
JITProp(Set)13100%n/a010401
isDone()10100%1375%130101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +JITProp

JITProp

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total55 of 21173%20 of 3441%182693729
addPending(Set)220%40%336611
lambda$execute$0(GraphNode)101458%4233%341601
finishedCount()90%20%221111
execute()61571%2250%231601
toString()54389%2250%230301
pendingCount()3666%1150%120101
noteFinished(GraphNode)55100%4450%450901
JITProp(Set)13100%n/a010401
isDone()10100%1375%130101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/JITProp.java.html b/docs/coverage/test/html/neureka.autograd/JITProp.java.html index 3cbc52d7c..d7e1a25bf 100644 --- a/docs/coverage/test/html/neureka.autograd/JITProp.java.html +++ b/docs/coverage/test/html/neureka.autograd/JITProp.java.html @@ -96,4 +96,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/NodePayload.html b/docs/coverage/test/html/neureka.autograd/NodePayload.html index 54a3f582c..b84ea30c8 100644 --- a/docs/coverage/test/html/neureka.autograd/NodePayload.html +++ b/docs/coverage/test/html/neureka.autograd/NodePayload.html @@ -1 +1 @@ -NodePayload

NodePayload

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total11 of 7585%3 of 862%41011716
NodePayload(Tensor)44191%1375%1301201
payloadDataType()30%n/a111111
getPayloadShape()21184%1150%120101
getPayload()2880%1150%120101
payloadReferenceVersion()3100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NodePayload

NodePayload

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total11 of 7585%3 of 862%41011716
NodePayload(Tensor)44191%1375%1301201
payloadDataType()30%n/a111111
getPayloadShape()21184%1150%120101
getPayload()2880%1150%120101
payloadReferenceVersion()3100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/NodePayload.java.html b/docs/coverage/test/html/neureka.autograd/NodePayload.java.html index 2a71852c5..5a1688a9c 100644 --- a/docs/coverage/test/html/neureka.autograd/NodePayload.java.html +++ b/docs/coverage/test/html/neureka.autograd/NodePayload.java.html @@ -64,4 +64,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/PendingError.html b/docs/coverage/test/html/neureka.autograd/PendingError.html index 99c18ca5f..3673f8d35 100644 --- a/docs/coverage/test/html/neureka.autograd/PendingError.html +++ b/docs/coverage/test/html/neureka.autograd/PendingError.html @@ -1 +1 @@ -PendingError

PendingError

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total55 of 12054%1 of 475%51152049
toString()260%n/a111111
accumulate(Tensor)203060%1150%121601
getGeneration()30%n/a111111
getReceived()30%n/a111111
getExpectedToBeReceived()30%n/a111111
PendingError(Tensor, int, int)15100%n/a010601
isFullyAccumulated()9100%2100%020101
lambda$accumulate$0(Tensor)8100%n/a010201
getAccumulatedError()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +PendingError

PendingError

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total55 of 12054%1 of 475%51152049
toString()260%n/a111111
accumulate(Tensor)203060%1150%121601
getGeneration()30%n/a111111
getReceived()30%n/a111111
getExpectedToBeReceived()30%n/a111111
PendingError(Tensor, int, int)15100%n/a010601
isFullyAccumulated()9100%2100%020101
lambda$accumulate$0(Tensor)8100%n/a010201
getAccumulatedError()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.autograd/PendingError.java.html b/docs/coverage/test/html/neureka.autograd/PendingError.java.html index 22b8d8cdc..17d5593aa 100644 --- a/docs/coverage/test/html/neureka.autograd/PendingError.java.html +++ b/docs/coverage/test/html/neureka.autograd/PendingError.java.html @@ -65,4 +65,4 @@ public Tensor<V> getAccumulatedError() { return _accumulatedError; } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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Created with JaCoCo 0.8.11.202310140853
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neureka.autograd

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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JITProp.java5515673%201441%18269372901
PendingError.java556554%375%5115204901
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ADTarget.java1885%n/a14181401
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GraphNodeUtility.java92100%18100%0100160101
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.autograd

neureka.autograd

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total652 of 2,90877%98 of 34271%1003149352723142012
GraphNode.java4781,64177%6918773%6622267374139302
JITProp.java5515673%201441%18269372901
PendingError.java556554%375%5115204901
ADAction.java255869%675%387162401
DefaultADAction.java254363%3562%383130401
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ADTarget.java1885%n/a14181401
BackPropTargetCollector.java97100%6100%0110180802
GraphNodeUtility.java92100%18100%0100160101
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry$1$1.html b/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry$1$1.html index c67f63d3a..f9cfbdfa8 100644 --- a/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry$1$1.html +++ b/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry$1$1.html @@ -1 +1 @@ -BackendRegistry.1.new ReceiveForOperation() {...}

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{...}9100%n/a010101
Created with JaCoCo 0.8.9.202303310957
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BackendRegistry.1.new ReceiveForOperation() {...}

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set(Class, Function)14100%n/a010201
{...}9100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry$1.html b/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry$1.html index e49808762..0f70bbc5d 100644 --- a/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry$1.html +++ b/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry$1.html @@ -1 +1 @@ -BackendRegistry.new ReceiveForDevice() {...}

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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{...}9100%n/a010101
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BackendRegistry.new ReceiveForDevice() {...}

BackendRegistry.new ReceiveForDevice() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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set(Class, Class, Function)110%n/a112211
{...}9100%n/a010101
forOperation(Class)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry.html b/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry.html index 5c68f55fc..555c1435d 100644 --- a/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry.html +++ b/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry.html @@ -1 +1 @@ -BackendRegistry

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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BackendRegistry(ImplementationReceiver)6100%n/a010301
forDevice(Class)6100%n/a010101
of(ImplementationReceiver)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BackendRegistry

BackendRegistry

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 17100%0 of 0n/a030503
BackendRegistry(ImplementationReceiver)6100%n/a010301
forDevice(Class)6100%n/a010101
of(ImplementationReceiver)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry.java.html b/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry.java.html index 1d06f59b7..e0c909b8f 100644 --- a/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry.java.html +++ b/docs/coverage/test/html/neureka.backend.api.ini/BackendRegistry.java.html @@ -51,4 +51,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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neureka.backend.api.ini

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.api.ini

neureka.backend.api.ini

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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BackendRegistry.new ReceiveForDevice() {...}111557%n/a13241301
BackendRegistry.1.new ReceiveForOperation() {...}23100%n/a02030201
BackendRegistry17100%n/a03050301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.ini/index.source.html b/docs/coverage/test/html/neureka.backend.api.ini/index.source.html index 9985a4123..79bde3e94 100644 --- a/docs/coverage/test/html/neureka.backend.api.ini/index.source.html +++ b/docs/coverage/test/html/neureka.backend.api.ini/index.source.html @@ -1 +1 @@ -neureka.backend.api.ini

neureka.backend.api.ini

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total11 of 6683%0 of 0n/a182101803
BackendRegistry.java115583%n/a182101803
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.api.ini

neureka.backend.api.ini

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total11 of 6683%0 of 0n/a182101803
BackendRegistry.java115583%n/a182101803
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractAlgorithm.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractAlgorithm.html index 9e864d36d..01cdaad8f 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractAlgorithm.html @@ -1 +1 @@ -AbstractAlgorithm

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
AbstractAlgorithm(String)6100%n/a010101
getName()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractAlgorithm

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
AbstractAlgorithm(String)6100%n/a010101
getName()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractAlgorithm.java.html index 3a0c1c862..afcb5e44f 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractAlgorithm.java.html @@ -27,4 +27,4 @@ public String getName() { return _name; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractDeviceAlgorithm.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractDeviceAlgorithm.html index 7325dc9cc..572544713 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractDeviceAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractDeviceAlgorithm.html @@ -1 +1 @@ -AbstractDeviceAlgorithm

AbstractDeviceAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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lambda$_deepDerivative$6(ExecutionCall, Function[], FinalExecutor)5819677%111963%91654501
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lambda$executeOnCommonDevice$8(Device, Tensor)5100%n/a010101
static {...}4100%n/a010101
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lambda$_deepActivation$3(int)3100%n/a010101
lambda$executeOnCommonDevice$9(Tensor)100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractDeviceAlgorithm

AbstractDeviceAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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lambda$_deepDerivative$6(ExecutionCall, Function[], FinalExecutor)5819677%111963%91654501
_deepActivation(ExecutionCall, Function[], boolean, boolean, FinalExecutor)3513178%71568%61242701
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lambda$_deepActivation$4(String, boolean, Tensor[])11100%n/a010101
lambda$_deepActivation$2(int)11100%n/a010101
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lambda$_deepDerivative$7(ExecutionCall, Function[], FinalExecutor)9100%n/a010101
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lambda$executeOnCommonDevice$8(Device, Tensor)5100%n/a010101
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lambda$_deepDerivative$5(ExecutionCall)3100%n/a010101
lambda$_deepActivation$3(int)3100%n/a010101
lambda$executeOnCommonDevice$9(Tensor)100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractDeviceAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractDeviceAlgorithm.java.html index 2894a5be0..c4dd72782 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractDeviceAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractDeviceAlgorithm.java.html @@ -216,7 +216,7 @@ ExecutionCall<?> flattenedCall = _flatten( call.withArgs( Arg.VarIdx.of(j) ), nodes ); - if ( + if ( !isFlat && j < 0 && ( call.getOperation().isOperator() || @@ -412,7 +412,7 @@ private static void _deleteIfNotIn(Tensor<?>[] array, Tensor<?> tensor ) { if ( Neureka.get().settings().debug().isDeletingIntermediateTensors() ) { for ( int i = 1; i < array.length; i++ ) - if ( array[i] == tensor ) return; + if ( array[i] == tensor ) return; if ( !tensor.isDeleted() ) tensor.mut().delete(); } @@ -470,4 +470,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunAlgorithm.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunAlgorithm.html index 5e5780530..cace2574f 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunAlgorithm.html @@ -1 +1 @@ -AbstractFunAlgorithm

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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_checked(Object, Object, Class)36716%4233%344701
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_checkReadiness()21416%1150%122401
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lambda$execute$0(Function, ExecutionCall)6100%n/a010101
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractFunAlgorithm

AbstractFunAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total103 of 21151%10 of 1844%9211239012
_checked(Object, Object, Class)36716%4233%344701
execute(Function, ExecutionCall)301938%2250%234901
_checkReadiness()21416%1150%122401
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static {...}4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunAlgorithm.java.html index 917bd5faa..71bdee67e 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunAlgorithm.java.html @@ -165,4 +165,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunDeviceAlgorithm.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunDeviceAlgorithm.html index c0f74db2b..42bb3ccae 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunDeviceAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunDeviceAlgorithm.html @@ -1 +1 @@ -AbstractFunDeviceAlgorithm

AbstractFunDeviceAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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execute(Function, ExecutionCall)393043%3562%3551301
_checked(Object, Object, Class)162762%1583%142701
buildFunAlgorithm()39100%4866%470501
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_checkReadiness()25100%2100%020401
lambda$execute$3(Function, ExecutionCall)18100%2100%020301
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AbstractFunDeviceAlgorithm(String)7100%n/a010201
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lambda$prepare$0(Tensor, Tensor)7100%2100%020101
lambda$prepare$2(Tensor)6100%n/a010101
lambda$prepare$1(Tensor[], Tensor)6100%n/a010201
static {...}4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractFunDeviceAlgorithm

AbstractFunDeviceAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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execute(Function, ExecutionCall)393043%3562%3551301
_checked(Object, Object, Class)162762%1583%142701
buildFunAlgorithm()39100%4866%470501
prepare(ExecutionCall)26100%2100%020901
_checkReadiness()25100%2100%020401
lambda$execute$3(Function, ExecutionCall)18100%2100%020301
setIsSuitableFor(SuitabilityPredicate)11100%n/a010201
setSupplyADActionFor(ADActionSupplier)11100%n/a010201
setCallPreparation(ExecutionPreparation)11100%n/a010201
setAutogradModeFor(ADSupportPredicate)11100%n/a010201
setExecution(Execution)11100%n/a010201
AbstractFunDeviceAlgorithm(String)7100%n/a010201
isSuitableFor(ExecutionCall)7100%n/a010201
autoDiffModeFrom(ExecutionCall)7100%n/a010201
lambda$prepare$0(Tensor, Tensor)7100%2100%020101
lambda$prepare$2(Tensor)6100%n/a010101
lambda$prepare$1(Tensor[], Tensor)6100%n/a010201
static {...}4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunDeviceAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunDeviceAlgorithm.java.html index b9c1e12c8..231af881c 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunDeviceAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/AbstractFunDeviceAlgorithm.java.html @@ -261,4 +261,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FallbackAlgorithm.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FallbackAlgorithm.html index c00173e89..868cc57fe 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FallbackAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FallbackAlgorithm.html @@ -1 +1 @@ -FallbackAlgorithm

FallbackAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total122 of 65581%15 of 5974%125323125122
lambda$new$2(double[], ExecutionCall, Function, int, int)470%40%335511
lambda$new$4(Operation, ExecutionCall)216174%2466%2462301
_tryExecute(ExecutionCall, Class)214467%7436%5841601
_tryExecute(Method, Object[], int)193866%2100%023701
prepare(ExecutionCall)94483%2250%2331501
_findMethod(String, Class)59595%12100%0722101
isSuitableFor(ExecutionCall)45100%10100%060701
lambda$_tryExecute$8(ExecutionCall, Method, int, int)43100%4100%030601
lambda$new$3(ExecutionCall, int, int)37100%4100%030601
ADAction(Function, ExecutionCall)35100%2100%020701
FallbackAlgorithm(String, int, Operation)14100%n/a010501
lambda$ADAction$7(Function, Tensor, ADTarget)14100%n/a010101
lambda$ADAction$5(Function, Tensor, ADTarget)14100%n/a010101
setAt(Tensor, int, Object)10100%n/a010201
execute(Function, ExecutionCall)8100%n/a010101
lambda$ADAction$6(Function, ExecutionCall)7100%n/a010101
dispatch(Function, ExecutionCall)5100%n/a010101
lambda$new$0(Tensor)5100%n/a010101
supplyADActionFor(Function, ExecutionCall)4100%n/a010101
lambda$new$1(Tensor)4100%n/a010101
static {...}4100%n/a010101
autoDiffModeFrom(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FallbackAlgorithm

FallbackAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total122 of 65581%15 of 5974%125323125122
lambda$new$2(double[], ExecutionCall, Function, int, int)470%40%335511
lambda$new$4(Operation, ExecutionCall)216174%2466%2462301
_tryExecute(ExecutionCall, Class)214467%7436%5841601
_tryExecute(Method, Object[], int)193866%2100%023701
prepare(ExecutionCall)94483%2250%2331501
_findMethod(String, Class)59595%12100%0722101
isSuitableFor(ExecutionCall)45100%10100%060701
lambda$_tryExecute$8(ExecutionCall, Method, int, int)43100%4100%030601
lambda$new$3(ExecutionCall, int, int)37100%4100%030601
ADAction(Function, ExecutionCall)35100%2100%020701
FallbackAlgorithm(String, int, Operation)14100%n/a010501
lambda$ADAction$7(Function, Tensor, ADTarget)14100%n/a010101
lambda$ADAction$5(Function, Tensor, ADTarget)14100%n/a010101
setAt(Tensor, int, Object)10100%n/a010201
execute(Function, ExecutionCall)8100%n/a010101
lambda$ADAction$6(Function, ExecutionCall)7100%n/a010101
dispatch(Function, ExecutionCall)5100%n/a010101
lambda$new$0(Tensor)5100%n/a010101
supplyADActionFor(Function, ExecutionCall)4100%n/a010101
lambda$new$1(Tensor)4100%n/a010101
static {...}4100%n/a010101
autoDiffModeFrom(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FallbackAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FallbackAlgorithm.java.html index 7e60ef85a..4710ceea8 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FallbackAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FallbackAlgorithm.java.html @@ -245,4 +245,4 @@ return Result.of(this.dispatch(caller, call)).withAutoDiff(this); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunAlgorithm.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunAlgorithm.html index c9a4a86cc..5719b1359 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunAlgorithm.html @@ -1 +1 @@ -FunAlgorithm

FunAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010101
FunAlgorithm(String)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FunAlgorithm

FunAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010101
FunAlgorithm(String)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunAlgorithm.java.html index 0611af91d..76abbad16 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunAlgorithm.java.html @@ -4,4 +4,4 @@ { public FunAlgorithm( String name ) { super(name); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunDeviceAlgorithm.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunDeviceAlgorithm.html index 91f654109..86cc707b5 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunDeviceAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunDeviceAlgorithm.html @@ -1 +1 @@ -FunDeviceAlgorithm

FunDeviceAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010101
FunDeviceAlgorithm(String)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FunDeviceAlgorithm

FunDeviceAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010101
FunDeviceAlgorithm(String)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunDeviceAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunDeviceAlgorithm.java.html index d5c2ba6c6..fa05d852f 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunDeviceAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/FunDeviceAlgorithm.java.html @@ -4,4 +4,4 @@ { public FunDeviceAlgorithm( String name ) { super( name ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/index.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/index.html index 700ca34a2..823748a4a 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/index.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/index.html @@ -1 +1 @@ -neureka.backend.api.template.algorithms

neureka.backend.api.template.algorithms

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total446 of 2,49082%65 of 27376%592266043928807
AbstractDeviceAlgorithm1661,12287%3213080%301131821113201
FallbackAlgorithm12253381%154474%12532312512201
AbstractFunAlgorithm10310851%10844%921123901201
AbstractFunDeviceAlgorithm5526482%82676%83576001801
AbstractAlgorithm100%n/a02020201
FunDeviceAlgorithm100%n/a01010101
FunAlgorithm100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.api.template.algorithms

neureka.backend.api.template.algorithms

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total447 of 2,49082%67 of 27375%612266043928807
AbstractDeviceAlgorithm1671,12187%3412879%321131821113201
FallbackAlgorithm12253381%154474%12532312512201
AbstractFunAlgorithm10310851%10844%921123901201
AbstractFunDeviceAlgorithm5526482%82676%83576001801
AbstractAlgorithm100%n/a02020201
FunDeviceAlgorithm100%n/a01010101
FunAlgorithm100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.algorithms/index.source.html b/docs/coverage/test/html/neureka.backend.api.template.algorithms/index.source.html index f62968357..38859d1d1 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.algorithms/index.source.html +++ b/docs/coverage/test/html/neureka.backend.api.template.algorithms/index.source.html @@ -1 +1 @@ -neureka.backend.api.template.algorithms

neureka.backend.api.template.algorithms

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total446 of 2,49082%65 of 27376%592266043928807
AbstractDeviceAlgorithm.java1661,12287%3213080%301131821113201
FallbackAlgorithm.java12253381%154474%12532312512201
AbstractFunAlgorithm.java10310851%10844%921123901201
AbstractFunDeviceAlgorithm.java5526482%82676%83576001801
AbstractAlgorithm.java100%n/a02020201
FunAlgorithm.java100%n/a01010101
FunDeviceAlgorithm.java100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.api.template.algorithms

neureka.backend.api.template.algorithms

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total447 of 2,49082%67 of 27375%612266043928807
AbstractDeviceAlgorithm.java1671,12187%3412879%321131821113201
FallbackAlgorithm.java12253381%154474%12532312512201
AbstractFunAlgorithm.java10310851%10844%921123901201
AbstractFunDeviceAlgorithm.java5526482%82676%83576001801
AbstractAlgorithm.java100%n/a02020201
FunAlgorithm.java100%n/a01010101
FunDeviceAlgorithm.java100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.implementations/AbstractImplementationFor.html b/docs/coverage/test/html/neureka.backend.api.template.implementations/AbstractImplementationFor.html index c9cf4c093..e7f818039 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.implementations/AbstractImplementationFor.html +++ b/docs/coverage/test/html/neureka.backend.api.template.implementations/AbstractImplementationFor.html @@ -1 +1 @@ -AbstractImplementationFor

AbstractImplementationFor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total34 of 6245%3 of 650%2511002
run(ExecutionCall)341935%3350%241601
AbstractImplementationFor(ImplementationFor, int)9100%n/a010401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractImplementationFor

AbstractImplementationFor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total34 of 6245%3 of 650%2511002
run(ExecutionCall)341935%3350%241601
AbstractImplementationFor(ImplementationFor, int)9100%n/a010401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.implementations/AbstractImplementationFor.java.html b/docs/coverage/test/html/neureka.backend.api.template.implementations/AbstractImplementationFor.java.html index 27672a23c..dfe585459 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.implementations/AbstractImplementationFor.java.html +++ b/docs/coverage/test/html/neureka.backend.api.template.implementations/AbstractImplementationFor.java.html @@ -30,4 +30,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.implementations/index.html b/docs/coverage/test/html/neureka.backend.api.template.implementations/index.html index 29560314c..30dbfba9f 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.implementations/index.html +++ b/docs/coverage/test/html/neureka.backend.api.template.implementations/index.html @@ -1 +1 @@ -neureka.backend.api.template.implementations

neureka.backend.api.template.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total34 of 6245%3 of 650%251100201
AbstractImplementationFor342845%3350%251100201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.api.template.implementations

neureka.backend.api.template.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total34 of 6245%3 of 650%251100201
AbstractImplementationFor342845%3350%251100201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.implementations/index.source.html b/docs/coverage/test/html/neureka.backend.api.template.implementations/index.source.html index 3abe6d791..6d1a04454 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.implementations/index.source.html +++ b/docs/coverage/test/html/neureka.backend.api.template.implementations/index.source.html @@ -1 +1 @@ -neureka.backend.api.template.implementations

neureka.backend.api.template.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total34 of 6245%3 of 650%251100201
AbstractImplementationFor.java342845%3350%251100201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.api.template.implementations

neureka.backend.api.template.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total34 of 6245%3 of 650%251100201
AbstractImplementationFor.java342845%3350%251100201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.operations/AbstractOperation.html b/docs/coverage/test/html/neureka.backend.api.template.operations/AbstractOperation.html index 2ba8b0566..3c50b0011 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.operations/AbstractOperation.html +++ b/docs/coverage/test/html/neureka.backend.api.template.operations/AbstractOperation.html @@ -1 +1 @@ -AbstractOperation

AbstractOperation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total54 of 39686%4 of 2684%635777222
getAlgorithmFor(ExecutionCall)196577%1990%1631801
setAlgorithm(Class, Algorithm)151346%1150%122501
asDerivative(Function[], int)150%n/a111111
isDifferentiable()30%n/a111111
toString()24395%1150%120501
stringify(String[])88100%1990%1601401
AbstractOperation(OperationBuilder)52100%n/a0101201
getAlgorithm(Class)24100%2100%020901
getAllAlgorithms()8100%n/a010101
lambda$getAlgorithm$0(Class, Map.Entry)6100%n/a010101
supportsAlgorithm(Class)5100%n/a010101
supports(Class)5100%n/a010101
operationName()4100%n/a010101
lambda$getAlgorithm$1(Map.Entry)4100%n/a010101
static {...}4100%n/a010101
isOperator()3100%n/a010101
getIdentifier()3100%n/a010101
getOperator()3100%n/a010101
getArity()3100%n/a010101
isIndexer()3100%n/a010101
isInline()3100%n/a010101
getDefaultAlgorithm()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractOperation

AbstractOperation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total59 of 39685%4 of 2684%735877322
getAlgorithmFor(ExecutionCall)196577%1990%1631801
setAlgorithm(Class, Algorithm)151346%1150%122501
asDerivative(Function[], int)150%n/a111111
supportsAlgorithm(Class)50%n/a111111
isDifferentiable()30%n/a111111
toString()24395%1150%120501
stringify(String[])88100%1990%1601401
AbstractOperation(OperationBuilder)52100%n/a0101201
getAlgorithm(Class)24100%2100%020901
getAllAlgorithms()8100%n/a010101
lambda$getAlgorithm$0(Class, Map.Entry)6100%n/a010101
supports(Class)5100%n/a010101
operationName()4100%n/a010101
lambda$getAlgorithm$1(Map.Entry)4100%n/a010101
static {...}4100%n/a010101
isOperator()3100%n/a010101
getIdentifier()3100%n/a010101
getOperator()3100%n/a010101
getArity()3100%n/a010101
isIndexer()3100%n/a010101
isInline()3100%n/a010101
getDefaultAlgorithm()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.operations/AbstractOperation.java.html b/docs/coverage/test/html/neureka.backend.api.template.operations/AbstractOperation.java.html index f6bee2a9f..3aa649438 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.operations/AbstractOperation.java.html +++ b/docs/coverage/test/html/neureka.backend.api.template.operations/AbstractOperation.java.html @@ -150,7 +150,7 @@ */ @Override public final <T extends Algorithm> boolean supportsAlgorithm( Class<T> type ) { - return _algorithms.containsKey( type ); + return _algorithms.containsKey( type ); } /** @@ -280,4 +280,4 @@ return this.getClass().getSimpleName(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder$1.html b/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder$1.html index 731117e88..156a49e3b 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder$1.html +++ b/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder$1.html @@ -1 +1 @@ -OperationBuilder.new AbstractOperation() {...}

OperationBuilder.new AbstractOperation() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 4641%3 of 425%475925
asDerivative(Function[], int)160%20%223311
calculate(double[], int, int, Function[])70%n/a111111
stringify(String[])41071%1150%121301
{...}7100%n/a010101
operationName()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OperationBuilder.new AbstractOperation() {...}

OperationBuilder.new AbstractOperation() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 4636%3 of 425%576935
asDerivative(Function[], int)160%20%223311
calculate(double[], int, int, Function[])70%n/a111111
stringify(String[])41071%1150%121301
operationName()20%n/a111111
{...}7100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder.html b/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder.html index 2801c4345..382e4829a 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder.html +++ b/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder.html @@ -1 +1 @@ -OperationBuilder

OperationBuilder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total153 of 34655%29 of 5446%3049771322
_listOfMissingProperties()282749%7750%780901
setDerivation(OperationBuilder.Derivation)210%40%334411
build()181545%2250%231501
arity(int)101254%2250%230401
isOperator(boolean)101254%2250%230401
isIndexer(boolean)101254%2250%230401
isDifferentiable(boolean)101254%2250%230401
isInline(boolean)101254%2250%230401
stringifier(OperationBuilder.Stringifier)101152%2250%230401
identifier(String)101152%2250%230401
operator(String)101152%2250%230401
getStringifier()30%n/a111111
getDerivator()30%n/a111111
OperationBuilder()33100%n/a0101101
dispose()4100%n/a010101
getIdentifier()3100%n/a010101
getOperator()3100%n/a010101
getArity()3100%n/a010101
getIsOperator()3100%n/a010101
getIsIndexer()3100%n/a010101
getIsDifferentiable()3100%n/a010101
getIsInline()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OperationBuilder

OperationBuilder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total153 of 34655%29 of 5446%3049771322
_listOfMissingProperties()282749%7750%780901
setDerivation(OperationBuilder.Derivation)210%40%334411
build()181545%2250%231501
arity(int)101254%2250%230401
isOperator(boolean)101254%2250%230401
isIndexer(boolean)101254%2250%230401
isDifferentiable(boolean)101254%2250%230401
isInline(boolean)101254%2250%230401
stringifier(OperationBuilder.Stringifier)101152%2250%230401
identifier(String)101152%2250%230401
operator(String)101152%2250%230401
getStringifier()30%n/a111111
getDerivator()30%n/a111111
OperationBuilder()33100%n/a0101101
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder.java.html b/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder.java.html index 3ef5d42a2..16de03ea7 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder.java.html +++ b/docs/coverage/test/html/neureka.backend.api.template.operations/OperationBuilder.java.html @@ -188,11 +188,11 @@ return src[ 0 ].call( inputs, j ); } - @Override protected String operationName() { return "OptimizedOperation"; } + @Override protected String operationName() { return "OptimizedOperation"; } }; } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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AbstractOperation5933785%42284%73587732201
OperationBuilder.new AbstractOperation() {...}291736%3125%57693501
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api.template.operations/index.source.html b/docs/coverage/test/html/neureka.backend.api.template.operations/index.source.html index a99874097..a057fcd90 100644 --- a/docs/coverage/test/html/neureka.backend.api.template.operations/index.source.html +++ b/docs/coverage/test/html/neureka.backend.api.template.operations/index.source.html @@ -1 +1 @@ -neureka.backend.api.template.operations

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total234 of 78870%36 of 8457%40911915674903
OperationBuilder.java18021254%322644%3456127952702
AbstractOperation.java5434286%42284%63577722201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.api.template.operations

neureka.backend.api.template.operations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total241 of 78869%36 of 8457%42912115694903
OperationBuilder.java18221053%322644%3556137962702
AbstractOperation.java5933785%42284%73587732201
Created with JaCoCo 0.8.11.202310140853
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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withName(String)11100%n/a010201
Created with JaCoCo 0.8.11.202310140853
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Created with JaCoCo 0.8.9.202303310957
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AutoDiffMode

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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static {...}27100%n/a010501
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allowsBackward()10100%4100%030101
Created with JaCoCo 0.8.9.202303310957
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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static {...}44100%n/a010501
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allowsBackward()10100%4100%030101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/AutoDiffMode.java.html b/docs/coverage/test/html/neureka.backend.api/AutoDiffMode.java.html index 0ee92ea1d..ece26fd25 100644 --- a/docs/coverage/test/html/neureka.backend.api/AutoDiffMode.java.html +++ b/docs/coverage/test/html/neureka.backend.api/AutoDiffMode.java.html @@ -16,4 +16,4 @@ } } -
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\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/BackendContext$1.html b/docs/coverage/test/html/neureka.backend.api/BackendContext$1.html index 19f07b0a1..21340a75d 100644 --- a/docs/coverage/test/html/neureka.backend.api/BackendContext$1.html +++ b/docs/coverage/test/html/neureka.backend.api/BackendContext$1.html @@ -1 +1 @@ -BackendContext.new ImplementationReceiver() {...}

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.9.202303310957
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accept(Class, Class, Class, Function)15100%n/a010201
{...}9100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/BackendContext$2.html b/docs/coverage/test/html/neureka.backend.api/BackendContext$2.html index ae62e3195..a974c4e42 100644 --- a/docs/coverage/test/html/neureka.backend.api/BackendContext$2.html +++ b/docs/coverage/test/html/neureka.backend.api/BackendContext$2.html @@ -1 +1 @@ -BackendContext.new LoadingContext() {...}

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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{...}12100%n/a010101
getOperationIdentidier()4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BackendContext.new LoadingContext() {...}

BackendContext.new LoadingContext() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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getAlgorithmName()40%n/a111111
getOperationIdentidier()40%n/a111111
{...}12100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/BackendContext$Registered.html b/docs/coverage/test/html/neureka.backend.api/BackendContext$Registered.html index 9d08757ea..3805a8204 100644 --- a/docs/coverage/test/html/neureka.backend.api/BackendContext$Registered.html +++ b/docs/coverage/test/html/neureka.backend.api/BackendContext$Registered.html @@ -1 +1 @@ -BackendContext.Registered

BackendContext.Registered

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a010601
BackendContext.Registered(BackendContext, Class, Class, Class, Function)18100%n/a010601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BackendContext.Registered

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a010601
BackendContext.Registered(BackendContext, Class, Class, Class, Function)18100%n/a010601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/BackendContext$Runner.html b/docs/coverage/test/html/neureka.backend.api/BackendContext$Runner.html index b9fca75d6..c30adb780 100644 --- a/docs/coverage/test/html/neureka.backend.api/BackendContext$Runner.html +++ b/docs/coverage/test/html/neureka.backend.api/BackendContext$Runner.html @@ -1 +1 @@ -BackendContext.Runner

BackendContext.Runner

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 4893%1 of 250%1601505
BackendContext.Runner(BackendContext, BackendContext)31280%1150%120501
runAndGet(Supplier)13100%n/a010401
run(Runnable)12100%n/a010401
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invoke(Supplier)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BackendContext.Runner

BackendContext.Runner

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 4893%1 of 250%1601505
BackendContext.Runner(BackendContext, BackendContext)31280%1150%120501
runAndGet(Supplier)13100%n/a010401
run(Runnable)12100%n/a010401
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invoke(Supplier)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/BackendContext.html b/docs/coverage/test/html/neureka.backend.api/BackendContext.html index 689fc0fbf..3d68ddaef 100644 --- a/docs/coverage/test/html/neureka.backend.api/BackendContext.html +++ b/docs/coverage/test/html/neureka.backend.api/BackendContext.html @@ -1 +1 @@ -BackendContext

BackendContext

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total55 of 39686%6 of 2272%835772224
set(BackendExtension)216375%2466%2421401
reset()141856%2100%022701
addOperation(Operation)85086%2250%2301001
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_register(BackendContext.Registered)26496%2880%2611101
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toString()16100%n/a010101
runner()8100%n/a010101
getOperation(String)7100%n/a010101
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getAutogradFunction()5100%n/a010101
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lambda$new$1()5100%n/a010101
lambda$new$0()5100%n/a010101
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getOperations()4100%n/a010101
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Created with JaCoCo 0.8.9.202303310957
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BackendContext

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total61 of 39684%6 of 2272%935872324
set(BackendExtension)216375%2466%2421401
reset()141856%2100%022701
addOperation(Operation)85086%2250%2301001
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lambda$new$1()5100%n/a010101
lambda$new$0()5100%n/a010101
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getOperations()4100%n/a010101
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getFunctionCache()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/BackendContext.java.html b/docs/coverage/test/html/neureka.backend.api/BackendContext.java.html index 1875ff799..6de2abc4f 100644 --- a/docs/coverage/test/html/neureka.backend.api/BackendContext.java.html +++ b/docs/coverage/test/html/neureka.backend.api/BackendContext.java.html @@ -186,7 +186,7 @@ * @return The truth value determining if the provided {@link Operation} is part of this {@link BackendContext}. */ public boolean hasOperation( Operation operation ) { - return _lookup.containsKey( operation.getIdentifier() ); + return _lookup.containsKey( operation.getIdentifier() ); } /** @@ -333,7 +333,7 @@ registered.deviceType, registered.function.apply(new LoadingContext() { @Override public String getAlgorithmName() { return da.getName(); } - @Override public String getOperationIdentidier() { return o.getIdentifier(); } + @Override public String getOperationIdentidier() { return o.getIdentifier(); } }) ); return true; @@ -445,4 +445,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.9.202303310957
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 15100%0 of 0n/a030603
BackendExtension.DeviceOption(Device, double)9100%n/a010401
device()3100%n/a010101
confidence()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/BackendExtension.html b/docs/coverage/test/html/neureka.backend.api/BackendExtension.html index 308cc9aa8..88d0272cb 100644 --- a/docs/coverage/test/html/neureka.backend.api/BackendExtension.html +++ b/docs/coverage/test/html/neureka.backend.api/BackendExtension.html @@ -1 +1 @@ -BackendExtension

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 1100%0 of 0n/a010101
reset()1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
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BackendExtension

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 1100%0 of 0n/a010101
reset()1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/BackendExtension.java.html b/docs/coverage/test/html/neureka.backend.api/BackendExtension.java.html index 29b035afe..f1fe64030 100644 --- a/docs/coverage/test/html/neureka.backend.api/BackendExtension.java.html +++ b/docs/coverage/test/html/neureka.backend.api/BackendExtension.java.html @@ -68,4 +68,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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andArgs(Arg[])14100%n/a010201
get()12100%n/a010101
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Created with JaCoCo 0.8.9.202303310957
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Call.Builder(Device)21100%n/a010201
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get()12100%n/a010101
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/Call$Validator$Estimator.html b/docs/coverage/test/html/neureka.backend.api/Call$Validator$Estimator.html index 1e79f670c..b72464bf1 100644 --- a/docs/coverage/test/html/neureka.backend.api/Call$Validator$Estimator.html +++ b/docs/coverage/test/html/neureka.backend.api/Call$Validator$Estimator.html @@ -1 +1 @@ -Call.Validator.Estimator

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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goodIfAll(Call.TensorCondition)100%20%221111
badIfAll(Call.TensorCondition)100%20%221111
goodIfAll(Call.TensorCompare)100%20%221111
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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badIfAll(Call.TensorCondition)100%20%221111
goodIfAll(Call.TensorCompare)100%20%221111
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Call.Validator.Estimator(Call.Validator, boolean)11292%1150%120301
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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allShare(Function)52100%3975%3701101
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lambda$ifValid$1(Object)2100%n/a010101
lambda$ifValid$0(Object, Object)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
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Call.Validator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total118 of 47475%29 of 8867%23681677524
_anyNotNullMatch(Call.TensorCondition)340%80%554411
last(Call.TensorCondition)250%40%333311
first(Call.TensorCondition)210%40%333311
any(Call.TensorCondition)180%40%333311
anyNotNull(Call.TensorCondition)150%20%223311
all(Call.TensorCompare)31583%2250%230301
_allMatch(Call.TensorCompare)23394%1583%140601
allShare(Function)52100%3975%3701101
_allHaveSame(Call.TensorProperty)48100%8100%0501101
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_anyMatch(Call.TensorCondition)32100%6100%040301
_allMatch(Call.TensorCondition)32100%6100%040301
tensors(Call.TensorsCondition)20100%4100%030301
allNotNull(Call.TensorCondition)18100%4100%030301
all(Call.TensorCondition)15100%2100%020301
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Call.Validator(Call)9100%n/a010201
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isValid()3100%n/a010101
lambda$ifValid$1(Object)2100%n/a010101
lambda$ifValid$0(Object, Object)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/Call.html b/docs/coverage/test/html/neureka.backend.api/Call.html index fa49e7690..aca731dc6 100644 --- a/docs/coverage/test/html/neureka.backend.api/Call.html +++ b/docs/coverage/test/html/neureka.backend.api/Call.html @@ -1 +1 @@ -Call

Call

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total38 of 21982%2 of 1080%220438015
input(Class, int)382236%2466%2441001
Call(Tensor[], Object, List)50100%2100%020901
rearrangeInputs(int[])29100%2100%020501
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allMetaArgs()11100%n/a010101
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getDerivativeIndex()6100%n/a010101
inputs()5100%n/a010101
input(int)5100%n/a010101
validate()5100%n/a010101
arity()4100%n/a010101
getDevice()3100%n/a010101
lambda$allMetaArgs$0(Arg)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Call

Call

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total38 of 21982%2 of 1080%220438015
input(Class, int)382236%2466%2441001
Call(Tensor[], Object, List)50100%2100%020901
rearrangeInputs(int[])29100%2100%020501
get(Class)12100%n/a010201
allMetaArgs()11100%n/a010101
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getDevice()3100%n/a010101
lambda$allMetaArgs$0(Arg)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/Call.java.html b/docs/coverage/test/html/neureka.backend.api/Call.java.html index 73ee65c01..58a36a8a1 100644 --- a/docs/coverage/test/html/neureka.backend.api/Call.java.html +++ b/docs/coverage/test/html/neureka.backend.api/Call.java.html @@ -341,11 +341,11 @@ public Estimator badIfAll( TensorCondition condition ) { if ( _allMatch( condition ) ) _mod(-0.5f); return this; } - public Estimator goodIfAnyNonNull( TensorCondition condition ) { return goodIfAny( t -> t != null && condition.check(t) ); } + public Estimator goodIfAnyNonNull( TensorCondition condition ) { return goodIfAny( t -> t != null && condition.check(t) ); } public Estimator goodIfAny( TensorCondition condition ) { if ( _anyMatch( condition ) ) _mod(0.5f); return this; } - public Estimator badIfAnyNonNull( TensorCondition condition ) { return badIfAny( t -> t != null && condition.check(t) ); } + public Estimator badIfAnyNonNull( TensorCondition condition ) { return badIfAny( t -> t != null && condition.check(t) ); } public Estimator badIfAny( TensorCondition condition ) { if ( _anyMatch( condition ) ) _mod(-0.5f); return this; } @@ -366,4 +366,4 @@ public interface OperationCondition { boolean check( Operation type ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/DeviceAlgorithm.html b/docs/coverage/test/html/neureka.backend.api/DeviceAlgorithm.html index 2ed609432..661076cd2 100644 --- a/docs/coverage/test/html/neureka.backend.api/DeviceAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.api/DeviceAlgorithm.html @@ -1 +1 @@ -DeviceAlgorithm

DeviceAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 2564%2 of 20%241413
hasImplementationFor(Device)90%20%221111
withName(String)11100%n/a010201
getImplementationFor(Device)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DeviceAlgorithm

DeviceAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 2564%2 of 20%241413
hasImplementationFor(Device)90%20%221111
withName(String)11100%n/a010201
getImplementationFor(Device)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/DeviceAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.api/DeviceAlgorithm.java.html index eaca2e174..cd0c87465 100644 --- a/docs/coverage/test/html/neureka.backend.api/DeviceAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.api/DeviceAlgorithm.java.html @@ -70,4 +70,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/ExecutionCall$Builder.html b/docs/coverage/test/html/neureka.backend.api/ExecutionCall$Builder.html index cbe2d6482..5d032cef7 100644 --- a/docs/coverage/test/html/neureka.backend.api/ExecutionCall$Builder.html +++ b/docs/coverage/test/html/neureka.backend.api/ExecutionCall$Builder.html @@ -1 +1 @@ -ExecutionCall.Builder

ExecutionCall.Builder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 11385%1 of 250%1611405
running(Operation)162863%1150%121501
ExecutionCall.Builder(Tensor[])24100%n/a010201
on(Device)18100%n/a010201
andArgs(Arg[])14100%n/a010201
andArgs(List)13100%n/a010301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ExecutionCall.Builder

ExecutionCall.Builder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 11385%1 of 250%1611405
running(Operation)162863%1150%121501
ExecutionCall.Builder(Tensor[])24100%n/a010201
on(Device)18100%n/a010201
andArgs(Arg[])14100%n/a010201
andArgs(List)13100%n/a010301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/ExecutionCall.html b/docs/coverage/test/html/neureka.backend.api/ExecutionCall.html index 1d0ef1c7e..c007ba263 100644 --- a/docs/coverage/test/html/neureka.backend.api/ExecutionCall.html +++ b/docs/coverage/test/html/neureka.backend.api/ExecutionCall.html @@ -1 +1 @@ -ExecutionCall

ExecutionCall

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total37 of 39590%6 of 1457%625350018
lambda$new$0()17934%1150%122501
lambda$new$1()161344%2250%231501
checkArity()25696%2466%240801
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withArgs(Arg[])40100%n/a010501
withInputAt(int, Tensor)30100%n/a010301
withAddedInputAt(int, Tensor)29100%n/a010301
withRemovedInputAt(int)29100%n/a010301
withInputs(Tensor[])20100%n/a010201
withOperation(Operation)20100%n/a010201
ExecutionCall(Device, Operation, Tensor[], List)19100%n/a010501
of(Tensor[])12100%n/a010201
lambda$withArgs$3(Arg[], Arg)6100%n/a010101
lambda$withArgs$2(Arg, Arg)6100%n/a010101
getAlgorithm()5100%n/a010101
autogradMode()5100%n/a010101
static {...}4100%n/a010101
getOperation()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ExecutionCall

ExecutionCall

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total37 of 39590%6 of 1457%625350018
lambda$new$0()17934%1150%122501
lambda$new$1()161344%2250%231501
checkArity()25696%2466%240801
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withArgs(Arg[])40100%n/a010501
withInputAt(int, Tensor)30100%n/a010301
withAddedInputAt(int, Tensor)29100%n/a010301
withRemovedInputAt(int)29100%n/a010301
withInputs(Tensor[])20100%n/a010201
withOperation(Operation)20100%n/a010201
ExecutionCall(Device, Operation, Tensor[], List)19100%n/a010501
of(Tensor[])12100%n/a010201
lambda$withArgs$3(Arg[], Arg)6100%n/a010101
lambda$withArgs$2(Arg, Arg)6100%n/a010101
getAlgorithm()5100%n/a010101
autogradMode()5100%n/a010101
static {...}4100%n/a010101
getOperation()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/ExecutionCall.java.html b/docs/coverage/test/html/neureka.backend.api/ExecutionCall.java.html index af4e65bba..4bc437c1d 100644 --- a/docs/coverage/test/html/neureka.backend.api/ExecutionCall.java.html +++ b/docs/coverage/test/html/neureka.backend.api/ExecutionCall.java.html @@ -262,4 +262,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/Extensions.html b/docs/coverage/test/html/neureka.backend.api/Extensions.html index 434eb7803..b42acaa28 100644 --- a/docs/coverage/test/html/neureka.backend.api/Extensions.html +++ b/docs/coverage/test/html/neureka.backend.api/Extensions.html @@ -1 +1 @@ -Extensions

Extensions

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 771%0 of 0n/a131313
_removeOrReject(Component)20%n/a111111
Extensions()3100%n/a010101
_setOrReject(Component)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Extensions

Extensions

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 771%0 of 0n/a131313
_removeOrReject(Component)20%n/a111111
Extensions()3100%n/a010101
_setOrReject(Component)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/Extensions.java.html b/docs/coverage/test/html/neureka.backend.api/Extensions.java.html index 0c2bcb2e6..487506daf 100644 --- a/docs/coverage/test/html/neureka.backend.api/Extensions.java.html +++ b/docs/coverage/test/html/neureka.backend.api/Extensions.java.html @@ -18,4 +18,4 @@ return newComponent; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/LazyRef.html b/docs/coverage/test/html/neureka.backend.api/LazyRef.html index ef0acadf3..8af89a328 100644 --- a/docs/coverage/test/html/neureka.backend.api/LazyRef.html +++ b/docs/coverage/test/html/neureka.backend.api/LazyRef.html @@ -1 +1 @@ -LazyRef

LazyRef

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total32 of 7959%1 of 475%1641304
toString()321633%1150%124601
get()17100%2100%020401
LazyRef(Supplier)9100%n/a010201
of(Supplier)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +LazyRef

LazyRef

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total32 of 7959%1 of 475%1641304
toString()321633%1150%124601
get()17100%2100%020401
LazyRef(Supplier)9100%n/a010201
of(Supplier)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/LazyRef.java.html b/docs/coverage/test/html/neureka.backend.api/LazyRef.java.html index 252457fb2..86410638f 100644 --- a/docs/coverage/test/html/neureka.backend.api/LazyRef.java.html +++ b/docs/coverage/test/html/neureka.backend.api/LazyRef.java.html @@ -41,4 +41,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/Operation.html b/docs/coverage/test/html/neureka.backend.api/Operation.html index 45714dbec..d06f06013 100644 --- a/docs/coverage/test/html/neureka.backend.api/Operation.html +++ b/docs/coverage/test/html/neureka.backend.api/Operation.html @@ -1 +1 @@ -Operation

Operation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total63 of 18165%3 of 2085%31662306
lambda$execute$1(ExecutionCall, Function)634240%3770%3661201
execute(Function, ExecutionCall)60100%10100%060901
setAlgorithm(Algorithm)6100%n/a010101
builder()4100%n/a010101
lambda$execute$2(Tensor)3100%n/a010101
lambda$execute$0(Function)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Operation

Operation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total63 of 18365%3 of 2085%31662406
lambda$execute$1(ExecutionCall, Function)634441%3770%3661301
execute(Function, ExecutionCall)60100%10100%060901
setAlgorithm(Algorithm)6100%n/a010101
builder()4100%n/a010101
lambda$execute$2(Tensor)3100%n/a010101
lambda$execute$0(Function)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/Operation.java.html b/docs/coverage/test/html/neureka.backend.api/Operation.java.html index d4c6ddbe2..0437f3b1c 100644 --- a/docs/coverage/test/html/neureka.backend.api/Operation.java.html +++ b/docs/coverage/test/html/neureka.backend.api/Operation.java.html @@ -43,9 +43,11 @@ import neureka.autograd.GraphNode; import neureka.backend.api.fun.Execution; import neureka.backend.api.template.operations.OperationBuilder; +import neureka.devices.Device; import neureka.math.Function; import neureka.math.implementations.FunctionConstant; -import neureka.devices.Device; + +import java.util.List; /** * This interface is part of the backend API, and it embodies the top layer of the 3 tier backend architecture. @@ -64,7 +66,7 @@ */ public interface Operation { - static OperationBuilder builder() { return new OperationBuilder(); } + static OperationBuilder builder() { return new OperationBuilder(); } Algorithm[] getAllAlgorithms(); @@ -95,7 +97,7 @@ <T extends Algorithm> Operation setAlgorithm( Class<T> type, T instance ); default <T extends Algorithm> Operation setAlgorithm( T instance ) { - return setAlgorithm( (Class<T>) instance.getClass(), instance ); + return setAlgorithm( (Class<T>) instance.getClass(), instance ); } /** @@ -197,39 +199,41 @@ default Result execute( Function caller, ExecutionCall<?> call ) { - LazyRef<Result> ref = LazyRef.of(()->{ - int d = call.getDerivativeIndex(); - if ( d >= 0 && !caller.dependsOn(d) ) - throw new IllegalArgumentException("Cannot derive w.r.t. to input index " + d + " in function '" + caller + "', because there is no input with index "+d+"!"); - - if ( caller.getSubFunctions().stream().allMatch( f -> f instanceof FunctionConstant) ) { - if ( d < 0 ) return Result.of(Tensor.like((Tensor<Number>)call.input(0)).all(caller.call(new double[0])).mut().setIsIntermediate(true)); - else return Result.of(Tensor.like((Tensor<Number>)call.input(0)).all(0).mut().setIsIntermediate(true)); + LazyRef<Result> ref = LazyRef.of(()->{ + int d = call.getDerivativeIndex(); + if ( d >= 0 && !caller.dependsOn(d) ) + throw new IllegalArgumentException("Cannot derive w.r.t. to input index " + d + " in function '" + caller + "', because there is no input with index "+d+"!"); + + List<Function> subFunctions = caller.getSubFunctions(); + + if ( subFunctions.stream().allMatch( f -> f instanceof FunctionConstant) ) { + if ( d < 0 ) return Result.of(Tensor.like((Tensor<Number>)call.input(0)).all(caller.call(new double[0])).mut().setIsIntermediate(true)); + else return Result.of(Tensor.like((Tensor<Number>)call.input(0)).all(0).mut().setIsIntermediate(true)); } - Result result = call.getAlgorithm().execute( caller, call ); - if ( result != null ) return result; - throw new IllegalStateException( - "Missing return value of " + Execution.class.getSimpleName() + " in algorithm '" + - call.getAlgorithm().getClass().getSimpleName() + "' in operation '" + - call.getOperation().getClass().getName()+"'" + Result result = call.getAlgorithm().execute( caller, call ); + if ( result != null ) return result; + throw new IllegalStateException( + "Missing return value of " + Execution.class.getSimpleName() + " in algorithm '" + + call.getAlgorithm().getClass().getSimpleName() + "' in operation '" + + call.getOperation().getClass().getName()+"'" ); }); - for ( Tensor<?> t : call.inputs() ) - if ( !t.graphNode().isPresent() ) - new GraphNode<>( caller, null, () -> Result.of(t) ); + for ( Tensor<?> t : call.inputs() ) + if ( !t.graphNode().isPresent() ) + new GraphNode<>( caller, null, () -> Result.of(t) ); - if ( caller.isFlat() ) + if ( caller.isFlat() ) { - call.checkArity(); + call.checkArity(); /* The following code is reached in flat functions only: Autograd-Graph will be generated below for the new GraphNode: only flat functions can be executed directly */ - if ( call.getDerivativeIndex() < 0 && caller.isDoingAD() ) - new GraphNode<>( caller, (ExecutionCall<Device<?>>) call, ref::get ); + if ( call.getDerivativeIndex() < 0 && caller.isDoingAD() ) + new GraphNode<>( caller, (ExecutionCall<Device<?>>) call, ref::get ); } - return ref.get(); + return ref.get(); } /** @@ -261,4 +265,4 @@ double calculate( double[] inputs, int j, int d, Function[] src ); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/Result.html b/docs/coverage/test/html/neureka.backend.api/Result.html index 6b717d001..4c25e6429 100644 --- a/docs/coverage/test/html/neureka.backend.api/Result.html +++ b/docs/coverage/test/html/neureka.backend.api/Result.html @@ -1 +1 @@ -Result

Result

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 6091%1 of 250%1811307
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of(Tensor)16100%n/a010201
Result(Tensor, ADActionSupplier)9100%n/a010401
withADAction(ADAction)5100%n/a010101
get()3100%n/a010101
getAgentSupplier()3100%n/a010101
lambda$withADAction$0(ADAction, Function, ExecutionCall)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Result

Result

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 6091%1 of 250%1811307
withAutoDiff(ADActionSupplier)51676%1150%121401
of(Tensor)16100%n/a010201
Result(Tensor, ADActionSupplier)9100%n/a010401
withADAction(ADAction)5100%n/a010101
get()3100%n/a010101
getAgentSupplier()3100%n/a010101
lambda$withADAction$0(ADAction, Function, ExecutionCall)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/Result.java.html b/docs/coverage/test/html/neureka.backend.api/Result.java.html index 08f24e593..be39c5256 100644 --- a/docs/coverage/test/html/neureka.backend.api/Result.java.html +++ b/docs/coverage/test/html/neureka.backend.api/Result.java.html @@ -41,4 +41,4 @@ public ADActionSupplier getAgentSupplier() { return _agent; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.api/index.html b/docs/coverage/test/html/neureka.backend.api/index.html index f271ea5b7..e7ac91193 100644 --- a/docs/coverage/test/html/neureka.backend.api/index.html +++ b/docs/coverage/test/html/neureka.backend.api/index.html @@ -1 +1 @@ -neureka.backend.api

neureka.backend.api

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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Operation6311865%31785%3166230601
BackendContext5534186%61672%83577222401
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Call3818182%2880%22043801501
ExecutionCall3735890%6857%62535001801
LazyRef324759%1375%164130401
ExecutionCall.Builder169785%1150%161140501
DeviceAlgorithm91664%20%24141301
Result55591%1150%181130701
BackendContext.new LoadingContext() {...}41680%n/a13131301
BackendContext.Runner4593%1150%160150501
Extensions571%n/a13131301
Call.Builder82100%2100%060110501
AutoDiffMode47100%1787%17070301
BackendContext.new ImplementationReceiver() {...}24100%n/a02030201
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Algorithm11100%n/a01020101
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.api

neureka.backend.api

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total433 of 2,36281%64 of 19867%642455137716146020
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Operation6312065%31785%3166240601
BackendContext6133584%61672%93587232401
Call.Validator.Estimator418767%111150%112441641301
Call3818182%2880%22043801501
ExecutionCall3735890%6857%62535001801
LazyRef324759%1375%164130401
ExecutionCall.Builder169785%1150%161140501
DeviceAlgorithm91664%20%24141301
BackendContext.new LoadingContext() {...}81260%n/a23232301
Result55591%1150%181130701
BackendContext.Runner4593%1150%160150501
Extensions571%n/a13131301
Call.Builder82100%2100%060110501
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BackendContext.new ImplementationReceiver() {...}24100%n/a02030201
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neureka.backend.api

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Created with JaCoCo 0.8.11.202310140853
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lambda$_load$46(LoadingContext)5100%n/a010101
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lambda$_load$20(LoadingContext)4100%n/a010101
lambda$_load$19(LoadingContext)4100%n/a010101
lambda$_load$18(LoadingContext)4100%n/a010101
lambda$_load$17(LoadingContext)4100%n/a010101
lambda$_load$16(LoadingContext)4100%n/a010101
lambda$_load$15(LoadingContext)4100%n/a010101
lambda$_load$14(LoadingContext)4100%n/a010101
lambda$_load$13(LoadingContext)4100%n/a010101
lambda$_load$12(LoadingContext)4100%n/a010101
lambda$_load$11(LoadingContext)4100%n/a010101
lambda$_load$10(LoadingContext)4100%n/a010101
lambda$_load$9(LoadingContext)4100%n/a010101
lambda$_load$8(LoadingContext)4100%n/a010101
lambda$_load$7(LoadingContext)4100%n/a010101
lambda$_load$6(LoadingContext)4100%n/a010101
lambda$_load$5(LoadingContext)4100%n/a010101
lambda$_load$4(LoadingContext)4100%n/a010101
lambda$_load$3(LoadingContext)4100%n/a010101
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBackend

CPUBackend

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_load(ReceiveForDevice)368100%n/a01011101
lambda$getLoader$0(BackendRegistry)6100%n/a010101
lambda$_load$73(LoadingContext)5100%n/a010101
lambda$_load$72(LoadingContext)5100%n/a010101
lambda$_load$71(LoadingContext)5100%n/a010101
lambda$_load$70(LoadingContext)5100%n/a010101
lambda$_load$69(LoadingContext)5100%n/a010101
lambda$_load$68(LoadingContext)5100%n/a010101
lambda$_load$67(LoadingContext)5100%n/a010101
lambda$_load$66(LoadingContext)5100%n/a010101
lambda$_load$65(LoadingContext)5100%n/a010101
lambda$_load$64(LoadingContext)5100%n/a010101
lambda$_load$63(LoadingContext)5100%n/a010101
lambda$_load$62(LoadingContext)5100%n/a010101
lambda$_load$61(LoadingContext)5100%n/a010101
lambda$_load$60(LoadingContext)5100%n/a010101
lambda$_load$59(LoadingContext)5100%n/a010101
lambda$_load$58(LoadingContext)5100%n/a010101
lambda$_load$57(LoadingContext)5100%n/a010101
lambda$_load$56(LoadingContext)5100%n/a010101
lambda$_load$55(LoadingContext)5100%n/a010101
lambda$_load$54(LoadingContext)5100%n/a010101
lambda$_load$53(LoadingContext)5100%n/a010101
lambda$_load$52(LoadingContext)5100%n/a010101
lambda$_load$51(LoadingContext)5100%n/a010101
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lambda$_load$45(LoadingContext)5100%n/a010101
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lambda$_load$41(LoadingContext)5100%n/a010101
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lambda$_load$16(LoadingContext)4100%n/a010101
lambda$_load$15(LoadingContext)4100%n/a010101
lambda$_load$14(LoadingContext)4100%n/a010101
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lambda$_load$12(LoadingContext)4100%n/a010101
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lambda$_load$7(LoadingContext)4100%n/a010101
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.cpu/CPUBackend.java.html b/docs/coverage/test/html/neureka.backend.cpu/CPUBackend.java.html index 9afe688e9..d3ab93a2a 100644 --- a/docs/coverage/test/html/neureka.backend.cpu/CPUBackend.java.html +++ b/docs/coverage/test/html/neureka.backend.cpu/CPUBackend.java.html @@ -166,4 +166,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.cpu/index.html b/docs/coverage/test/html/neureka.backend.cpu/index.html index efb6fe1b3..b3c9bb387 100644 --- a/docs/coverage/test/html/neureka.backend.cpu/index.html +++ b/docs/coverage/test/html/neureka.backend.cpu/index.html @@ -1 +1 @@ -neureka.backend.cpu

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.cpu

neureka.backend.cpu

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.cpu/index.source.html b/docs/coverage/test/html/neureka.backend.cpu/index.source.html index e4ba29a0a..53a0bb51c 100644 --- a/docs/coverage/test/html/neureka.backend.cpu/index.source.html +++ b/docs/coverage/test/html/neureka.backend.cpu/index.source.html @@ -1 +1 @@ -neureka.backend.cpu

neureka.backend.cpu

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total9 of 75698%1 of 683%282111817901
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.cpu

neureka.backend.cpu

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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CPUBackend.java974798%1583%282111817901
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/BiElementwise.html b/docs/coverage/test/html/neureka.backend.main.algorithms/BiElementwise.html index 9b01aa0c8..c170ad0c9 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/BiElementwise.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/BiElementwise.html @@ -1 +1 @@ -BiElementwise

BiElementwise

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BiElementwise

BiElementwise

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lambda$new$0(Tensor)5100%n/a010101
lambda$new$3(ExecutionCall)3100%n/a010101
lambda$new$2(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/BiElementwise.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/BiElementwise.java.html index 30c020d8d..21f60ebb5 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/BiElementwise.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/BiElementwise.java.html @@ -2,6 +2,7 @@ import neureka.Tensor; import neureka.backend.api.AutoDiffMode; +import neureka.backend.api.ExecutionCall; import neureka.backend.api.Result; import neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm; import neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm; @@ -12,45 +13,52 @@ public final class BiElementwise extends AbstractFunDeviceAlgorithm<BiElementwise> { public BiElementwise() { - super("elementwise"); - setIsSuitableFor( - call -> call - .validate() - .allNotNullHaveSame(NDimensional::size) - .allNotNullHaveSame(NDimensional::shape) - .allNotNull( t -> t.getDataType().typeClassImplements( NumericType.class ) ) - .basicSuitability() + super("elementwise"); + setIsSuitableFor( + call -> call + .validate() + .allNotNullHaveSame(NDimensional::size) + .allNotNullHaveSame(NDimensional::shape) + .allNotNull( t -> t.getDataType().typeClassImplements( NumericType.class ) ) + .basicSuitability() ); - setAutogradModeFor( call -> AutoDiffMode.FORWARD_AND_BACKWARD ); - setExecution( + setAutogradModeFor( call -> AutoDiffMode.FORWARD_AND_BACKWARD ); + setExecution( (outerCaller, outerCall) -> - Result.of(AbstractDeviceAlgorithm.executeFor( + Result.of(AbstractDeviceAlgorithm.executeFor( outerCaller, outerCall, - innerCall -> AbstractDeviceAlgorithm.executeDeviceAlgorithm( innerCall ) + innerCall -> AbstractDeviceAlgorithm.executeDeviceAlgorithm( innerCall ) )) + //(outerCaller, outerCall) -> { + // ExecutionCall<? extends Device<?>> finalOuterCall = _prepare(outerCall); + // return Result.of(executeOnCommonDevice(finalOuterCall, ()->{ + // return AbstractDeviceAlgorithm.executeDeviceAlgorithm(finalOuterCall); + // })); + //} ); - setCallPreparation( - call -> { - if ( call.arity() < 3 ) call = call.withAddedInputAt(0, null); - Device<Object> device = (Device<Object>) call.getDevice(); - if ( call.input( 0 ) == null ) // Creating a new tensor: - { - int[] outShape = call.input( 1 ).getNDConf().shape(); + setCallPreparation(this::_prepare); + } - Class<Object> type = (Class<Object>) call.input( 1 ).getItemType(); - Tensor<Object> output = Tensor.of( type ).withShape( outShape ).all( 0.0 ).mut().setIsIntermediate( true ); - output.mut().setIsVirtual( false ); - try { - device.store( output ); - } catch( Exception e ) { - e.printStackTrace(); - } - call = call.withInputAt( 0, output ); - } - return call; - } - ); - } + private ExecutionCall<?> _prepare( final ExecutionCall<?> inputCall ) { + ExecutionCall<?> call = inputCall; + if ( call.arity() < 3 ) call = call.withAddedInputAt(0, null); + Device<Object> device = (Device<Object>) call.getDevice(); + if ( call.input( 0 ) == null ) // Creating a new tensor: + { + int[] outShape = call.input( 1 ).getNDConf().shape(); + + Class<Object> type = (Class<Object>) call.input( 1 ).getItemType(); + Tensor<Object> output = Tensor.of( type ).withShape( outShape ).all( 0.0 ).mut().setIsIntermediate( true ); + output.mut().setIsVirtual( false ); + try { + device.store( output ); + } catch( Exception e ) { + e.printStackTrace(); + } + call = call.withInputAt( 0, output ); + } + return call; + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/BiScalarBroadcast.html b/docs/coverage/test/html/neureka.backend.main.algorithms/BiScalarBroadcast.html index a637c30f8..47a2f4bb3 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/BiScalarBroadcast.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/BiScalarBroadcast.html @@ -1 +1 @@ -BiScalarBroadcast

BiScalarBroadcast

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BiScalarBroadcast

BiScalarBroadcast

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BiScalarBroadcast()16100%n/a010501
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lambda$new$0(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/BiScalarBroadcast.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/BiScalarBroadcast.java.html index a0aacb9d5..0a9372e61 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/BiScalarBroadcast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/BiScalarBroadcast.java.html @@ -47,4 +47,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/Broadcast.html b/docs/coverage/test/html/neureka.backend.main.algorithms/Broadcast.html index c1af2b38f..f2cb6e789 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/Broadcast.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/Broadcast.html @@ -1 +1 @@ -Broadcast

Broadcast

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Broadcast()20100%n/a010601
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lambda$new$4(ExecutionCall)3100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Broadcast

Broadcast

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lambda$new$1(ExecutionCall)29797%12596%11401601
Broadcast()20100%n/a010601
lambda$new$5(Function, ExecutionCall)5100%n/a010101
lambda$new$0(Tensor)5100%n/a010101
lambda$new$4(ExecutionCall)3100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/Broadcast.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/Broadcast.java.html index 23892eb8f..f4a9c3257 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/Broadcast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/Broadcast.java.html @@ -99,4 +99,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/DotProductAlgorithm.html b/docs/coverage/test/html/neureka.backend.main.algorithms/DotProductAlgorithm.html index a62c3f193..aab1fa532 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/DotProductAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/DotProductAlgorithm.html @@ -1 +1 @@ -DotProductAlgorithm

DotProductAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total51 of 45588%16 of 4866%1644478120
_withDimTrim(ExecutionCall)175977%5758%470801
_autoClone(ExecutionCall)142058%2466%242701
_toInline(Tensor, NDConfiguration.Layout)63986%1583%141701
lambda$new$7(Function, ExecutionCall)53988%1150%121701
_prepare(ExecutionCall)42787%3350%340601
lambda$new$9(ExecutionCall)20%n/a111111
_checkAndPrepareLayout(ExecutionCall, Tensor)17698%1583%1401901
_isSimpleSymmetric(Tensor)11292%2250%230101
lambda$new$2(Tensor)1990%1150%120101
_withNewOutput(ExecutionCall)37100%n/a010501
DotProductAlgorithm()20100%n/a010601
lambda$new$6(Function, Tensor, ADTarget)14100%n/a010101
lambda$new$4(ExecutionCall)13100%n/a010601
lambda$new$8(Function, ExecutionCall)9100%n/a010401
lambda$new$1(Integer)8100%2100%020101
isSymmetric(NDConfiguration.Layout)7100%2100%020101
lambda$new$0(Tensor)5100%n/a010101
lambda$new$3(Tensor)4100%n/a010101
static {...}4100%n/a010101
lambda$new$5(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DotProductAlgorithm

DotProductAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total51 of 45588%16 of 4866%1644478120
_withDimTrim(ExecutionCall)175977%5758%470801
_autoClone(ExecutionCall)142058%2466%242701
_toInline(Tensor, NDConfiguration.Layout)63986%1583%141701
lambda$new$7(Function, ExecutionCall)53988%1150%121701
_prepare(ExecutionCall)42787%3350%340601
lambda$new$9(ExecutionCall)20%n/a111111
_checkAndPrepareLayout(ExecutionCall, Tensor)17698%1583%1401901
_isSimpleSymmetric(Tensor)11292%2250%230101
lambda$new$2(Tensor)1990%1150%120101
_withNewOutput(ExecutionCall)37100%n/a010501
DotProductAlgorithm()20100%n/a010601
lambda$new$6(Function, Tensor, ADTarget)14100%n/a010101
lambda$new$4(ExecutionCall)13100%n/a010601
lambda$new$8(Function, ExecutionCall)9100%n/a010401
lambda$new$1(Integer)8100%2100%020101
isSymmetric(NDConfiguration.Layout)7100%2100%020101
lambda$new$0(Tensor)5100%n/a010101
lambda$new$3(Tensor)4100%n/a010101
static {...}4100%n/a010101
lambda$new$5(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/DotProductAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/DotProductAlgorithm.java.html index 6ef6bbabd..9ba30dcb2 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/DotProductAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/DotProductAlgorithm.java.html @@ -174,4 +174,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/ElementwiseAlgorithm.html b/docs/coverage/test/html/neureka.backend.main.algorithms/ElementwiseAlgorithm.html index 8c4c5893e..949a9c6f7 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/ElementwiseAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/ElementwiseAlgorithm.html @@ -1 +1 @@ -ElementwiseAlgorithm

ElementwiseAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 11094%2 of 450%31002518
lambda$new$2(Tensor, Tensor)60%n/a111111
lambda$new$6(ExecutionCall)55100%2250%2301001
ElementwiseAlgorithm()20100%n/a010601
lambda$new$3(ExecutionCall)10100%n/a010401
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$5(Function, ExecutionCall)5100%n/a010101
lambda$new$0(Tensor)5100%n/a010101
lambda$new$4(ExecutionCall)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ElementwiseAlgorithm

ElementwiseAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 11094%2 of 450%31002518
lambda$new$2(Tensor, Tensor)60%n/a111111
lambda$new$6(ExecutionCall)55100%2250%2301001
ElementwiseAlgorithm()20100%n/a010601
lambda$new$3(ExecutionCall)10100%n/a010401
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$5(Function, ExecutionCall)5100%n/a010101
lambda$new$0(Tensor)5100%n/a010101
lambda$new$4(ExecutionCall)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/ElementwiseAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/ElementwiseAlgorithm.java.html index 7f1868870..9378cb177 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/ElementwiseAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/ElementwiseAlgorithm.java.html @@ -55,4 +55,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/MatMulAlgorithm.html b/docs/coverage/test/html/neureka.backend.main.algorithms/MatMulAlgorithm.html index 98c55fbf4..6bb12ea2a 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/MatMulAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/MatMulAlgorithm.html @@ -1 +1 @@ -MatMulAlgorithm

MatMulAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 44395%9 of 4379%940273018
_prepare(ExecutionCall)91967%3350%340501
lambda$new$7(Function, ExecutionCall)54088%1150%121701
lambda$new$6(int, Function, Tensor, ADTarget)53487%1266%131701
_checkAndPrepareLayout(ExecutionCall, Tensor)213898%1990%1602201
_withNewOutput(ExecutionCall)51100%n/a010601
_autoClone(ExecutionCall)39100%1787%150701
MatMulAlgorithm()20100%n/a010601
_isSimpleColumnMajorMatrix(Tensor)13100%1375%130101
_isSimpleRowMajorMatrix(Tensor)13100%1375%130101
lambda$new$3(ExecutionCall)11100%n/a010601
isRMOrCM(NDConfiguration.Layout)10100%4100%030101
lambda$new$8(Function, ExecutionCall)8100%n/a010201
lambda$new$2(Tensor)8100%2100%020101
lambda$new$0(Tensor)5100%n/a010101
lambda$new$1(Tensor)4100%n/a010101
static {...}4100%n/a010101
lambda$new$5(ExecutionCall)3100%n/a010101
lambda$new$4(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +MatMulAlgorithm

MatMulAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 43996%9 of 4379%939272017
lambda$new$6(Function, ExecutionCall)54088%1150%121701
lambda$new$5(int, Function, Tensor, ADTarget)53487%1266%131701
_prepare(ExecutionCall)42485%3350%340501
_checkAndPrepareLayout(ExecutionCall, Tensor)213898%1990%1602201
_withNewOutput(ExecutionCall)51100%n/a010601
_autoClone(ExecutionCall)39100%1787%150701
MatMulAlgorithm()20100%n/a010601
_isSimpleColumnMajorMatrix(Tensor)13100%1375%130101
_isSimpleRowMajorMatrix(Tensor)13100%1375%130101
lambda$new$3(ExecutionCall)11100%n/a010601
isRMOrCM(NDConfiguration.Layout)10100%4100%030101
lambda$new$2(Tensor)8100%2100%020101
lambda$new$7(Function, ExecutionCall)7100%n/a010201
lambda$new$0(Tensor)5100%n/a010101
lambda$new$1(Tensor)4100%n/a010101
static {...}4100%n/a010101
lambda$new$4(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/MatMulAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/MatMulAlgorithm.java.html index 1711ec0bb..970245b73 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/MatMulAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/MatMulAlgorithm.java.html @@ -33,99 +33,96 @@ setAutogradModeFor( call -> AutoDiffMode.BACKWARD_ONLY ); setExecution( (outerCaller, outerCall) -> - Result.of(AbstractDeviceAlgorithm.executeFor( - outerCaller, outerCall, - innerCall -> AbstractDeviceAlgorithm.executeDeviceAlgorithm( innerCall ) - )) - .withAutoDiff( (Function f, ExecutionCall<? extends Device<?>> adCall ) -> + Result.of(AbstractDeviceAlgorithm.executeDeviceAlgorithm(_prepare(outerCall))) + .withAutoDiff( (Function f, ExecutionCall<? extends Device<?>> adCall ) -> { - if ( adCall.autogradMode().allowsForward() ) - throw new IllegalArgumentException("Matrix multiplication does not support forward-AD!"); - Function matMul = Neureka.get().backend().getFunction().matMul(); - int d = ( 1 + adCall.getValOf( Arg.DerivIdx.class ) ) % 2; - Tensor<?> derivative = Util.transpose(adCall.input( d )).deepCopy().mut().setIsIntermediate( true ); // We need to clone it to make it have a simple nd configuration... - derivative.to(adCall.getDevice()); - return ADAction.of(target -> { + if ( adCall.autogradMode().allowsForward() ) + throw new IllegalArgumentException("Matrix multiplication does not support forward-AD!"); + Function matMul = Neureka.get().backend().getFunction().matMul(); + int d = ( 1 + adCall.getValOf( Arg.DerivIdx.class ) ) % 2; + Tensor<?> derivative = Util.transpose(adCall.input( d )).deepCopy().mut().setIsIntermediate( true ); // We need to clone it to make it have a simple nd configuration... + derivative.to(adCall.getDevice()); + return ADAction.of(target -> { Tensor<?> result; - switch ( d ) { + switch ( d ) { case 0: - result = matMul.execute(derivative, target.error()); - break; + result = matMul.execute(derivative, target.error()); + break; case 1: - result = matMul.execute(target.error(), derivative); - break; + result = matMul.execute(target.error(), derivative); + break; default: - throw new IllegalStateException("This should never happen!"); + throw new IllegalStateException("This should never happen!"); } - return result; + return result; }); }) ); - setCallPreparation(MatMulAlgorithm::_prepare); - } + setCallPreparation(MatMulAlgorithm::_prepare); + } private static ExecutionCall<Device<Object>> _prepare( ExecutionCall<?> call ) { - assert call.arity() <= 3; - if ( call.arity() == 2 ) call = call.withAddedInputAt(0, null); - if ( call.input( 0 ) == null ) // Creating a new tensor: - call = _withNewOutput( call ); + assert call.arity() <= 3; + if ( call.arity() == 2 ) call = call.withAddedInputAt(0, null); + if ( call.input( 0 ) == null ) // Creating a new tensor: + call = _withNewOutput( call ); - return (ExecutionCall<Device<Object>>) _autoClone( call ); + return (ExecutionCall<Device<Object>>) _autoClone( call ); } private static ExecutionCall<?> _withNewOutput( ExecutionCall<?> call ) { - Class<Number> type = (Class<Number>) call.input( 1 ).getDataType().getItemTypeClass(); + Class<Number> type = (Class<Number>) call.input( 1 ).getDataType().getItemTypeClass(); - int[] shp = new int[]{ call.input( 1 ).shape(0), call.input( 2 ).shape(1) }; - Tensor<Number> output = Tensor.of( type ).withShape( shp ).all( 0 ).mut().setIsIntermediate( true ); + int[] shp = new int[]{ call.input( 1 ).shape(0), call.input( 2 ).shape(1) }; + Tensor<Number> output = Tensor.of( type ).withShape( shp ).all( 0 ).mut().setIsIntermediate( true ); - call = _checkAndPrepareLayout( call, output ); + call = _checkAndPrepareLayout( call, output ); - call.getDeviceFor(Number.class).store( output ); - return call.withInputAt( 0, output ); + call.getDeviceFor(Number.class).store( output ); + return call.withInputAt( 0, output ); } private static ExecutionCall<?> _checkAndPrepareLayout( ExecutionCall<?> call, Tensor<?> c ) { - Tensor<?> a = call.input( 1 ); - Tensor<?> b = call.input( 2 ); + Tensor<?> a = call.input( 1 ); + Tensor<?> b = call.input( 2 ); // We need to make sure that the matrices have a common/compatible layout, // ..before we can before the actual a @ b = c matrix multiplication! - NDConfiguration.Layout layoutA = a.getNDConf().getLayout(); - NDConfiguration.Layout layoutB = b.getNDConf().getLayout(); - NDConfiguration.Layout layoutC = c.getNDConf().getLayout(); - - boolean aIsCompatible = isRMOrCM( layoutA ); - boolean bIsCompatible = isRMOrCM( layoutB ); - - Function relayout = Neureka.get().backend().getFunction().relayout(); - - if ( aIsCompatible ) { - if ( layoutB != NDConfiguration.Layout.SYMMETRIC ) - b = relayout.with(Arg.Layout.of(layoutA)).call(b); // We choose a valid layout based on a - layoutC = layoutA; - } else if ( bIsCompatible ) { - if ( layoutA != NDConfiguration.Layout.SYMMETRIC ) - a = relayout.with(Arg.Layout.of(layoutB)).call(a); // We choose a valid layout based on b - layoutC = layoutB; + NDConfiguration.Layout layoutA = a.getNDConf().getLayout(); + NDConfiguration.Layout layoutB = b.getNDConf().getLayout(); + NDConfiguration.Layout layoutC = c.getNDConf().getLayout(); + + boolean aIsCompatible = isRMOrCM( layoutA ); + boolean bIsCompatible = isRMOrCM( layoutB ); + + Function relayout = Neureka.get().backend().getFunction().relayout(); + + if ( aIsCompatible ) { + if ( layoutB != NDConfiguration.Layout.SYMMETRIC ) + b = relayout.with(Arg.Layout.of(layoutA)).call(b); // We choose a valid layout based on a + layoutC = layoutA; + } else if ( bIsCompatible ) { + if ( layoutA != NDConfiguration.Layout.SYMMETRIC ) + a = relayout.with(Arg.Layout.of(layoutB)).call(a); // We choose a valid layout based on b + layoutC = layoutB; } else { // Ok so the inputs are unspecific/symmetric/ (not RM or CM) // So we just need to decide on any valid layout really: - layoutC = isRMOrCM(layoutC) ? layoutC : NDConfiguration.Layout.ROW_MAJOR; - a = relayout.with(Arg.Layout.of(layoutC)).call(a); - b = relayout.with(Arg.Layout.of(layoutC)).call(b); + layoutC = isRMOrCM(layoutC) ? layoutC : NDConfiguration.Layout.ROW_MAJOR; + a = relayout.with(Arg.Layout.of(layoutC)).call(a); + b = relayout.with(Arg.Layout.of(layoutC)).call(b); } - c.mut().toLayout( layoutC ); - c.mut().setIsVirtual( false ); // This statement is after the layout conversion for performance reasons (virtual tensors barely need copying). + c.mut().toLayout( layoutC ); + c.mut().setIsVirtual( false ); // This statement is after the layout conversion for performance reasons (virtual tensors barely need copying). - return call.withInputAt( 1, a ).withInputAt( 2, b ); + return call.withInputAt( 1, a ).withInputAt( 2, b ); } private static boolean isRMOrCM(NDConfiguration.Layout layout ) { - return layout == NDConfiguration.Layout.ROW_MAJOR || + return layout == NDConfiguration.Layout.ROW_MAJOR || layout == NDConfiguration.Layout.COLUMN_MAJOR; } @@ -139,14 +136,14 @@ * @param call The execution call whose tensors ought to be cloned based on the complexity of their access patterns. */ private static ExecutionCall<?> _autoClone( ExecutionCall<?> call ) { - for ( int i = 0; i < call.arity(); i++ ) - if ( - (!_isSimpleRowMajorMatrix( call.input( i ) ) && !_isSimpleColumnMajorMatrix( call.input( i ) )) + for ( int i = 0; i < call.arity(); i++ ) + if ( + (!_isSimpleRowMajorMatrix( call.input( i ) ) && !_isSimpleColumnMajorMatrix( call.input( i ) )) || - call.input( i ).isPartialSlice() + call.input( i ).isPartialSlice() ) { - _LOG.debug("Auto cloning a tensor which does not have a simple ND configuration..."); - call = call.withInputAt( i, call.input( i ).deepCopy().mut().setIsIntermediate( true ) ); + _LOG.debug("Auto cloning a tensor which does not have a simple ND configuration..."); + call = call.withInputAt( i, call.input( i ).deepCopy().mut().setIsIntermediate( true ) ); /* The user should do cloning explicitly because using slices will cause the backend to perform auto cloning every time the @@ -154,16 +151,16 @@ */ } - return call; + return call; } private static boolean _isSimpleColumnMajorMatrix( Tensor<?> t ) { - return t.rank() == 2 && t.getNDConf().getLayout() == NDConfiguration.Layout.COLUMN_MAJOR; + return t.rank() == 2 && t.getNDConf().getLayout() == NDConfiguration.Layout.COLUMN_MAJOR; } private static boolean _isSimpleRowMajorMatrix( Tensor<?> t ) { - return t.rank() == 2 && t.getNDConf().getLayout() == NDConfiguration.Layout.ROW_MAJOR; + return t.rank() == 2 && t.getNDConf().getLayout() == NDConfiguration.Layout.ROW_MAJOR; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/NDConvolution.html b/docs/coverage/test/html/neureka.backend.main.algorithms/NDConvolution.html index 7cc695339..7ff8d06e1 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/NDConvolution.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/NDConvolution.html @@ -1 +1 @@ -NDConvolution

NDConvolution

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a030603
NDConvolution()8100%n/a010301
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$0(Tensor)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDConvolution

NDConvolution

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a030603
NDConvolution()8100%n/a010301
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$0(Tensor)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/NDConvolution.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/NDConvolution.java.html index a1f783882..463f891d7 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/NDConvolution.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/NDConvolution.java.html @@ -16,4 +16,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarAlgorithm.html b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarAlgorithm.html index 2b784577e..82f7a011e 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarAlgorithm.html @@ -1 +1 @@ -ScalarAlgorithm

ScalarAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total80 of 13942%10 of 1637%916132738
lambda$new$4(ExecutionCall)410%20%227711
lambda$new$2(Tensor[])222452%8642%581401
lambda$new$0(ExecutionCall)100%n/a113311
lambda$new$5(Function, ExecutionCall)70%n/a112211
ScalarAlgorithm()20100%n/a010601
lambda$new$3(ExecutionCall)9100%n/a010401
lambda$new$1(Tensor)5100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarAlgorithm

ScalarAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total80 of 13942%10 of 1637%916132738
lambda$new$4(ExecutionCall)410%20%227711
lambda$new$2(Tensor[])222452%8642%581401
lambda$new$0(ExecutionCall)100%n/a113311
lambda$new$5(Function, ExecutionCall)70%n/a112211
ScalarAlgorithm()20100%n/a010601
lambda$new$3(ExecutionCall)9100%n/a010401
lambda$new$1(Tensor)5100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarAlgorithm.java.html index ca1bf4eac..1df49e7dc 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarAlgorithm.java.html @@ -53,4 +53,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarBroadcast.html b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarBroadcast.html index 7f55b46f1..a46054a10 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarBroadcast.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarBroadcast.html @@ -1 +1 @@ -ScalarBroadcast

ScalarBroadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total76 of 22065%9 of 1850%1018164539
lambda$new$4(ExecutionCall)500%20%22111111
lambda$new$0(ExecutionCall)100%n/a113311
lambda$new$2(Tensor[])73984%6857%580401
lambda$new$5(Function, ExecutionCall)70%n/a112211
lambda$new$6(ScalarFun, ExecutionCall)25696%1150%1201201
ScalarBroadcast(ScalarFun)34100%n/a010801
lambda$new$3(ExecutionCall)9100%n/a010401
lambda$new$1(Tensor)5100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarBroadcast

ScalarBroadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total132 of 22040%10 of 1844%1118284549
lambda$new$6(ScalarFun, ExecutionCall)580%20%22121211
lambda$new$4(ExecutionCall)500%20%22111111
lambda$new$0(ExecutionCall)100%n/a113311
lambda$new$2(Tensor[])73984%6857%580401
lambda$new$5(Function, ExecutionCall)70%n/a112211
ScalarBroadcast(ScalarFun)34100%n/a010801
lambda$new$3(ExecutionCall)9100%n/a010401
lambda$new$1(Tensor)5100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarBroadcast.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarBroadcast.java.html index fdfe9a4bb..ef6f68237 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarBroadcast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarBroadcast.java.html @@ -65,22 +65,22 @@ setImplementationFor( OpenCLDevice.class, call -> { - int d = call.getValOf(Arg.DerivIdx.class); - CPUFun f = d < 0 ? fun.getActivation() : fun.getDerivative(); - double value = f.invoke( call.input( Number.class, 1 ).at(0).get().doubleValue() ); - Tensor<Number> t = call.input( Number.class, 0 ); - int gwz = t.size(); - call.getDevice() - .getKernel("scalar_broadcast") - .passAllOf(t) - .pass((float) value) - .pass(t.rank()) - .call( gwz ); + int d = call.getValOf(Arg.DerivIdx.class); + CPUFun f = d < 0 ? fun.getActivation() : fun.getDerivative(); + double value = f.invoke( call.input( Number.class, 1 ).at(0).get().doubleValue() ); + Tensor<Number> t = call.input( Number.class, 0 ); + int gwz = t.size(); + call.getDevice() + .getKernel("scalar_broadcast") + .passAllOf(t) + .pass((float) value) + .pass(t.rank()) + .call( gwz ); - return call.input(0); + return call.input(0); } ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarSumAlgorithm.html b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarSumAlgorithm.html index 8e4941b33..97a4826be 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarSumAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarSumAlgorithm.html @@ -1 +1 @@ -ScalarSumAlgorithm

ScalarSumAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 12676%3 of 862%31242518
lambda$new$4(Shape, ADTarget)220%20%223311
lambda$new$5(Function, ExecutionCall)75688%1150%1211101
ScalarSumAlgorithm()13100%n/a010601
lambda$new$1(Tensor)11100%4100%030101
lambda$new$2(ExecutionCall)9100%n/a010401
lambda$new$0(Tensor)5100%n/a010101
lambda$new$3(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSumAlgorithm

ScalarSumAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 12676%3 of 862%31242518
lambda$new$4(Shape, ADTarget)220%20%223311
lambda$new$5(Function, ExecutionCall)75688%1150%1211101
ScalarSumAlgorithm()13100%n/a010601
lambda$new$1(Tensor)11100%4100%030101
lambda$new$2(ExecutionCall)9100%n/a010401
lambda$new$0(Tensor)5100%n/a010101
lambda$new$3(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarSumAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarSumAlgorithm.java.html index 8c2ebd9e5..81c124788 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarSumAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/ScalarSumAlgorithm.java.html @@ -40,4 +40,4 @@ .buildFunAlgorithm(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/SumAlgorithm.html b/docs/coverage/test/html/neureka.backend.main.algorithms/SumAlgorithm.html index 4c05b1954..557ec23bf 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/SumAlgorithm.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/SumAlgorithm.html @@ -1 +1 @@ -SumAlgorithm

SumAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 9782%3 of 425%31032418
lambda$new$5(ExecutionCall)130%20%223311
lambda$new$3(Shape, ADTarget)41881%1150%120301
lambda$new$4(Function, ExecutionCall)33100%n/a010701
SumAlgorithm()15100%n/a010701
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$0(Tensor)5100%n/a010101
lambda$new$2(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SumAlgorithm

SumAlgorithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 9782%3 of 425%31032418
lambda$new$5(ExecutionCall)130%20%223311
lambda$new$3(Shape, ADTarget)41881%1150%120301
lambda$new$4(Function, ExecutionCall)33100%n/a010701
SumAlgorithm()15100%n/a010701
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$0(Tensor)5100%n/a010101
lambda$new$2(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/SumAlgorithm.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/SumAlgorithm.java.html index f8d2cef56..8be2b8b77 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/SumAlgorithm.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/SumAlgorithm.java.html @@ -42,4 +42,4 @@ .buildFunAlgorithm(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/Util.html b/docs/coverage/test/html/neureka.backend.main.algorithms/Util.html index 15a65f422..841dd2197 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/Util.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/Util.html @@ -1 +1 @@ -Util

Util

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 9096%0 of 6100%1511412
Util()30%n/a111111
transpose(Tensor)87100%6100%0401301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Util

Util

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 9096%0 of 6100%1511412
Util()30%n/a111111
transpose(Tensor)87100%6100%0401301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/Util.java.html b/docs/coverage/test/html/neureka.backend.main.algorithms/Util.java.html index 8a7aed536..f632d4c82 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/Util.java.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/Util.java.html @@ -26,4 +26,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/index.html b/docs/coverage/test/html/neureka.backend.main.algorithms/index.html index 839163c2d..08214b43c 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/index.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/index.html @@ -1 +1 @@ -neureka.backend.main.algorithms

neureka.backend.main.algorithms

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total318 of 2,30786%65 of 22571%692204942213107012
ScalarAlgorithm805942%10637%91613273801
ScalarBroadcast7614465%9950%101816453901
DotProductAlgorithm5140488%163266%164447812001
ScalarSumAlgorithm299776%3562%3124251801
Broadcast2731992%64287%8334532901
MatMulAlgorithm2142295%93479%94027301801
SumAlgorithm178082%3125%3103241801
ElementwiseAlgorithm610494%2250%3100251801
BiScalarBroadcast515296%62076%6200250701
BiElementwise10297%1375%192270701
Util8796%6100%151141201
NDConvolution19100%n/a03060301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.algorithms

neureka.backend.main.algorithms

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total369 of 2,30683%66 of 22570%702196142214106012
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ScalarAlgorithm805942%10637%91613273801
DotProductAlgorithm5140488%163266%164447812001
ScalarSumAlgorithm299776%3562%3124251801
Broadcast2731992%64287%8334532901
SumAlgorithm178082%3125%3103241801
MatMulAlgorithm1642396%93479%93927201701
ElementwiseAlgorithm610494%2250%3100251801
BiScalarBroadcast515296%62076%6200250701
BiElementwise10597%1375%192280701
Util8796%6100%151141201
NDConvolution19100%n/a03060301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.algorithms/index.source.html b/docs/coverage/test/html/neureka.backend.main.algorithms/index.source.html index 222bdf57f..5f2031673 100644 --- a/docs/coverage/test/html/neureka.backend.main.algorithms/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.algorithms/index.source.html @@ -1 +1 @@ -neureka.backend.main.algorithms

neureka.backend.main.algorithms

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total318 of 2,30786%65 of 22571%692204942213107012
ScalarAlgorithm.java805942%10637%91613273801
ScalarBroadcast.java7614465%9950%101816453901
DotProductAlgorithm.java5140488%163266%164447812001
ScalarSumAlgorithm.java299776%3562%3124251801
Broadcast.java2731992%64287%8334532901
MatMulAlgorithm.java2142295%93479%94027301801
SumAlgorithm.java178082%3125%3103241801
ElementwiseAlgorithm.java610494%2250%3100251801
BiScalarBroadcast.java515296%62076%6200250701
BiElementwise.java10297%1375%192270701
Util.java8796%6100%151141201
NDConvolution.java19100%n/a03060301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.algorithms

neureka.backend.main.algorithms

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total369 of 2,30683%66 of 22570%702196142214106012
ScalarBroadcast.java1328840%10844%111828454901
ScalarAlgorithm.java805942%10637%91613273801
DotProductAlgorithm.java5140488%163266%164447812001
ScalarSumAlgorithm.java299776%3562%3124251801
Broadcast.java2731992%64287%8334532901
SumAlgorithm.java178082%3125%3103241801
MatMulAlgorithm.java1642396%93479%93927201701
ElementwiseAlgorithm.java610494%2250%3100251801
BiScalarBroadcast.java515296%62076%6200250701
BiElementwise.java10597%1375%192280701
Util.java8796%6100%151141201
NDConvolution.java19100%n/a03060301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcast.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcast.html index 46d20fe3f..54d604e96 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcast.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcast.html @@ -1 +1 @@ -CLBroadcast

CLBroadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 8793%2 of 450%2501503
lambda$new$0(ExecutionCall)66191%2250%2301101
CLBroadcast(String, String, String)13100%n/a010301
lambda$new$1(KernelCode)7100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLBroadcast

CLBroadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total87 of 870%4 of 40%55151533
lambda$new$0(ExecutionCall)670%40%33111111
CLBroadcast(String, String, String)130%n/a113311
lambda$new$1(KernelCode)70%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcast.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcast.java.html index 48a771eca..a0d3d67b6 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcast.java.html @@ -8,29 +8,29 @@ public class CLBroadcast extends ParsedCLImplementation { protected CLBroadcast(String postfix, String forward, String backward) { - super( + super( call -> { - int offset = ( call.input( Number.class, 0 ) != null ? 0 : 1 ); - int gwz = ( call.input( Number.class, 0 ) != null ? call.input( Number.class, 0 ).size() : call.input( Number.class, 1 ).size() ); - call.getDevice() - .getKernel(call) - .passAllOf( call.input( Number.class, offset ) ) - .passAllOf( call.input( Number.class, offset + 1 ) ) - .passAllOf( call.input( Number.class, offset + 2 ) ) - .pass( call.input( Number.class, 0 ).rank() ) - .pass( call.getValOf( Arg.DerivIdx.class ) ) - .call( gwz ); + int offset = ( call.input( Number.class, 0 ) != null ? 0 : 1 ); + int gwz = ( call.input( Number.class, 0 ) != null ? call.input( Number.class, 0 ).size() : call.input( Number.class, 1 ).size() ); + call.getDevice() + .getKernel(call) + .passAllOf( call.input( Number.class, offset ) ) + .passAllOf( call.input( Number.class, offset + 1 ) ) + .passAllOf( call.input( Number.class, offset + 2 ) ) + .pass( call.input( Number.class, 0 ).rank() ) + .pass( call.getValOf( Arg.DerivIdx.class ) ) + .call( gwz ); - return call.input( 0 ); + return call.input( 0 ); }, 3, - Neureka.get().utility().readResource("kernels/broadcast_template.cl"), + Neureka.get().utility().readResource("kernels/broadcast_template.cl"), forward, backward, postfix, - kernelCode -> new KernelCode[]{kernelCode} + kernelCode -> new KernelCode[]{kernelCode} ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastAddition.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastAddition.html index b45bd4faf..3a5e6afcb 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastAddition.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastAddition.html @@ -1 +1 @@ -CLBroadcastAddition

CLBroadcastAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 6100%0 of 0n/a010201
CLBroadcastAddition(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLBroadcastAddition

CLBroadcastAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 60%0 of 0n/a112211
CLBroadcastAddition(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastAddition.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastAddition.java.html index bc2f8daa1..0de2d2fb3 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastAddition.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastAddition.java.html @@ -3,7 +3,7 @@ public class CLBroadcastAddition extends CLBroadcast { public CLBroadcastAddition(String id) { - super(id, "value += src1 + src2;\n", "value += 1 * drain;\n"); - } + super(id, "value += src1 + src2;\n", "value += 1 * drain;\n"); + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastDivision.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastDivision.html index 124e1afff..9fb4ff333 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastDivision.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastDivision.html @@ -1 +1 @@ -CLBroadcastDivision

CLBroadcastDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 6100%0 of 0n/a010201
CLBroadcastDivision(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLBroadcastDivision

CLBroadcastDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 60%0 of 0n/a112211
CLBroadcastDivision(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastDivision.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastDivision.java.html index 3065771d2..70a675da5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastDivision.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastDivision.java.html @@ -3,7 +3,7 @@ public class CLBroadcastDivision extends CLBroadcast { public CLBroadcastDivision(String id) { - super( + super( id, "value = ((int)src1) % ((int)src2);\n", "if ( d == 0 ) {\n" + @@ -12,6 +12,6 @@ " value += (-(handle /(float)pow(target, (float)2)) ) * drain;\n" + "}" ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastModulo.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastModulo.html index e10e0fa28..75118a0ee 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastModulo.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastModulo.html @@ -1 +1 @@ -CLBroadcastModulo

CLBroadcastModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 6100%0 of 0n/a010201
CLBroadcastModulo(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
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CLBroadcastModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 60%0 of 0n/a112211
CLBroadcastModulo(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastModulo.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastModulo.java.html index 79e417753..c1e9cad78 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastModulo.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastModulo.java.html @@ -3,7 +3,7 @@ public class CLBroadcastModulo extends CLBroadcast { public CLBroadcastModulo(String id) { - super( + super( id, "value = ((int)src1) % ((int)src2);\n", "if ( d == 0 ) {\n" + @@ -12,6 +12,6 @@ " value += (-(handle /(float)pow(target, (float)2)) ) * drain;\n" + "}" ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastMultiplication.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastMultiplication.html index ec2295ce6..f4e8d088d 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastMultiplication.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastMultiplication.html @@ -1 +1 @@ -CLBroadcastMultiplication

CLBroadcastMultiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 6100%0 of 0n/a010201
CLBroadcastMultiplication(String)6100%n/a010201
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\ No newline at end of file +CLBroadcastMultiplication

CLBroadcastMultiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 60%0 of 0n/a112211
CLBroadcastMultiplication(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastMultiplication.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastMultiplication.java.html index d52450cb3..d0056b7dd 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastMultiplication.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastMultiplication.java.html @@ -3,7 +3,7 @@ public class CLBroadcastMultiplication extends CLBroadcast { public CLBroadcastMultiplication(String id) { - super(id, "value = src1 * src2;\n", "value += ( d == 0 ? drain : handle );\n"); - } + super(id, "value = src1 * src2;\n", "value += ( d == 0 ? drain : handle );\n"); + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastPower.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastPower.html index 7b1963e9a..1dc2352ea 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastPower.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastPower.html @@ -1 +1 @@ -CLBroadcastPower

CLBroadcastPower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 6100%0 of 0n/a010201
CLBroadcastPower(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
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CLBroadcastPower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 60%0 of 0n/a112211
CLBroadcastPower(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastPower.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastPower.java.html index 0c89d17ab..4188b80c9 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastPower.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastPower.java.html @@ -3,7 +3,7 @@ public class CLBroadcastPower extends CLBroadcast { public CLBroadcastPower(String id) { - super( + super( id, "value += pow(src1, src2);", "if ( d == 0 ) {\n" + @@ -12,6 +12,6 @@ " value += (pow(target, handle) * log(handle)) * drain;\n" + "}" ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastSubtraction.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastSubtraction.html index 5a9f7b665..8ff176e29 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastSubtraction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastSubtraction.html @@ -1 +1 @@ -CLBroadcastSubtraction

CLBroadcastSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 6100%0 of 0n/a010201
CLBroadcastSubtraction(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
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CLBroadcastSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 60%0 of 0n/a112211
CLBroadcastSubtraction(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastSubtraction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastSubtraction.java.html index 4a87ab175..bac64da38 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastSubtraction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLBroadcastSubtraction.java.html @@ -3,7 +3,7 @@ public class CLBroadcastSubtraction extends CLBroadcast { public CLBroadcastSubtraction(String id) { - super(id, "value += src1 - src2;\n", "value += src1 + src2 * -((d * 2) -1);\n"); - } + super(id, "value += src1 - src2;\n", "value += src1 + src2 * -((d * 2) -1);\n"); + } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcast.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcast.html index 8e78bda65..32ad9f631 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcast.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcast.html @@ -1 +1 @@ -CLScalarBroadcast

CLScalarBroadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total49 of 16269%1 of 785%211123115
lambda$new$0(ExecutionCall)460%n/a11111111
lambda$new$1(KernelCode, String)36195%1685%1711301
lambda$new$3(KernelCode)36100%n/a010401
CLScalarBroadcast(String, String, String)13100%n/a010301
lambda$new$2(int)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLScalarBroadcast

CLScalarBroadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total162 of 1620%7 of 70%1111313155
lambda$new$1(KernelCode, String)640%70%77131311
lambda$new$0(ExecutionCall)460%n/a11111111
lambda$new$3(KernelCode)360%n/a114411
CLScalarBroadcast(String, String, String)130%n/a113311
lambda$new$2(int)30%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcast.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcast.java.html index 933c1941b..8901702e5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcast.java.html @@ -16,7 +16,7 @@ public CLScalarBroadcast( String postfix, String activation, String derivation ) { - super( + super( call->{ Tensor<Number> t = call.input( Number.class, 0 ); int gwz = t.size(); @@ -32,35 +32,35 @@ return call.input(0); }, 2, - Neureka.get().utility().readResource("kernels/scalarization_template.cl"), + Neureka.get().utility().readResource("kernels/scalarization_template.cl"), activation, derivation, postfix, kernelCode -> { - String[] types = new String[]{ + String[] types = new String[]{ "float", "double", "int", "long", "short", "char" }; - return - Arrays.stream(types).map( type -> { - String newName = kernelCode.getName() + ("_" + type); - String newCode = kernelCode.getCode() - .replace(TYPE, type) - .replace(kernelCode.getName(), newName); + return + Arrays.stream(types).map( type -> { + String newName = kernelCode.getName() + ("_" + type); + String newCode = kernelCode.getCode() + .replace(TYPE, type) + .replace(kernelCode.getName(), newName); DataType<?> dt; - switch (type) { - case "float": dt = DataType.of(Float.class); break; - case "double": dt = DataType.of(Double.class); break; - case "int": dt = DataType.of(Integer.class); break; - case "long": dt = DataType.of(Long.class); break; - case "short": dt = DataType.of(Short.class); break; - case "char": dt = DataType.of(Byte.class); break; + switch (type) { + case "float": dt = DataType.of(Float.class); break; + case "double": dt = DataType.of(Double.class); break; + case "int": dt = DataType.of(Integer.class); break; + case "long": dt = DataType.of(Long.class); break; + case "short": dt = DataType.of(Short.class); break; + case "char": dt = DataType.of(Byte.class); break; default: dt = DataType.of(Float.class); break; } - return new KernelCode(newName, newCode, dt); + return new KernelCode(newName, newCode, dt); }) - .toArray(KernelCode[]::new); + .toArray(KernelCode[]::new); } ); - } + } } -
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastAddition.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastAddition.html index b9bea87a8..765084d4c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastAddition.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastAddition.html @@ -1 +1 @@ -CLScalarBroadcastAddition

CLScalarBroadcastAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total24 of 9273%3 of 650%3621903
run(ExecutionCall)246171%3350%3421601
CLScalarBroadcastAddition(String)6100%n/a010201
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLScalarBroadcastAddition

CLScalarBroadcastAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total92 of 920%6 of 60%66191933
run(ExecutionCall)850%60%44161611
CLScalarBroadcastAddition(String)60%n/a112211
static {...}10%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastAddition.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastAddition.java.html index 7b0daa95b..ced929d20 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastAddition.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastAddition.java.html @@ -5,32 +5,32 @@ import neureka.devices.opencl.OpenCLDevice; import neureka.math.args.Arg; -public class CLScalarBroadcastAddition extends CLScalarBroadcast +public class CLScalarBroadcastAddition extends CLScalarBroadcast { public CLScalarBroadcastAddition(String id) { - super( id, "output = input1 + value;\n", "output = 1;\n" ); - } + super( id, "output = input1 + value;\n", "output = 1;\n" ); + } @Override public Tensor<?> run(ExecutionCall<OpenCLDevice> call) { - assert call.arity() == 3; - if ( call.getDerivativeIndex() == 0 ) + assert call.arity() == 3; + if ( call.getDerivativeIndex() == 0 ) return Tensor.of( call.input(1).shape(), 1d ).mut().setIsIntermediate( true ); - else if ( call.getDerivativeIndex() == 1 ) + else if ( call.getDerivativeIndex() == 1 ) return Tensor.of( call.input( 2 ).shape(), 1d ).mut().setIsIntermediate( true ); else { - int gwz = call.input(Number.class, 0).size(); - float value = call.input(Number.class, 2).item(0).floatValue(); - call.getDevice() - .getKernel(call) - .passAllOf(call.input(Number.class, 0)) - .passAllOf(call.input(Number.class, 1)) - .pass(value) - .pass(call.input(Number.class, 0).rank()) - .pass(call.getValOf(Arg.DerivIdx.class)) - .call(gwz); + int gwz = call.input(Number.class, 0).size(); + float value = call.input(Number.class, 2).item(0).floatValue(); + call.getDevice() + .getKernel(call) + .passAllOf(call.input(Number.class, 0)) + .passAllOf(call.input(Number.class, 1)) + .pass(value) + .pass(call.input(Number.class, 0).rank()) + .pass(call.getValOf(Arg.DerivIdx.class)) + .call(gwz); } - return call.input( 0 ); + return call.input( 0 ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastDivision.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastDivision.html index 20ee6c35c..db1ec8b44 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastDivision.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastDivision.html @@ -1 +1 @@ -CLScalarBroadcastDivision

CLScalarBroadcastDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total72 of 787%4 of 40%34101212
run(ExecutionCall)720%40%33101011
CLScalarBroadcastDivision(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLScalarBroadcastDivision

CLScalarBroadcastDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total78 of 780%4 of 40%44121222
run(ExecutionCall)720%40%33101011
CLScalarBroadcastDivision(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastDivision.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastDivision.java.html index afa429a81..e2972575b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastDivision.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastDivision.java.html @@ -8,7 +8,7 @@ public class CLScalarBroadcastDivision extends CLScalarBroadcast { public CLScalarBroadcastDivision( String id ) { - super( + super( id, "output = ("+TYPE+")(((float)input1) / ((float)value));\n", "if ( d == 0 ) { \n" + @@ -17,7 +17,7 @@ " output = -(("+TYPE+")(((float)value) /(float)pow((float)input1, 2.0f))); \n" + "} \n" ); - } + } @Override public Tensor<?> run(ExecutionCall<OpenCLDevice> call) { @@ -34,4 +34,4 @@ return call.input( 0 ); } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastIdentity.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastIdentity.html index e53e6319b..6369c79ea 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastIdentity.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastIdentity.html @@ -1 +1 @@ -CLScalarBroadcastIdentity

CLScalarBroadcastIdentity

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 51100%0 of 0n/a0201302
run(ExecutionCall)45100%n/a0101101
CLScalarBroadcastIdentity(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
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CLScalarBroadcastIdentity

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total51 of 510%0 of 0n/a22131322
run(ExecutionCall)450%n/a11111111
CLScalarBroadcastIdentity(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastIdentity.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastIdentity.java.html index 895efdf22..98298ddbf 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastIdentity.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastIdentity.java.html @@ -8,27 +8,27 @@ public class CLScalarBroadcastIdentity extends CLScalarBroadcast { public CLScalarBroadcastIdentity(String id) { - super( + super( id, "output = value;\n", "output = value;\n" ); - } + } @Override public Tensor<?> run(ExecutionCall<OpenCLDevice> call) { - Tensor<Number> t = call.input( Number.class, 0 ); - int gwz = t.size(); - call.getDevice() - .getKernel(call) - .passAllOf( t ) - .passAllOf( t ) - .pass( call.input( Number.class, 1 ).at(0).get() ) - .pass( t.rank() ) - .pass( call.getValOf( Arg.DerivIdx.class ) ) - .call( gwz ); + Tensor<Number> t = call.input( Number.class, 0 ); + int gwz = t.size(); + call.getDevice() + .getKernel(call) + .passAllOf( t ) + .passAllOf( t ) + .pass( call.input( Number.class, 1 ).at(0).get() ) + .pass( t.rank() ) + .pass( call.getValOf( Arg.DerivIdx.class ) ) + .call( gwz ); - return call.input(0); + return call.input(0); } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastModulo.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastModulo.html index a741ea7f2..c5b8c72a3 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastModulo.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastModulo.html @@ -1 +1 @@ -CLScalarBroadcastModulo

CLScalarBroadcastModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total72 of 787%4 of 40%34111312
run(ExecutionCall)720%40%33111111
CLScalarBroadcastModulo(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLScalarBroadcastModulo

CLScalarBroadcastModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total78 of 780%4 of 40%44131322
run(ExecutionCall)720%40%33111111
CLScalarBroadcastModulo(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastModulo.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastModulo.java.html index da12b7c61..2955f292c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastModulo.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastModulo.java.html @@ -8,7 +8,7 @@ public class CLScalarBroadcastModulo extends CLScalarBroadcast { public CLScalarBroadcastModulo(String id ) { - super( + super( id, "output = ("+TYPE+")(((int)input1) % ((int)value)); \n", " if ( d == 0 ) { \n" + @@ -17,7 +17,7 @@ " output = ("+TYPE+")(-value /(float)pow((float)input1, 2.0f)); \n" + " }" ); - } + } @Override public Tensor<?> run(ExecutionCall<OpenCLDevice> call) { @@ -35,4 +35,4 @@ return call.input( 0 ); } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastMultiplication.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastMultiplication.html index f49375582..3f44488f6 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastMultiplication.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastMultiplication.html @@ -1 +1 @@ -CLScalarBroadcastMultiplication

CLScalarBroadcastMultiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total1 of 10299%1 of 887%1601702
run(ExecutionCall)19598%1787%1501501
CLScalarBroadcastMultiplication(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
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CLScalarBroadcastMultiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total102 of 1020%8 of 80%66171722
run(ExecutionCall)960%80%55151511
CLScalarBroadcastMultiplication(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastMultiplication.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastMultiplication.java.html index 76220aeab..b27f1cb31 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastMultiplication.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastMultiplication.java.html @@ -8,28 +8,28 @@ public class CLScalarBroadcastMultiplication extends CLScalarBroadcast { public CLScalarBroadcastMultiplication(String id) { - super( id, "output = input1 * value;\n", "if ( d == 0 ) {output = value;}else{output = input1;}\n" ); - } + super( id, "output = input1 * value;\n", "if ( d == 0 ) {output = value;}else{output = input1;}\n" ); + } @Override public Tensor<?> run(ExecutionCall<OpenCLDevice> call) { - if ( call.getDerivativeIndex() == 0 ) - return call.input( 2 ).shallowCopy().mut().setIsIntermediate( true ); - else if ( call.getDerivativeIndex() == 1 ) - return call.input( 1 ).shallowCopy().mut().setIsIntermediate( true ); + if ( call.getDerivativeIndex() == 0 ) + return call.input( 2 ).shallowCopy().mut().setIsIntermediate( true ); + else if ( call.getDerivativeIndex() == 1 ) + return call.input( 1 ).shallowCopy().mut().setIsIntermediate( true ); else { - int offset = (call.input(Number.class, 2).isVirtual() || call.input(Number.class, 2).size() == 1) ? 1 : 0; - int gwz = call.input(Number.class, 0).size(); - call.getDevice() - .getKernel(call) - .passAllOf(call.input(Number.class, 0)) - .passAllOf(call.input(Number.class, 0 + offset)) - .pass( call.input( Number.class, 1 + offset ).at( 0 ).get() ) - .pass(call.input(Number.class, 0).rank()) - .pass(call.getValOf(Arg.DerivIdx.class)) - .call(gwz); + int offset = (call.input(Number.class, 2).isVirtual() || call.input(Number.class, 2).size() == 1) ? 1 : 0; + int gwz = call.input(Number.class, 0).size(); + call.getDevice() + .getKernel(call) + .passAllOf(call.input(Number.class, 0)) + .passAllOf(call.input(Number.class, 0 + offset)) + .pass( call.input( Number.class, 1 + offset ).at( 0 ).get() ) + .pass(call.input(Number.class, 0).rank()) + .pass(call.getValOf(Arg.DerivIdx.class)) + .call(gwz); } - return call.input( 0 ); + return call.input( 0 ); } } -
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastPower.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastPower.html index 797e90374..3dddbeee9 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastPower.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastPower.html @@ -1 +1 @@ -CLScalarBroadcastPower

CLScalarBroadcastPower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total72 of 787%4 of 40%34111312
run(ExecutionCall)720%40%33111111
CLScalarBroadcastPower(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLScalarBroadcastPower

CLScalarBroadcastPower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total78 of 780%4 of 40%44131322
run(ExecutionCall)720%40%33111111
CLScalarBroadcastPower(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastPower.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastPower.java.html index d6fdfddfd..5e3a9d5a5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastPower.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastPower.java.html @@ -8,7 +8,7 @@ public class CLScalarBroadcastPower extends CLScalarBroadcast { public CLScalarBroadcastPower( String id ) { - super( + super( id, "output = ("+TYPE+") pow( (float) input1, (float) value );", " if ( d == 0 ) \n" + @@ -16,7 +16,7 @@ " else \n" + " output = ("+TYPE+") ( pow( (float) input1, (float) value ) * log( (float) value ) ); \n" ); - } + } @Override public Tensor<?> run(ExecutionCall<OpenCLDevice> call) { int offset = (call.input( Number.class, 2 ).isVirtual() || call.input( Number.class, 2 ).size() == 1)?1:0; @@ -33,4 +33,4 @@ return call.input(0); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastSubtraction.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastSubtraction.html index 57d3676e5..cc0ab1a09 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastSubtraction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastSubtraction.html @@ -1 +1 @@ -CLScalarBroadcastSubtraction

CLScalarBroadcastSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total73 of 797%4 of 40%34111312
run(ExecutionCall)730%40%33111111
CLScalarBroadcastSubtraction(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLScalarBroadcastSubtraction

CLScalarBroadcastSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total79 of 790%4 of 40%44131322
run(ExecutionCall)730%40%33111111
CLScalarBroadcastSubtraction(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastSubtraction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastSubtraction.java.html index 552c3b7e4..91ffde7be 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastSubtraction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CLScalarBroadcastSubtraction.java.html @@ -8,8 +8,8 @@ public class CLScalarBroadcastSubtraction extends CLScalarBroadcast { public CLScalarBroadcastSubtraction(String id) { - super( id, "output = input1 - value;\n", "if (d==0) { output = 1; } else { output = -1; }" ); - } + super( id, "output = input1 - value;\n", "if (d==0) { output = 1; } else { output = -1; }" ); + } @Override public Tensor<?> run(ExecutionCall<OpenCLDevice> call) { @@ -27,4 +27,4 @@ return call.input(0); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcast.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcast.html index fc3ff75ec..db43f781c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcast.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcast.html @@ -1 +1 @@ -CPUBroadcast

CPUBroadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total35 of 76295%10 of 9088%1053216008
_newWorkloadFor(ExecutionCall)156781%2880%2621601
_broadcastF64(Tensor, Tensor, Tensor, int, int, int, CPUBiFun)1031196%43690%42106801
_broadcastF32(Tensor, Tensor, Tensor, int, int, int, CPUBiFun)1031196%43690%42106801
run(ExecutionCall)16100%n/a010601
lambda$_newWorkloadFor$1(Tensor, Tensor, Tensor, int, CPUBiFun, int, int)9100%n/a010101
lambda$_newWorkloadFor$0(Tensor, Tensor, Tensor, int, CPUBiFun, int, int)9100%n/a010101
CPUBroadcast()3100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcast

CPUBroadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total35 of 76295%10 of 9088%1053216008
_newWorkloadFor(ExecutionCall)156781%2880%2621601
_broadcastF64(Tensor, Tensor, Tensor, int, int, int, CPUBiFun)1031196%43690%42106801
_broadcastF32(Tensor, Tensor, Tensor, int, int, int, CPUBiFun)1031196%43690%42106801
run(ExecutionCall)16100%n/a010601
lambda$_newWorkloadFor$1(Tensor, Tensor, Tensor, int, CPUBiFun, int, int)9100%n/a010101
lambda$_newWorkloadFor$0(Tensor, Tensor, Tensor, int, CPUBiFun, int, int)9100%n/a010101
CPUBroadcast()3100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcast.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcast.java.html index ed4c11f78..2ee47cfe5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcast.java.html @@ -261,4 +261,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition$1.html index 8a22c078d..0169526a7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition$1.html @@ -1 +1 @@ -CPUBroadcastAddition.new CPUBiFun() {...}

CPUBroadcastAddition.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total13 of 2751%4 of 40%472525
invoke(boolean, boolean)80%40%331111
invoke(char, char)50%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastAddition.new CPUBiFun() {...}

CPUBroadcastAddition.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total13 of 2751%4 of 40%472525
invoke(boolean, boolean)80%40%331111
invoke(char, char)50%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition.html index 79ae05a8d..42ba1f83c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition.html @@ -1 +1 @@ -CPUBroadcastAddition

CPUBroadcastAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a040404
_getFun()5100%n/a010101
CPUBroadcastAddition()3100%n/a010101
_getDeriveAt0()3100%n/a010101
_getDeriveAt1()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastAddition

CPUBroadcastAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a040404
_getFun()5100%n/a010101
CPUBroadcastAddition()3100%n/a010101
_getDeriveAt0()3100%n/a010101
_getDeriveAt1()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition.java.html index 20a3247d2..701d3f80c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastAddition.java.html @@ -20,4 +20,4 @@ @Override protected CPUBiFun _getDeriveAt1() { return _getFun(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$1.html index ef5bc5089..dd886c64a 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$1.html @@ -1 +1 @@ -CPUBroadcastDivision.new CPUBiFun() {...}

CPUBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastDivision.new CPUBiFun() {...}

CPUBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$2.html index cbc07abd5..dcd9f8b7c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$2.html @@ -1 +1 @@ -CPUBroadcastDivision.new CPUBiFun() {...}

CPUBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a333333
{...}60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastDivision.new CPUBiFun() {...}

CPUBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a333333
{...}60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$3.html index a38b03794..573a8ff73 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision$3.html @@ -1 +1 @@ -CPUBroadcastDivision.new CPUBiFun() {...}

CPUBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total23 of 230%0 of 0n/a333333
invoke(float, float)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastDivision.new CPUBiFun() {...}

CPUBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total23 of 230%0 of 0n/a333333
invoke(float, float)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision.html index 15e272e67..ff905b1d4 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision.html @@ -1 +1 @@ -CPUBroadcastDivision

CPUBroadcastDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 1844%0 of 0n/a242424
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
CPUBroadcastDivision()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastDivision

CPUBroadcastDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 1844%0 of 0n/a242424
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
CPUBroadcastDivision()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision.java.html index 15d2bb964..3566a62d1 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastDivision.java.html @@ -30,4 +30,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$1.html index 7cfba3a05..d763ee855 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$1.html @@ -1 +1 @@ -CPUBroadcastModulo.new CPUBiFun() {...}

CPUBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1471%0 of 0n/a131313
invoke(float, float)40%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastModulo.new CPUBiFun() {...}

CPUBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1471%0 of 0n/a131313
invoke(float, float)40%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$2.html index 6a610015b..4003e3f48 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$2.html @@ -1 +1 @@ -CPUBroadcastModulo.new CPUBiFun() {...}

CPUBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a333333
{...}60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastModulo.new CPUBiFun() {...}

CPUBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a333333
{...}60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$3.html index 5fc253a1f..3246f66d7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo$3.html @@ -1 +1 @@ -CPUBroadcastModulo.new CPUBiFun() {...}

CPUBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total23 of 230%0 of 0n/a333333
invoke(float, float)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastModulo.new CPUBiFun() {...}

CPUBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total23 of 230%0 of 0n/a333333
invoke(float, float)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo.html index 61db2a4f4..4bfac2146 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo.html @@ -1 +1 @@ -CPUBroadcastModulo

CPUBroadcastModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 1844%0 of 0n/a242424
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
CPUBroadcastModulo()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastModulo

CPUBroadcastModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 1844%0 of 0n/a242424
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
CPUBroadcastModulo()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo.java.html index bcc1bacfb..7bee20708 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastModulo.java.html @@ -30,4 +30,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$1.html index 96302aeac..4481fd21e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$1.html @@ -1 +1 @@ -CPUBroadcastMultiplication.new CPUBiFun() {...}

CPUBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastMultiplication.new CPUBiFun() {...}

CPUBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$2.html index 19b59da07..51ad34795 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$2.html @@ -1 +1 @@ -CPUBroadcastMultiplication.new CPUBiFun() {...}

CPUBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 100%0 of 0n/a333333
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastMultiplication.new CPUBiFun() {...}

CPUBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 100%0 of 0n/a333333
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$3.html index 26cc5614f..490aec5b8 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication$3.html @@ -1 +1 @@ -CPUBroadcastMultiplication.new CPUBiFun() {...}

CPUBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 100%0 of 0n/a333333
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastMultiplication.new CPUBiFun() {...}

CPUBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 100%0 of 0n/a333333
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication.html index da7546fc2..46193bc59 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication.html @@ -1 +1 @@ -CPUBroadcastMultiplication

CPUBroadcastMultiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 1844%0 of 0n/a242424
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
CPUBroadcastMultiplication()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastMultiplication

CPUBroadcastMultiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 1844%0 of 0n/a242424
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
CPUBroadcastMultiplication()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication.java.html index 339038d25..77d39d5dc 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastMultiplication.java.html @@ -30,4 +30,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$1.html index cb1c87e49..1ebdc4e77 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$1.html @@ -1 +1 @@ -CPUBroadcastPower.new CPUBiFun() {...}

CPUBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1776%0 of 0n/a131313
invoke(double, double)40%n/a111111
invoke(float, float)7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastPower.new CPUBiFun() {...}

CPUBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1776%0 of 0n/a131313
invoke(double, double)40%n/a111111
invoke(float, float)7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$2.html index 7c9eb6c8c..c3257b8d0 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$2.html @@ -1 +1 @@ -CPUBroadcastPower.new CPUBiFun() {...}

CPUBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total26 of 260%0 of 0n/a333333
invoke(float, float)120%n/a111111
invoke(double, double)80%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastPower.new CPUBiFun() {...}

CPUBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total26 of 260%0 of 0n/a333333
invoke(float, float)120%n/a111111
invoke(double, double)80%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$3.html index 2ea2d0f4c..c8b425655 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower$3.html @@ -1 +1 @@ -CPUBroadcastPower.new CPUBiFun() {...}

CPUBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total24 of 240%0 of 0n/a333333
invoke(float, float)110%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastPower.new CPUBiFun() {...}

CPUBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total24 of 240%0 of 0n/a333333
invoke(float, float)110%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower.html index 2d8fc73d9..d4698cfd9 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower.html @@ -1 +1 @@ -CPUBroadcastPower

CPUBroadcastPower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 1844%0 of 0n/a242424
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
CPUBroadcastPower()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastPower

CPUBroadcastPower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 1844%0 of 0n/a242424
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
CPUBroadcastPower()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower.java.html index ab67584a7..9b6245466 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastPower.java.html @@ -30,4 +30,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$1.html index 6c938e26b..80450f53b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$1.html @@ -1 +1 @@ -CPUBroadcastSubtraction.new CPUBiFun() {...}

CPUBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastSubtraction.new CPUBiFun() {...}

CPUBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$2.html index 9890c35fe..367b80b94 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$2.html @@ -1 +1 @@ -CPUBroadcastSubtraction.new CPUBiFun() {...}

CPUBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastSubtraction.new CPUBiFun() {...}

CPUBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$3.html index f9f96dcd8..7eaea7bdc 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction$3.html @@ -1 +1 @@ -CPUBroadcastSubtraction.new CPUBiFun() {...}

CPUBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastSubtraction.new CPUBiFun() {...}

CPUBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction.html index defc17922..8d0debb47 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction.html @@ -1 +1 @@ -CPUBroadcastSubtraction

CPUBroadcastSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
_getDeriveAt1()5100%n/a010101
CPUBroadcastSubtraction()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastSubtraction

CPUBroadcastSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
_getDeriveAt1()5100%n/a010101
CPUBroadcastSubtraction()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction.java.html index 97c09562b..f09dd1ed2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSubtraction.java.html @@ -30,4 +30,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$1.html index 12f275b7f..5cc4251aa 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$1.html @@ -1 +1 @@ -CPUBroadcastSummation.new CPUBiFun() {...}

CPUBroadcastSummation.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total35 of 350%4 of 40%997777
invoke(boolean, boolean)80%40%331111
{...}60%n/a111111
invoke(char, char)50%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
invoke(int, int)40%n/a111111
invoke(long, long)40%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastSummation.new CPUBiFun() {...}

CPUBroadcastSummation.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total35 of 350%4 of 40%997777
invoke(boolean, boolean)80%40%331111
{...}60%n/a111111
invoke(char, char)50%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
invoke(int, int)40%n/a111111
invoke(long, long)40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$2.html index 85cf0ec61..17af1c165 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$2.html @@ -1 +1 @@ -CPUBroadcastSummation.new CPUBiFun() {...}

CPUBroadcastSummation.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 270%4 of 40%997777
invoke(boolean, boolean)80%40%331111
{...}60%n/a111111
invoke(char, char)50%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastSummation.new CPUBiFun() {...}

CPUBroadcastSummation.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 270%4 of 40%997777
invoke(boolean, boolean)80%40%331111
{...}60%n/a111111
invoke(char, char)50%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$3.html index 6ef1faf5f..dcc760442 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation$3.html @@ -1 +1 @@ -CPUBroadcastSummation.new CPUBiFun() {...}

CPUBroadcastSummation.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 270%4 of 40%997777
invoke(boolean, boolean)80%40%331111
{...}60%n/a111111
invoke(char, char)50%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastSummation.new CPUBiFun() {...}

CPUBroadcastSummation.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 270%4 of 40%997777
invoke(boolean, boolean)80%40%331111
{...}60%n/a111111
invoke(char, char)50%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation.html index 28bd68e93..c34e216fa 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation.html @@ -1 +1 @@ -CPUBroadcastSummation

CPUBroadcastSummation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 180%0 of 0n/a444444
_getFun()50%n/a111111
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
CPUBroadcastSummation()30%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBroadcastSummation

CPUBroadcastSummation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 180%0 of 0n/a444444
_getFun()50%n/a111111
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
CPUBroadcastSummation()30%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation.java.html index dda7127dc..4a1908e8c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUBroadcastSummation.java.html @@ -42,4 +42,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$1.html index 203405479..203195c3e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$1.html @@ -1 +1 @@ -CPUScalaBroadcastPower.new CPUBiFun() {...}

CPUScalaBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total32 of 320%0 of 0n/a555555
invoke(int, int)80%n/a111111
invoke(float, float)70%n/a111111
invoke(long, long)70%n/a111111
{...}60%n/a111111
invoke(double, double)40%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalaBroadcastPower.new CPUBiFun() {...}

CPUScalaBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total32 of 320%0 of 0n/a555555
invoke(int, int)80%n/a111111
invoke(float, float)70%n/a111111
invoke(long, long)70%n/a111111
{...}60%n/a111111
invoke(double, double)40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$2.html index f2cd17cd8..8bdbea8e8 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$2.html @@ -1 +1 @@ -CPUScalaBroadcastPower.new CPUBiFun() {...}

CPUScalaBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total51 of 510%0 of 0n/a555555
invoke(int, int)130%n/a111111
invoke(float, float)120%n/a111111
invoke(long, long)120%n/a111111
invoke(double, double)80%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalaBroadcastPower.new CPUBiFun() {...}

CPUScalaBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total51 of 510%0 of 0n/a555555
invoke(int, int)130%n/a111111
invoke(float, float)120%n/a111111
invoke(long, long)120%n/a111111
invoke(double, double)80%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$3.html index 1f9853a5f..dee50cf50 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower$3.html @@ -1 +1 @@ -CPUScalaBroadcastPower.new CPUBiFun() {...}

CPUScalaBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total47 of 470%0 of 0n/a555555
invoke(int, int)120%n/a111111
invoke(float, float)110%n/a111111
invoke(long, long)110%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalaBroadcastPower.new CPUBiFun() {...}

CPUScalaBroadcastPower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total47 of 470%0 of 0n/a555555
invoke(int, int)120%n/a111111
invoke(float, float)110%n/a111111
invoke(long, long)110%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower.html index 56485d315..16dbb3f97 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower.html @@ -1 +1 @@ -CPUScalaBroadcastPower

CPUScalaBroadcastPower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 1816%0 of 0n/a343434
_getFun()50%n/a111111
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
CPUScalaBroadcastPower()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalaBroadcastPower

CPUScalaBroadcastPower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 1816%0 of 0n/a343434
_getFun()50%n/a111111
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
CPUScalaBroadcastPower()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower.java.html index bfc91cd23..96bc242c0 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalaBroadcastPower.java.html @@ -34,4 +34,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcast.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcast.html index b3f7cbd37..521ae155b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcast.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcast.html @@ -1 +1 @@ -CPUScalarBroadcast

CPUScalarBroadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 76997%3 of 4493%3341152012
_workloadFor(ExecutionCall)1839995%32388%31415501
lambda$_workloadFor$8(Tensor, Tensor, Object[], CPUBiFun, Object[], Object, int, int)37100%2100%0201001
lambda$_workloadFor$7(Tensor, Tensor, boolean[], CPUBiFun, boolean[], boolean, int, int)37100%2100%0201001
lambda$_workloadFor$6(Tensor, Tensor, char[], CPUBiFun, char[], char, int, int)37100%2100%0201001
lambda$_workloadFor$5(Tensor, Tensor, byte[], CPUBiFun, byte[], byte, int, int)37100%2100%0201001
lambda$_workloadFor$4(Tensor, Tensor, short[], CPUBiFun, short[], short, int, int)37100%2100%0201001
lambda$_workloadFor$3(Tensor, Tensor, long[], CPUBiFun, long[], long, int, int)37100%2100%0201001
lambda$_workloadFor$2(Tensor, Tensor, int[], CPUBiFun, int[], int, int, int)37100%2100%0201001
lambda$_workloadFor$1(Tensor, Tensor, float[], CPUBiFun, float[], float, int, int)37100%2100%0201001
lambda$_workloadFor$0(Tensor, Tensor, double[], CPUBiFun, double[], double, int, int)37100%2100%0201001
run(ExecutionCall)16100%n/a010601
CPUScalarBroadcast()100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcast

CPUScalarBroadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 76997%3 of 4493%3341152012
_workloadFor(ExecutionCall)1839995%32388%31415501
lambda$_workloadFor$8(Tensor, Tensor, Object[], CPUBiFun, Object[], Object, int, int)37100%2100%0201001
lambda$_workloadFor$7(Tensor, Tensor, boolean[], CPUBiFun, boolean[], boolean, int, int)37100%2100%0201001
lambda$_workloadFor$6(Tensor, Tensor, char[], CPUBiFun, char[], char, int, int)37100%2100%0201001
lambda$_workloadFor$5(Tensor, Tensor, byte[], CPUBiFun, byte[], byte, int, int)37100%2100%0201001
lambda$_workloadFor$4(Tensor, Tensor, short[], CPUBiFun, short[], short, int, int)37100%2100%0201001
lambda$_workloadFor$3(Tensor, Tensor, long[], CPUBiFun, long[], long, int, int)37100%2100%0201001
lambda$_workloadFor$2(Tensor, Tensor, int[], CPUBiFun, int[], int, int, int)37100%2100%0201001
lambda$_workloadFor$1(Tensor, Tensor, float[], CPUBiFun, float[], float, int, int)37100%2100%0201001
lambda$_workloadFor$0(Tensor, Tensor, double[], CPUBiFun, double[], double, int, int)37100%2100%0201001
run(ExecutionCall)16100%n/a010601
CPUScalarBroadcast()100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcast.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcast.java.html index a5570ec14..7dd0a8585 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcast.java.html @@ -228,4 +228,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition$1.html index 61ed4d69f..cf1481854 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition$1.html @@ -1 +1 @@ -CPUScalarBroadcastAddition.new CPUBiFun() {...}

CPUScalarBroadcastAddition.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total13 of 3562%4 of 40%492727
invoke(boolean, boolean)80%40%331111
invoke(char, char)50%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
invoke(long, long)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastAddition.new CPUBiFun() {...}

CPUScalarBroadcastAddition.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total13 of 3562%4 of 40%492727
invoke(boolean, boolean)80%40%331111
invoke(char, char)50%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
invoke(long, long)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition$2.html index b464a9e46..d28c7e76c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition$2.html @@ -1 +1 @@ -CPUScalarBroadcastAddition.new CPUBiFun() {...}

CPUScalarBroadcastAddition.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 180%0 of 0n/a777777
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
invoke(char, char)20%n/a111111
invoke(boolean, boolean)20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastAddition.new CPUBiFun() {...}

CPUScalarBroadcastAddition.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 180%0 of 0n/a777777
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
invoke(char, char)20%n/a111111
invoke(boolean, boolean)20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition.html index 880f9c437..1034ddc98 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition.html @@ -1 +1 @@ -CPUScalarBroadcastAddition

CPUScalarBroadcastAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total32 of 5743%3 of 650%5941026
run(ExecutionCall)241640%3350%342601
_getDeriveAt0()50%n/a111111
_getDeriveAt1()30%n/a111111
_getFun()5100%n/a010101
CPUScalarBroadcastAddition()3100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastAddition

CPUScalarBroadcastAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total32 of 5743%3 of 650%5941026
run(ExecutionCall)241640%3350%342601
_getDeriveAt0()50%n/a111111
_getDeriveAt1()30%n/a111111
_getFun()5100%n/a010101
CPUScalarBroadcastAddition()3100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition.java.html index 637808743..69afd90bc 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastAddition.java.html @@ -47,4 +47,4 @@ return _getDeriveAt0(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$1.html index e91050233..c682007fb 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$1.html @@ -1 +1 @@ -CPUScalarBroadcastDivision.new CPUBiFun() {...}

CPUScalarBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 220%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
invoke(int, int)40%n/a111111
invoke(long, long)40%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastDivision.new CPUBiFun() {...}

CPUScalarBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 220%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
invoke(int, int)40%n/a111111
invoke(long, long)40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$2.html index d604ada3a..922371302 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$2.html @@ -1 +1 @@ -CPUScalarBroadcastDivision.new CPUBiFun() {...}

CPUScalarBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 270%0 of 0n/a555555
invoke(int, int)70%n/a111111
{...}60%n/a111111
invoke(long, long)60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastDivision.new CPUBiFun() {...}

CPUScalarBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 270%0 of 0n/a555555
invoke(int, int)70%n/a111111
{...}60%n/a111111
invoke(long, long)60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$3.html index 88dc62686..40f32d297 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision$3.html @@ -1 +1 @@ -CPUScalarBroadcastDivision.new CPUBiFun() {...}

CPUScalarBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total44 of 440%0 of 0n/a555555
invoke(int, int)110%n/a111111
invoke(float, float)100%n/a111111
invoke(long, long)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastDivision.new CPUBiFun() {...}

CPUScalarBroadcastDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total44 of 440%0 of 0n/a555555
invoke(int, int)110%n/a111111
invoke(float, float)100%n/a111111
invoke(long, long)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision.html index 325922690..81f3c4257 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision.html @@ -1 +1 @@ -CPUScalarBroadcastDivision

CPUScalarBroadcastDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 1816%0 of 0n/a343434
_getFun()50%n/a111111
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
CPUScalarBroadcastDivision()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastDivision

CPUScalarBroadcastDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 1816%0 of 0n/a343434
_getFun()50%n/a111111
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
CPUScalarBroadcastDivision()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision.java.html index 00582c21f..871ed1140 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastDivision.java.html @@ -34,4 +34,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity$1.html index 6e2ca4cb9..98a80bec6 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity$1.html @@ -1 +1 @@ -CPUScalarBroadcastIdentity.new CPUBiFun() {...}

CPUScalarBroadcastIdentity.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 22100%0 of 0n/a090909
{...}6100%n/a010101
invoke(double, double)2100%n/a010101
invoke(float, float)2100%n/a010101
invoke(byte, byte)2100%n/a010101
invoke(int, int)2100%n/a010101
invoke(boolean, boolean)2100%n/a010101
invoke(char, char)2100%n/a010101
invoke(long, long)2100%n/a010101
invoke(Object, Object)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastIdentity.new CPUBiFun() {...}

CPUScalarBroadcastIdentity.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 22100%0 of 0n/a090909
{...}6100%n/a010101
invoke(double, double)2100%n/a010101
invoke(float, float)2100%n/a010101
invoke(byte, byte)2100%n/a010101
invoke(int, int)2100%n/a010101
invoke(boolean, boolean)2100%n/a010101
invoke(char, char)2100%n/a010101
invoke(long, long)2100%n/a010101
invoke(Object, Object)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity.html index 47db61cb3..0ac28c1b9 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity.html @@ -1 +1 @@ -CPUScalarBroadcastIdentity

CPUScalarBroadcastIdentity

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 1457%0 of 0n/a242424
_getDeriveAt0()30%n/a111111
_getDeriveAt1()30%n/a111111
_getFun()5100%n/a010101
CPUScalarBroadcastIdentity()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastIdentity

CPUScalarBroadcastIdentity

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 1457%0 of 0n/a242424
_getDeriveAt0()30%n/a111111
_getDeriveAt1()30%n/a111111
_getFun()5100%n/a010101
CPUScalarBroadcastIdentity()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity.java.html index 67fc96471..3a24ae013 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastIdentity.java.html @@ -28,4 +28,4 @@ return _getFun(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$1.html index 292ebf3d3..87742bf5b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$1.html @@ -1 +1 @@ -CPUScalarBroadcastModulo.new CPUBiFun() {...}

CPUScalarBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 220%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
invoke(int, int)40%n/a111111
invoke(long, long)40%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastModulo.new CPUBiFun() {...}

CPUScalarBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 220%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
invoke(int, int)40%n/a111111
invoke(long, long)40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$2.html index 54c12b1ee..3f4f31ae8 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$2.html @@ -1 +1 @@ -CPUScalarBroadcastModulo.new CPUBiFun() {...}

CPUScalarBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 270%0 of 0n/a555555
invoke(int, int)70%n/a111111
{...}60%n/a111111
invoke(long, long)60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastModulo.new CPUBiFun() {...}

CPUScalarBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 270%0 of 0n/a555555
invoke(int, int)70%n/a111111
{...}60%n/a111111
invoke(long, long)60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$3.html index 66ff0d48e..f5be3bf77 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo$3.html @@ -1 +1 @@ -CPUScalarBroadcastModulo.new CPUBiFun() {...}

CPUScalarBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total44 of 440%0 of 0n/a555555
invoke(int, int)110%n/a111111
invoke(float, float)100%n/a111111
invoke(long, long)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastModulo.new CPUBiFun() {...}

CPUScalarBroadcastModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total44 of 440%0 of 0n/a555555
invoke(int, int)110%n/a111111
invoke(float, float)100%n/a111111
invoke(long, long)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo.html index bd3511a20..c5b517825 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo.html @@ -1 +1 @@ -CPUScalarBroadcastModulo

CPUScalarBroadcastModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 1816%0 of 0n/a343434
_getFun()50%n/a111111
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
CPUScalarBroadcastModulo()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastModulo

CPUScalarBroadcastModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 1816%0 of 0n/a343434
_getFun()50%n/a111111
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
CPUScalarBroadcastModulo()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo.java.html index 2987adb0d..ab6b65bc2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastModulo.java.html @@ -34,4 +34,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$1.html index 5b8f0de01..4b9ac66de 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$1.html @@ -1 +1 @@ -CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2281%0 of 0n/a151515
invoke(long, long)40%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2281%0 of 0n/a151515
invoke(long, long)40%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$2.html index 846ee71d3..183f9e5c4 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$2.html @@ -1 +1 @@ -CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$3.html index a478abedf..e299e223f 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication$3.html @@ -1 +1 @@ -CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

CPUScalarBroadcastMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication.html index ab9367725..b1f2e02fc 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication.html @@ -1 +1 @@ -CPUScalarBroadcastMultiplication

CPUScalarBroadcastMultiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 5574%1 of 683%3921026
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
run(ExecutionCall)43288%1583%140601
_getFun()5100%n/a010101
CPUScalarBroadcastMultiplication()3100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastMultiplication

CPUScalarBroadcastMultiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 5574%1 of 683%3921026
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
run(ExecutionCall)43288%1583%140601
_getFun()5100%n/a010101
CPUScalarBroadcastMultiplication()3100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication.java.html index 73c87e6e8..35cb9733d 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastMultiplication.java.html @@ -48,4 +48,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$1.html index 4c962d034..cd060a6e5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$1.html @@ -1 +1 @@ -CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 220%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
invoke(int, int)40%n/a111111
invoke(long, long)40%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 220%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
invoke(int, int)40%n/a111111
invoke(long, long)40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$2.html index 5e638015d..32895e04c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$2.html @@ -1 +1 @@ -CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$3.html index b532b9e48..88ddd0465 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction$3.html @@ -1 +1 @@ -CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

CPUScalarBroadcastSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 140%0 of 0n/a555555
{...}60%n/a111111
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction.html index fb8331e7a..d808ab320 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction.html @@ -1 +1 @@ -CPUScalarBroadcastSubtraction

CPUScalarBroadcastSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 1816%0 of 0n/a343434
_getFun()50%n/a111111
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
CPUScalarBroadcastSubtraction()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastSubtraction

CPUScalarBroadcastSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 1816%0 of 0n/a343434
_getFun()50%n/a111111
_getDeriveAt0()50%n/a111111
_getDeriveAt1()50%n/a111111
CPUScalarBroadcastSubtraction()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction.java.html index 0ad53ef7f..76c779150 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/CPUScalarBroadcastSubtraction.java.html @@ -34,4 +34,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/index.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/index.html index 94f1623f0..5e6d548ac 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/index.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/index.html @@ -1 +1 @@ -neureka.backend.main.implementations.broadcast

neureka.backend.main.implementations.broadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total1,275 of 3,54664%60 of 20771%2063841956701642782768
CLScalarBroadcastSubtraction737%40%3411131201
CLScalarBroadcastModulo727%40%3411131201
CLScalarBroadcastPower727%40%3411131201
CLScalarBroadcastDivision727%40%3410121201
CPUScalaBroadcastPower.new CPUBiFun() {...}510%n/a55555511
CLScalarBroadcast4911369%1685%21112311501
CPUScalaBroadcastPower.new CPUBiFun() {...}470%n/a55555511
CPUScalarBroadcastDivision.new CPUBiFun() {...}440%n/a55555511
CPUScalarBroadcastModulo.new CPUBiFun() {...}440%n/a55555511
CPUBroadcast3572795%108088%105321600801
CPUBroadcastSummation.new CPUBiFun() {...}350%40%99777711
CPUScalarBroadcastAddition322543%3350%594102601
CPUScalaBroadcastPower.new CPUBiFun() {...}320%n/a55555511
CPUScalarBroadcastDivision.new CPUBiFun() {...}270%n/a55555511
CPUBroadcastSummation.new CPUBiFun() {...}270%40%99777711
CPUBroadcastSummation.new CPUBiFun() {...}270%40%99777711
CPUScalarBroadcastModulo.new CPUBiFun() {...}270%n/a55555511
CPUBroadcastPower.new CPUBiFun() {...}260%n/a33333311
CLScalarBroadcastAddition246873%3350%362190301
CPUBroadcastPower.new CPUBiFun() {...}240%n/a33333311
CPUBroadcastModulo.new CPUBiFun() {...}230%n/a33333311
CPUBroadcastDivision.new CPUBiFun() {...}230%n/a33333311
CPUScalarBroadcastDivision.new CPUBiFun() {...}220%n/a55555511
CPUScalarBroadcastSubtraction.new CPUBiFun() {...}220%n/a55555511
CPUScalarBroadcastModulo.new CPUBiFun() {...}220%n/a55555511
CPUScalarBroadcast1875197%34193%334115201201
CPUBroadcastSummation180%n/a44444411
CPUScalarBroadcastAddition.new CPUBiFun() {...}180%n/a77777711
CPUScalarBroadcastSubtraction1516%n/a34343401
CPUScalarBroadcastDivision1516%n/a34343401
CPUScalaBroadcastPower1516%n/a34343401
CPUScalarBroadcastModulo1516%n/a34343401
CPUScalarBroadcastMultiplication144174%1583%392102601
CPUBroadcastModulo.new CPUBiFun() {...}140%n/a33333311
CPUBroadcastDivision.new CPUBiFun() {...}140%n/a33333311
CPUScalarBroadcastMultiplication.new CPUBiFun() {...}140%n/a55555511
CPUScalarBroadcastMultiplication.new CPUBiFun() {...}140%n/a55555511
CPUScalarBroadcastSubtraction.new CPUBiFun() {...}140%n/a55555511
CPUScalarBroadcastSubtraction.new CPUBiFun() {...}140%n/a55555511
CPUScalarBroadcastAddition.new CPUBiFun() {...}132262%40%49272701
CPUBroadcastAddition.new CPUBiFun() {...}131451%40%47252501
CPUBroadcastPower10844%n/a24242401
CPUBroadcastModulo10844%n/a24242401
CPUBroadcastDivision10844%n/a24242401
CPUBroadcastMultiplication10844%n/a24242401
CPUBroadcastMultiplication.new CPUBiFun() {...}100%n/a33333311
CPUBroadcastMultiplication.new CPUBiFun() {...}100%n/a33333311
CLBroadcast8193%2250%250150301
CPUScalarBroadcastIdentity857%n/a24242401
CPUScalarBroadcastMultiplication.new CPUBiFun() {...}1881%n/a15151501
CPUBroadcastPower.new CPUBiFun() {...}1376%n/a13131301
CPUBroadcastModulo.new CPUBiFun() {...}1071%n/a13131301
CLScalarBroadcastMultiplication10199%1787%160170201
CLScalarBroadcastIdentity51100%n/a020130201
CPUScalarBroadcastIdentity.new CPUBiFun() {...}22100%n/a09090901
CPUBroadcastSubtraction18100%n/a04040401
CPUBroadcastDivision.new CPUBiFun() {...}14100%n/a03030301
CPUBroadcastAddition14100%n/a04040401
CPUBroadcastSubtraction.new CPUBiFun() {...}14100%n/a03030301
CPUBroadcastSubtraction.new CPUBiFun() {...}14100%n/a03030301
CPUBroadcastSubtraction.new CPUBiFun() {...}14100%n/a03030301
CPUBroadcastMultiplication.new CPUBiFun() {...}14100%n/a03030301
CLBroadcastDivision100%n/a01020101
CLBroadcastModulo100%n/a01020101
CLBroadcastSubtraction100%n/a01020101
CLBroadcastAddition100%n/a01020101
CLBroadcastPower100%n/a01020101
CLBroadcastMultiplication100%n/a01020101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.broadcast

neureka.backend.main.implementations.broadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total1,749 of 3,54650%78 of 20762%2383842966701882784268
CLScalarBroadcast1620%70%111131315511
CLScalarBroadcastMultiplication1020%80%6617172211
CLScalarBroadcastAddition920%60%6619193311
CLBroadcast870%40%5515153311
CLScalarBroadcastSubtraction790%40%4413132211
CLScalarBroadcastModulo780%40%4413132211
CLScalarBroadcastPower780%40%4413132211
CLScalarBroadcastDivision780%40%4412122211
CPUScalaBroadcastPower.new CPUBiFun() {...}510%n/a55555511
CLScalarBroadcastIdentity510%n/a2213132211
CPUScalaBroadcastPower.new CPUBiFun() {...}470%n/a55555511
CPUScalarBroadcastDivision.new CPUBiFun() {...}440%n/a55555511
CPUScalarBroadcastModulo.new CPUBiFun() {...}440%n/a55555511
CPUBroadcast3572795%108088%105321600801
CPUBroadcastSummation.new CPUBiFun() {...}350%40%99777711
CPUScalarBroadcastAddition322543%3350%594102601
CPUScalaBroadcastPower.new CPUBiFun() {...}320%n/a55555511
CPUScalarBroadcastDivision.new CPUBiFun() {...}270%n/a55555511
CPUBroadcastSummation.new CPUBiFun() {...}270%40%99777711
CPUBroadcastSummation.new CPUBiFun() {...}270%40%99777711
CPUScalarBroadcastModulo.new CPUBiFun() {...}270%n/a55555511
CPUBroadcastPower.new CPUBiFun() {...}260%n/a33333311
CPUBroadcastPower.new CPUBiFun() {...}240%n/a33333311
CPUBroadcastModulo.new CPUBiFun() {...}230%n/a33333311
CPUBroadcastDivision.new CPUBiFun() {...}230%n/a33333311
CPUScalarBroadcastDivision.new CPUBiFun() {...}220%n/a55555511
CPUScalarBroadcastSubtraction.new CPUBiFun() {...}220%n/a55555511
CPUScalarBroadcastModulo.new CPUBiFun() {...}220%n/a55555511
CPUScalarBroadcast1875197%34193%334115201201
CPUBroadcastSummation180%n/a44444411
CPUScalarBroadcastAddition.new CPUBiFun() {...}180%n/a77777711
CPUScalarBroadcastSubtraction1516%n/a34343401
CPUScalarBroadcastDivision1516%n/a34343401
CPUScalaBroadcastPower1516%n/a34343401
CPUScalarBroadcastModulo1516%n/a34343401
CPUScalarBroadcastMultiplication144174%1583%392102601
CPUBroadcastModulo.new CPUBiFun() {...}140%n/a33333311
CPUBroadcastDivision.new CPUBiFun() {...}140%n/a33333311
CPUScalarBroadcastMultiplication.new CPUBiFun() {...}140%n/a55555511
CPUScalarBroadcastMultiplication.new CPUBiFun() {...}140%n/a55555511
CPUScalarBroadcastSubtraction.new CPUBiFun() {...}140%n/a55555511
CPUScalarBroadcastSubtraction.new CPUBiFun() {...}140%n/a55555511
CPUScalarBroadcastAddition.new CPUBiFun() {...}132262%40%49272701
CPUBroadcastAddition.new CPUBiFun() {...}131451%40%47252501
CPUBroadcastPower10844%n/a24242401
CPUBroadcastModulo10844%n/a24242401
CPUBroadcastDivision10844%n/a24242401
CPUBroadcastMultiplication10844%n/a24242401
CPUBroadcastMultiplication.new CPUBiFun() {...}100%n/a33333311
CPUBroadcastMultiplication.new CPUBiFun() {...}100%n/a33333311
CPUScalarBroadcastIdentity857%n/a24242401
CLBroadcastDivision0%n/a11221111
CLBroadcastModulo0%n/a11221111
CLBroadcastSubtraction0%n/a11221111
CLBroadcastAddition0%n/a11221111
CLBroadcastPower0%n/a11221111
CLBroadcastMultiplication0%n/a11221111
CPUScalarBroadcastMultiplication.new CPUBiFun() {...}1881%n/a15151501
CPUBroadcastPower.new CPUBiFun() {...}1376%n/a13131301
CPUBroadcastModulo.new CPUBiFun() {...}1071%n/a13131301
CPUScalarBroadcastIdentity.new CPUBiFun() {...}22100%n/a09090901
CPUBroadcastSubtraction18100%n/a04040401
CPUBroadcastDivision.new CPUBiFun() {...}14100%n/a03030301
CPUBroadcastAddition14100%n/a04040401
CPUBroadcastSubtraction.new CPUBiFun() {...}14100%n/a03030301
CPUBroadcastSubtraction.new CPUBiFun() {...}14100%n/a03030301
CPUBroadcastSubtraction.new CPUBiFun() {...}14100%n/a03030301
CPUBroadcastMultiplication.new CPUBiFun() {...}14100%n/a03030301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/index.source.html b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/index.source.html index cfd8e596a..15425edf5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.broadcast/index.source.html @@ -1 +1 @@ -neureka.backend.main.implementations.broadcast

neureka.backend.main.implementations.broadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total1,275 of 3,54664%60 of 20771%2063841956701642782768
CPUScalaBroadcastPower.java1452%n/a18191516181934
CPUScalarBroadcastDivision.java1082%n/a18191516181934
CPUScalarBroadcastModulo.java1082%n/a18191516181934
CPUBroadcastSummation.java1070%120%31312222252544
CLScalarBroadcastSubtraction.java737%40%3411131201
CLScalarBroadcastModulo.java727%40%3411131201
CLScalarBroadcastDivision.java727%40%3410121201
CLScalarBroadcastPower.java727%40%3411131201
CPUScalarBroadcastSubtraction.java654%n/a18191516181934
CPUBroadcastPower.java642124%n/a91371091324
CPUScalarBroadcastAddition.java634742%7330%16251222112013
CPUBroadcastModulo.java511826%n/a91371091324
CLScalarBroadcast.java4911369%1685%21112311501
CPUBroadcastDivision.java472231%n/a81361081324
CPUScalarBroadcastMultiplication.java465956%1583%14241122132124
CPUBroadcast.java3572795%108088%105321600801
CPUBroadcastMultiplication.java302242%n/a81361081324
CLScalarBroadcastAddition.java246873%3350%362190301
CPUScalarBroadcast.java1875197%34193%334115201201
CPUBroadcastAddition.java132868%40%411282902
CLBroadcast.java8193%2250%250150301
CPUScalarBroadcastIdentity.java3083%n/a21321221302
CLScalarBroadcastMultiplication.java10199%1787%160170201
CPUBroadcastSubtraction.java60100%n/a01301001304
CLScalarBroadcastIdentity.java51100%n/a020130201
CLBroadcastPower.java100%n/a01020101
CLBroadcastSubtraction.java100%n/a01020101
CLBroadcastModulo.java100%n/a01020101
CLBroadcastMultiplication.java100%n/a01020101
CLBroadcastDivision.java100%n/a01020101
CLBroadcastAddition.java100%n/a01020101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.broadcast

neureka.backend.main.implementations.broadcast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total1,749 of 3,54650%78 of 20762%2383842966701882784268
CLScalarBroadcast.java1620%70%111131315511
CPUScalaBroadcastPower.java1452%n/a18191516181934
CPUScalarBroadcastDivision.java1082%n/a18191516181934
CPUScalarBroadcastModulo.java1082%n/a18191516181934
CPUBroadcastSummation.java1070%120%31312222252544
CLScalarBroadcastMultiplication.java1020%80%6617172211
CLScalarBroadcastAddition.java920%60%6619193311
CLBroadcast.java870%40%5515153311
CLScalarBroadcastSubtraction.java790%40%4413132211
CLScalarBroadcastPower.java780%40%4413132211
CLScalarBroadcastModulo.java780%40%4413132211
CLScalarBroadcastDivision.java780%40%4412122211
CPUScalarBroadcastSubtraction.java654%n/a18191516181934
CPUBroadcastPower.java642124%n/a91371091324
CPUScalarBroadcastAddition.java634742%7330%16251222112013
CPUBroadcastModulo.java511826%n/a91371091324
CLScalarBroadcastIdentity.java510%n/a2213132211
CPUBroadcastDivision.java472231%n/a81361081324
CPUScalarBroadcastMultiplication.java465956%1583%14241122132124
CPUBroadcast.java3572795%108088%105321600801
CPUBroadcastMultiplication.java302242%n/a81361081324
CPUScalarBroadcast.java1875197%34193%334115201201
CPUBroadcastAddition.java132868%40%411282902
CPUScalarBroadcastIdentity.java3083%n/a21321221302
CLBroadcastSubtraction.java0%n/a11221111
CLBroadcastDivision.java0%n/a11221111
CLBroadcastPower.java0%n/a11221111
CLBroadcastModulo.java0%n/a11221111
CLBroadcastMultiplication.java0%n/a11221111
CLBroadcastAddition.java0%n/a11221111
CPUBroadcastSubtraction.java60100%n/a01301001304
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/AbstractCPUConvolution.html b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/AbstractCPUConvolution.html index dddffffbb..f710f2ea2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/AbstractCPUConvolution.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/AbstractCPUConvolution.html @@ -1 +1 @@ -AbstractCPUConvolution

AbstractCPUConvolution

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total160 of 1,30387%31 of 16481%259529261013
_deConvolve64(Tensor, Tensor, Tensor, int, int, CPUBiFun)7723975%123473%924136401
_deConvolve32(Tensor, Tensor, Tensor, int, int, CPUBiFun)6523077%93177%621136101
_workloadFor(ExecutionCall)147383%2880%2611801
_convolve64(Tensor, Tensor, Tensor, int, int, CPUBiFun)26099%42887%41715301
_convolve32(Tensor, Tensor, Tensor, int, int, CPUBiFun)26099%42887%41715301
run(ExecutionCall)32100%4100%030501
_doNDConvolutionFor(ExecutionCall)13100%n/a010601
lambda$_workloadFor$3(Tensor, Tensor, Tensor, CPUBiFun, int, int)8100%n/a010101
lambda$_workloadFor$2(Tensor, Tensor, Tensor, CPUBiFun, int, int)8100%n/a010101
lambda$_workloadFor$1(Tensor, Tensor, Tensor, CPUBiFun, int, int)8100%n/a010101
lambda$_workloadFor$0(Tensor, Tensor, Tensor, CPUBiFun, int, int)8100%n/a010101
AbstractCPUConvolution()3100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractCPUConvolution

AbstractCPUConvolution

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total160 of 1,30387%31 of 16481%259529261013
_deConvolve64(Tensor, Tensor, Tensor, int, int, CPUBiFun)7723975%123473%924136401
_deConvolve32(Tensor, Tensor, Tensor, int, int, CPUBiFun)6523077%93177%621136101
_workloadFor(ExecutionCall)147383%2880%2611801
_convolve64(Tensor, Tensor, Tensor, int, int, CPUBiFun)26099%42887%41715301
_convolve32(Tensor, Tensor, Tensor, int, int, CPUBiFun)26099%42887%41715301
run(ExecutionCall)32100%4100%030501
_doNDConvolutionFor(ExecutionCall)13100%n/a010601
lambda$_workloadFor$3(Tensor, Tensor, Tensor, CPUBiFun, int, int)8100%n/a010101
lambda$_workloadFor$2(Tensor, Tensor, Tensor, CPUBiFun, int, int)8100%n/a010101
lambda$_workloadFor$1(Tensor, Tensor, Tensor, CPUBiFun, int, int)8100%n/a010101
lambda$_workloadFor$0(Tensor, Tensor, Tensor, CPUBiFun, int, int)8100%n/a010101
AbstractCPUConvolution()3100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/AbstractCPUConvolution.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/AbstractCPUConvolution.java.html index 0fe996737..20ad1daa6 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/AbstractCPUConvolution.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/AbstractCPUConvolution.java.html @@ -408,4 +408,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/CLConvolution.html b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/CLConvolution.html index 973b36230..455219452 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/CLConvolution.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/CLConvolution.html @@ -1 +1 @@ -CLConvolution

CLConvolution

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 8793%2 of 450%2501503
lambda$new$0(ExecutionCall)66191%2250%2301101
CLConvolution(String)13100%n/a010301
lambda$new$1(KernelCode)7100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLConvolution

CLConvolution

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total87 of 870%4 of 40%55151533
lambda$new$0(ExecutionCall)670%40%33111111
CLConvolution(String)130%n/a113311
lambda$new$1(KernelCode)70%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/CLConvolution.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/CLConvolution.java.html index 6223120d2..2d5615d57 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/CLConvolution.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/CLConvolution.java.html @@ -8,28 +8,28 @@ public class CLConvolution extends ParsedCLImplementation { public CLConvolution( String id ) { - super( call -> { - int offset = ( call.input( Number.class, 0 ) != null ) ? 0 : 1; - int gwz = ( call.input( Number.class, 0 ) != null ) ? call.input( Number.class, 0 ).size() : call.input( Number.class, 1 ).size(); - call.getDevice() - .getKernel(call) - .passAllOf( call.input( Number.class, offset ) ) - .passAllOf( call.input( Number.class, offset + 1 ) ) - .passAllOf( call.input( Number.class, offset + 2 ) ) - .pass( call.input( Number.class, 0 ).rank() ) - .pass( call.getValOf( Arg.DerivIdx.class ) ) - .call( gwz ); + super( call -> { + int offset = ( call.input( Number.class, 0 ) != null ) ? 0 : 1; + int gwz = ( call.input( Number.class, 0 ) != null ) ? call.input( Number.class, 0 ).size() : call.input( Number.class, 1 ).size(); + call.getDevice() + .getKernel(call) + .passAllOf( call.input( Number.class, offset ) ) + .passAllOf( call.input( Number.class, offset + 1 ) ) + .passAllOf( call.input( Number.class, offset + 2 ) ) + .pass( call.input( Number.class, 0 ).rank() ) + .pass( call.getValOf( Arg.DerivIdx.class ) ) + .call( gwz ); - return call.input( 0 ); + return call.input( 0 ); }, 3, - Neureka.get().utility().readResource("kernels/convolution_template.cl"), + Neureka.get().utility().readResource("kernels/convolution_template.cl"), "value = src1 * src2;\n", "value += handle * drain;\n", id, - kernelCode -> new KernelCode[]{kernelCode} + kernelCode -> new KernelCode[]{kernelCode} ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
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Created with JaCoCo 0.8.11.202310140853
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution$ImplF64.html b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution$ImplF64.html index f1a8c45b6..5473ee397 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution$ImplF64.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution$ImplF64.html @@ -1 +1 @@ -SimpleCPUConvolution.ImplF64

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution.html b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution.html index 4e5cffe35..891d219a7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution.html @@ -1 +1 @@ -SimpleCPUConvolution

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution.java.html index 57ce98d42..ab6c5e354 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/SimpleCPUConvolution.java.html @@ -233,4 +233,4 @@ } } -
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\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/index.html b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/index.html index bf8a1d64f..2471f85fa 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/index.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/index.html @@ -1 +1 @@ -neureka.backend.main.implementations.convolution

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/index.source.html b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/index.source.html index 364b6628a..35972a6c5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.convolution/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.convolution/index.source.html @@ -1 +1 @@ -neureka.backend.main.implementations.convolution

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CPUConvolution.java2273%n/a27262702
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwise.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwise.html index 9bb570e9c..017ab5544 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwise.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwise.html @@ -1 +1 @@ -CLBiElementwise

CLBiElementwise

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLBiElementwise

CLBiElementwise

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwise.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwise.java.html index 667b05fff..8728a2bc3 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwise.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwise.java.html @@ -7,28 +7,28 @@ public class CLBiElementwise extends ParsedCLImplementation { public CLBiElementwise( String postfix, String activationSource, String differentiationSource ) { - super( + super( call -> { - int offset = (call.input( Number.class, 0 ) != null) ? 0 : 1; - int gwz = (call.input( Number.class, 0 ) != null) ? call.input( Number.class, 0 ).size() : call.input( Number.class, 1 ).size(); - call.getDevice() - .getKernel(call) - .passAllOf( call.input( Number.class, offset ) ) - .passAllOf( call.input( Number.class, offset + 1 ) ) - .passAllOf( call.input( Number.class, offset + 2 ) ) - .pass( call.input( Number.class, 0 ).rank() ) - .pass( call.getDerivativeIndex() ) - .call( gwz ); + int offset = (call.input( Number.class, 0 ) != null) ? 0 : 1; + int gwz = (call.input( Number.class, 0 ) != null) ? call.input( Number.class, 0 ).size() : call.input( Number.class, 1 ).size(); + call.getDevice() + .getKernel(call) + .passAllOf( call.input( Number.class, offset ) ) + .passAllOf( call.input( Number.class, offset + 1 ) ) + .passAllOf( call.input( Number.class, offset + 2 ) ) + .pass( call.input( Number.class, 0 ).rank() ) + .pass( call.getDerivativeIndex() ) + .call( gwz ); - return call.input( 0 ); + return call.input( 0 ); }, -1, - Neureka.get().utility().readResource("kernels/elementwise_template.cl"), + Neureka.get().utility().readResource("kernels/elementwise_template.cl"), activationSource, differentiationSource, postfix, - kernelCode -> new KernelCode[]{kernelCode} + kernelCode -> new KernelCode[]{kernelCode} ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseAddition.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseAddition.html index ecc9fb962..51e4b0ee8 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseAddition.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseAddition.html @@ -1 +1 @@ -CLBiElementwiseAddition

CLBiElementwiseAddition

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CLBiElementwiseAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseAddition.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseAddition.java.html index 305126e18..6d5eab99b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseAddition.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseAddition.java.html @@ -3,11 +3,11 @@ public class CLBiElementwiseAddition extends CLBiElementwise { public CLBiElementwiseAddition(String postfix) { - super( + super( postfix, "output = input1 + input2;\n", "output = 1;\n" ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseDivision.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseDivision.html index f33d3e987..8991e0dd7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseDivision.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseDivision.html @@ -1 +1 @@ -CLBiElementwiseDivision

CLBiElementwiseDivision

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CLBiElementwiseDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseDivision.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseDivision.java.html index d1128f9a4..353f598a4 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseDivision.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseDivision.java.html @@ -3,11 +3,11 @@ public class CLBiElementwiseDivision extends CLBiElementwise { public CLBiElementwiseDivision(String postfix) { - super( + super( postfix, "output = input1 / input2;\n", "output = ( d == 0 ? 1 / input2 : -input2 / (float)pow(input1, 2.0f) ); \n" ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseModulo.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseModulo.html index 35a8deee6..310301619 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseModulo.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseModulo.html @@ -1 +1 @@ -CLBiElementwiseModulo

CLBiElementwiseModulo

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CLBiElementwiseModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseModulo.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseModulo.java.html index 0c9b2fc46..cc6c2019c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseModulo.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseModulo.java.html @@ -3,11 +3,11 @@ public class CLBiElementwiseModulo extends CLBiElementwise { public CLBiElementwiseModulo(String postfix) { - super( + super( postfix, "output = ((int)input1) % ((int)input2);\n", "output = ( d == 0 ? 1/input2 : -input2 / (float) pow(input1, 2.0f) );\n" ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseMultiplication.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseMultiplication.html index 7eba44d56..c48223586 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseMultiplication.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseMultiplication.html @@ -1 +1 @@ -CLBiElementwiseMultiplication

CLBiElementwiseMultiplication

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CLBiElementwiseMultiplication

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseMultiplication.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseMultiplication.java.html index 42a4d69fc..58181d8df 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseMultiplication.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseMultiplication.java.html @@ -3,11 +3,11 @@ public class CLBiElementwiseMultiplication extends CLBiElementwise { public CLBiElementwiseMultiplication(String postfix) { - super( + super( postfix, "output = input1 * input2;\n", "output = input2;\n" ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwisePower.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwisePower.html index 3cbd3cb30..33de0be96 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwisePower.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwisePower.html @@ -1 +1 @@ -CLBiElementwisePower

CLBiElementwisePower

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CLBiElementwisePower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 60%0 of 0n/a112211
CLBiElementwisePower(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwisePower.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwisePower.java.html index b743ba297..c2174f73f 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwisePower.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwisePower.java.html @@ -3,7 +3,7 @@ public class CLBiElementwisePower extends CLBiElementwise { public CLBiElementwisePower(String postfix) { - super(postfix, + super(postfix, "output = pow(input1, input2);", "if ( d == 0 ) { \n" + " output = input2 * pow(input1, input2-1.0f); \n" + @@ -11,6 +11,6 @@ " output = pow(input1, input2) * log(input1); \n" + "}" ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseSubtraction.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseSubtraction.html index b29e50b5a..b9d386b13 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseSubtraction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseSubtraction.html @@ -1 +1 @@ -CLBiElementwiseSubtraction

CLBiElementwiseSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 6100%0 of 0n/a010201
CLBiElementwiseSubtraction(String)6100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLBiElementwiseSubtraction

CLBiElementwiseSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 60%0 of 0n/a112211
CLBiElementwiseSubtraction(String)60%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseSubtraction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseSubtraction.java.html index ca34f6aad..7a1a4bae6 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseSubtraction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLBiElementwiseSubtraction.java.html @@ -3,11 +3,11 @@ public class CLBiElementwiseSubtraction extends CLBiElementwise { public CLBiElementwiseSubtraction(String postfix) { - super( + super( postfix, "output = input1 - input2;\n", "output = 1;\n" ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLElementwiseFunction.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLElementwiseFunction.html index 2f893b65e..b3a1303d5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLElementwiseFunction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLElementwiseFunction.html @@ -1 +1 @@ -CLElementwiseFunction

CLElementwiseFunction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 9993%3 of 650%3601803
_run(ExecutionCall)67092%3350%3401101
CLElementwiseFunction(ScalarFun)16100%n/a010601
lambda$new$0(KernelCode)7100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLElementwiseFunction

CLElementwiseFunction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total99 of 990%6 of 60%66181833
_run(ExecutionCall)760%60%44111111
CLElementwiseFunction(ScalarFun)160%n/a116611
lambda$new$0(KernelCode)70%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLElementwiseFunction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLElementwiseFunction.java.html index ddfdd11e3..14e392629 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLElementwiseFunction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLElementwiseFunction.java.html @@ -13,33 +13,33 @@ { public CLElementwiseFunction( ScalarFun fun ) { - super( + super( CLElementwiseFunction::_run, 2, - Neureka.get().utility().readResource("kernels/activation_template.cl"), - fun.activationCode(), - fun.derivationCode(), - fun.id(), - kernelCode -> new KernelCode[]{kernelCode} + Neureka.get().utility().readResource("kernels/activation_template.cl"), + fun.activationCode(), + fun.derivationCode(), + fun.id(), + kernelCode -> new KernelCode[]{kernelCode} ); - } + } private static Tensor<?> _run(ExecutionCall<OpenCLDevice> call ) { - int offset = call.input( Number.class, 0 ) != null ? 0 : 1; - int gwz = call.input( Number.class, 0 ) != null ? call.input( Number.class, 0 ).size() : call.input( Number.class, 1 ).size(); + int offset = call.input( Number.class, 0 ) != null ? 0 : 1; + int gwz = call.input( Number.class, 0 ) != null ? call.input( Number.class, 0 ).size() : call.input( Number.class, 1 ).size(); // Drain tensor needs to be 'actual'! : - if ( !call.input( Number.class, offset + 1).isVirtual() ) call.input( Number.class, offset).mut().setIsVirtual( false ); - call.getDevice() - .getKernel(call) - .passAllOf( call.input( Number.class, offset ) ) - .passAllOf( call.input( Number.class, offset + 1 ) ) - .pass( call.input( Number.class, 0 ).rank() ) - .pass( call.getValOf( Arg.DerivIdx.class ) ) - .call( gwz ); + if ( !call.input( Number.class, offset + 1).isVirtual() ) call.input( Number.class, offset).mut().setIsVirtual( false ); + call.getDevice() + .getKernel(call) + .passAllOf( call.input( Number.class, offset ) ) + .passAllOf( call.input( Number.class, offset + 1 ) ) + .pass( call.input( Number.class, 0 ).rank() ) + .pass( call.getValOf( Arg.DerivIdx.class ) ) + .call( gwz ); - return call.input( 0 ); + return call.input( 0 ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLRandomization.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLRandomization.html index 8bf7b8883..c7e3a05b7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLRandomization.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLRandomization.html @@ -1 +1 @@ -CLRandomization

CLRandomization

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 837%0 of 0n/a121212
run(ExecutionCall)50%n/a111111
CLRandomization()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLRandomization

CLRandomization

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 80%0 of 0n/a222222
run(ExecutionCall)50%n/a111111
CLRandomization()30%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLRandomization.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLRandomization.java.html index a127251f7..aedc956d5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLRandomization.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CLRandomization.java.html @@ -5,10 +5,10 @@ import neureka.backend.api.ImplementationFor; import neureka.devices.opencl.OpenCLDevice; -public class CLRandomization implements ImplementationFor<OpenCLDevice> { +public class CLRandomization implements ImplementationFor<OpenCLDevice> { @Override public Tensor<?> run(ExecutionCall<OpenCLDevice> call) { throw new IllegalStateException("Not yet implemented"); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWise.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWise.html index b0bbfda19..891cf37fb 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWise.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWise.html @@ -1 +1 @@ -CPUBiElementWise

CPUBiElementWise

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total118 of 64281%28 of 8667%25595109316
_newWorkloadI32(Tensor, Tensor, Tensor, CPUBiFun)247475%91155%71111401
_newWorkloadF64(Tensor, Tensor, Tensor, CPUBiFun)247375%81260%61111401
_newWorkloadF32(Tensor, Tensor, Tensor, CPUBiFun)247375%91155%71111401
lambda$_newWorkloadI32$6(int[], CPUBiFun, int[], int[], int, int)120%n/a111111
lambda$_newWorkloadF32$3(float[], CPUBiFun, float[], float[], int, int)120%n/a111111
lambda$_newWorkloadF64$0(double[], CPUBiFun, double[], double[], int, int)120%n/a111111
_workloadFor(ExecutionCall)57393%11191%1711301
run(ExecutionCall)52080%1150%121801
lambda$_newWorkloadI32$8(Tensor, Tensor, Tensor, int[], CPUBiFun, int[], int[], int, int)50100%2100%0201301
lambda$_newWorkloadF32$5(Tensor, Tensor, Tensor, float[], CPUBiFun, float[], float[], int, int)50100%2100%0201301
lambda$_newWorkloadF64$2(Tensor, Tensor, Tensor, double[], CPUBiFun, double[], double[], int, int)50100%2100%0201301
lambda$_newWorkloadI32$7(int[], CPUBiFun, int[], int[], int, int)19100%2100%020201
lambda$_newWorkloadF32$4(float[], CPUBiFun, float[], float[], int, int)19100%2100%020201
lambda$_newWorkloadF64$1(double[], CPUBiFun, double[], double[], int, int)19100%2100%020201
CPUBiElementWise()3100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWise

CPUBiElementWise

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total118 of 64281%28 of 8667%25595109316
_newWorkloadI32(Tensor, Tensor, Tensor, CPUBiFun)247475%91155%71111401
_newWorkloadF64(Tensor, Tensor, Tensor, CPUBiFun)247375%81260%61111401
_newWorkloadF32(Tensor, Tensor, Tensor, CPUBiFun)247375%91155%71111401
lambda$_newWorkloadI32$6(int[], CPUBiFun, int[], int[], int, int)120%n/a111111
lambda$_newWorkloadF32$3(float[], CPUBiFun, float[], float[], int, int)120%n/a111111
lambda$_newWorkloadF64$0(double[], CPUBiFun, double[], double[], int, int)120%n/a111111
_workloadFor(ExecutionCall)57393%11191%1711301
run(ExecutionCall)52080%1150%121801
lambda$_newWorkloadI32$8(Tensor, Tensor, Tensor, int[], CPUBiFun, int[], int[], int, int)50100%2100%0201301
lambda$_newWorkloadF32$5(Tensor, Tensor, Tensor, float[], CPUBiFun, float[], float[], int, int)50100%2100%0201301
lambda$_newWorkloadF64$2(Tensor, Tensor, Tensor, double[], CPUBiFun, double[], double[], int, int)50100%2100%0201301
lambda$_newWorkloadI32$7(int[], CPUBiFun, int[], int[], int, int)19100%2100%020201
lambda$_newWorkloadF32$4(float[], CPUBiFun, float[], float[], int, int)19100%2100%020201
lambda$_newWorkloadF64$1(double[], CPUBiFun, double[], double[], int, int)19100%2100%020201
CPUBiElementWise()3100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWise.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWise.java.html index 504dff251..d4ac7fc03 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWise.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWise.java.html @@ -186,4 +186,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition$1.html index 237e548fa..5e45ff0b2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition$1.html @@ -1 +1 @@ -CPUBiElementWiseAddition.new CPUBiFun() {...}

CPUBiElementWiseAddition.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 3551%4 of 40%593737
invoke(boolean, boolean)80%40%331111
invoke(char, char)50%n/a111111
invoke(long, long)40%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseAddition.new CPUBiFun() {...}

CPUBiElementWiseAddition.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 3551%4 of 40%593737
invoke(boolean, boolean)80%40%331111
invoke(char, char)50%n/a111111
invoke(long, long)40%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition$2.html index 911c65c86..08d23480f 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition$2.html @@ -1 +1 @@ -CPUBiElementWiseAddition.new CPUBiFun() {...}

CPUBiElementWiseAddition.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total12 of 1833%0 of 0n/a676767
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
invoke(boolean, boolean)20%n/a111111
invoke(char, char)20%n/a111111
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseAddition.new CPUBiFun() {...}

CPUBiElementWiseAddition.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total12 of 1833%0 of 0n/a676767
invoke(double, double)20%n/a111111
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
invoke(long, long)20%n/a111111
invoke(boolean, boolean)20%n/a111111
invoke(char, char)20%n/a111111
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition.html index b8caa5cfb..70f056b41 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition.html @@ -1 +1 @@ -CPUBiElementWiseAddition

CPUBiElementWiseAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 1681%0 of 0n/a141414
_getDeriveAt1()30%n/a111111
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
CPUBiElementWiseAddition()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseAddition

CPUBiElementWiseAddition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 1681%0 of 0n/a141414
_getDeriveAt1()30%n/a111111
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
CPUBiElementWiseAddition()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition.java.html index 025dab610..6eea7bcd8 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseAddition.java.html @@ -34,4 +34,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$1.html index e8b87d1d2..d1ee80cd4 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$1.html @@ -1 +1 @@ -CPUBiElementWiseDivision.new CPUBiFun() {...}

CPUBiElementWiseDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2281%0 of 0n/a151515
invoke(long, long)40%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseDivision.new CPUBiFun() {...}

CPUBiElementWiseDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2281%0 of 0n/a151515
invoke(long, long)40%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$2.html index 61b9d48c8..287679a51 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$2.html @@ -1 +1 @@ -CPUBiElementWiseDivision.new CPUBiFun() {...}

CPUBiElementWiseDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 2722%0 of 0n/a454545
invoke(int, int)70%n/a111111
invoke(long, long)60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseDivision.new CPUBiFun() {...}

CPUBiElementWiseDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 2722%0 of 0n/a454545
invoke(int, int)70%n/a111111
invoke(long, long)60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$3.html index 560de1668..d8ea05348 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision$3.html @@ -1 +1 @@ -CPUBiElementWiseDivision.new CPUBiFun() {...}

CPUBiElementWiseDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total44 of 440%0 of 0n/a555555
invoke(int, int)110%n/a111111
invoke(float, float)100%n/a111111
invoke(long, long)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseDivision.new CPUBiFun() {...}

CPUBiElementWiseDivision.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total44 of 440%0 of 0n/a555555
invoke(int, int)110%n/a111111
invoke(float, float)100%n/a111111
invoke(long, long)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision.html index 73ccd7607..36e72e117 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision.html @@ -1 +1 @@ -CPUBiElementWiseDivision

CPUBiElementWiseDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1872%0 of 0n/a141414
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
CPUBiElementWiseDivision()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseDivision

CPUBiElementWiseDivision

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1872%0 of 0n/a141414
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
CPUBiElementWiseDivision()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision.java.html index 730b9862b..7d093bbc4 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseDivision.java.html @@ -34,4 +34,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$1.html index 98ee6104c..87289b275 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$1.html @@ -1 +1 @@ -CPUBiElementWiseModulo.new CPUBiFun() {...}

CPUBiElementWiseModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2281%0 of 0n/a151515
invoke(long, long)40%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseModulo.new CPUBiFun() {...}

CPUBiElementWiseModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2281%0 of 0n/a151515
invoke(long, long)40%n/a111111
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$2.html index 68eff0843..058e6b4e7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$2.html @@ -1 +1 @@ -CPUBiElementWiseModulo.new CPUBiFun() {...}

CPUBiElementWiseModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 2722%0 of 0n/a454545
invoke(int, int)70%n/a111111
invoke(long, long)60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseModulo.new CPUBiFun() {...}

CPUBiElementWiseModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 2722%0 of 0n/a454545
invoke(int, int)70%n/a111111
invoke(long, long)60%n/a111111
invoke(double, double)40%n/a111111
invoke(float, float)40%n/a111111
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$3.html index 3caa8e387..0b3b88362 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo$3.html @@ -1 +1 @@ -CPUBiElementWiseModulo.new CPUBiFun() {...}

CPUBiElementWiseModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total44 of 440%0 of 0n/a555555
invoke(int, int)110%n/a111111
invoke(float, float)100%n/a111111
invoke(long, long)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseModulo.new CPUBiFun() {...}

CPUBiElementWiseModulo.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total44 of 440%0 of 0n/a555555
invoke(int, int)110%n/a111111
invoke(float, float)100%n/a111111
invoke(long, long)100%n/a111111
invoke(double, double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo.html index 5e5c3479b..4b6b61c43 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo.html @@ -1 +1 @@ -CPUBiElementWiseModulo

CPUBiElementWiseModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1872%0 of 0n/a141414
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
CPUBiElementWiseModulo()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseModulo

CPUBiElementWiseModulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1872%0 of 0n/a141414
_getDeriveAt1()50%n/a111111
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
CPUBiElementWiseModulo()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo.java.html index bdb9407f0..669b0c59e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseModulo.java.html @@ -34,4 +34,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$1.html index 64692c415..2c54762cf 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$1.html @@ -1 +1 @@ -CPUBiElementWiseMultiplication.new CPUBiFun() {...}

CPUBiElementWiseMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseMultiplication.new CPUBiFun() {...}

CPUBiElementWiseMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$2.html index 5e6c6976b..6e5909da6 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$2.html @@ -1 +1 @@ -CPUBiElementWiseMultiplication.new CPUBiFun() {...}

CPUBiElementWiseMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1266%0 of 0n/a242424
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
{...}6100%n/a010101
invoke(double, double)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseMultiplication.new CPUBiFun() {...}

CPUBiElementWiseMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1266%0 of 0n/a242424
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
{...}6100%n/a010101
invoke(double, double)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$3.html index 0177172c1..ff8fab391 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication$3.html @@ -1 +1 @@ -CPUBiElementWiseMultiplication.new CPUBiFun() {...}

CPUBiElementWiseMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1266%0 of 0n/a242424
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
{...}6100%n/a010101
invoke(double, double)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseMultiplication.new CPUBiFun() {...}

CPUBiElementWiseMultiplication.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1266%0 of 0n/a242424
invoke(float, float)20%n/a111111
invoke(int, int)20%n/a111111
{...}6100%n/a010101
invoke(double, double)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication.html index 0a1a26d5a..3f63fd263 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication.html @@ -1 +1 @@ -CPUBiElementWiseMultiplication

CPUBiElementWiseMultiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
_getDeriveAt1()5100%n/a010101
CPUBiElementWiseMultiplication()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseMultiplication

CPUBiElementWiseMultiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
_getDeriveAt1()5100%n/a010101
CPUBiElementWiseMultiplication()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication.java.html index 75b28a363..4401825b8 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseMultiplication.java.html @@ -31,4 +31,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$1.html index adde17123..edda14547 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$1.html @@ -1 +1 @@ -CPUBiElementWisePower.new CPUBiFun() {...}

CPUBiElementWisePower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 3253%0 of 0n/a252525
invoke(int, int)80%n/a111111
invoke(long, long)70%n/a111111
invoke(float, float)7100%n/a010101
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWisePower.new CPUBiFun() {...}

CPUBiElementWisePower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 3253%0 of 0n/a252525
invoke(int, int)80%n/a111111
invoke(long, long)70%n/a111111
invoke(float, float)7100%n/a010101
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$2.html index 64a9d4194..89c6b4ff3 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$2.html @@ -1 +1 @@ -CPUBiElementWisePower.new CPUBiFun() {...}

CPUBiElementWisePower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 5150%0 of 0n/a252525
invoke(int, int)130%n/a111111
invoke(long, long)120%n/a111111
invoke(float, float)12100%n/a010101
invoke(double, double)8100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWisePower.new CPUBiFun() {...}

CPUBiElementWisePower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 5150%0 of 0n/a252525
invoke(int, int)130%n/a111111
invoke(long, long)120%n/a111111
invoke(float, float)12100%n/a010101
invoke(double, double)8100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$3.html index f340010af..17a9c8881 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower$3.html @@ -1 +1 @@ -CPUBiElementWisePower.new CPUBiFun() {...}

CPUBiElementWisePower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total23 of 4751%0 of 0n/a252525
invoke(int, int)120%n/a111111
invoke(long, long)110%n/a111111
invoke(float, float)11100%n/a010101
invoke(double, double)7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWisePower.new CPUBiFun() {...}

CPUBiElementWisePower.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total23 of 4751%0 of 0n/a252525
invoke(int, int)120%n/a111111
invoke(long, long)110%n/a111111
invoke(float, float)11100%n/a010101
invoke(double, double)7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower.html index 34b6ad3c2..157a36b6f 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower.html @@ -1 +1 @@ -CPUBiElementWisePower

CPUBiElementWisePower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
_getDeriveAt1()5100%n/a010101
CPUBiElementWisePower()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWisePower

CPUBiElementWisePower

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
_getDeriveAt1()5100%n/a010101
CPUBiElementWisePower()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower.java.html index ee69bd6ad..880a4b8ae 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWisePower.java.html @@ -34,4 +34,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$1.html index 0f8a2c1ab..a19a1e331 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$1.html @@ -1 +1 @@ -CPUBiElementWiseSubtraction.new CPUBiFun() {...}

CPUBiElementWiseSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseSubtraction.new CPUBiFun() {...}

CPUBiElementWiseSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
{...}6100%n/a010101
invoke(double, double)4100%n/a010101
invoke(float, float)4100%n/a010101
invoke(int, int)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$2.html index 69dc74c15..d0e3c463a 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$2.html @@ -1 +1 @@ -CPUBiElementWiseSubtraction.new CPUBiFun() {...}

CPUBiElementWiseSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 1283%0 of 0n/a141414
invoke(int, int)20%n/a111111
{...}6100%n/a010101
invoke(double, double)2100%n/a010101
invoke(float, float)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseSubtraction.new CPUBiFun() {...}

CPUBiElementWiseSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 1283%0 of 0n/a141414
invoke(int, int)20%n/a111111
{...}6100%n/a010101
invoke(double, double)2100%n/a010101
invoke(float, float)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$3.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$3.html index ec42ca6da..4675ba125 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$3.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction$3.html @@ -1 +1 @@ -CPUBiElementWiseSubtraction.new CPUBiFun() {...}

CPUBiElementWiseSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 1283%0 of 0n/a141414
invoke(int, int)20%n/a111111
{...}6100%n/a010101
invoke(double, double)2100%n/a010101
invoke(float, float)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseSubtraction.new CPUBiFun() {...}

CPUBiElementWiseSubtraction.new CPUBiFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 1283%0 of 0n/a141414
invoke(int, int)20%n/a111111
{...}6100%n/a010101
invoke(double, double)2100%n/a010101
invoke(float, float)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction.html index 3eee71c2c..50820edff 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction.html @@ -1 +1 @@ -CPUBiElementWiseSubtraction

CPUBiElementWiseSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
_getDeriveAt1()5100%n/a010101
CPUBiElementWiseSubtraction()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiElementWiseSubtraction

CPUBiElementWiseSubtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040404
_getFun()5100%n/a010101
_getDeriveAt0()5100%n/a010101
_getDeriveAt1()5100%n/a010101
CPUBiElementWiseSubtraction()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction.java.html index 37615ebfa..14bb9936d 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUBiElementWiseSubtraction.java.html @@ -31,4 +31,4 @@ }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseAssignFun.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseAssignFun.html index c52e53acf..e3e436800 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseAssignFun.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseAssignFun.html @@ -1 +1 @@ -CPUElementwiseAssignFun

CPUElementwiseAssignFun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total45 of 39388%12 of 4271%102546604
run(ExecutionCall)4533988%123071%102246401
CPUElementwiseAssignFun()4100%n/a010101
lambda$run$0(Tensor)4100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUElementwiseAssignFun

CPUElementwiseAssignFun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total45 of 39388%12 of 4271%102546604
run(ExecutionCall)4533988%123071%102246401
CPUElementwiseAssignFun()4100%n/a010101
lambda$run$0(Tensor)4100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseAssignFun.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseAssignFun.java.html index baa403d58..78d80be57 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseAssignFun.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseAssignFun.java.html @@ -97,4 +97,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseFunction.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseFunction.html index bd5bca829..0cb3d04e5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseFunction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseFunction.html @@ -1 +1 @@ -CPUElementwiseFunction

CPUElementwiseFunction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total97 of 91689%12 of 8686%126613208323
_workloadFor(ExecutionCall)4933987%64287%62578201
lambda$_workloadFor$15(char[], CPUFun, char[], int, int)160%20%222211
lambda$_workloadFor$13(boolean[], CPUFun, boolean[], int, int)160%20%222211
lambda$_workloadFor$11(short[], CPUFun, short[], int, int)160%20%222211
lambda$_workloadFor$0(Tensor, Tensor, double[], CPUFun, int[], int, int)37100%2100%0201001
lambda$_workloadFor$18(Tensor, Tensor, Object[], CPUFun, Object[], int, int)36100%2100%0201001
lambda$_workloadFor$16(Tensor, Tensor, char[], CPUFun, char[], int, int)36100%2100%0201001
lambda$_workloadFor$14(Tensor, Tensor, boolean[], CPUFun, boolean[], int, int)36100%2100%0201001
lambda$_workloadFor$12(Tensor, Tensor, short[], CPUFun, short[], int, int)36100%2100%0201001
lambda$_workloadFor$10(Tensor, Tensor, byte[], CPUFun, byte[], int, int)36100%2100%0201001
lambda$_workloadFor$8(Tensor, Tensor, long[], CPUFun, long[], int, int)36100%2100%0201001
lambda$_workloadFor$6(Tensor, Tensor, int[], CPUFun, int[], int, int)36100%2100%0201001
lambda$_workloadFor$4(Tensor, Tensor, float[], CPUFun, float[], int, int)36100%2100%0201001
lambda$_workloadFor$2(Tensor, Tensor, double[], CPUFun, double[], int, int)36100%2100%0201001
run(ExecutionCall)16100%n/a010601
lambda$_workloadFor$17(Object[], CPUFun, Object[], int, int)16100%2100%020201
lambda$_workloadFor$9(byte[], CPUFun, byte[], int, int)16100%2100%020201
lambda$_workloadFor$7(long[], CPUFun, long[], int, int)16100%2100%020201
lambda$_workloadFor$5(int[], CPUFun, int[], int, int)16100%2100%020201
lambda$_workloadFor$3(float[], CPUFun, float[], int, int)16100%2100%020201
lambda$_workloadFor$1(double[], CPUFun, double[], int, int)16100%2100%020201
CPUElementwiseFunction(ScalarFun)6100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUElementwiseFunction

CPUElementwiseFunction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total97 of 91689%12 of 8686%126613208323
_workloadFor(ExecutionCall)4933987%64287%62578201
lambda$_workloadFor$15(char[], CPUFun, char[], int, int)160%20%222211
lambda$_workloadFor$13(boolean[], CPUFun, boolean[], int, int)160%20%222211
lambda$_workloadFor$11(short[], CPUFun, short[], int, int)160%20%222211
lambda$_workloadFor$0(Tensor, Tensor, double[], CPUFun, int[], int, int)37100%2100%0201001
lambda$_workloadFor$18(Tensor, Tensor, Object[], CPUFun, Object[], int, int)36100%2100%0201001
lambda$_workloadFor$16(Tensor, Tensor, char[], CPUFun, char[], int, int)36100%2100%0201001
lambda$_workloadFor$14(Tensor, Tensor, boolean[], CPUFun, boolean[], int, int)36100%2100%0201001
lambda$_workloadFor$12(Tensor, Tensor, short[], CPUFun, short[], int, int)36100%2100%0201001
lambda$_workloadFor$10(Tensor, Tensor, byte[], CPUFun, byte[], int, int)36100%2100%0201001
lambda$_workloadFor$8(Tensor, Tensor, long[], CPUFun, long[], int, int)36100%2100%0201001
lambda$_workloadFor$6(Tensor, Tensor, int[], CPUFun, int[], int, int)36100%2100%0201001
lambda$_workloadFor$4(Tensor, Tensor, float[], CPUFun, float[], int, int)36100%2100%0201001
lambda$_workloadFor$2(Tensor, Tensor, double[], CPUFun, double[], int, int)36100%2100%0201001
run(ExecutionCall)16100%n/a010601
lambda$_workloadFor$17(Object[], CPUFun, Object[], int, int)16100%2100%020201
lambda$_workloadFor$9(byte[], CPUFun, byte[], int, int)16100%2100%020201
lambda$_workloadFor$7(long[], CPUFun, long[], int, int)16100%2100%020201
lambda$_workloadFor$5(int[], CPUFun, int[], int, int)16100%2100%020201
lambda$_workloadFor$3(float[], CPUFun, float[], int, int)16100%2100%020201
lambda$_workloadFor$1(double[], CPUFun, double[], int, int)16100%2100%020201
CPUElementwiseFunction(ScalarFun)6100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseFunction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseFunction.java.html index aebee139b..b3e7f0174 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseFunction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPUElementwiseFunction.java.html @@ -295,4 +295,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization$1.html index 0885a9958..2784b8cb7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization$1.html @@ -1 +1 @@ -CPURandomization.new CPURandomization.DataProvider() {...}

CPURandomization.new CPURandomization.DataProvider() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
{...}6100%n/a010101
get(Class)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPURandomization.new CPURandomization.DataProvider() {...}

CPURandomization.new CPURandomization.DataProvider() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
{...}6100%n/a010101
get(Class)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization$2.html index bb80f7519..ccc614c18 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization$2.html @@ -1 +1 @@ -CPURandomization.new CPURandomization.DataProvider() {...}

CPURandomization.new CPURandomization.DataProvider() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 12100%0 of 0n/a020202
{...}6100%n/a010101
get(Class)6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPURandomization.new CPURandomization.DataProvider() {...}

CPURandomization.new CPURandomization.DataProvider() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 12100%0 of 0n/a020202
{...}6100%n/a010101
get(Class)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization.html index 324e71787..2c171c238 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization.html @@ -1 +1 @@ -CPURandomization

CPURandomization

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total12 of 1,01398%3 of 11097%3911194036
_newWorkloadFor(long, Class, CPURandomization.NDIteratorProvider, CPURandomization.DataProvider)1215892%13397%11814201
fillRandomly(Object, long)109100%18100%01001501
lambda$_newWorkloadFor$4(CPURandomization.NDIteratorProvider, long, float[], int, int)70100%6100%0401101
gaussianFrom(long, double[])67100%1375%1301301
lambda$_newWorkloadFor$3(long, float[], int, int)67100%6100%0401001
lambda$_newWorkloadFor$2(CPURandomization.NDIteratorProvider, long, double[], int, int)67100%6100%0401101
lambda$_newWorkloadFor$1(long, double[], int, int)64100%6100%0401001
_newWorkloadFor(ExecutionCall)38100%2100%020801
seedIndexScramble(long, long)30100%n/a010401
lambda$_newWorkloadFor$16(CPURandomization.NDIteratorProvider, char[], long, int, int)19100%2100%020401
lambda$_newWorkloadFor$14(CPURandomization.NDIteratorProvider, boolean[], long, int, int)19100%2100%020401
lambda$_newWorkloadFor$12(CPURandomization.NDIteratorProvider, short[], long, int, int)19100%2100%020401
lambda$_newWorkloadFor$10(CPURandomization.NDIteratorProvider, byte[], long, int, int)19100%2100%020401
lambda$_newWorkloadFor$8(CPURandomization.NDIteratorProvider, int[], long, int, int)19100%2100%020401
lambda$_newWorkloadFor$6(CPURandomization.NDIteratorProvider, long[], long, int, int)19100%2100%020401
_nextLong(long)18100%n/a010301
_nextSeed(long)18100%1150%120401
run(ExecutionCall)15100%n/a010701
_nextDouble(long, long)15100%n/a010101
lambda$_newWorkloadFor$15(char[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$13(boolean[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$11(short[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$9(byte[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$7(int[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$5(long[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$0(Tensor, int)10100%n/a010301
_nextBoolean(long)9100%2100%020101
fillRandomly(Object, String)8100%n/a010101
fillRandomly(Object, Arg.Seed)7100%n/a010101
_next(int, long)7100%n/a010101
initialScramble(long)6100%n/a010101
_nextInt(long)5100%n/a010101
_nextByte(long)4100%n/a010101
_nextShort(long)4100%n/a010101
_nextChar(long)4100%n/a010101
CPURandomization()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPURandomization

CPURandomization

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total12 of 1,01398%3 of 11097%3911194036
_newWorkloadFor(long, Class, CPURandomization.NDIteratorProvider, CPURandomization.DataProvider)1215892%13397%11814201
fillRandomly(Object, long)109100%18100%01001501
lambda$_newWorkloadFor$4(CPURandomization.NDIteratorProvider, long, float[], int, int)70100%6100%0401101
gaussianFrom(long, double[])67100%1375%1301301
lambda$_newWorkloadFor$3(long, float[], int, int)67100%6100%0401001
lambda$_newWorkloadFor$2(CPURandomization.NDIteratorProvider, long, double[], int, int)67100%6100%0401101
lambda$_newWorkloadFor$1(long, double[], int, int)64100%6100%0401001
_newWorkloadFor(ExecutionCall)38100%2100%020801
seedIndexScramble(long, long)30100%n/a010401
lambda$_newWorkloadFor$16(CPURandomization.NDIteratorProvider, char[], long, int, int)19100%2100%020401
lambda$_newWorkloadFor$14(CPURandomization.NDIteratorProvider, boolean[], long, int, int)19100%2100%020401
lambda$_newWorkloadFor$12(CPURandomization.NDIteratorProvider, short[], long, int, int)19100%2100%020401
lambda$_newWorkloadFor$10(CPURandomization.NDIteratorProvider, byte[], long, int, int)19100%2100%020401
lambda$_newWorkloadFor$8(CPURandomization.NDIteratorProvider, int[], long, int, int)19100%2100%020401
lambda$_newWorkloadFor$6(CPURandomization.NDIteratorProvider, long[], long, int, int)19100%2100%020401
_nextLong(long)18100%n/a010301
_nextSeed(long)18100%1150%120401
run(ExecutionCall)15100%n/a010701
_nextDouble(long, long)15100%n/a010101
lambda$_newWorkloadFor$15(char[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$13(boolean[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$11(short[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$9(byte[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$7(int[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$5(long[], long, int, int)14100%2100%020301
lambda$_newWorkloadFor$0(Tensor, int)10100%n/a010301
_nextBoolean(long)9100%2100%020101
fillRandomly(Object, String)8100%n/a010101
fillRandomly(Object, Arg.Seed)7100%n/a010101
_next(int, long)7100%n/a010101
initialScramble(long)6100%n/a010101
_nextInt(long)5100%n/a010101
_nextByte(long)4100%n/a010101
_nextShort(long)4100%n/a010101
_nextChar(long)4100%n/a010101
CPURandomization()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization.java.html index 52968f89b..6f0d2499e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/CPURandomization.java.html @@ -327,4 +327,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/index.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/index.html index b31b2dffc..eec67bbc1 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/index.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/index.html @@ -1 +1 @@ -neureka.backend.main.implementations.elementwise

neureka.backend.main.implementations.elementwise

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total544 of 3,77285%64 of 33881%1023736671851204238
CPUBiElementWise11852481%285867%2559510931601
CPUElementwiseFunction9781989%127486%12661320832301
CPUElementwiseAssignFun4534888%123071%10254660401
CPUBiElementWiseDivision.new CPUBiFun() {...}440%n/a55555511
CPUBiElementWiseModulo.new CPUBiFun() {...}440%n/a55555511
CPUBiElementWisePower.new CPUBiFun() {...}252650%n/a25252501
CPUBiElementWisePower.new CPUBiFun() {...}232451%n/a25252501
CPUBiElementWiseDivision.new CPUBiFun() {...}2122%n/a45454501
CPUBiElementWiseModulo.new CPUBiFun() {...}2122%n/a45454501
CPUBiElementWiseAddition.new CPUBiFun() {...}171851%40%59373701
CPUBiElementWisePower.new CPUBiFun() {...}151753%n/a25252501
CPURandomization121,00198%310797%391119403601
CPUBiElementWiseAddition.new CPUBiFun() {...}1233%n/a67676701
CLElementwiseFunction9393%3350%360180301
CLBiElementwise7992%2250%250150301
CPUBiElementWiseDivision1372%n/a14141401
CPUBiElementWiseModulo1372%n/a14141401
CLRandomization37%n/a12121201
CPUBiElementWiseDivision.new CPUBiFun() {...}1881%n/a15151501
CPUBiElementWiseModulo.new CPUBiFun() {...}1881%n/a15151501
CPUBiElementWiseMultiplication.new CPUBiFun() {...}66%n/a24242401
CPUBiElementWiseMultiplication.new CPUBiFun() {...}66%n/a24242401
CPUBiElementWiseAddition1381%n/a14141401
CPUBiElementWiseSubtraction.new CPUBiFun() {...}1083%n/a14141401
CPUBiElementWiseSubtraction.new CPUBiFun() {...}1083%n/a14141401
CPUBiElementWiseMultiplication18100%n/a04040401
CPUBiElementWiseSubtraction18100%n/a04040401
CPUBiElementWisePower18100%n/a04040401
CPUBiElementWiseMultiplication.new CPUBiFun() {...}18100%n/a04040401
CPUBiElementWiseSubtraction.new CPUBiFun() {...}18100%n/a04040401
CPURandomization.new CPURandomization.DataProvider() {...}12100%n/a02020201
CPURandomization.new CPURandomization.DataProvider() {...}9100%n/a02020201
CLBiElementwiseMultiplication100%n/a01020101
CLBiElementwiseAddition100%n/a01020101
CLBiElementwiseSubtraction100%n/a01020101
CLBiElementwisePower100%n/a01020101
CLBiElementwiseModulo100%n/a01020101
CLBiElementwiseDivision100%n/a01020101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.elementwise

neureka.backend.main.implementations.elementwise

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total755 of 3,77279%69 of 33879%115373112718642041138
CPUBiElementWise11852481%285867%2559510931601
CLElementwiseFunction990%60%6618183311
CPUElementwiseFunction9781989%127486%12661320832301
CLBiElementwise850%40%5515153311
CPUElementwiseAssignFun4534888%123071%10254660401
CPUBiElementWiseDivision.new CPUBiFun() {...}440%n/a55555511
CPUBiElementWiseModulo.new CPUBiFun() {...}440%n/a55555511
CPUBiElementWisePower.new CPUBiFun() {...}252650%n/a25252501
CPUBiElementWisePower.new CPUBiFun() {...}232451%n/a25252501
CPUBiElementWiseDivision.new CPUBiFun() {...}2122%n/a45454501
CPUBiElementWiseModulo.new CPUBiFun() {...}2122%n/a45454501
CPUBiElementWiseAddition.new CPUBiFun() {...}171851%40%59373701
CPUBiElementWisePower.new CPUBiFun() {...}151753%n/a25252501
CPURandomization121,00198%310797%391119403601
CPUBiElementWiseAddition.new CPUBiFun() {...}1233%n/a67676701
CLRandomization0%n/a22222211
CLBiElementwiseMultiplication0%n/a11221111
CLBiElementwiseAddition0%n/a11221111
CLBiElementwiseSubtraction0%n/a11221111
CLBiElementwisePower0%n/a11221111
CLBiElementwiseModulo0%n/a11221111
CLBiElementwiseDivision0%n/a11221111
CPUBiElementWiseDivision1372%n/a14141401
CPUBiElementWiseModulo1372%n/a14141401
CPUBiElementWiseDivision.new CPUBiFun() {...}1881%n/a15151501
CPUBiElementWiseModulo.new CPUBiFun() {...}1881%n/a15151501
CPUBiElementWiseMultiplication.new CPUBiFun() {...}66%n/a24242401
CPUBiElementWiseMultiplication.new CPUBiFun() {...}66%n/a24242401
CPUBiElementWiseAddition1381%n/a14141401
CPUBiElementWiseSubtraction.new CPUBiFun() {...}1083%n/a14141401
CPUBiElementWiseSubtraction.new CPUBiFun() {...}1083%n/a14141401
CPUBiElementWiseMultiplication18100%n/a04040401
CPUBiElementWiseSubtraction18100%n/a04040401
CPUBiElementWisePower18100%n/a04040401
CPUBiElementWiseMultiplication.new CPUBiFun() {...}18100%n/a04040401
CPUBiElementWiseSubtraction.new CPUBiFun() {...}18100%n/a04040401
CPURandomization.new CPURandomization.DataProvider() {...}12100%n/a02020201
CPURandomization.new CPURandomization.DataProvider() {...}9100%n/a02020201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/index.source.html b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/index.source.html index fd096d70a..8e80802b2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.elementwise/index.source.html @@ -1 +1 @@ -neureka.backend.main.implementations.elementwise

neureka.backend.main.implementations.elementwise

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total544 of 3,77285%64 of 33881%1023736671851204238
CPUBiElementWise.java11852481%285867%2559510931601
CPUElementwiseFunction.java9781989%127486%12661320832301
CPUBiElementWiseModulo.java743733%n/a11191016111914
CPUBiElementWiseDivision.java743733%n/a11191016111914
CPUBiElementWisePower.java638557%n/a61961661904
CPUElementwiseAssignFun.java4534888%123071%10254660401
CPUBiElementWiseAddition.java323753%40%12201016101803
CPURandomization.java121,02298%310797%395119804003
CPUBiElementWiseMultiplication.java5286%n/a41641341604
CLElementwiseFunction.java9393%3350%360180301
CLBiElementwise.java7992%2250%250150301
CLRandomization.java37%n/a12121201
CPUBiElementWiseSubtraction.java5693%n/a21621321604
CLBiElementwiseSubtraction.java100%n/a01020101
CLBiElementwiseModulo.java100%n/a01020101
CLBiElementwiseDivision.java100%n/a01020101
CLBiElementwisePower.java100%n/a01020101
CLBiElementwiseMultiplication.java100%n/a01020101
CLBiElementwiseAddition.java100%n/a01020101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.elementwise

neureka.backend.main.implementations.elementwise

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total755 of 3,77279%69 of 33879%115373112718642041138
CPUBiElementWise.java11852481%285867%2559510931601
CLElementwiseFunction.java990%60%6618183311
CPUElementwiseFunction.java9781989%127486%12661320832301
CLBiElementwise.java850%40%5515153311
CPUBiElementWiseModulo.java743733%n/a11191016111914
CPUBiElementWiseDivision.java743733%n/a11191016111914
CPUBiElementWisePower.java638557%n/a61961661904
CPUElementwiseAssignFun.java4534888%123071%10254660401
CPUBiElementWiseAddition.java323753%40%12201016101803
CPURandomization.java121,02298%310797%395119804003
CPUBiElementWiseMultiplication.java5286%n/a41641341604
CLRandomization.java0%n/a22222211
CLBiElementwiseAddition.java0%n/a11221111
CLBiElementwiseSubtraction.java0%n/a11221111
CLBiElementwiseModulo.java0%n/a11221111
CLBiElementwiseDivision.java0%n/a11221111
CLBiElementwisePower.java0%n/a11221111
CLBiElementwiseMultiplication.java0%n/a11221111
CPUBiElementWiseSubtraction.java5693%n/a21621321604
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUBiFun.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUBiFun.html index 26f05b81a..29bb668fc 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUBiFun.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUBiFun.html @@ -1 +1 @@ -CPUBiFun

CPUBiFun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total76 of 8611%6 of 60%10117878
invoke(boolean, boolean)190%60%441111
invoke(Object, Object)140%n/a111111
invoke(byte, byte)100%n/a111111
invoke(int, int)90%n/a111111
invoke(float, float)80%n/a111111
invoke(long, long)80%n/a111111
invoke(char, char)80%n/a111111
invoke(short, short)10100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUBiFun

CPUBiFun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total72 of 8212%6 of 60%10117878
invoke(boolean, boolean)170%60%441111
invoke(Object, Object)140%n/a111111
invoke(byte, byte)100%n/a111111
invoke(int, int)90%n/a111111
invoke(float, float)80%n/a111111
invoke(long, long)80%n/a111111
invoke(char, char)60%n/a111111
invoke(short, short)10100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUBiFun.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUBiFun.java.html index 3798f0b70..315b55911 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUBiFun.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUBiFun.java.html @@ -4,20 +4,36 @@ { double invoke(double a, double b); - default float invoke(float a, float b) { return (float) invoke( (double) a, (double) b ); } + default float invoke(float a, float b) { + return (float) invoke( a, (double) b ); + } - default int invoke(int a, int b) { return (int) Math.round( invoke( (double) a, (double) b ) ); } + default int invoke(int a, int b) { + return (int) Math.round( invoke( a, (double) b ) ); + } - default long invoke(long a, long b) { return Math.round( invoke( (double) a, (double) b ) ); } + default long invoke(long a, long b) { + return Math.round( invoke( (double) a, (double) b ) ); + } - default byte invoke(byte a, byte b) { return (byte) Math.round( invoke( (double) a, (double) b ) ); } + default byte invoke(byte a, byte b) { + return (byte) Math.round( invoke( a, (double) b ) ); + } - default short invoke(short a, short b) { return (short) Math.round( invoke( (double) a, (double) b ) ); } + default short invoke(short a, short b) { + return (short) Math.round( invoke( a, (double) b ) ); + } - default boolean invoke(boolean a, boolean b) { return Math.round( invoke( a ? 1 : 0, b ? 1 : 0 ) ) != 0; } // Some default behaviors, it might make sense to override this for some activations. + default boolean invoke(boolean a, boolean b) { + return invoke( a ? 1 : 0, b ? 1 : 0 ) != 0; // Some default behaviors, it might make sense to override this for some activations. + } - default char invoke(char a, char b) { return (char) Math.round( invoke( (int) a, (int) b ) ); } // Some default behaviors, it might make sense to override this for some activations. + default char invoke(char a, char b) { + return (char) invoke( a, (int) b ); // Some default behaviors, it might make sense to override this for some activations. + } - default Object invoke(Object a, Object b) { throw new IllegalStateException("Not implemented for operation "+getClass().getSimpleName()); } + default Object invoke(Object a, Object b) { + throw new IllegalStateException("Not implemented for operation "+getClass().getSimpleName()); + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUFun.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUFun.html index 5ab8d9275..7b4f3e3a4 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUFun.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUFun.html @@ -1 +1 @@ -CPUFun

CPUFun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total41 of 7041%4 of 40%6104848
invoke(boolean)140%40%331111
invoke(Object)140%n/a111111
invoke(char)70%n/a111111
invoke(long)60%n/a111111
invoke(byte)8100%n/a010101
invoke(short)8100%n/a010101
invoke(int)7100%n/a010101
invoke(float)6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUFun

CPUFun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total37 of 6643%4 of 40%6104848
invoke(Object)140%n/a111111
invoke(boolean)120%40%331111
invoke(long)60%n/a111111
invoke(char)50%n/a111111
invoke(byte)8100%n/a010101
invoke(short)8100%n/a010101
invoke(int)7100%n/a010101
invoke(float)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUFun.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUFun.java.html index a653614af..eca64af10 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUFun.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/CPUFun.java.html @@ -4,21 +4,37 @@ double invoke(double x); - default float invoke(float x) { return (float) invoke( (double) x ); } + default float invoke(float x) { + return (float) invoke( (double) x ); + } - default int invoke(int x) { return (int) Math.round( invoke( (double) x ) ); } + default int invoke(int x) { + return (int) Math.round( invoke( (double) x ) ); + } - default long invoke(long x) { return Math.round( invoke( (double) x ) ); } + default long invoke(long x) { + return Math.round( invoke( (double) x ) ); + } - default byte invoke(byte x) { return (byte) Math.round( invoke( (double) x ) ); } + default byte invoke(byte x) { + return (byte) Math.round( invoke( (double) x ) ); + } - default short invoke(short x) { return (short) Math.round( invoke( (double) x ) ); } + default short invoke(short x) { + return (short) Math.round( invoke( (double) x ) ); + } - default boolean invoke(boolean x) { return Math.round( invoke( x ? 1 : 0 ) ) != 0; } // Some default behaviors, it might make sense to override this for some activations. + default boolean invoke(boolean x) { + return invoke( x ? 1 : 0 ) != 0; // Some default behaviors, it might make sense to override this for some activations. + } - default char invoke(char x) { return (char) Math.round( invoke( (int) x ) ); } // Some default behaviors, it might make sense to override this for some activations. + default char invoke(char x) { + return (char) invoke( (int) x ); // Some default behaviors, it might make sense to override this for some activations. + } - default Object invoke(Object x) { throw new IllegalStateException("Not implemented for operation "+getClass().getSimpleName()); } + default Object invoke(Object x) { + throw new IllegalStateException("Not implemented for operation "+getClass().getSimpleName()); + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/ScalarFun.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/ScalarFun.html index 3dee9da92..607972c02 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/ScalarFun.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/ScalarFun.html @@ -1 +1 @@ -ScalarFun

ScalarFun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 105100%0 of 2100%0302602
static {...}93100%n/a0102301
calculate(double, boolean)12100%2100%020301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarFun

ScalarFun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 105100%0 of 2100%0302602
static {...}93100%n/a0102301
calculate(double, boolean)12100%2100%020301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/ScalarFun.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/ScalarFun.java.html index a155ac0c1..5477dade2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/ScalarFun.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/ScalarFun.java.html @@ -46,4 +46,4 @@ CPUFun getDerivative(); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/index.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/index.html index 13291aad3..c14b146bc 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/index.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/index.html @@ -1 +1 @@ -neureka.backend.main.implementations.fun.api

neureka.backend.main.implementations.fun.api

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total117 of 26155%10 of 1216%16241142111803
CPUBiFun761011%60%1011787801
CPUFun412941%40%610484801
ScalarFun105100%2100%030260201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.fun.api

neureka.backend.main.implementations.fun.api

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total109 of 25356%10 of 1216%16241142111803
CPUBiFun721012%60%1011787801
CPUFun372943%40%610484801
ScalarFun105100%2100%030260201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/index.source.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/index.source.html index 86d7ae217..b952fb1e8 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun.api/index.source.html @@ -1 +1 @@ -neureka.backend.main.implementations.fun.api

neureka.backend.main.implementations.fun.api

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total117 of 26155%10 of 1216%16241142111803
CPUBiFun.java761011%60%1011787801
CPUFun.java412941%40%610484801
ScalarFun.java105100%2100%030260201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.fun.api

neureka.backend.main.implementations.fun.api

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total109 of 25356%10 of 1216%16241142111803
CPUBiFun.java721012%60%1011787801
CPUFun.java372943%40%610484801
ScalarFun.java105100%2100%030260201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/FunUtil.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/FunUtil.html index d66e4240c..f662ce3dd 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/FunUtil.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/FunUtil.html @@ -1 +1 @@ -FunUtil

FunUtil

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 9996%0 of 0n/a1311713
FunUtil()30%n/a111111
invSqrt(double)58100%n/a010901
invSqrt(float)38100%n/a010701
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FunUtil

FunUtil

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 9996%0 of 0n/a1311713
FunUtil()30%n/a111111
invSqrt(double)58100%n/a010901
invSqrt(float)38100%n/a010701
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/FunUtil.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/FunUtil.java.html index e39dc4e4c..002685d2a 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/FunUtil.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/FunUtil.java.html @@ -28,4 +28,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute$1.html index 45f81447c..6d5844e01 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute$1.html @@ -1 +1 @@ -ScalarAbsolute.new CPUFun() {...}

ScalarAbsolute.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 15100%0 of 0n/a040404
{...}6100%n/a010101
invoke(double)3100%n/a010101
invoke(float)3100%n/a010101
invoke(int)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarAbsolute.new CPUFun() {...}

ScalarAbsolute.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 15100%0 of 0n/a040404
{...}6100%n/a010101
invoke(double)3100%n/a010101
invoke(float)3100%n/a010101
invoke(int)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute$2.html index 055365abd..65af3943d 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute$2.html @@ -1 +1 @@ -ScalarAbsolute.new CPUFun() {...}

ScalarAbsolute.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 2850%4 of 633%472424
invoke(float)80%20%221111
invoke(int)60%20%221111
invoke(double)8100%2100%020101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarAbsolute.new CPUFun() {...}

ScalarAbsolute.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 2850%4 of 633%472424
invoke(float)80%20%221111
invoke(int)60%20%221111
invoke(double)8100%2100%020101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute.html index e79e09740..cbed1579c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute.html @@ -1 +1 @@ -ScalarAbsolute

ScalarAbsolute

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarAbsolute()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarAbsolute

ScalarAbsolute

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarAbsolute()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute.java.html index a03c6d1fe..ace07ab17 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarAbsolute.java.html @@ -7,9 +7,9 @@ { @Override public String id() { return "abs"; } - @Override public String activationCode() { return "output = fabs( input );\n"; } + @Override public String activationCode() { return "output = fabs( input );\n"; } - @Override public String derivationCode() { return "output = ( input < 0 ) ? -1 : 1;\n"; } + @Override public String derivationCode() { return "output = ( input < 0 ) ? -1 : 1;\n"; } @Override public CPUFun getActivation() { @@ -32,4 +32,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt$1.html index 9f192ba32..78890c765 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt$1.html @@ -1 +1 @@ -ScalarCbrt.new CPUFun() {...}

ScalarCbrt.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1464%0 of 0n/a131313
invoke(float)50%n/a111111
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarCbrt.new CPUFun() {...}

ScalarCbrt.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1464%0 of 0n/a131313
invoke(float)50%n/a111111
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt$2.html index ca81f2e06..5a82a17e7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt$2.html @@ -1 +1 @@ -ScalarCbrt.new CPUFun() {...}

ScalarCbrt.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total24 of 240%0 of 0n/a333333
invoke(float)100%n/a111111
invoke(double)80%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarCbrt.new CPUFun() {...}

ScalarCbrt.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total24 of 240%0 of 0n/a333333
invoke(float)100%n/a111111
invoke(double)80%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt.html index 19d06d191..667972c52 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt.html @@ -1 +1 @@ -ScalarCbrt

ScalarCbrt

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1973%0 of 0n/a161616
getDerivative()50%n/a111111
getActivation()5100%n/a010101
ScalarCbrt()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarCbrt

ScalarCbrt

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 1952%0 of 0n/a363636
getDerivative()50%n/a111111
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
ScalarCbrt()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt.java.html index af2d50a5e..14542759f 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCbrt.java.html @@ -9,12 +9,12 @@ @Override public String activationCode() { - return "output = cbrt( input );\n"; + return "output = cbrt( input );\n"; } @Override public String derivationCode() { - return "output = 1 / ( 3 * pow( input, 2.0f / 3.0f ) );\n"; + return "output = 1 / ( 3 * pow( input, 2.0f / 3.0f ) );\n"; } @Override @@ -35,4 +35,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus$1.html index 0c35f1188..18a679904 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus$1.html @@ -1 +1 @@ -ScalarCosinus.new CPUFun() {...}

ScalarCosinus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarCosinus.new CPUFun() {...}

ScalarCosinus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus$2.html index 2b92f827a..6445c7f28 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus$2.html @@ -1 +1 @@ -ScalarCosinus.new CPUFun() {...}

ScalarCosinus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 10100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarCosinus.new CPUFun() {...}

ScalarCosinus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 10100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus.html index 607ff951a..eefd85769 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus.html @@ -1 +1 @@ -ScalarCosinus

ScalarCosinus

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarCosinus()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarCosinus

ScalarCosinus

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarCosinus()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus.java.html index d9323a94e..2265483d2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarCosinus.java.html @@ -8,10 +8,10 @@ @Override public String id() { return "cos"; } @Override - public String activationCode() { return "output = cos( input );\n"; } + public String activationCode() { return "output = cos( input );\n"; } @Override - public String derivationCode() { return "output = -sin( input );\n"; } + public String derivationCode() { return "output = -sin( input );\n"; } @Override public CPUFun getActivation() { @@ -30,4 +30,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp$1.html index f8ce46f7e..52ea0b0a0 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp$1.html @@ -1 +1 @@ -ScalarExp.new CPUFun() {...}

ScalarExp.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(float)5100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarExp.new CPUFun() {...}

ScalarExp.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(float)5100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp$2.html index f39681639..12c8f0133 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp$2.html @@ -1 +1 @@ -ScalarExp.new CPUFun() {...}

ScalarExp.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 1478%0 of 0n/a131313
invoke(double)30%n/a111111
{...}6100%n/a010101
invoke(float)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarExp.new CPUFun() {...}

ScalarExp.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 1478%0 of 0n/a131313
invoke(double)30%n/a111111
{...}6100%n/a010101
invoke(float)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp.html index 1eb65a3c6..d2533135f 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp.html @@ -1 +1 @@ -ScalarExp

ScalarExp

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarExp()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarExp

ScalarExp

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarExp()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp.java.html index 9f85b4872..f4b3eaf05 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarExp.java.html @@ -9,12 +9,12 @@ @Override public String activationCode() { - return "output = exp( input );\n"; + return "output = exp( input );\n"; } @Override public String derivationCode() { - return "output = exp( input );\n"; + return "output = exp( input );\n"; } @Override @@ -34,4 +34,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU$1.html index d7b94dd10..78a4c7b64 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU$1.html @@ -1 +1 @@ -ScalarGaSU.new CPUFun() {...}

ScalarGaSU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 20100%0 of 0n/a030303
invoke(double)7100%n/a010101
invoke(float)7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaSU.new CPUFun() {...}

ScalarGaSU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 20100%0 of 0n/a030303
invoke(double)7100%n/a010101
invoke(float)7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU$2.html index 1d4d54f48..359561768 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU$2.html @@ -1 +1 @@ -ScalarGaSU.new CPUFun() {...}

ScalarGaSU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 56100%0 of 0n/a030703
invoke(double)25100%n/a010301
invoke(float)25100%n/a010301
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaSU.new CPUFun() {...}

ScalarGaSU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 56100%0 of 0n/a030703
invoke(double)25100%n/a010301
invoke(float)25100%n/a010301
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU.html index 3076f2cfb..80f1c49f3 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU.html @@ -1 +1 @@ -ScalarGaSU

ScalarGaSU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarGaSU()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaSU

ScalarGaSU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarGaSU()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU.java.html index 09e8d32ee..76e21f4ab 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaSU.java.html @@ -17,13 +17,13 @@ @Override public String activationCode() { - return "float cubed = input * input * input; \n" + + return "float cubed = input * input * input; \n" + "output = cubed / ( 1.0f + fabs( cubed ) ); \n"; } @Override public String derivationCode() { - return "float x2 = input * input; \n" + + return "float x2 = input * input; \n" + "float x6 = x2 * x2 * x2; \n" + "output = 3.0f * x2 / ( 2.0f * x2 * fabs( input ) + x6 + 1.0f ); \n"; } @@ -55,4 +55,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU$1.html index b03f0c257..f0fec6be5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU$1.html @@ -1 +1 @@ -ScalarGaTU.new CPUFun() {...}

ScalarGaTU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 20100%0 of 0n/a030303
invoke(double)7100%n/a010101
invoke(float)7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaTU.new CPUFun() {...}

ScalarGaTU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 20100%0 of 0n/a030303
invoke(double)7100%n/a010101
invoke(float)7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU$2.html index d00589623..5c7d70d67 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU$2.html @@ -1 +1 @@ -ScalarGaTU.new CPUFun() {...}

ScalarGaTU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 56100%0 of 0n/a0301103
invoke(float)26100%n/a010501
invoke(double)24100%n/a010501
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaTU.new CPUFun() {...}

ScalarGaTU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 56100%0 of 0n/a0301103
invoke(float)26100%n/a010501
invoke(double)24100%n/a010501
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU.html index 4a97dab51..89aa5e389 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU.html @@ -1 +1 @@ -ScalarGaTU

ScalarGaTU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarGaTU()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaTU

ScalarGaTU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarGaTU()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU.java.html index 438370db3..02a37f9b2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaTU.java.html @@ -16,11 +16,11 @@ @Override public String id() { return "gatu"; } @Override - public String activationCode() { return "output = tanh(input*input*input);\n"; } + public String activationCode() { return "output = tanh(input*input*input);\n"; } @Override public String derivationCode() { - return "float x2 = input * input; \n" + + return "float x2 = input * input; \n" + "float x3 = x2 * input; \n" + "float temp = 3 * x2; \n" + "float tanh2 = pow(tanh(x3), 2); \n" + @@ -57,4 +57,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian$1.html index 54c781f43..9e054dcc6 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian$1.html @@ -1 +1 @@ -ScalarGaussian.new CPUFun() {...}

ScalarGaussian.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 12100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaussian.new CPUFun() {...}

ScalarGaussian.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 12100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian$2.html index 2e18e52ff..0eddb7538 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian$2.html @@ -1 +1 @@ -ScalarGaussian.new CPUFun() {...}

ScalarGaussian.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 160%0 of 0n/a222222
invoke(double)100%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaussian.new CPUFun() {...}

ScalarGaussian.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 160%0 of 0n/a222222
invoke(double)100%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian.html index 64b8730f2..f925627c0 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian.html @@ -1 +1 @@ -ScalarGaussian

ScalarGaussian

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1973%0 of 0n/a161616
getDerivative()50%n/a111111
getActivation()5100%n/a010101
ScalarGaussian()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaussian

ScalarGaussian

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 1952%0 of 0n/a363636
getDerivative()50%n/a111111
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
ScalarGaussian()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian.java.html index 24a2cc975..b0382f41b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussian.java.html @@ -7,9 +7,9 @@ { @Override public String id() { return "gaus"; } - @Override public String activationCode() { return "output = exp( -( input * input ) );\n"; } + @Override public String activationCode() { return "output = exp( -( input * input ) );\n"; } - @Override public String derivationCode() { return "output = -2 * input * exp( -( input * input ) );\n"; } + @Override public String derivationCode() { return "output = -2 * input * exp( -( input * input ) );\n"; } @Override public CPUFun getActivation() { @@ -26,4 +26,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast$1.html index 080459600..eabe39045 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast$1.html @@ -1 +1 @@ -ScalarGaussianFast.new CPUFun() {...}

ScalarGaussianFast.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 22100%0 of 0n/a030303
invoke(double)8100%n/a010101
invoke(float)8100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaussianFast.new CPUFun() {...}

ScalarGaussianFast.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 22100%0 of 0n/a030303
invoke(double)8100%n/a010101
invoke(float)8100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast$2.html index bcbb9459c..5ed1b6460 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast$2.html @@ -1 +1 @@ -ScalarGaussianFast.new CPUFun() {...}

ScalarGaussianFast.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 4257%0 of 0n/a132513
invoke(float)180%n/a112211
invoke(double)18100%n/a010201
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaussianFast.new CPUFun() {...}

ScalarGaussianFast.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 4257%0 of 0n/a132513
invoke(float)180%n/a112211
invoke(double)18100%n/a010201
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast.html index 94fdb8663..7ad2986b1 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast.html @@ -1 +1 @@ -ScalarGaussianFast

ScalarGaussianFast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarGaussianFast()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGaussianFast

ScalarGaussianFast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarGaussianFast()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast.java.html index 40ae53b44..599d9de55 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGaussianFast.java.html @@ -7,10 +7,10 @@ { @Override public String id() { return "fast_gaus"; } - @Override public String activationCode() { return "output = 1 / ( 1 + input * input );\n"; } + @Override public String activationCode() { return "output = 1 / ( 1 + input * input );\n"; } @Override public String derivationCode() { - return "float x2 = input * input;\n" + + return "float x2 = input * input;\n" + "output = -2 * input / ( x2 * x2 + 2 * x2 + 1 );\n"; } @@ -38,4 +38,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU$1.html index bace3101c..6531b7107 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU$1.html @@ -1 +1 @@ -ScalarGeLU.new CPUFun() {...}

ScalarGeLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(float)5100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGeLU.new CPUFun() {...}

ScalarGeLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(float)5100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU$2.html index 9bfbdef27..1e516deab 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU$2.html @@ -1 +1 @@ -ScalarGeLU.new CPUFun() {...}

ScalarGeLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 46100%0 of 0n/a030703
invoke(float)21100%n/a010301
invoke(double)19100%n/a010301
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGeLU.new CPUFun() {...}

ScalarGeLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 46100%0 of 0n/a030703
invoke(float)21100%n/a010301
invoke(double)19100%n/a010301
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU.html index fce508894..d8216e94b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU.html @@ -1 +1 @@ -ScalarGeLU

ScalarGeLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 26100%0 of 0n/a070707
gelu(double)7100%n/a010101
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarGeLU()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarGeLU

ScalarGeLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2684%0 of 0n/a272727
activationCode()20%n/a111111
derivationCode()20%n/a111111
gelu(double)7100%n/a010101
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarGeLU()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU.java.html index cf167872a..710c79cb3 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarGeLU.java.html @@ -17,10 +17,10 @@ @Override public String id() { return "gelu"; } - @Override public String activationCode() { return "output = input / ( 1 + (float) exp(-input * 1.702) );\n"; } + @Override public String activationCode() { return "output = input / ( 1 + (float) exp(-input * 1.702) );\n"; } @Override public String derivationCode() { - return "float sig = 1.0f / ( 1.0f + exp( -input * 1.702f ) );" + + return "float sig = 1.0f / ( 1.0f + exp( -input * 1.702f ) );" + "float ds = sig * ( 1.0f - sig );" + "output = sig + ds * input * 1.702;\n"; } @@ -54,4 +54,4 @@ public static double gelu(double x) { return x * ScalarSigmoid.sig(x * 1.702); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity$1.html index ab39d1a06..b2e829602 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity$1.html @@ -1 +1 @@ -ScalarIdentity.new CPUFun() {...}

ScalarIdentity.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 20100%0 of 0n/a080808
{...}6100%n/a010101
invoke(double)2100%n/a010101
invoke(float)2100%n/a010101
invoke(int)2100%n/a010101
invoke(long)2100%n/a010101
invoke(boolean)2100%n/a010101
invoke(char)2100%n/a010101
invoke(Object)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarIdentity.new CPUFun() {...}

ScalarIdentity.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 20100%0 of 0n/a080808
{...}6100%n/a010101
invoke(double)2100%n/a010101
invoke(float)2100%n/a010101
invoke(int)2100%n/a010101
invoke(long)2100%n/a010101
invoke(boolean)2100%n/a010101
invoke(char)2100%n/a010101
invoke(Object)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity$2.html index 363ba16aa..afcc3818e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity$2.html @@ -1 +1 @@ -ScalarIdentity.new CPUFun() {...}

ScalarIdentity.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 160%0 of 0n/a666666
{...}60%n/a111111
invoke(double)20%n/a111111
invoke(float)20%n/a111111
invoke(int)20%n/a111111
invoke(long)20%n/a111111
invoke(Object)20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarIdentity.new CPUFun() {...}

ScalarIdentity.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 160%0 of 0n/a666666
{...}60%n/a111111
invoke(double)20%n/a111111
invoke(float)20%n/a111111
invoke(int)20%n/a111111
invoke(long)20%n/a111111
invoke(Object)20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity.html index 5b040f3eb..c244249e7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity.html @@ -1 +1 @@ -ScalarIdentity

ScalarIdentity

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1973%0 of 0n/a161616
getDerivative()50%n/a111111
getActivation()5100%n/a010101
ScalarIdentity()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarIdentity

ScalarIdentity

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 1952%0 of 0n/a363636
getDerivative()50%n/a111111
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
ScalarIdentity()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity.java.html index 5e2a68334..bf4b73717 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarIdentity.java.html @@ -7,9 +7,9 @@ { @Override public String id() { return "idy"; } - @Override public String activationCode() { return "output = input; \n"; } + @Override public String activationCode() { return "output = input; \n"; } - @Override public String derivationCode() { return "output = 1.0f; \n"; } + @Override public String derivationCode() { return "output = 1.0f; \n"; } @Override public CPUFun getActivation() { @@ -36,4 +36,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10$1.html index 7a18fad79..b9708a4e9 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10$1.html @@ -1 +1 @@ -ScalarLog10.new CPUFun() {...}

ScalarLog10.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1464%0 of 0n/a131313
invoke(float)50%n/a111111
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarLog10.new CPUFun() {...}

ScalarLog10.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1464%0 of 0n/a131313
invoke(float)50%n/a111111
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10$2.html index ea7e98f73..431402133 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10$2.html @@ -1 +1 @@ -ScalarLog10.new CPUFun() {...}

ScalarLog10.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 210%0 of 0n/a333333
invoke(float)80%n/a111111
invoke(double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarLog10.new CPUFun() {...}

ScalarLog10.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 210%0 of 0n/a333333
invoke(float)80%n/a111111
invoke(double)70%n/a111111
{...}60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10.html index e7e3e60f5..a3869034b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10.html @@ -1 +1 @@ -ScalarLog10

ScalarLog10

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1973%0 of 0n/a161616
getDerivative()50%n/a111111
getActivation()5100%n/a010101
ScalarLog10()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarLog10

ScalarLog10

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 1952%0 of 0n/a363636
getDerivative()50%n/a111111
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
ScalarLog10()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10.java.html index c4acd4d77..3711fb9ad 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLog10.java.html @@ -9,12 +9,12 @@ @Override public String activationCode() { - return "output = log10( input );\n"; + return "output = log10( input );\n"; } @Override public String derivationCode() { - return "output = 1.0f / ( input * log( 10.0f ) );\n"; + return "output = 1.0f / ( input * log( 10.0f ) );\n"; } @Override @@ -34,4 +34,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm$1.html index c392f0f86..29238a98d 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm$1.html @@ -1 +1 @@ -ScalarLogarithm.new CPUFun() {...}

ScalarLogarithm.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarLogarithm.new CPUFun() {...}

ScalarLogarithm.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm$2.html index b011f7653..9a67432f1 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm$2.html @@ -1 +1 @@ -ScalarLogarithm.new CPUFun() {...}

ScalarLogarithm.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1471%0 of 0n/a131313
invoke(float)40%n/a111111
{...}6100%n/a010101
invoke(double)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarLogarithm.new CPUFun() {...}

ScalarLogarithm.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1471%0 of 0n/a131313
invoke(float)40%n/a111111
{...}6100%n/a010101
invoke(double)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm.html index 312ff8f1b..111a1ecd0 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm.html @@ -1 +1 @@ -ScalarLogarithm

ScalarLogarithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarLogarithm()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarLogarithm

ScalarLogarithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarLogarithm()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm.java.html index bb45d1544..7ebddafdf 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarLogarithm.java.html @@ -8,10 +8,10 @@ @Override public String id() { return "ln"; } @Override - public String activationCode() { return "output = log( input );\n"; } + public String activationCode() { return "output = log( input );\n"; } @Override - public String derivationCode() { return "output = 1.0 / ( input );\n"; } + public String derivationCode() { return "output = 1.0 / ( input );\n"; } @Override public CPUFun getActivation() { @@ -29,4 +29,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic$1.html index 0c3dd6ea3..73140ef75 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic$1.html @@ -1 +1 @@ -ScalarQuadratic.new CPUFun() {...}

ScalarQuadratic.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 1855%0 of 0n/a242424
invoke(float)40%n/a111111
invoke(int)40%n/a111111
{...}6100%n/a010101
invoke(double)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarQuadratic.new CPUFun() {...}

ScalarQuadratic.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 1855%0 of 0n/a242424
invoke(float)40%n/a111111
invoke(int)40%n/a111111
{...}6100%n/a010101
invoke(double)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic$2.html index ebdf69061..f86ccbd45 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic$2.html @@ -1 +1 @@ -ScalarQuadratic.new CPUFun() {...}

ScalarQuadratic.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 1855%0 of 0n/a242424
invoke(float)40%n/a111111
invoke(int)40%n/a111111
{...}6100%n/a010101
invoke(double)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarQuadratic.new CPUFun() {...}

ScalarQuadratic.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 1855%0 of 0n/a242424
invoke(float)40%n/a111111
invoke(int)40%n/a111111
{...}6100%n/a010101
invoke(double)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic.html index 4f15610fa..9f907f3d7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic.html @@ -1 +1 @@ -ScalarQuadratic

ScalarQuadratic

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarQuadratic()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarQuadratic

ScalarQuadratic

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarQuadratic()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic.java.html index a9ee5b504..0fdf07959 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarQuadratic.java.html @@ -7,9 +7,9 @@ { @Override public String id() { return "quad"; } - @Override public String activationCode() { return "output = input * input;\n"; } + @Override public String activationCode() { return "output = input * input;\n"; } - @Override public String derivationCode() { return "output = 2 * input;\n"; } + @Override public String derivationCode() { return "output = 2 * input;\n"; } @Override public CPUFun getActivation() { @@ -30,4 +30,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU$1.html index bcbe09dad..d3439f15f 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU$1.html @@ -1 +1 @@ -ScalarReLU.new CPUFun() {...}

ScalarReLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 26100%0 of 4100%050303
invoke(double)10100%2100%020101
invoke(float)10100%2100%020101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarReLU.new CPUFun() {...}

ScalarReLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 26100%0 of 4100%050303
invoke(double)10100%2100%020101
invoke(float)10100%2100%020101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU$2.html index ae991e992..1bc959fab 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU$2.html @@ -1 +1 @@ -ScalarReLU.new CPUFun() {...}

ScalarReLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 2263%2 of 450%251313
invoke(float)80%20%221111
invoke(double)8100%2100%020101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarReLU.new CPUFun() {...}

ScalarReLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 2263%2 of 450%251313
invoke(float)80%20%221111
invoke(double)8100%2100%020101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU.html index c6eb4429c..bd7f31ef1 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU.html @@ -1 +1 @@ -ScalarReLU

ScalarReLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarReLU()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarReLU

ScalarReLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarReLU()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU.java.html index 4eaecc54f..e46dbb01a 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarReLU.java.html @@ -9,12 +9,12 @@ @Override public String activationCode() { - return "if (input >= 0) { output = input; } else { output = input * (float)0.01; }\n"; + return "if (input >= 0) { output = input; } else { output = input * (float)0.01; }\n"; } @Override public String derivationCode() { - return "if (input >= 0) { output = (float)1; } else { output = (float)0.01; }\n"; + return "if (input >= 0) { output = (float)1; } else { output = (float)0.01; }\n"; } @Override @@ -34,4 +34,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU$1.html index baf4ce741..40031dcd3 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU$1.html @@ -1 +1 @@ -ScalarSeLU.new CPUFun() {...}

ScalarSeLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(float)5100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSeLU.new CPUFun() {...}

ScalarSeLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(float)5100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU$2.html index a06a43a7d..60e2b9415 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU$2.html @@ -1 +1 @@ -ScalarSeLU.new CPUFun() {...}

ScalarSeLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 4264%4 of 850%373703
invoke(float)13631%3125%232301
invoke(double)21588%1375%131301
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSeLU.new CPUFun() {...}

ScalarSeLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 4264%4 of 850%373703
invoke(float)13631%3125%232301
invoke(double)21588%1375%131301
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU.html index bc98f5ffc..89b0fbce9 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU.html @@ -1 +1 @@ -ScalarSeLU

ScalarSeLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 4095%1 of 475%191907
selu(double)21990%1375%131301
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarSeLU()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSeLU

ScalarSeLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 4085%1 of 475%393927
selu(double)21990%1375%131301
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarSeLU()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU.java.html index 381b32f74..90c60f70c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSeLU.java.html @@ -25,13 +25,13 @@ @Override public String id() { return "selu"; } @Override public String activationCode() { - return "if ( input > 0 ) output = "+SCALE_F32+"f * input;\n" + + return "if ( input > 0 ) output = "+SCALE_F32+"f * input;\n" + "else if ( input <= 0 ) output = "+SCALE_F32+"f * "+ALPHA_F32+"f * (exp(input) - 1.0f);\n" + "else output = 0.0f;\n"; } @Override public String derivationCode() { - return "if ( input > 0 ) output = "+SCALE_F32+"f;\n" + + return "if ( input > 0 ) output = "+SCALE_F32+"f;\n" + "else if ( input <= 0 ) output = "+SCALE_F32+"f * "+ALPHA_F32+"f * exp(input);\n" + "else output = 1.0f;\n"; } @@ -71,4 +71,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU$1.html index fe5454c7b..fd44764e1 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU$1.html @@ -1 +1 @@ -ScalarSiLU.new CPUFun() {...}

ScalarSiLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(float)5100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSiLU.new CPUFun() {...}

ScalarSiLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(float)5100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU$2.html index 00396391e..4f6124552 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU$2.html @@ -1 +1 @@ -ScalarSiLU.new CPUFun() {...}

ScalarSiLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 34100%0 of 0n/a030503
invoke(float)15100%n/a010201
invoke(double)13100%n/a010201
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSiLU.new CPUFun() {...}

ScalarSiLU.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 34100%0 of 0n/a030503
invoke(float)15100%n/a010201
invoke(double)13100%n/a010201
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU.html index 3aedd29f1..0884e6149 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU.html @@ -1 +1 @@ -ScalarSiLU

ScalarSiLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 24100%0 of 0n/a070707
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
silu(double)5100%n/a010101
ScalarSiLU()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSiLU

ScalarSiLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2483%0 of 0n/a272727
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
silu(double)5100%n/a010101
ScalarSiLU()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU.java.html index 819acfbb0..a4be7da9e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSiLU.java.html @@ -13,10 +13,10 @@ { @Override public String id() { return "silu"; } - @Override public String activationCode() { return "output = input / ( 1 + (float) exp(-input) );\n"; } + @Override public String activationCode() { return "output = input / ( 1 + (float) exp(-input) );\n"; } @Override public String derivationCode() { - return "float sig = 1.0f / ( 1.0f + exp( -input ) );" + + return "float sig = 1.0f / ( 1.0f + exp( -input ) );" + "output = sig + ( input * sig * ( 1.0f - sig ) );\n"; } @@ -48,4 +48,4 @@ public static double silu(double x) { return x * ScalarSigmoid.sig(x); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid$1.html index fa6632705..372e8cbcb 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid$1.html @@ -1 +1 @@ -ScalarSigmoid.new CPUFun() {...}

ScalarSigmoid.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(float)5100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSigmoid.new CPUFun() {...}

ScalarSigmoid.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030303
{...}6100%n/a010101
invoke(float)5100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid$2.html index f11b5d9e2..f136785a1 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid$2.html @@ -1 +1 @@ -ScalarSigmoid.new CPUFun() {...}

ScalarSigmoid.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 26100%0 of 0n/a030503
invoke(float)11100%n/a010201
invoke(double)9100%n/a010201
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSigmoid.new CPUFun() {...}

ScalarSigmoid.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 26100%0 of 0n/a030503
invoke(float)11100%n/a010201
invoke(double)9100%n/a010201
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid.html index 21335650e..e6f5512fb 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid.html @@ -1 +1 @@ -ScalarSigmoid

ScalarSigmoid

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 27100%0 of 0n/a070707
sig(double)8100%n/a010101
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarSigmoid()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSigmoid

ScalarSigmoid

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2785%0 of 0n/a272727
activationCode()20%n/a111111
derivationCode()20%n/a111111
sig(double)8100%n/a010101
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarSigmoid()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid.java.html index 35f054602..540f45391 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSigmoid.java.html @@ -7,9 +7,9 @@ { @Override public String id() { return "sig"; } - @Override public String activationCode() { return "output = 1 / ( 1 + (float) exp(-input) );\n"; } + @Override public String activationCode() { return "output = 1 / ( 1 + (float) exp(-input) );\n"; } - @Override public String derivationCode() { return "output = input * ( 1 - input );\n"; } + @Override public String derivationCode() { return "output = input * ( 1 - input );\n"; } @Override public CPUFun getActivation() { @@ -38,4 +38,4 @@ public static double sig(double x) { return 1d / ( 1d + Math.exp( -x ) ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus$1.html index ae316c036..55096ca8c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus$1.html @@ -1 +1 @@ -ScalarSinus.new CPUFun() {...}

ScalarSinus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSinus.new CPUFun() {...}

ScalarSinus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus$2.html index c600358a2..11c26c979 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus$2.html @@ -1 +1 @@ -ScalarSinus.new CPUFun() {...}

ScalarSinus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSinus.new CPUFun() {...}

ScalarSinus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus.html index d8d3704a3..e38a5c0fe 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus.html @@ -1 +1 @@ -ScalarSinus

ScalarSinus

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarSinus()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSinus

ScalarSinus

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarSinus()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus.java.html index b65279d60..e0c82f0bf 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSinus.java.html @@ -7,9 +7,9 @@ { @Override public String id() { return "sin"; } - @Override public String activationCode() { return "output = sin( input );\n"; } + @Override public String activationCode() { return "output = sin( input );\n"; } - @Override public String derivationCode() { return "output = cos( input );\n"; } + @Override public String derivationCode() { return "output = cos( input );\n"; } @Override public CPUFun getActivation() { @@ -26,4 +26,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus$1.html index 1cc9e1ebc..7e04f6ec1 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus$1.html @@ -1 +1 @@ -ScalarSoftplus.new CPUFun() {...}

ScalarSoftplus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 12100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSoftplus.new CPUFun() {...}

ScalarSoftplus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 12100%0 of 0n/a020202
{...}6100%n/a010101
invoke(double)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus$2.html index ec4f6fa45..bb42e4318 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus$2.html @@ -1 +1 @@ -ScalarSoftplus.new CPUFun() {...}

ScalarSoftplus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a020202
invoke(double)8100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSoftplus.new CPUFun() {...}

ScalarSoftplus.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a020202
invoke(double)8100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus.html index 61c31eaa5..ac7f1623e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus.html @@ -1 +1 @@ -ScalarSoftplus

ScalarSoftplus

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarSoftplus()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSoftplus

ScalarSoftplus

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarSoftplus()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus.java.html index 6338d2030..a843e7ce3 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftplus.java.html @@ -11,9 +11,9 @@ { @Override public String id() { return "softplus"; } - @Override public String activationCode() { return "output = log( 1.0f + exp( input ) );"; } + @Override public String activationCode() { return "output = log( 1.0f + exp( input ) );"; } - @Override public String derivationCode() { return "output = 1.0f / ( 1.0f + exp( -input ) );\n"; } + @Override public String derivationCode() { return "output = 1.0f / ( 1.0f + exp( -input ) );\n"; } @Override public CPUFun getActivation() { @@ -30,4 +30,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign$1.html index 45c961067..9e542d2e9 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign$1.html @@ -1 +1 @@ -ScalarSoftsign.new CPUFun() {...}

ScalarSoftsign.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 12100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double)3100%n/a010101
invoke(float)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSoftsign.new CPUFun() {...}

ScalarSoftsign.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 12100%0 of 0n/a030303
{...}6100%n/a010101
invoke(double)3100%n/a010101
invoke(float)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign$2.html index bf36ccd0b..4e37db70d 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign$2.html @@ -1 +1 @@ -ScalarSoftsign.new CPUFun() {...}

ScalarSoftsign.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total13 of 3259%0 of 0n/a131313
invoke(float)130%n/a111111
invoke(double)13100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSoftsign.new CPUFun() {...}

ScalarSoftsign.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total13 of 3259%0 of 0n/a131313
invoke(float)130%n/a111111
invoke(double)13100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign.html index 4538fbd61..103a038db 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign.html @@ -1 +1 @@ -ScalarSoftsign

ScalarSoftsign

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 33100%0 of 0n/a080808
softsign(double)7100%n/a010101
softsign(float)7100%n/a010101
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarSoftsign()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSoftsign

ScalarSoftsign

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 3387%0 of 0n/a282828
activationCode()20%n/a111111
derivationCode()20%n/a111111
softsign(double)7100%n/a010101
softsign(float)7100%n/a010101
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarSoftsign()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign.java.html index 4e8b8a3d6..b36faab85 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSoftsign.java.html @@ -17,12 +17,12 @@ @Override public String activationCode() { - return "output = input / ( 1.0f + fabs( input ) );\n"; + return "output = input / ( 1.0f + fabs( input ) );\n"; } @Override public String derivationCode() { - return "output = 1.0f / ( 2.0f * fabs( input ) + input * input + 1.0f );\n"; + return "output = 1.0f / ( 2.0f * fabs( input ) + input * input + 1.0f );\n"; } @Override @@ -46,4 +46,4 @@ public static float softsign(float x) { return x / ( 1f + Math.abs( x ) ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt$1.html index 9d3f178b1..3a4f153d5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt$1.html @@ -1 +1 @@ -ScalarSqrt.new CPUFun() {...}

ScalarSqrt.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1464%0 of 0n/a131313
invoke(float)50%n/a111111
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSqrt.new CPUFun() {...}

ScalarSqrt.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1464%0 of 0n/a131313
invoke(float)50%n/a111111
{...}6100%n/a010101
invoke(double)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt$2.html index 9efe2e25b..b7578b8d2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt$2.html @@ -1 +1 @@ -ScalarSqrt.new CPUFun() {...}

ScalarSqrt.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 160%0 of 0n/a333333
{...}60%n/a111111
invoke(double)50%n/a111111
invoke(float)50%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSqrt.new CPUFun() {...}

ScalarSqrt.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 160%0 of 0n/a333333
{...}60%n/a111111
invoke(double)50%n/a111111
invoke(float)50%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt.html index 88bd51b8d..c084f6851 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt.html @@ -1 +1 @@ -ScalarSqrt

ScalarSqrt

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1973%0 of 0n/a161616
getDerivative()50%n/a111111
getActivation()5100%n/a010101
ScalarSqrt()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarSqrt

ScalarSqrt

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 1952%0 of 0n/a363636
getDerivative()50%n/a111111
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
ScalarSqrt()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt.java.html index 635f804a1..94be28ad2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarSqrt.java.html @@ -9,12 +9,12 @@ @Override public String activationCode() { - return "output = sqrt( input );\n"; + return "output = sqrt( input );\n"; } @Override public String derivationCode() { - return "output = 0.5f * fast_inverse_sqrt( input );\n"; + return "output = 0.5f * fast_inverse_sqrt( input );\n"; } @Override @@ -34,4 +34,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh$1.html index c7a828084..8a0f785a4 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh$1.html @@ -1 +1 @@ -ScalarTanh.new CPUFun() {...}

ScalarTanh.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 32100%0 of 0n/a030303
invoke(float)14100%n/a010101
invoke(double)12100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarTanh.new CPUFun() {...}

ScalarTanh.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 32100%0 of 0n/a030303
invoke(float)14100%n/a010101
invoke(double)12100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh$2.html index 07d453ce6..44a39f29e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh$2.html @@ -1 +1 @@ -ScalarTanh.new CPUFun() {...}

ScalarTanh.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 22100%0 of 0n/a030303
invoke(float)9100%n/a010101
invoke(double)7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarTanh.new CPUFun() {...}

ScalarTanh.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 22100%0 of 0n/a030303
invoke(float)9100%n/a010101
invoke(double)7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh.html index ca8b5a241..92068baa5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh.html @@ -1 +1 @@ -ScalarTanh

ScalarTanh

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 45100%0 of 0n/a080808
tanh(float)14100%n/a010101
tanh(double)12100%n/a010101
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarTanh()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarTanh

ScalarTanh

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 4591%0 of 0n/a282828
activationCode()20%n/a111111
derivationCode()20%n/a111111
tanh(float)14100%n/a010101
tanh(double)12100%n/a010101
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarTanh()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh.java.html index dca463d0f..40c952d5e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanh.java.html @@ -9,12 +9,12 @@ @Override public String activationCode() { - return "output = tanh(input);\n"; + return "output = tanh(input);\n"; } @Override public String derivationCode() { - return "output = 1 - pow( tanh(input), 2.0f );\n"; + return "output = 1 - pow( tanh(input), 2.0f );\n"; } @Override @@ -39,4 +39,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast$1.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast$1.html index 48f9a3351..2d6366f77 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast$1.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast$1.html @@ -1 +1 @@ -ScalarTanhFast.new CPUFun() {...}

ScalarTanhFast.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 24100%0 of 0n/a030303
invoke(double)9100%n/a010101
invoke(float)9100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarTanhFast.new CPUFun() {...}

ScalarTanhFast.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 24100%0 of 0n/a030303
invoke(double)9100%n/a010101
invoke(float)9100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast$2.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast$2.html index b201ea058..727ba53ae 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast$2.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast$2.html @@ -1 +1 @@ -ScalarTanhFast.new CPUFun() {...}

ScalarTanhFast.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total19 of 4254%0 of 0n/a133713
invoke(float)190%n/a113311
invoke(double)17100%n/a010301
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarTanhFast.new CPUFun() {...}

ScalarTanhFast.new CPUFun() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total19 of 4254%0 of 0n/a133713
invoke(float)190%n/a113311
invoke(double)17100%n/a010301
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast.html index a742dede8..035c4c91d 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast.html @@ -1 +1 @@ -ScalarTanhFast

ScalarTanhFast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 19100%0 of 0n/a060606
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarTanhFast()3100%n/a010101
id()2100%n/a010101
activationCode()2100%n/a010101
derivationCode()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ScalarTanhFast

ScalarTanhFast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1978%0 of 0n/a262626
activationCode()20%n/a111111
derivationCode()20%n/a111111
getActivation()5100%n/a010101
getDerivative()5100%n/a010101
ScalarTanhFast()3100%n/a010101
id()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast.java.html index a7866c396..3915f793e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/ScalarTanhFast.java.html @@ -9,12 +9,12 @@ @Override public String activationCode() { - return "output = input * fast_inverse_sqrt( 1.0f + input * input );\n"; + return "output = input * fast_inverse_sqrt( 1.0f + input * input );\n"; } @Override public String derivationCode() { - return "float temp1 = input * input;\n" + + return "float temp1 = input * input;\n" + "float temp2 = sqrt( 1 + temp1 );\n" + "output = 1 / ( temp1 * temp2 + temp2 );\n"; } @@ -47,4 +47,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/index.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/index.html index cdc5fe601..35a715dc0 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/index.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/index.html @@ -1 +1 @@ -neureka.backend.main.implementations.fun

neureka.backend.main.implementations.fun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total248 of 1,60984%11 of 2657%453034029038290570
ScalarCbrt.new CPUFun() {...}240%n/a33333311
ScalarLog10.new CPUFun() {...}210%n/a33333311
ScalarTanhFast.new CPUFun() {...}192354%n/a13371301
ScalarGaussianFast.new CPUFun() {...}182457%n/a13251301
ScalarSqrt.new CPUFun() {...}160%n/a33333311
ScalarGaussian.new CPUFun() {...}160%n/a22222211
ScalarIdentity.new CPUFun() {...}160%n/a66666611
ScalarSeLU.new CPUFun() {...}152764%4450%37370301
ScalarAbsolute.new CPUFun() {...}141450%4233%47242401
ScalarSoftsign.new CPUFun() {...}131959%n/a13131301
ScalarReLU.new CPUFun() {...}81463%2250%25131301
ScalarQuadratic.new CPUFun() {...}81055%n/a24242401
ScalarQuadratic.new CPUFun() {...}81055%n/a24242401
ScalarLog1051473%n/a16161601
ScalarSqrt51473%n/a16161601
ScalarIdentity51473%n/a16161601
ScalarGaussian51473%n/a16161601
ScalarCbrt51473%n/a16161601
ScalarSqrt.new CPUFun() {...}5964%n/a13131301
ScalarCbrt.new CPUFun() {...}5964%n/a13131301
ScalarLog10.new CPUFun() {...}5964%n/a13131301
ScalarLogarithm.new CPUFun() {...}41071%n/a13131301
FunUtil39696%n/a131171301
ScalarExp.new CPUFun() {...}31178%n/a13131301
ScalarSeLU23895%1375%19190701
ScalarGaTU.new CPUFun() {...}56100%n/a030110301
ScalarGaSU.new CPUFun() {...}56100%n/a03070301
ScalarGeLU.new CPUFun() {...}46100%n/a03070301
ScalarTanh45100%n/a08080801
ScalarSiLU.new CPUFun() {...}34100%n/a03050301
ScalarSoftsign33100%n/a08080801
ScalarTanh.new CPUFun() {...}32100%n/a03030301
ScalarSigmoid27100%n/a07070701
ScalarReLU.new CPUFun() {...}26100%4100%05030301
ScalarSigmoid.new CPUFun() {...}26100%n/a03050301
ScalarGeLU26100%n/a07070701
ScalarSiLU24100%n/a07070701
ScalarTanhFast.new CPUFun() {...}24100%n/a03030301
ScalarTanh.new CPUFun() {...}22100%n/a03030301
ScalarGaussianFast.new CPUFun() {...}22100%n/a03030301
ScalarGaTU.new CPUFun() {...}20100%n/a03030301
ScalarGaSU.new CPUFun() {...}20100%n/a03030301
ScalarIdentity.new CPUFun() {...}20100%n/a08080801
ScalarCosinus19100%n/a06060601
ScalarExp19100%n/a06060601
ScalarQuadratic19100%n/a06060601
ScalarGaTU19100%n/a06060601
ScalarGaussianFast19100%n/a06060601
ScalarTanhFast19100%n/a06060601
ScalarLogarithm19100%n/a06060601
ScalarGaSU19100%n/a06060601
ScalarAbsolute19100%n/a06060601
ScalarSoftplus19100%n/a06060601
ScalarSinus19100%n/a06060601
ScalarReLU19100%n/a06060601
ScalarAbsolute.new CPUFun() {...}15100%n/a04040401
ScalarExp.new CPUFun() {...}14100%n/a03030301
ScalarSoftplus.new CPUFun() {...}14100%n/a02020201
ScalarSiLU.new CPUFun() {...}14100%n/a03030301
ScalarSigmoid.new CPUFun() {...}14100%n/a03030301
ScalarSeLU.new CPUFun() {...}14100%n/a03030301
ScalarGeLU.new CPUFun() {...}14100%n/a03030301
ScalarGaussian.new CPUFun() {...}12100%n/a02020201
ScalarSoftplus.new CPUFun() {...}12100%n/a02020201
ScalarSoftsign.new CPUFun() {...}12100%n/a03030301
ScalarCosinus.new CPUFun() {...}10100%n/a02020201
ScalarSinus.new CPUFun() {...}9100%n/a02020201
ScalarSinus.new CPUFun() {...}9100%n/a02020201
ScalarLogarithm.new CPUFun() {...}9100%n/a02020201
ScalarCosinus.new CPUFun() {...}9100%n/a02020201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.fun

neureka.backend.main.implementations.fun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total340 of 1,60978%11 of 2657%913038629084290570
ScalarCbrt.new CPUFun() {...}240%n/a33333311
ScalarLog10.new CPUFun() {...}210%n/a33333311
ScalarTanhFast.new CPUFun() {...}192354%n/a13371301
ScalarGaussianFast.new CPUFun() {...}182457%n/a13251301
ScalarSqrt.new CPUFun() {...}160%n/a33333311
ScalarGaussian.new CPUFun() {...}160%n/a22222211
ScalarIdentity.new CPUFun() {...}160%n/a66666611
ScalarSeLU.new CPUFun() {...}152764%4450%37370301
ScalarAbsolute.new CPUFun() {...}141450%4233%47242401
ScalarSoftsign.new CPUFun() {...}131959%n/a13131301
ScalarLog1091052%n/a36363601
ScalarSqrt91052%n/a36363601
ScalarIdentity91052%n/a36363601
ScalarGaussian91052%n/a36363601
ScalarCbrt91052%n/a36363601
ScalarReLU.new CPUFun() {...}81463%2250%25131301
ScalarQuadratic.new CPUFun() {...}81055%n/a24242401
ScalarQuadratic.new CPUFun() {...}81055%n/a24242401
ScalarSeLU63485%1375%39392701
ScalarSqrt.new CPUFun() {...}5964%n/a13131301
ScalarCbrt.new CPUFun() {...}5964%n/a13131301
ScalarLog10.new CPUFun() {...}5964%n/a13131301
ScalarTanh44191%n/a28282801
ScalarSoftsign42987%n/a28282801
ScalarSigmoid42385%n/a27272701
ScalarGeLU42284%n/a27272701
ScalarSiLU42083%n/a27272701
ScalarCosinus41578%n/a26262601
ScalarExp41578%n/a26262601
ScalarQuadratic41578%n/a26262601
ScalarGaTU41578%n/a26262601
ScalarGaussianFast41578%n/a26262601
ScalarTanhFast41578%n/a26262601
ScalarLogarithm41578%n/a26262601
ScalarGaSU41578%n/a26262601
ScalarAbsolute41578%n/a26262601
ScalarSoftplus41578%n/a26262601
ScalarSinus41578%n/a26262601
ScalarReLU41578%n/a26262601
ScalarLogarithm.new CPUFun() {...}41071%n/a13131301
FunUtil39696%n/a131171301
ScalarExp.new CPUFun() {...}31178%n/a13131301
ScalarGaTU.new CPUFun() {...}56100%n/a030110301
ScalarGaSU.new CPUFun() {...}56100%n/a03070301
ScalarGeLU.new CPUFun() {...}46100%n/a03070301
ScalarSiLU.new CPUFun() {...}34100%n/a03050301
ScalarTanh.new CPUFun() {...}32100%n/a03030301
ScalarReLU.new CPUFun() {...}26100%4100%05030301
ScalarSigmoid.new CPUFun() {...}26100%n/a03050301
ScalarTanhFast.new CPUFun() {...}24100%n/a03030301
ScalarTanh.new CPUFun() {...}22100%n/a03030301
ScalarGaussianFast.new CPUFun() {...}22100%n/a03030301
ScalarGaTU.new CPUFun() {...}20100%n/a03030301
ScalarGaSU.new CPUFun() {...}20100%n/a03030301
ScalarIdentity.new CPUFun() {...}20100%n/a08080801
ScalarAbsolute.new CPUFun() {...}15100%n/a04040401
ScalarExp.new CPUFun() {...}14100%n/a03030301
ScalarSoftplus.new CPUFun() {...}14100%n/a02020201
ScalarSiLU.new CPUFun() {...}14100%n/a03030301
ScalarSigmoid.new CPUFun() {...}14100%n/a03030301
ScalarSeLU.new CPUFun() {...}14100%n/a03030301
ScalarGeLU.new CPUFun() {...}14100%n/a03030301
ScalarGaussian.new CPUFun() {...}12100%n/a02020201
ScalarSoftplus.new CPUFun() {...}12100%n/a02020201
ScalarSoftsign.new CPUFun() {...}12100%n/a03030301
ScalarCosinus.new CPUFun() {...}10100%n/a02020201
ScalarSinus.new CPUFun() {...}9100%n/a02020201
ScalarSinus.new CPUFun() {...}9100%n/a02020201
ScalarLogarithm.new CPUFun() {...}9100%n/a02020201
ScalarCosinus.new CPUFun() {...}9100%n/a02020201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.fun/index.source.html b/docs/coverage/test/html/neureka.backend.main.implementations.fun/index.source.html index 5a7331404..c3b3b938c 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.fun/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.fun/index.source.html @@ -1 +1 @@ -neureka.backend.main.implementations.fun

neureka.backend.main.implementations.fun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total248 of 1,60984%11 of 2657%453034029038290570
ScalarCbrt.java342340%n/a51241051213
ScalarLog10.java312342%n/a51241051213
ScalarSqrt.java262346%n/a51241051213
ScalarIdentity.java213461%n/a72061872013
ScalarGaussian.java212655%n/a3102831013
ScalarTanhFast.java196677%n/a11231411203
ScalarGaussianFast.java186578%n/a11221211203
ScalarSeLU.java177982%5758%41941701303
ScalarQuadratic.java163970%n/a41441241403
ScalarAbsolute.java144877%4233%41721221403
ScalarSoftsign.java136483%n/a11411211403
ScalarReLU.java85988%2675%21611011203
ScalarLogarithm.java43890%n/a1111911103
FunUtil.java39696%n/a131171301
ScalarExp.java34493%n/a11211011203
ScalarTanh.java99100%n/a01401201403
ScalarGaTU.java95100%n/a01201801203
ScalarGaSU.java95100%n/a01201401203
ScalarGeLU.java86100%n/a01301501303
ScalarSiLU.java72100%n/a01301301303
ScalarSigmoid.java67100%n/a01301301303
ScalarSoftplus.java45100%n/a0100801003
ScalarCosinus.java38100%n/a0100801003
ScalarSinus.java37100%n/a0100801003
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.fun

neureka.backend.main.implementations.fun

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total340 of 1,60978%11 of 2657%913038629084290570
ScalarCbrt.java381933%n/a71261071213
ScalarLog10.java351935%n/a71261071213
ScalarSqrt.java301938%n/a71261071213
ScalarIdentity.java253054%n/a92081892013
ScalarGaussian.java252246%n/a5104851013
ScalarTanhFast.java236272%n/a31251431203
ScalarGaussianFast.java226173%n/a31241231203
ScalarSeLU.java217578%5758%61961721303
ScalarQuadratic.java203563%n/a61461261403
ScalarAbsolute.java184470%4233%61741241403
ScalarSoftsign.java176077%n/a31431231403
ScalarReLU.java125582%2675%41631031203
ScalarLogarithm.java83480%n/a3113931103
ScalarExp.java74085%n/a31231031203
ScalarTanh.java49595%n/a21421221403
ScalarGaTU.java49195%n/a21221821203
ScalarGaSU.java49195%n/a21221421203
ScalarGeLU.java48295%n/a21321521303
ScalarSiLU.java46894%n/a21321321303
ScalarSigmoid.java46394%n/a21321321303
ScalarSoftplus.java44191%n/a2102821003
ScalarCosinus.java43489%n/a2102821003
ScalarSinus.java43389%n/a2102821003
FunUtil.java39696%n/a131171301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.linear/CLDot.html b/docs/coverage/test/html/neureka.backend.main.implementations.linear/CLDot.html index efb78ecc2..15834b869 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.linear/CLDot.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.linear/CLDot.html @@ -1 +1 @@ -CLDot

CLDot

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 12491%3 of 650%3621903
run(ExecutionCall)1010090%3350%3421701
lambda$run$0(String)11100%n/a010101
CLDot()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLDot

CLDot

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 12483%3 of 650%4631913
lambda$run$0(String)110%n/a111111
run(ExecutionCall)1010090%3350%3421701
CLDot()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.linear/CLDot.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.linear/CLDot.java.html index c1a249c01..57d62a543 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.linear/CLDot.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.linear/CLDot.java.html @@ -33,7 +33,7 @@ // First we multiply the two vectors: String kernelName = "multiply_arrays_for_dot_product"; Supplier<String> code = () -> - "__kernel void " + kernelName + "(__global const float* a, \n" + + "__kernel void " + kernelName + "(__global const float* a, \n" + " __global const float* b, \n" + " __global float* c,\n" + " const int n) {\n" + @@ -55,4 +55,4 @@ return c; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.linear/CPUDot.html b/docs/coverage/test/html/neureka.backend.main.implementations.linear/CPUDot.html index d9ff30860..3cbf73dda 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.linear/CPUDot.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.linear/CPUDot.html @@ -1 +1 @@ -CPUDot

CPUDot

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total35 of 29788%7 of 2065%71764807
run(ExecutionCall)3520785%71365%71163901
execute(double[], double[], double[], int)11100%n/a010201
execute(float[], float[], float[], int)11100%n/a010201
execute(long[], long[], long[], int)11100%n/a010201
execute(int[], int[], int[], int)11100%n/a010201
lambda$run$0(Tensor, Tensor)8100%n/a010101
CPUDot()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUDot

CPUDot

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total35 of 29788%7 of 2065%71764807
run(ExecutionCall)3520785%71365%71163901
execute(double[], double[], double[], int)11100%n/a010201
execute(float[], float[], float[], int)11100%n/a010201
execute(long[], long[], long[], int)11100%n/a010201
execute(int[], int[], int[], int)11100%n/a010201
lambda$run$0(Tensor, Tensor)8100%n/a010101
CPUDot()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.linear/CPUDot.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.linear/CPUDot.java.html index aa805d2d3..ba17536ec 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.linear/CPUDot.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.linear/CPUDot.java.html @@ -86,4 +86,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.linear/index.html b/docs/coverage/test/html/neureka.backend.main.implementations.linear/index.html index d4491a0a0..d1358d47b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.linear/index.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.linear/index.html @@ -1 +1 @@ -neureka.backend.main.implementations.linear

neureka.backend.main.implementations.linear

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total45 of 42189%10 of 2661%102386701002
CPUDot3526288%71365%7176480701
CLDot1011491%3350%362190301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.linear

neureka.backend.main.implementations.linear

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total56 of 42186%10 of 2661%112396711002
CPUDot3526288%71365%7176480701
CLDot2110383%3350%463191301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.linear/index.source.html b/docs/coverage/test/html/neureka.backend.main.implementations.linear/index.source.html index 2ca4ba355..8e79379f5 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.linear/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.linear/index.source.html @@ -1 +1 @@ -neureka.backend.main.implementations.linear

neureka.backend.main.implementations.linear

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total45 of 42189%10 of 2661%102386701002
CPUDot.java3526288%71365%7176480701
CLDot.java1011491%3350%362190301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.linear

neureka.backend.main.implementations.linear

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total56 of 42186%10 of 2661%112396711002
CPUDot.java3526288%71365%7176480701
CLDot.java2110383%3350%463191301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CLMatMul.html b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CLMatMul.html index ddb700ca0..d2d2116c2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CLMatMul.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CLMatMul.html @@ -1 +1 @@ -CLMatMul

CLMatMul

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 90100%0 of 4100%0501803
lambda$new$1(ExecutionCall)74100%2100%0201601
lambda$new$0(Tensor)9100%2100%020101
CLMatMul()7100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLMatMul

CLMatMul

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total90 of 900%4 of 40%55181833
lambda$new$1(ExecutionCall)740%20%22161611
lambda$new$0(Tensor)90%20%221111
CLMatMul()70%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CLMatMul.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CLMatMul.java.html index 78a0c16de..bf1798d8f 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CLMatMul.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CLMatMul.java.html @@ -7,27 +7,27 @@ public class CLMatMul extends SimpleCLImplementation { public CLMatMul() { - super( + super( call -> { - if ( - call.validate() - .all( t -> t.getNDConf().getLayout() == NDConfiguration.Layout.COLUMN_MAJOR ) - .isValid() + if ( + call.validate() + .all( t -> t.getNDConf().getLayout() == NDConfiguration.Layout.COLUMN_MAJOR ) + .isValid() ) { - return new CLGEMM().run( call ); + return new CLGEMM().run( call ); } else { - int M = call.input(1).shape(0); - int N = call.input(2).shape(1); - int K = call.input(1).shape(1); - call.getDevice() - .getKernel(call) - .pass(M).pass(N).pass(K) - .pass(call.input(Number.class, 1)) - .pass(call.input(Number.class, 2)) - .pass(call.input(Number.class, 0)) - .call(new long[]{M, N}, null); + int M = call.input(1).shape(0); + int N = call.input(2).shape(1); + int K = call.input(1).shape(1); + call.getDevice() + .getKernel(call) + .pass(M).pass(N).pass(K) + .pass(call.input(Number.class, 1)) + .pass(call.input(Number.class, 2)) + .pass(call.input(Number.class, 0)) + .call(new long[]{M, N}, null); - return call.input(0); + return call.input(0); } }, 3, @@ -50,6 +50,6 @@ " C[ n + m * N ] = acc; \n" + " } \n" ); - } + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CPUMatMul.html b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CPUMatMul.html index 908829611..bb51b94c6 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CPUMatMul.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CPUMatMul.html @@ -1 +1 @@ -CPUMatMul

CPUMatMul

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total45 of 37588%7 of 3680%72555507
run(ExecutionCall)4527185%72980%71954601
execute(boolean, double[], double[], double[], int, int, int)12100%n/a010201
execute(boolean, float[], float[], float[], int, int, int)12100%n/a010201
execute(boolean, long[], long[], long[], int, int, int)12100%n/a010201
execute(boolean, int[], int[], int[], int, int, int)12100%n/a010201
lambda$run$0(Tensor, Tensor)8100%n/a010101
CPUMatMul()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUMatMul

CPUMatMul

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total45 of 37588%7 of 3680%72555507
run(ExecutionCall)4527185%72980%71954601
execute(boolean, double[], double[], double[], int, int, int)12100%n/a010201
execute(boolean, float[], float[], float[], int, int, int)12100%n/a010201
execute(boolean, long[], long[], long[], int, int, int)12100%n/a010201
execute(boolean, int[], int[], int[], int, int, int)12100%n/a010201
lambda$run$0(Tensor, Tensor)8100%n/a010101
CPUMatMul()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CPUMatMul.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CPUMatMul.java.html index 7005ecd21..bbbc223e6 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CPUMatMul.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/CPUMatMul.java.html @@ -109,4 +109,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/index.html b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/index.html index cd2cd201f..d85870d9b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/index.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/index.html @@ -1 +1 @@ -neureka.backend.main.implementations.matmul

neureka.backend.main.implementations.matmul

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total45 of 46590%7 of 4082%73057301002
CPUMatMul4533088%72980%7255550701
CLMatMul90100%4100%050180301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.matmul

neureka.backend.main.implementations.matmul

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total135 of 46570%11 of 4072%1230237331012
CLMatMul900%40%5518183311
CPUMatMul4533088%72980%7255550701
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/index.source.html b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/index.source.html index 4adb1c4bc..b00ac6b22 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.matmul/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.matmul/index.source.html @@ -1 +1 @@ -neureka.backend.main.implementations.matmul

neureka.backend.main.implementations.matmul

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total45 of 46590%7 of 4082%73057301002
CPUMatMul.java4533088%72980%7255550701
CLMatMul.java90100%4100%050180301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.matmul

neureka.backend.main.implementations.matmul

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total135 of 46570%11 of 4072%1230237331012
CLMatMul.java900%40%5518183311
CPUMatMul.java4533088%72980%7255550701
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CLScalarFunction.html b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CLScalarFunction.html index a3d908ff0..3ee41a122 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CLScalarFunction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CLScalarFunction.html @@ -1 +1 @@ -CLScalarFunction

CLScalarFunction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total43 of 4912%2 of 20%236912
run(ExecutionCall)430%20%226611
CLScalarFunction(ScalarFun)6100%n/a010301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLScalarFunction

CLScalarFunction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total49 of 490%2 of 20%339922
run(ExecutionCall)430%20%226611
CLScalarFunction(ScalarFun)60%n/a113311
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CLScalarFunction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CLScalarFunction.java.html index b856621bf..1af0feea4 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CLScalarFunction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CLScalarFunction.java.html @@ -12,9 +12,9 @@ { private final ScalarFun _fun; - public CLScalarFunction(ScalarFun fun) { - _fun = fun; - } + public CLScalarFunction(ScalarFun fun) { + _fun = fun; + } @Override public Tensor<?> run(ExecutionCall<OpenCLDevice> call) { @@ -26,4 +26,4 @@ return call.input(0); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarBroadcastFunction.html b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarBroadcastFunction.html index ee0c27737..1ddb498b2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarBroadcastFunction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarBroadcastFunction.html @@ -1 +1 @@ -CPUScalarBroadcastFunction

CPUScalarBroadcastFunction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total183 of 31241%12 of 2040%1217437637
_workloadFor(ExecutionCall)907645%6650%67132901
lambda$_workloadFor$3(Tensor, Tensor, Object[], Object, int, int)310%20%22101011
lambda$_workloadFor$2(Tensor, Tensor, int[], int, int, int)310%20%22101011
lambda$_workloadFor$1(Tensor, Tensor, float[], float, int, int)310%20%22101011
lambda$_workloadFor$0(Tensor, Tensor, double[], double, int, int)31100%2100%0201001
run(ExecutionCall)16100%n/a010601
CPUScalarBroadcastFunction(ScalarFun)6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarBroadcastFunction

CPUScalarBroadcastFunction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total183 of 31241%12 of 2040%1217437637
_workloadFor(ExecutionCall)907645%6650%67132901
lambda$_workloadFor$3(Tensor, Tensor, Object[], Object, int, int)310%20%22101011
lambda$_workloadFor$2(Tensor, Tensor, int[], int, int, int)310%20%22101011
lambda$_workloadFor$1(Tensor, Tensor, float[], float, int, int)310%20%22101011
lambda$_workloadFor$0(Tensor, Tensor, double[], double, int, int)31100%2100%0201001
run(ExecutionCall)16100%n/a010601
CPUScalarBroadcastFunction(ScalarFun)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarBroadcastFunction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarBroadcastFunction.java.html index eec8fb318..225186289 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarBroadcastFunction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarBroadcastFunction.java.html @@ -132,4 +132,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarFunction.html b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarFunction.html index ffc244d2f..6ae86c90a 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarFunction.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarFunction.html @@ -1 +1 @@ -CPUScalarFunction

CPUScalarFunction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total45 of 5111%2 of 20%237812
run(ExecutionCall)450%20%227711
CPUScalarFunction(ScalarFun)6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUScalarFunction

CPUScalarFunction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total45 of 5111%2 of 20%237812
run(ExecutionCall)450%20%227711
CPUScalarFunction(ScalarFun)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarFunction.java.html b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarFunction.java.html index 0261d0fad..23cd3cd4d 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarFunction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/CPUScalarFunction.java.html @@ -26,4 +26,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/index.html b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/index.html index 4a7c87631..0767b40cf 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/index.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/index.html @@ -1 +1 @@ -neureka.backend.main.implementations.scalar

neureka.backend.main.implementations.scalar

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total271 of 41234%16 of 2433%1623569351103
CPUScalarBroadcastFunction18312941%12840%121743763701
CPUScalarFunction45611%20%23781201
CLScalarFunction43612%20%23691201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.scalar

neureka.backend.main.implementations.scalar

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total277 of 41232%16 of 2433%1723599361113
CPUScalarBroadcastFunction18312941%12840%121743763701
CLScalarFunction490%20%33992211
CPUScalarFunction45611%20%23781201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/index.source.html b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/index.source.html index d30ef441e..21c110522 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations.scalar/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations.scalar/index.source.html @@ -1 +1 @@ -neureka.backend.main.implementations.scalar

neureka.backend.main.implementations.scalar

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total271 of 41234%16 of 2433%1623569351103
CPUScalarBroadcastFunction.java18312941%12840%121743763701
CPUScalarFunction.java45611%20%23781201
CLScalarFunction.java43612%20%23691201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations.scalar

neureka.backend.main.implementations.scalar

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total277 of 41232%16 of 2433%1723599361113
CPUScalarBroadcastFunction.java18312941%12840%121743763701
CLScalarFunction.java490%20%33992211
CPUScalarFunction.java45611%20%23781201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations/CLImplementation.html b/docs/coverage/test/html/neureka.backend.main.implementations/CLImplementation.html index 2e85b7fc2..d1807704f 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations/CLImplementation.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations/CLImplementation.html @@ -1 +1 @@ -CLImplementation

CLImplementation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 5100%0 of 0n/a010201
CLImplementation(ImplementationFor, int)5100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLImplementation

CLImplementation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 50%0 of 0n/a112211
CLImplementation(ImplementationFor, int)50%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations/CLImplementation.java.html b/docs/coverage/test/html/neureka.backend.main.implementations/CLImplementation.java.html index f7cf8b3da..bd2fb9cf7 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations/CLImplementation.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations/CLImplementation.java.html @@ -18,7 +18,7 @@ ImplementationFor<OpenCLDevice> execution, int arity ) { - super( execution, arity ); - } + super( execution, arity ); + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations/CPUImplementation.html b/docs/coverage/test/html/neureka.backend.main.implementations/CPUImplementation.html index 9a5c5641f..48a1420bb 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations/CPUImplementation.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations/CPUImplementation.html @@ -1 +1 @@ -CPUImplementation

CPUImplementation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030203
lambda$withArity$0(int, ImplementationFor)6100%n/a010101
CPUImplementation(ImplementationFor, int)5100%n/a010101
withArity(int)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUImplementation

CPUImplementation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 14100%0 of 0n/a030203
lambda$withArity$0(int, ImplementationFor)6100%n/a010101
CPUImplementation(ImplementationFor, int)5100%n/a010101
withArity(int)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations/CPUImplementation.java.html b/docs/coverage/test/html/neureka.backend.main.implementations/CPUImplementation.java.html index 3d1eda177..1ef65c7da 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations/CPUImplementation.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations/CPUImplementation.java.html @@ -27,4 +27,4 @@ CPUImplementation andImplementation( ImplementationFor<CPU> creator ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations/ParsedCLImplementation.html b/docs/coverage/test/html/neureka.backend.main.implementations/ParsedCLImplementation.html index a063824e6..e9970a44e 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations/ParsedCLImplementation.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations/ParsedCLImplementation.html @@ -1 +1 @@ -ParsedCLImplementation

ParsedCLImplementation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total12 of 30096%8 of 2263%71935508
ParsedCLImplementation(ImplementationFor, int, String, String, String, String, Function)128587%5337%4531801
lambda$_getParsedKernelsFromTemplate$2(String[], String, Map, String, String, String)90100%31178%380901
lambda$new$0(String)41100%n/a0101101
_getParsedKernelsFromTemplate(String, String, String, String, String)31100%n/a010701
getKernelFor(ExecutionCall)19100%n/a010501
lambda$new$1(String)14100%n/a010401
lambda$getKernelFor$3(DataType, KernelCode)5100%n/a010101
getKernelCode()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ParsedCLImplementation

ParsedCLImplementation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total300 of 3000%22 of 220%1919555588
ParsedCLImplementation(ImplementationFor, int, String, String, String, String, Function)970%80%55181811
lambda$_getParsedKernelsFromTemplate$2(String[], String, Map, String, String, String)900%140%889911
lambda$new$0(String)410%n/a11111111
_getParsedKernelsFromTemplate(String, String, String, String, String)310%n/a117711
getKernelFor(ExecutionCall)190%n/a115511
lambda$new$1(String)140%n/a114411
lambda$getKernelFor$3(DataType, KernelCode)50%n/a111111
getKernelCode()30%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations/ParsedCLImplementation.java.html b/docs/coverage/test/html/neureka.backend.main.implementations/ParsedCLImplementation.java.html index ba51164e7..60e15f8b6 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations/ParsedCLImplementation.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations/ParsedCLImplementation.java.html @@ -14,27 +14,27 @@ public class ParsedCLImplementation extends CLImplementation { - private final java.util.function.Function<String, String> _aliasSwapper = + private final java.util.function.Function<String, String> _aliasSwapper = s -> - "//-=<PARSED>=-//\n" + - s.replace("src1", "src1[_i_of_idx_on_tln(prv_src1_cfg, rank)]") - .replace("src2", "src2[_i_of_idx_on_tln(prv_src2_cfg, rank)]") - .replace("input1", "src1[_i_of_i(i, prv_src1_cfg, rank)]") - .replace("input2", "src2[_i_of_i(i, prv_src2_cfg, rank)]") - .replace("input", "src1[_i_of_i(i, prv_src1_cfg, rank)]") - .replace("output", "drn[_i_of_i(i, prv_drn_cfg, rank)]") - .replace("handle", "src1[_i_of_idx_on_tln(prv_src1_cfg, rank)]") - .replace("drain", "src2[_i_of_idx_on_tln(prv_src2_cfg, rank)]") - .replace("origin", "drn[di]") - .replace("target", "frn[_i_of_idx_on_tln(prv_frn_cfg, rank)]") + + "//-=<PARSED>=-//\n" + + s.replace("src1", "src1[_i_of_idx_on_tln(prv_src1_cfg, rank)]") + .replace("src2", "src2[_i_of_idx_on_tln(prv_src2_cfg, rank)]") + .replace("input1", "src1[_i_of_i(i, prv_src1_cfg, rank)]") + .replace("input2", "src2[_i_of_i(i, prv_src2_cfg, rank)]") + .replace("input", "src1[_i_of_i(i, prv_src1_cfg, rank)]") + .replace("output", "drn[_i_of_i(i, prv_drn_cfg, rank)]") + .replace("handle", "src1[_i_of_idx_on_tln(prv_src1_cfg, rank)]") + .replace("drain", "src2[_i_of_idx_on_tln(prv_src2_cfg, rank)]") + .replace("origin", "drn[di]") + .replace("target", "frn[_i_of_idx_on_tln(prv_frn_cfg, rank)]") + "\n//-=<PARSED>=-//"; - private final java.util.function.Function<String, String> asAdvanced = + private final java.util.function.Function<String, String> asAdvanced = s -> - s.replace("target", "frn[_i_of_idx_on_tln(prv_frn2_cfg, rank)]") - .replace("input3","frn[_i_of_idx_on_tln(prv_frn2_cfg, rank)]") - .replace("//-=<ARGUMENT>=-//", "") - .replace("//-=<CONFIGURATION>=-//", ""); + s.replace("target", "frn[_i_of_idx_on_tln(prv_frn2_cfg, rank)]") + .replace("input3","frn[_i_of_idx_on_tln(prv_frn2_cfg, rank)]") + .replace("//-=<ARGUMENT>=-//", "") + .replace("//-=<CONFIGURATION>=-//", ""); private final KernelCode[] _kernels; @@ -47,33 +47,33 @@ String postfix, Function<KernelCode, KernelCode[]> dataTypeAdapter ) { - super( lambda, arity ); - String parsedCode = null; - String parsedName = null; - if ( activationSource == null && differentiationSource == null ) { + super( lambda, arity ); + String parsedCode = null; + String parsedName = null; + if ( activationSource == null && differentiationSource == null ) { parsedCode = kernelSource; parsedName = postfix; - } else if (kernelSource.contains("__kernel")) { + } else if (kernelSource.contains("__kernel")) { boolean templateFound; - String[] parts = kernelSource.split("__kernel")[ 1 ].split("\\(")[ 0 ].split(" "); + String[] parts = kernelSource.split("__kernel")[ 1 ].split("\\(")[ 0 ].split(" "); - templateFound = parts[parts.length - 1].contains("template"); - if (!templateFound) + templateFound = parts[parts.length - 1].contains("template"); + if (!templateFound) throw new IllegalStateException("Invalid source code passed to AbstractCLExecution!"); else { - Map<String, String> map = _getParsedKernelsFromTemplate( + Map<String, String> map = _getParsedKernelsFromTemplate( parts[parts.length - 1], kernelSource, activationSource, differentiationSource, postfix ); - parsedName = map.keySet().toArray(new String[ 0 ])[ 0 ]; - parsedCode = map.values().toArray(new String[ 0 ])[ 0 ]; + parsedName = map.keySet().toArray(new String[ 0 ])[ 0 ]; + parsedCode = map.values().toArray(new String[ 0 ])[ 0 ]; } } - _kernels = dataTypeAdapter.apply( new KernelCode( parsedName, parsedCode ) ); - } + _kernels = dataTypeAdapter.apply( new KernelCode( parsedName, parsedCode ) ); + } private Map<String, String> _getParsedKernelsFromTemplate( String templateName, @@ -82,49 +82,49 @@ String differentiationSource, String postfix ) { - Map<String, String> code = new HashMap<>(); - String preName = templateName.replace("template", ""); - String source = kernelSource.replace("template", ""); - String[] parts = source.split("//-=<OPERATION>=-//"); + Map<String, String> code = new HashMap<>(); + String preName = templateName.replace("template", ""); + String source = kernelSource.replace("template", ""); + String[] parts = source.split("//-=<OPERATION>=-//"); - Parser parser = ( n, f, s ) -> { - String convcode = - parts[ 0 ].replace(preName, preName + n) + - _aliasSwapper.apply(f) + + Parser parser = ( n, f, s ) -> { + String convcode = + parts[ 0 ].replace(preName, preName + n) + + _aliasSwapper.apply(f) + parts[ 2 ] + - _aliasSwapper.apply(s) + + _aliasSwapper.apply(s) + parts[4]; - boolean isAdvanced = s.contains("target")&&s.contains("drain")&&s.contains("handle") - || s.contains("input1")&&s.contains("input2")&&s.contains("input3"); - convcode = (isAdvanced) ? asAdvanced.apply(convcode) : convcode; - code.put(preName + n, convcode); - }; + boolean isAdvanced = s.contains("target")&&s.contains("drain")&&s.contains("handle") + || s.contains("input1")&&s.contains("input2")&&s.contains("input3"); + convcode = (isAdvanced) ? asAdvanced.apply(convcode) : convcode; + code.put(preName + n, convcode); + }; //Tensor t0_origin, Tensor t1_handle, Tensor t2_drain ... when d>=0 //Tensor t0_drain, Tensor t1_src1, Tensor t2_src2 //drn[di], src1[_i_of_idx_on_tln(prv_src1_cfg, rank)], src2[_i_of_idx_on_tln(prv_src2_cfg, rank)] //default: src1 o src2 -> drain //inverse: src1/fdrn <-src2 <- drain //=========================================================================== - parser.apply( + parser.apply( postfix, activationSource, differentiationSource ); - return code; + return code; } @Override public KernelCode getKernelFor( ExecutionCall<OpenCLDevice> call ) { - DataType<?> callType = call.input(0 ).getDataType(); - return Arrays.stream(_kernels) - .filter( k -> k.getDataType().equals( callType ) ) - .findFirst() - .orElse(_kernels[0]); + DataType<?> callType = call.input(0 ).getDataType(); + return Arrays.stream(_kernels) + .filter( k -> k.getDataType().equals( callType ) ) + .findFirst() + .orElse(_kernels[0]); } @Override public KernelCode[] getKernelCode() { - return _kernels; + return _kernels; } private interface Parser @@ -133,4 +133,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations/SimpleCLImplementation.html b/docs/coverage/test/html/neureka.backend.main.implementations/SimpleCLImplementation.html index 899102dd2..91e0df23b 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations/SimpleCLImplementation.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations/SimpleCLImplementation.html @@ -1 +1 @@ -SimpleCLImplementation

SimpleCLImplementation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 23100%0 of 0n/a030503
SimpleCLImplementation(ImplementationFor, int, String, String)12100%n/a010301
getKernelCode()8100%n/a010101
getKernelFor(ExecutionCall)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SimpleCLImplementation

SimpleCLImplementation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total23 of 230%0 of 0n/a335533
SimpleCLImplementation(ImplementationFor, int, String, String)120%n/a113311
getKernelCode()80%n/a111111
getKernelFor(ExecutionCall)30%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations/SimpleCLImplementation.java.html b/docs/coverage/test/html/neureka.backend.main.implementations/SimpleCLImplementation.java.html index 156ddd270..aff914292 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations/SimpleCLImplementation.java.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations/SimpleCLImplementation.java.html @@ -15,18 +15,18 @@ String kernelName, String kernelSource ) { - super(execution, arity); - _kernel = new KernelCode( kernelName, kernelSource ); - } + super(execution, arity); + _kernel = new KernelCode( kernelName, kernelSource ); + } @Override public KernelCode getKernelFor( ExecutionCall<OpenCLDevice> call ) { - return _kernel; + return _kernel; } @Override public KernelCode[] getKernelCode() { - return new KernelCode[]{ _kernel }; + return new KernelCode[]{ _kernel }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.implementations/index.html b/docs/coverage/test/html/neureka.backend.main.implementations/index.html index 38264b19e..0f83ccce2 100644 --- a/docs/coverage/test/html/neureka.backend.main.implementations/index.html +++ b/docs/coverage/test/html/neureka.backend.main.implementations/index.html @@ -1 +1 @@ -neureka.backend.main.implementations

neureka.backend.main.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total12 of 34296%8 of 2263%72636401504
ParsedCLImplementation1228896%81463%7193550801
SimpleCLImplementation23100%n/a03050301
CPUImplementation14100%n/a03020301
CLImplementation5100%n/a01020101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.implementations

neureka.backend.main.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total328 of 3424%22 of 220%23266264121534
ParsedCLImplementation3000%220%191955558811
SimpleCLImplementation230%n/a33553311
CLImplementation50%n/a11221111
CPUImplementation14100%n/a03020301
Created with JaCoCo 0.8.11.202310140853
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CPUImplementation.java14100%n/a03020301
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.memory/MemUtil.html b/docs/coverage/test/html/neureka.backend.main.memory/MemUtil.html index 2a4626ef9..2b192ad2d 100644 --- a/docs/coverage/test/html/neureka.backend.main.memory/MemUtil.html +++ b/docs/coverage/test/html/neureka.backend.main.memory/MemUtil.html @@ -1 +1 @@ -MemUtil

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\ No newline at end of file +MemUtil

MemUtil

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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lambda$new$1(Tensor)21285%1375%130101
lambda$new$5(Tensor[], Result, int)1292%1150%120101
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lambda$new$6(Tensor[], Result, int)10100%2100%020101
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.memory/MemValidator.java.html b/docs/coverage/test/html/neureka.backend.main.memory/MemValidator.java.html index 1f13045c9..0f03dfa74 100644 --- a/docs/coverage/test/html/neureka.backend.main.memory/MemValidator.java.html +++ b/docs/coverage/test/html/neureka.backend.main.memory/MemValidator.java.html @@ -113,4 +113,4 @@ public Result getResult() { return _result; } } -
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MemUtil121100%11191%1140190801
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\ No newline at end of file +neureka.backend.main.memory

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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MemUtil121100%11191%1140190801
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.memory/index.source.html b/docs/coverage/test/html/neureka.backend.main.memory/index.source.html index a23e460e8..f73771157 100644 --- a/docs/coverage/test/html/neureka.backend.main.memory/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.memory/index.source.html @@ -1 +1 @@ -neureka.backend.main.memory

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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MemUtil.java121100%11191%1140190801
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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MemValidator.java1319293%52583%52723701201
MemUtil.java121100%11191%1140190801
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Absolute.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Absolute.html index 775183476..1d5aa33df 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Absolute.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Absolute.html @@ -1 +1 @@ -Absolute

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Absolute()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Absolute.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Absolute.java.html index 858fa6c0b..b212ef891 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Absolute.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Absolute.java.html @@ -8,4 +8,4 @@ super(ScalarFun.ABSOLUTE); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/AbstractActivationOperation.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/AbstractActivationOperation.html index c89510b1d..1cacab21e 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/AbstractActivationOperation.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/AbstractActivationOperation.html @@ -1 +1 @@ -AbstractActivationOperation

AbstractActivationOperation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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AbstractActivationOperation(ScalarFun)53100%n/a0101701
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractActivationOperation

AbstractActivationOperation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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AbstractActivationOperation(ScalarFun)53100%n/a0101701
stringify(String[])36100%1375%130301
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/AbstractActivationOperation.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/AbstractActivationOperation.java.html index cd84cf075..fa48708bc 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/AbstractActivationOperation.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/AbstractActivationOperation.java.html @@ -96,4 +96,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Cbrt.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Cbrt.html index e69d4d8a2..3a34732cc 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Cbrt.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Cbrt.html @@ -1 +1 @@ -Cbrt

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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\ No newline at end of file +Cbrt

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Cbrt.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Cbrt.java.html index 83d61cc1d..41b74c702 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Cbrt.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Cbrt.java.html @@ -8,4 +8,4 @@ super(ScalarFun.CBRT); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Cosinus.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Cosinus.html index 3b426668c..24df57642 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Cosinus.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Cosinus.html @@ -1 +1 @@ -Cosinus

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Cosinus.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Cosinus.java.html index 85f77b907..cbb0f60ee 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Cosinus.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Cosinus.java.html @@ -8,4 +8,4 @@ super(ScalarFun.COSINUS); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Exp.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Exp.html index f65025554..a6b526c2b 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Exp.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Exp.html @@ -1 +1 @@ -Exp

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Exp.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Exp.java.html index 2b11046d0..b76e6731e 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Exp.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Exp.java.html @@ -8,4 +8,4 @@ super(ScalarFun.EXP); } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaSU.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaSU.html index b1afb820e..601df8f2f 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaSU.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaSU.html @@ -1 +1 @@ -GaSU

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaSU.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaSU.java.html index b5f2e433b..914d7c294 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaSU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaSU.java.html @@ -17,4 +17,4 @@ } } -
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaTU.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaTU.html index 582e416a1..3d656c76d 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaTU.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaTU.html @@ -1 +1 @@ -GaTU

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaTU.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaTU.java.html index e866c3c9d..ec28c8ca0 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaTU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaTU.java.html @@ -16,4 +16,4 @@ super(ScalarFun.GATU); } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Gaussian.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Gaussian.html index e3298e17f..67b349c03 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Gaussian.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Gaussian.html @@ -1 +1 @@ -Gaussian

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Gaussian.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Gaussian.java.html index a441c7ab5..df17cc55b 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Gaussian.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Gaussian.java.html @@ -8,4 +8,4 @@ super(ScalarFun.GAUSSIAN); } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaussianFast.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaussianFast.html index f8ab2d606..229ba1707 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaussianFast.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaussianFast.html @@ -1 +1 @@ -GaussianFast

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaussianFast.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaussianFast.java.html index 7e9aa0013..ac9fd5304 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/GaussianFast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/GaussianFast.java.html @@ -8,4 +8,4 @@ super(ScalarFun.GAUSSIAN_FAST); } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
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GeLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
GeLU()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/GeLU.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/GeLU.java.html index 907740699..82ddee63b 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/GeLU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/GeLU.java.html @@ -15,4 +15,4 @@ super(ScalarFun.GELU); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Identity.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Identity.html index fc666b148..d7104973b 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Identity.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Identity.html @@ -1 +1 @@ -Identity

Identity

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Identity()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Identity

Identity

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Identity()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Identity.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Identity.java.html index f898d4559..f628e9810 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Identity.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Identity.java.html @@ -8,4 +8,4 @@ super(ScalarFun.IDENTITY); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Log10.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Log10.html index a2d879c03..d80f300f8 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Log10.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Log10.html @@ -1 +1 @@ -Log10

Log10

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Log10()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Log10

Log10

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Log10()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Log10.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Log10.java.html index 07dbe87b7..c816d940a 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Log10.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Log10.java.html @@ -8,4 +8,4 @@ super(ScalarFun.LOG10); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Logarithm.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Logarithm.html index dbe7c2eb6..584ee31bf 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Logarithm.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Logarithm.html @@ -1 +1 @@ -Logarithm

Logarithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 3183%1 of 250%130402
asDerivative(Function[], int)52281%1150%120201
Logarithm()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Logarithm

Logarithm

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 3183%1 of 250%130402
asDerivative(Function[], int)52281%1150%120201
Logarithm()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Logarithm.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Logarithm.java.html index fd20535f5..d40dd1191 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Logarithm.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Logarithm.java.html @@ -14,4 +14,4 @@ return children[0].getDerivative(derivationIndex)+" / "+children[0].toString(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Quadratic.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Quadratic.html index dc37d70e5..39067ae84 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Quadratic.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Quadratic.html @@ -1 +1 @@ -Quadratic

Quadratic

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Quadratic()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Quadratic

Quadratic

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Quadratic()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Quadratic.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Quadratic.java.html index 90088f075..b77cebbe3 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Quadratic.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Quadratic.java.html @@ -8,4 +8,4 @@ super(ScalarFun.QUADRATIC); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/ReLU.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/ReLU.html index 9d7ce1db5..a95eb352f 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/ReLU.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/ReLU.html @@ -1 +1 @@ -ReLU

ReLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
ReLU()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ReLU

ReLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
ReLU()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/ReLU.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/ReLU.java.html index b3d2bc134..48d3e5a0d 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/ReLU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/ReLU.java.html @@ -8,4 +8,4 @@ super(ScalarFun.RELU); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/SeLU.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/SeLU.html index f83099c44..b5601e7fb 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/SeLU.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/SeLU.html @@ -1 +1 @@ -SeLU

SeLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
SeLU()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SeLU

SeLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
SeLU()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/SeLU.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/SeLU.java.html index c7d637127..a85c5a9b2 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/SeLU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/SeLU.java.html @@ -19,4 +19,4 @@ super(ScalarFun.SELU); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/SiLU.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/SiLU.html index 888b707e3..656ea6ebd 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/SiLU.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/SiLU.html @@ -1 +1 @@ -SiLU

SiLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
SiLU()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SiLU

SiLU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
SiLU()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/SiLU.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/SiLU.java.html index 4a6e1682c..0164adc8c 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/SiLU.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/SiLU.java.html @@ -14,4 +14,4 @@ super(ScalarFun.SILU); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sigmoid.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sigmoid.html index d96e070e7..07f7723fd 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sigmoid.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sigmoid.html @@ -1 +1 @@ -Sigmoid

Sigmoid

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Sigmoid()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sigmoid

Sigmoid

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Sigmoid()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sigmoid.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sigmoid.java.html index 83bac56b3..1b7c87977 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sigmoid.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sigmoid.java.html @@ -8,4 +8,4 @@ super(ScalarFun.SIGMOID); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sinus.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sinus.html index 5485f0b18..f53761419 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sinus.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sinus.html @@ -1 +1 @@ -Sinus

Sinus

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Sinus()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sinus

Sinus

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Sinus()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sinus.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sinus.java.html index 1be604ac0..c494dcec8 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sinus.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sinus.java.html @@ -8,4 +8,4 @@ super(ScalarFun.SINUS); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Softplus.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Softplus.html index 37c9c4263..75dfb2fe1 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Softplus.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Softplus.html @@ -1 +1 @@ -Softplus

Softplus

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Softplus()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Softplus

Softplus

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Softplus()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Softplus.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Softplus.java.html index b30c32065..cf27b5c73 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Softplus.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Softplus.java.html @@ -12,4 +12,4 @@ super(ScalarFun.SOFTPLUS); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Softsign.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Softsign.html index c9c044eb6..c494398a0 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Softsign.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Softsign.html @@ -1 +1 @@ -Softsign

Softsign

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Softsign()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Softsign

Softsign

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Softsign()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Softsign.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Softsign.java.html index 33076e9af..125ad4aef 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Softsign.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Softsign.java.html @@ -16,4 +16,4 @@ super(ScalarFun.SOFTSIGN); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sqrt.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sqrt.html index 6bd667995..d9e350f64 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sqrt.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sqrt.html @@ -1 +1 @@ -Sqrt

Sqrt

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Sqrt()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sqrt

Sqrt

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Sqrt()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sqrt.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sqrt.java.html index e3c3f87f6..2089bc70a 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Sqrt.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Sqrt.java.html @@ -8,4 +8,4 @@ super(ScalarFun.SQRT); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Tanh.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Tanh.html index 9bfb3f841..b9a50e0d4 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Tanh.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Tanh.html @@ -1 +1 @@ -Tanh

Tanh

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Tanh()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Tanh

Tanh

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
Tanh()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/Tanh.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/Tanh.java.html index 2db6c8a5e..1c734906e 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/Tanh.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/Tanh.java.html @@ -9,4 +9,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/TanhFast.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/TanhFast.html index cf819b2db..6d5cc685d 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/TanhFast.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/TanhFast.html @@ -1 +1 @@ -TanhFast

TanhFast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
TanhFast()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +TanhFast

TanhFast

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 4100%0 of 0n/a010201
TanhFast()4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/TanhFast.java.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/TanhFast.java.html index aca946de4..95c8965a6 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/TanhFast.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/TanhFast.java.html @@ -8,4 +8,4 @@ super(ScalarFun.TANH_FAST); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/index.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/index.html index b4e1b7931..5513fd373 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/index.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/index.html @@ -1 +1 @@ -neureka.backend.main.operations.functions

neureka.backend.main.operations.functions

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total5 of 37898%2 of 1485%235091028024
Logarithm52683%1150%13040201
AbstractActivationOperation259100%11191%1100430401
TanhFast4100%n/a01020101
GaTU4100%n/a01020101
Identity4100%n/a01020101
GaSU4100%n/a01020101
Tanh4100%n/a01020101
Exp4100%n/a01020101
Sigmoid4100%n/a01020101
Sqrt4100%n/a01020101
Absolute4100%n/a01020101
SiLU4100%n/a01020101
Softplus4100%n/a01020101
Softsign4100%n/a01020101
Log104100%n/a01020101
GaussianFast4100%n/a01020101
SeLU4100%n/a01020101
Cosinus4100%n/a01020101
Gaussian4100%n/a01020101
Cbrt4100%n/a01020101
GeLU4100%n/a01020101
ReLU4100%n/a01020101
Sinus4100%n/a01020101
Quadratic4100%n/a01020101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.functions

neureka.backend.main.operations.functions

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total5 of 37898%2 of 1485%235091028024
Logarithm52683%1150%13040201
AbstractActivationOperation259100%11191%1100430401
TanhFast4100%n/a01020101
GaTU4100%n/a01020101
Identity4100%n/a01020101
GaSU4100%n/a01020101
Tanh4100%n/a01020101
Exp4100%n/a01020101
Sigmoid4100%n/a01020101
Sqrt4100%n/a01020101
Absolute4100%n/a01020101
SiLU4100%n/a01020101
Softplus4100%n/a01020101
Softsign4100%n/a01020101
Log104100%n/a01020101
GaussianFast4100%n/a01020101
SeLU4100%n/a01020101
Cosinus4100%n/a01020101
Gaussian4100%n/a01020101
Cbrt4100%n/a01020101
GeLU4100%n/a01020101
ReLU4100%n/a01020101
Sinus4100%n/a01020101
Quadratic4100%n/a01020101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.functions/index.source.html b/docs/coverage/test/html/neureka.backend.main.operations.functions/index.source.html index 8a5ae0f7a..9943ca1b5 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.functions/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.functions/index.source.html @@ -1 +1 @@ -neureka.backend.main.operations.functions

neureka.backend.main.operations.functions

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total5 of 37898%2 of 1485%235091028024
Logarithm.java52683%1150%13040201
AbstractActivationOperation.java259100%11191%1100430401
GaussianFast.java4100%n/a01020101
SiLU.java4100%n/a01020101
Sigmoid.java4100%n/a01020101
Absolute.java4100%n/a01020101
ReLU.java4100%n/a01020101
GaSU.java4100%n/a01020101
GeLU.java4100%n/a01020101
GaTU.java4100%n/a01020101
Sqrt.java4100%n/a01020101
Softsign.java4100%n/a01020101
TanhFast.java4100%n/a01020101
Softplus.java4100%n/a01020101
Cbrt.java4100%n/a01020101
Quadratic.java4100%n/a01020101
Gaussian.java4100%n/a01020101
Cosinus.java4100%n/a01020101
Tanh.java4100%n/a01020101
Exp.java4100%n/a01020101
Sinus.java4100%n/a01020101
Identity.java4100%n/a01020101
Log10.java4100%n/a01020101
SeLU.java4100%n/a01020101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.functions

neureka.backend.main.operations.functions

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total5 of 37898%2 of 1485%235091028024
Logarithm.java52683%1150%13040201
AbstractActivationOperation.java259100%11191%1100430401
GaussianFast.java4100%n/a01020101
ReLU.java4100%n/a01020101
GaTU.java4100%n/a01020101
Softplus.java4100%n/a01020101
Quadratic.java4100%n/a01020101
Cosinus.java4100%n/a01020101
Tanh.java4100%n/a01020101
Exp.java4100%n/a01020101
Log10.java4100%n/a01020101
SiLU.java4100%n/a01020101
Sigmoid.java4100%n/a01020101
Absolute.java4100%n/a01020101
GaSU.java4100%n/a01020101
GeLU.java4100%n/a01020101
Sqrt.java4100%n/a01020101
Softsign.java4100%n/a01020101
TanhFast.java4100%n/a01020101
Cbrt.java4100%n/a01020101
Gaussian.java4100%n/a01020101
Sinus.java4100%n/a01020101
Identity.java4100%n/a01020101
SeLU.java4100%n/a01020101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.indexer/Product.html b/docs/coverage/test/html/neureka.backend.main.operations.indexer/Product.html index 63ca96a4c..8334f5a02 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.indexer/Product.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.indexer/Product.html @@ -1 +1 @@ -Product

Product

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total70 of 39882%7 of 3278%62396907
calculate(double[], int, int, Function[])593637%4660%3681701
calculate(double[], int, Function[])68193%1787%1501601
execute(Function, ExecutionCall)517597%21083%2712701
Product()20100%n/a010901
lambda$execute$1(int, int)7100%2100%020101
lambda$execute$2(Tensor[], Integer)5100%n/a010101
lambda$execute$0(Function, int, int)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Product

Product

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total70 of 39882%7 of 3278%62396907
calculate(double[], int, int, Function[])593637%4660%3681701
calculate(double[], int, Function[])68193%1787%1501601
execute(Function, ExecutionCall)517597%21083%2712701
Product()20100%n/a010901
lambda$execute$1(int, int)7100%2100%020101
lambda$execute$2(Tensor[], Integer)5100%n/a010101
lambda$execute$0(Function, int, int)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.indexer/Product.java.html b/docs/coverage/test/html/neureka.backend.main.operations.indexer/Product.java.html index 54eaf4f08..459637490 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.indexer/Product.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.indexer/Product.java.html @@ -141,4 +141,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.indexer/Summation.html b/docs/coverage/test/html/neureka.backend.main.operations.indexer/Summation.html index d85c0d39a..2b3220843 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.indexer/Summation.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.indexer/Summation.html @@ -1 +1 @@ -Summation

Summation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total26 of 18085%5 of 1668%41323705
calculate(double[], int, int, Function[])14733%3125%232301
calculate(double[], int, Function[])63685%1583%1401001
_calculate(double[], Function[])62781%1375%130701
execute(Function, ExecutionCall)64100%2100%020801
Summation()20100%n/a010901
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Summation

Summation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total26 of 18085%5 of 1668%41323705
calculate(double[], int, int, Function[])14733%3125%232301
calculate(double[], int, Function[])63685%1583%1401001
_calculate(double[], Function[])62781%1375%130701
execute(Function, ExecutionCall)64100%2100%020801
Summation()20100%n/a010901
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.indexer/Summation.java.html b/docs/coverage/test/html/neureka.backend.main.operations.indexer/Summation.java.html index 9f2e19c87..51eb0803b 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.indexer/Summation.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.indexer/Summation.java.html @@ -93,4 +93,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.indexer/index.html b/docs/coverage/test/html/neureka.backend.main.operations.indexer/index.html index 805b0d071..54c4f0613 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.indexer/index.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.indexer/index.html @@ -1 +1 @@ -neureka.backend.main.operations.indexer

neureka.backend.main.operations.indexer

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total96 of 57883%12 of 4875%10361110601202
Product7032882%72578%6239690701
Summation2615485%51168%4132370501
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.indexer

neureka.backend.main.operations.indexer

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total96 of 57883%12 of 4875%10361110601202
Product7032882%72578%6239690701
Summation2615485%51168%4132370501
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.indexer/index.source.html b/docs/coverage/test/html/neureka.backend.main.operations.indexer/index.source.html index ec3a25c22..934fc80a6 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.indexer/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.indexer/index.source.html @@ -1 +1 @@ -neureka.backend.main.operations.indexer

neureka.backend.main.operations.indexer

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total96 of 57883%12 of 4875%10361110601202
Product.java7032882%72578%6239690701
Summation.java2615485%51168%4132370501
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.indexer

neureka.backend.main.operations.indexer

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total96 of 57883%12 of 4875%10361110601202
Product.java7032882%72578%6239690701
Summation.java2615485%51168%4132370501
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/AXPY.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/AXPY.html index 7f227910e..3a3db31ac 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/AXPY.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/AXPY.html @@ -1 +1 @@ -AXPY

AXPY

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 4993%0 of 4100%151713
AXPY()30%n/a111111
invoke(double[], int, double, double[], int, int, int)23100%2100%020301
invoke(float[], int, float, float[], int, int, int)23100%2100%020301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AXPY

AXPY

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 4993%0 of 4100%151713
AXPY()30%n/a111111
invoke(double[], int, double, double[], int, int, int)23100%2100%020301
invoke(float[], int, float, float[], int, int, int)23100%2100%020301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/AXPY.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/AXPY.java.html index 5c84e45b9..4d231a4d0 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/AXPY.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/AXPY.java.html @@ -59,4 +59,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/COPY.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/COPY.html index 57ad936b9..111bbcffb 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/COPY.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/COPY.html @@ -1 +1 @@ -COPY

COPY

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2185%0 of 0n/a121512
COPY()30%n/a111111
copyOf(Object[])18100%n/a010401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +COPY

COPY

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2185%0 of 0n/a121512
COPY()30%n/a111111
copyOf(Object[])18100%n/a010401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/COPY.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/COPY.java.html index abb003114..f42a80cbc 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/COPY.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/COPY.java.html @@ -39,4 +39,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/DOT.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/DOT.html index 4d37b612f..e6751d72a 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/DOT.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/DOT.html @@ -1 +1 @@ -DOT

DOT

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total119 of 59580%2 of 1687%31799319
unrolled04(long[], int, long[], int, int, int)588258%1375%1342201
unrolled04(int[], int, int[], int, int, int)588258%1375%1342201
DOT()30%n/a111111
unrolled04(double[], int, double[], int, int, int)140100%4100%0302201
unrolled04(float[], int, float[], int, int, int)140100%4100%0302201
invoke(double[], int, double[], int, int, int)8100%n/a010101
invoke(float[], int, float[], int, int, int)8100%n/a010101
invoke(long[], int, long[], int, int, int)8100%n/a010101
invoke(int[], int, int[], int, int, int)8100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DOT

DOT

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total119 of 59580%2 of 1687%31799319
unrolled04(long[], int, long[], int, int, int)588258%1375%1342201
unrolled04(int[], int, int[], int, int, int)588258%1375%1342201
DOT()30%n/a111111
unrolled04(double[], int, double[], int, int, int)140100%4100%0302201
unrolled04(float[], int, float[], int, int, int)140100%4100%0302201
invoke(double[], int, double[], int, int, int)8100%n/a010101
invoke(float[], int, float[], int, int, int)8100%n/a010101
invoke(long[], int, long[], int, int, int)8100%n/a010101
invoke(int[], int, int[], int, int, int)8100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/DOT.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/DOT.java.html index f9e08ccbd..0e6287292 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/DOT.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/DOT.java.html @@ -179,4 +179,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/GEMM.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/GEMM.html index 336ad2f75..afbf96717 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/GEMM.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/GEMM.html @@ -1 +1 @@ -GEMM

GEMM

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2,36199%2 of 12898%31011517137
GEMM()0%n/a111111
full_F64_5x5_CM(double[], double[], int, double[])374100%2100%0209701
full_F64_4x4_CM(double[], double[], int, double[])253100%2100%0206601
full_F64_0xN_CM(double[], double[], int, double[])195100%4100%0303701
full_F64_9xN_CM(double[], double[], int, double[])179100%4100%0303401
full_F64_8xN_CM(double[], double[], int, double[])163100%4100%0303101
full_F64_3x3_CM(double[], double[], int, double[])156100%2100%0204101
full_F64_7xN_CM(double[], double[], int, double[])147100%4100%0302801
full_F64_6xN_CM(double[], double[], int, double[])131100%4100%0302501
operationForF64(boolean, long, long)124100%14998%12603001
full_F64_2x2_CM(double[], double[], int, double[])83100%2100%0202201
operationForF32(boolean, long, long)40100%11593%1901701
partial_F64_MxN_CM(double[], int, int, double[], int, double[])38100%4100%030501
partial_F64_MxN_RM(double[], int, int, double[], int, double[])38100%4100%030501
partial_F32_MxN_CM(float[], int, int, float[], int, float[])38100%4100%030501
partial_F32_MxN_RM(float[], int, int, float[], int, float[])38100%4100%030501
full_F64_1x1_CM(double[], double[], int, double[])31100%2100%020601
full_F64_Mx1_RM(double[], double[], int, double[])28100%2100%020601
full_F32_Mx1_RM(float[], float[], int, float[])28100%2100%020601
full_F64_1xN_RM(double[], double[], int, double[])25100%2100%020401
full_F32_1xN_RM(float[], float[], int, float[])25100%2100%020401
full_F64_Mx1_CM(double[], double[], int, double[])23100%2100%020401
full_F32_Mx1_CM(float[], float[], int, float[])23100%2100%020401
full_F64_1xN_CM(double[], double[], int, double[])23100%2100%020301
full_F32_1xN_CM(float[], float[], int, float[])23100%2100%020301
threaded_F64_MxN_CM(double[], double[], int, double[])14100%n/a010401
threaded_F32_MxN_CM(float[], float[], int, float[])14100%n/a010401
threaded_F32_MxN_RM(float[], float[], int, float[])14100%n/a010401
threaded_F64_MxN_RM(double[], double[], int, double[])14100%n/a010401
full_F64_MxN_CM(double[], double[], int, double[])11100%n/a010201
full_F64_MxN_RM(double[], double[], int, double[])11100%n/a010201
full_F32_MxN_CM(float[], float[], int, float[])11100%n/a010201
full_F32_MxN_RM(float[], float[], int, float[])11100%n/a010201
lambda$threaded_F64_MxN_RM$3(double[], double[], int, double[], int, int)8100%n/a010101
lambda$threaded_F32_MxN_RM$2(float[], float[], int, float[], int, int)8100%n/a010101
lambda$threaded_F32_MxN_CM$1(float[], float[], int, float[], int, int)8100%n/a010101
lambda$threaded_F64_MxN_CM$0(double[], double[], int, double[], int, int)8100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +GEMM

GEMM

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2,36199%2 of 12898%31011517137
GEMM()0%n/a111111
full_F64_5x5_CM(double[], double[], int, double[])374100%2100%0209701
full_F64_4x4_CM(double[], double[], int, double[])253100%2100%0206601
full_F64_0xN_CM(double[], double[], int, double[])195100%4100%0303701
full_F64_9xN_CM(double[], double[], int, double[])179100%4100%0303401
full_F64_8xN_CM(double[], double[], int, double[])163100%4100%0303101
full_F64_3x3_CM(double[], double[], int, double[])156100%2100%0204101
full_F64_7xN_CM(double[], double[], int, double[])147100%4100%0302801
full_F64_6xN_CM(double[], double[], int, double[])131100%4100%0302501
operationForF64(boolean, long, long)124100%14998%12603001
full_F64_2x2_CM(double[], double[], int, double[])83100%2100%0202201
operationForF32(boolean, long, long)40100%11593%1901701
partial_F64_MxN_CM(double[], int, int, double[], int, double[])38100%4100%030501
partial_F64_MxN_RM(double[], int, int, double[], int, double[])38100%4100%030501
partial_F32_MxN_CM(float[], int, int, float[], int, float[])38100%4100%030501
partial_F32_MxN_RM(float[], int, int, float[], int, float[])38100%4100%030501
full_F64_1x1_CM(double[], double[], int, double[])31100%2100%020601
full_F64_Mx1_RM(double[], double[], int, double[])28100%2100%020601
full_F32_Mx1_RM(float[], float[], int, float[])28100%2100%020601
full_F64_1xN_RM(double[], double[], int, double[])25100%2100%020401
full_F32_1xN_RM(float[], float[], int, float[])25100%2100%020401
full_F64_Mx1_CM(double[], double[], int, double[])23100%2100%020401
full_F32_Mx1_CM(float[], float[], int, float[])23100%2100%020401
full_F64_1xN_CM(double[], double[], int, double[])23100%2100%020301
full_F32_1xN_CM(float[], float[], int, float[])23100%2100%020301
threaded_F64_MxN_CM(double[], double[], int, double[])14100%n/a010401
threaded_F32_MxN_CM(float[], float[], int, float[])14100%n/a010401
threaded_F32_MxN_RM(float[], float[], int, float[])14100%n/a010401
threaded_F64_MxN_RM(double[], double[], int, double[])14100%n/a010401
full_F64_MxN_CM(double[], double[], int, double[])11100%n/a010201
full_F64_MxN_RM(double[], double[], int, double[])11100%n/a010201
full_F32_MxN_CM(float[], float[], int, float[])11100%n/a010201
full_F32_MxN_RM(float[], float[], int, float[])11100%n/a010201
lambda$threaded_F64_MxN_RM$3(double[], double[], int, double[], int, int)8100%n/a010101
lambda$threaded_F32_MxN_RM$2(float[], float[], int, float[], int, int)8100%n/a010101
lambda$threaded_F32_MxN_CM$1(float[], float[], int, float[], int, int)8100%n/a010101
lambda$threaded_F64_MxN_CM$0(double[], double[], int, double[], int, int)8100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/GEMM.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/GEMM.java.html index 79e94a0e3..c767ca9d8 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/GEMM.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/GEMM.java.html @@ -963,4 +963,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IAXPY.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IAXPY.html index a395fbe19..05bdbe2e2 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IAXPY.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IAXPY.html @@ -1 +1 @@ -IAXPY

IAXPY

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 4993%0 of 4100%151713
IAXPY()30%n/a111111
invoke(long[], int, long, long[], int, int, int)23100%2100%020301
invoke(int[], int, int, int[], int, int, int)23100%2100%020301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +IAXPY

IAXPY

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 4993%0 of 4100%151713
IAXPY()30%n/a111111
invoke(long[], int, long, long[], int, int, int)23100%2100%020301
invoke(int[], int, int, int[], int, int, int)23100%2100%020301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IAXPY.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IAXPY.java.html index 1f9a195c9..3f8a7ab63 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IAXPY.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IAXPY.java.html @@ -59,4 +59,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IDOT.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IDOT.html index a1992d0f2..9c6544ddc 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IDOT.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IDOT.html @@ -1 +1 @@ -IDOT

IDOT

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total119 of 29960%2 of 875%3994715
unrolled04(long[], int, long[], int, int, int)588258%1375%1342201
unrolled04(int[], int, int[], int, int, int)588258%1375%1342201
IDOT()30%n/a111111
invoke(long[], int, long[], int, int, int)8100%n/a010101
invoke(int[], int, int[], int, int, int)8100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +IDOT

IDOT

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total119 of 29960%2 of 875%3994715
unrolled04(long[], int, long[], int, int, int)588258%1375%1342201
unrolled04(int[], int, int[], int, int, int)588258%1375%1342201
IDOT()30%n/a111111
invoke(long[], int, long[], int, int, int)8100%n/a010101
invoke(int[], int, int[], int, int, int)8100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IDOT.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IDOT.java.html index 5a90fa4ca..7afd9756f 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IDOT.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IDOT.java.html @@ -105,4 +105,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IGEMM.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IGEMM.html index 3a07cd75c..8f467015d 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IGEMM.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IGEMM.html @@ -1 +1 @@ -IGEMM

IGEMM

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2,36199%2 of 12898%31011517137
IGEMM()0%n/a111111
full_I64_5x5_CM(long[], long[], int, long[])374100%2100%0209701
full_I64_4x4_CM(long[], long[], int, long[])253100%2100%0206601
full_I64_0xN_CM(long[], long[], int, long[])195100%4100%0303701
full_I64_9xN_CM(long[], long[], int, long[])179100%4100%0303401
full_I64_8xN_CM(long[], long[], int, long[])163100%4100%0303101
full_I64_3x3_CM(long[], long[], int, long[])156100%2100%0204101
full_I64_7xN_CM(long[], long[], int, long[])147100%4100%0302801
full_I64_6xN_CM(long[], long[], int, long[])131100%4100%0302501
operationForI64(boolean, long, long)124100%14998%12603001
full_I64_2x2_CM(long[], long[], int, long[])83100%2100%0202201
operationForI32(boolean, long, long)40100%11593%1901701
partial_I64_MxN_CM(long[], int, int, long[], int, long[])38100%4100%030501
partial_I64_MxN_RM(long[], int, int, long[], int, long[])38100%4100%030501
partial_I32_MxN_CM(int[], int, int, int[], int, int[])38100%4100%030501
partial_I32_MxN_RM(int[], int, int, int[], int, int[])38100%4100%030501
full_I64_1x1_CM(long[], long[], int, long[])31100%2100%020601
full_I64_Mx1_RM(long[], long[], int, long[])28100%2100%020601
full_I32_Mx1_RM(int[], int[], int, int[])28100%2100%020601
full_I64_1xN_RM(long[], long[], int, long[])25100%2100%020401
full_I32_1xN_RM(int[], int[], int, int[])25100%2100%020401
full_I64_Mx1_CM(long[], long[], int, long[])23100%2100%020401
full_I32_Mx1_CM(int[], int[], int, int[])23100%2100%020401
full_I64_1xN_CM(long[], long[], int, long[])23100%2100%020301
full_I32_1xN_CM(int[], int[], int, int[])23100%2100%020301
threaded_I64_MxN_CM(long[], long[], int, long[])14100%n/a010401
threaded_I32_MxN_CM(int[], int[], int, int[])14100%n/a010401
threaded_I32_MxN_RM(int[], int[], int, int[])14100%n/a010401
threaded_I64_MxN_RM(long[], long[], int, long[])14100%n/a010401
full_I64_MxN_CM(long[], long[], int, long[])11100%n/a010201
full_I64_MxN_RM(long[], long[], int, long[])11100%n/a010201
full_I32_MxN_CM(int[], int[], int, int[])11100%n/a010201
full_I32_MxN_RM(int[], int[], int, int[])11100%n/a010201
lambda$threaded_I64_MxN_RM$3(long[], long[], int, long[], int, int)8100%n/a010101
lambda$threaded_I32_MxN_RM$2(int[], int[], int, int[], int, int)8100%n/a010101
lambda$threaded_I32_MxN_CM$1(int[], int[], int, int[], int, int)8100%n/a010101
lambda$threaded_I64_MxN_CM$0(long[], long[], int, long[], int, int)8100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +IGEMM

IGEMM

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2,36199%2 of 12898%31011517137
IGEMM()0%n/a111111
full_I64_5x5_CM(long[], long[], int, long[])374100%2100%0209701
full_I64_4x4_CM(long[], long[], int, long[])253100%2100%0206601
full_I64_0xN_CM(long[], long[], int, long[])195100%4100%0303701
full_I64_9xN_CM(long[], long[], int, long[])179100%4100%0303401
full_I64_8xN_CM(long[], long[], int, long[])163100%4100%0303101
full_I64_3x3_CM(long[], long[], int, long[])156100%2100%0204101
full_I64_7xN_CM(long[], long[], int, long[])147100%4100%0302801
full_I64_6xN_CM(long[], long[], int, long[])131100%4100%0302501
operationForI64(boolean, long, long)124100%14998%12603001
full_I64_2x2_CM(long[], long[], int, long[])83100%2100%0202201
operationForI32(boolean, long, long)40100%11593%1901701
partial_I64_MxN_CM(long[], int, int, long[], int, long[])38100%4100%030501
partial_I64_MxN_RM(long[], int, int, long[], int, long[])38100%4100%030501
partial_I32_MxN_CM(int[], int, int, int[], int, int[])38100%4100%030501
partial_I32_MxN_RM(int[], int, int, int[], int, int[])38100%4100%030501
full_I64_1x1_CM(long[], long[], int, long[])31100%2100%020601
full_I64_Mx1_RM(long[], long[], int, long[])28100%2100%020601
full_I32_Mx1_RM(int[], int[], int, int[])28100%2100%020601
full_I64_1xN_RM(long[], long[], int, long[])25100%2100%020401
full_I32_1xN_RM(int[], int[], int, int[])25100%2100%020401
full_I64_Mx1_CM(long[], long[], int, long[])23100%2100%020401
full_I32_Mx1_CM(int[], int[], int, int[])23100%2100%020401
full_I64_1xN_CM(long[], long[], int, long[])23100%2100%020301
full_I32_1xN_CM(int[], int[], int, int[])23100%2100%020301
threaded_I64_MxN_CM(long[], long[], int, long[])14100%n/a010401
threaded_I32_MxN_CM(int[], int[], int, int[])14100%n/a010401
threaded_I32_MxN_RM(int[], int[], int, int[])14100%n/a010401
threaded_I64_MxN_RM(long[], long[], int, long[])14100%n/a010401
full_I64_MxN_CM(long[], long[], int, long[])11100%n/a010201
full_I64_MxN_RM(long[], long[], int, long[])11100%n/a010201
full_I32_MxN_CM(int[], int[], int, int[])11100%n/a010201
full_I32_MxN_RM(int[], int[], int, int[])11100%n/a010201
lambda$threaded_I64_MxN_RM$3(long[], long[], int, long[], int, int)8100%n/a010101
lambda$threaded_I32_MxN_RM$2(int[], int[], int, int[], int, int)8100%n/a010101
lambda$threaded_I32_MxN_CM$1(int[], int[], int, int[], int, int)8100%n/a010101
lambda$threaded_I64_MxN_CM$0(long[], long[], int, long[], int, int)8100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IGEMM.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IGEMM.java.html index b79d9ff60..ff9376398 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IGEMM.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/IGEMM.java.html @@ -963,4 +963,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/index.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/index.html index 3d519137d..a3c53c50e 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/index.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/index.html @@ -1 +1 @@ -neureka.backend.main.operations.linear.internal.blas

neureka.backend.main.operations.linear.internal.blas

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total253 of 5,73595%8 of 28897%15240231,19379607
DOT11947680%21487%3179931901
IDOT11918060%2675%399471501
GEMM2,35899%212698%3101151713701
IGEMM2,35899%212698%3101151713701
IAXPY4693%4100%15171301
AXPY4693%4100%15171301
COPY85%n/a12151201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.linear.internal.blas

neureka.backend.main.operations.linear.internal.blas

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total253 of 5,73595%8 of 28897%15240231,19379607
DOT11947680%21487%3179931901
IDOT11918060%2675%399471501
GEMM2,35899%212698%3101151713701
IGEMM2,35899%212698%3101151713701
IAXPY4693%4100%15171301
AXPY4693%4100%15171301
COPY85%n/a12151201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/index.source.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/index.source.html index 086828b47..abaf303fa 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.blas/index.source.html @@ -1 +1 @@ -neureka.backend.main.operations.linear.internal.blas

neureka.backend.main.operations.linear.internal.blas

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total253 of 5,73595%8 of 28897%15240231,19379607
DOT.java11947680%21487%3179931901
IDOT.java11918060%2675%399471501
IGEMM.java2,35899%212698%3101151713701
GEMM.java2,35899%212698%3101151713701
IAXPY.java4693%4100%15171301
AXPY.java4693%4100%15171301
COPY.java85%n/a12151201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.linear.internal.blas

neureka.backend.main.operations.linear.internal.blas

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total253 of 5,73595%8 of 28897%15240231,19379607
DOT.java11947680%21487%3179931901
IDOT.java11918060%2675%399471501
GEMM.java2,35899%212698%3101151713701
IGEMM.java2,35899%212698%3101151713701
AXPY.java4693%4100%15171301
IAXPY.java4693%4100%15171301
COPY.java85%n/a12151201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLGEMM.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLGEMM.html index 3adf5535a..25c0b0d88 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLGEMM.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLGEMM.html @@ -1 +1 @@ -CLGEMM

CLGEMM

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 32094%4 of 1877%51413215
run(ExecutionCall)1321794%41071%4812601
lambda$run$1()50%n/a111111
lambda$run$0(int, int, int, int, int, int, String)81100%4100%030501
CLGEMM()3100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLGEMM

CLGEMM

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 32091%7 of 1861%81413215
run(ExecutionCall)1321794%5964%5812601
lambda$run$0(int, int, int, int, int, int, String)107187%2250%230501
lambda$run$1()50%n/a111111
CLGEMM()3100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLGEMM.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLGEMM.java.html index a6037b85e..1b5d2f70a 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLGEMM.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLGEMM.java.html @@ -34,7 +34,7 @@ for ( int s : new int[]{16,8,4,2,1} ) if ( M % s == 0 ) { MW = s; break; } for ( int s : new int[]{8,4,2,1} ) - if ( N % s == 0 && K % s == 0 ) { KW = s; break; } + if ( N % s == 0 && K % s == 0 ) { KW = s; break; } int NW = KW; @@ -49,8 +49,8 @@ " #define KW "+ finalKW +" // K tile Width \n" + " #define MT "+(int)Math.floor(M/ finalMW)+" // MT is max for 'mt' (M tile count) \n" + " #define KT "+(int)Math.floor(K/ finalKW)+" // KT is max for 'kt' (K tile count) \n" + - " #define floatMW "+(finalMW != 1 ? "float"+ finalMW : "float")+" \n" + - " #define floatKW "+(finalKW != 1 ? "float"+ finalKW : "float")+" \n" + + " #define floatMW "+(finalMW != 1 ? "float"+ finalMW : "float")+" \n" + + " #define floatKW "+(finalKW != 1 ? "float"+ finalKW : "float")+" \n" + " __kernel void "+kernelName+"( \n" + " const __global floatMW* restrict A, \n" + " const __global floatKW* restrict B, \n" + @@ -100,4 +100,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce$Type.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce$Type.html index 909be9c9e..b7b44fd05 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce$Type.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce$Type.html @@ -1 +1 @@ -CLReduce.Type

CLReduce.Type

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 15100%0 of 0n/a010101
static {...}15100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLReduce.Type

CLReduce.Type

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 24100%0 of 0n/a010101
static {...}24100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce.html index 1567c6646..9bd48823c 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce.html @@ -1 +1 @@ -CLReduce

CLReduce

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total64 of 37682%11 of 2556%112155518
_runRecursively(Tensor, OpenCLDevice)2513784%4450%3532501
CLReduce(CLReduce.Type)122062%1266%131801
lambda$_fetch$2(String)120%n/a111111
_fetch(Tensor, Tensor, OpenCLDevice)85988%1150%1211101
run(ExecutionCall)76289%5758%570801
lambda$_runRecursively$0(long, String)20100%n/a010101
lambda$_fetch$1(String)11100%n/a010101
static {...}3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLReduce

CLReduce

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total96 of 37674%13 of 2548%132165528
_runRecursively(Tensor, OpenCLDevice)2513784%4450%3532501
lambda$_runRecursively$0(long, String)200%n/a111111
run(ExecutionCall)195072%7541%670801
CLReduce(CLReduce.Type)122062%1266%131801
lambda$_fetch$2(String)120%n/a111111
_fetch(Tensor, Tensor, OpenCLDevice)85988%1150%1211101
lambda$_fetch$1(String)11100%n/a010101
static {...}3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce.java.html index eb0a1250a..06708f301 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLReduce.java.html @@ -35,7 +35,7 @@ public Tensor<Integer> run(ExecutionCall<OpenCLDevice> call) { CLBackend context = Neureka.get().backend().find(CLBackend.class).orElse(null); CLSettings settings = context == null ? null : context.getSettings(); - boolean autoConvert = context == null || settings.isAutoConvertToFloat(); + boolean autoConvert = context == null || settings.isAutoConvertToFloat(); if ( settings != null ) settings.setAutoConvertToFloat(false); Tensor<Float> in = call.input(0) == null ? call.input(Float.class, 1) : call.input(Float.class, 0); int index = _runRecursively(in, call.getDevice()); @@ -67,7 +67,7 @@ String kernelName = "fast_"+_type.name().toLowerCase()+"_reduce_RTS"+RTS; Supplier<String> code = () -> - " #define RTS "+RTS+" \n" + + " #define RTS "+RTS+" \n" + " __kernel void "+kernelName+"( \n" + " const int size, \n" + " const __global float* in, \n" + @@ -151,4 +151,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLSum.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLSum.html index 04336f49e..284caf499 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLSum.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLSum.html @@ -1 +1 @@ -CLSum

CLSum

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 21098%1 of 887%11113407
run(Tensor, OpenCLDevice)414097%1787%1512401
_processPrivate(long, OpenCLDevice)18100%n/a010301
lambda$_processPrivate$0(long, String)15100%n/a010101
lambda$_processLocal$1(String)11100%n/a010101
_processLocal(OpenCLDevice)10100%n/a010301
run(ExecutionCall)9100%n/a010101
CLSum()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLSum

CLSum

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total30 of 21085%1 of 887%31133427
lambda$_processPrivate$0(long, String)150%n/a111111
lambda$_processLocal$1(String)110%n/a111111
run(Tensor, OpenCLDevice)414097%1787%1512401
_processPrivate(long, OpenCLDevice)18100%n/a010301
_processLocal(OpenCLDevice)10100%n/a010301
run(ExecutionCall)9100%n/a010101
CLSum()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLSum.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLSum.java.html index 4b22bd383..1b302d78d 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLSum.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/CLSum.java.html @@ -67,7 +67,7 @@ { String kernelName = "fast_private_sum_reduction_RTS"+RTS; Supplier<String> code = () -> - " #define RTS "+RTS+" \n" + + " #define RTS "+RTS+" \n" + " __kernel void "+kernelName+"( \n" + " const int size, \n" + " const __global float* in, \n" + @@ -95,7 +95,7 @@ ) { String kernelName = "fast_local_mem_based_sum"; Supplier<String> code = () -> - " \n" + + " \n" + " int div(int i) { return i % 2 == 1 ? (i+1) / 2 : i / 2; } \n" + " \n" + " __kernel void "+kernelName+" ( \n" + @@ -132,4 +132,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/index.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/index.html index e9828a375..f4f55bd92 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/index.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/index.html @@ -1 +1 @@ -neureka.backend.main.operations.linear.internal.opencl

neureka.backend.main.operations.linear.internal.opencl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total86 of 92190%16 of 5168%1747712222104
CLReduce6431282%111456%11215551801
CLGEMM1830294%41477%5141321501
CLSum420698%1787%1111340701
CLReduce.Type15100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.linear.internal.opencl

neureka.backend.main.operations.linear.internal.opencl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total154 of 93083%21 of 5158%24471012252104
CLReduce9628074%131248%13216552801
CLSum3018085%1787%3113342701
CLGEMM2829291%71161%8141321501
CLReduce.Type24100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/index.source.html b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/index.source.html index 71f927b16..a14d94375 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear.internal.opencl/index.source.html @@ -1 +1 @@ -neureka.backend.main.operations.linear.internal.opencl

neureka.backend.main.operations.linear.internal.opencl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total86 of 92190%16 of 5168%1747712222104
CLReduce.java6432783%111456%11225561902
CLGEMM.java1830294%41477%5141321501
CLSum.java420698%1787%1111340701
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.linear.internal.opencl

neureka.backend.main.operations.linear.internal.opencl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total154 of 93083%21 of 5158%24471012252104
CLReduce.java9630476%131248%13226562902
CLSum.java3018085%1787%3113342701
CLGEMM.java2829291%71161%8141321501
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/Convolution.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/Convolution.html index 627d24e47..3baf1460f 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/Convolution.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/Convolution.html @@ -1 +1 @@ -Convolution

Convolution

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total117 of 47775%25 of 5453%20381274111
lambda$new$3(Function, ExecutionCall)598157%141246%111451701
reducePairwise(Function)33921%3125%234701
lambda$new$0(ExecutionCall)182255%5337%352601
calculate(double[], int, int, Function[])70%n/a111111
execute(Function, ExecutionCall)132100%21285%2801401
lambda$new$5(ExecutionCall)47100%1150%120801
Convolution()35100%n/a0101401
lambda$new$2(Function, Tensor, Shape, Number, ADTarget)23100%n/a010301
lambda$new$4(Function, ExecutionCall)7100%n/a010201
lambda$new$1(ExecutionCall)3100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Convolution

Convolution

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total117 of 47775%25 of 5453%20381274111
lambda$new$3(Function, ExecutionCall)598157%141246%111451701
reducePairwise(Function)33921%3125%234701
lambda$new$0(ExecutionCall)182255%5337%352601
calculate(double[], int, int, Function[])70%n/a111111
execute(Function, ExecutionCall)132100%21285%2801401
lambda$new$5(ExecutionCall)47100%1150%120801
Convolution()35100%n/a0101401
lambda$new$2(Function, Tensor, Shape, Number, ADTarget)23100%n/a010301
lambda$new$4(Function, ExecutionCall)7100%n/a010201
lambda$new$1(ExecutionCall)3100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/Convolution.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/Convolution.java.html index ad996c066..c19b0db75 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/Convolution.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/Convolution.java.html @@ -153,4 +153,4 @@ @Override public double calculate( double[] inputs, int j, int d, Function[] src ) { return src[ 0 ].call( inputs, j ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/DotProduct.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/DotProduct.html index 68054c0a5..149912926 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/DotProduct.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/DotProduct.html @@ -1 +1 @@ -DotProduct

DotProduct

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 3287%0 of 0n/a1211212
calculate(double[], int, int, Function[])40%n/a111111
DotProduct()28100%n/a0101101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DotProduct

DotProduct

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 3287%0 of 0n/a1211212
calculate(double[], int, int, Function[])40%n/a111111
DotProduct()28100%n/a0101101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/DotProduct.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/DotProduct.java.html index 80c245b4e..b87315953 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/DotProduct.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/DotProduct.java.html @@ -28,4 +28,4 @@ throw new UnsupportedOperationException(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/MatMul.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/MatMul.html index 0c5cfcb1f..a9a9e95d5 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/MatMul.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/MatMul.html @@ -1 +1 @@ -MatMul

MatMul

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 3580%0 of 0n/a1211212
calculate(double[], int, int, Function[])70%n/a111111
MatMul()28100%n/a0101101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +MatMul

MatMul

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total73 of 12039%4 of 633%4792514
execute(Function, ExecutionCall)331023%1150%124601
reducePairwise(Function)33921%3125%234701
calculate(double[], int, int, Function[])70%n/a111111
MatMul()28100%n/a0101101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/MatMul.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/MatMul.java.html index 2f367da2f..c23a3339c 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/MatMul.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/MatMul.java.html @@ -1,31 +1,68 @@ MatMul.java

MatMul.java

package neureka.backend.main.operations.linear;
 
+import neureka.Neureka;
+import neureka.backend.api.ExecutionCall;
+import neureka.backend.api.Result;
+import neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm;
 import neureka.backend.api.template.operations.AbstractOperation;
 import neureka.backend.api.template.operations.OperationBuilder;
 import neureka.backend.main.algorithms.MatMulAlgorithm;
 import neureka.math.Function;
+import neureka.math.args.Arg;
+import neureka.math.parsing.FunctionParser;
 
 public class MatMul extends AbstractOperation
 {
     public MatMul()
     {
-        super(
+        super(
             new OperationBuilder()
-                .identifier(       "matMul"    )
-                .operator(         "@"         )
-                .arity(            2           )
-                .isOperator(       true        )
-                .isIndexer(        false       )
-                .isDifferentiable( true        )
-                .isInline(         false       )
+                .identifier(       "matMul"    )
+                .operator(         "@"         )
+                .arity(            2           )
+                .isOperator(       true        )
+                .isIndexer(        false       )
+                .isDifferentiable( true        )
+                .isInline(         false       )
         );
 
-        setAlgorithm(
-            new MatMulAlgorithm().buildFunAlgorithm()
+        setAlgorithm(
+            new MatMulAlgorithm().buildFunAlgorithm()
         );
-    }
+    }
 
     @Override
-    public double calculate( double[] inputs, int j, int d, Function[] src ) { return src[ 0 ].call( inputs, j ); }
+    public Result execute( final Function caller, final ExecutionCall<?> call )
+    {
+        if ( !caller.isFlat() ) {
+            Function reducedCaller = reducePairwise(caller);
+            ExecutionCall<?> flatCall = AbstractDeviceAlgorithm.flatten( reducedCaller, call.withArgs(Arg.DerivIdx.of(-1)) );
+            Function flat = new FunctionParser(Neureka.get().backend()).parse( flatCall.getOperation(), flatCall.arity(), true );
+            return super.execute( flat, flatCall );
+        }
+        return super.execute( reducePairwise(caller), call );
+    }
+
+    private Function reducePairwise( final Function fun ) {
+        Function reduced = fun;
+        if ( reduced.getSubFunctions().size() > 2 ) {
+            /*
+                So currently we have something like this: a@b@c@d...
+                However, this is how it is really executed:  ((((a@b)@c)@d)..)
+                ...so let's create a function that is nested like the above:
+            */
+            Function nested = reduced.getSubFunctions().get(0);
+            for ( int i = 1; i < reduced.getSubFunctions().size(); i++ )
+                nested = Function.of( nested + " @ " + reduced.getSubFunctions().get(i), true );
+
+            reduced = nested;
+        }
+        return reduced;
+    }
+
+    @Override
+    public double calculate( double[] inputs, int j, int d, Function[] src ) {
+        return src[ 0 ].call( inputs, j );
+    }
 }
-
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvLeft.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvLeft.html index 18f2ba0b3..1be2a96f1 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvLeft.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvLeft.html @@ -1 +1 @@ -XConvLeft

XConvLeft

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 7290%0 of 4100%1511813
calculate(double[], int, int, Function[])70%n/a111111
stringify(String[])39100%4100%030601
XConvLeft()26100%n/a0101101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +XConvLeft

XConvLeft

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 7290%0 of 4100%1511813
calculate(double[], int, int, Function[])70%n/a111111
stringify(String[])39100%4100%030601
XConvLeft()26100%n/a0101101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvLeft.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvLeft.java.html index 2d1f4202f..67d094ff7 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvLeft.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvLeft.java.html @@ -42,4 +42,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvRight.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvRight.html index 6aab209c0..35390143d 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvRight.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvRight.html @@ -1 +1 @@ -XConvRight

XConvRight

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 6797%0 of 4100%1511813
calculate(double[], int, int, Function[])20%n/a111111
stringify(String[])39100%4100%030601
XConvRight()26100%n/a0101101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +XConvRight

XConvRight

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 6797%0 of 4100%1511813
calculate(double[], int, int, Function[])20%n/a111111
stringify(String[])39100%4100%030601
XConvRight()26100%n/a0101101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvRight.java.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvRight.java.html index 4a08ab9d5..b56fa2616 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvRight.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/XConvRight.java.html @@ -42,4 +42,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/index.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/index.html index 0010ef015..574ab6cf6 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/index.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/index.html @@ -1 +1 @@ -neureka.backend.main.operations.linear

neureka.backend.main.operations.linear

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total137 of 68379%25 of 6259%24521613452105
Convolution11736075%252953%2038127411101
XConvLeft76590%4100%151181301
MatMul72880%n/a121121201
DotProduct42887%n/a121121201
XConvRight6597%4100%151181301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.linear

neureka.backend.main.operations.linear

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total203 of 76873%29 of 6857%27572414752305
Convolution11736075%252953%2038127411101
MatMul734739%4233%479251401
XConvLeft76590%4100%151181301
DotProduct42887%n/a121121201
XConvRight6597%4100%151181301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.linear/index.source.html b/docs/coverage/test/html/neureka.backend.main.operations.linear/index.source.html index 9b13d328b..9635ec57a 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.linear/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.linear/index.source.html @@ -1 +1 @@ -neureka.backend.main.operations.linear

neureka.backend.main.operations.linear

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total137 of 68379%25 of 6259%24521613452105
Convolution.java11736075%252953%2038127411101
XConvLeft.java76590%4100%151181301
MatMul.java72880%n/a121121201
DotProduct.java42887%n/a121121201
XConvRight.java6597%4100%151181301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.linear

neureka.backend.main.operations.linear

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total203 of 76873%29 of 6857%27572414752305
Convolution.java11736075%252953%2038127411101
MatMul.java734739%4233%479251401
XConvLeft.java76590%4100%151181301
DotProduct.java42887%n/a121121201
XConvRight.java6597%4100%151181301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Addition.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Addition.html index 4eb09e475..2b033e170 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Addition.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Addition.html @@ -1 +1 @@ -Addition

Addition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 68295%12 of 6681%11523121019
execute(Function, ExecutionCall)1823292%71770%61323801
lambda$new$3(Function, ExecutionCall)91664%2250%231501
lambda$new$4(Function, ExecutionCall)3995%3770%360601
calculate(double[], int, int, Function[])65100%8100%0501101
Addition()57100%n/a0102001
calculate(double[], int, Function[])55100%6100%0401001
reducePairwise(Function)42100%4100%030701
lambda$_autogradBroadcast$6(Device, Tensor, Tensor, int, ADTarget)39100%n/a010901
_autogradBroadcast(ExecutionCall)31100%2100%020501
asDerivative(Function[], int)25100%n/a010501
lambda$execute$7(int, int, Function)17100%8100%050101
lambda$execute$8(Function, int, int)8100%n/a010101
lambda$new$5(Function, ExecutionCall)8100%n/a010201
lambda$new$1(Function, ExecutionCall)5100%n/a010101
lambda$asDerivative$10(int, Function)4100%n/a010101
lambda$asDerivative$9(int, Function)4100%n/a010101
lambda$new$0(ExecutionCall)3100%n/a010101
lambda$new$2(ExecutionCall)100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Addition

Addition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 68295%12 of 6681%11523121019
execute(Function, ExecutionCall)1823292%71770%61323801
lambda$new$3(Function, ExecutionCall)91664%2250%231501
lambda$new$4(Function, ExecutionCall)3995%3770%360601
calculate(double[], int, int, Function[])65100%8100%0501101
Addition()57100%n/a0102001
calculate(double[], int, Function[])55100%6100%0401001
reducePairwise(Function)42100%4100%030701
lambda$_autogradBroadcast$6(Device, Tensor, Tensor, int, ADTarget)39100%n/a010901
_autogradBroadcast(ExecutionCall)31100%2100%020501
asDerivative(Function[], int)25100%n/a010501
lambda$execute$7(int, int, Function)17100%8100%050101
lambda$execute$8(Function, int, int)8100%n/a010101
lambda$new$5(Function, ExecutionCall)8100%n/a010201
lambda$new$1(Function, ExecutionCall)5100%n/a010101
lambda$asDerivative$10(int, Function)4100%n/a010101
lambda$asDerivative$9(int, Function)4100%n/a010101
lambda$new$0(ExecutionCall)3100%n/a010101
lambda$new$2(ExecutionCall)100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Addition.java.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Addition.java.html index 0233bee25..0f99e30b1 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Addition.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Addition.java.html @@ -237,4 +237,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Division.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Division.html index 856549647..8b6c8cc52 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Division.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Division.html @@ -1 +1 @@ -Division

Division

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total74 of 75990%11 of 5680%13455122317
_asDerivative(Function[], int, int)308974%5758%4721201
lambda$new$1(Function, Tensor, ADTarget)140%n/a111111
execute(Function, ExecutionCall)1015994%41680%41112901
lambda$new$3(Function, ExecutionCall)103075%2250%232901
lambda$new$6(Function, ExecutionCall)80%n/a111111
lambda$new$5(ExecutionCall)20%n/a111111
calculate(double[], int, int, Function[])99100%8100%0501501
calculate(double[], int, Function[])90100%6100%0401601
_deriveB(ExecutionCall, Function, boolean, Tensor, Tensor, Tensor)69100%2100%020901
Division()60100%n/a0102101
reducePairwise(Function)42100%4100%030701
lambda$new$2(Function, Tensor, ADTarget)14100%n/a010101
lambda$new$0(ExecutionCall)10100%n/a010301
asDerivative(Function[], int)9100%n/a010101
lambda$execute$8(Tensor, Tensor)6100%n/a010101
lambda$execute$7(Tensor)6100%n/a010101
lambda$new$4(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Division

Division

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total74 of 75990%11 of 5680%13455122317
_asDerivative(Function[], int, int)308974%5758%4721201
lambda$new$1(Function, Tensor, ADTarget)140%n/a111111
execute(Function, ExecutionCall)1015994%41680%41112901
lambda$new$3(Function, ExecutionCall)103075%2250%232901
lambda$new$6(Function, ExecutionCall)80%n/a111111
lambda$new$5(ExecutionCall)20%n/a111111
calculate(double[], int, int, Function[])99100%8100%0501501
calculate(double[], int, Function[])90100%6100%0401601
_deriveB(ExecutionCall, Function, boolean, Tensor, Tensor, Tensor)69100%2100%020901
Division()60100%n/a0102101
reducePairwise(Function)42100%4100%030701
lambda$new$2(Function, Tensor, ADTarget)14100%n/a010101
lambda$new$0(ExecutionCall)10100%n/a010301
asDerivative(Function[], int)9100%n/a010101
lambda$execute$8(Tensor, Tensor)6100%n/a010101
lambda$execute$7(Tensor)6100%n/a010101
lambda$new$4(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Division.java.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Division.java.html index 1ca76f1c6..5c128efeb 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Division.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Division.java.html @@ -258,4 +258,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Modulo.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Modulo.html index 8ce2b2773..b12607e6e 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Modulo.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Modulo.html @@ -1 +1 @@ -Modulo

Modulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total158 of 52870%13 of 4067%16352198615
lambda$new$3(Function, ExecutionCall)400%40%339911
reducePairwise(Function)33921%3125%234701
execute(Function, ExecutionCall)3219786%61672%61233701
lambda$new$2(Function, Tensor, ADTarget)140%n/a111111
lambda$new$1(Function, Tensor, ADTarget)140%n/a111111
lambda$new$5(ExecutionCall)100%n/a114411
lambda$new$6(Function, ExecutionCall)80%n/a111111
asDerivative(Function[], int)70%n/a111111
Modulo()60100%n/a0102101
calculate(double[], int, int, Function[])45100%6100%040801
calculate(double[], int, Function[])35100%4100%030701
lambda$new$0(ExecutionCall)10100%n/a010401
lambda$execute$8(Tensor, Tensor)6100%n/a010101
lambda$execute$7(Tensor)6100%n/a010101
lambda$new$4(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Modulo

Modulo

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total158 of 52870%13 of 4067%16352198615
lambda$new$3(Function, ExecutionCall)400%40%339911
reducePairwise(Function)33921%3125%234701
execute(Function, ExecutionCall)3219786%61672%61233701
lambda$new$2(Function, Tensor, ADTarget)140%n/a111111
lambda$new$1(Function, Tensor, ADTarget)140%n/a111111
lambda$new$5(ExecutionCall)100%n/a114411
lambda$new$6(Function, ExecutionCall)80%n/a111111
asDerivative(Function[], int)70%n/a111111
Modulo()60100%n/a0102101
calculate(double[], int, int, Function[])45100%6100%040801
calculate(double[], int, Function[])35100%4100%030701
lambda$new$0(ExecutionCall)10100%n/a010401
lambda$execute$8(Tensor, Tensor)6100%n/a010101
lambda$execute$7(Tensor)6100%n/a010101
lambda$new$4(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Modulo.java.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Modulo.java.html index 8d051a2bf..81d1cae40 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Modulo.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Modulo.java.html @@ -200,4 +200,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Multiplication.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Multiplication.html index 474fe4c8f..61f161160 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Multiplication.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Multiplication.html @@ -1 +1 @@ -Multiplication

Multiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total83 of 67087%6 of 4686%84210113419
lambda$new$4(Function, ExecutionCall)430%40%339911
lambda$new$3(Function, Tensor, ADTarget)140%n/a111111
lambda$new$1(Function, Tensor, ADTarget)140%n/a111111
lambda$new$2(Function, ExecutionCall, int)60%n/a111111
execute(Function, ExecutionCall)59695%1583%1411901
lambda$asDerivative$11(int, Function[], Function)2996%1150%120601
derive(int[], Tensor[], Function)126100%12100%0701701
calculate(double[], int, int, Function[])89100%8100%0501501
calculate(double[], int, Function[])76100%6100%0401401
Multiplication()56100%n/a0101901
reducePairwise(Function)42100%4100%030701
lambda$execute$8(Function, ExecutionCall, Integer)22100%2100%020201
asDerivative(Function[], int)16100%n/a010501
lambda$execute$7(Function, int, int)8100%n/a010101
lambda$new$5(Function, ExecutionCall)8100%n/a010101
lambda$asDerivative$10(Function, Function)7100%2100%020101
lambda$execute$6(Tensor, Tensor)6100%n/a010101
lambda$asDerivative$9(int, Function)4100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Multiplication

Multiplication

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total83 of 75789%8 of 6086%105010125420
lambda$new$4(Function, ExecutionCall)430%40%339911
lambda$new$3(Function, Tensor, ADTarget)140%n/a111111
lambda$new$1(Function, Tensor, ADTarget)140%n/a111111
lambda$new$2(Function, ExecutionCall, int)60%n/a111111
execute(Function, ExecutionCall)514896%21285%2812601
lambda$asDerivative$11(int, Function[], Function)2996%1150%120601
derive(int[], Tensor[], Function)126100%12100%0701701
calculate(double[], int, int, Function[])89100%8100%0501501
calculate(double[], int, Function[])76100%6100%0401401
Multiplication()56100%n/a0101901
reducePairwise(Function)42100%4100%030701
_deleteIfNotIn(Tensor[], Tensor)35100%1583%140501
lambda$execute$8(Function, ExecutionCall, Integer)22100%2100%020201
asDerivative(Function[], int)16100%n/a010501
lambda$execute$7(Function, int, int)8100%n/a010101
lambda$new$5(Function, ExecutionCall)8100%n/a010101
lambda$asDerivative$10(Function, Function)7100%2100%020101
lambda$execute$6(Tensor, Tensor)6100%n/a010101
lambda$asDerivative$9(int, Function)4100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Multiplication.java.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Multiplication.java.html index 6893edaad..2c40edb9c 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Multiplication.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Multiplication.java.html @@ -3,6 +3,7 @@ import neureka.Neureka; import neureka.Tensor; import neureka.autograd.ADAction; +import neureka.autograd.GraphNode; import neureka.backend.api.AutoDiffMode; import neureka.backend.api.Call; import neureka.backend.api.ExecutionCall; @@ -29,200 +30,223 @@ { public Multiplication() { - super( + super( new OperationBuilder() - .identifier( "multiply" ) - .operator( "*" ) - .arity( -1 ) - .isOperator( true ) - .isIndexer( false ) - .isDifferentiable( true ) - .isInline( false ) + .identifier( "multiply" ) + .operator( "*" ) + .arity( -1 ) + .isOperator( true ) + .isIndexer( false ) + .isDifferentiable( true ) + .isInline( false ) ); - setAlgorithm( + setAlgorithm( BiElementwise.class, new BiElementwise() - .setSupplyADActionFor( getDefaultAlgorithm() ) - .buildFunAlgorithm() + .setSupplyADActionFor( getDefaultAlgorithm() ) + .buildFunAlgorithm() ); - setAlgorithm( + setAlgorithm( Broadcast.class, new Broadcast() - .setAutogradModeFor( call -> AutoDiffMode.BACKWARD_ONLY ) - .setSupplyADActionFor( + .setAutogradModeFor( call -> AutoDiffMode.BACKWARD_ONLY ) + .setSupplyADActionFor( ( Function f, ExecutionCall<? extends Device<?>> call ) -> { - if ( call.autogradMode().allowsForward() ) - throw new IllegalArgumentException("Broadcast implementation does not support forward-AD!"); - Tensor<?> ctxDerivative = (Tensor<?>) call.getValOf(Arg.Derivative.class); - Function mul = Neureka.get().backend().getFunction().mul(); - if ( ctxDerivative != null ) { - return ADAction.of( target -> mul.execute( target.error(), ctxDerivative ) ); + if ( call.autogradMode().allowsForward() ) + throw new IllegalArgumentException("Broadcast implementation does not support forward-AD!"); + Tensor<?> ctxDerivative = (Tensor<?>) call.getValOf(Arg.Derivative.class); + Function mul = Neureka.get().backend().getFunction().mul(); + if ( ctxDerivative != null ) { + return ADAction.of( target -> mul.execute( target.error(), ctxDerivative ) ); } - int d = call.getDerivativeIndex(); - Tensor<?> derivative = MemUtil.keep( call.inputs(), () -> f.executeDerive( call.inputs(), d ) ); - return ADAction.of( target -> mul.execute( target.error(), derivative ) ); + int d = call.getDerivativeIndex(); + Tensor<?> derivative = MemUtil.keep( call.inputs(), () -> f.executeDerive( call.inputs(), d ) ); + return ADAction.of( target -> mul.execute( target.error(), derivative ) ); } ) - .buildFunAlgorithm() + .buildFunAlgorithm() ); - setAlgorithm( + setAlgorithm( BiScalarBroadcast.class, new BiScalarBroadcast() - .setExecution( (caller, call) -> Result.of(AbstractDeviceAlgorithm.executeFor(caller, call, AbstractDeviceAlgorithm::executeDeviceAlgorithm)).withAutoDiff( FallbackAlgorithm::ADAction )) - .buildFunAlgorithm() + .setExecution( (caller, call) -> Result.of(AbstractDeviceAlgorithm.executeFor(caller, call, AbstractDeviceAlgorithm::executeDeviceAlgorithm)).withAutoDiff( FallbackAlgorithm::ADAction )) + .buildFunAlgorithm() ); - } + } @Override public Result execute( final Function caller, final ExecutionCall<?> call ) { - if ( !caller.isFlat() ) { - int d = call.getDerivativeIndex(); - if ( d < 0 ) { - Function reducedCaller = reducePairwise(caller); - ExecutionCall<?> flatCall = AbstractDeviceAlgorithm.flatten( reducedCaller, call.withArgs(Arg.DerivIdx.of(-1)) ); - Function flat = new FunctionParser(Neureka.get().backend()).parse( flatCall.getOperation(), flatCall.arity(), true ); - Result r = super.execute( flat, flatCall ); - //for ( int i = 0; i < flatCall.inputs().length; i++ ) - // _deleteIfNotIn(call.inputs(), flatCall.input(i)); // TODO: Make it possible to delete more stuff - return r; + int d = call.getDerivativeIndex(); + if ( !caller.isFlat() ) { + if ( d < 0 ) { + Function reducedCaller = reducePairwise(caller); + ExecutionCall<?> flatCall = AbstractDeviceAlgorithm.flatten( reducedCaller, call.withArgs(Arg.DerivIdx.of(-1)) ); + for ( Tensor<?> input : flatCall.inputs() ) + input.mut().setIsIntermediate( false ); + Function flat = new FunctionParser(Neureka.get().backend()).parse( flatCall.getOperation(), flatCall.arity(), true ); + Result r = super.execute( flat, flatCall ); + for ( int i = 0; i < flatCall.inputs().length; i++ ) + _deleteIfNotIn(call.inputs(), flatCall.input(i)); + return r; } else { - if ( !call.validate().all( (a, b) -> Util.canBeBroadcast(a.shape(), b.shape()) ).isValid() ) - throw new IllegalArgumentException("The shapes of the operands of the multiplication operation must be equal or broadcast compatible! (when deriving nested functions)"); + if ( !call.validate().all( (a, b) -> Util.canBeBroadcast(a.shape(), b.shape()) ).isValid() ) + throw new IllegalArgumentException("The shapes of the operands of the multiplication operation must be equal or broadcast compatible! (when deriving nested functions)"); - Function noAd = Function.of( caller.toString(), false ); - ExecutionCall<?> flatCall = AbstractDeviceAlgorithm.flatten( noAd, call.withArgs(Arg.DerivIdx.of(-1)) ); + Function noAd = Function.of( caller.toString(), false ); + ExecutionCall<?> flatCall = AbstractDeviceAlgorithm.flatten( noAd, call.withArgs(Arg.DerivIdx.of(-1)) ); - Tensor[] results = flatCall.inputs(); - Function finalCaller = caller; - int[] toBeDerived = IntStream.range(0,caller.getSubFunctions().size()) - .filter( i -> finalCaller.getSubFunctions().get(i).dependsOn(d) ) - .toArray(); + Tensor[] results = flatCall.inputs(); + Function finalCaller = caller; + int[] toBeDerived = IntStream.range(0,caller.getSubFunctions().size()) + .filter( i -> finalCaller.getSubFunctions().get(i).dependsOn(d) ) + .toArray(); - return derive( toBeDerived, results, i->{ - Function noAD = Function.of( caller.getSubFunctions().get( i ).toString(), false ); - return noAD.call( (Call) (noAD.getOperation() == null ? call : call.withOperation(noAD.getOperation())) ); + return derive( toBeDerived, results, i->{ + Function noAD = Function.of( caller.getSubFunctions().get( i ).toString(), false ); + return noAD.call( (Call) (noAD.getOperation() == null ? call : call.withOperation(noAD.getOperation())) ); } ); } } - return super.execute( reducePairwise(caller), call ); + + + Function reduced = reducePairwise(caller); + //ExecutionCall<?> flatCall = call; + //Function flat = caller; + //if ( d < 0 && caller.isFlat() && subFunctions.stream().anyMatch( f -> f instanceof FunctionConstant) ) { + // Function noAd = Function.of( caller.toString(), false ); + // ExecutionCall<?> flatCall = AbstractDeviceAlgorithm.flatten( noAd, call.withArgs(Arg.DerivIdx.of(-1)) ); + // return super.execute( reducePairwise(caller), call ); + //} + if ( reduced.equals(caller) && reduced.isFlat() ) + return super.execute( reduced, call ); + else + return this.execute( reduced, call ); } + private void _deleteIfNotIn( Tensor<?>[] inputs, Tensor<?> input ) { + for ( Tensor<?> i : inputs ) { + if ( i == input ) return; + } + if ( input.getGraphNode().map(GraphNode::canBeDeleted).orElse(true) ) + input.mut().delete(); + } + public static Result derive( int[] toBeDerived, Tensor[] results, java.util.function.Function<Integer, Tensor<?>> deriveAt ) { - Tensor[] derivatives = new Tensor[ toBeDerived.length ]; - Function mul = Neureka.get().backend().getFunction().mul(); - Function add = Neureka.get().backend().getFunction().add(); - Tensor<?> finalDerivative = null; - for ( int i = 0; i < derivatives.length; i++ ) { - Tensor<?> deriv = deriveAt.apply( toBeDerived[i] ); - derivatives[ i ] = deriv; - Tensor<?> localDeriv = null; - for ( int j = 0; j < results.length; j++ ) { + Tensor[] derivatives = new Tensor[ toBeDerived.length ]; + Function mul = Neureka.get().backend().getFunction().mul(); + Function add = Neureka.get().backend().getFunction().add(); + Tensor<?> finalDerivative = null; + for ( int i = 0; i < derivatives.length; i++ ) { + Tensor<?> deriv = deriveAt.apply( toBeDerived[i] ); + derivatives[ i ] = deriv; + Tensor<?> localDeriv = null; + for ( int j = 0; j < results.length; j++ ) { // Now we calculate the local derivatives of the multiplication operation: - if ( j == toBeDerived[i] ) { - if ( localDeriv == null ) localDeriv = derivatives[ i ]; - else localDeriv = mul.call( localDeriv, derivatives[ i ] ); + if ( j == toBeDerived[i] ) { + if ( localDeriv == null ) localDeriv = derivatives[ i ]; + else localDeriv = mul.call( localDeriv, derivatives[ i ] ); } else { - if ( localDeriv == null ) localDeriv = results[ j ].mut().setIsIntermediate(false); - else localDeriv = mul.call( localDeriv, results[ j ].mut().setIsIntermediate(false) ); + if ( localDeriv == null ) localDeriv = results[ j ].mut().setIsIntermediate(false); + else localDeriv = mul.call( localDeriv, results[ j ].mut().setIsIntermediate(false) ); } } - if ( finalDerivative == null ) finalDerivative = localDeriv; - else finalDerivative = add.call( (Tensor<Object>) finalDerivative, (Tensor<Object>) localDeriv ); + if ( finalDerivative == null ) finalDerivative = localDeriv; + else finalDerivative = add.call( (Tensor<Object>) finalDerivative, (Tensor<Object>) localDeriv ); } - return Result.of( finalDerivative.mut().setIsIntermediate(true) ); + return Result.of( finalDerivative.mut().setIsIntermediate(true) ); } private Function reducePairwise( final Function fun ) { - Function reduced = fun; - if ( reduced.getSubFunctions().size() > 2 ) { + Function reduced = fun; + if ( reduced.getSubFunctions().size() > 2 ) { /* So currently we have something like this: a*b*c*d... However, this is how it is really executed: ((((a*b)*c)*d)..) ...so let's create a function that is nested like the above: */ - Function nested = reduced.getSubFunctions().get(0); - for ( int i = 1; i < reduced.getSubFunctions().size(); i++ ) - nested = Function.of( nested + " * " + reduced.getSubFunctions().get(i), true ); + Function nested = reduced.getSubFunctions().get(0); + for ( int i = 1; i < reduced.getSubFunctions().size(); i++ ) + nested = Function.of( nested + " * " + reduced.getSubFunctions().get(i), true ); - reduced = nested; + reduced = nested; } - return reduced; + return reduced; } @Override public String asDerivative( Function[] children, int derivationIndex) { - return Arrays.stream( children ) - .filter( child -> child.dependsOn(derivationIndex) ) - .map( child -> { - String derivative = child.getDerivative(derivationIndex).toString(); - return ( derivative.equals("1.0") ? "" : " * " ) + - Arrays.stream( children ) - .filter( inner -> inner != child ) - .map( Object::toString ) - .collect( Collectors.joining( " * " ) ); + return Arrays.stream( children ) + .filter( child -> child.dependsOn(derivationIndex) ) + .map( child -> { + String derivative = child.getDerivative(derivationIndex).toString(); + return ( derivative.equals("1.0") ? "" : " * " ) + + Arrays.stream( children ) + .filter( inner -> inner != child ) + .map( Object::toString ) + .collect( Collectors.joining( " * " ) ); } ) - .map( Object::toString ) - .collect( Collectors.joining( " + " ) ); + .map( Object::toString ) + .collect( Collectors.joining( " + " ) ); } @Override public double calculate( double[] inputs, int j, int d, Function[] src ) { - if ( j < 0 ) return calculate( inputs, d, src ); - if ( d < 0 ) { - double result = src[ 0 ].call( inputs, j ); - for ( int i = 1; i < src.length; i++ ) { - final double current = src[ i ].call( inputs, j ); - result *= current; + if ( j < 0 ) return calculate( inputs, d, src ); + if ( d < 0 ) { + double result = src[ 0 ].call( inputs, j ); + for ( int i = 1; i < src.length; i++ ) { + final double current = src[ i ].call( inputs, j ); + result *= current; } - return result; + return result; } else { double u, ud, v, vd; - u = src[ 0 ].call( inputs, j ); - ud = src[ 0 ].derive( inputs, d, j ); - - for ( int ji = 1; ji < src.length; ji++ ) { - v = src[ ji ].call( inputs, j ); - vd = src[ ji ].derive( inputs, d, j ); - ud = u * vd + v * ud; - u *= v; + u = src[ 0 ].call( inputs, j ); + ud = src[ 0 ].derive( inputs, d, j ); + + for ( int ji = 1; ji < src.length; ji++ ) { + v = src[ ji ].call( inputs, j ); + vd = src[ ji ].derive( inputs, d, j ); + ud = u * vd + v * ud; + u *= v; } - return ud; + return ud; } } public static double calculate( double[] inputs, int d, Function[] src ) { - if ( d < 0 ) { - double result = src[ 0 ].call( inputs ); - for ( int i = 1; i < src.length; i++ ) { - final double current = src[ i ].call( inputs ); - result *= current; + if ( d < 0 ) { + double result = src[ 0 ].call( inputs ); + for ( int i = 1; i < src.length; i++ ) { + final double current = src[ i ].call( inputs ); + result *= current; } - return result; + return result; } else { double u, ud, v, vd; - u = src[ 0 ].call( inputs ); - ud = src[ 0 ].derive( inputs, d ); - for ( int j = 1; j < src.length; j++ ) { - v = src[ j ].call( inputs ); - vd = src[ j ].derive( inputs, d ); - - ud = u * vd + v * ud; - u *= v; // ...this step can be avoided (TODO optimize) + u = src[ 0 ].call( inputs ); + ud = src[ 0 ].derive( inputs, d ); + for ( int j = 1; j < src.length; j++ ) { + v = src[ j ].call( inputs ); + vd = src[ j ].derive( inputs, d ); + + ud = u * vd + v * ud; + u *= v; // ...this step can be avoided (TODO optimize) } - return ud; + return ud; } } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Power.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Power.html index b0416b127..023d28ec0 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Power.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Power.html @@ -1 +1 @@ -Power

Power

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total118 of 65782%11 of 5680%144212109514
lambda$new$3(Function, ExecutionCall)400%40%339911
asDerivative(Function[], int)3116484%41881%41212601
lambda$new$2(Function, Tensor, ADTarget)140%n/a111111
lambda$new$1(Function, Tensor, ADTarget)140%n/a111111
calculate(double[], int, Function[])611995%21083%2702101
lambda$new$6(Function, ExecutionCall)60%n/a112211
calculate(double[], int, int, Function[])513396%11392%1802201
lambda$new$5(ExecutionCall)20%n/a111111
Power()60100%n/a0102101
reducePairwise(Function)47100%4100%030701
execute(Function, ExecutionCall)7100%n/a010101
lambda$asDerivative$7(Function[], int)5100%n/a010101
lambda$new$4(ExecutionCall)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Power

Power

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total118 of 65782%11 of 5680%144212109514
lambda$new$3(Function, ExecutionCall)400%40%339911
asDerivative(Function[], int)3116484%41881%41212601
lambda$new$2(Function, Tensor, ADTarget)140%n/a111111
lambda$new$1(Function, Tensor, ADTarget)140%n/a111111
calculate(double[], int, Function[])611995%21083%2702101
lambda$new$6(Function, ExecutionCall)60%n/a112211
calculate(double[], int, int, Function[])513396%11392%1802201
lambda$new$5(ExecutionCall)20%n/a111111
Power()60100%n/a0102101
reducePairwise(Function)47100%4100%030701
execute(Function, ExecutionCall)7100%n/a010101
lambda$asDerivative$7(Function[], int)5100%n/a010101
lambda$new$4(ExecutionCall)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Power.java.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Power.java.html index 684dcbe11..35fcdf51b 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Power.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Power.java.html @@ -203,4 +203,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Subtraction.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Subtraction.html index 58be821c7..efe421827 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Subtraction.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Subtraction.html @@ -1 +1 @@ -Subtraction

Subtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total57 of 55089%7 of 4484%7376101115
reducePairwise(Function)33921%3125%234701
lambda$new$4(Function, ExecutionCall)94382%2466%2411001
lambda$new$1(Function, ExecutionCall)80%n/a111111
execute(Function, ExecutionCall)516297%11392%1812301
asDerivative(Function[], int)22893%1150%120601
calculate(double[], int, int, Function[])72100%10100%0601301
calculate(double[], int, Function[])61100%8100%0501201
Subtraction()58100%n/a0102001
lambda$new$3(Device, Tensor, Tensor, int, ADTarget)39100%n/a010901
lambda$execute$5(Function, int, int)8100%n/a010101
lambda$asDerivative$7(int, Function)4100%n/a010101
lambda$asDerivative$6(int, Function)4100%n/a010101
lambda$new$2(ExecutionCall)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Subtraction

Subtraction

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total57 of 55089%7 of 4484%7376101115
reducePairwise(Function)33921%3125%234701
lambda$new$4(Function, ExecutionCall)94382%2466%2411001
lambda$new$1(Function, ExecutionCall)80%n/a111111
execute(Function, ExecutionCall)516297%11392%1812301
asDerivative(Function[], int)22893%1150%120601
calculate(double[], int, int, Function[])72100%10100%0601301
calculate(double[], int, Function[])61100%8100%0501201
Subtraction()58100%n/a0102001
lambda$new$3(Device, Tensor, Tensor, int, ADTarget)39100%n/a010901
lambda$execute$5(Function, int, int)8100%n/a010101
lambda$asDerivative$7(int, Function)4100%n/a010101
lambda$asDerivative$6(int, Function)4100%n/a010101
lambda$new$2(ExecutionCall)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Subtraction.java.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Subtraction.java.html index 8a61dd849..4a3aae090 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Subtraction.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Subtraction.java.html @@ -198,4 +198,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Util.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Util.html index e1192047a..ded983228 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Util.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Util.html @@ -1 +1 @@ -Util

Util

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 4785%3 of 1275%482812
canBeBroadcast(Shape, Shape)44090%3975%371701
Util()30%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Util

Util

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 4785%3 of 1275%482812
canBeBroadcast(Shape, Shape)44090%3975%371701
Util()30%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/Util.java.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/Util.java.html index ecb1b3bbd..eb5303d1c 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/Util.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/Util.java.html @@ -16,4 +16,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/index.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/index.html index 92d18384e..d74314e7c 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/index.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/index.html @@ -1 +1 @@ -neureka.backend.main.operations.operator

neureka.backend.main.operations.operator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total526 of 3,89386%63 of 32080%73261596722010107
Modulo15837070%132767%1635219861501
Power11853982%114580%14421210951401
Multiplication8358787%64086%8421011341901
Division7468590%114580%1345512231701
Subtraction5749389%73784%737610111501
Addition2965395%125481%1152312101901
Util74085%3975%48281201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.operator

neureka.backend.main.operations.operator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total526 of 3,98086%65 of 33480%75269596842010207
Modulo15837070%132767%1635219861501
Power11853982%114580%14421210951401
Multiplication8367489%85286%10501012542001
Division7468590%114580%1345512231701
Subtraction5749389%73784%737610111501
Addition2965395%125481%1152312101901
Util74085%3975%48281201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.operator/index.source.html b/docs/coverage/test/html/neureka.backend.main.operations.operator/index.source.html index f4cbb2bda..21b2cc735 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.operator/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.operator/index.source.html @@ -1 +1 @@ -neureka.backend.main.operations.operator

neureka.backend.main.operations.operator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total526 of 3,89386%63 of 32080%73261596722010107
Modulo.java15837070%132767%1635219861501
Power.java11853982%114580%14421210951401
Multiplication.java8358787%64086%8421011341901
Division.java7468590%114580%1345512231701
Subtraction.java5749389%73784%737610111501
Addition.java2965395%125481%1152312101901
Util.java74085%3975%48281201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.operator

neureka.backend.main.operations.operator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total526 of 3,98086%65 of 33480%75269596842010207
Modulo.java15837070%132767%1635219861501
Power.java11853982%114580%14421210951401
Multiplication.java8367489%85286%10501012542001
Division.java7468590%114580%1345512231701
Subtraction.java5749389%73784%737610111501
Addition.java2965395%125481%1152312101901
Util.java74085%3975%48281201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce$Type.html b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce$Type.html index 0c956d99e..adb1d39c9 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce$Type.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce$Type.html @@ -1 +1 @@ -CPUReduce.Type

CPUReduce.Type

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total72 of 23168%6 of 4285%643626019
getFloatComparator()12942%1266%131401
getDoubleComparator()12942%1266%131401
getIntComparator()12942%1266%131401
getLongComparator()12942%1266%131401
getByteComparator()12942%1266%131401
getShortComparator()12942%1266%131401
static {...}15100%n/a010201
lambda$getLongComparator$7(long, long)8100%2100%020101
lambda$getLongComparator$6(long, long)8100%2100%020101
lambda$getDoubleComparator$3(double, double)8100%2100%020101
lambda$getDoubleComparator$2(double, double)8100%2100%020101
lambda$getFloatComparator$1(float, float)8100%2100%020101
lambda$getFloatComparator$0(float, float)8100%2100%020101
lambda$getShortComparator$11(short, short)7100%2100%020101
lambda$getShortComparator$10(short, short)7100%2100%020101
lambda$getByteComparator$9(byte, byte)7100%2100%020101
lambda$getByteComparator$8(byte, byte)7100%2100%020101
lambda$getIntComparator$5(int, int)7100%2100%020101
lambda$getIntComparator$4(int, int)7100%2100%020101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUReduce.Type

CPUReduce.Type

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total72 of 24070%6 of 4285%643626019
getFloatComparator()12942%1266%131401
getDoubleComparator()12942%1266%131401
getIntComparator()12942%1266%131401
getLongComparator()12942%1266%131401
getByteComparator()12942%1266%131401
getShortComparator()12942%1266%131401
static {...}24100%n/a010201
lambda$getLongComparator$7(long, long)8100%2100%020101
lambda$getLongComparator$6(long, long)8100%2100%020101
lambda$getDoubleComparator$3(double, double)8100%2100%020101
lambda$getDoubleComparator$2(double, double)8100%2100%020101
lambda$getFloatComparator$1(float, float)8100%2100%020101
lambda$getFloatComparator$0(float, float)8100%2100%020101
lambda$getShortComparator$11(short, short)7100%2100%020101
lambda$getShortComparator$10(short, short)7100%2100%020101
lambda$getByteComparator$9(byte, byte)7100%2100%020101
lambda$getByteComparator$8(byte, byte)7100%2100%020101
lambda$getIntComparator$5(int, int)7100%2100%020101
lambda$getIntComparator$4(int, int)7100%2100%020101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce.html b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce.html index 556324a0f..2c55f3e40 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce.html @@ -1 +1 @@ -CPUReduce

CPUReduce

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total26 of 77196%6 of 7091%5514136016
_runRecursively(Tensor, CPU)1736195%42686%31636101
run(ExecutionCall)92976%2250%231501
lambda$_runRecursively$10(int, int, byte[], CPUReduce.ComparatorI8, int[], int)42100%4100%0301101
lambda$_runRecursively$8(int, int, short[], CPUReduce.ComparatorI16, int[], int)42100%4100%0301101
lambda$_runRecursively$6(int, int, long[], CPUReduce.ComparatorI64, int[], int)42100%4100%0301101
lambda$_runRecursively$4(int, int, int[], CPUReduce.ComparatorI32, int[], int)42100%4100%0301101
lambda$_runRecursively$2(int, int, double[], CPUReduce.ComparatorF64, int[], int)42100%4100%0301101
lambda$_runRecursively$0(int, int, float[], CPUReduce.ComparatorF32, int[], int)42100%4100%0301101
lambda$_runRecursively$11(byte[], byte[], int[], int, int)16100%2100%020101
lambda$_runRecursively$9(short[], short[], int[], int, int)16100%2100%020101
lambda$_runRecursively$7(long[], long[], int[], int, int)16100%2100%020101
lambda$_runRecursively$5(int[], int[], int[], int, int)16100%2100%020101
lambda$_runRecursively$3(double[], double[], int[], int, int)16100%2100%020101
lambda$_runRecursively$1(float[], float[], int[], int, int)16100%2100%020101
CPUReduce(CPUReduce.Type)6100%n/a010301
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUReduce

CPUReduce

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total26 of 77196%6 of 7091%5514136016
_runRecursively(Tensor, CPU)1736195%42686%31636101
run(ExecutionCall)92976%2250%231501
lambda$_runRecursively$10(int, int, byte[], CPUReduce.ComparatorI8, int[], int)42100%4100%0301101
lambda$_runRecursively$8(int, int, short[], CPUReduce.ComparatorI16, int[], int)42100%4100%0301101
lambda$_runRecursively$6(int, int, long[], CPUReduce.ComparatorI64, int[], int)42100%4100%0301101
lambda$_runRecursively$4(int, int, int[], CPUReduce.ComparatorI32, int[], int)42100%4100%0301101
lambda$_runRecursively$2(int, int, double[], CPUReduce.ComparatorF64, int[], int)42100%4100%0301101
lambda$_runRecursively$0(int, int, float[], CPUReduce.ComparatorF32, int[], int)42100%4100%0301101
lambda$_runRecursively$11(byte[], byte[], int[], int, int)16100%2100%020101
lambda$_runRecursively$9(short[], short[], int[], int, int)16100%2100%020101
lambda$_runRecursively$7(long[], long[], int[], int, int)16100%2100%020101
lambda$_runRecursively$5(int[], int[], int[], int, int)16100%2100%020101
lambda$_runRecursively$3(double[], double[], int[], int, int)16100%2100%020101
lambda$_runRecursively$1(float[], float[], int[], int, int)16100%2100%020101
CPUReduce(CPUReduce.Type)6100%n/a010301
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce.java.html index 4a9aca035..5034f1b6a 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUReduce.java.html @@ -253,4 +253,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUSum.html b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUSum.html index fed511388..81566c0aa 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUSum.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUSum.html @@ -1 +1 @@ -CPUSum

CPUSum

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 59795%5 of 5090%5353115010
_runRecursively(Tensor, CPU)1832594%32990%31726601
run(ExecutionCall)92270%2250%231501
lambda$_runRecursively$6(int, int, Object[], Number[], int)38100%2100%020701
lambda$_runRecursively$5(int, int, byte[], byte[], int)31100%2100%020601
lambda$_runRecursively$4(int, int, short[], short[], int)31100%2100%020601
lambda$_runRecursively$3(int, int, long[], long[], int)30100%2100%020601
lambda$_runRecursively$2(int, int, int[], int[], int)30100%2100%020601
lambda$_runRecursively$1(int, int, double[], double[], int)30100%2100%020601
lambda$_runRecursively$0(int, int, float[], float[], int)30100%2100%020601
CPUSum()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPUSum

CPUSum

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total27 of 59795%5 of 5090%5353115010
_runRecursively(Tensor, CPU)1832594%32990%31726601
run(ExecutionCall)92270%2250%231501
lambda$_runRecursively$6(int, int, Object[], Number[], int)38100%2100%020701
lambda$_runRecursively$5(int, int, byte[], byte[], int)31100%2100%020601
lambda$_runRecursively$4(int, int, short[], short[], int)31100%2100%020601
lambda$_runRecursively$3(int, int, long[], long[], int)30100%2100%020601
lambda$_runRecursively$2(int, int, int[], int[], int)30100%2100%020601
lambda$_runRecursively$1(int, int, double[], double[], int)30100%2100%020601
lambda$_runRecursively$0(int, int, float[], float[], int)30100%2100%020601
CPUSum()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUSum.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUSum.java.html index b62f10f56..4229e879e 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUSum.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/CPUSum.java.html @@ -160,4 +160,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/index.html b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/index.html index 841a59f3d..41b561157 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/index.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/index.html @@ -1 +1 @@ -neureka.backend.main.operations.other.internal

neureka.backend.main.operations.other.internal

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total125 of 1,59992%17 of 16289%161291327704503
CPUReduce.Type7215968%63685%64362601901
CPUSum2757095%54590%535311501001
CPUReduce2674596%66491%551413601601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.other.internal

neureka.backend.main.operations.other.internal

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total125 of 1,60892%17 of 16289%161291327704503
CPUReduce.Type7216870%63685%64362601901
CPUSum2757095%54590%535311501001
CPUReduce2674596%66491%551413601601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/index.source.html b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/index.source.html index aff57dc68..c43088723 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other.internal/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other.internal/index.source.html @@ -1 +1 @@ -neureka.backend.main.operations.other.internal

neureka.backend.main.operations.other.internal

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total125 of 1,59992%17 of 16289%161291327704503
CPUReduce.java9890490%1210089%11941016203502
CPUSum.java2757095%54590%535311501001
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.other.internal

neureka.backend.main.operations.other.internal

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total125 of 1,60892%17 of 16289%161291327704503
CPUReduce.java9891390%1210089%11941016203502
CPUSum.java2757095%54590%535311501001
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/AssignLeft.html b/docs/coverage/test/html/neureka.backend.main.operations.other/AssignLeft.html index 0c8977398..82e808b22 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/AssignLeft.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/AssignLeft.html @@ -1 +1 @@ -AssignLeft

AssignLeft

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total164 of 42161%16 of 3452%15342074417
lambda$new$5(ExecutionCall)470%20%227711
reducePairwise(Function)38919%3125%234701
lambda$new$11(ExecutionCall)350%20%224411
calculate(double[], int, int, Function[])220%20%222211
lambda$new$2(ExecutionCall)103577%2675%2521101
execute(Function, ExecutionCall)55892%1375%131701
lambda$new$1(Tensor[])5758%3125%230101
lambda$new$3(ExecutionCall)20%n/a111111
AssignLeft()54100%n/a0102101
stringify(String[])39100%4100%030501
lambda$new$7(Tensor[])12100%1375%130101
lambda$new$10(Function, ExecutionCall)11100%n/a010301
lambda$new$4(Function, ExecutionCall)11100%n/a010301
lambda$new$8(ExecutionCall)9100%n/a010401
lambda$new$6(Tensor)5100%n/a010101
lambda$new$0(Tensor)5100%n/a010101
lambda$new$9(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AssignLeft

AssignLeft

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total164 of 42161%16 of 3452%15342074417
lambda$new$5(ExecutionCall)470%20%227711
reducePairwise(Function)38919%3125%234701
lambda$new$11(ExecutionCall)350%20%224411
calculate(double[], int, int, Function[])220%20%222211
lambda$new$2(ExecutionCall)103577%2675%2521101
execute(Function, ExecutionCall)55892%1375%131701
lambda$new$1(Tensor[])5758%3125%230101
lambda$new$3(ExecutionCall)20%n/a111111
AssignLeft()54100%n/a0102101
stringify(String[])39100%4100%030501
lambda$new$7(Tensor[])12100%1375%130101
lambda$new$10(Function, ExecutionCall)11100%n/a010301
lambda$new$4(Function, ExecutionCall)11100%n/a010301
lambda$new$8(ExecutionCall)9100%n/a010401
lambda$new$6(Tensor)5100%n/a010101
lambda$new$0(Tensor)5100%n/a010101
lambda$new$9(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/AssignLeft.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/AssignLeft.java.html index 51f40b809..e678b09c8 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/AssignLeft.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/AssignLeft.java.html @@ -145,4 +145,4 @@ return d >= 0 ? src[ right ].derive( inputs, d, j ) : src[ right ].call( inputs, j ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Cat.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Cat.html index 8638ea984..cbdcd8d2b 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Cat.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Cat.html @@ -1 +1 @@ -Cat

Cat

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 59195%10 of 5481%11447106117
_catFrames(Tensor[], Tensor, int)1023395%62278%61525201
calculate(double[], int, int, Function[])70%n/a111111
execute(Function, ExecutionCall)51473%2250%231301
lambda$new$0(ExecutionCall)43990%2675%251801
lambda$new$5(Function, ExecutionCall)317198%12100%0722301
lambda$new$4(List, Integer, ADTarget)35100%2100%020401
Cat()33100%n/a0101501
lambda$_catFrames$7(Map)6100%n/a010101
lambda$new$2(Integer, Tensor)6100%n/a010101
lambda$_catFrames$8(int, List)4100%n/a010101
lambda$_catFrames$6(Tensor)4100%n/a010101
lambda$_catFrames$12(Object)3100%n/a010101
lambda$_catFrames$11(Object)3100%n/a010101
lambda$_catFrames$10(Object)3100%n/a010101
lambda$_catFrames$9(Object)3100%n/a010101
lambda$new$3(Integer)3100%n/a010101
lambda$new$1(ExecutionCall)100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Cat

Cat

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 59195%10 of 5481%11447106117
_catFrames(Tensor[], Tensor, int)1023395%62278%61525201
calculate(double[], int, int, Function[])70%n/a111111
execute(Function, ExecutionCall)51473%2250%231301
lambda$new$0(ExecutionCall)43990%2675%251801
lambda$new$5(Function, ExecutionCall)317198%12100%0722301
lambda$new$4(List, Integer, ADTarget)35100%2100%020401
Cat()33100%n/a0101501
lambda$_catFrames$7(Map)6100%n/a010101
lambda$new$2(Integer, Tensor)6100%n/a010101
lambda$_catFrames$8(int, List)4100%n/a010101
lambda$_catFrames$6(Tensor)4100%n/a010101
lambda$_catFrames$12(Object)3100%n/a010101
lambda$_catFrames$11(Object)3100%n/a010101
lambda$_catFrames$10(Object)3100%n/a010101
lambda$_catFrames$9(Object)3100%n/a010101
lambda$new$3(Integer)3100%n/a010101
lambda$new$1(ExecutionCall)100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Cat.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Cat.java.html index cb25e68ff..a10a48948 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Cat.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Cat.java.html @@ -193,4 +193,4 @@ @Override public double calculate( double[] inputs, int j, int d, Function[] src ) { return src[ 0 ].call( inputs, j ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/DimFit.html b/docs/coverage/test/html/neureka.backend.main.operations.other/DimFit.html index 578d0131d..8b2d8368a 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/DimFit.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/DimFit.html @@ -1 +1 @@ -DimFit

DimFit

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total214 of 2140%28 of 280%2020393966
lambda$new$2(Function, ExecutionCall)1700%280%1515222211
DimFit()320%n/a11151511
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$1(ExecutionCall)20%n/a111111
lambda$new$0(ExecutionCall)20%n/a111111
static {...}0%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DimFit

DimFit

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total214 of 2140%28 of 280%2020393966
lambda$new$2(Function, ExecutionCall)1700%280%1515222211
DimFit()320%n/a11151511
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$1(ExecutionCall)20%n/a111111
lambda$new$0(ExecutionCall)20%n/a111111
static {...}0%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/DimFit.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/DimFit.java.html index 96794a22f..b3c3b111e 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/DimFit.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/DimFit.java.html @@ -77,4 +77,4 @@ return src[ 0 ].call( inputs, j ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/DimTrim.html b/docs/coverage/test/html/neureka.backend.main.operations.other/DimTrim.html index 3fc4f41f2..bfcd44361 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/DimTrim.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/DimTrim.html @@ -1 +1 @@ -DimTrim

DimTrim

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total73 of 61588%7 of 3479%8379112120
lambda$new$3(Function, ExecutionCall)353146%2250%233901
lambda$new$2(ExecutionCall, Function, ADTarget)193464%1150%123901
calculate(double[], int, int, Function[])70%n/a111111
_pad(Tensor, int[], boolean)618996%2880%2613901
_trim(Tensor, boolean)617196%2880%2613301
endsFrom(int[])50100%8100%050501
DimTrim()32100%n/a0101501
lambda$_trim$13(Integer)3100%n/a010101
lambda$_trim$12(Integer)3100%n/a010101
lambda$_trim$11(Integer)3100%n/a010101
lambda$_trim$10(Integer)3100%n/a010101
lambda$_trim$9(Integer)3100%n/a010101
lambda$_pad$8(Integer)3100%n/a010101
lambda$_pad$7(Integer)3100%n/a010101
lambda$_pad$6(Integer)3100%n/a010101
lambda$_pad$5(Integer)3100%n/a010101
lambda$_pad$4(Integer)3100%n/a010101
lambda$new$1(ExecutionCall)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DimTrim

DimTrim

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total73 of 61588%7 of 3479%8379112120
lambda$new$3(Function, ExecutionCall)353146%2250%233901
lambda$new$2(ExecutionCall, Function, ADTarget)193464%1150%123901
calculate(double[], int, int, Function[])70%n/a111111
_pad(Tensor, int[], boolean)618996%2880%2613901
_trim(Tensor, boolean)617196%2880%2613301
endsFrom(int[])50100%8100%050501
DimTrim()32100%n/a0101501
lambda$_trim$13(Integer)3100%n/a010101
lambda$_trim$12(Integer)3100%n/a010101
lambda$_trim$11(Integer)3100%n/a010101
lambda$_trim$10(Integer)3100%n/a010101
lambda$_trim$9(Integer)3100%n/a010101
lambda$_pad$8(Integer)3100%n/a010101
lambda$_pad$7(Integer)3100%n/a010101
lambda$_pad$6(Integer)3100%n/a010101
lambda$_pad$5(Integer)3100%n/a010101
lambda$_pad$4(Integer)3100%n/a010101
lambda$new$1(ExecutionCall)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/DimTrim.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/DimTrim.java.html index 96fe1d71f..514672c63 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/DimTrim.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/DimTrim.java.html @@ -178,4 +178,4 @@ return src[ 0 ].call( inputs, j ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Max.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Max.html index c41d3d153..10c4caecd 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Max.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Max.html @@ -1 +1 @@ -Max

Max

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 19185%4 of 633%51244329
lambda$new$5(ExecutionCall)130%20%223311
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$4(Function, ExecutionCall)47094%1150%1201101
lambda$new$3(Class, Shape, Device, int, ADTarget)43088%1150%120601
Max()49100%n/a0101801
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$0(Tensor)5100%n/a010101
lambda$new$2(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Max

Max

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 19185%4 of 633%51244329
lambda$new$5(ExecutionCall)130%20%223311
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$4(Function, ExecutionCall)47094%1150%1201101
lambda$new$3(Class, Shape, Device, int, ADTarget)43088%1150%120601
Max()49100%n/a0101801
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$0(Tensor)5100%n/a010101
lambda$new$2(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Max.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Max.java.html index f1c1b924f..a988c0ed3 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Max.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Max.java.html @@ -77,4 +77,4 @@ @Override public double calculate( double[] inputs, int j, int d, Function[] src ) { return src[ 0 ].call( inputs, j ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Min.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Min.html index 064c5f975..9d4a3ab32 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Min.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Min.html @@ -1 +1 @@ -Min

Min

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 19285%4 of 633%51244329
lambda$new$5(ExecutionCall)130%20%223311
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$4(Function, ExecutionCall)47194%1150%1201101
lambda$new$3(Class, Shape, Device, int, ADTarget)43088%1150%120601
Min()49100%n/a0101801
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$0(Tensor)5100%n/a010101
lambda$new$2(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Min

Min

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 19285%4 of 633%51244329
lambda$new$5(ExecutionCall)130%20%223311
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$4(Function, ExecutionCall)47194%1150%1201101
lambda$new$3(Class, Shape, Device, int, ADTarget)43088%1150%120601
Min()49100%n/a0101801
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$0(Tensor)5100%n/a010101
lambda$new$2(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Min.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Min.java.html index 55e22c69b..ee398c85d 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Min.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Min.java.html @@ -77,4 +77,4 @@ @Override public double calculate( double[] inputs, int j, int d, Function[] src ) { return src[ 0 ].call( inputs, j ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Permute.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Permute.html index 058497622..d9f985a8a 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Permute.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Permute.html @@ -1 +1 @@ -Permute

Permute

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total53 of 49789%8 of 5284%838895112
_shapeCheck(int[], Tensor)29617%3125%234601
stringify(String[])118488%2880%2621701
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$3(Function, ExecutionCall)56692%1787%1511201
_rearrangeAxisOf(Tensor, int[], boolean)3897%2250%230901
makeFit(Tensor[], boolean)139100%18100%01002001
invert(int[])46100%8100%0501201
Permute()32100%n/a0101501
lambda$new$2(Function, ADTarget)19100%n/a010101
lambda$stringify$4(String)10100%n/a010301
lambda$new$1(ExecutionCall)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Permute

Permute

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total53 of 49789%8 of 5284%838895112
_shapeCheck(int[], Tensor)29617%3125%234601
stringify(String[])118488%2880%2621701
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$3(Function, ExecutionCall)56692%1787%1511201
_rearrangeAxisOf(Tensor, int[], boolean)3897%2250%230901
makeFit(Tensor[], boolean)139100%18100%01002001
invert(int[])46100%8100%0501201
Permute()32100%n/a0101501
lambda$new$2(Function, ADTarget)19100%n/a010101
lambda$stringify$4(String)10100%n/a010301
lambda$new$1(ExecutionCall)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Permute.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Permute.java.html index d67a5efec..2f14266e0 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Permute.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Permute.java.html @@ -182,4 +182,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Randomization.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Randomization.html index 6b14d55ec..7014799eb 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Randomization.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Randomization.html @@ -1 +1 @@ -Randomization

Randomization

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 13288%2 of 1080%31223017
lambda$new$4(ExecutionCall)75087%1375%131801
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$0(Tensor)11694%1583%140301
Randomization()36100%n/a0101501
lambda$new$3(Function, ExecutionCall)7100%n/a010101
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$2(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Randomization

Randomization

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 13288%2 of 1080%31223017
lambda$new$4(ExecutionCall)75087%1375%131801
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$0(Tensor)11694%1583%140301
Randomization()36100%n/a0101501
lambda$new$3(Function, ExecutionCall)7100%n/a010101
lambda$new$1(ExecutionCall)6100%n/a010301
lambda$new$2(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Randomization.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Randomization.java.html index 7c7285126..eebc67781 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Randomization.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Randomization.java.html @@ -78,4 +78,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/ReLayout.html b/docs/coverage/test/html/neureka.backend.main.operations.other/ReLayout.html index db913c365..6a900c03c 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/ReLayout.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/ReLayout.html @@ -1 +1 @@ -ReLayout

ReLayout

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total61 of 29579%7 of 2875%1030971316
_checkLayoutConversion(NDConfiguration, NDConfiguration, NDConfiguration.Layout)29923%2250%234701
toLayout(Tensor, NDConfiguration.Layout)104481%31178%3821401
lambda$new$2(NDConfiguration.Layout, ADTarget)90%n/a112211
calculate(double[], int, int, Function[])70%n/a111111
_createNewNDCFrom(NDConfiguration, int[])41578%1150%120301
lambda$new$1(ExecutionCall)20%n/a111111
ReLayout()32100%n/a0101501
lambda$new$3(Function, ExecutionCall)32100%n/a010701
_fromRMToCM(Tensor)31100%2100%020801
_fromCMToRM(Tensor)29100%1375%130801
lambda$_assignIfActual$6(Tensor, Tensor)16100%n/a010101
_assignIfActual(Tensor, Supplier)15100%2100%020401
lambda$_fromRMToCM$5(Tensor)6100%n/a010101
lambda$_fromCMToRM$4(Tensor)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ReLayout

ReLayout

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total61 of 29579%7 of 2875%1030971316
_checkLayoutConversion(NDConfiguration, NDConfiguration, NDConfiguration.Layout)29923%2250%234701
toLayout(Tensor, NDConfiguration.Layout)104481%31178%3821401
lambda$new$2(NDConfiguration.Layout, ADTarget)90%n/a112211
calculate(double[], int, int, Function[])70%n/a111111
_createNewNDCFrom(NDConfiguration, int[])41578%1150%120301
lambda$new$1(ExecutionCall)20%n/a111111
ReLayout()32100%n/a0101501
lambda$new$3(Function, ExecutionCall)32100%n/a010701
_fromRMToCM(Tensor)31100%2100%020801
_fromCMToRM(Tensor)29100%1375%130801
lambda$_assignIfActual$6(Tensor, Tensor)16100%n/a010101
_assignIfActual(Tensor, Supplier)15100%2100%020401
lambda$_fromRMToCM$5(Tensor)6100%n/a010101
lambda$_fromCMToRM$4(Tensor)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/ReLayout.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/ReLayout.java.html index d6ac5eda2..662c5e075 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/ReLayout.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/ReLayout.java.html @@ -155,4 +155,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Reshape.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Reshape.html index 3b6bd8587..e66e27362 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Reshape.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Reshape.html @@ -1 +1 @@ -Reshape

Reshape

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 18890%2 of 1283%31335517
calculate(double[], int, int, Function[])70%n/a111111
lambda$new$3(Function, ExecutionCall)57093%1375%1311901
_resolveNewShape(int, int[])55391%1787%1511501
Reshape()32100%n/a0101501
lambda$new$2(NDConfiguration, ADTarget)12100%n/a010501
lambda$new$1(ExecutionCall)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Reshape

Reshape

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 18888%3 of 1275%41345517
lambda$new$3(Function, ExecutionCall)106586%2250%2321901
calculate(double[], int, int, Function[])70%n/a111111
_resolveNewShape(int, int[])55391%1787%1511501
Reshape()32100%n/a0101501
lambda$new$2(NDConfiguration, ADTarget)12100%n/a010501
lambda$new$1(ExecutionCall)2100%n/a010101
lambda$new$0(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Reshape.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Reshape.java.html index 58780fe85..8970ea970 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Reshape.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Reshape.java.html @@ -57,8 +57,8 @@ parent.addChild( reshaped ); input.set( parent ); - if ( input.isOutsourced() ) - input.getDevice().store( reshaped ); + if ( input.isOutsourced() ) + input.getDevice().store( reshaped ); NDConfiguration originalConfig = input.getNDConf(); @@ -120,4 +120,4 @@ return src[ 0 ].call( inputs, j ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Slice.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Slice.html index a933581cd..8f9ae3cca 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Slice.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Slice.html @@ -1 +1 @@ -Slice

Slice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 56394%6 of 5088%7363106111
_slice(int[], int[], int[], Tensor)1625394%43489%42014401
calculate(double[], int, int, Function[])70%n/a111111
lambda$_sliceFrame$5(int[], int[], int[], Tensor, NDFrame)67692%2880%2611801
lambda$new$2(Class, Shape, boolean, Device, int[], int[], int[], ADTarget)76100%n/a0101001
lambda$new$3(Function, ExecutionCall)59100%n/a0101301
Slice()33100%n/a0101501
_sliceFrame(Tensor, Tensor, int[], int[], int[])27100%2100%020401
static {...}4100%n/a010101
lambda$_slice$4(Relation)100%n/a010101
lambda$new$1(ExecutionCall)100%n/a010101
lambda$new$0(ExecutionCall)100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Slice

Slice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total34 of 56393%7 of 5086%8364106111
_slice(int[], int[], int[], Tensor)2124892%53386%52024401
calculate(double[], int, int, Function[])70%n/a111111
lambda$_sliceFrame$5(int[], int[], int[], Tensor, NDFrame)67692%2880%2611801
lambda$new$2(Class, Shape, boolean, Device, int[], int[], int[], ADTarget)76100%n/a0101001
lambda$new$3(Function, ExecutionCall)59100%n/a0101301
Slice()33100%n/a0101501
_sliceFrame(Tensor, Tensor, int[], int[], int[])27100%2100%020401
static {...}4100%n/a010101
lambda$_slice$4(Relation)100%n/a010101
lambda$new$1(ExecutionCall)100%n/a010101
lambda$new$0(ExecutionCall)100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Slice.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Slice.java.html index 87246dd7c..fdad455d9 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Slice.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Slice.java.html @@ -174,8 +174,8 @@ parent.addChild( subset ); input.set( parent ); - if ( input.isOutsourced() ) - input.getDevice().store( subset ); + if ( input.isOutsourced() ) + input.getDevice().store( subset ); if ( input.isVirtual() ) subset.mut().setIsVirtual( true ); @@ -218,4 +218,4 @@ public double calculate( double[] inputs, int j, int d, Function[] src ) { return src[ 0 ].call( inputs, j ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Sum.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Sum.html index 2a576c429..65a98e1a1 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Sum.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Sum.html @@ -1 +1 @@ -Sum

Sum

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 3982%0 of 0n/a1211212
calculate(double[], int, int, Function[])70%n/a111111
Sum()32100%n/a0101101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sum

Sum

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 3982%0 of 0n/a1211212
calculate(double[], int, int, Function[])70%n/a111111
Sum()32100%n/a0101101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/Sum.java.html b/docs/coverage/test/html/neureka.backend.main.operations.other/Sum.java.html index 79010da94..984ffbcb7 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/Sum.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/Sum.java.html @@ -27,4 +27,4 @@ @Override public double calculate( double[] inputs, int j, int d, Function[] src ) { return src[ 0 ].call( inputs, j ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/index.html b/docs/coverage/test/html/neureka.backend.main.operations.other/index.html index 35cd02096..eee140e1b 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/index.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/index.html @@ -1 +1 @@ -neureka.backend.main.operations.other

neureka.backend.main.operations.other

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total718 of 3,93881%94 of 31470%9629010978624133112
DimFit2140%280%202039396611
AssignLeft16425761%161852%1534207441701
DimTrim7354288%72779%837911212001
ReLayout6123479%72175%103097131601
Permute5344489%84484%83889511201
Cat2956295%104481%1144710611701
Slice2953494%64488%736310611101
Min2816485%4233%5124432901
Max2816385%4233%5124432901
Reshape1717190%21083%3133551701
Randomization1511788%2880%3122301701
Sum73282%n/a121121201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.other

neureka.backend.main.operations.other

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total728 of 3,93881%96 of 31469%9829011178624133112
DimFit2140%280%202039396611
AssignLeft16425761%161852%1534207441701
DimTrim7354288%72779%837911212001
ReLayout6123479%72175%103097131601
Permute5344489%84484%83889511201
Slice3452993%74386%836410611101
Cat2956295%104481%1144710611701
Min2816485%4233%5124432901
Max2816385%4233%5124432901
Reshape2216688%3975%4134551701
Randomization1511788%2880%3122301701
Sum73282%n/a121121201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations.other/index.source.html b/docs/coverage/test/html/neureka.backend.main.operations.other/index.source.html index 415b475ef..04daffe82 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations.other/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.operations.other/index.source.html @@ -1 +1 @@ -neureka.backend.main.operations.other

neureka.backend.main.operations.other

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total718 of 3,93881%94 of 31470%9629010978624133112
DimFit.java2140%280%202039396611
AssignLeft.java16425761%161852%1534207441701
DimTrim.java7354288%72779%837911212001
ReLayout.java6123479%72175%103097131601
Permute.java5344489%84484%83889511201
Cat.java2956295%104481%1144710611701
Slice.java2953494%64488%736310611101
Min.java2816485%4233%5124432901
Max.java2816385%4233%5124432901
Reshape.java1717190%21083%3133551701
Randomization.java1511788%2880%3122301701
Sum.java73282%n/a121121201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations.other

neureka.backend.main.operations.other

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total728 of 3,93881%96 of 31469%9829011178624133112
DimFit.java2140%280%202039396611
AssignLeft.java16425761%161852%1534207441701
DimTrim.java7354288%72779%837911212001
ReLayout.java6123479%72175%103097131601
Permute.java5344489%84484%83889511201
Slice.java3452993%74386%836410611101
Cat.java2956295%104481%1144710611701
Min.java2816485%4233%5124432901
Max.java2816385%4233%5124432901
Reshape.java2216688%3975%4134551701
Randomization.java1511788%2880%3122301701
Sum.java73282%n/a121121201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations/ConvUtil.html b/docs/coverage/test/html/neureka.backend.main.operations/ConvUtil.html index fa0e6e1c2..ef7426e7b 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations/ConvUtil.html +++ b/docs/coverage/test/html/neureka.backend.main.operations/ConvUtil.html @@ -1 +1 @@ -ConvUtil

ConvUtil

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ConvUtil

ConvUtil

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations/ConvUtil.java.html b/docs/coverage/test/html/neureka.backend.main.operations/ConvUtil.java.html index ed37f0cdf..1f54c4011 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations/ConvUtil.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations/ConvUtil.java.html @@ -88,4 +88,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations/ElemWiseUtil.html b/docs/coverage/test/html/neureka.backend.main.operations/ElemWiseUtil.html index 0cc86f204..863bd025d 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations/ElemWiseUtil.html +++ b/docs/coverage/test/html/neureka.backend.main.operations/ElemWiseUtil.html @@ -1 +1 @@ -ElemWiseUtil

ElemWiseUtil

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ElemWiseUtil

ElemWiseUtil

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations/ElemWiseUtil.java.html b/docs/coverage/test/html/neureka.backend.main.operations/ElemWiseUtil.java.html index fd1f389a9..dac7c93c8 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations/ElemWiseUtil.java.html +++ b/docs/coverage/test/html/neureka.backend.main.operations/ElemWiseUtil.java.html @@ -42,4 +42,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations/index.html b/docs/coverage/test/html/neureka.backend.main.operations/index.html index 6b483221b..dcddc4303 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations/index.html +++ b/docs/coverage/test/html/neureka.backend.main.operations/index.html @@ -1 +1 @@ -neureka.backend.main.operations

neureka.backend.main.operations

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations

neureka.backend.main.operations

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.main.operations/index.source.html b/docs/coverage/test/html/neureka.backend.main.operations/index.source.html index 3a0d11ea9..6279181b4 100644 --- a/docs/coverage/test/html/neureka.backend.main.operations/index.source.html +++ b/docs/coverage/test/html/neureka.backend.main.operations/index.source.html @@ -1 +1 @@ -neureka.backend.main.operations

neureka.backend.main.operations

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.main.operations

neureka.backend.main.operations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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ConvUtil.java8319169%141246%1123144421001
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.ocl/CLBackend.html b/docs/coverage/test/html/neureka.backend.ocl/CLBackend.html index 4e6f64370..470a97ae4 100644 --- a/docs/coverage/test/html/neureka.backend.ocl/CLBackend.html +++ b/docs/coverage/test/html/neureka.backend.ocl/CLBackend.html @@ -1 +1 @@ -CLBackend

CLBackend

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLBackend

CLBackend

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lambda$_load$21(LoadingContext)60%n/a111111
lambda$_load$20(LoadingContext)60%n/a111111
lambda$_load$19(LoadingContext)60%n/a111111
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lambda$_load$16(LoadingContext)60%n/a111111
lambda$_load$15(LoadingContext)60%n/a111111
lambda$_load$14(LoadingContext)60%n/a111111
lambda$_load$13(LoadingContext)60%n/a111111
lambda$_load$12(LoadingContext)60%n/a111111
lambda$_load$11(LoadingContext)60%n/a111111
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lambda$_load$24(LoadingContext)50%n/a111111
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.ocl/CLBackend.java.html b/docs/coverage/test/html/neureka.backend.ocl/CLBackend.java.html index 3e774b990..714e5ecaa 100644 --- a/docs/coverage/test/html/neureka.backend.ocl/CLBackend.java.html +++ b/docs/coverage/test/html/neureka.backend.ocl/CLBackend.java.html @@ -70,20 +70,20 @@ * @return The number of all {@link OpenCLDevice} instances across all {@link OpenCLPlatform}s. */ public int getTotalNumberOfDevices() { - List<OpenCLPlatform> platforms = getPlatforms(); - if ( getPlatforms().isEmpty() ) return 0; - return platforms.stream().mapToInt( p -> p.getDevices().size() ).sum(); + List<OpenCLPlatform> platforms = getPlatforms(); + if ( getPlatforms().isEmpty() ) return 0; + return platforms.stream().mapToInt( p -> p.getDevices().size() ).sum(); } /** * @return A list of context specific {@link OpenCLPlatform} instances possible containing {@link OpenCLDevice}s. */ - public List<OpenCLPlatform> getPlatforms() { return Collections.unmodifiableList( _platforms ); } + public List<OpenCLPlatform> getPlatforms() { return Collections.unmodifiableList( _platforms ); } /** * @return A container for OpenCL specific settings. */ - public CLSettings getSettings() { return _settings; } + public CLSettings getSettings() { return _settings; } /** * Updating the CLContext will cause the list of existing {@link OpenCLPlatform} instances to be @@ -94,17 +94,17 @@ */ @Override public boolean update( OwnerChangeRequest<Extensions> changeRequest ) { - _platforms.clear(); - _platforms.addAll( _findLoadAndCompileForAllPlatforms() ); - changeRequest.executeChange(); // This can be an 'add', 'remove' or 'transfer' of this component! - return true; + _platforms.clear(); + _platforms.addAll( _findLoadAndCompileForAllPlatforms() ); + changeRequest.executeChange(); // This can be an 'add', 'remove' or 'transfer' of this component! + return true; } @Override public String toString() { - return this.getClass().getSimpleName()+"@"+Integer.toHexString(hashCode())+"[" + + return this.getClass().getSimpleName()+"@"+Integer.toHexString(hashCode())+"[" + "platforms=["+ - _platforms.stream().map(Object::toString).collect(Collectors.joining(","))+ + _platforms.stream().map(Object::toString).collect(Collectors.joining(","))+ "]" + "]"; } @@ -115,68 +115,68 @@ private static List<OpenCLPlatform> _findLoadAndCompileForAllPlatforms() { // Obtain the number of platforms - int[] numPlatforms = new int[ 1 ]; - clGetPlatformIDs( 0, null, numPlatforms ); + int[] numPlatforms = new int[ 1 ]; + clGetPlatformIDs( 0, null, numPlatforms ); // Obtain the platform IDs - cl_platform_id[] platforms = new cl_platform_id[ numPlatforms[ 0 ] ]; - clGetPlatformIDs( platforms.length, platforms, null ); + cl_platform_id[] platforms = new cl_platform_id[ numPlatforms[ 0 ] ]; + clGetPlatformIDs( platforms.length, platforms, null ); - List<OpenCLPlatform> loadedPlatforms = new ArrayList<>(); - List<String> failures = new ArrayList<>(); - for ( cl_platform_id id : platforms ) { - OpenCLPlatform newPlatform = null; + List<OpenCLPlatform> loadedPlatforms = new ArrayList<>(); + List<String> failures = new ArrayList<>(); + for ( cl_platform_id id : platforms ) { + OpenCLPlatform newPlatform = null; try { - newPlatform = new OpenCLPlatform( id ); + newPlatform = new OpenCLPlatform( id ); } catch ( Exception e ) { String message = "Failed to instantiate '"+OpenCLPlatform.class.getSimpleName()+"' " + "with id '0x"+Long.toHexString(id.getNativePointer())+"'!"; _LOG.error( message, e ); failures.add( message + " Reason: " + e.getMessage() ); - } - if ( newPlatform != null ) - loadedPlatforms.add( newPlatform ); + } + if ( newPlatform != null ) + loadedPlatforms.add( newPlatform ); } - if ( loadedPlatforms.isEmpty() || loadedPlatforms.stream().allMatch( p -> p.getDevices().isEmpty() ) ) + if ( loadedPlatforms.isEmpty() || loadedPlatforms.stream().allMatch( p -> p.getDevices().isEmpty() ) ) _LOG.info( Messages.clContextCouldNotFindAnyDevices() ); - if ( loadedPlatforms.isEmpty() && platforms.length > 0 ) + if ( loadedPlatforms.isEmpty() && platforms.length > 0 ) // There should be at least one platform with at least one device! throw new RuntimeException( "Failed to instantiate any '"+OpenCLPlatform.class.getSimpleName()+"' instance!\n" + "Reasons: \n " + failures.stream().collect(Collectors.joining("\n ")) ); - return loadedPlatforms; + return loadedPlatforms; } @Override public DeviceOption find( String searchKey ) { - Device<Number> result = null; - double score = 0; - for ( OpenCLPlatform p : _platforms ) { - for ( OpenCLDevice d : p.getDevices() ) { - double similarity = Stream.of("opencl",d.type().name(),d.name(),d.vendor()) - .map( word -> word.trim().toLowerCase() ) - .mapToDouble( word -> ParseUtil.similarity( word, searchKey ) ) - .max() - .orElse(0); - if ( similarity > score ) { - result = d; - score = similarity; - if ( score == 1 ) - return new DeviceOption( result, score ); + Device<Number> result = null; + double score = 0; + for ( OpenCLPlatform p : _platforms ) { + for ( OpenCLDevice d : p.getDevices() ) { + double similarity = Stream.of("opencl",d.type().name(),d.name(),d.vendor()) + .map( word -> word.trim().toLowerCase() ) + .mapToDouble( word -> ParseUtil.similarity( word, searchKey ) ) + .max() + .orElse(0); + if ( similarity > score ) { + result = d; + score = similarity; + if ( score == 1 ) + return new DeviceOption( result, score ); } - } - } - return new DeviceOption( result, score ); + } + } + return new DeviceOption( result, score ); } @Override public void reset() { - _settings.reset(); - } + _settings.reset(); + } /** * This method will free all the resources occupied by this context, @@ -185,12 +185,12 @@ */ @Override public void dispose() { - for ( OpenCLPlatform platform : _platforms ) { - for ( OpenCLDevice device : platform.getDevices() ) device.dispose(); - platform.dispose(); - } - _platforms.clear(); - } + for ( OpenCLPlatform platform : _platforms ) { + for ( OpenCLDevice device : platform.getDevices() ) device.dispose(); + platform.dispose(); + } + _platforms.clear(); + } @Override public BackendLoader getLoader() { @@ -200,129 +200,129 @@ private void _load( ReceiveForDevice<OpenCLDevice> receive ) { receive.forOperation( Power.class ) - .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastPower( context.getOperationIdentidier() ) ) - .set( Broadcast.class, context -> new CLBroadcastPower( context.getOperationIdentidier() ) ) - .set( BiElementwise.class, context -> new CLBiElementwisePower( context.getOperationIdentidier() ) ); + .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastPower( context.getOperationIdentidier() ) ) + .set( Broadcast.class, context -> new CLBroadcastPower( context.getOperationIdentidier() ) ) + .set( BiElementwise.class, context -> new CLBiElementwisePower( context.getOperationIdentidier() ) ); receive.forOperation( Addition.class ) - .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastAddition(context.getOperationIdentidier()) ) - .set( Broadcast.class, context -> new CLBroadcastAddition( context.getOperationIdentidier() ) ) - .set( BiElementwise.class, context -> new CLBiElementwiseAddition( context.getOperationIdentidier() )); + .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastAddition(context.getOperationIdentidier()) ) + .set( Broadcast.class, context -> new CLBroadcastAddition( context.getOperationIdentidier() ) ) + .set( BiElementwise.class, context -> new CLBiElementwiseAddition( context.getOperationIdentidier() )); receive.forOperation( Subtraction.class ) - .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastSubtraction( context.getOperationIdentidier() ) ) - .set( Broadcast.class, context -> new CLBroadcastSubtraction( context.getOperationIdentidier() ) ) - .set( BiElementwise.class, context -> new CLBiElementwiseSubtraction( context.getOperationIdentidier() ) ); + .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastSubtraction( context.getOperationIdentidier() ) ) + .set( Broadcast.class, context -> new CLBroadcastSubtraction( context.getOperationIdentidier() ) ) + .set( BiElementwise.class, context -> new CLBiElementwiseSubtraction( context.getOperationIdentidier() ) ); receive.forOperation( Multiplication.class ) - .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastMultiplication( context.getOperationIdentidier() ) ) - .set( Broadcast.class, context -> new CLBroadcastMultiplication( context.getOperationIdentidier() ) ) - .set( BiElementwise.class, context -> new CLBiElementwiseMultiplication( context.getOperationIdentidier() ) ); + .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastMultiplication( context.getOperationIdentidier() ) ) + .set( Broadcast.class, context -> new CLBroadcastMultiplication( context.getOperationIdentidier() ) ) + .set( BiElementwise.class, context -> new CLBiElementwiseMultiplication( context.getOperationIdentidier() ) ); receive.forOperation( Division.class ) - .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastDivision( context.getOperationIdentidier() ) ) - .set( Broadcast.class, context -> new CLBroadcastDivision( context.getOperationIdentidier() ) ) - .set( BiElementwise.class, context -> new CLBiElementwiseDivision( context.getOperationIdentidier() ) ); + .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastDivision( context.getOperationIdentidier() ) ) + .set( Broadcast.class, context -> new CLBroadcastDivision( context.getOperationIdentidier() ) ) + .set( BiElementwise.class, context -> new CLBiElementwiseDivision( context.getOperationIdentidier() ) ); receive.forOperation( Modulo.class ) - .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastModulo( context.getOperationIdentidier() ) ) - .set( Broadcast.class, context -> new CLBroadcastModulo( context.getOperationIdentidier() ) ) - .set( BiElementwise.class, context -> new CLBiElementwiseModulo( context.getOperationIdentidier() ) ); + .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastModulo( context.getOperationIdentidier() ) ) + .set( Broadcast.class, context -> new CLBroadcastModulo( context.getOperationIdentidier() ) ) + .set( BiElementwise.class, context -> new CLBiElementwiseModulo( context.getOperationIdentidier() ) ); receive.forOperation( AssignLeft.class ) - .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastIdentity( context.getOperationIdentidier() ) ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.IDENTITY ) ); + .set( BiScalarBroadcast.class, context -> new CLScalarBroadcastIdentity( context.getOperationIdentidier() ) ) + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.IDENTITY ) ); receive.forOperation( Convolution.class ) - .set( NDConvolution.class, context -> new CLConvolution( context.getOperationIdentidier() ) ); + .set( NDConvolution.class, context -> new CLConvolution( context.getOperationIdentidier() ) ); receive.forOperation( XConvLeft.class ) - .set( NDConvolution.class, context -> new CLConvolution( context.getOperationIdentidier() ) ); + .set( NDConvolution.class, context -> new CLConvolution( context.getOperationIdentidier() ) ); receive.forOperation( XConvRight.class ) - .set( NDConvolution.class, context -> new CLConvolution( context.getOperationIdentidier() ) ); + .set( NDConvolution.class, context -> new CLConvolution( context.getOperationIdentidier() ) ); receive.forOperation( MatMul.class ) - .set( MatMulAlgorithm.class, context -> new CLMatMul() ); + .set( MatMulAlgorithm.class, context -> new CLMatMul() ); receive.forOperation( DotProduct.class ) - .set( DotProductAlgorithm.class, context -> new CLDot() ); + .set( DotProductAlgorithm.class, context -> new CLDot() ); receive.forOperation( Sum.class ) - .set( SumAlgorithm.class, context -> new CLSum() ); + .set( SumAlgorithm.class, context -> new CLSum() ); receive.forOperation( Randomization.class ) - .set( ElementwiseAlgorithm.class, context -> new CLRandomization() ); + .set( ElementwiseAlgorithm.class, context -> new CLRandomization() ); receive.forOperation( Absolute.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.ABSOLUTE) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.ABSOLUTE) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.ABSOLUTE) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.ABSOLUTE) ); receive.forOperation( Cosinus.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.COSINUS) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.COSINUS) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.COSINUS) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.COSINUS) ); receive.forOperation( GaSU.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.GASU) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.GASU) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.GASU) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.GASU) ); receive.forOperation( GaTU.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.GATU) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.GATU) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.GATU) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.GATU) ); receive.forOperation( Gaussian.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.GAUSSIAN) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.GAUSSIAN) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.GAUSSIAN) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.GAUSSIAN) ); receive.forOperation( GaussianFast.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.GAUSSIAN_FAST) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.GAUSSIAN_FAST) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.GAUSSIAN_FAST) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.GAUSSIAN_FAST) ); receive.forOperation( GeLU.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.GELU) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.GELU) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.GELU) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.GELU) ); receive.forOperation( Identity.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.IDENTITY) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.IDENTITY) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.IDENTITY) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.IDENTITY) ); receive.forOperation( Logarithm.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.LOGARITHM) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.LOGARITHM) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.LOGARITHM) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.LOGARITHM) ); receive.forOperation( Quadratic.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.QUADRATIC) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.QUADRATIC) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.QUADRATIC) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.QUADRATIC) ); receive.forOperation( ReLU.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.RELU) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.RELU) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.RELU) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.RELU) ); receive.forOperation( SeLU.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SELU) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SELU) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SELU) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SELU) ); receive.forOperation( Sigmoid.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SIGMOID) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SIGMOID) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SIGMOID) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SIGMOID) ); receive.forOperation( SiLU.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SILU) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SILU) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SILU) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SILU) ); receive.forOperation( Sinus.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SINUS) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SINUS) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SINUS) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SINUS) ); receive.forOperation( Softplus.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SOFTPLUS) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SOFTPLUS) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SOFTPLUS) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SOFTPLUS) ); receive.forOperation( Softsign.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SOFTSIGN) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SOFTSIGN) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SOFTSIGN) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SOFTSIGN) ); receive.forOperation( Tanh.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.TANH) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.TANH) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.TANH) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.TANH) ); receive.forOperation( TanhFast.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.TANH_FAST) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.TANH_FAST) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.TANH_FAST) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.TANH_FAST) ); receive.forOperation( Exp.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.EXP) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.EXP) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.EXP) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.EXP) ); receive.forOperation( Cbrt.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.CBRT) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.CBRT) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.CBRT) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.CBRT) ); receive.forOperation( Log10.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.LOG10) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.LOG10) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.LOG10) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.LOG10) ); receive.forOperation( Sqrt.class ) - .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SQRT) ) - .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SQRT) ); + .set( ElementwiseAlgorithm.class, context -> new CLElementwiseFunction( ScalarFun.SQRT) ) + .set( ScalarAlgorithm.class, context -> new CLScalarFunction(ScalarFun.SQRT) ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.ocl/CLSettings.html b/docs/coverage/test/html/neureka.backend.ocl/CLSettings.html index bb82333d8..0497bcb7e 100644 --- a/docs/coverage/test/html/neureka.backend.ocl/CLSettings.html +++ b/docs/coverage/test/html/neureka.backend.ocl/CLSettings.html @@ -1 +1 @@ -CLSettings

CLSettings

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 18100%0 of 0n/a040704
CLSettings()6100%n/a010201
setAutoConvertToFloat(boolean)5100%n/a010201
reset()4100%n/a010201
isAutoConvertToFloat()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CLSettings

CLSettings

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total12 of 1833%0 of 0n/a345734
setAutoConvertToFloat(boolean)50%n/a112211
reset()40%n/a112211
isAutoConvertToFloat()30%n/a111111
CLSettings()6100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.ocl/CLSettings.java.html b/docs/coverage/test/html/neureka.backend.ocl/CLSettings.java.html index 329966407..2add3c07e 100644 --- a/docs/coverage/test/html/neureka.backend.ocl/CLSettings.java.html +++ b/docs/coverage/test/html/neureka.backend.ocl/CLSettings.java.html @@ -7,16 +7,16 @@ private boolean _autoConvertToFloat = false; - public boolean isAutoConvertToFloat() { return _autoConvertToFloat; } + public boolean isAutoConvertToFloat() { return _autoConvertToFloat; } public CLSettings setAutoConvertToFloat(boolean autoConvertToFloat) { - _autoConvertToFloat = autoConvertToFloat; - return this; + _autoConvertToFloat = autoConvertToFloat; + return this; } public void reset() { - _autoConvertToFloat = false; - } + _autoConvertToFloat = false; + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.ocl/index.html b/docs/coverage/test/html/neureka.backend.ocl/index.html index 8121df71f..d67097132 100644 --- a/docs/coverage/test/html/neureka.backend.ocl/index.html +++ b/docs/coverage/test/html/neureka.backend.ocl/index.html @@ -1 +1 @@ -neureka.backend.ocl

neureka.backend.ocl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total67 of 1,13094%8 of 2669%71081018409502
CLBackend671,04593%81869%71041017709101
CLSettings18100%n/a04070401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.ocl

neureka.backend.ocl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total730 of 1,13035%26 of 260%10210866184899502
CLBackend71839435%260%9910461177869101
CLSettings1233%n/a34573401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.backend.ocl/index.source.html b/docs/coverage/test/html/neureka.backend.ocl/index.source.html index 356aa7184..63da83af7 100644 --- a/docs/coverage/test/html/neureka.backend.ocl/index.source.html +++ b/docs/coverage/test/html/neureka.backend.ocl/index.source.html @@ -1 +1 @@ -neureka.backend.ocl

neureka.backend.ocl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total67 of 1,13094%8 of 2669%71081018409502
CLBackend.java671,04593%81869%71041017709101
CLSettings.java18100%n/a04070401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.backend.ocl

neureka.backend.ocl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total730 of 1,13035%26 of 260%10210866184899502
CLBackend.java71839435%260%9910461177869101
CLSettings.java1233%n/a34573401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$1.html b/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$1.html index e425d0aec..1b01d54bf 100644 --- a/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$1.html +++ b/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$1.html @@ -1 +1 @@ -AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 30100%0 of 0n/a040604
{...}12100%n/a010101
executeChange()12100%n/a010301
getOldOwner()4100%n/a010101
getNewOwner()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 30100%0 of 0n/a040604
{...}12100%n/a010101
executeChange()12100%n/a010301
getOldOwner()4100%n/a010101
getNewOwner()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$2.html b/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$2.html index 68fdc07f9..3ca95d8fd 100644 --- a/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$2.html +++ b/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$2.html @@ -1 +1 @@ -AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 33100%0 of 0n/a040604
executeChange()15100%n/a010301
{...}12100%n/a010101
getNewOwner()4100%n/a010101
getOldOwner()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 33100%0 of 0n/a040604
executeChange()15100%n/a010301
{...}12100%n/a010101
getNewOwner()4100%n/a010101
getOldOwner()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$3.html b/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$3.html index 33ea409d7..292bd65a9 100644 --- a/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$3.html +++ b/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner$3.html @@ -1 +1 @@ -AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total19 of 190%0 of 0n/a444444
{...}90%n/a111111
getOldOwner()40%n/a111111
getNewOwner()40%n/a111111
executeChange()20%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

AbstractComponentOwner.new Component.OwnerChangeRequest() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total19 of 190%0 of 0n/a444444
{...}90%n/a111111
getOldOwner()40%n/a111111
getNewOwner()40%n/a111111
executeChange()20%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner.html b/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner.html index 98b3a2675..60e9b2049 100644 --- a/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner.html +++ b/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner.html @@ -1 +1 @@ -AbstractComponentOwner

AbstractComponentOwner

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total36 of 42391%15 of 6476%1448678116
_transferFrom(AbstractComponentOwner)350%40%336611
_add(Component)16598%1583%140801
get(Class)58100%10100%0601001
_addOrRemoveComp(Component, boolean)51100%21083%2701101
getAll(Class)43100%3562%350801
_remove(Component)36100%3562%350701
_newArrayWithout(int, Component[])36100%2250%230601
remove(Class)31100%6100%040501
set(Component)27100%4100%030701
has(Class)14100%2100%020201
AbstractComponentOwner()6100%n/a010201
_set(Component)5100%n/a010201
find(Class)5100%n/a010101
_setComps(Component[])4100%n/a010101
_deleteComponents()4100%n/a010101
_this()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractComponentOwner

AbstractComponentOwner

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total36 of 42391%15 of 6476%1448678116
_transferFrom(AbstractComponentOwner)350%40%336611
_add(Component)16598%1583%140801
get(Class)58100%10100%0601001
_addOrRemoveComp(Component, boolean)51100%21083%2701101
getAll(Class)43100%3562%350801
_remove(Component)36100%3562%350701
_newArrayWithout(int, Component[])36100%2250%230601
remove(Class)31100%6100%040501
set(Component)27100%4100%030701
has(Class)14100%2100%020201
AbstractComponentOwner()6100%n/a010201
_set(Component)5100%n/a010201
find(Class)5100%n/a010101
_setComps(Component[])4100%n/a010101
_deleteComponents()4100%n/a010101
_this()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner.java.html b/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner.java.html index 29e4b221b..105b9bd1f 100644 --- a/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner.java.html +++ b/docs/coverage/test/html/neureka.common.composition/AbstractComponentOwner.java.html @@ -328,4 +328,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/Component$IsBeing.html b/docs/coverage/test/html/neureka.common.composition/Component$IsBeing.html index 29b774eaf..374596206 100644 --- a/docs/coverage/test/html/neureka.common.composition/Component$IsBeing.html +++ b/docs/coverage/test/html/neureka.common.composition/Component$IsBeing.html @@ -1 +1 @@ -Component.IsBeing

Component.IsBeing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 27100%0 of 0n/a010101
static {...}27100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Component.IsBeing

Component.IsBeing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 44100%0 of 0n/a010101
static {...}44100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/Component$OwnerChangeRequest.html b/docs/coverage/test/html/neureka.common.composition/Component$OwnerChangeRequest.html index 6a28a345a..ced73c14b 100644 --- a/docs/coverage/test/html/neureka.common.composition/Component$OwnerChangeRequest.html +++ b/docs/coverage/test/html/neureka.common.composition/Component$OwnerChangeRequest.html @@ -1 +1 @@ -Component.OwnerChangeRequest

Component.OwnerChangeRequest

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2684%4 of 1266%471401
type()42284%4866%471401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Component.OwnerChangeRequest

Component.OwnerChangeRequest

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 2684%4 of 1266%471401
type()42284%4866%471401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/Component.html b/docs/coverage/test/html/neureka.common.composition/Component.html index 9fa30706d..e66547427 100644 --- a/docs/coverage/test/html/neureka.common.composition/Component.html +++ b/docs/coverage/test/html/neureka.common.composition/Component.html @@ -1 +1 @@ -Component

Component

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 5100%0 of 0n/a010201
update(Component.OwnerChangeRequest)5100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Component

Component

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 5100%0 of 0n/a010201
update(Component.OwnerChangeRequest)5100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/Component.java.html b/docs/coverage/test/html/neureka.common.composition/Component.java.html index 01d01fa32..231cdf623 100644 --- a/docs/coverage/test/html/neureka.common.composition/Component.java.html +++ b/docs/coverage/test/html/neureka.common.composition/Component.java.html @@ -92,4 +92,4 @@ return true; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/index.html b/docs/coverage/test/html/neureka.common.composition/index.html index 3835a967f..4fba8f322 100644 --- a/docs/coverage/test/html/neureka.common.composition/index.html +++ b/docs/coverage/test/html/neureka.common.composition/index.html @@ -1 +1 @@ -neureka.common.composition

neureka.common.composition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total59 of 56389%19 of 7675%22691110153117
AbstractComponentOwner3638791%154976%144867811601
AbstractComponentOwner.new Component.OwnerChangeRequest() {...}190%n/a44444411
Component.OwnerChangeRequest42284%4866%47140101
AbstractComponentOwner.new Component.OwnerChangeRequest() {...}33100%n/a04060401
AbstractComponentOwner.new Component.OwnerChangeRequest() {...}30100%n/a04060401
Component.IsBeing27100%n/a01010101
Component5100%n/a01020101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.common.composition

neureka.common.composition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total59 of 58089%19 of 7675%22691110153117
AbstractComponentOwner3638791%154976%144867811601
AbstractComponentOwner.new Component.OwnerChangeRequest() {...}190%n/a44444411
Component.OwnerChangeRequest42284%4866%47140101
Component.IsBeing44100%n/a01010101
AbstractComponentOwner.new Component.OwnerChangeRequest() {...}33100%n/a04060401
AbstractComponentOwner.new Component.OwnerChangeRequest() {...}30100%n/a04060401
Component5100%n/a01020101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.composition/index.source.html b/docs/coverage/test/html/neureka.common.composition/index.source.html index bb18830d3..a9a7830ba 100644 --- a/docs/coverage/test/html/neureka.common.composition/index.source.html +++ b/docs/coverage/test/html/neureka.common.composition/index.source.html @@ -1 +1 @@ -neureka.common.composition

neureka.common.composition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total59 of 56389%19 of 7675%22691110153117
AbstractComponentOwner.java5545089%154976%1860109452814
Component.java5493%4866%49170303
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.common.composition

neureka.common.composition

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total59 of 58089%19 of 7675%22691110153117
AbstractComponentOwner.java5545089%154976%1860109452814
Component.java7194%4866%49170303
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/Cache$LazyEntry.html b/docs/coverage/test/html/neureka.common.utility/Cache$LazyEntry.html index 900c70759..d26610d97 100644 --- a/docs/coverage/test/html/neureka.common.utility/Cache$LazyEntry.html +++ b/docs/coverage/test/html/neureka.common.utility/Cache$LazyEntry.html @@ -1 +1 @@ -Cache.LazyEntry

Cache.LazyEntry

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 5792%3 of 862%3801204
equals(Object)41982%3350%340401
getValue()13100%2100%020201
Cache.LazyEntry(Object, Function)12100%n/a010501
hashCode()9100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Cache.LazyEntry

Cache.LazyEntry

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 5792%3 of 862%3801204
equals(Object)41982%3350%340401
getValue()13100%2100%020201
Cache.LazyEntry(Object, Function)12100%n/a010501
hashCode()9100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/Cache.html b/docs/coverage/test/html/neureka.common.utility/Cache.html index 4810bb767..89dcb03f7 100644 --- a/docs/coverage/test/html/neureka.common.utility/Cache.html +++ b/docs/coverage/test/html/neureka.common.utility/Cache.html @@ -1 +1 @@ -Cache

Cache

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total59 of 24075%18 of 4257%1331841210
_index(int[])398468%111150%61251801
has(Object)170%40%332211
size()30%n/a111111
_equalsFor(Object, Object)22100%2675%250501
process(Object)21100%2100%020501
_setAt(int, Object)19100%1375%130301
_indexFor(Object)13100%2100%020101
Cache(int)10100%n/a010401
_index(int)7100%n/a010101
_getAt(int)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Cache

Cache

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total59 of 24075%18 of 4257%1331841210
_index(int[])398468%111150%61251801
has(Object)170%40%332211
size()30%n/a111111
_equalsFor(Object, Object)22100%2675%250501
process(Object)21100%2100%020501
_setAt(int, Object)19100%1375%130301
_indexFor(Object)13100%2100%020101
Cache(int)10100%n/a010401
_index(int)7100%n/a010101
_getAt(int)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/Cache.java.html b/docs/coverage/test/html/neureka.common.utility/Cache.java.html index a0e18708a..6c434be2c 100644 --- a/docs/coverage/test/html/neureka.common.utility/Cache.java.html +++ b/docs/coverage/test/html/neureka.common.utility/Cache.java.html @@ -134,4 +134,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/DataConverter$ForTensor.html b/docs/coverage/test/html/neureka.common.utility/DataConverter$ForTensor.html index be021cb0c..f3e9d48e8 100644 --- a/docs/coverage/test/html/neureka.common.utility/DataConverter$ForTensor.html +++ b/docs/coverage/test/html/neureka.common.utility/DataConverter$ForTensor.html @@ -1 +1 @@ -DataConverter.ForTensor

DataConverter.ForTensor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 206100%0 of 0n/a015032015
toFloatArray(Function)15100%n/a010401
toByteArray(Function)15100%n/a010401
toLongArray(Function)15100%n/a010401
toIntArray(Function)15100%n/a010401
toDoubleArray(Function)15100%n/a010401
toShortArray(Function)15100%n/a010401
toObjectArray(Function)15100%n/a010401
lambda$toShortArray$5(short[], Function, int)13100%n/a010101
lambda$toDoubleArray$4(double[], Function, int)13100%n/a010101
lambda$toIntArray$3(int[], Function, int)13100%n/a010101
lambda$toLongArray$2(long[], Function, int)13100%n/a010101
lambda$toByteArray$1(byte[], Function, int)13100%n/a010101
lambda$toFloatArray$0(float[], Function, int)13100%n/a010101
DataConverter.ForTensor(Tensor)12100%n/a010401
lambda$toObjectArray$6(Object[], Function, int)11100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DataConverter.ForTensor

DataConverter.ForTensor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 206100%0 of 0n/a015032015
toFloatArray(Function)15100%n/a010401
toByteArray(Function)15100%n/a010401
toLongArray(Function)15100%n/a010401
toIntArray(Function)15100%n/a010401
toDoubleArray(Function)15100%n/a010401
toShortArray(Function)15100%n/a010401
toObjectArray(Function)15100%n/a010401
lambda$toShortArray$5(short[], Function, int)13100%n/a010101
lambda$toDoubleArray$4(double[], Function, int)13100%n/a010101
lambda$toIntArray$3(int[], Function, int)13100%n/a010101
lambda$toLongArray$2(long[], Function, int)13100%n/a010101
lambda$toByteArray$1(byte[], Function, int)13100%n/a010101
lambda$toFloatArray$0(float[], Function, int)13100%n/a010101
DataConverter.ForTensor(Tensor)12100%n/a010401
lambda$toObjectArray$6(Object[], Function, int)11100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/DataConverter$Utility.html b/docs/coverage/test/html/neureka.common.utility/DataConverter$Utility.html index e96d12ef3..3fe0af142 100644 --- a/docs/coverage/test/html/neureka.common.utility/DataConverter$Utility.html +++ b/docs/coverage/test/html/neureka.common.utility/DataConverter$Utility.html @@ -1 +1 @@ -DataConverter.Utility

DataConverter.Utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total170 of 1,43788%57 of 21072%5616215232557
doubleToBool(double[])310%60%444411
boolToFloat(boolean[])280%60%444411
doubleToFloat(double[])112871%2466%243801
floatToDouble(float[])112871%2466%243801
lambda$floatToDouble$1(double[], float[], int)80%n/a111111
lambda$doubleToFloat$0(float[], double[], int)80%n/a111111
DataConverter.Utility()30%n/a111111
boolToDouble(boolean[])22692%1583%140401
floatToBigInteger(float[])22592%1375%130401
byteToBigInteger(byte[])22492%1375%130401
doubleToByte(double[])22492%1375%130401
doubleToShort(double[])22492%1375%130401
floatToByte(float[])22492%1375%130401
floatToShort(float[])22492%1375%130401
shortToBigInteger(short[])22492%1375%130401
doubleToBigInteger(double[])22492%1375%130401
intToBigInteger(int[])22492%1375%130401
longToByte(long[])22492%1375%130401
longToShort(long[])22492%1375%130401
byteToShort(byte[])22392%1375%130401
doubleToLong(double[])22392%1375%130401
floatToLong(float[])22392%1375%130401
shortToDouble(short[])22392%1375%130401
byteToDouble(byte[])22392%1375%130401
byteToFloat(byte[])22392%1375%130401
shortToFloat(short[])22392%1375%130401
shortToByte(short[])22392%1375%130401
byteToLong(byte[])22392%1375%130401
shortToLong(short[])22392%1375%130401
intToFloat(int[])22392%1375%130401
floatToInt(float[])22392%1375%130401
doubleToInt(double[])22392%1375%130401
intToDouble(int[])22392%1375%130401
intToLong(int[])22392%1375%130401
intToShort(int[])22392%1375%130401
intToByte(int[])22392%1375%130401
longToInt(long[])22392%1375%130401
longToFloat(long[])22392%1375%130401
longToDouble(long[])22392%1375%130401
longToBigInteger(long[])22392%1375%130401
byteToInt(byte[])22291%1375%130401
shortToInt(short[])22291%1375%130401
objectsToDoubles(Object[], int)53100%1583%140701
objectsToFloats(Object[], int)32100%1375%130501
objectsToShorts(Object[], int)32100%1375%130501
objectsToBytes(Object[], int)32100%1375%130501
objectsToLongs(Object[], int)32100%1375%130501
objectsToInts(Object[], int)32100%1375%130501
objFloatsToPrimFloats(Float[])21100%2100%020401
objDoublesToPrimDoubles(Double[])21100%2100%020401
objIntsToPrimInts(Integer[])21100%2100%020401
objLongsToPrimLongs(Long[])21100%2100%020401
objShortsToPrimShorts(Short[])21100%2100%020401
objBytesToPrimBytes(Byte[])21100%2100%020401
objBooleansToPrimBooleans(Boolean[])21100%2100%020401
objCharsToPrimChars(Character[])21100%2100%020401
intStream(int, int)12100%2100%020301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DataConverter.Utility

DataConverter.Utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total170 of 1,43788%57 of 21072%5616215232557
doubleToBool(double[])310%60%444411
boolToFloat(boolean[])280%60%444411
doubleToFloat(double[])112871%2466%243801
floatToDouble(float[])112871%2466%243801
lambda$floatToDouble$1(double[], float[], int)80%n/a111111
lambda$doubleToFloat$0(float[], double[], int)80%n/a111111
DataConverter.Utility()30%n/a111111
boolToDouble(boolean[])22692%1583%140401
floatToBigInteger(float[])22592%1375%130401
byteToBigInteger(byte[])22492%1375%130401
doubleToByte(double[])22492%1375%130401
doubleToShort(double[])22492%1375%130401
floatToByte(float[])22492%1375%130401
floatToShort(float[])22492%1375%130401
shortToBigInteger(short[])22492%1375%130401
doubleToBigInteger(double[])22492%1375%130401
intToBigInteger(int[])22492%1375%130401
longToByte(long[])22492%1375%130401
longToShort(long[])22492%1375%130401
byteToShort(byte[])22392%1375%130401
doubleToLong(double[])22392%1375%130401
floatToLong(float[])22392%1375%130401
shortToDouble(short[])22392%1375%130401
byteToDouble(byte[])22392%1375%130401
byteToFloat(byte[])22392%1375%130401
shortToFloat(short[])22392%1375%130401
shortToByte(short[])22392%1375%130401
byteToLong(byte[])22392%1375%130401
shortToLong(short[])22392%1375%130401
intToFloat(int[])22392%1375%130401
floatToInt(float[])22392%1375%130401
doubleToInt(double[])22392%1375%130401
intToDouble(int[])22392%1375%130401
intToLong(int[])22392%1375%130401
intToShort(int[])22392%1375%130401
intToByte(int[])22392%1375%130401
longToInt(long[])22392%1375%130401
longToFloat(long[])22392%1375%130401
longToDouble(long[])22392%1375%130401
longToBigInteger(long[])22392%1375%130401
byteToInt(byte[])22291%1375%130401
shortToInt(short[])22291%1375%130401
objectsToDoubles(Object[], int)53100%1583%140701
objectsToFloats(Object[], int)32100%1375%130501
objectsToShorts(Object[], int)32100%1375%130501
objectsToBytes(Object[], int)32100%1375%130501
objectsToLongs(Object[], int)32100%1375%130501
objectsToInts(Object[], int)32100%1375%130501
objFloatsToPrimFloats(Float[])21100%2100%020401
objDoublesToPrimDoubles(Double[])21100%2100%020401
objIntsToPrimInts(Integer[])21100%2100%020401
objLongsToPrimLongs(Long[])21100%2100%020401
objShortsToPrimShorts(Short[])21100%2100%020401
objBytesToPrimBytes(Byte[])21100%2100%020401
objBooleansToPrimBooleans(Boolean[])21100%2100%020401
objCharsToPrimChars(Character[])21100%2100%020401
intStream(int, int)12100%2100%020301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/DataConverter.html b/docs/coverage/test/html/neureka.common.utility/DataConverter.html index 306affc8a..1942bb17b 100644 --- a/docs/coverage/test/html/neureka.common.utility/DataConverter.html +++ b/docs/coverage/test/html/neureka.common.utility/DataConverter.html @@ -1 +1 @@ -DataConverter

DataConverter

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total320 of 1,31075%38 of 6238%5588131943357
convert(Object, Class)607254%31785%31182501
lambda$new$50(Object[], int)301126%3125%234601
_set(Class, Class, DataConverter.Conversion)204267%1375%1311101
lambda$new$32(Float)100%20%221111
lambda$new$24(Long)100%20%221111
lambda$new$14(BigDecimal)90%20%221111
lambda$new$12(BigInteger)90%20%221111
lambda$new$46(Boolean)80%20%221111
lambda$new$45(Boolean)80%20%221111
lambda$new$44(Boolean)80%20%221111
lambda$new$43(Boolean)80%20%221111
lambda$new$42(Boolean)80%20%221111
lambda$new$41(Boolean)80%20%221111
lambda$new$36(Byte)80%20%221111
lambda$new$20(Short)80%20%221111
lambda$new$16(Integer)80%20%221111
lambda$new$48(Boolean)70%20%221111
lambda$new$47(Boolean)70%20%221111
lambda$new$33(Float)60%n/a111111
lambda$new$15(BigDecimal)60%n/a111111
lambda$new$37(Byte)0%n/a111111
lambda$new$25(Long)0%n/a111111
lambda$new$23(Short)0%n/a111111
lambda$new$22(Short)0%n/a111111
lambda$new$21(Short)0%n/a111111
lambda$new$18(Integer)0%n/a111111
lambda$new$17(Integer)0%n/a111111
lambda$new$13(BigInteger)0%n/a111111
lambda$new$39(Byte)0%n/a111111
lambda$new$38(Byte)0%n/a111111
lambda$new$35(Float)0%n/a111111
lambda$new$34(Float)0%n/a111111
lambda$new$31(Double)0%n/a111111
lambda$new$30(Double)0%n/a111111
lambda$new$27(Long)0%n/a111111
lambda$new$26(Long)0%n/a111111
lambda$new$28(Double)880%1150%120101
DataConverter()679100%n/a01013601
lambda$new$11(List)16100%n/a010301
lambda$new$9(List)16100%n/a010301
lambda$new$7(List)16100%n/a010301
static {...}13100%n/a010301
lambda$new$10(float[], List, int)12100%n/a010101
lambda$new$8(byte[], List, int)12100%n/a010101
lambda$new$6(short[], List, int)12100%n/a010101
lambda$new$51(Object[])9100%n/a010301
lambda$new$49(String)9100%n/a010101
lambda$new$40(Boolean)8100%2100%020101
lambda$new$5(List)7100%n/a010101
lambda$new$4(Object)7100%n/a010101
lambda$new$3(List)7100%n/a010101
lambda$new$2(Object)7100%n/a010101
lambda$new$1(List)7100%n/a010101
lambda$new$0(Object)7100%n/a010101
lambda$new$29(Double)6100%n/a010101
lambda$new$19(Integer)100%n/a010101
get()100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DataConverter

DataConverter

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total320 of 1,31075%38 of 6238%5588131943357
convert(Object, Class)607254%31785%31182501
lambda$new$50(Object[], int)301126%3125%234601
_set(Class, Class, DataConverter.Conversion)204267%1375%1311101
lambda$new$32(Float)100%20%221111
lambda$new$24(Long)100%20%221111
lambda$new$14(BigDecimal)90%20%221111
lambda$new$12(BigInteger)90%20%221111
lambda$new$46(Boolean)80%20%221111
lambda$new$45(Boolean)80%20%221111
lambda$new$44(Boolean)80%20%221111
lambda$new$43(Boolean)80%20%221111
lambda$new$42(Boolean)80%20%221111
lambda$new$41(Boolean)80%20%221111
lambda$new$36(Byte)80%20%221111
lambda$new$20(Short)80%20%221111
lambda$new$16(Integer)80%20%221111
lambda$new$48(Boolean)70%20%221111
lambda$new$47(Boolean)70%20%221111
lambda$new$33(Float)60%n/a111111
lambda$new$15(BigDecimal)60%n/a111111
lambda$new$37(Byte)0%n/a111111
lambda$new$25(Long)0%n/a111111
lambda$new$23(Short)0%n/a111111
lambda$new$22(Short)0%n/a111111
lambda$new$21(Short)0%n/a111111
lambda$new$18(Integer)0%n/a111111
lambda$new$17(Integer)0%n/a111111
lambda$new$13(BigInteger)0%n/a111111
lambda$new$39(Byte)0%n/a111111
lambda$new$38(Byte)0%n/a111111
lambda$new$35(Float)0%n/a111111
lambda$new$34(Float)0%n/a111111
lambda$new$31(Double)0%n/a111111
lambda$new$30(Double)0%n/a111111
lambda$new$27(Long)0%n/a111111
lambda$new$26(Long)0%n/a111111
lambda$new$28(Double)880%1150%120101
DataConverter()679100%n/a01013601
lambda$new$11(List)16100%n/a010301
lambda$new$9(List)16100%n/a010301
lambda$new$7(List)16100%n/a010301
static {...}13100%n/a010301
lambda$new$10(float[], List, int)12100%n/a010101
lambda$new$8(byte[], List, int)12100%n/a010101
lambda$new$6(short[], List, int)12100%n/a010101
lambda$new$51(Object[])9100%n/a010301
lambda$new$49(String)9100%n/a010101
lambda$new$40(Boolean)8100%2100%020101
lambda$new$5(List)7100%n/a010101
lambda$new$4(Object)7100%n/a010101
lambda$new$3(List)7100%n/a010101
lambda$new$2(Object)7100%n/a010101
lambda$new$1(List)7100%n/a010101
lambda$new$0(Object)7100%n/a010101
lambda$new$29(Double)6100%n/a010101
lambda$new$19(Integer)100%n/a010101
get()100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/DataConverter.java.html b/docs/coverage/test/html/neureka.common.utility/DataConverter.java.html index dd03b8181..acecf08e3 100644 --- a/docs/coverage/test/html/neureka.common.utility/DataConverter.java.html +++ b/docs/coverage/test/html/neureka.common.utility/DataConverter.java.html @@ -911,4 +911,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/ListReader$Result.html b/docs/coverage/test/html/neureka.common.utility/ListReader$Result.html index c3eb8401d..1ad90b2d3 100644 --- a/docs/coverage/test/html/neureka.common.utility/ListReader$Result.html +++ b/docs/coverage/test/html/neureka.common.utility/ListReader$Result.html @@ -1 +1 @@ -ListReader.Result

ListReader.Result

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 38100%0 of 0n/a040904
ListReader.Result(Object, Function)29100%n/a010601
getType()3100%n/a010101
getShape()3100%n/a010101
getData()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ListReader.Result

ListReader.Result

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 38100%0 of 0n/a040904
ListReader.Result(Object, Function)29100%n/a010601
getType()3100%n/a010101
getShape()3100%n/a010101
getData()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/ListReader.html b/docs/coverage/test/html/neureka.common.utility/ListReader.html index 962da2d92..6602b80da 100644 --- a/docs/coverage/test/html/neureka.common.utility/ListReader.html +++ b/docs/coverage/test/html/neureka.common.utility/ListReader.html @@ -1 +1 @@ -ListReader

ListReader

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 31292%5 of 2882%529241015
ListReader(Object, int, List, List, Function)187981%3770%3622001
_isLeave(Object)41376%2466%240301
_findSize(List, int)71100%2100%020801
_findType(List)58100%2100%020901
lambda$new$0(int, List, List, Function, Object)11100%n/a010101
lambda$_findSize$8(int, Integer)8100%2100%020101
lambda$_findSize$7(int, Integer)8100%2100%020101
read(List, Function)7100%n/a010101
lambda$_findType$4(Class, Class)7100%2100%020101
lambda$_findType$3(Class, Class)7100%2100%020101
lambda$_findSize$6(List)5100%n/a010101
lambda$_findType$2(List)5100%n/a010101
lambda$_findSize$9(ListReader)4100%n/a010101
lambda$_findSize$5(ListReader)4100%n/a010101
lambda$_findType$1(ListReader)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ListReader

ListReader

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 31292%5 of 2882%529241015
ListReader(Object, int, List, List, Function)187981%3770%3622001
_isLeave(Object)41376%2466%240301
_findSize(List, int)71100%2100%020801
_findType(List)58100%2100%020901
lambda$new$0(int, List, List, Function, Object)11100%n/a010101
lambda$_findSize$8(int, Integer)8100%2100%020101
lambda$_findSize$7(int, Integer)8100%2100%020101
read(List, Function)7100%n/a010101
lambda$_findType$4(Class, Class)7100%2100%020101
lambda$_findType$3(Class, Class)7100%2100%020101
lambda$_findSize$6(List)5100%n/a010101
lambda$_findType$2(List)5100%n/a010101
lambda$_findSize$9(ListReader)4100%n/a010101
lambda$_findSize$5(ListReader)4100%n/a010101
lambda$_findType$1(ListReader)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/ListReader.java.html b/docs/coverage/test/html/neureka.common.utility/ListReader.java.html index 917eb2653..a59fa895b 100644 --- a/docs/coverage/test/html/neureka.common.utility/ListReader.java.html +++ b/docs/coverage/test/html/neureka.common.utility/ListReader.java.html @@ -127,4 +127,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/LogUtil.html b/docs/coverage/test/html/neureka.common.utility/LogUtil.html index 68d08f027..769314645 100644 --- a/docs/coverage/test/html/neureka.common.utility/LogUtil.html +++ b/docs/coverage/test/html/neureka.common.utility/LogUtil.html @@ -1 +1 @@ -LogUtil

LogUtil

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 5290%1 of 475%251913
LogUtil()30%n/a111111
nullArgCheck(Object, String, Class, String[])24295%1375%130701
format(String, Object[])5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +LogUtil

LogUtil

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 5290%1 of 475%251913
LogUtil()30%n/a111111
nullArgCheck(Object, String, Class, String[])24295%1375%130701
format(String, Object[])5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/LogUtil.java.html b/docs/coverage/test/html/neureka.common.utility/LogUtil.java.html index e4a0a8b25..b24eba43b 100644 --- a/docs/coverage/test/html/neureka.common.utility/LogUtil.java.html +++ b/docs/coverage/test/html/neureka.common.utility/LogUtil.java.html @@ -33,4 +33,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/SettingsLoader$TypeChecker.html b/docs/coverage/test/html/neureka.common.utility/SettingsLoader$TypeChecker.html index 8786576f6..774cc7f2e 100644 --- a/docs/coverage/test/html/neureka.common.utility/SettingsLoader$TypeChecker.html +++ b/docs/coverage/test/html/neureka.common.utility/SettingsLoader$TypeChecker.html @@ -1 +1 @@ -SettingsLoader.TypeChecker

SettingsLoader.TypeChecker

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total76 of 16453%3 of 1478%39113202
checkAndAssign(String, Class, Consumer)768251%31178%38113101
SettingsLoader.TypeChecker(Properties)6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SettingsLoader.TypeChecker

SettingsLoader.TypeChecker

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total76 of 16453%3 of 1478%39113202
checkAndAssign(String, Class, Consumer)768251%31178%38113101
SettingsLoader.TypeChecker(Properties)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/SettingsLoader.html b/docs/coverage/test/html/neureka.common.utility/SettingsLoader.html index 40d861547..d18d5089d 100644 --- a/docs/coverage/test/html/neureka.common.utility/SettingsLoader.html +++ b/docs/coverage/test/html/neureka.common.utility/SettingsLoader.html @@ -1 +1 @@ -SettingsLoader

SettingsLoader

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 39895%1 of 475%130752028
tryGroovyClosureOn(Object, Object)74285%n/a013701
loadProperties(Neureka)514396%n/a0123501
tryGroovyScriptsOn(Neureka, Consumer)52281%1375%132901
lambda$loadProperties$20(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$16(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$15(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$14(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$13(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$12(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$11(Neureka.Settings, Integer)8100%n/a010101
lambda$loadProperties$10(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$9(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$8(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$7(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$6(Neureka.Settings, Integer)8100%n/a010101
lambda$loadProperties$19(Neureka.Settings, String)7100%n/a010101
lambda$loadProperties$18(Neureka.Settings, String)7100%n/a010101
lambda$loadProperties$17(Neureka.Settings, String)7100%n/a010101
lambda$loadProperties$23(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$21(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$5(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$4(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$3(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$2(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$1(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$0(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$22(Neureka.Settings, Class)5100%n/a010101
static {...}4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SettingsLoader

SettingsLoader

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 40095%1 of 475%130753028
tryGroovyClosureOn(Object, Object)74285%n/a013701
loadProperties(Neureka)514596%n/a0123601
tryGroovyScriptsOn(Neureka, Consumer)52281%1375%132901
lambda$loadProperties$20(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$16(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$15(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$14(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$13(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$12(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$11(Neureka.Settings, Integer)8100%n/a010101
lambda$loadProperties$10(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$9(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$8(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$7(Neureka.Settings, Boolean)8100%n/a010101
lambda$loadProperties$6(Neureka.Settings, Integer)8100%n/a010101
lambda$loadProperties$19(Neureka.Settings, String)7100%n/a010101
lambda$loadProperties$18(Neureka.Settings, String)7100%n/a010101
lambda$loadProperties$17(Neureka.Settings, String)7100%n/a010101
lambda$loadProperties$23(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$21(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$5(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$4(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$3(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$2(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$1(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$0(Neureka.Settings, Boolean)6100%n/a010101
lambda$loadProperties$22(Neureka.Settings, Class)5100%n/a010101
static {...}4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/SettingsLoader.java.html b/docs/coverage/test/html/neureka.common.utility/SettingsLoader.java.html index b803816e0..bc8851195 100644 --- a/docs/coverage/test/html/neureka.common.utility/SettingsLoader.java.html +++ b/docs/coverage/test/html/neureka.common.utility/SettingsLoader.java.html @@ -61,7 +61,7 @@ private SettingsLoader() {/* This is a utility class! */} public static void loadProperties( Neureka instance ) { - try ( + try ( final InputStream stream = instance.getClass() .getClassLoader() .getResourceAsStream( "library_settings.properties" ) @@ -183,4 +183,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/index.html b/docs/coverage/test/html/neureka.common.utility/index.html index 00f6287a0..5ebadd9d3 100644 --- a/docs/coverage/test/html/neureka.common.utility/index.html +++ b/docs/coverage/test/html/neureka.common.utility/index.html @@ -1 +1 @@ -neureka.common.utility

neureka.common.utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total673 of 4,21484%126 of 37266%1383815765441195010
DataConverter32099075%382438%558813194335701
DataConverter.Utility1701,26788%5715372%561621523255701
SettingsLoader.TypeChecker768853%31178%3911320201
Cache5918175%182457%133184121001
ListReader2229092%52382%52924101501
SettingsLoader1738195%375%13075202801
LogUtil4790%375%25191301
Cache.LazyEntry5392%3562%380120401
DataConverter.ForTensor206100%n/a01503201501
ListReader.Result38100%n/a04090401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.common.utility

neureka.common.utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total673 of 4,21684%126 of 37266%1383815765541195010
DataConverter32099075%382438%558813194335701
DataConverter.Utility1701,26788%5715372%561621523255701
SettingsLoader.TypeChecker768853%31178%3911320201
Cache5918175%182457%133184121001
ListReader2229092%52382%52924101501
SettingsLoader1738395%375%13075302801
LogUtil4790%375%25191301
Cache.LazyEntry5392%3562%380120401
DataConverter.ForTensor206100%n/a01503201501
ListReader.Result38100%n/a04090401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.common.utility/index.source.html b/docs/coverage/test/html/neureka.common.utility/index.source.html index 777d9457c..d1bcccd1f 100644 --- a/docs/coverage/test/html/neureka.common.utility/index.source.html +++ b/docs/coverage/test/html/neureka.common.utility/index.source.html @@ -1 +1 @@ -neureka.common.utility

neureka.common.utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total673 of 4,21484%126 of 37266%1383815765441195010
DataConverter.java4902,46383%9517765%111265284583812903
SettingsLoader.java9346983%41477%439188403002
Cache.java6323478%212958%163985321402
ListReader.java32893%52382%53325001902
LogUtil.java4790%375%25191301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.common.utility

neureka.common.utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total673 of 4,21684%126 of 37266%1383815765541195010
DataConverter.java4902,46383%9517765%111265284583812903
SettingsLoader.java9347183%41477%439188503002
Cache.java6323478%212958%163985321402
ListReader.java32893%52382%53325001902
LogUtil.java4790%375%25191301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/AbstractFileHandle.html b/docs/coverage/test/html/neureka.devices.file/AbstractFileHandle.html index 4982b2995..a07167dc0 100644 --- a/docs/coverage/test/html/neureka.devices.file/AbstractFileHandle.html +++ b/docs/coverage/test/html/neureka.devices.file/AbstractFileHandle.html @@ -1 +1 @@ -AbstractFileHandle

AbstractFileHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total227 of 37439%20 of 3237%16273569311
restore(Tensor)704338%13735%711122001
AbstractFileHandle(String, FileType)704237%3350%34102001
free()251740%1150%123701
_loadFile()251130%1150%123601
_loadFileInputStream()221337%n/a014901
isEmpty()70%20%221111
contains(Tensor)50%n/a111111
numberOfStored()30%n/a111111
getFileName()15100%n/a010201
getLocation()3100%n/a010101
extension()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractFileHandle

AbstractFileHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total227 of 37439%20 of 3237%16273569311
restore(Tensor)704338%13735%711122001
AbstractFileHandle(String, FileType)704237%3350%34102001
free()251740%1150%123701
_loadFile()251130%1150%123601
_loadFileInputStream()221337%n/a014901
isEmpty()70%20%221111
contains(Tensor)50%n/a111111
numberOfStored()30%n/a111111
getFileName()15100%n/a010201
getLocation()3100%n/a010101
extension()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/AbstractFileHandle.java.html b/docs/coverage/test/html/neureka.devices.file/AbstractFileHandle.java.html index c5684f190..034775cca 100644 --- a/docs/coverage/test/html/neureka.devices.file/AbstractFileHandle.java.html +++ b/docs/coverage/test/html/neureka.devices.file/AbstractFileHandle.java.html @@ -140,4 +140,4 @@ @Override public final String extension() { return _extension; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/AbstractImageFileHandle.html b/docs/coverage/test/html/neureka.devices.file/AbstractImageFileHandle.html index 46e09e5ea..4690ef062 100644 --- a/docs/coverage/test/html/neureka.devices.file/AbstractImageFileHandle.html +++ b/docs/coverage/test/html/neureka.devices.file/AbstractImageFileHandle.html @@ -1 +1 @@ -AbstractImageFileHandle

AbstractImageFileHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total206 of 46855%9 of 2259%10222772111
_loadHead()652729%2250%2381601
store(Tensor)643736%2250%2381501
AbstractImageFileHandle(Tensor, String, ImageFileType)405658%3562%3551601
_loadData()274864%2250%2351401
getDataSize()100%n/a111111
load()33100%n/a010501
getShape()20100%n/a010101
lambda$_loadData$0(short[], UI8, byte[], int, int)18100%2100%020101
getValueSize()10100%n/a010101
getTotalSize()10100%n/a010101
getDataType()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractImageFileHandle

AbstractImageFileHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total206 of 46855%9 of 2259%10222772111
_loadHead()652729%2250%2381601
store(Tensor)643736%2250%2381501
AbstractImageFileHandle(Tensor, String, ImageFileType)405658%3562%3551601
_loadData()274864%2250%2351401
getDataSize()100%n/a111111
load()33100%n/a010501
getShape()20100%n/a010101
lambda$_loadData$0(short[], UI8, byte[], int, int)18100%2100%020101
getValueSize()10100%n/a010101
getTotalSize()10100%n/a010101
getDataType()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/AbstractImageFileHandle.java.html b/docs/coverage/test/html/neureka.devices.file/AbstractImageFileHandle.java.html index 554b70857..24302373d 100644 --- a/docs/coverage/test/html/neureka.devices.file/AbstractImageFileHandle.java.html +++ b/docs/coverage/test/html/neureka.devices.file/AbstractImageFileHandle.java.html @@ -163,4 +163,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/CSVHandle$CSVType.html b/docs/coverage/test/html/neureka.devices.file/CSVHandle$CSVType.html index 2d12221e0..eafd2718a 100644 --- a/docs/coverage/test/html/neureka.devices.file/CSVHandle$CSVType.html +++ b/docs/coverage/test/html/neureka.devices.file/CSVHandle$CSVType.html @@ -1 +1 @@ -CSVHandle.CSVType

CSVHandle.CSVType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 2100%0 of 0n/a010101
defaultExtension()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CSVHandle.CSVType

CSVHandle.CSVType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 2100%0 of 0n/a010101
defaultExtension()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/CSVHandle.html b/docs/coverage/test/html/neureka.devices.file/CSVHandle.html index 8c57d6b69..4c7e73c96 100644 --- a/docs/coverage/test/html/neureka.devices.file/CSVHandle.html +++ b/docs/coverage/test/html/neureka.devices.file/CSVHandle.html @@ -1 +1 @@ -CSVHandle

CSVHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total94 of 78688%14 of 8483%226222146920
_lazyLoad()2424991%32790%21695701
CSVHandle(Tensor, String)1720092%52784%51723701
load()911092%1990%1601801
getDataSize()7750%1150%122301
getValueSize()61368%2250%230401
store(Tensor)50%n/a111111
_loadData()30%n/a111111
getDelimiter()30%n/a111111
isFirstRowIsLabels()30%n/a111111
getColLabels()30%n/a111111
isFirstColIsIndex()30%n/a111111
getRowLabels()30%n/a111111
getNumberOfRows()30%n/a111111
getNumberOfColumns()30%n/a111111
_parseTensorNameFromFileName()3093%2250%230401
CSVHandle(String, Map)57100%2100%0201301
getShape()16100%n/a010101
static {...}4100%n/a010201
getTotalSize()3100%n/a010101
getDataType()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CSVHandle

CSVHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total94 of 78888%14 of 8483%226222147920
_lazyLoad()2425191%32790%21695801
CSVHandle(Tensor, String)1720092%52784%51723701
load()911092%1990%1601801
getDataSize()7750%1150%122301
getValueSize()61368%2250%230401
store(Tensor)50%n/a111111
_loadData()30%n/a111111
getDelimiter()30%n/a111111
isFirstRowIsLabels()30%n/a111111
getColLabels()30%n/a111111
isFirstColIsIndex()30%n/a111111
getRowLabels()30%n/a111111
getNumberOfRows()30%n/a111111
getNumberOfColumns()30%n/a111111
_parseTensorNameFromFileName()3093%2250%230401
CSVHandle(String, Map)57100%2100%0201301
getShape()16100%n/a010101
static {...}4100%n/a010201
getTotalSize()3100%n/a010101
getDataType()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/CSVHandle.java.html b/docs/coverage/test/html/neureka.devices.file/CSVHandle.java.html index d342c60f3..8208620dc 100644 --- a/docs/coverage/test/html/neureka.devices.file/CSVHandle.java.html +++ b/docs/coverage/test/html/neureka.devices.file/CSVHandle.java.html @@ -113,7 +113,7 @@ } List<String[]> table = new ArrayList<>(); List<String> rowLabels = ( _firstColIsIndex ) ? new ArrayList<>() : null; - try ( + try ( BufferedReader br = new BufferedReader( new InputStreamReader( fis, StandardCharsets.UTF_8 ) ) ) { String line; @@ -279,4 +279,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/FileDevice$1.html b/docs/coverage/test/html/neureka.devices.file/FileDevice$1.html index d6551be5a..ddf63a74a 100644 --- a/docs/coverage/test/html/neureka.devices.file/FileDevice$1.html +++ b/docs/coverage/test/html/neureka.devices.file/FileDevice$1.html @@ -1 +1 @@ -FileDevice.new AbstractDeviceData() {...}

FileDevice.new AbstractDeviceData() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 10100%0 of 0n/a010101
{...}10100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FileDevice.new AbstractDeviceData() {...}

FileDevice.new AbstractDeviceData() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 10100%0 of 0n/a010101
{...}10100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/FileDevice.html b/docs/coverage/test/html/neureka.devices.file/FileDevice.html index 362942a4f..98532fef3 100644 --- a/docs/coverage/test/html/neureka.devices.file/FileDevice.html +++ b/docs/coverage/test/html/neureka.devices.file/FileDevice.html @@ -1 +1 @@ -FileDevice

FileDevice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total206 of 75072%14 of 3458%214441141827
load(String, Map)657653%5550%5672301
update(Component.OwnerChangeRequest)350%20%228811
store(Tensor)168283%1150%1261501
access(Tensor)140%n/a112211
approve(ExecutionCall)140%n/a112211
optimizedOperationOf(Function, String)140%n/a112211
dispose()130%n/a115511
restore(Tensor)83480%1150%1231101
free(Tensor)83280%1150%1231101
allocate(DataType, NDConfiguration)50%n/a111111
allocateFromOne(DataType, NDConfiguration, Object)50%n/a111111
allocateFromAll(DataType, NDConfiguration, Object)50%n/a111111
_updateFolderView()47995%2880%2601501
store(Tensor, String, Map)82100%1375%1301601
toString()34100%n/a010401
lambda$_updateFolderView$0(String)25100%1150%120401
FileDevice(String)23100%n/a010701
at(String)17100%n/a010201
fileHandleOf(Tensor)12100%n/a010201
has(Tensor)11100%n/a010201
static {...}9100%n/a010201
getLoaded()7100%n/a010101
store(Tensor, String)6100%n/a010101
getLoadable()6100%n/a010101
load(String)5100%n/a010101
getDirectory()3100%n/a010101
lambda$store$1()100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FileDevice

FileDevice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total202 of 75073%13 of 3461%204441141827
load(String, Map)657653%5550%5672301
update(Component.OwnerChangeRequest)350%20%228811
store(Tensor)168283%1150%1261501
access(Tensor)140%n/a112211
approve(ExecutionCall)140%n/a112211
optimizedOperationOf(Function, String)140%n/a112211
dispose()130%n/a115511
restore(Tensor)83480%1150%1231101
free(Tensor)83280%1150%1231101
allocate(DataType, NDConfiguration)50%n/a111111
allocateFromOne(DataType, NDConfiguration, Object)50%n/a111111
allocateFromAll(DataType, NDConfiguration, Object)50%n/a111111
_updateFolderView()83100%1990%1601501
store(Tensor, String, Map)82100%1375%1301601
toString()34100%n/a010401
lambda$_updateFolderView$0(String)25100%1150%120401
FileDevice(String)23100%n/a010701
at(String)17100%n/a010201
fileHandleOf(Tensor)12100%n/a010201
has(Tensor)11100%n/a010201
static {...}9100%n/a010201
getLoaded()7100%n/a010101
store(Tensor, String)6100%n/a010101
getLoadable()6100%n/a010101
load(String)5100%n/a010101
getDirectory()3100%n/a010101
lambda$store$1()100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/FileDevice.java.html b/docs/coverage/test/html/neureka.devices.file/FileDevice.java.html index f2ab77359..61be2ab04 100644 --- a/docs/coverage/test/html/neureka.devices.file/FileDevice.java.html +++ b/docs/coverage/test/html/neureka.devices.file/FileDevice.java.html @@ -81,7 +81,7 @@ private void _updateFolderView() { _loadable.clear(); File dir = new File( _directory ); - if ( ! dir.exists() ) dir.mkdirs(); + if ( ! dir.exists() ) dir.mkdirs(); else { List<String> found = new ArrayList<>(); File[] files = dir.listFiles(); @@ -333,4 +333,4 @@ public List<String> getLoaded() { return new ArrayList<>(_loaded.keySet()); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/FileHandle.html b/docs/coverage/test/html/neureka.devices.file/FileHandle.html index 794855d28..cbf8327f9 100644 --- a/docs/coverage/test/html/neureka.devices.file/FileHandle.html +++ b/docs/coverage/test/html/neureka.devices.file/FileHandle.html @@ -1 +1 @@ -FileHandle

FileHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 5100%0 of 0n/a010101
static {...}5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FileHandle

FileHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 5100%0 of 0n/a010101
static {...}5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/FileHandle.java.html b/docs/coverage/test/html/neureka.devices.file/FileHandle.java.html index e3a3df4ac..ec768d7ad 100644 --- a/docs/coverage/test/html/neureka.devices.file/FileHandle.java.html +++ b/docs/coverage/test/html/neureka.devices.file/FileHandle.java.html @@ -94,4 +94,4 @@ */ String extension(); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/HandleFactory.html b/docs/coverage/test/html/neureka.devices.file/HandleFactory.html index d958b13c4..da1704149 100644 --- a/docs/coverage/test/html/neureka.devices.file/HandleFactory.html +++ b/docs/coverage/test/html/neureka.devices.file/HandleFactory.html @@ -1 +1 @@ -HandleFactory

HandleFactory

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 12896%0 of 0n/a113016113
lambda$new$0(String, Map)50%n/a111111
HandleFactory()61100%n/a0101201
getLoader(String)6100%n/a010101
getSaver(String)6100%n/a010101
lambda$new$7(String, Tensor, Map)6100%n/a010101
lambda$new$6(String, Tensor, Map)6100%n/a010101
lambda$new$5(String, Tensor, Map)6100%n/a010101
lambda$new$4(String, Tensor, Map)6100%n/a010101
lambda$new$3(String, Map)6100%n/a010101
hasLoader(String)5100%n/a010101
hasSaver(String)5100%n/a010101
lambda$new$2(String, Map)5100%n/a010101
lambda$new$1(String, Map)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +HandleFactory

HandleFactory

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 12896%0 of 0n/a113016113
lambda$new$0(String, Map)50%n/a111111
HandleFactory()61100%n/a0101201
getLoader(String)6100%n/a010101
getSaver(String)6100%n/a010101
lambda$new$7(String, Tensor, Map)6100%n/a010101
lambda$new$6(String, Tensor, Map)6100%n/a010101
lambda$new$5(String, Tensor, Map)6100%n/a010101
lambda$new$4(String, Tensor, Map)6100%n/a010101
lambda$new$3(String, Map)6100%n/a010101
hasLoader(String)5100%n/a010101
hasSaver(String)5100%n/a010101
lambda$new$2(String, Map)5100%n/a010101
lambda$new$1(String, Map)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/HandleFactory.java.html b/docs/coverage/test/html/neureka.devices.file/HandleFactory.java.html index 5821d01a6..bb55d61cb 100644 --- a/docs/coverage/test/html/neureka.devices.file/HandleFactory.java.html +++ b/docs/coverage/test/html/neureka.devices.file/HandleFactory.java.html @@ -53,4 +53,4 @@ return _SAVERS.get(name); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/IDXHandle$IDXType.html b/docs/coverage/test/html/neureka.devices.file/IDXHandle$IDXType.html index 2a7640cff..b9e189e29 100644 --- a/docs/coverage/test/html/neureka.devices.file/IDXHandle$IDXType.html +++ b/docs/coverage/test/html/neureka.devices.file/IDXHandle$IDXType.html @@ -1 +1 @@ -IDXHandle.IDXType

IDXHandle.IDXType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 2100%0 of 0n/a010101
defaultExtension()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +IDXHandle.IDXType

IDXHandle.IDXType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 2100%0 of 0n/a010101
defaultExtension()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/IDXHandle.html b/docs/coverage/test/html/neureka.devices.file/IDXHandle.html index 2482f7fee..7d9a0e8f4 100644 --- a/docs/coverage/test/html/neureka.devices.file/IDXHandle.html +++ b/docs/coverage/test/html/neureka.devices.file/IDXHandle.html @@ -1 +1 @@ -IDXHandle

IDXHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total64 of 46786%7 of 1861%82116107112
store(Tensor)4011273%2466%24103901
_loadData()85186%2250%2321101
IDXHandle(String)61164%n/a013701
_loadHead()48595%1375%1301701
getShape()30%n/a111111
load()22592%1150%120501
getDataSize()1794%1150%120401
static {...}65100%n/a0101401
IDXHandle(Tensor, String)25100%n/a010601
getTotalSize()6100%n/a010101
getDataType()3100%n/a010101
getValueSize()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +IDXHandle

IDXHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total64 of 46786%7 of 1861%82116107112
store(Tensor)4011273%2466%24103901
_loadData()85186%2250%2321101
IDXHandle(String)61164%n/a013701
_loadHead()48595%1375%1301701
getShape()30%n/a111111
load()22592%1150%120501
getDataSize()1794%1150%120401
static {...}65100%n/a0101401
IDXHandle(Tensor, String)25100%n/a010601
getTotalSize()6100%n/a010101
getDataType()3100%n/a010101
getValueSize()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/IDXHandle.java.html b/docs/coverage/test/html/neureka.devices.file/IDXHandle.java.html index 9bce47558..dc0462cf8 100644 --- a/docs/coverage/test/html/neureka.devices.file/IDXHandle.java.html +++ b/docs/coverage/test/html/neureka.devices.file/IDXHandle.java.html @@ -220,4 +220,4 @@ @Override public String defaultExtension() { return "idx"; } } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/ImageFileType.html b/docs/coverage/test/html/neureka.devices.file/ImageFileType.html index 321451687..2d25b4921 100644 --- a/docs/coverage/test/html/neureka.devices.file/ImageFileType.html +++ b/docs/coverage/test/html/neureka.devices.file/ImageFileType.html @@ -1 +1 @@ -ImageFileType

ImageFileType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 16100%0 of 0n/a030303
numericTypeRepresentation()8100%n/a010101
numberOfChannels()4100%n/a010101
targetedValueType()4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ImageFileType

ImageFileType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 16100%0 of 0n/a030303
numericTypeRepresentation()8100%n/a010101
numberOfChannels()4100%n/a010101
targetedValueType()4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/ImageFileType.java.html b/docs/coverage/test/html/neureka.devices.file/ImageFileType.java.html index 2735b3095..463a2bf1b 100644 --- a/docs/coverage/test/html/neureka.devices.file/ImageFileType.java.html +++ b/docs/coverage/test/html/neureka.devices.file/ImageFileType.java.html @@ -21,4 +21,4 @@ String imageTypeName(); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/JPEGHandle$1.html b/docs/coverage/test/html/neureka.devices.file/JPEGHandle$1.html index 9354207cd..17341ed86 100644 --- a/docs/coverage/test/html/neureka.devices.file/JPEGHandle$1.html +++ b/docs/coverage/test/html/neureka.devices.file/JPEGHandle$1.html @@ -1 +1 @@ -JPEGHandle.new ImageFileType() {...}

JPEGHandle.new ImageFileType() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 977%0 of 0n/a141414
imageTypeName()20%n/a111111
{...}3100%n/a010101
imageType()2100%n/a010101
defaultExtension()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +JPEGHandle.new ImageFileType() {...}

JPEGHandle.new ImageFileType() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 977%0 of 0n/a141414
imageTypeName()20%n/a111111
{...}3100%n/a010101
imageType()2100%n/a010101
defaultExtension()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/JPEGHandle.html b/docs/coverage/test/html/neureka.devices.file/JPEGHandle.html index bd88ae645..0a0c90afa 100644 --- a/docs/coverage/test/html/neureka.devices.file/JPEGHandle.html +++ b/docs/coverage/test/html/neureka.devices.file/JPEGHandle.html @@ -1 +1 @@ -JPEGHandle

JPEGHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 17100%0 of 0n/a030503
JPEGHandle(Tensor, String)8100%n/a010201
JPEGHandle(String)5100%n/a010101
static {...}4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +JPEGHandle

JPEGHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 17100%0 of 0n/a030503
JPEGHandle(Tensor, String)8100%n/a010201
JPEGHandle(String)5100%n/a010101
static {...}4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/JPEGHandle.java.html b/docs/coverage/test/html/neureka.devices.file/JPEGHandle.java.html index bebad11e1..85b43f7c6 100644 --- a/docs/coverage/test/html/neureka.devices.file/JPEGHandle.java.html +++ b/docs/coverage/test/html/neureka.devices.file/JPEGHandle.java.html @@ -32,4 +32,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/NumberReader.html b/docs/coverage/test/html/neureka.devices.file/NumberReader.html index 743691417..6637a959c 100644 --- a/docs/coverage/test/html/neureka.devices.file/NumberReader.html +++ b/docs/coverage/test/html/neureka.devices.file/NumberReader.html @@ -1 +1 @@ -NumberReader

NumberReader

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 8892%1 of 250%2611515
read(NumericType)43188%1150%120301
getStream()30%n/a111111
NumberReader(FileInputStream)46100%n/a010901
bytesRead()3100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NumberReader

NumberReader

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 8892%1 of 250%2611515
read(NumericType)43188%1150%120301
getStream()30%n/a111111
NumberReader(FileInputStream)46100%n/a010901
bytesRead()3100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/NumberReader.java.html b/docs/coverage/test/html/neureka.devices.file/NumberReader.java.html index e59d51142..cec2549e2 100644 --- a/docs/coverage/test/html/neureka.devices.file/NumberReader.java.html +++ b/docs/coverage/test/html/neureka.devices.file/NumberReader.java.html @@ -40,4 +40,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/PNGHandle$1.html b/docs/coverage/test/html/neureka.devices.file/PNGHandle$1.html index 06bb66339..fb50aae3d 100644 --- a/docs/coverage/test/html/neureka.devices.file/PNGHandle$1.html +++ b/docs/coverage/test/html/neureka.devices.file/PNGHandle$1.html @@ -1 +1 @@ -PNGHandle.new ImageFileType() {...}

PNGHandle.new ImageFileType() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 977%0 of 0n/a141414
imageTypeName()20%n/a111111
{...}3100%n/a010101
imageType()2100%n/a010101
defaultExtension()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +PNGHandle.new ImageFileType() {...}

PNGHandle.new ImageFileType() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 977%0 of 0n/a141414
imageTypeName()20%n/a111111
{...}3100%n/a010101
imageType()2100%n/a010101
defaultExtension()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/PNGHandle.html b/docs/coverage/test/html/neureka.devices.file/PNGHandle.html index 8e98dde52..a1e684af0 100644 --- a/docs/coverage/test/html/neureka.devices.file/PNGHandle.html +++ b/docs/coverage/test/html/neureka.devices.file/PNGHandle.html @@ -1 +1 @@ -PNGHandle

PNGHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 17100%0 of 0n/a030503
PNGHandle(Tensor, String)8100%n/a010201
PNGHandle(String)5100%n/a010101
static {...}4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +PNGHandle

PNGHandle

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 17100%0 of 0n/a030503
PNGHandle(Tensor, String)8100%n/a010201
PNGHandle(String)5100%n/a010101
static {...}4100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/PNGHandle.java.html b/docs/coverage/test/html/neureka.devices.file/PNGHandle.java.html index 5a5405298..574dd88bd 100644 --- a/docs/coverage/test/html/neureka.devices.file/PNGHandle.java.html +++ b/docs/coverage/test/html/neureka.devices.file/PNGHandle.java.html @@ -23,4 +23,4 @@ ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/index.html b/docs/coverage/test/html/neureka.devices.file/index.html index e85a210c8..aad84d319 100644 --- a/docs/coverage/test/html/neureka.devices.file/index.html +++ b/docs/coverage/test/html/neureka.devices.file/index.html @@ -1 +1 @@ -neureka.devices.file

neureka.devices.file

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total813 of 3,14874%65 of 19266%8221614459026120016
AbstractFileHandle22714739%201237%1627356931101
FileDevice20654472%142058%21444114182701
AbstractImageFileHandle20626255%91359%1022277211101
CSVHandle9469288%147083%22622214692001
IDXHandle6440386%71161%8211610711201
NumberReader78192%1150%261151501
HandleFactory12396%n/a11301611301
PNGHandle.new ImageFileType() {...}777%n/a14141401
JPEGHandle.new ImageFileType() {...}777%n/a14141401
JPEGHandle17100%n/a03050301
PNGHandle17100%n/a03050301
ImageFileType16100%n/a03030301
FileDevice.new AbstractDeviceData() {...}10100%n/a01010101
FileHandle100%n/a01010101
CSVHandle.CSVType100%n/a01010101
IDXHandle.IDXType100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices.file

neureka.devices.file

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total809 of 3,15074%64 of 19266%8121614459126120016
AbstractFileHandle22714739%201237%1627356931101
AbstractImageFileHandle20626255%91359%1022277211101
FileDevice20254873%132161%20444114182701
CSVHandle9469488%147083%22622214792001
IDXHandle6440386%71161%8211610711201
NumberReader78192%1150%261151501
HandleFactory12396%n/a11301611301
PNGHandle.new ImageFileType() {...}777%n/a14141401
JPEGHandle.new ImageFileType() {...}777%n/a14141401
JPEGHandle17100%n/a03050301
PNGHandle17100%n/a03050301
ImageFileType16100%n/a03030301
FileDevice.new AbstractDeviceData() {...}10100%n/a01010101
FileHandle100%n/a01010101
CSVHandle.CSVType100%n/a01010101
IDXHandle.IDXType100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.file/index.source.html b/docs/coverage/test/html/neureka.devices.file/index.source.html index 9b4a61971..1a6bebd15 100644 --- a/docs/coverage/test/html/neureka.devices.file/index.source.html +++ b/docs/coverage/test/html/neureka.devices.file/index.source.html @@ -1 +1 @@ -neureka.devices.file

neureka.devices.file

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total813 of 3,14874%65 of 19266%8221614459026120016
AbstractFileHandle.java22714739%201237%1627356931101
FileDevice.java20655472%142058%21454114182802
AbstractImageFileHandle.java20626255%91359%1022277211101
CSVHandle.java9469488%147083%22632214792102
IDXHandle.java6440586%71161%8221610811302
NumberReader.java78192%1150%261151501
HandleFactory.java12396%n/a11301611301
PNGHandle.java2492%n/a17191702
JPEGHandle.java2492%n/a17191702
ImageFileType.java16100%n/a03030301
FileHandle.java100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices.file

neureka.devices.file

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total809 of 3,15074%64 of 19266%8121614459126120016
AbstractFileHandle.java22714739%201237%1627356931101
AbstractImageFileHandle.java20626255%91359%1022277211101
FileDevice.java20255873%132161%20454114182802
CSVHandle.java9469688%147083%22632214892102
IDXHandle.java6440586%71161%8221610811302
NumberReader.java78192%1150%261151501
HandleFactory.java12396%n/a11301611301
PNGHandle.java2492%n/a17191702
JPEGHandle.java2492%n/a17191702
ImageFileType.java16100%n/a03030301
FileHandle.java100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.concurrent/Parallelism.html b/docs/coverage/test/html/neureka.devices.host.concurrent/Parallelism.html index 0b6e58b11..23604921b 100644 --- a/docs/coverage/test/html/neureka.devices.host.concurrent/Parallelism.html +++ b/docs/coverage/test/html/neureka.devices.host.concurrent/Parallelism.html @@ -1 +1 @@ -Parallelism

Parallelism

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 8182%0 of 0n/a610010610
lambda$static$2()30%n/a111111
lambda$static$1()30%n/a111111
lambda$static$6()20%n/a111111
lambda$static$5()20%n/a111111
lambda$static$4()20%n/a111111
lambda$static$3()20%n/a111111
static {...}52100%n/a010801
Parallelism(String, int, IntSupplier)8100%n/a010101
getAsInt()4100%n/a010101
lambda$static$0()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Parallelism

Parallelism

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 11087%0 of 0n/a610010610
lambda$static$2()30%n/a111111
lambda$static$1()30%n/a111111
lambda$static$6()20%n/a111111
lambda$static$5()20%n/a111111
lambda$static$4()20%n/a111111
lambda$static$3()20%n/a111111
static {...}81100%n/a010801
Parallelism(String, int, IntSupplier)8100%n/a010101
getAsInt()4100%n/a010101
lambda$static$0()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.concurrent/Parallelism.java.html b/docs/coverage/test/html/neureka.devices.host.concurrent/Parallelism.java.html index b61cd46ee..14e69c333 100644 --- a/docs/coverage/test/html/neureka.devices.host.concurrent/Parallelism.java.html +++ b/docs/coverage/test/html/neureka.devices.host.concurrent/Parallelism.java.html @@ -72,4 +72,4 @@ public int getAsInt() { return _supplier.getAsInt(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler$Divider.html b/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler$Divider.html index f7a74c190..9d0ef5b03 100644 --- a/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler$Divider.html +++ b/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler$Divider.html @@ -1 +1 @@ -WorkScheduler.Divider

WorkScheduler.Divider

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 16989%1 of 1090%215638110
_divide(ExecutorService, int, int, int, int, CPU.RangeWorkload)65389%4100%0321401
submit(int, CPU.IndexedWorkload)64287%4100%0321001
divide(int, CPU.RangeWorkload)60%n/a112211
WorkScheduler.Divider(ExecutorService)12100%n/a010501
divide(int, int, CPU.RangeWorkload)12100%n/a010301
lambda$_divide$2(ExecutorService, int, int, int, int, CPU.RangeWorkload)8100%n/a010101
lambda$_divide$1(ExecutorService, int, int, int, int, CPU.RangeWorkload)8100%n/a010101
parallelism(IntSupplier)7100%1150%120201
threshold(int)5100%n/a010201
lambda$submit$0(CPU.IndexedWorkload, int)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +WorkScheduler.Divider

WorkScheduler.Divider

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 16989%1 of 1090%215638110
_divide(ExecutorService, int, int, int, int, CPU.RangeWorkload)65389%4100%0321401
submit(int, CPU.IndexedWorkload)64287%4100%0321001
divide(int, CPU.RangeWorkload)60%n/a112211
WorkScheduler.Divider(ExecutorService)12100%n/a010501
divide(int, int, CPU.RangeWorkload)12100%n/a010301
lambda$_divide$2(ExecutorService, int, int, int, int, CPU.RangeWorkload)8100%n/a010101
lambda$_divide$1(ExecutorService, int, int, int, int, CPU.RangeWorkload)8100%n/a010101
parallelism(IntSupplier)7100%1150%120201
threshold(int)5100%n/a010201
lambda$submit$0(CPU.IndexedWorkload, int)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler.html b/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler.html index 8caad1d6e..cbab361f0 100644 --- a/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler.html +++ b/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler.html @@ -1 +1 @@ -WorkScheduler

WorkScheduler

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 150%0 of 0n/a224422
invoke(ExecutorService, int, int, int)120%n/a113311
WorkScheduler()30%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +WorkScheduler

WorkScheduler

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 150%0 of 0n/a224422
invoke(ExecutorService, int, int, int)120%n/a113311
WorkScheduler()30%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler.java.html b/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler.java.html index 45be193b6..bca2f6a54 100644 --- a/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler.java.html +++ b/docs/coverage/test/html/neureka.devices.host.concurrent/WorkScheduler.java.html @@ -161,4 +161,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.concurrent/index.html b/docs/coverage/test/html/neureka.devices.host.concurrent/index.html index dfa2995d4..91aa53fd0 100644 --- a/docs/coverage/test/html/neureka.devices.host.concurrent/index.html +++ b/docs/coverage/test/html/neureka.devices.host.concurrent/index.html @@ -1 +1 @@ -neureka.devices.host.concurrent

neureka.devices.host.concurrent

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total47 of 26582%1 of 1090%1027105292213
WorkScheduler.Divider1815189%1990%21563811001
WorkScheduler150%n/a22442211
Parallelism146782%n/a61001061001
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices.host.concurrent

neureka.devices.host.concurrent

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total47 of 29484%1 of 1090%1027105292213
WorkScheduler.Divider1815189%1990%21563811001
WorkScheduler150%n/a22442211
Parallelism149687%n/a61001061001
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.concurrent/index.source.html b/docs/coverage/test/html/neureka.devices.host.concurrent/index.source.html index 4df56fc49..1fbf7d003 100644 --- a/docs/coverage/test/html/neureka.devices.host.concurrent/index.source.html +++ b/docs/coverage/test/html/neureka.devices.host.concurrent/index.source.html @@ -1 +1 @@ -neureka.devices.host.concurrent

neureka.devices.host.concurrent

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total47 of 26582%1 of 1090%1027105292213
WorkScheduler.java3315182%1990%417104231212
Parallelism.java146782%n/a61001061001
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices.host.concurrent

neureka.devices.host.concurrent

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total47 of 29484%1 of 1090%1027105292213
WorkScheduler.java3315182%1990%417104231212
Parallelism.java149687%n/a61001061001
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.machine/BasicMachine.html b/docs/coverage/test/html/neureka.devices.host.machine/BasicMachine.html index 0efe2a30e..3d6e0b44b 100644 --- a/docs/coverage/test/html/neureka.devices.host.machine/BasicMachine.html +++ b/docs/coverage/test/html/neureka.devices.host.machine/BasicMachine.html @@ -1 +1 @@ -BasicMachine

BasicMachine

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total167 of 1765%24 of 240%1718232745
toString()1020%140%99101011
equals(Object)340%100%667711
hashCode()260%n/a115511
BasicMachine()50%n/a111111
BasicMachine(long, int)9100%n/a010401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +BasicMachine

BasicMachine

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total167 of 1765%24 of 240%1718232745
toString()1020%140%99101011
equals(Object)340%100%667711
hashCode()260%n/a115511
BasicMachine()50%n/a111111
BasicMachine(long, int)9100%n/a010401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.machine/BasicMachine.java.html b/docs/coverage/test/html/neureka.devices.host.machine/BasicMachine.java.html index 35de9a519..72bf7fe99 100644 --- a/docs/coverage/test/html/neureka.devices.host.machine/BasicMachine.java.html +++ b/docs/coverage/test/html/neureka.devices.host.machine/BasicMachine.java.html @@ -72,4 +72,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.machine/CommonMachine.html b/docs/coverage/test/html/neureka.devices.host.machine/CommonMachine.html index 0f8334b3c..bcac7cef0 100644 --- a/docs/coverage/test/html/neureka.devices.host.machine/CommonMachine.html +++ b/docs/coverage/test/html/neureka.devices.host.machine/CommonMachine.html @@ -1 +1 @@ -CommonMachine

CommonMachine

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total106 of 17639%20 of 200%1214193124
equals(Object)590%180%1010121211
hashCode()470%20%227711
CommonMachine(String, BasicMachine[])41100%n/a010601
CommonMachine(Hardware, Runtime)29100%n/a010601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CommonMachine

CommonMachine

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total106 of 17639%20 of 200%1214193124
equals(Object)590%180%1010121211
hashCode()470%20%227711
CommonMachine(String, BasicMachine[])41100%n/a010601
CommonMachine(Hardware, Runtime)29100%n/a010601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.machine/CommonMachine.java.html b/docs/coverage/test/html/neureka.devices.host.machine/CommonMachine.java.html index 2ed0e95e6..50cdd45c6 100644 --- a/docs/coverage/test/html/neureka.devices.host.machine/CommonMachine.java.html +++ b/docs/coverage/test/html/neureka.devices.host.machine/CommonMachine.java.html @@ -111,4 +111,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.machine/ConcreteMachine.html b/docs/coverage/test/html/neureka.devices.host.machine/ConcreteMachine.html index 8134ea905..afe23da85 100644 --- a/docs/coverage/test/html/neureka.devices.host.machine/ConcreteMachine.html +++ b/docs/coverage/test/html/neureka.devices.host.machine/ConcreteMachine.html @@ -1 +1 @@ -ConcreteMachine

ConcreteMachine

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total81 of 17152%18 of 2630%1421153838
equals(Object)350%100%667711
hashCode()200%20%224411
toString()140%n/a111111
static {...}85988%4660%3611401
getArchitecture()41477%2250%232601
ConcreteMachine(Hardware, Runtime)11100%n/a010401
getMemory()3100%n/a010101
getThreads()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ConcreteMachine

ConcreteMachine

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total98 of 17142%16 of 2638%1321173838
equals(Object)350%100%667711
static {...}254262%2880%2631401
hashCode()200%20%224411
toString()140%n/a111111
getArchitecture()41477%2250%232601
ConcreteMachine(Hardware, Runtime)11100%n/a010401
getMemory()3100%n/a010101
getThreads()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.machine/ConcreteMachine.java.html b/docs/coverage/test/html/neureka.devices.host.machine/ConcreteMachine.java.html index 3e32ca7c9..642c30ffa 100644 --- a/docs/coverage/test/html/neureka.devices.host.machine/ConcreteMachine.java.html +++ b/docs/coverage/test/html/neureka.devices.host.machine/ConcreteMachine.java.html @@ -38,17 +38,17 @@ int threads = ConcreteMachine.getThreads(); for (Hardware hw : Hardware.PREDEFINED) { - if (hw.architecture.equals(architecture) && (hw.threads == threads) && (hw.memory >= memory)) - ENVIRONMENT = hw.virtualize(); + if (hw.architecture.equals(architecture) && (hw.threads == threads) && (hw.memory >= memory)) + ENVIRONMENT = hw.virtualize(); } if (ENVIRONMENT == null) { - _LOG.debug( + _LOG.debug( "No matching hardware profile found for this system. " + "Instantiating a default hardware profile with the following main properties: " + - "Architecture={} Threads={} Memory={}", architecture, threads, memory + "Architecture={} Threads={} Memory={}", architecture, threads, memory ); - ENVIRONMENT = Hardware.makeSimple(architecture, memory, threads).virtualize(); + ENVIRONMENT = Hardware.makeSimple(architecture, memory, threads).virtualize(); } } @@ -117,4 +117,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.machine/Hardware.html b/docs/coverage/test/html/neureka.devices.host.machine/Hardware.html index fd36247a6..cfe036d92 100644 --- a/docs/coverage/test/html/neureka.devices.host.machine/Hardware.html +++ b/docs/coverage/test/html/neureka.devices.host.machine/Hardware.html @@ -1 +1 @@ -Hardware

Hardware

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Hardware

Hardware

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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toString()770%40%338811
equals(Object)290%80%55101011
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isL2Specified()90%20%221111
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.machine/Hardware.java.html b/docs/coverage/test/html/neureka.devices.host.machine/Hardware.java.html index a40eaa8df..0381c4c7b 100644 --- a/docs/coverage/test/html/neureka.devices.host.machine/Hardware.java.html +++ b/docs/coverage/test/html/neureka.devices.host.machine/Hardware.java.html @@ -513,14 +513,14 @@ public static Hardware makeSimple(final String systemArchitecture, final long systemMemory, final int systemThreads) { - if (systemThreads > 8) { + if (systemThreads > 8) { // Assume hyperthreading, L3 cache and more than 1 CPU - final BasicMachine tmpL1Machine = new BasicMachine(32L * K, 2); //Hyperthreading - final BasicMachine tmpL2Machine = new BasicMachine(256L * K, tmpL1Machine.threads); - final BasicMachine tmpL3Machine = new BasicMachine(4L * K * K, systemThreads / ((systemThreads + 7) / 8)); //More than 1 CPU - final BasicMachine tmpSystemMachine = new BasicMachine(systemMemory, systemThreads); - return new Hardware(systemArchitecture, new BasicMachine[] { tmpSystemMachine, tmpL3Machine, tmpL2Machine, tmpL1Machine }); + final BasicMachine tmpL1Machine = new BasicMachine(32L * K, 2); //Hyperthreading + final BasicMachine tmpL2Machine = new BasicMachine(256L * K, tmpL1Machine.threads); + final BasicMachine tmpL3Machine = new BasicMachine(4L * K * K, systemThreads / ((systemThreads + 7) / 8)); //More than 1 CPU + final BasicMachine tmpSystemMachine = new BasicMachine(systemMemory, systemThreads); + return new Hardware(systemArchitecture, new BasicMachine[] { tmpSystemMachine, tmpL3Machine, tmpL2Machine, tmpL1Machine }); } else if (systemThreads >= 4) { // Assume hyperthreading, L3 cache but only 1 CPU @@ -636,4 +636,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.machine/index.html b/docs/coverage/test/html/neureka.devices.host.machine/index.html index 2562892c6..68e7741b8 100644 --- a/docs/coverage/test/html/neureka.devices.host.machine/index.html +++ b/docs/coverage/test/html/neureka.devices.host.machine/index.html @@ -1 +1 @@ -neureka.devices.host.machine

neureka.devices.host.machine

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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Hardware24365672%211134%1827378661101
BasicMachine16795%240%171823274501
CommonMachine1067039%200%121419312401
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices.host.machine

neureka.devices.host.machine

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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BasicMachine16795%240%171823274501
CommonMachine1067039%200%121419312401
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host.machine/index.source.html b/docs/coverage/test/html/neureka.devices.host.machine/index.source.html index d5c1ab878..73d15cb41 100644 --- a/docs/coverage/test/html/neureka.devices.host.machine/index.source.html +++ b/docs/coverage/test/html/neureka.devices.host.machine/index.source.html @@ -1 +1 @@ -neureka.devices.host.machine

neureka.devices.host.machine

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total597 of 1,42258%83 of 10218%618094182152804
Hardware.java24365672%211134%1827378661101
BasicMachine.java16795%240%171823274501
CommonMachine.java1067039%200%121419312401
ConcreteMachine.java819052%18830%142115383801
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices.host.machine

neureka.devices.host.machine

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total671 of 1,42252%82 of 10219%6180102182162804
Hardware.java30059966%221031%1927438671101
BasicMachine.java16795%240%171823274501
CommonMachine.java1067039%200%121419312401
ConcreteMachine.java987342%161038%132117383801
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host/CPU$JVMExecutor.html b/docs/coverage/test/html/neureka.devices.host/CPU$JVMExecutor.html index 7dc86eac4..715deda3b 100644 --- a/docs/coverage/test/html/neureka.devices.host/CPU$JVMExecutor.html +++ b/docs/coverage/test/html/neureka.devices.host/CPU$JVMExecutor.html @@ -1 +1 @@ -CPU.JVMExecutor

CPU.JVMExecutor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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static {...}10100%n/a010201
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getCorePoolSize()4100%n/a010101
getCompletedTaskCount()4100%n/a010101
lambda$threaded$1(CPU.IndexedWorkload, int)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPU.JVMExecutor

CPU.JVMExecutor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 16493%2 of 875%217035013
_newThreadFactory(ThreadGroup, String)81157%1150%120201
threaded(int, CPU.RangeWorkload)23294%1583%140701
lambda$_newThreadFactory$0(ThreadGroup, String, Runnable)20100%n/a010401
CPU.JVMExecutor()18100%n/a010301
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static {...}10100%n/a010201
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getCorePoolSize()4100%n/a010101
getCompletedTaskCount()4100%n/a010101
lambda$threaded$1(CPU.IndexedWorkload, int)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host/CPU.html b/docs/coverage/test/html/neureka.devices.host/CPU.html index 5fc5fb5c2..e2b8e9e49 100644 --- a/docs/coverage/test/html/neureka.devices.host/CPU.html +++ b/docs/coverage/test/html/neureka.devices.host/CPU.html @@ -1 +1 @@ -CPU

CPU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total439 of 2,59983%60 of 39684%5423647413338
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static {...}17100%n/a010501
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lambda$_autoConvertAndOptimizeObjectArray$0(DataType, Object)11100%4100%030101
CPU()8100%n/a010201
_createDataFor(Object, DataType)7100%n/a010101
update(Component.OwnerChangeRequest)6100%n/a010201
lambda$_compactAndSizeObjectArray$5(Object[], int)6100%n/a010101
lambda$_compactAndSizeObjectArray$4(Object[], int)6100%n/a010101
lambda$_compactAndSizeObjectArray$3(Object[], int)6100%n/a010101
lambda$_compactAndSizeObjectArray$2(Object[], int)5100%n/a010101
lambda$_compactAndSizeObjectArray$1(int)4100%n/a010101
getExecutor()100%n/a010101
getCoreCount()100%n/a010101
get()100%n/a010101
_approveExecutionOf(Tensor[], int, Operation)100%n/a010101
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CPU

CPU

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total463 of 2,59982%63 of 39684%5723652413438
allocateFromAll(DataType, NDConfiguration, Object)16919453%203664%1429135701
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lambda$_autoConvertAndOptimizeObjectArray$0(DataType, Object)11100%4100%030101
CPU()8100%n/a010201
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lambda$_compactAndSizeObjectArray$4(Object[], int)6100%n/a010101
lambda$_compactAndSizeObjectArray$3(Object[], int)6100%n/a010101
lambda$_compactAndSizeObjectArray$2(Object[], int)5100%n/a010101
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host/CPU.java.html b/docs/coverage/test/html/neureka.devices.host/CPU.java.html index 7d2b6c012..fc852e285 100644 --- a/docs/coverage/test/html/neureka.devices.host/CPU.java.html +++ b/docs/coverage/test/html/neureka.devices.host/CPU.java.html @@ -246,11 +246,11 @@ protected final <T> void _writeArray( Tensor<T> tensor, Object array, int offset, int start, int size ) { - Object data = tensor.getMut().getData() == null ? null : tensor.getMut().getData().getOrNull(); - if ( data == null ) { - DataType<?> dataType = tensor.getDataType() != null ? tensor.getDataType() : _dataTypeOf(array); - tensor.getMut().setData( _createDataFor( array, (DataType<T>) dataType) ); - return; + Object data = tensor.getMut().getData() == null ? null : tensor.getMut().getData().getOrNull(); + if ( data == null ) { + DataType<?> dataType = tensor.getDataType() != null ? tensor.getDataType() : _dataTypeOf(array); + tensor.getMut().setData( _createDataFor( array, (DataType<T>) dataType) ); + return; } Class<?> arrayType = data.getClass(); if ( arrayType == float[].class ) { @@ -628,8 +628,8 @@ */ @Override public boolean update( OwnerChangeRequest<Tensor<Object>> changeRequest ) { - super.update( changeRequest ); - return false; // This type of device can not be a component simply because it is the default device + super.update( changeRequest ); + return false; // This type of device can not be a component simply because it is the default device } /** @@ -798,4 +798,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host/CPUData.html b/docs/coverage/test/html/neureka.devices.host/CPUData.html index 7b170d861..c9d9d52ba 100644 --- a/docs/coverage/test/html/neureka.devices.host/CPUData.html +++ b/docs/coverage/test/html/neureka.devices.host/CPUData.html @@ -1 +1 @@ -CPUData

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.9.202303310957
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CPUData(AbstractBaseDevice, Object, DataType)7100%n/a010201
lambda$new$0()1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host/CPUData.java.html b/docs/coverage/test/html/neureka.devices.host/CPUData.java.html index 4bdeedadc..3a65ddfd8 100644 --- a/docs/coverage/test/html/neureka.devices.host/CPUData.java.html +++ b/docs/coverage/test/html/neureka.devices.host/CPUData.java.html @@ -18,4 +18,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.host/index.html b/docs/coverage/test/html/neureka.devices.host/index.html index 4c965edeb..e2990e7a9 100644 --- a/docs/coverage/test/html/neureka.devices.host/index.html +++ b/docs/coverage/test/html/neureka.devices.host/index.html @@ -1 +1 @@ -neureka.devices.host

neureka.devices.host

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total449 of 2,77183%62 of 40484%562554745135303
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CPU.JVMExecutor15493%675%21703501301
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Created with JaCoCo 0.8.9.202303310957
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neureka.devices.host

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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CPU.JVMExecutor15493%675%21703501301
CPUData100%n/a02030201
Created with JaCoCo 0.8.11.202310140853
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neureka.devices.host

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total449 of 2,77183%62 of 40484%562554745135303
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CPUData.java100%n/a02030201
Created with JaCoCo 0.8.9.202303310957
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neureka.devices.host

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total473 of 2,77182%65 of 40483%592555245145303
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl.utility/CLFunctionCompiler.html b/docs/coverage/test/html/neureka.devices.opencl.utility/CLFunctionCompiler.html index 8b5e5f6f6..a152eb31a 100644 --- a/docs/coverage/test/html/neureka.devices.opencl.utility/CLFunctionCompiler.html +++ b/docs/coverage/test/html/neureka.devices.opencl.utility/CLFunctionCompiler.html @@ -1 +1 @@ -CLFunctionCompiler

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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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_clTypeOf(Tensor)192455%2250%232801
lambda$optimize$3(String[])182356%3125%231401
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CLFunctionCompiler(OpenCLDevice, Function, String)23100%n/a0101101
lambda$_adHocKernelFor$17(List, int)20100%n/a010101
lambda$_adHocKernelFor$15(List, int)16100%n/a010101
lambda$_adHocKernelFor$11(ExecutionCall, int)9100%n/a010101
lambda$_adHocKernelFor$14(int[])8100%n/a010301
lambda$optimize$9(Function, ExecutionCall)8100%n/a010201
lambda$_clTypeOf$19(Class)7100%n/a010301
lambda$_adHocKernelFor$13(Tensor)4100%n/a010101
lambda$new$1(Function)4100%n/a010101
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lambda$optimize$2(Function)3100%n/a010101
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lambda$optimize$4(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
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CLFunctionCompiler

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_clTypeOf(Tensor)192455%2250%232801
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optimize()46193%2250%2312201
lambda$_adHocKernelFor$12(Tensor)42887%1150%120601
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CLFunctionCompiler(OpenCLDevice, Function, String)23100%n/a0101101
lambda$_adHocKernelFor$17(List, int)20100%n/a010101
lambda$_adHocKernelFor$15(List, int)16100%n/a010101
lambda$_adHocKernelFor$11(ExecutionCall, int)9100%n/a010101
lambda$_adHocKernelFor$14(int[])8100%n/a010301
lambda$optimize$9(Function, ExecutionCall)8100%n/a010201
lambda$_clTypeOf$19(Class)7100%n/a010301
lambda$_adHocKernelFor$13(Tensor)4100%n/a010101
lambda$new$1(Function)4100%n/a010101
lambda$optimize$6(ExecutionCall)3100%n/a010101
lambda$optimize$2(Function)3100%n/a010101
lambda$new$0(Function)3100%n/a010101
lambda$optimize$5(ExecutionCall)2100%n/a010101
lambda$optimize$4(ExecutionCall)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl.utility/CLFunctionCompiler.java.html b/docs/coverage/test/html/neureka.devices.opencl.utility/CLFunctionCompiler.java.html index c6364ebe8..b1fd9d81a 100644 --- a/docs/coverage/test/html/neureka.devices.opencl.utility/CLFunctionCompiler.java.html +++ b/docs/coverage/test/html/neureka.devices.opencl.utility/CLFunctionCompiler.java.html @@ -236,4 +236,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl.utility/DeviceQuery.html b/docs/coverage/test/html/neureka.devices.opencl.utility/DeviceQuery.html index 18347dbbc..7c1cf4231 100644 --- a/docs/coverage/test/html/neureka.devices.opencl.utility/DeviceQuery.html +++ b/docs/coverage/test/html/neureka.devices.opencl.utility/DeviceQuery.html @@ -1 +1 @@ -DeviceQuery

DeviceQuery

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total56 of 93594%11 of 2657%1022311609
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getLong(cl_device_id, int)7100%n/a010101
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DeviceQuery

DeviceQuery

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total935 of 9350%26 of 260%222211611699
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getLong(cl_device_id, int)70%n/a111111
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl.utility/DeviceQuery.java.html b/docs/coverage/test/html/neureka.devices.opencl.utility/DeviceQuery.java.html index 991fa0d12..ce6fba033 100644 --- a/docs/coverage/test/html/neureka.devices.opencl.utility/DeviceQuery.java.html +++ b/docs/coverage/test/html/neureka.devices.opencl.utility/DeviceQuery.java.html @@ -28,166 +28,166 @@ */ public static String query() { - String result = "[DEVICE QUERY]:\n========================================================\n"; + String result = "[DEVICE QUERY]:\n========================================================\n"; // Obtain the number of platforms - int[] numPlatforms = new int[ 1 ]; - clGetPlatformIDs(0, null, numPlatforms); + int[] numPlatforms = new int[ 1 ]; + clGetPlatformIDs(0, null, numPlatforms); - result+=("Number of platforms: "+numPlatforms[ 0 ]+"\n"); + result+=("Number of platforms: "+numPlatforms[ 0 ]+"\n"); // Obtain the platform IDs - cl_platform_id[] platforms = new cl_platform_id[numPlatforms[ 0 ]]; - clGetPlatformIDs(platforms.length, platforms, null); + cl_platform_id[] platforms = new cl_platform_id[numPlatforms[ 0 ]]; + clGetPlatformIDs(platforms.length, platforms, null); // Collect all devices of all platforms - List<cl_device_id> devices = new ArrayList<cl_device_id>(); - for (cl_platform_id platform : platforms) + List<cl_device_id> devices = new ArrayList<cl_device_id>(); + for (cl_platform_id platform : platforms) { - String platformName = getString(platform, CL_PLATFORM_NAME); + String platformName = getString(platform, CL_PLATFORM_NAME); // Obtain the number of devices for the current platform - int[] numDevices = new int[ 1 ]; - clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, 0, null, numDevices); + int[] numDevices = new int[ 1 ]; + clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, 0, null, numDevices); - result += ("Number of devices in platform " + platformName + ": " + numDevices[ 0 ] + "\n"); + result += ("Number of devices in platform " + platformName + ": " + numDevices[ 0 ] + "\n"); - cl_device_id[] devicesArray = new cl_device_id[numDevices[ 0 ]]; - clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, numDevices[ 0 ], devicesArray, null); + cl_device_id[] devicesArray = new cl_device_id[numDevices[ 0 ]]; + clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, numDevices[ 0 ], devicesArray, null); - devices.addAll(Arrays.asList(devicesArray)); + devices.addAll(Arrays.asList(devicesArray)); } - result += "========================================================\n"; + result += "========================================================\n"; // Print the infos about all devices - for (cl_device_id device : devices) + for (cl_device_id device : devices) { // CL_DEVICE_NAME - String deviceName = getString(device, CL_DEVICE_NAME); - result+=("\n[Info for device "+deviceName+"]: \n--------------------------------------------------------\n"); - result+=("CL_DEVICE_NAME: "+deviceName+"\n"); + String deviceName = getString(device, CL_DEVICE_NAME); + result+=("\n[Info for device "+deviceName+"]: \n--------------------------------------------------------\n"); + result+=("CL_DEVICE_NAME: "+deviceName+"\n"); // CL_DEVICE_VENDOR - String deviceVendor = getString(device, CL_DEVICE_VENDOR); - result+=("CL_DEVICE_VENDOR: "+deviceVendor+"\n"); + String deviceVendor = getString(device, CL_DEVICE_VENDOR); + result+=("CL_DEVICE_VENDOR: "+deviceVendor+"\n"); // CL_DRIVER_VERSION - String driverVersion = getString(device, CL_DRIVER_VERSION); - result+=("CL_DRIVER_VERSION: "+driverVersion+"\n"); + String driverVersion = getString(device, CL_DRIVER_VERSION); + result+=("CL_DRIVER_VERSION: "+driverVersion+"\n"); // CL_DEVICE_TYPE - long deviceType = getLong(device, CL_DEVICE_TYPE); - if ( (deviceType & CL_DEVICE_TYPE_CPU) != 0) result+=("CL_DEVICE_TYPE: CL_DEVICE_TYPE_CPU\n"); - if ( (deviceType & CL_DEVICE_TYPE_GPU) != 0) result+=("CL_DEVICE_TYPE: CL_DEVICE_TYPE_GPU\n"); - if ( (deviceType & CL_DEVICE_TYPE_ACCELERATOR) != 0) result+=("CL_DEVICE_TYPE: CL_DEVICE_TYPE_ACCELERATOR\n"); - if ( (deviceType & CL_DEVICE_TYPE_DEFAULT) != 0) result+=("CL_DEVICE_TYPE: CL_DEVICE_TYPE_DEFAULT\n"); + long deviceType = getLong(device, CL_DEVICE_TYPE); + if ( (deviceType & CL_DEVICE_TYPE_CPU) != 0) result+=("CL_DEVICE_TYPE: CL_DEVICE_TYPE_CPU\n"); + if ( (deviceType & CL_DEVICE_TYPE_GPU) != 0) result+=("CL_DEVICE_TYPE: CL_DEVICE_TYPE_GPU\n"); + if ( (deviceType & CL_DEVICE_TYPE_ACCELERATOR) != 0) result+=("CL_DEVICE_TYPE: CL_DEVICE_TYPE_ACCELERATOR\n"); + if ( (deviceType & CL_DEVICE_TYPE_DEFAULT) != 0) result+=("CL_DEVICE_TYPE: CL_DEVICE_TYPE_DEFAULT\n"); // CL_DEVICE_MAX_COMPUTE_UNITS - int maxComputeUnits = getInt(device, CL_DEVICE_MAX_COMPUTE_UNITS); - result += ("CL_DEVICE_MAX_COMPUTE_UNITS: "+ maxComputeUnits+"\n"); + int maxComputeUnits = getInt(device, CL_DEVICE_MAX_COMPUTE_UNITS); + result += ("CL_DEVICE_MAX_COMPUTE_UNITS: "+ maxComputeUnits+"\n"); // CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS - long maxWorkItemDimensions = getLong(device, CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS); - result += ("CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS: "+ maxWorkItemDimensions+"\n"); + long maxWorkItemDimensions = getLong(device, CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS); + result += ("CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS: "+ maxWorkItemDimensions+"\n"); // CL_DEVICE_MAX_WORK_ITEM_SIZES - long[] maxWorkItemSizes = getSizes(device, CL_DEVICE_MAX_WORK_ITEM_SIZES, 3); - result += ("CL_DEVICE_MAX_WORK_ITEM_SIZES: "+maxWorkItemSizes[ 0 ]+", "+ maxWorkItemSizes[ 1 ]+", "+maxWorkItemSizes[ 2 ]+"\n"); + long[] maxWorkItemSizes = getSizes(device, CL_DEVICE_MAX_WORK_ITEM_SIZES, 3); + result += ("CL_DEVICE_MAX_WORK_ITEM_SIZES: "+maxWorkItemSizes[ 0 ]+", "+ maxWorkItemSizes[ 1 ]+", "+maxWorkItemSizes[ 2 ]+"\n"); // CL_DEVICE_MAX_WORK_GROUP_SIZE - long maxWorkGroupSize = getSize(device, CL_DEVICE_MAX_WORK_GROUP_SIZE); - result += ("CL_DEVICE_MAX_WORK_GROUP_SIZE: "+ maxWorkGroupSize+"\n"); + long maxWorkGroupSize = getSize(device, CL_DEVICE_MAX_WORK_GROUP_SIZE); + result += ("CL_DEVICE_MAX_WORK_GROUP_SIZE: "+ maxWorkGroupSize+"\n"); // CL_DEVICE_MAX_CLOCK_FREQUENCY - long maxClockFrequency = getLong(device, CL_DEVICE_MAX_CLOCK_FREQUENCY); - result += ("CL_DEVICE_MAX_CLOCK_FREQUENCY: "+ maxClockFrequency+" MHz\n"); + long maxClockFrequency = getLong(device, CL_DEVICE_MAX_CLOCK_FREQUENCY); + result += ("CL_DEVICE_MAX_CLOCK_FREQUENCY: "+ maxClockFrequency+" MHz\n"); // CL_DEVICE_ADDRESS_BITS - int addressBits = getInt(device, CL_DEVICE_ADDRESS_BITS); - result += ("CL_DEVICE_ADDRESS_BITS: "+ addressBits+"\n"); + int addressBits = getInt(device, CL_DEVICE_ADDRESS_BITS); + result += ("CL_DEVICE_ADDRESS_BITS: "+ addressBits+"\n"); // CL_DEVICE_MAX_MEM_ALLOC_SIZE - long maxMemAllocSize = getLong(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE); - result+=("CL_DEVICE_MAX_MEM_ALLOC_SIZE: "+ (int)(maxMemAllocSize / (1024 * 1024))+" MByte\n"); + long maxMemAllocSize = getLong(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE); + result+=("CL_DEVICE_MAX_MEM_ALLOC_SIZE: "+ (int)(maxMemAllocSize / (1024 * 1024))+" MByte\n"); // CL_DEVICE_GLOBAL_MEM_SIZE - long globalMemSize = getLong(device, CL_DEVICE_GLOBAL_MEM_SIZE); - result += ("CL_DEVICE_GLOBAL_MEM_SIZE: "+(int)(globalMemSize / (1024 * 1024))+" MByte\n"); + long globalMemSize = getLong(device, CL_DEVICE_GLOBAL_MEM_SIZE); + result += ("CL_DEVICE_GLOBAL_MEM_SIZE: "+(int)(globalMemSize / (1024 * 1024))+" MByte\n"); // CL_DEVICE_ERROR_CORRECTION_SUPPORT - int errorCorrectionSupport = getInt(device, CL_DEVICE_ERROR_CORRECTION_SUPPORT); - result += ("CL_DEVICE_ERROR_CORRECTION_SUPPORT: "+(errorCorrectionSupport != 0 ? "yes" : "no")+"\n"); + int errorCorrectionSupport = getInt(device, CL_DEVICE_ERROR_CORRECTION_SUPPORT); + result += ("CL_DEVICE_ERROR_CORRECTION_SUPPORT: "+(errorCorrectionSupport != 0 ? "yes" : "no")+"\n"); // CL_DEVICE_LOCAL_MEM_TYPE - int localMemType = getInt(device, CL_DEVICE_LOCAL_MEM_TYPE); - result += ("CL_DEVICE_LOCAL_MEM_TYPE: "+(localMemType == 1 ? "local" : "global")+"\n"); + int localMemType = getInt(device, CL_DEVICE_LOCAL_MEM_TYPE); + result += ("CL_DEVICE_LOCAL_MEM_TYPE: "+(localMemType == 1 ? "local" : "global")+"\n"); // CL_DEVICE_LOCAL_MEM_SIZE - long localMemSize = getLong(device, CL_DEVICE_LOCAL_MEM_SIZE); - result += ("CL_DEVICE_LOCAL_MEM_SIZE: "+(int)(localMemSize / 1024)+" KByte\n"); + long localMemSize = getLong(device, CL_DEVICE_LOCAL_MEM_SIZE); + result += ("CL_DEVICE_LOCAL_MEM_SIZE: "+(int)(localMemSize / 1024)+" KByte\n"); // CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE - long maxConstantBufferSize = getLong(device, CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE); - result += ("CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE: "+(int)(maxConstantBufferSize / 1024)+" KByte\n"); + long maxConstantBufferSize = getLong(device, CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE); + result += ("CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE: "+(int)(maxConstantBufferSize / 1024)+" KByte\n"); // CL_DEVICE_QUEUE_PROPERTIES - long queueProperties = getLong(device, CL_DEVICE_QUEUE_PROPERTIES); - if ( ( queueProperties & CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE ) != 0 ) + long queueProperties = getLong(device, CL_DEVICE_QUEUE_PROPERTIES); + if ( ( queueProperties & CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE ) != 0 ) result += ("CL_DEVICE_QUEUE_PROPERTIES: CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE\n"); - if(( queueProperties & CL_QUEUE_PROFILING_ENABLE ) != 0) - result += ("CL_DEVICE_QUEUE_PROPERTIES: CL_QUEUE_PROFILING_ENABLE\n"); + if(( queueProperties & CL_QUEUE_PROFILING_ENABLE ) != 0) + result += ("CL_DEVICE_QUEUE_PROPERTIES: CL_QUEUE_PROFILING_ENABLE\n"); // CL_DEVICE_IMAGE_SUPPORT - int imageSupport = getInt(device, CL_DEVICE_IMAGE_SUPPORT); - result += ("CL_DEVICE_IMAGE_SUPPORT: "+imageSupport+"\n"); + int imageSupport = getInt(device, CL_DEVICE_IMAGE_SUPPORT); + result += ("CL_DEVICE_IMAGE_SUPPORT: "+imageSupport+"\n"); // CL_DEVICE_MAX_READ_IMAGE_ARGS - int maxReadImageArgs = getInt(device, CL_DEVICE_MAX_READ_IMAGE_ARGS); - result += ("CL_DEVICE_MAX_READ_IMAGE_ARGS: "+maxReadImageArgs+"\n"); + int maxReadImageArgs = getInt(device, CL_DEVICE_MAX_READ_IMAGE_ARGS); + result += ("CL_DEVICE_MAX_READ_IMAGE_ARGS: "+maxReadImageArgs+"\n"); // CL_DEVICE_MAX_WRITE_IMAGE_ARGS - int maxWriteImageArgs = getInt(device, CL_DEVICE_MAX_WRITE_IMAGE_ARGS); - result += ("CL_DEVICE_MAX_WRITE_IMAGE_ARGS: "+maxWriteImageArgs+"\n"); + int maxWriteImageArgs = getInt(device, CL_DEVICE_MAX_WRITE_IMAGE_ARGS); + result += ("CL_DEVICE_MAX_WRITE_IMAGE_ARGS: "+maxWriteImageArgs+"\n"); // CL_DEVICE_SINGLE_FP_CONFIG - long singleFpConfig = getLong(device, CL_DEVICE_SINGLE_FP_CONFIG); - result+=("CL_DEVICE_SINGLE_FP_CONFIG: "+stringFor_cl_device_fp_config(singleFpConfig)+"\n"); + long singleFpConfig = getLong(device, CL_DEVICE_SINGLE_FP_CONFIG); + result+=("CL_DEVICE_SINGLE_FP_CONFIG: "+stringFor_cl_device_fp_config(singleFpConfig)+"\n"); // CL_DEVICE_IMAGE2D_MAX_WIDTH - long image2dMaxWidth = getSize(device, CL_DEVICE_IMAGE2D_MAX_WIDTH); - result += ("CL_DEVICE_2D_MAX_WIDTH "+image2dMaxWidth+"\n"); + long image2dMaxWidth = getSize(device, CL_DEVICE_IMAGE2D_MAX_WIDTH); + result += ("CL_DEVICE_2D_MAX_WIDTH "+image2dMaxWidth+"\n"); // CL_DEVICE_IMAGE2D_MAX_HEIGHT - long image2dMaxHeight = getSize(device, CL_DEVICE_IMAGE2D_MAX_HEIGHT); - result += ("CL_DEVICE_2D_MAX_HEIGHT "+image2dMaxHeight+"\n"); + long image2dMaxHeight = getSize(device, CL_DEVICE_IMAGE2D_MAX_HEIGHT); + result += ("CL_DEVICE_2D_MAX_HEIGHT "+image2dMaxHeight+"\n"); // CL_DEVICE_IMAGE3D_MAX_WIDTH - long image3dMaxWidth = getSize(device, CL_DEVICE_IMAGE3D_MAX_WIDTH); - result += ("CL_DEVICE_3D_MAX_WIDTH "+image3dMaxWidth+"\n"); + long image3dMaxWidth = getSize(device, CL_DEVICE_IMAGE3D_MAX_WIDTH); + result += ("CL_DEVICE_3D_MAX_WIDTH "+image3dMaxWidth+"\n"); // CL_DEVICE_IMAGE3D_MAX_HEIGHT - long image3dMaxHeight = getSize(device, CL_DEVICE_IMAGE3D_MAX_HEIGHT); - result+=("CL_DEVICE_3D_MAX_HEIGHT "+image3dMaxHeight+"\n"); + long image3dMaxHeight = getSize(device, CL_DEVICE_IMAGE3D_MAX_HEIGHT); + result+=("CL_DEVICE_3D_MAX_HEIGHT "+image3dMaxHeight+"\n"); // CL_DEVICE_IMAGE3D_MAX_DEPTH - long image3dMaxDepth = getSize(device, CL_DEVICE_IMAGE3D_MAX_DEPTH); - result += ("CL_DEVICE_3D_MAX_DEPTH "+image3dMaxDepth+"\n"); + long image3dMaxDepth = getSize(device, CL_DEVICE_IMAGE3D_MAX_DEPTH); + result += ("CL_DEVICE_3D_MAX_DEPTH "+image3dMaxDepth+"\n"); // CL_DEVICE_PREFERRED_VECTOR_WIDTH_<type> - result += ("CL_DEVICE_PREFERRED_VECTOR_WIDTH_<t>\n"); - int preferredVectorWidthChar = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_CHAR); - int preferredVectorWidthShort = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_SHORT); - int preferredVectorWidthInt = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT); - int preferredVectorWidthLong = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_LONG); - int preferredVectorWidthFloat = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT); - int preferredVectorWidthDouble = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE); - result += ("CHAR "+preferredVectorWidthChar+ + result += ("CL_DEVICE_PREFERRED_VECTOR_WIDTH_<t>\n"); + int preferredVectorWidthChar = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_CHAR); + int preferredVectorWidthShort = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_SHORT); + int preferredVectorWidthInt = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT); + int preferredVectorWidthLong = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_LONG); + int preferredVectorWidthFloat = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT); + int preferredVectorWidthDouble = getInt(device, CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE); + result += ("CHAR "+preferredVectorWidthChar+ ", SHORT "+preferredVectorWidthShort+ ", INT "+preferredVectorWidthInt + ", LONG "+preferredVectorWidthLong+ ", FLOAT "+preferredVectorWidthFloat+ ", DOUBLE "+ preferredVectorWidthDouble+"\n"); - } - return result; + } + return result; } /** @@ -199,7 +199,7 @@ */ private static int getInt(cl_device_id device, int paramName) { - return getInts(device, paramName, 1)[ 0 ]; + return getInts(device, paramName, 1)[ 0 ]; } /** @@ -212,9 +212,9 @@ */ private static int[] getInts(cl_device_id device, int paramName, int numValues) { - int values[] = new int[numValues]; - clGetDeviceInfo(device, paramName, Sizeof.cl_int * numValues, Pointer.to(values), null); - return values; + int values[] = new int[numValues]; + clGetDeviceInfo(device, paramName, Sizeof.cl_int * numValues, Pointer.to(values), null); + return values; } /** @@ -226,7 +226,7 @@ */ private static long getLong(cl_device_id device, int paramName) { - return getLongs(device, paramName, 1)[ 0 ]; + return getLongs(device, paramName, 1)[ 0 ]; } /** @@ -239,9 +239,9 @@ */ private static long[] getLongs(cl_device_id device, int paramName, int numValues) { - long values[] = new long[numValues]; - clGetDeviceInfo(device, paramName, Sizeof.cl_long * numValues, Pointer.to(values), null); - return values; + long values[] = new long[numValues]; + clGetDeviceInfo(device, paramName, Sizeof.cl_long * numValues, Pointer.to(values), null); + return values; } /** @@ -254,15 +254,15 @@ private static String getString(cl_device_id device, int paramName) { // Obtain the length of the string that will be queried - long size[] = new long[ 1 ]; - clGetDeviceInfo(device, paramName, 0, null, size); + long size[] = new long[ 1 ]; + clGetDeviceInfo(device, paramName, 0, null, size); // Create a buffer of the appropriate size and fill it with the info - byte buffer[] = new byte[(int)size[ 0 ]]; - clGetDeviceInfo(device, paramName, buffer.length, Pointer.to(buffer), null); + byte buffer[] = new byte[(int)size[ 0 ]]; + clGetDeviceInfo(device, paramName, buffer.length, Pointer.to(buffer), null); // Create a string from the buffer (excluding the trailing \0 byte) - return new String(buffer, 0, buffer.length-1); + return new String(buffer, 0, buffer.length-1); } /** @@ -275,15 +275,15 @@ private static String getString(cl_platform_id platform, int paramName) { // Obtain the length of the string that will be queried - long size[] = new long[ 1 ]; - clGetPlatformInfo(platform, paramName, 0, null, size); + long size[] = new long[ 1 ]; + clGetPlatformInfo(platform, paramName, 0, null, size); // Create a buffer of the appropriate size and fill it with the info - byte buffer[] = new byte[(int)size[ 0 ]]; - clGetPlatformInfo(platform, paramName, buffer.length, Pointer.to(buffer), null); + byte buffer[] = new byte[(int)size[ 0 ]]; + clGetPlatformInfo(platform, paramName, buffer.length, Pointer.to(buffer), null); // Create a string from the buffer (excluding the trailing \0 byte) - return new String(buffer, 0, buffer.length-1); + return new String(buffer, 0, buffer.length-1); } /** @@ -295,7 +295,7 @@ */ private static long getSize(cl_device_id device, int paramName) { - return getSizes(device, paramName, 1)[ 0 ]; + return getSizes(device, paramName, 1)[ 0 ]; } /** @@ -310,20 +310,20 @@ { // The size of the returned data has to depend on // the size of a size_t, which is handled here - ByteBuffer buffer = ByteBuffer.allocate( - numValues * Sizeof.size_t).order(ByteOrder.nativeOrder()); - clGetDeviceInfo(device, paramName, Sizeof.size_t * numValues, - Pointer.to(buffer), null); - long values[] = new long[numValues]; - if (Sizeof.size_t == 4) + ByteBuffer buffer = ByteBuffer.allocate( + numValues * Sizeof.size_t).order(ByteOrder.nativeOrder()); + clGetDeviceInfo(device, paramName, Sizeof.size_t * numValues, + Pointer.to(buffer), null); + long values[] = new long[numValues]; + if (Sizeof.size_t == 4) for ( int i = 0; i < numValues; i++ ) values[ i ] = buffer.getInt(i * Sizeof.size_t); else - for ( int i = 0; i < numValues; i++ ) - values[ i ] = buffer.getLong(i * Sizeof.size_t); + for ( int i = 0; i < numValues; i++ ) + values[ i ] = buffer.getLong(i * Sizeof.size_t); - return values; + return values; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl.utility/Messages$Tips.html b/docs/coverage/test/html/neureka.devices.opencl.utility/Messages$Tips.html index b9f304c34..dfd7d39bc 100644 --- a/docs/coverage/test/html/neureka.devices.opencl.utility/Messages$Tips.html +++ b/docs/coverage/test/html/neureka.devices.opencl.utility/Messages$Tips.html @@ -1 +1 @@ -Messages.Tips

Messages.Tips

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Created with JaCoCo 0.8.9.202303310957
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Messages.Tips

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Created with JaCoCo 0.8.11.202310140853
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl.utility/Messages.java.html b/docs/coverage/test/html/neureka.devices.opencl.utility/Messages.java.html index c910a6227..aa29883c9 100644 --- a/docs/coverage/test/html/neureka.devices.opencl.utility/Messages.java.html +++ b/docs/coverage/test/html/neureka.devices.opencl.utility/Messages.java.html @@ -15,11 +15,11 @@ private Messages() {/* This is a utility class! */} public static String clContextCreationFailed() { - return LogUtil.format( + return LogUtil.format( "OpenCL not available!\n" + - "Skipped creating and adding a new '"+ CLBackend.class.getSimpleName()+"' " + - "to the current '"+ BackendContext.class.getSimpleName()+"'...\n" + - findTip().bootstrapTip() + "Skipped creating and adding a new '"+ CLBackend.class.getSimpleName()+"' " + + "to the current '"+ BackendContext.class.getSimpleName()+"'...\n" + + findTip().bootstrapTip() ); } @@ -108,4 +108,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl.utility/index.html b/docs/coverage/test/html/neureka.devices.opencl.utility/index.html index 058322a1b..91a3a7f22 100644 --- a/docs/coverage/test/html/neureka.devices.opencl.utility/index.html +++ b/docs/coverage/test/html/neureka.devices.opencl.utility/index.html @@ -1 +1 @@ -neureka.devices.opencl.utility

neureka.devices.opencl.utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total212 of 1,80788%30 of 6251%32722928644004
CLFunctionCompiler9655785%121045%1336812222501
Messages599561%6650%81017362301
DeviceQuery5687994%111557%102231160901
Messages.Tips6498%1150%141120301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices.opencl.utility

neureka.devices.opencl.utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total1,068 of 1,82441%45 of 6227%4372138286124014
DeviceQuery9350%260%22221161169911
CLFunctionCompiler9655785%121045%1336812222501
Messages3611876%6650%71013361301
Messages.Tips8198%1150%141120301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl.utility/index.source.html b/docs/coverage/test/html/neureka.devices.opencl.utility/index.source.html index f9fc5deea..b84fabfca 100644 --- a/docs/coverage/test/html/neureka.devices.opencl.utility/index.source.html +++ b/docs/coverage/test/html/neureka.devices.opencl.utility/index.source.html @@ -1 +1 @@ -neureka.devices.opencl.utility

neureka.devices.opencl.utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total212 of 1,80788%30 of 6251%32722928644004
CLFunctionCompiler.java9655785%121045%1336812222501
Messages.java6015972%7750%91418482602
DeviceQuery.java5687994%111557%102231160901
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices.opencl.utility

neureka.devices.opencl.utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total1,068 of 1,82441%45 of 6227%4372138286124014
DeviceQuery.java9350%260%22221161169911
CLFunctionCompiler.java9655785%121045%1336812222501
Messages.java3719984%7750%81414481602
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/JVMData.html b/docs/coverage/test/html/neureka.devices.opencl/JVMData.html index 5fa866801..99ce70d4e 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/JVMData.html +++ b/docs/coverage/test/html/neureka.devices.opencl/JVMData.html @@ -1 +1 @@ -JVMData

JVMData

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total74 of 74790%11 of 14492%109011135018
of(Class, int)234767%31178%2831101
_fillArray(Object, int, int)1415291%13597%11913901
_allocArrayFromNumber(Number, int)137885%11191%1712501
getElementAt(int)46694%11191%171701
getLength()46093%11191%171701
getPointer()45493%11191%171701
lengthOf(Object)44792%11392%181801
getItemSize()43690%11191%171701
getType()43690%11191%171701
_preprocess(Object, int, int, boolean, boolean)27100%6100%040601
JVMData(Object, int, int, boolean, boolean)16100%n/a010401
of(Object)10100%n/a010101
of(Object, boolean)10100%n/a010101
isVirtual()10100%2100%020101
of(Object, int, boolean, boolean)9100%n/a010101
of(Object, int, int)9100%n/a010101
getArray()3100%n/a010101
getTargetLength()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +JVMData

JVMData

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total114 of 74784%15 of 14489%179016135418
of(Class, int)234767%31178%2831101
_fillArray(Object, int, int)1415291%13597%11913901
_allocArrayFromNumber(Number, int)137885%11191%1712501
of(Object, boolean)100%n/a111111
isVirtual()100%20%221111
of(Object, int, boolean, boolean)90%n/a111111
_preprocess(Object, int, int, boolean, boolean)81970%2466%241601
getElementAt(int)46694%11191%171701
getLength()46093%11191%171701
getPointer()45493%11191%171701
lengthOf(Object)44792%11392%181801
getItemSize()43690%11191%171701
getType()43690%11191%171701
getTargetLength()30%n/a111111
JVMData(Object, int, int, boolean, boolean)16100%n/a010401
of(Object)10100%n/a010101
of(Object, int, int)9100%n/a010101
getArray()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/JVMData.java.html b/docs/coverage/test/html/neureka.devices.opencl/JVMData.java.html index 7f45b2811..a5c32ca76 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/JVMData.java.html +++ b/docs/coverage/test/html/neureka.devices.opencl/JVMData.java.html @@ -40,11 +40,11 @@ } public static JVMData of( Object data, boolean convertToFloat ) { - return new JVMData( data, 0, lengthOf(data), convertToFloat, false ); + return new JVMData( data, 0, lengthOf(data), convertToFloat, false ); } public static JVMData of( Object data, int size, boolean convertToFloat, boolean virtual ) { - return new JVMData( data, 0, size, convertToFloat, virtual ); + return new JVMData( data, 0, size, convertToFloat, virtual ); } public static JVMData of( Object data, int size, int start ) { @@ -60,13 +60,13 @@ private Object _preprocess( Object data, int start, int targetSize, boolean convertToFloat, boolean allowVirtual ) { - int size = allowVirtual ? lengthOf(data) : targetSize; + int size = allowVirtual ? lengthOf(data) : targetSize; if ( data instanceof Number ) data = _allocArrayFromNumber( (Number) data, size ); - if ( convertToFloat ) - data = DataConverter.get().convert( data, float[].class ); + if ( convertToFloat ) + data = DataConverter.get().convert( data, float[].class ); return _fillArray( data, start, size ); // Make sure the array is of the correct size! } @@ -169,7 +169,7 @@ throw new IllegalStateException(); } - public long getTargetLength() { return _size; } + public long getTargetLength() { return _size; } int getItemSize() { if ( _data instanceof float[] ) return Sizeof.cl_float; @@ -182,7 +182,7 @@ } boolean isVirtual() { - return _size != getLength(); + return _size != getLength(); } OpenCLDevice.cl_dtype getType() { @@ -217,4 +217,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/KernelCache$1.html b/docs/coverage/test/html/neureka.devices.opencl/KernelCache$1.html index fbee84120..ad63afd4a 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/KernelCache$1.html +++ b/docs/coverage/test/html/neureka.devices.opencl/KernelCache$1.html @@ -1 +1 @@ -KernelCache.new LinkedHashMap() {...}

KernelCache.new LinkedHashMap() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 1586%1 of 250%130202
removeEldestEntry(Map.Entry)2675%1150%120101
{...}7100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +KernelCache.new LinkedHashMap() {...}

KernelCache.new LinkedHashMap() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 150%2 of 20%332222
removeEldestEntry(Map.Entry)80%20%221111
{...}70%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/KernelCache.html b/docs/coverage/test/html/neureka.devices.opencl/KernelCache.html index 92d3faacd..8f10fc87f 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/KernelCache.html +++ b/docs/coverage/test/html/neureka.devices.opencl/KernelCache.html @@ -1 +1 @@ -KernelCache

KernelCache

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 28100%0 of 0n/a040604
KernelCache()10100%n/a010201
put(String, OpenCLDevice.cl_ad_hoc)7100%n/a010201
get(String)6100%n/a010101
has(String)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +KernelCache

KernelCache

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 280%0 of 0n/a446644
KernelCache()100%n/a112211
put(String, OpenCLDevice.cl_ad_hoc)70%n/a112211
get(String)60%n/a111111
has(String)50%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/KernelCache.java.html b/docs/coverage/test/html/neureka.devices.opencl/KernelCache.java.html index be3443c86..410c9caf7 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/KernelCache.java.html +++ b/docs/coverage/test/html/neureka.devices.opencl/KernelCache.java.html @@ -12,30 +12,30 @@ * anywhere but within this library. <br> * This class or its public methods might change or get removed in future versions!</b> */ -public final class KernelCache { +public final class KernelCache { private final static int CAPACITY = 256; - private final Map<String, OpenCLDevice.cl_ad_hoc> _adhocKernels = - new LinkedHashMap<String, OpenCLDevice.cl_ad_hoc>(CAPACITY) { + private final Map<String, OpenCLDevice.cl_ad_hoc> _adhocKernels = + new LinkedHashMap<String, OpenCLDevice.cl_ad_hoc>(CAPACITY) { @Override protected boolean removeEldestEntry(final Map.Entry eldest) { - return size() > CAPACITY; + return size() > CAPACITY; } }; public void put( String name, OpenCLDevice.cl_ad_hoc kernel ) { // Storing the ad hoc object in a fixed size map for fast access by operations: - _adhocKernels.put( name, kernel ); - } + _adhocKernels.put( name, kernel ); + } public boolean has( String name ) { - return _adhocKernels.containsKey( name ); + return _adhocKernels.containsKey( name ); } public OpenCLDevice.cl_ad_hoc get( String name ) { - return _adhocKernels.get( name ); + return _adhocKernels.get( name ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/KernelCaller.html b/docs/coverage/test/html/neureka.devices.opencl/KernelCaller.html index 15dd5b5d1..cc00c2332 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/KernelCaller.html +++ b/docs/coverage/test/html/neureka.devices.opencl/KernelCaller.html @@ -1 +1 @@ -KernelCaller

KernelCaller

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total97 of 55582%11 of 3063%9381177123
_releaseEvents(Tensor[])450%40%335511
call(long[], long[])173567%2466%242701
pass(Number)144877%11191%171701
call(int)133271%1150%122601
_getWaitList(Tensor[])83782%3350%241601
pass(Tensor)33100%n/a010401
passAllOf(Tensor)32100%n/a010401
passConfOf(Tensor)25100%n/a010401
pass(int[])21100%n/a010301
pass(float[])21100%n/a010301
pass(double[])21100%n/a010301
pass(short[])21100%n/a010301
pass(long[])21100%n/a010301
pass(byte[])21100%n/a010301
passLocalFloats(long)18100%n/a010301
KernelCaller(cl_kernel, cl_command_queue)17100%n/a010601
pass(int)9100%n/a010101
pass(float)9100%n/a010101
pass(double)9100%n/a010101
pass(short)9100%n/a010101
pass(long)9100%n/a010101
pass(byte)9100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +KernelCaller

KernelCaller

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total554 of 5550%30 of 300%373876772223
pass(Number)620%120%777711
call(long[], long[])520%60%447711
call(int)450%20%226611
_releaseEvents(Tensor[])450%40%335511
_getWaitList(Tensor[])450%60%446611
pass(Tensor)330%n/a114411
passAllOf(Tensor)320%n/a114411
passConfOf(Tensor)250%n/a114411
pass(int[])210%n/a113311
pass(float[])210%n/a113311
pass(double[])210%n/a113311
pass(short[])210%n/a113311
pass(long[])210%n/a113311
pass(byte[])210%n/a113311
passLocalFloats(long)180%n/a113311
KernelCaller(cl_kernel, cl_command_queue)170%n/a116611
pass(int)90%n/a111111
pass(float)90%n/a111111
pass(double)90%n/a111111
pass(short)90%n/a111111
pass(long)90%n/a111111
pass(byte)90%n/a111111
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/KernelCaller.java.html b/docs/coverage/test/html/neureka.devices.opencl/KernelCaller.java.html index 3b1273d10..f9c2da5c1 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/KernelCaller.java.html +++ b/docs/coverage/test/html/neureka.devices.opencl/KernelCaller.java.html @@ -19,18 +19,18 @@ private final cl_kernel _kernel; private final List<Tensor<Number>> _inputs; - private int _argId = 0; + private int _argId = 0; /** * * @param kernel The kernel which ought to be called. * @param queue The queue on which calls ought to be dispatched. */ - public KernelCaller( cl_kernel kernel, cl_command_queue queue ) { - _queue = queue; - _kernel = kernel; - _inputs = new ArrayList<>(); - } + public KernelCaller( cl_kernel kernel, cl_command_queue queue ) { + _queue = queue; + _kernel = kernel; + _inputs = new ArrayList<>(); + } /** * This method passes 2 arguments to the kernel. @@ -39,10 +39,10 @@ * @return This very KernelCaller instance (factory pattern). */ public KernelCaller passAllOf( Tensor<Number> tensor ) { - _inputs.add( tensor ); - clSetKernelArg( _kernel, _argId, Sizeof.cl_mem, Pointer.to( tensor.getMut().getData().as( OpenCLDevice.cl_tsr.class ).value.data ) ); - _argId++; - return passConfOf( tensor ); + _inputs.add( tensor ); + clSetKernelArg( _kernel, _argId, Sizeof.cl_mem, Pointer.to( tensor.getMut().getData().as( OpenCLDevice.cl_tsr.class ).value.data ) ); + _argId++; + return passConfOf( tensor ); } /** @@ -55,10 +55,10 @@ * @return This very KernelCaller instance (factory pattern). */ public KernelCaller passConfOf( Tensor<Number> tensor ) { - OpenCLDevice device = (OpenCLDevice) tensor.getDevice(); - clSetKernelArg( _kernel, _argId, Sizeof.cl_mem, Pointer.to( device.clConfigOf(tensor ).data ) ); - _argId++; - return this; + OpenCLDevice device = (OpenCLDevice) tensor.getDevice(); + clSetKernelArg( _kernel, _argId, Sizeof.cl_mem, Pointer.to( device.clConfigOf(tensor ).data ) ); + _argId++; + return this; } /** @@ -68,10 +68,10 @@ * @return This very KernelCaller instance (factory pattern). */ public <T extends Number> KernelCaller pass( Tensor<T> tensor ) { - _inputs.add( tensor.getMut().upcast(Number.class) ); - clSetKernelArg( _kernel, _argId, Sizeof.cl_mem, Pointer.to( tensor.getMut().getData().as( OpenCLDevice.cl_tsr.class ).value.data ) ); - _argId++; - return this; + _inputs.add( tensor.getMut().upcast(Number.class) ); + clSetKernelArg( _kernel, _argId, Sizeof.cl_mem, Pointer.to( tensor.getMut().getData().as( OpenCLDevice.cl_tsr.class ).value.data ) ); + _argId++; + return this; } /** @@ -80,7 +80,7 @@ * @return This very KernelCaller instance (factory pattern). */ public KernelCaller pass( int value ) { - return this.pass( new int[]{ value } ); + return this.pass( new int[]{ value } ); } /** @@ -90,9 +90,9 @@ * @return This very KernelCaller instance (factory pattern). */ public KernelCaller pass( int... values ) { - clSetKernelArg( _kernel, _argId, Sizeof.cl_int * (long) values.length, Pointer.to( values ) ); - _argId++; - return this; + clSetKernelArg( _kernel, _argId, Sizeof.cl_int * (long) values.length, Pointer.to( values ) ); + _argId++; + return this; } /** @@ -102,33 +102,33 @@ * @return This very KernelCaller instance (factory pattern). */ public KernelCaller pass( float... values ) { - clSetKernelArg( _kernel, _argId, Sizeof.cl_float * (long) values.length, Pointer.to( values ) ); - _argId++; - return this; + clSetKernelArg( _kernel, _argId, Sizeof.cl_float * (long) values.length, Pointer.to( values ) ); + _argId++; + return this; } public KernelCaller pass( double... values ) { - clSetKernelArg( _kernel, _argId, Sizeof.cl_double * (long) values.length, Pointer.to( values ) ); - _argId++; - return this; + clSetKernelArg( _kernel, _argId, Sizeof.cl_double * (long) values.length, Pointer.to( values ) ); + _argId++; + return this; } public KernelCaller pass( short... values ) { - clSetKernelArg( _kernel, _argId, Sizeof.cl_short * (long) values.length, Pointer.to( values ) ); - _argId++; - return this; + clSetKernelArg( _kernel, _argId, Sizeof.cl_short * (long) values.length, Pointer.to( values ) ); + _argId++; + return this; } public KernelCaller pass( long... values ) { - clSetKernelArg( _kernel, _argId, Sizeof.cl_long * (long) values.length, Pointer.to( values ) ); - _argId++; - return this; + clSetKernelArg( _kernel, _argId, Sizeof.cl_long * (long) values.length, Pointer.to( values ) ); + _argId++; + return this; } public KernelCaller pass( byte... values ) { - clSetKernelArg( _kernel, _argId, Sizeof.cl_char * (long) values.length, Pointer.to( values ) ); - _argId++; - return this; + clSetKernelArg( _kernel, _argId, Sizeof.cl_char * (long) values.length, Pointer.to( values ) ); + _argId++; + return this; } /** @@ -136,39 +136,39 @@ * @return This very KernelCaller instance (factory pattern). */ public KernelCaller pass( float value ) { - return this.pass( new float[]{ value } ); + return this.pass( new float[]{ value } ); } public KernelCaller pass( double value ) { - return this.pass( new double[]{ value } ); + return this.pass( new double[]{ value } ); } public KernelCaller pass( short value ) { - return this.pass( new short[]{ value } ); + return this.pass( new short[]{ value } ); } public KernelCaller pass( long value ) { - return this.pass( new long[]{ value } ); + return this.pass( new long[]{ value } ); } public KernelCaller pass( byte value ) { - return this.pass( new byte[]{ value } ); + return this.pass( new byte[]{ value } ); } public KernelCaller pass( Number value ) { - if ( value instanceof Float ) return this.pass( value.floatValue() ); - else if ( value instanceof Double ) return this.pass( value.doubleValue() ); - else if ( value instanceof Integer ) return this.pass( value.intValue() ); - else if ( value instanceof Long ) return this.pass( value.longValue() ); - else if ( value instanceof Short ) return this.pass( value.shortValue() ); - else if ( value instanceof Byte ) return this.pass( value.byteValue() ); + if ( value instanceof Float ) return this.pass( value.floatValue() ); + else if ( value instanceof Double ) return this.pass( value.doubleValue() ); + else if ( value instanceof Integer ) return this.pass( value.intValue() ); + else if ( value instanceof Long ) return this.pass( value.longValue() ); + else if ( value instanceof Short ) return this.pass( value.shortValue() ); + else if ( value instanceof Byte ) return this.pass( value.byteValue() ); else throw new IllegalArgumentException( "Unsupported number type: " + value.getClass().getName() ); } public KernelCaller passLocalFloats( long size ) { - clSetKernelArg( _kernel, _argId, Sizeof.cl_float * (long) size, null ); - _argId++; - return this; + clSetKernelArg( _kernel, _argId, Sizeof.cl_float * (long) size, null ); + _argId++; + return this; } /** @@ -177,12 +177,12 @@ */ public void call( int globalWorkSize ) { - cl_event[] events = _getWaitList( _inputs.toArray( new Tensor[ 0 ] ) ); - if ( events.length > 0 ) { + cl_event[] events = _getWaitList( _inputs.toArray( new Tensor[ 0 ] ) ); + if ( events.length > 0 ) { clWaitForEvents( events.length, events ); _releaseEvents( _inputs.toArray( new Tensor[ 0 ] ) ); } - clEnqueueNDRangeKernel( + clEnqueueNDRangeKernel( _queue, _kernel, 1, null, @@ -192,7 +192,7 @@ null, null ); - } + } /** * Use this to call the kernel with 2 long arrays defining how the kernel should be indexed and parallelized. @@ -215,13 +215,13 @@ */ public void call( long[] globalWorkSizes, long[] localWorkSizes ) { - cl_event[] events = _getWaitList( _inputs.toArray( new Tensor[ 0 ] ) ); - if ( events.length > 0 ) { + cl_event[] events = _getWaitList( _inputs.toArray( new Tensor[ 0 ] ) ); + if ( events.length > 0 ) { clWaitForEvents( events.length, events ); _releaseEvents( _inputs.toArray( new Tensor[ 0 ] ) ); } - assert localWorkSizes == null || globalWorkSizes.length == localWorkSizes.length; - clEnqueueNDRangeKernel( + assert localWorkSizes == null || globalWorkSizes.length == localWorkSizes.length; + clEnqueueNDRangeKernel( _queue, _kernel, globalWorkSizes.length, null, @@ -231,7 +231,7 @@ null, null ); - } + } private void _releaseEvents( Tensor<Number>[] tensors ) { @@ -245,15 +245,15 @@ private cl_event[] _getWaitList( Tensor<Number>[] tensors ) { - List<cl_event> list = new ArrayList<>(); - for ( Tensor<Number> t : tensors ) { - cl_event event = t.getMut().getData().as( OpenCLDevice.cl_tsr.class ).value.event; - if ( event != null && !list.contains(event) ) { + List<cl_event> list = new ArrayList<>(); + for ( Tensor<Number> t : tensors ) { + cl_event event = t.getMut().getData().as( OpenCLDevice.cl_tsr.class ).value.event; + if ( event != null && !list.contains(event) ) { list.add( event ); } } - return list.toArray( new cl_event[ 0 ] ); + return list.toArray( new cl_event[ 0 ] ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/KernelCode.html b/docs/coverage/test/html/neureka.devices.opencl/KernelCode.html index 3777fbbae..28f756e56 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/KernelCode.html +++ b/docs/coverage/test/html/neureka.devices.opencl/KernelCode.html @@ -1 +1 @@ -KernelCode

KernelCode

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total32 of 6046%6 of 60%51051527
equals(Object)230%60%444411
hashCode()90%n/a111111
KernelCode(String, String, DataType)12100%n/a010501
KernelCode(String, String)7100%n/a010201
getName()3100%n/a010101
getCode()3100%n/a010101
getDataType()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +KernelCode

KernelCode

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total60 of 600%6 of 60%1010151577
equals(Object)230%60%444411
KernelCode(String, String, DataType)120%n/a115511
hashCode()90%n/a111111
KernelCode(String, String)70%n/a112211
getName()30%n/a111111
getCode()30%n/a111111
getDataType()30%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/KernelCode.java.html b/docs/coverage/test/html/neureka.devices.opencl/KernelCode.java.html index 800391bb1..250a6e9a8 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/KernelCode.java.html +++ b/docs/coverage/test/html/neureka.devices.opencl/KernelCode.java.html @@ -11,20 +11,20 @@ private final DataType<?> _dataType; public KernelCode( String name, String code ) { - this( name, code, DataType.of(Float.class) ); - } + this( name, code, DataType.of(Float.class) ); + } - public KernelCode( String name, String code, DataType<?> dataType ) { - _name = name; - _code = code; - _dataType = dataType; - } + public KernelCode( String name, String code, DataType<?> dataType ) { + _name = name; + _code = code; + _dataType = dataType; + } - public String getName() { return _name; } + public String getName() { return _name; } - public String getCode() { return _code; } + public String getCode() { return _code; } - public DataType<?> getDataType() { return _dataType; } + public DataType<?> getDataType() { return _dataType; } @Override public boolean equals( Object o ) { @@ -40,4 +40,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$CLData.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$CLData.html index e273bf8d8..ff1eae96e 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$CLData.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$CLData.html @@ -1 +1 @@ -OpenCLDevice.CLData

OpenCLDevice.CLData

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 4190%1 of 475%150803
OpenCLDevice.CLData(AbstractBaseDevice, Object, DataType)41275%1150%120301
lambda$new$0(Object)24100%2100%020401
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OpenCLDevice.CLData

OpenCLDevice.CLData

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total41 of 410%4 of 40%558833
lambda$new$0(Object)240%20%224411
OpenCLDevice.CLData(AbstractBaseDevice, Object, DataType)160%20%223311
static {...}10%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$Query.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$Query.html index 73c901a24..3becbc449 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$Query.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$Query.html @@ -1 +1 @@ -OpenCLDevice.Query

OpenCLDevice.Query

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total56 of 19270%3 of 650%413831210
getString(cl_platform_id, int)360%n/a115511
getLongs(int, ByteBuffer, long[])172054%3350%242601
OpenCLDevice.Query()30%n/a111111
getString(cl_device_id, int)36100%n/a010501
getSizes(cl_device_id, int, int)27100%n/a010501
getInts(cl_device_id, int, int)16100%n/a010301
getLongs(cl_device_id, int, int)16100%n/a010301
getInt(cl_device_id, int)7100%n/a010101
getLong(cl_device_id, int)7100%n/a010101
getSize(cl_device_id, int)7100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OpenCLDevice.Query

OpenCLDevice.Query

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total192 of 1920%6 of 60%131331311010
getLongs(int, ByteBuffer, long[])370%60%446611
getString(cl_device_id, int)360%n/a115511
getString(cl_platform_id, int)360%n/a115511
getSizes(cl_device_id, int, int)270%n/a115511
getInts(cl_device_id, int, int)160%n/a113311
getLongs(cl_device_id, int, int)160%n/a113311
getInt(cl_device_id, int)70%n/a111111
getLong(cl_device_id, int)70%n/a111111
getSize(cl_device_id, int)70%n/a111111
OpenCLDevice.Query()30%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$Type.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$Type.html index 339dc0b6d..05cee36f6 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$Type.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$Type.html @@ -1 +1 @@ -OpenCLDevice.Type

OpenCLDevice.Type

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 45100%0 of 0n/a010201
static {...}45100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OpenCLDevice.Type

OpenCLDevice.Type

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total74 of 740%0 of 0n/a112211
static {...}740%n/a112211
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_ad_hoc.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_ad_hoc.html index 0e2303183..18b74aa13 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_ad_hoc.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_ad_hoc.html @@ -1 +1 @@ -OpenCLDevice.cl_ad_hoc

OpenCLDevice.cl_ad_hoc

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 12100%0 of 0n/a010501
OpenCLDevice.cl_ad_hoc(String, cl_kernel, cl_program)12100%n/a010501
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OpenCLDevice.cl_ad_hoc

OpenCLDevice.cl_ad_hoc

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total12 of 120%0 of 0n/a115511
OpenCLDevice.cl_ad_hoc(String, cl_kernel, cl_program)120%n/a115511
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_config.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_config.html index 8ce569629..d09c7f7aa 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_config.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_config.html @@ -1 +1 @@ -OpenCLDevice.cl_config

OpenCLDevice.cl_config

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 3100%0 of 0n/a010101
OpenCLDevice.cl_config()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OpenCLDevice.cl_config

OpenCLDevice.cl_config

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 30%0 of 0n/a111111
OpenCLDevice.cl_config()30%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_dtype.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_dtype.html index d1281ad5b..0c920479f 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_dtype.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_dtype.html @@ -1 +1 @@ -OpenCLDevice.cl_dtype

OpenCLDevice.cl_dtype

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 57100%0 of 0n/a010101
static {...}57100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OpenCLDevice.cl_dtype

OpenCLDevice.cl_dtype

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 94100%0 of 0n/a010101
static {...}94100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_tsr$cl_value.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_tsr$cl_value.html index 73d7f2e76..1d7e80e09 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_tsr$cl_value.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_tsr$cl_value.html @@ -1 +1 @@ -OpenCLDevice.cl_tsr.cl_value

OpenCLDevice.cl_tsr.cl_value

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 6100%0 of 0n/a010101
OpenCLDevice.cl_tsr.cl_value(int)6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OpenCLDevice.cl_tsr.cl_value

OpenCLDevice.cl_tsr.cl_value

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total6 of 60%0 of 0n/a111111
OpenCLDevice.cl_tsr.cl_value(int)60%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_tsr.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_tsr.html index 1c2695d2f..9ad0f1a5c 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_tsr.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice$cl_tsr.html @@ -1 +1 @@ -OpenCLDevice.cl_tsr

OpenCLDevice.cl_tsr

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2487%2 of 450%240602
equals(Object)31280%2250%230201
OpenCLDevice.cl_tsr(OpenCLDevice.cl_tsr.cl_value, OpenCLDevice.cl_dtype)9100%n/a010401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OpenCLDevice.cl_tsr

OpenCLDevice.cl_tsr

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 280%4 of 40%557733
equals(Object)150%40%332211
OpenCLDevice.cl_tsr(OpenCLDevice.cl_tsr.cl_value, OpenCLDevice.cl_dtype)90%n/a114411
hashCode()40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice.html index 0e501858d..3d0d3cbc8 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice.html @@ -1 +1 @@ -OpenCLDevice

OpenCLDevice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total344 of 1,58578%32 of 9566%49138502912188
getKernel(ExecutionCall)384956%5550%5631101
type()361934%10216%675801
compileAdHocKernel(String, String)3212279%4660%4632501
_swap(Tensor, Tensor)230%n/a114411
_dataTypeOf(Object)203664%2777%2821201
allocate(DataType, NDConfiguration)170%n/a113311
getAdHocKernel(String)141551%1150%121301
getKernel(String)141450%1150%121401
allocateFromAll(DataType, NDConfiguration, Object)140%n/a113311
_add(Tensor, Data, Runnable)139087%3975%3742701
lambda$compileAndGetAdHocKernel$1(String)120%n/a111111
maxConstantBufferSizeKB()90%n/a111111
lambda$free$7(Tensor, Device)71466%1150%123701
lambda$compileAdHocKernel$2(cl_kernel, cl_program)70%n/a113311
maxWorkItemSizes()60%n/a111111
_actualize(Tensor)53487%1150%120701
_virtualize(Tensor)53487%1150%120701
maxComputeUnits()50%n/a111111
maxWorkItemSimensions()50%n/a111111
maxAddressBits()50%n/a111111
maxMemAllocSize()50%n/a111111
errorCorrectionSupport()50%n/a111111
localMemType()50%n/a111111
localMemSize()50%n/a111111
imageSupport()50%n/a111111
maxReadImageArgs()50%n/a111111
singleFPConfig()50%n/a111111
lambda$_add$5()50%n/a111111
lambda$_store$3(Tensor)50%n/a111111
lambda$new$0()50%n/a111111
getId()30%n/a111111
free(Tensor)22392%1150%120501
findAdHocKernel(String)21688%1150%121301
_store(JVMData, OpenCLDevice.cl_tsr, boolean)68100%4100%0301501
_overwrite(Tensor, long, JVMData)65100%4100%030901
restore(Tensor)61100%4100%0301101
_writeNewNDConfig(NDConfiguration)49100%n/a010901
_read(JVMData, Tensor, int)34100%n/a010601
OpenCLDevice(OpenCLPlatform, cl_device_id)32100%n/a010901
_storeNew(JVMData, boolean)26100%2100%020401
update(Component.OwnerChangeRequest)26100%2100%020701
findOrCompileAdHocKernel(String, Supplier)22100%2100%020301
toString()21100%n/a010101
_store(Tensor, Tensor)21100%2100%020501
clConfigOf(NDConfiguration)20100%2100%020501
_findRoot(Tensor)20100%2100%020501
allocateFromOne(DataType, NDConfiguration, Number)18100%n/a010301
_readAll(Tensor, boolean)18100%n/a010201
_updateInternal(Tensor, Runnable)17100%2100%020401
of(OpenCLPlatform, cl_device_id)16100%1150%120201
_readItem(Tensor, int)14100%n/a010101
_readArray(Tensor, Class, int, int)13100%n/a010101
dispose()12100%n/a010401
compileAndGetAdHocKernel(String, String)11100%n/a010301
lambda$free$9(Tensor, Device)11100%n/a010301
_writeItem(Tensor, Number, int, int)10100%n/a010201
_writeArray(Tensor, Object, int, int, int)10100%n/a010201
_sizeOccupiedBy(Tensor)9100%n/a010101
optimizedOperationOf(Function, String)8100%n/a010101
lambda$free$8(Tensor)8100%n/a010101
_dataArrayOf(Object, DataType)7100%n/a010101
hasAdHocKernel(String)5100%n/a010101
_storeNew(JVMData)5100%n/a010101
clConfigOf(Tensor)5100%n/a010101
name()5100%n/a010101
vendor()5100%n/a010101
version()5100%n/a010101
maxWorkGroupSize()5100%n/a010101
maxClockFrequenzy()5100%n/a010101
globalMemSize()5100%n/a010101
maxConstantBufferSize()5100%n/a010101
maxWriteImageArgs()5100%n/a010101
image2DMaxWidth()5100%n/a010101
image2DMaxHeight()5100%n/a010101
image3DMaxWidth()5100%n/a010101
image3DMaxHeight()5100%n/a010101
image3DMaxDepth()5100%n/a010101
prefVecWidthChar()5100%n/a010101
prefVecWidthShort()5100%n/a010101
prefVecWidthInt()5100%n/a010101
prefVecWidthLong()5100%n/a010101
prefVecWidthFloat()5100%n/a010101
prefVecWidthDouble()5100%n/a010101
lambda$_add$4(CLBackend)5100%n/a010101
lambda$_writeNewNDConfig$6(cl_mem)4100%n/a010101
static {...}4100%n/a010101
getPlatform()3100%n/a010101
_approveExecutionOf(Tensor[], int, Operation)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OpenCLDevice

OpenCLDevice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total1,582 of 1,5860%95 of 950%1371382902918788
compileAdHocKernel(String, String)1540%100%66252511
_add(Tensor, Data, Runnable)1030%120%77272711
getKernel(ExecutionCall)870%100%66111111
_store(JVMData, OpenCLDevice.cl_tsr, boolean)680%40%33151511
_overwrite(Tensor, long, JVMData)650%40%339911
restore(Tensor)610%40%33111111
_dataTypeOf(Object)560%90%88121211
type()550%120%778811
_writeNewNDConfig(NDConfiguration)490%n/a119911
_actualize(Tensor)390%20%227711
_virtualize(Tensor)390%20%227711
_read(JVMData, Tensor, int)340%n/a116611
OpenCLDevice(OpenCLPlatform, cl_device_id)320%n/a119911
getAdHocKernel(String)290%20%223311
getKernel(String)280%20%224411
_storeNew(JVMData, boolean)260%20%224411
update(Component.OwnerChangeRequest)260%20%227711
free(Tensor)250%20%225511
_swap(Tensor, Tensor)230%n/a114411
findOrCompileAdHocKernel(String, Supplier)220%20%223311
toString()210%n/a111111
_store(Tensor, Tensor)210%20%225511
lambda$free$7(Tensor, Device)210%20%227711
clConfigOf(NDConfiguration)200%20%225511
_findRoot(Tensor)200%20%225511
findAdHocKernel(String)180%20%223311
allocateFromOne(DataType, NDConfiguration, Number)180%n/a113311
_readAll(Tensor, boolean)180%n/a112211
allocate(DataType, NDConfiguration)170%n/a113311
_updateInternal(Tensor, Runnable)170%20%224411
of(OpenCLPlatform, cl_device_id)160%20%222211
_readItem(Tensor, int)140%n/a111111
allocateFromAll(DataType, NDConfiguration, Object)140%n/a113311
_readArray(Tensor, Class, int, int)130%n/a111111
dispose()120%n/a114411
lambda$compileAndGetAdHocKernel$1(String)120%n/a111111
compileAndGetAdHocKernel(String, String)110%n/a113311
lambda$free$9(Tensor, Device)110%n/a113311
_writeItem(Tensor, Number, int, int)100%n/a112211
_writeArray(Tensor, Object, int, int, int)100%n/a112211
_sizeOccupiedBy(Tensor)90%n/a111111
maxConstantBufferSizeKB()90%n/a111111
optimizedOperationOf(Function, String)80%n/a111111
lambda$free$8(Tensor)80%n/a111111
_dataArrayOf(Object, DataType)70%n/a111111
lambda$compileAdHocKernel$2(cl_kernel, cl_program)70%n/a113311
maxWorkItemSizes()60%n/a111111
lambda$_store$3(Tensor)60%n/a111111
hasAdHocKernel(String)50%n/a111111
_storeNew(JVMData)50%n/a111111
clConfigOf(Tensor)50%n/a111111
name()50%n/a111111
vendor()50%n/a111111
version()50%n/a111111
maxComputeUnits()50%n/a111111
maxWorkItemSimensions()50%n/a111111
maxWorkGroupSize()50%n/a111111
maxClockFrequenzy()50%n/a111111
maxAddressBits()50%n/a111111
maxMemAllocSize()50%n/a111111
globalMemSize()50%n/a111111
errorCorrectionSupport()50%n/a111111
localMemType()50%n/a111111
localMemSize()50%n/a111111
maxConstantBufferSize()50%n/a111111
imageSupport()50%n/a111111
maxReadImageArgs()50%n/a111111
maxWriteImageArgs()50%n/a111111
singleFPConfig()50%n/a111111
image2DMaxWidth()50%n/a111111
image2DMaxHeight()50%n/a111111
image3DMaxWidth()50%n/a111111
image3DMaxHeight()50%n/a111111
image3DMaxDepth()50%n/a111111
prefVecWidthChar()50%n/a111111
prefVecWidthShort()50%n/a111111
prefVecWidthInt()50%n/a111111
prefVecWidthLong()50%n/a111111
prefVecWidthFloat()50%n/a111111
prefVecWidthDouble()50%n/a111111
lambda$_add$5()50%n/a111111
lambda$_add$4(CLBackend)50%n/a111111
lambda$new$0()50%n/a111111
lambda$_writeNewNDConfig$6(cl_mem)40%n/a111111
getId()30%n/a111111
getPlatform()30%n/a111111
_approveExecutionOf(Tensor[], int, Operation)20%n/a111111
static {...}4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice.java.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice.java.html index 004ec5df9..0fb05a798 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice.java.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLDevice.java.html @@ -88,12 +88,12 @@ private static final Logger _LOG = LoggerFactory.getLogger(OpenCLDevice.class); static OpenCLDevice of( OpenCLPlatform platform, cl_device_id did ) { - if (!platform.has(did)) platform.put(did, new OpenCLDevice(platform, did)); - return platform.get(did); + if (!platform.has(did)) platform.put(did, new OpenCLDevice(platform, did)); + return platform.get(did); } - public enum Type { - CPU, GPU, ACCELERATOR, DEFAULT, CUSTOM, ALL, UNKNOWN + public enum Type { + CPU, GPU, ACCELERATOR, DEFAULT, CUSTOM, ALL, UNKNOWN } enum cl_dtype { F32, F64, I64, I32, I16, I8, U32, U16, U8 } @@ -104,7 +104,7 @@ | --------------------------- */ - private final KernelCache _kernelCache = new KernelCache(); + private final KernelCache _kernelCache = new KernelCache(); private final cl_device_id _deviceId; @@ -121,7 +121,7 @@ private final cl_command_queue _queue; - private final Map<NDConfiguration, cl_config> _configs = new WeakHashMap<>(); + private final Map<NDConfiguration, cl_config> _configs = new WeakHashMap<>(); /*================================================================================================================== | @@ -134,38 +134,38 @@ * @param deviceId The underlying OpenCL id of this device. */ private OpenCLDevice( OpenCLPlatform platform, cl_device_id deviceId ) { - super(); - _deviceId = deviceId; - _platform = platform; - _queue = clCreateCommandQueueWithProperties(// Create a command-queue for the selected device - platform.getContext(), deviceId, + super(); + _deviceId = deviceId; + _platform = platform; + _queue = clCreateCommandQueueWithProperties(// Create a command-queue for the selected device + platform.getContext(), deviceId, null, null ); - _cleaning(this, () -> clReleaseCommandQueue(_queue)); - } + _cleaning(this, () -> clReleaseCommandQueue(_queue)); + } public final String toString() { - return "OpenCLDevice[id=0x" + Long.toHexString(_deviceId.getNativePointer()) + ",platform=0x" + Long.toHexString(_platform.getId()) + "]"; + return "OpenCLDevice[id=0x" + Long.toHexString(_deviceId.getNativePointer()) + ",platform=0x" + Long.toHexString(_platform.getId()) + "]"; } public final cl_device_id getId() { return _deviceId; } - public final OpenCLPlatform getPlatform() { return _platform; } + public final OpenCLPlatform getPlatform() { return _platform; } /** * @param name The name of the kernel whose presents should be checked. * @return True if the kernel is present in the cache, false otherwise. */ - public boolean hasAdHocKernel( String name ) { return _kernelCache.has(name); } + public boolean hasAdHocKernel( String name ) { return _kernelCache.has(name); } /** * @param name The name of the kernel which should be retrieved. * @return The kernel with the given name if it is present in the cache, throws an exception otherwise. */ public KernelCaller getAdHocKernel( String name ) { - cl_ad_hoc adHoc = _kernelCache.get(name); - if (adHoc != null) return new KernelCaller(adHoc.kernel, _queue); + cl_ad_hoc adHoc = _kernelCache.get(name); + if (adHoc != null) return new KernelCaller(adHoc.kernel, _queue); else throw new IllegalArgumentException("No ad hoc kernel with name '" + name + "' found!"); } @@ -174,8 +174,8 @@ * @return An {@link Optional} containing the kernel with the given name if it is present in the cache, an empty optional otherwise. */ public Optional<KernelCaller> findAdHocKernel( String name ) { - cl_ad_hoc adHoc = _kernelCache.get(name); - if (adHoc != null) return Optional.of(new KernelCaller(adHoc.kernel, _queue)); + cl_ad_hoc adHoc = _kernelCache.get(name); + if (adHoc != null) return Optional.of(new KernelCaller(adHoc.kernel, _queue)); else return Optional.empty(); } @@ -186,9 +186,9 @@ * or compiled from the given source code if it was not present in the cache. */ public KernelCaller findOrCompileAdHocKernel( String name, Supplier<String> source ) { - cl_ad_hoc adHoc = _kernelCache.get(name); - if ( adHoc != null ) return new KernelCaller(adHoc.kernel, _queue); - else return compileAndGetAdHocKernel(name, source.get()); + cl_ad_hoc adHoc = _kernelCache.get(name); + if ( adHoc != null ) return new KernelCaller(adHoc.kernel, _queue); + else return compileAndGetAdHocKernel(name, source.get()); } /** @@ -206,9 +206,9 @@ * @return The {@link KernelCaller} for the compiled kernel. */ public synchronized KernelCaller compileAndGetAdHocKernel( String name, String source ) { - return compileAdHocKernel( name, source ) - .findAdHocKernel( name ) - .orElseThrow(() -> new RuntimeException("Failed to compile kernel: " + name)); + return compileAdHocKernel( name, source ) + .findAdHocKernel( name ) + .orElseThrow(() -> new RuntimeException("Failed to compile kernel: " + name)); } /** @@ -226,22 +226,22 @@ * @return This very instance in order to enable the factory pattern. */ public synchronized OpenCLDevice compileAdHocKernel( String name, String source ) { - if (this.hasAdHocKernel(name)) { - cl_ad_hoc adHoc = _kernelCache.get(name); - String message = + if (this.hasAdHocKernel(name)) { + cl_ad_hoc adHoc = _kernelCache.get(name); + String message = "Cannot compile kernel source for name '" + name + "' because the name is already taken.\n" + "Use another name or find out why this kernel already exists.\n" + ( - adHoc.source.equals(source) - ? "Besides the name, the source code of the existing kernel is also identical.\n" : "" + adHoc.source.equals(source) + ? "Besides the name, the source code of the existing kernel is also identical.\n" : "" ); - _log.error(message); - throw new IllegalArgumentException(message); + _log.error(message); + throw new IllegalArgumentException(message); } // Create the program for the kernel - cl_program cpProgram = clCreateProgramWithSource( - getPlatform().getContext(), + cl_program cpProgram = clCreateProgramWithSource( + getPlatform().getContext(), 1, new String[]{source}, null, @@ -249,7 +249,7 @@ ); // Build the program - int err = clBuildProgram( + int err = clBuildProgram( cpProgram, 1, new cl_device_id[]{_deviceId}, @@ -258,39 +258,39 @@ null ); - if ( err != CL_SUCCESS ) + if ( err != CL_SUCCESS ) _log.error("Error when trying to compile 'ad hoc kernel' named '"+name+"'! Error code: "+err); //TODO: check compilation errors! cl_kernel kernel; try { // Create the kernel - kernel = clCreateKernel(cpProgram, name, null); - } catch (Exception e) { - if (e.getMessage().equals("CL_INVALID_KERNEL_NAME") && !source.contains("__kernel void " + name)) { - String message = "Method 'clCreateKernel' failed! The name of the '__kernel' method declared inside \n" + + kernel = clCreateKernel(cpProgram, name, null); + } catch (Exception e) { + if (e.getMessage().equals("CL_INVALID_KERNEL_NAME") && !source.contains("__kernel void " + name)) { + String message = "Method 'clCreateKernel' failed! The name of the '__kernel' method declared inside \n" + "the source String does not match the provided name needed for kernel creation."; - _log.error(message, e); - throw new IllegalArgumentException(message); + _log.error(message, e); + throw new IllegalArgumentException(message); } _log.error("Method call 'clCreateKernel(.., name=\"" + name + "\", ..)' failed!", e); throw e; - } - cl_ad_hoc adHoc = new cl_ad_hoc(source, kernel, cpProgram); + } + cl_ad_hoc adHoc = new cl_ad_hoc(source, kernel, cpProgram); // Storing the ad hoc object in a weak hash map for fast access by operations : - _kernelCache.put( name, adHoc ); + _kernelCache.put( name, adHoc ); - _cleaning(adHoc, () -> { + _cleaning(adHoc, () -> { clReleaseKernel(kernel); clReleaseProgram(cpProgram); }); - return this; + return this; } @Override public Operation optimizedOperationOf( Function function, String name ) { - return new CLFunctionCompiler( this, function, name ).optimize(); + return new CLFunctionCompiler( this, function, name ).optimize(); } /** @@ -298,10 +298,10 @@ */ @Override public void dispose() { - _numberOfTensors = 0; - clFinish( _queue ); - clReleaseCommandQueue( _queue ); - } + _numberOfTensors = 0; + clFinish( _queue ); + clReleaseCommandQueue( _queue ); + } /** * This method assumes that the passed tensor is stored on this device instance. @@ -314,23 +314,23 @@ */ @Override public Device<Number> restore( Tensor<Number> tensor ) { - if ( !this.has( tensor ) ) { - String message = "The passed tensor cannot be restored from this OpenCL device " + + if ( !this.has( tensor ) ) { + String message = "The passed tensor cannot be restored from this OpenCL device " + "because the tensor is not stored on the device.\n"; - _log.error(message); - throw new IllegalArgumentException(message); + _log.error(message); + throw new IllegalArgumentException(message); } - Object value = _read(JVMData.of(tensor.itemType(), tensor.isVirtual() ? 1 : tensor.size()), tensor, 0).getArray(); + Object value = _read(JVMData.of(tensor.itemType(), tensor.isVirtual() ? 1 : tensor.size()), tensor, 0).getArray(); - Class<?> arrayType = Objects.requireNonNull(tensor.getDataType().getTypeClassInstance(NumericType.class)).holderArrayType(); + Class<?> arrayType = Objects.requireNonNull(tensor.getDataType().getTypeClassInstance(NumericType.class)).holderArrayType(); - value = DataConverter.get().convert( value, arrayType ); + value = DataConverter.get().convert( value, arrayType ); - this.free( tensor ); - tensor.find( Tensor.class ).ifPresent( this::restore ); - tensor.getMut().setItems( value ); - return this; + this.free( tensor ); + tensor.find( Tensor.class ).ifPresent( this::restore ); + tensor.getMut().setItems( value ); + return this; } @@ -344,45 +344,45 @@ * @param tensor The tensor whose data ought to be stored. */ private <T extends Number> void _store(Tensor<T> tensor, Tensor<T> parent ) { - if (!parent.isOutsourced()) throw new IllegalStateException("Data parent is not outsourced!"); - _add( - tensor.getMut().upcast(Number.class), - parent.getMut().getData(), + if (!parent.isOutsourced()) throw new IllegalStateException("Data parent is not outsourced!"); + _add( + tensor.getMut().upcast(Number.class), + parent.getMut().getData(), () -> tensor.set((Component) this) ); - } + } private <T extends Number> void _add( Tensor<Number> tensor, Data<T> parentData, Runnable migration // Causes the device to be a component of the tensor! ) { - if ( this.has( tensor ) ) { - _LOG.debug("Trying to add a tensor to a device which already reports hosting it."); - return; + if ( this.has( tensor ) ) { + _LOG.debug("Trying to add a tensor to a device which already reports hosting it."); + return; } - boolean convertToFloat = Neureka.get() - .backend() - .find(CLBackend.class) - .map( it -> it.getSettings().isAutoConvertToFloat() ) - .orElse(false); + boolean convertToFloat = Neureka.get() + .backend() + .find(CLBackend.class) + .map( it -> it.getSettings().isAutoConvertToFloat() ) + .orElse(false); Data<Number> data; - if ( parentData == null ) { - if ( tensor.getMut().getData().owner() == this ) { + if ( parentData == null ) { + if ( tensor.getMut().getData().owner() == this ) { migration.run(); return; } - JVMData jvmData = null; - jvmData = JVMData.of( tensor.getMut().getData().getOrNull(), convertToFloat ); + JVMData jvmData = null; + jvmData = JVMData.of( tensor.getMut().getData().getOrNull(), convertToFloat ); cl_tsr<Number, Number> newClt; - newClt = _storeNew( jvmData ); - if ( tensor.rqsGradient() && tensor.hasGradient() ) + newClt = _storeNew( jvmData ); + if ( tensor.rqsGradient() && tensor.hasGradient() ) this.store(tensor.gradient().orElseThrow(()->new IllegalStateException("Gradient missing!"))); - cl_mem[] memos = new cl_mem[]{newClt.value.data}; - clEnqueueMigrateMemObjects( + cl_mem[] memos = new cl_mem[]{newClt.value.data}; + clEnqueueMigrateMemObjects( _queue, memos.length, memos, CL_MIGRATE_MEM_OBJECT_HOST, 0, @@ -390,69 +390,69 @@ null ); - data = _dataArrayOf(newClt, (DataType<Number>) _dataTypeOf(newClt)); - } + data = _dataArrayOf(newClt, (DataType<Number>) _dataTypeOf(newClt)); + } else data = (Data<Number>) parentData; - tensor.getMut().setData( data ); - migration.run(); + tensor.getMut().setData( data ); + migration.run(); // When tensors get stored on this device, // they can be implicitly converted to a float tensor: - if ( convertToFloat ) - tensor.getMut().toType(F32.class); - } + if ( convertToFloat ) + tensor.getMut().toType(F32.class); + } private cl_tsr<Number, Number> _storeNew( JVMData jvmData ) { - return _storeNew( jvmData, false ); + return _storeNew( jvmData, false ); } private cl_tsr<Number, Number> _storeNew( JVMData jvmData, boolean allocateTargetSize ) { - cl_tsr.cl_value newVal = new cl_tsr.cl_value((int) (allocateTargetSize ? jvmData.getTargetLength() : jvmData.getLength())); - cl_tsr<Number, Number> newClt = new cl_tsr<>(newVal, jvmData.getType()); - _store( jvmData, newClt, allocateTargetSize ); - return newClt; + cl_tsr.cl_value newVal = new cl_tsr.cl_value((int) (allocateTargetSize ? jvmData.getTargetLength() : jvmData.getLength())); + cl_tsr<Number, Number> newClt = new cl_tsr<>(newVal, jvmData.getType()); + _store( jvmData, newClt, allocateTargetSize ); + return newClt; } public cl_config clConfigOf(Tensor<?> t ) { - return clConfigOf( t.getNDConf() ); + return clConfigOf( t.getNDConf() ); } public cl_config clConfigOf(NDConfiguration ndc ) { - cl_config config = _configs.get(ndc); - if ( config == null ) { - config = _writeNewNDConfig( ndc ); - _configs.put(ndc, config); + cl_config config = _configs.get(ndc); + if ( config == null ) { + config = _writeNewNDConfig( ndc ); + _configs.put(ndc, config); } - return config; + return config; } private cl_config _writeNewNDConfig(NDConfiguration ndc ) { - cl_config clf = new cl_config(); + cl_config clf = new cl_config(); //Config format: <[ shape | strides | indicesMap | indices | scale ]> - int[] config = ndc.asInlineArray(); + int[] config = ndc.asInlineArray(); //shape/strides/map/offset/spread - clf.data = clCreateBuffer( - _platform.getContext(), + clf.data = clCreateBuffer( + _platform.getContext(), CL_MEM_READ_WRITE, (long) config.length * Sizeof.cl_int, null, null ); - clEnqueueWriteBuffer( + clEnqueueWriteBuffer( _queue, clf.data, CL_TRUE, 0, (long) config.length * Sizeof.cl_int, - Pointer.to(config), + Pointer.to(config), 0, null, null ); - final cl_mem clConfMem = clf.data; - _cleaning( clf, () -> clReleaseMemObject(clConfMem) ); - return clf; + final cl_mem clConfMem = clf.data; + _cleaning( clf, () -> clReleaseMemObject(clConfMem) ); + return clf; } private void _store( @@ -460,53 +460,53 @@ cl_tsr<?, ?> newClTensor, boolean allocateTarget ) { - long bufferLength = allocateTarget ? jvmData.getTargetLength() : jvmData.getLength(); + long bufferLength = allocateTarget ? jvmData.getTargetLength() : jvmData.getLength(); - cl_mem mem = clCreateBuffer( - _platform.getContext(), + cl_mem mem = clCreateBuffer( + _platform.getContext(), CL_MEM_READ_WRITE, - (long) jvmData.getItemSize() * bufferLength, + (long) jvmData.getItemSize() * bufferLength, null, null ); - newClTensor.value.data = mem; + newClTensor.value.data = mem; // Virtual means that there is only a single value in the JVM array. // So we don't have to write the whole array to the device! // Instead, we can just fill the device memory with the single value. - boolean isASingleValue = jvmData.isVirtual(); + boolean isASingleValue = jvmData.isVirtual(); - if ( isASingleValue ) - clEnqueueFillBuffer( - _queue, mem, jvmData.getPointer(), // pattern - jvmData.getItemSize(), 0, - (long) jvmData.getItemSize() * bufferLength, + if ( isASingleValue ) + clEnqueueFillBuffer( + _queue, mem, jvmData.getPointer(), // pattern + jvmData.getItemSize(), 0, + (long) jvmData.getItemSize() * bufferLength, 0, null, null ); else - clEnqueueWriteBuffer( + clEnqueueWriteBuffer( _queue, mem, CL_TRUE, 0, - (long) jvmData.getItemSize() * bufferLength, - jvmData.getPointer(), 0, null, null + (long) jvmData.getItemSize() * bufferLength, + jvmData.getPointer(), 0, null, null ); - } + } @Override public final <T extends Number> Device<Number> free( Tensor<T> tensor ) { - cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); - if ( clt == null ) return this; - tensor.getMut().setData(null); - tensor.find(Device.class).ifPresent( + cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); + if ( clt == null ) return this; + tensor.getMut().setData(null); + tensor.find(Device.class).ifPresent( device -> { - tensor.remove( Device.class ); - tensor.find(Tensor.class).ifPresent( + tensor.remove( Device.class ); + tensor.find(Tensor.class).ifPresent( gradient -> - ( (Tensor<Number>) gradient ).find(Device.class).ifPresent( + ( (Tensor<Number>) gradient ).find(Device.class).ifPresent( gradDevice -> { try { - if ( this.has( gradient ) ) gradDevice.restore( gradient ); + if ( this.has( gradient ) ) gradDevice.restore( gradient ); } catch ( Exception exception ) { _LOG.error( @@ -514,29 +514,29 @@ exception ); throw exception; - } - gradient.remove( Device.class ); - }) + } + gradient.remove( Device.class ); + }) ); - } + } ); - return this; + return this; } @Override protected final <T extends Number> T _readItem( Tensor<T> tensor, int index ) { - return (T) _read(JVMData.of(tensor.itemType(), 1), tensor.getMut().upcast(Number.class), index).getElementAt(0); + return (T) _read(JVMData.of(tensor.itemType(), 1), tensor.getMut().upcast(Number.class), index).getElementAt(0); } @Override protected final <T extends Number, A> A _readArray( Tensor<T> tensor, Class<A> arrayType, int start, int size ) { - return (A) _read(JVMData.of(tensor.itemType(), size), tensor.getMut().upcast(Number.class), start).getArray(); + return (A) _read(JVMData.of(tensor.itemType(), size), tensor.getMut().upcast(Number.class), start).getArray(); } @Override protected final <T extends Number> void _writeItem( Tensor<T> tensor, T item, int start, int size ) { - _overwrite( tensor, start, JVMData.of(item, size, 0) ); - } + _overwrite( tensor, start, JVMData.of(item, size, 0) ); + } @Override protected final <T extends Number> void _writeArray( @@ -546,8 +546,8 @@ int start, int size ) { - _overwrite( tensor, start, JVMData.of(array, size, offset) ); - } + _overwrite( tensor, start, JVMData.of(array, size, offset) ); + } @Override public <T extends Number> Data<T> allocate( DataType<T> dataType, NDConfiguration ndc ) { @@ -558,9 +558,9 @@ @Override public <T extends Number> Data<T> allocateFromOne( DataType<T> dataType, NDConfiguration ndc, T initialValue ) { - JVMData jvmData = JVMData.of( initialValue, ndc.size(), false, true ); - cl_tsr<Number, Number> clt = _storeNew(jvmData ); - return (Data<T>) _dataArrayOf(clt, (DataType<Number>) _dataTypeOf(clt)); + JVMData jvmData = JVMData.of( initialValue, ndc.size(), false, true ); + cl_tsr<Number, Number> clt = _storeNew(jvmData ); + return (Data<T>) _dataArrayOf(clt, (DataType<Number>) _dataTypeOf(clt)); } @Override @@ -572,41 +572,41 @@ @Override protected Data<Number> _actualize( Tensor<?> tensor ) { - NDConfiguration ndc = tensor.getNDConf(); - Object initialValue = tensor.item(); - cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); - if ( clt == null ) throw new IllegalStateException("The tensor has no device component!"); - JVMData jvmData = JVMData.of( initialValue, ndc.size(), false, true ); - clt = _storeNew( jvmData, true ); - return _dataArrayOf(clt, (DataType<Number>) _dataTypeOf(clt)); + NDConfiguration ndc = tensor.getNDConf(); + Object initialValue = tensor.item(); + cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); + if ( clt == null ) throw new IllegalStateException("The tensor has no device component!"); + JVMData jvmData = JVMData.of( initialValue, ndc.size(), false, true ); + clt = _storeNew( jvmData, true ); + return _dataArrayOf(clt, (DataType<Number>) _dataTypeOf(clt)); } @Override protected Data<Number> _virtualize( Tensor<?> tensor ) { - NDConfiguration ndc = tensor.getNDConf(); - Object initialValue = tensor.item(); - cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); - if ( clt == null ) throw new IllegalStateException("The tensor has no device component!"); - JVMData jvmData = JVMData.of( initialValue, ndc.size(), false, true ); - clt = _storeNew( jvmData, false ); - return _dataArrayOf(clt, (DataType<Number>) _dataTypeOf(clt)); + NDConfiguration ndc = tensor.getNDConf(); + Object initialValue = tensor.item(); + cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); + if ( clt == null ) throw new IllegalStateException("The tensor has no device component!"); + JVMData jvmData = JVMData.of( initialValue, ndc.size(), false, true ); + clt = _storeNew( jvmData, false ); + return _dataArrayOf(clt, (DataType<Number>) _dataTypeOf(clt)); } @Override protected final DataType<?> _dataTypeOf( Object rawData ) { - LogUtil.nullArgCheck( rawData, "rawData", Object.class ); - if ( rawData instanceof cl_tsr ) { - cl_dtype type = ((cl_tsr) rawData).dtype; - switch ( type ) { - case F32: return DataType.of( Float.class ); - case F64: return DataType.of( Double.class ); + LogUtil.nullArgCheck( rawData, "rawData", Object.class ); + if ( rawData instanceof cl_tsr ) { + cl_dtype type = ((cl_tsr) rawData).dtype; + switch ( type ) { + case F32: return DataType.of( Float.class ); + case F64: return DataType.of( Double.class ); case I32: case U32: - return DataType.of( Integer.class ); - case I64: return DataType.of( Long.class ); + return DataType.of( Integer.class ); + case I64: return DataType.of( Long.class ); case I16: case U16: - return DataType.of( Short.class ); + return DataType.of( Short.class ); case I8: case U8: - return DataType.of( Byte.class ); + return DataType.of( Byte.class ); default: throw new IllegalStateException("Unknown OpenCL data type!"); } } @@ -616,20 +616,20 @@ private void _overwrite( Tensor<?> tensor, long offset, JVMData jvmData ) { - if ( jvmData.getLength() == 0 ) return; - cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); - - if ( clt.value.event != null ) clWaitForEvents(1, new cl_event[]{clt.value.event}); - clt.value.event = new cl_event(); - long start = offset * jvmData.getItemSize(); - long size = jvmData.getItemSize() * jvmData.getLength(); - clEnqueueWriteBuffer( + if ( jvmData.getLength() == 0 ) return; + cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); + + if ( clt.value.event != null ) clWaitForEvents(1, new cl_event[]{clt.value.event}); + clt.value.event = new cl_event(); + long start = offset * jvmData.getItemSize(); + long size = jvmData.getItemSize() * jvmData.getLength(); + clEnqueueWriteBuffer( _queue, clt.value.data, CL_TRUE, start, size, - jvmData.getPointer(), 0, null, + jvmData.getPointer(), 0, null, clt.value.event ); - } + } @Override protected final <T extends Number> void _swap( Tensor<T> former, Tensor<T> replacement) { @@ -640,53 +640,53 @@ @Override public boolean update( OwnerChangeRequest<Tensor<Number>> changeRequest ) { - super.update(changeRequest); - if ( changeRequest.type() == IsBeing.ADDED ) { - Tensor<Number> newOwner = changeRequest.getNewOwner(); - _updateInternal(newOwner, changeRequest::executeChange); - } else - changeRequest.executeChange(); // This can be an 'add', 'remove' or 'transfer' of this component! - return true; + super.update(changeRequest); + if ( changeRequest.type() == IsBeing.ADDED ) { + Tensor<Number> newOwner = changeRequest.getNewOwner(); + _updateInternal(newOwner, changeRequest::executeChange); + } else + changeRequest.executeChange(); // This can be an 'add', 'remove' or 'transfer' of this component! + return true; } @Override - protected <T extends Number> int _sizeOccupiedBy( Tensor<T> tensor ) { return tensor.getMut().getData().as( cl_tsr.class).value.size; } + protected <T extends Number> int _sizeOccupiedBy( Tensor<T> tensor ) { return tensor.getMut().getData().as( cl_tsr.class).value.size; } @Override protected <T extends Number> Object _readAll( Tensor<T> tensor, boolean clone ) { - cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); - return _readArray( tensor, tensor.getDataType().dataArrayType(), 0, clt.value.size ); + cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); + return _readArray( tensor, tensor.getDataType().dataArrayType(), 0, clt.value.size ); } private void _updateInternal( Tensor<Number> newOwner, Runnable migration) { - Tensor<Number> root = _findRoot( newOwner ); - if (root != null) _store(newOwner, root); - else _add( newOwner, null, migration ); - } + Tensor<Number> root = _findRoot( newOwner ); + if (root != null) _store(newOwner, root); + else _add( newOwner, null, migration ); + } private Tensor<Number> _findRoot( Tensor<Number> newOwner ) { - Tensor<Number> root = null; - Relation<Number> relation = newOwner.get(Relation.class); - if ( relation != null ) - root = ((Relation<Number>) newOwner.get(Relation.class)).findRootTensor().orElse(null); + Tensor<Number> root = null; + Relation<Number> relation = newOwner.get(Relation.class); + if ( relation != null ) + root = ((Relation<Number>) newOwner.get(Relation.class)).findRootTensor().orElse(null); - return root; + return root; } private JVMData _read( JVMData jvmData, Tensor<Number> tensor, int offset ) { - cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); - clEnqueueReadBuffer( + cl_tsr<?, ?> clt = tensor.getMut().getData().as( cl_tsr.class); + clEnqueueReadBuffer( _queue, clt.value.data, CL_TRUE, - (long) offset * jvmData.getItemSize(), // one double == eight byte - (long) jvmData.getItemSize() * jvmData.getLength(), - jvmData.getPointer(), + (long) offset * jvmData.getItemSize(), // one double == eight byte + (long) jvmData.getItemSize() * jvmData.getLength(), + jvmData.getPointer(), 0, null, null ); - return jvmData; + return jvmData; } /** @@ -695,23 +695,23 @@ */ public KernelCaller getKernel( ExecutionCall<OpenCLDevice> call ) { String chosen; - Algorithm algorithm = call.getAlgorithm(); - DeviceAlgorithm<?> deviceAlgorithm = ( algorithm instanceof DeviceAlgorithm ? ((DeviceAlgorithm<?>) algorithm) : null ); + Algorithm algorithm = call.getAlgorithm(); + DeviceAlgorithm<?> deviceAlgorithm = ( algorithm instanceof DeviceAlgorithm ? ((DeviceAlgorithm<?>) algorithm) : null ); // We create the kernel name from the chosen algorithm: - ImplementationFor<OpenCLDevice> impl = ( deviceAlgorithm == null ? null : deviceAlgorithm.getImplementationFor(OpenCLDevice.class) ); - if ( impl instanceof CLImplementation && _platform.hasKernel(((CLImplementation) impl).getKernelFor(call).getName()) ) { - chosen = ((CLImplementation) impl).getKernelFor( call ).getName(); + ImplementationFor<OpenCLDevice> impl = ( deviceAlgorithm == null ? null : deviceAlgorithm.getImplementationFor(OpenCLDevice.class) ); + if ( impl instanceof CLImplementation && _platform.hasKernel(((CLImplementation) impl).getKernelFor(call).getName()) ) { + chosen = ((CLImplementation) impl).getKernelFor( call ).getName(); } else chosen = call.getAlgorithm().getName() + "_" + call.getOperation().getIdentifier(); - cl_kernel kernel = _platform.getKernel( chosen ); - if ( kernel == null ) + cl_kernel kernel = _platform.getKernel( chosen ); + if ( kernel == null ) throw new IllegalStateException( "No kernel found for signature '" + chosen + "' for operation '" + call.getOperation().getIdentifier() + "'." ); - return new KernelCaller(kernel, _queue); + return new KernelCaller(kernel, _queue); } /** @@ -719,14 +719,14 @@ * @return A {@link KernelCaller} for calling the requested kernel. */ public KernelCaller getKernel( String name ) { - cl_kernel kernel = _platform.getKernel( name ); - if ( kernel == null ) + cl_kernel kernel = _platform.getKernel( name ); + if ( kernel == null ) throw new IllegalStateException("No kernel found with name '" + name + "'."); - return new KernelCaller(kernel, _queue); + return new KernelCaller(kernel, _queue); } @Override - protected boolean _approveExecutionOf(Tensor<?>[] tensors, int d, Operation type ) { return true; } + protected boolean _approveExecutionOf(Tensor<?>[] tensors, int d, Operation type ) { return true; } /*================================================================================================================== @@ -735,16 +735,16 @@ | --------------------------- */ - public String name() { return Query.getString( _deviceId, CL_DEVICE_NAME ); } + public String name() { return Query.getString( _deviceId, CL_DEVICE_NAME ); } - public String vendor() { return Query.getString(_deviceId, CL_DEVICE_VENDOR); } + public String vendor() { return Query.getString(_deviceId, CL_DEVICE_VENDOR); } - public String version() { return Query.getString(_deviceId, CL_DRIVER_VERSION); } + public String version() { return Query.getString(_deviceId, CL_DRIVER_VERSION); } public Type type() { - long deviceType = Query.getLong(_deviceId, CL_DEVICE_TYPE); - if ( (deviceType & CL_DEVICE_TYPE_CPU ) != 0 ) return Type.CPU; - if ( (deviceType & CL_DEVICE_TYPE_GPU ) != 0 ) return Type.GPU; + long deviceType = Query.getLong(_deviceId, CL_DEVICE_TYPE); + if ( (deviceType & CL_DEVICE_TYPE_CPU ) != 0 ) return Type.CPU; + if ( (deviceType & CL_DEVICE_TYPE_GPU ) != 0 ) return Type.GPU; if ( (deviceType & CL_DEVICE_TYPE_ACCELERATOR ) != 0 ) return Type.ACCELERATOR; if ( (deviceType & CL_DEVICE_TYPE_DEFAULT ) != 0 ) return Type.DEFAULT; if ( (deviceType & CL_DEVICE_TYPE_CUSTOM ) != 0 ) return Type.CUSTOM; @@ -758,15 +758,15 @@ public long[] maxWorkItemSizes() { return Query.getSizes(_deviceId, CL_DEVICE_MAX_WORK_ITEM_SIZES, 3); } - public long maxWorkGroupSize() { return Query.getSize(_deviceId, CL_DEVICE_MAX_WORK_GROUP_SIZE); } + public long maxWorkGroupSize() { return Query.getSize(_deviceId, CL_DEVICE_MAX_WORK_GROUP_SIZE); } - public long maxClockFrequenzy() { return Query.getLong(_deviceId, CL_DEVICE_MAX_CLOCK_FREQUENCY); } + public long maxClockFrequenzy() { return Query.getLong(_deviceId, CL_DEVICE_MAX_CLOCK_FREQUENCY); } public int maxAddressBits() { return Query.getInt(_deviceId, CL_DEVICE_ADDRESS_BITS); } public long maxMemAllocSize() { return Query.getLong(_deviceId, CL_DEVICE_MAX_MEM_ALLOC_SIZE); } - public long globalMemSize() { return Query.getLong(_deviceId, CL_DEVICE_GLOBAL_MEM_SIZE); } + public long globalMemSize() { return Query.getLong(_deviceId, CL_DEVICE_GLOBAL_MEM_SIZE); } public int errorCorrectionSupport() { return Query.getInt(_deviceId, CL_DEVICE_ERROR_CORRECTION_SUPPORT); } @@ -774,7 +774,7 @@ public long localMemSize() { return Query.getLong(_deviceId, CL_DEVICE_LOCAL_MEM_SIZE); } - public long maxConstantBufferSize() { return Query.getLong(_deviceId, CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE); } + public long maxConstantBufferSize() { return Query.getLong(_deviceId, CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE); } public long maxConstantBufferSizeKB() { return (int) (Query.getLong(_deviceId, CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE) / 1024); } @@ -782,31 +782,31 @@ public int maxReadImageArgs() { return Query.getInt(_deviceId, CL_DEVICE_MAX_READ_IMAGE_ARGS); } - public int maxWriteImageArgs() { return Query.getInt(_deviceId, CL_DEVICE_MAX_WRITE_IMAGE_ARGS); } + public int maxWriteImageArgs() { return Query.getInt(_deviceId, CL_DEVICE_MAX_WRITE_IMAGE_ARGS); } public long singleFPConfig() { return Query.getLong(_deviceId, CL_DEVICE_SINGLE_FP_CONFIG); } - public long image2DMaxWidth() { return Query.getSize(_deviceId, CL_DEVICE_IMAGE2D_MAX_WIDTH); } + public long image2DMaxWidth() { return Query.getSize(_deviceId, CL_DEVICE_IMAGE2D_MAX_WIDTH); } - public long image2DMaxHeight() { return Query.getSize(_deviceId, CL_DEVICE_IMAGE2D_MAX_HEIGHT); } + public long image2DMaxHeight() { return Query.getSize(_deviceId, CL_DEVICE_IMAGE2D_MAX_HEIGHT); } - public long image3DMaxWidth() { return Query.getSize(_deviceId, CL_DEVICE_IMAGE3D_MAX_WIDTH); } + public long image3DMaxWidth() { return Query.getSize(_deviceId, CL_DEVICE_IMAGE3D_MAX_WIDTH); } - public long image3DMaxHeight() { return Query.getSize(_deviceId, CL_DEVICE_IMAGE3D_MAX_HEIGHT); } + public long image3DMaxHeight() { return Query.getSize(_deviceId, CL_DEVICE_IMAGE3D_MAX_HEIGHT); } - public long image3DMaxDepth() { return Query.getSize(_deviceId, CL_DEVICE_IMAGE3D_MAX_DEPTH); } + public long image3DMaxDepth() { return Query.getSize(_deviceId, CL_DEVICE_IMAGE3D_MAX_DEPTH); } - public int prefVecWidthChar() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_CHAR); } + public int prefVecWidthChar() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_CHAR); } - public int prefVecWidthShort() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_SHORT); } + public int prefVecWidthShort() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_SHORT); } - public int prefVecWidthInt() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT); } + public int prefVecWidthInt() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT); } - public int prefVecWidthLong() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_LONG); } + public int prefVecWidthLong() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_LONG); } - public int prefVecWidthFloat() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT); } + public int prefVecWidthFloat() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT); } - public int prefVecWidthDouble() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE); } + public int prefVecWidthDouble() { return Query.getInt(_deviceId, CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE); } public static class Query { /** @@ -817,7 +817,7 @@ * @return The value */ public static int getInt(cl_device_id device, int paramName) { - return getInts(device, paramName, 1)[0]; + return getInts(device, paramName, 1)[0]; } /** @@ -829,9 +829,9 @@ * @return The value */ public static int[] getInts(cl_device_id device, int paramName, int numValues) { - int[] values = new int[numValues]; - clGetDeviceInfo(device, paramName, (long) Sizeof.cl_int * numValues, Pointer.to(values), null); - return values; + int[] values = new int[numValues]; + clGetDeviceInfo(device, paramName, (long) Sizeof.cl_int * numValues, Pointer.to(values), null); + return values; } /** @@ -842,7 +842,7 @@ * @return The value */ public static long getLong(cl_device_id device, int paramName) { - return getLongs(device, paramName, 1)[0]; + return getLongs(device, paramName, 1)[0]; } /** @@ -854,9 +854,9 @@ * @return The value */ public static long[] getLongs(cl_device_id device, int paramName, int numValues) { - long[] values = new long[numValues]; - clGetDeviceInfo(device, paramName, (long) Sizeof.cl_long * numValues, Pointer.to(values), null); - return values; + long[] values = new long[numValues]; + clGetDeviceInfo(device, paramName, (long) Sizeof.cl_long * numValues, Pointer.to(values), null); + return values; } /** @@ -868,15 +868,15 @@ */ public static String getString(cl_device_id device, int paramName) { // Obtain the length of the string that will be queried - long[] size = new long[1]; - clGetDeviceInfo(device, paramName, 0, null, size); + long[] size = new long[1]; + clGetDeviceInfo(device, paramName, 0, null, size); // Create a buffer of the appropriate size and fill it with the info - byte[] buffer = new byte[(int) size[0]]; - clGetDeviceInfo(device, paramName, buffer.length, Pointer.to(buffer), null); + byte[] buffer = new byte[(int) size[0]]; + clGetDeviceInfo(device, paramName, buffer.length, Pointer.to(buffer), null); // Create a string from the buffer (excluding the trailing \0 byte) - return new String(buffer, 0, buffer.length - 1); + return new String(buffer, 0, buffer.length - 1); } /** @@ -907,7 +907,7 @@ * @return The value64 */ public static long getSize(cl_device_id device, int paramName) { - return getSizes(device, paramName, 1)[0]; + return getSizes(device, paramName, 1)[0]; } /** @@ -921,47 +921,47 @@ public static long[] getSizes(cl_device_id device, int paramName, int numValues) { // The size of the returned data has to depend on // the size of a size_t, which is handled here - ByteBuffer buffer = ByteBuffer.allocate(numValues * Sizeof.size_t).order(ByteOrder.nativeOrder()); - clGetDeviceInfo( + ByteBuffer buffer = ByteBuffer.allocate(numValues * Sizeof.size_t).order(ByteOrder.nativeOrder()); + clGetDeviceInfo( device, paramName, (long) Sizeof.size_t * numValues, - Pointer.to(buffer), + Pointer.to(buffer), null ); - long[] values = new long[numValues]; - return getLongs(numValues, buffer, values); + long[] values = new long[numValues]; + return getLongs(numValues, buffer, values); } public static long[] getLongs(int numValues, ByteBuffer buffer, long[] values) { - if (Sizeof.size_t == 4) + if (Sizeof.size_t == 4) for (int i = 0; i < numValues; i++) values[i] = buffer.getInt(i * Sizeof.size_t); else - for ( int i = 0; i < numValues; i++ ) - values[i] = buffer.getLong(i * Sizeof.size_t); + for ( int i = 0; i < numValues; i++ ) + values[i] = buffer.getLong(i * Sizeof.size_t); - return values; + return values; } } private <T extends Number> Data<T> _dataArrayOf( Object data, DataType<T> dataType ) { - return (Data<T>) new CLData(this, data, (DataType<Number>) dataType); + return (Data<T>) new CLData(this, data, (DataType<Number>) dataType); } - private static class CLData extends AbstractDeviceData<Number> { + private static class CLData extends AbstractDeviceData<Number> { public CLData( AbstractBaseDevice<Number> owner, Object dataRef, DataType<Number> dataType ) { - super(owner, dataRef, dataType, ()->{ + super(owner, dataRef, dataType, ()->{ // In this lambda we free the memory, because the data is no longer needed! - cl_tsr<?,?> clTsr = (cl_tsr<?,?>) dataRef; - if ( clTsr.value.event != null ) clWaitForEvents(1, new cl_event[]{clTsr.value.event}); - clReleaseMemObject(clTsr.value.data); // Removing data from the device! - }); - assert !(dataRef instanceof Data); - } + cl_tsr<?,?> clTsr = (cl_tsr<?,?>) dataRef; + if ( clTsr.value.event != null ) clWaitForEvents(1, new cl_event[]{clTsr.value.event}); + clReleaseMemObject(clTsr.value.data); // Removing data from the device! + }); + assert !(dataRef instanceof Data); + } } @@ -981,10 +981,10 @@ */ static class cl_tsr<V, T extends V> { - cl_tsr(cl_tsr.cl_value value, cl_dtype dtype) { - this.value = value; - this.dtype = dtype; - } + cl_tsr(cl_tsr.cl_value value, cl_dtype dtype) { + this.value = value; + this.dtype = dtype; + } /** * This class is responsible for representing the @@ -996,7 +996,7 @@ */ static class cl_value { - cl_value( int size ) { this.size = size; } + cl_value( int size ) { this.size = size; } public final int size; public cl_mem data; @@ -1008,8 +1008,12 @@ @Override public boolean equals(Object obj) { - if ( !(obj instanceof cl_tsr) ) return false; - return ((cl_tsr) obj).value == this.value; + if ( !(obj instanceof cl_tsr) ) return false; + return ((cl_tsr) obj).value == this.value; + } + + @Override public int hashCode() { + return value.hashCode(); } } @@ -1030,11 +1034,11 @@ public cl_ad_hoc( String source, cl_kernel kernel, cl_program program - ) { - this.source = source; - this.kernel = kernel; - this.program = program; - } + ) { + this.source = source; + this.kernel = kernel; + this.program = program; + } } /** @@ -1044,8 +1048,8 @@ * Meaning this inner memory object "cl_mem" will * be freed via a call hook stored inside a Cleaner instance... */ - static final class cl_config { + static final class cl_config { public cl_mem data; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLPlatform.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLPlatform.html index e7912e1c9..611191524 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLPlatform.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLPlatform.html @@ -1 +1 @@ -OpenCLPlatform

OpenCLPlatform

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total148 of 77981%12 of 7283%105219115116
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_compile(cl_device_id[])2740993%65690%53235801
recompile()270%20%225511
toString()37100%n/a010401
getDevices()11100%n/a010301
lambda$_compile$0(ArrayList, ArrayList, String, String)9100%n/a010101
put(cl_device_id, OpenCLDevice)7100%n/a010201
get(cl_device_id)6100%n/a010101
getKernel(String)6100%n/a010101
has(cl_device_id)5100%n/a010101
hasKernel(String)5100%n/a010101
dispose()5100%n/a010201
lambda$getDevices$1(List, cl_device_id, OpenCLDevice)5100%n/a010101
getId()4100%n/a010101
static {...}4100%n/a010101
getContext()100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +OpenCLPlatform

OpenCLPlatform

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total816 of 8160%74 of 740%53531201201616
_compile(cl_device_id[])4360%620%3232585811
OpenCLPlatform(cl_platform_id)2460%100%66393911
toString()370%n/a114411
recompile()270%20%225511
getDevices()110%n/a113311
lambda$_compile$0(ArrayList, ArrayList, String, String)90%n/a111111
put(cl_device_id, OpenCLDevice)70%n/a112211
get(cl_device_id)60%n/a111111
getKernel(String)60%n/a111111
has(cl_device_id)50%n/a111111
hasKernel(String)50%n/a111111
dispose()50%n/a112211
lambda$getDevices$1(List, cl_device_id, OpenCLDevice)50%n/a111111
getId()40%n/a111111
static {...}40%n/a111111
getContext()0%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/OpenCLPlatform.java.html b/docs/coverage/test/html/neureka.devices.opencl/OpenCLPlatform.java.html index 6aa401dd7..73226c54b 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/OpenCLPlatform.java.html +++ b/docs/coverage/test/html/neureka.devices.opencl/OpenCLPlatform.java.html @@ -38,75 +38,87 @@ */ public class OpenCLPlatform { - private final static Logger _LOG = LoggerFactory.getLogger( OpenCLPlatform.class ); + private final static Logger _LOG = LoggerFactory.getLogger( OpenCLPlatform.class ); private final cl_platform_id _pid; private final cl_context _context; private final Map<cl_device_id, OpenCLDevice> _id_device; - private final Map<String, cl_kernel> _kernels = new HashMap<>(); + private final Map<String, cl_kernel> _kernels = new HashMap<>(); public OpenCLPlatform(cl_platform_id pid) - { - _id_device = new TreeMap<>(Comparator.comparingInt(NativePointerObject::hashCode)); - _pid = pid; + { + _id_device = new TreeMap<>(Comparator.comparingInt(NativePointerObject::hashCode)); + _pid = pid; // Obtain the number of devices for the current platform - int[] numDevices = new int[ 1 ]; - clGetDeviceIDs(pid, CL_DEVICE_TYPE_ALL, 0, null, numDevices); - cl_device_id[] devicesArray = new cl_device_id[numDevices[ 0 ]]; - clGetDeviceIDs(pid, CL_DEVICE_TYPE_ALL, numDevices[ 0 ], devicesArray, null); + int[] numDevices = new int[ 1 ]; + clGetDeviceIDs(pid, CL_DEVICE_TYPE_ALL, 0, null, numDevices); + cl_device_id[] devicesArray = new cl_device_id[numDevices[ 0 ]]; + clGetDeviceIDs(pid, CL_DEVICE_TYPE_ALL, numDevices[ 0 ], devicesArray, null); + if ( numDevices[0] == 0 ) { + String vendor = OpenCLDevice.Query.getString(pid, CL_PLATFORM_VENDOR); + String platformName = OpenCLDevice.Query.getString(pid, CL_PLATFORM_NAME); + _LOG.warn( + "Could not find any OpenCL devices for platform '{}' with id '0x{}' from vendor '{}'. \n" + + "Although an OpenCL platform is present, it does not seem to find any devices. \n" + + "Does your hardware support OpenCL? \n", + platformName, Long.toHexString(pid.getNativePointer()), vendor, + new Throwable() + ); + } + // Enable exceptions and subsequently omit error checks in this sample - setExceptionsEnabled( true ); + setExceptionsEnabled( true ); // Initialize the context properties - cl_context_properties contextProperties = new cl_context_properties(); - contextProperties.addProperty(CL_CONTEXT_PLATFORM, pid); + cl_context_properties contextProperties = new cl_context_properties(); + contextProperties.addProperty(CL_CONTEXT_PLATFORM, pid); // Create a context for the selected device - _context = clCreateContext( + _context = clCreateContext( contextProperties, devicesArray.length, devicesArray, null, null, null ); - List<cl_device_id> successfullyLoaded = new ArrayList<>(); + List<cl_device_id> successfullyLoaded = new ArrayList<>(); - List<String> failures = new ArrayList<>(); + List<String> failures = new ArrayList<>(); // Collect all devices of this platform - for (cl_device_id did : devicesArray) { + for (cl_device_id did : devicesArray) { try { - OpenCLDevice clDevice = OpenCLDevice.of(this, did); - _id_device.put(did, clDevice); - successfullyLoaded.add(did); - } catch ( Exception e ) { - String message = - "Failed to create '"+OpenCLDevice.class.getSimpleName()+"' instance for " + - "OpenCL device id '0x" + Long.toHexString(did.getNativePointer()) + "' under platform id '0x"+Long.toHexString(pid.getNativePointer())+"'!"; - _LOG.error(message, e); - failures.add(message + " Reason: " + e.getMessage()); - } + OpenCLDevice clDevice = OpenCLDevice.of(this, did); + _id_device.put(did, clDevice); + successfullyLoaded.add(did); + } catch ( Exception e ) { + String message = + "Failed to create '"+OpenCLDevice.class.getSimpleName()+"' instance for " + + "OpenCL device id '0x" + Long.toHexString(did.getNativePointer()) + "' under platform id '0x"+Long.toHexString(pid.getNativePointer())+"'!"; + _LOG.error(message, e); + failures.add(message + " Reason: " + e.getMessage()); + } } - if ( !successfullyLoaded.isEmpty() ) - _compile(successfullyLoaded.toArray(new cl_device_id[0])); + if ( !successfullyLoaded.isEmpty() ) + _compile(successfullyLoaded.toArray(new cl_device_id[0])); else - _LOG.warn( - "'"+this.getClass().getSimpleName()+"' with id '"+Long.toHexString(pid.getNativePointer())+"' does not have a valid device attached to it!" + _LOG.warn( + "'"+this.getClass().getSimpleName()+"' with id '"+Long.toHexString(pid.getNativePointer())+"' does not have a valid device attached to it!" ); - if ( successfullyLoaded.isEmpty() && devicesArray.length > 0 ) - throw new RuntimeException( - "Failed to create '"+OpenCLDevice.class.getSimpleName()+"' instances for all devices of platform id '0x"+Long.toHexString(pid.getNativePointer())+"'! \n" + - "Reasons: \n " + failures.stream().collect(Collectors.joining("\n ")) + if ( successfullyLoaded.isEmpty() && devicesArray.length > 0 ) + throw new RuntimeException( + "Failed to create '"+OpenCLDevice.class.getSimpleName()+"' instances for all devices of platform id '0x"+Long.toHexString(pid.getNativePointer())+"'! \n" + + "Reasons: \n " + failures.stream().collect(Collectors.joining("\n ")) ); - } + } public void recompile() { - List<OpenCLDevice> devices = getDevices(); - cl_device_id[] devicesArray = new cl_device_id[devices.size()]; - for ( int i = 0; i < devicesArray.length; i++) devicesArray[ i ] = devices.get( i ).getId(); - _compile(devicesArray); - } + List<OpenCLDevice> devices = getDevices(); + cl_device_id[] devicesArray = new cl_device_id[devices.size()]; + for ( int i = 0; i < devicesArray.length; i++) devicesArray[ i ] = devices.get( i ).getId(); + _compile(devicesArray); + } /** * This is where all the kernels defined by all the {@link CLImplementation} @@ -120,9 +132,9 @@ private void _compile( cl_device_id[] devicesArray ) { //Reading all kernels! - List<String> templateSources = new ArrayList<>(); + List<String> templateSources = new ArrayList<>(); - String[] fileNames = { + String[] fileNames = { "activation_template.cl", "broadcast_template.cl", "convolution_template.cl", @@ -131,90 +143,90 @@ "scalar_broadcast.cl", "utility.cl" }; - for ( String name : fileNames ) - templateSources.add(Neureka.get().utility().readResource("kernels/"+name)); + for ( String name : fileNames ) + templateSources.add(Neureka.get().utility().readResource("kernels/"+name)); - ArrayList<String> names = new ArrayList<>(); - ArrayList<String> sources = new ArrayList<>(); - for ( int i = 0; i < fileNames.length; i++ ) + ArrayList<String> names = new ArrayList<>(); + ArrayList<String> sources = new ArrayList<>(); + for ( int i = 0; i < fileNames.length; i++ ) { - String kernelSource = templateSources.get( i ); - boolean templateFound = false; - if ( kernelSource.contains( "__kernel" ) ) + String kernelSource = templateSources.get( i ); + boolean templateFound = false; + if ( kernelSource.contains( "__kernel" ) ) { - String[] parts = kernelSource.split("__kernel")[ 1 ].split("\\(")[ 0 ].split(" "); + String[] parts = kernelSource.split("__kernel")[ 1 ].split("\\(")[ 0 ].split(" "); - templateFound = parts[parts.length - 1].contains("template"); - if ( !templateFound ) names.add(parts[parts.length - 1]); + templateFound = parts[parts.length - 1].contains("template"); + if ( !templateFound ) names.add(parts[parts.length - 1]); else { - String preName = parts[ parts.length - 1 ].replace("template", ""); + String preName = parts[ parts.length - 1 ].replace("template", ""); // Tensor t0_origin, Tensor t1_handle, Tsr t2_drain ... when d>=0 // Tsr t0_drain, Tsr t1_src1, Tsr t2_src2 // drn[di], src1[_i_of_idx_on_tln(prv_src1_cfg, rank)], src2[_i_of_idx_on_tln(prv_src2_cfg, rank)] // default: src1 o src2 -> drain // inverse: src1/fdrn <- src2 <- drain //=========================================================================== - Map<String, String> code = new HashMap<>(); - ImplementationFor<OpenCLDevice> impl = null; - for ( Operation type : Neureka.get().backend().getOperations() ) { - if ( preName.contains("activation") && type.supportsAlgorithm(ElementwiseAlgorithm.class) ) - impl = type.getAlgorithm(ElementwiseAlgorithm.class).getImplementationFor( OpenCLDevice.class ); - else if ( preName.contains("elementwise") && type.supportsAlgorithm(BiElementwise.class) ) - impl = type.getAlgorithm(BiElementwise.class).getImplementationFor( OpenCLDevice.class ); - else if ( preName.contains("scalarization") && type.supportsAlgorithm(BiScalarBroadcast.class) ) - impl = type.getAlgorithm(BiScalarBroadcast.class).getImplementationFor( OpenCLDevice.class ); - else if ( preName.contains("broadcast") && type.supportsAlgorithm(Broadcast.class) ) - impl = type.getAlgorithm(Broadcast.class).getImplementationFor( OpenCLDevice.class ); - else if ( preName.contains("convolution") && type.supportsAlgorithm(NDConvolution.class) ) - impl = type.getAlgorithm(NDConvolution.class).getImplementationFor( OpenCLDevice.class ); - else if ( - type.supportsAlgorithm(DeviceAlgorithm.class) + Map<String, String> code = new HashMap<>(); + ImplementationFor<OpenCLDevice> impl = null; + for ( Operation type : Neureka.get().backend().getOperations() ) { + if ( preName.contains("activation") && type.supportsAlgorithm(ElementwiseAlgorithm.class) ) + impl = type.getAlgorithm(ElementwiseAlgorithm.class).getImplementationFor( OpenCLDevice.class ); + else if ( preName.contains("elementwise") && type.supportsAlgorithm(BiElementwise.class) ) + impl = type.getAlgorithm(BiElementwise.class).getImplementationFor( OpenCLDevice.class ); + else if ( preName.contains("scalarization") && type.supportsAlgorithm(BiScalarBroadcast.class) ) + impl = type.getAlgorithm(BiScalarBroadcast.class).getImplementationFor( OpenCLDevice.class ); + else if ( preName.contains("broadcast") && type.supportsAlgorithm(Broadcast.class) ) + impl = type.getAlgorithm(Broadcast.class).getImplementationFor( OpenCLDevice.class ); + else if ( preName.contains("convolution") && type.supportsAlgorithm(NDConvolution.class) ) + impl = type.getAlgorithm(NDConvolution.class).getImplementationFor( OpenCLDevice.class ); + else if ( + type.supportsAlgorithm(DeviceAlgorithm.class) && - preName.contains(type.getAlgorithm(DeviceAlgorithm.class).getName()) + preName.contains(type.getAlgorithm(DeviceAlgorithm.class).getName()) ) { // TODO: cover! - impl = type.getAlgorithm(DeviceAlgorithm.class).getImplementationFor( OpenCLDevice.class ); + impl = type.getAlgorithm(DeviceAlgorithm.class).getImplementationFor( OpenCLDevice.class ); } - if ( impl instanceof CLImplementation ) { - for ( KernelCode kernelCode : ((CLImplementation) impl).getKernelCode() ) { - if (kernelCode.getCode() != null) - code.put(kernelCode.getName(), kernelCode.getCode()); + if ( impl instanceof CLImplementation ) { + for ( KernelCode kernelCode : ((CLImplementation) impl).getKernelCode() ) { + if (kernelCode.getCode() != null) + code.put(kernelCode.getName(), kernelCode.getCode()); } } - } - code.forEach( ( n, s ) -> { names.add( n ); sources.add( s ); } ); + } + code.forEach( ( n, s ) -> { names.add( n ); sources.add( s ); } ); } } - if ( !templateFound ) sources.add( kernelSource ); + if ( !templateFound ) sources.add( kernelSource ); } - for ( Operation type : Neureka.get().backend().getOperations() ) { - for ( Algorithm algorithm : type.getAllAlgorithms()) { - DeviceAlgorithm<?> deviceAlgorithm = ( algorithm instanceof DeviceAlgorithm ? ((DeviceAlgorithm<?>) algorithm) : null ); - ImplementationFor<OpenCLDevice> impl = ( deviceAlgorithm == null ? null : deviceAlgorithm.getImplementationFor(OpenCLDevice.class) ); - if ( impl instanceof CLImplementation ) { - CLImplementation cli = ((CLImplementation) impl); - if ( cli instanceof SimpleCLImplementation ) { - for ( KernelCode kernelCode : cli.getKernelCode() ) { - names.add( kernelCode.getName() ); - sources.add( kernelCode.getCode() ); + for ( Operation type : Neureka.get().backend().getOperations() ) { + for ( Algorithm algorithm : type.getAllAlgorithms()) { + DeviceAlgorithm<?> deviceAlgorithm = ( algorithm instanceof DeviceAlgorithm ? ((DeviceAlgorithm<?>) algorithm) : null ); + ImplementationFor<OpenCLDevice> impl = ( deviceAlgorithm == null ? null : deviceAlgorithm.getImplementationFor(OpenCLDevice.class) ); + if ( impl instanceof CLImplementation ) { + CLImplementation cli = ((CLImplementation) impl); + if ( cli instanceof SimpleCLImplementation ) { + for ( KernelCode kernelCode : cli.getKernelCode() ) { + names.add( kernelCode.getName() ); + sources.add( kernelCode.getCode() ); } } } } - } + } // Create the program - cl_program cpProgram = clCreateProgramWithSource( + cl_program cpProgram = clCreateProgramWithSource( _context, - sources.size(), - sources.toArray( new String[ 0 ] ), + sources.size(), + sources.toArray( new String[ 0 ] ), null, null ); // Build the program - int err = clBuildProgram( + int err = clBuildProgram( cpProgram, devicesArray.length, devicesArray, @@ -222,62 +234,62 @@ null, null ); - if ( err != CL_SUCCESS ) - _LOG.error("Failed to compile the OpenCL code of the current context. Error code: '"+err+"'."); + if ( err != CL_SUCCESS ) + _LOG.error("Failed to compile the OpenCL code of the current context. Error code: '"+err+"'."); //TODO: check compilation errors! // Create the kernels - for ( String name : names ) - if ( name != null ) _kernels.put( name, clCreateKernel( cpProgram, name, null ) ); - } + for ( String name : names ) + if ( name != null ) _kernels.put( name, clCreateKernel( cpProgram, name, null ) ); + } public List<OpenCLDevice> getDevices() { - List<OpenCLDevice> devices = new ArrayList<>(); - _id_device.forEach( ( k, v ) -> devices.add( v ) ); - return devices; + List<OpenCLDevice> devices = new ArrayList<>(); + _id_device.forEach( ( k, v ) -> devices.add( v ) ); + return devices; } /** * @param did The {@link cl_device_id} representing an OpenCL supporting device. * @return The truth value determining if this platform hosts the device represented by the provided id. */ - public boolean has( cl_device_id did ) { return _id_device.containsKey( did ); } + public boolean has( cl_device_id did ) { return _id_device.containsKey( did ); } public OpenCLDevice get( cl_device_id did ) { - return _id_device.get( did ); + return _id_device.get( did ); } void put( cl_device_id did, OpenCLDevice device ) { - _id_device.put( did, device ); - } + _id_device.put( did, device ); + } public cl_kernel getKernel( String kernelName ) { - return _kernels.get( kernelName ); + return _kernels.get( kernelName ); } public boolean hasKernel( String kernelName ) { - return _kernels.containsKey( kernelName ); + return _kernels.containsKey( kernelName ); } - public final long getId() { return _pid.getNativePointer(); } + public final long getId() { return _pid.getNativePointer(); } public cl_context getContext() { - return _context; + return _context; } public void dispose() { - clReleaseContext( _context ); - } + clReleaseContext( _context ); + } @Override public String toString() { - return this.getClass().getSimpleName()+"@"+Integer.toHexString(hashCode())+"[" + - "id=0x" + Long.toHexString(_pid.getNativePointer()) + "," + - "context=0x"+Long.toHexString(_context.getNativePointer()) + "," + - "kernels=[.."+_kernels.size()+"..]" + + return this.getClass().getSimpleName()+"@"+Integer.toHexString(hashCode())+"[" + + "id=0x" + Long.toHexString(_pid.getNativePointer()) + "," + + "context=0x"+Long.toHexString(_context.getNativePointer()) + "," + + "kernels=[.."+_kernels.size()+"..]" + "]"; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices.opencl/index.html b/docs/coverage/test/html/neureka.devices.opencl/index.html index ba8a5a59f..318473f50 100644 --- a/docs/coverage/test/html/neureka.devices.opencl/index.html +++ b/docs/coverage/test/html/neureka.devices.opencl/index.html @@ -1 +1 @@ -neureka.devices.opencl

neureka.devices.opencl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices.opencl

neureka.devices.opencl

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OpenCLPlatform8160%740%5353120120161611
KernelCaller5540%300%37387677222301
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Created with JaCoCo 0.8.11.202310140853
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neureka.devices.opencl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total760 of 4,14981%79 of 36378%9136210469627178015
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OpenCLPlatform.java14863181%126083%10521911511601
KernelCaller.java9745882%111963%938117712301
JVMData.java7467390%1113392%10901113501801
KernelCode.java322846%60%5105152701
KernelCache.java4195%50%17080602
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices.opencl

neureka.devices.opencl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total3,525 of 4,25717%236 of 36535%2883645807021621791115
OpenCLDevice.java1,938984%1090%16416634534710710979
OpenCLPlatform.java8160%740%5353120120161611
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JVMData.java11463384%1512989%17901613541801
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/AbstractBaseDevice.html b/docs/coverage/test/html/neureka.devices/AbstractBaseDevice.html index de31670f9..a896c8fb8 100644 --- a/docs/coverage/test/html/neureka.devices/AbstractBaseDevice.html +++ b/docs/coverage/test/html/neureka.devices/AbstractBaseDevice.html @@ -1 +1 @@ -AbstractBaseDevice

AbstractBaseDevice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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numberOfStored()3100%n/a010101
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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractBaseDevice

AbstractBaseDevice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 4295%1 of 683%1901006
contains(Tensor)21487%1375%130301
AbstractBaseDevice()9100%n/a010301
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has(Tensor)4100%n/a010101
numberOfStored()3100%n/a010101
numberOfDataObjects()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/AbstractBaseDevice.java.html b/docs/coverage/test/html/neureka.devices/AbstractBaseDevice.java.html index c72adc0c6..3b0f8e52d 100644 --- a/docs/coverage/test/html/neureka.devices/AbstractBaseDevice.java.html +++ b/docs/coverage/test/html/neureka.devices/AbstractBaseDevice.java.html @@ -66,7 +66,7 @@ @Override public final boolean contains( Tensor<V> o ) { Data<V> data = o.mut().getData(); - if ( data == null ) return false; + if ( data == null ) return false; return data.owner() == this; } @@ -85,4 +85,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/AbstractDevice$1$1.html b/docs/coverage/test/html/neureka.devices/AbstractDevice$1$1.html index 3a8a1d25f..981f05d23 100644 --- a/docs/coverage/test/html/neureka.devices/AbstractDevice$1$1.html +++ b/docs/coverage/test/html/neureka.devices/AbstractDevice$1$1.html @@ -1 +1 @@ -AbstractDevice.1.new Device.Writer() {...}

AbstractDevice.1.new Device.Writer() {...}

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fully()150%n/a111111
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{...}9100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractDevice.1.new Device.Writer() {...}

AbstractDevice.1.new Device.Writer() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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fully()150%n/a111111
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{...}9100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/AbstractDevice$1$2.html b/docs/coverage/test/html/neureka.devices/AbstractDevice$1$2.html index dd485f2ac..d8d9354ad 100644 --- a/docs/coverage/test/html/neureka.devices/AbstractDevice$1$2.html +++ b/docs/coverage/test/html/neureka.devices/AbstractDevice$1$2.html @@ -1 +1 @@ -AbstractDevice.1.new Device.Writer() {...}

AbstractDevice.1.new Device.Writer() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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{...}12100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractDevice.1.new Device.Writer() {...}

AbstractDevice.1.new Device.Writer() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 45100%0 of 0n/a030303
fully()17100%n/a010101
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{...}12100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/AbstractDevice$1.html b/docs/coverage/test/html/neureka.devices/AbstractDevice$1.html index f0b628536..9783da77a 100644 --- a/docs/coverage/test/html/neureka.devices/AbstractDevice$1.html +++ b/docs/coverage/test/html/neureka.devices/AbstractDevice$1.html @@ -1 +1 @@ -AbstractDevice.new Device.Access() {...}

AbstractDevice.new Device.Access() {...}

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Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractDevice.new Device.Access() {...}

AbstractDevice.new Device.Access() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 7090%0 of 0n/a110110110
cleanup(Runnable)70%n/a111111
{...}9100%n/a010101
readArray(Class, int, int)9100%n/a010101
writeFrom(Object, int)7100%n/a010101
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/AbstractDevice.html b/docs/coverage/test/html/neureka.devices/AbstractDevice.html index 4ddd357dd..c19c9c813 100644 --- a/docs/coverage/test/html/neureka.devices/AbstractDevice.html +++ b/docs/coverage/test/html/neureka.devices/AbstractDevice.html @@ -1 +1 @@ -AbstractDevice

AbstractDevice

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_writeArrayInternal(Tensor, Object, int, int, int)30100%4100%030801
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AbstractDevice()8100%n/a010101
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access(Tensor)6100%n/a010101
_cleaning(Object, Runnable)5100%n/a010101
static {...}3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractDevice

AbstractDevice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total87 of 16446%18 of 2425%1321193639
update(Component.OwnerChangeRequest)670%160%99141411
store(Tensor)60%n/a112211
approve(ExecutionCall)51473%1150%121301
_cleaning(Object, Runnable)50%n/a111111
_writeArrayInternal(Tensor, Object, int, int, int)42686%1375%131801
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AbstractDevice()8100%n/a010101
access(Tensor)6100%n/a010101
static {...}3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/AbstractDevice.java.html b/docs/coverage/test/html/neureka.devices/AbstractDevice.java.html index 1241501e8..156b2083c 100644 --- a/docs/coverage/test/html/neureka.devices/AbstractDevice.java.html +++ b/docs/coverage/test/html/neureka.devices/AbstractDevice.java.html @@ -85,28 +85,28 @@ */ @Override public boolean update( OwnerChangeRequest<Tensor<V>> changeRequest ) { - Tensor<V> oldOwner = changeRequest.getOldOwner(); - Tensor<V> newOwner = changeRequest.getNewOwner(); - if ( changeRequest.type() == IsBeing.REPLACED ) _swap( oldOwner, newOwner ); - else if ( changeRequest.type() == IsBeing.ADDED ) { - if ( newOwner.has( Relation.class ) ) { - Relation<V> relation = newOwner.get(Relation.class); - if ( relation.hasParent() ) { // Root needs to be found ! : - Tensor<V> root = relation.findRootTensor().orElseThrow(IllegalStateException::new); - if ( !this.has(root) || !root.isOutsourced() ) - throw new IllegalStateException("Data parent is not outsourced!"); + Tensor<V> oldOwner = changeRequest.getOldOwner(); + Tensor<V> newOwner = changeRequest.getNewOwner(); + if ( changeRequest.type() == IsBeing.REPLACED ) _swap( oldOwner, newOwner ); + else if ( changeRequest.type() == IsBeing.ADDED ) { + if ( newOwner.has( Relation.class ) ) { + Relation<V> relation = newOwner.get(Relation.class); + if ( relation.hasParent() ) { // Root needs to be found ! : + Tensor<V> root = relation.findRootTensor().orElseThrow(IllegalStateException::new); + if ( !this.has(root) || !root.isOutsourced() ) + throw new IllegalStateException("Data parent is not outsourced!"); } } - Device<V> found = newOwner.getMut().getData().owner(); + Device<V> found = newOwner.getMut().getData().owner(); - if ( found != null && found != this ) - found.restore( newOwner ); + if ( found != null && found != this ) + found.restore( newOwner ); } - return true; + return true; } - protected void _cleaning( Object o, Runnable action ) { _CLEANER.register( o, action ); } + protected void _cleaning( Object o, Runnable action ) { _CLEANER.register( o, action ); } /** * <b>This method plays an important role in approving a provided {@link ExecutionCall}.</b> @@ -128,8 +128,8 @@ /** {@inheritDoc} */ @Override public <T extends V> Storage<V> store( Tensor<T> tensor ) { - tensor.set( (Component) this ); // This way we move the storing procedure to the update function! - return this; + tensor.set( (Component) this ); // This way we move the storing procedure to the update function! + return this; } /** {@inheritDoc} */ @@ -170,8 +170,8 @@ int offset, int start, int size ) { DataType<?> dataType = tensor.getDataType(); - if ( dataType == null ) - dataType = _dataTypeOf( array ); + if ( dataType == null ) + dataType = _dataTypeOf( array ); Class<?> arrayType = dataType.dataArrayType(); if ( !arrayType.isAssignableFrom( array.getClass() ) ) array = DataConverter.get().convert( array, arrayType ); @@ -206,4 +206,4 @@ protected abstract DataType<?> _dataTypeOf( Object rawData ); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/AbstractDeviceData.html b/docs/coverage/test/html/neureka.devices/AbstractDeviceData.html index 9bc2599f9..d4f9dca9e 100644 --- a/docs/coverage/test/html/neureka.devices/AbstractDeviceData.html +++ b/docs/coverage/test/html/neureka.devices/AbstractDeviceData.html @@ -1 +1 @@ -AbstractDeviceData

AbstractDeviceData

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 11596%0 of 8100%11312919
usages()40%n/a111111
AbstractDeviceData(AbstractBaseDevice, Object, DataType, Runnable)46100%n/a0101101
decrementUsageCount()16100%2100%020301
incrementUsageCount()15100%2100%020301
lambda$new$0(AbstractBaseDevice, Runnable, ReferenceCounter.ChangeEvent)13100%2100%020401
lambda$new$1(ReferenceCounter, AbstractBaseDevice)12100%2100%020401
owner()3100%n/a010101
getOrNull()3100%n/a010101
dataType()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractDeviceData

AbstractDeviceData

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 11596%0 of 8100%11312919
usages()40%n/a111111
AbstractDeviceData(AbstractBaseDevice, Object, DataType, Runnable)46100%n/a0101101
decrementUsageCount()16100%2100%020301
incrementUsageCount()15100%2100%020301
lambda$new$0(AbstractBaseDevice, Runnable, ReferenceCounter.ChangeEvent)13100%2100%020401
lambda$new$1(ReferenceCounter, AbstractBaseDevice)12100%2100%020401
owner()3100%n/a010101
getOrNull()3100%n/a010101
dataType()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/AbstractDeviceData.java.html b/docs/coverage/test/html/neureka.devices/AbstractDeviceData.java.html index f784dcb52..cf16d8781 100644 --- a/docs/coverage/test/html/neureka.devices/AbstractDeviceData.java.html +++ b/docs/coverage/test/html/neureka.devices/AbstractDeviceData.java.html @@ -57,4 +57,4 @@ @Override public final int usages() { return _refCounter.count(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner$ReferenceWithCleanup.html b/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner$ReferenceWithCleanup.html index e052b0476..181d9527b 100644 --- a/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner$ReferenceWithCleanup.html +++ b/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner$ReferenceWithCleanup.html @@ -1 +1 @@ -CustomDeviceCleaner.ReferenceWithCleanup

CustomDeviceCleaner.ReferenceWithCleanup

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 12100%0 of 0n/a020502
CustomDeviceCleaner.ReferenceWithCleanup(Object, Runnable, ReferenceQueue)8100%n/a010301
cleanup()4100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CustomDeviceCleaner.ReferenceWithCleanup

CustomDeviceCleaner.ReferenceWithCleanup

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 3354%1 of 250%132902
cleanup()151040%1150%122601
CustomDeviceCleaner.ReferenceWithCleanup(Object, Runnable, ReferenceQueue)8100%n/a010301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner.html b/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner.html index 6114787c3..96930494f 100644 --- a/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner.html +++ b/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner.html @@ -1 +1 @@ -CustomDeviceCleaner

CustomDeviceCleaner

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 10693%1 of 683%1742404
run()72275%1375%1341201
register(Object, Runnable)36100%2100%020601
toString()22100%n/a010101
CustomDeviceCleaner()19100%n/a010501
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +CustomDeviceCleaner

CustomDeviceCleaner

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total43 of 18576%4 of 1471%514154917
register(Object, Runnable)214668%1583%1461701
run()102772%2466%2441201
checkCleanup()101762%1150%1241001
getInstance()20%n/a111111
toString()23100%n/a010201
CustomDeviceCleaner()21100%n/a010501
static {...}8100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner.java.html b/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner.java.html index d29602395..ad1c2ffae 100644 --- a/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner.java.html +++ b/docs/coverage/test/html/neureka.devices/CustomDeviceCleaner.java.html @@ -1,5 +1,8 @@ CustomDeviceCleaner.java

CustomDeviceCleaner.java

package neureka.devices;
 
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
 import java.lang.ref.PhantomReference;
 import java.lang.ref.ReferenceQueue;
 import java.util.ArrayList;
@@ -15,63 +18,114 @@
  *  anywhere but within this library. <br>
  *  This class or its public methods might change or get removed in future versions!</b>
  */
-final class CustomDeviceCleaner implements DeviceCleaner, Runnable
+final class CustomDeviceCleaner implements DeviceCleaner
 {
-    private final ReferenceQueue<Object> _referenceQueue = new ReferenceQueue<>();
-    private final long _timeout = 60 * 1000;
-    private int _registered = 0;
+    private static final Logger log = LoggerFactory.getLogger(CustomDeviceCleaner.class);
+    private static final CustomDeviceCleaner _INSTANCE = new CustomDeviceCleaner();
+    private static final long _QUEUE_TIMEOUT = 60 * 1000;
+
+    private final ReferenceQueue<Object> _referenceQueue = new ReferenceQueue<>();
+    private final List<ReferenceWithCleanup<Object>> _toBeCleaned = new ArrayList<>();
+    private final Thread _thread;
+
+
+    public static CustomDeviceCleaner getInstance() {
+        return _INSTANCE;
+    }
+
+    CustomDeviceCleaner() {
+        _thread = new Thread(this::run, "Neureka-Cleaner");
+    }
 
-    List<Object> list = new ArrayList<>();
 
     static class ReferenceWithCleanup<T> extends PhantomReference<T>
     {
-        private final Runnable _action;
+        private Runnable _action;
 
-        ReferenceWithCleanup(T o, Runnable action, ReferenceQueue<T> queue) {
-            super( o, queue );
-            _action = action;
-        }
+        ReferenceWithCleanup( T o, Runnable action, ReferenceQueue<T> queue ) {
+            super( o, queue );
+            _action = action;
+        }
         public void cleanup() {
-            _action.run();
-        }
+            if ( _action != null ) {
+                try {
+                    _action.run();
+                } catch (Exception e) {
+                    log.error("Failed to execute cleanup action '"+_action+"'.", e);
+                } finally {
+                    _action = null;
+                }
+            }
+        }
     }
 
-    @Override
-    public void register(Object o, Runnable action) {
-        synchronized ( _referenceQueue ) {
-            list.add(new ReferenceWithCleanup<Object>(o, action, _referenceQueue));
-            _registered++;
-            if ( _registered == 1 ) new Thread( this::run ).start();
-        }
-    }
-
-    @Override
-    public void run() {
-        while ( _registered > 0 ) {
+    public void register( Object o, Runnable action ) {
+        if ( o == null ) {
+            log.warn("Attempt to register a null object for cleanup. This is not allowed!");
             try {
-                ReferenceWithCleanup ref = (ReferenceWithCleanup) _referenceQueue.remove(_timeout);
-                if ( ref != null ) {
-                    try {
-                        ref.cleanup();
-                    } catch ( Throwable e ) {
-                        e.printStackTrace();
-                        // ignore exceptions from the cleanup action
-                        // (including interruption of cleanup thread)
-                    }
-                    _registered--;
+                action.run();
+            } catch (Exception e) {
+                log.error("Failed to execute cleanup action '"+action+"'.", e);
+            }
+            return;
+        }
+        synchronized ( _referenceQueue ) {
+            _toBeCleaned.add(new ReferenceWithCleanup<>(o, action, _referenceQueue));
+            if ( _toBeCleaned.size() == 1 ) {
+                if ( !_thread.isAlive() ) {
+                    _thread.start();
                 }
-            } catch ( Throwable e ) {
-                e.printStackTrace(); // The queue failed
-            }
+                else {
+                    // We notify the cleaner thread that there are new items to be cleaned
+                    synchronized ( _thread ) {
+                        _thread.notify();
+                    }
+                }
+            }
+        }
+    }
+
+    private void run() {
+        if ( !_thread.isAlive() ) {
+            _thread.start();
         }
-    }
+        while ( _thread.isAlive() ) {
+            while ( !_toBeCleaned.isEmpty() ) {
+                checkCleanup();
+            }
+            try {
+                synchronized ( _thread ) {
+                    _thread.wait();
+                }
+            } catch (Exception e) {
+                log.error("Failed to make cleaner thread wait for cleaning notification!", e);
+            }
+        }
+    }
+
+    private void checkCleanup() {
+        try {
+            ReferenceWithCleanup<Object> ref = (ReferenceWithCleanup<Object>) _referenceQueue.remove(_QUEUE_TIMEOUT);
+            if ( ref != null ) {
+                try {
+                    ref.cleanup();
+                } catch ( Throwable e ) {
+                    log.error("Failed to perform cleanup!", e);
+                } finally {
+                    _toBeCleaned.remove(ref);
+                }
+            }
+        } catch ( Throwable e ) {
+            log.error("Failed to call 'remove()' on cleaner internal queue.", e);
+        }
+    }
 
     @Override
     public String toString() {
-        return this.getClass().getSimpleName()+"@"+Integer.toHexString(this.hashCode())+"[" +
-                    "registered=" + _registered +
+        return this.getClass().getSimpleName()+"@"+Integer.toHexString(this.hashCode())+"[" +
+                    "registered=" + _toBeCleaned.size() +
                 "]";
     }
 
 }
-
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/Device$1.html b/docs/coverage/test/html/neureka.devices/Device$1.html index 1d1300cf3..067235365 100644 --- a/docs/coverage/test/html/neureka.devices/Device$1.html +++ b/docs/coverage/test/html/neureka.devices/Device$1.html @@ -1 +1 @@ -Device.new Device.In() {...}

Device.new Device.In() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 67100%1 of 683%150702
in(Supplier)55100%1583%140601
{...}12100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Device.new Device.In() {...}

Device.new Device.In() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 67100%1 of 683%150702
in(Supplier)55100%1583%140601
{...}12100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/Device$Access.html b/docs/coverage/test/html/neureka.devices/Device$Access.html index c46ed8aee..ecbe14582 100644 --- a/docs/coverage/test/html/neureka.devices/Device$Access.html +++ b/docs/coverage/test/html/neureka.devices/Device$Access.html @@ -1 +1 @@ -Device.Access

Device.Access

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 6100%0 of 0n/a010101
writeFrom(Object)6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Device.Access

Device.Access

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 6100%0 of 0n/a010101
writeFrom(Object)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/Device$Writer.html b/docs/coverage/test/html/neureka.devices/Device$Writer.html index 2bda1ca1a..f7cb7a8ec 100644 --- a/docs/coverage/test/html/neureka.devices/Device$Writer.html +++ b/docs/coverage/test/html/neureka.devices/Device$Writer.html @@ -1 +1 @@ -Device.Writer

Device.Writer

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 7100%0 of 0n/a010101
at(int)7100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Device.Writer

Device.Writer

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 7100%0 of 0n/a010101
at(int)7100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/Device.html b/docs/coverage/test/html/neureka.devices/Device.html index 201712175..b85ed1a61 100644 --- a/docs/coverage/test/html/neureka.devices/Device.html +++ b/docs/coverage/test/html/neureka.devices/Device.html @@ -1 +1 @@ -Device

Device

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 15295%3 of 1070%31302908
get(Class, String[])71973%1150%120401
borrow(Tensor, Tensor[])44100%1375%130801
optimizedFunctionOf(Function, String)39100%1150%1201001
allocate(DataType, int)13100%n/a010101
any(String[])10100%2100%020201
get(String[])10100%n/a010201
find(String[])5100%n/a010101
find(Class, String[])5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Device

Device

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total56 of 15263%5 of 1050%513112918
optimizedFunctionOf(Function, String)390%20%22101011
borrow(Tensor, Tensor[])103477%2250%231801
get(Class, String[])71973%1150%120401
allocate(DataType, int)13100%n/a010101
any(String[])10100%2100%020201
get(String[])10100%n/a010201
find(String[])5100%n/a010101
find(Class, String[])5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/Device.java.html b/docs/coverage/test/html/neureka.devices/Device.java.html index 62918c248..b0d76653f 100644 --- a/docs/coverage/test/html/neureka.devices/Device.java.html +++ b/docs/coverage/test/html/neureka.devices/Device.java.html @@ -226,19 +226,19 @@ * @return An instance of the optimized function. */ default Function optimizedFunctionOf( Function function, String name ) { - LogUtil.nullArgCheck( function, "function", Function.class ); - LogUtil.nullArgCheck( name, "name", String.class ); + LogUtil.nullArgCheck( function, "function", Function.class ); + LogUtil.nullArgCheck( name, "name", String.class ); - Operation optimizedOperation = optimizedOperationOf( function, name ); - BackendContext currentContext = Neureka.get().backend(); - if ( !currentContext.hasOperation( optimizedOperation ) ) - currentContext.addOperation( optimizedOperation ); + Operation optimizedOperation = optimizedOperationOf( function, name ); + BackendContext currentContext = Neureka.get().backend(); + if ( !currentContext.hasOperation( optimizedOperation ) ) + currentContext.addOperation( optimizedOperation ); - return new FunctionParser( currentContext ) - .parse( + return new FunctionParser( currentContext ) + .parse( optimizedOperation, - function.numberOfArgs(), - function.isDoingAD() + function.numberOfArgs(), + function.isDoingAD() ); } @@ -261,8 +261,8 @@ LogUtil.nullArgCheck( rest, "rest", Tensor[].class ); List<Tensor<V>> tensors = new ArrayList<>(); if ( first != null ) tensors.add( first ); - if ( rest.length > 0 ) - tensors.addAll( Arrays.stream( rest ).filter(Objects::nonNull).collect(Collectors.toList()) ); + if ( rest.length > 0 ) + tensors.addAll( Arrays.stream( rest ).filter(Objects::nonNull).collect(Collectors.toList()) ); Device<?> thisDevice = this; return new In() { @Override @@ -408,4 +408,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/DeviceCleaner.html b/docs/coverage/test/html/neureka.devices/DeviceCleaner.html index 27810451c..670a3c4bd 100644 --- a/docs/coverage/test/html/neureka.devices/DeviceCleaner.html +++ b/docs/coverage/test/html/neureka.devices/DeviceCleaner.html @@ -1 +1 @@ -DeviceCleaner

DeviceCleaner

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
static {...}5100%n/a010101
getNewInstance()4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DeviceCleaner

DeviceCleaner

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
static {...}5100%n/a010101
getNewInstance()4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/DeviceCleaner.java.html b/docs/coverage/test/html/neureka.devices/DeviceCleaner.java.html index 339001372..fa7b33249 100644 --- a/docs/coverage/test/html/neureka.devices/DeviceCleaner.java.html +++ b/docs/coverage/test/html/neureka.devices/DeviceCleaner.java.html @@ -8,4 +8,4 @@ void register( Object o, Runnable action ); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/Query.html b/docs/coverage/test/html/neureka.devices/Query.html index 1aebcd4f2..0ee3db040 100644 --- a/docs/coverage/test/html/neureka.devices/Query.html +++ b/docs/coverage/test/html/neureka.devices/Query.html @@ -1 +1 @@ -Query

Query

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 37997%7 of 4684%734041011
_queryInternal(Class, String[])714095%63485%62102601
lambda$query$2(String)685%1150%120101
static {...}142100%n/a010301
query(Class, String[])22100%n/a0101001
lambda$query$4(String)19100%2100%020101
lambda$query$3(String)19100%2100%020101
lambda$_queryInternal$7(String[], String)6100%n/a010201
lambda$query$1(String)5100%n/a010101
lambda$query$0(String)5100%n/a010101
lambda$_queryInternal$6(String, String)4100%n/a010101
lambda$query$5(int)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Query

Query

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total57 of 37984%20 of 4656%1534441011
_queryInternal(Class, String[])3211578%172357%122142601
lambda$query$4(String)12736%1150%120101
lambda$query$3(String)12736%1150%120101
lambda$query$2(String)685%1150%120101
static {...}142100%n/a010301
query(Class, String[])22100%n/a0101001
lambda$_queryInternal$7(String[], String)6100%n/a010201
lambda$query$1(String)5100%n/a010101
lambda$query$0(String)5100%n/a010101
lambda$_queryInternal$6(String, String)4100%n/a010101
lambda$query$5(int)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/Query.java.html b/docs/coverage/test/html/neureka.devices/Query.java.html index 95d5a6cbd..c9618796e 100644 --- a/docs/coverage/test/html/neureka.devices/Query.java.html +++ b/docs/coverage/test/html/neureka.devices/Query.java.html @@ -33,8 +33,8 @@ .flatMap( key -> Arrays.stream(key.split("\\|\\|")) ) .map(String::trim) .filter( key -> !key.isEmpty() ) - .flatMap( key -> key.equals("amd") ? Stream.of("amd", "advanced micro devices") : Stream.of(key) ) - .flatMap( key -> key.equals("nvidia") ? Stream.of("nvidia", "nvidia corporation") : Stream.of(key) ) + .flatMap( key -> key.equals("amd") ? Stream.of("amd", "advanced micro devices") : Stream.of(key) ) + .flatMap( key -> key.equals("nvidia") ? Stream.of("nvidia", "nvidia corporation") : Stream.of(key) ) .toArray(String[]::new); return _queryInternal( deviceType, flattened ); @@ -68,11 +68,11 @@ BackendExtension.DeviceOption found = extension.find( currentKey ); if ( found == null ) continue; if ( found.device() == null ) continue; - if ( found.confidence() <= 0 ) continue; - if ( !deviceType.isAssignableFrom( found.device().getClass() ) ) continue; - if ( found.confidence() > ACCEPTABILITY && found.confidence() > desireForCPU || (justTakeFirstOne && probablyWantsGPU) ) - return (D) found.device(); - } + if ( found.confidence() <= 0 ) continue; + if ( !deviceType.isAssignableFrom( found.device().getClass() ) ) continue; + if ( found.confidence() > ACCEPTABILITY && found.confidence() > desireForCPU || (justTakeFirstOne && probablyWantsGPU) ) + return (D) found.device(); + } if ( probablyWantsGPU ) return null; // User wants OpenCL but cannot have it :/ @@ -83,4 +83,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/ReferenceCounter$ChangeEvent.html b/docs/coverage/test/html/neureka.devices/ReferenceCounter$ChangeEvent.html index 196dab16b..be89b7077 100644 --- a/docs/coverage/test/html/neureka.devices/ReferenceCounter$ChangeEvent.html +++ b/docs/coverage/test/html/neureka.devices/ReferenceCounter$ChangeEvent.html @@ -1 +1 @@ -ReferenceCounter.ChangeEvent

ReferenceCounter.ChangeEvent

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2185%0 of 0n/a141814
type()30%n/a111111
ReferenceCounter.ChangeEvent(ReferenceCounter.ChangeType, int, int)12100%n/a010501
change()3100%n/a010101
currentCount()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ReferenceCounter.ChangeEvent

ReferenceCounter.ChangeEvent

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 2185%0 of 0n/a141814
type()30%n/a111111
ReferenceCounter.ChangeEvent(ReferenceCounter.ChangeType, int, int)12100%n/a010501
change()3100%n/a010101
currentCount()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/ReferenceCounter$ChangeType.html b/docs/coverage/test/html/neureka.devices/ReferenceCounter$ChangeType.html index 32cbcbb0b..ae1b73857 100644 --- a/docs/coverage/test/html/neureka.devices/ReferenceCounter$ChangeType.html +++ b/docs/coverage/test/html/neureka.devices/ReferenceCounter$ChangeType.html @@ -1 +1 @@ -ReferenceCounter.ChangeType

ReferenceCounter.ChangeType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 21100%0 of 0n/a010101
static {...}21100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ReferenceCounter.ChangeType

ReferenceCounter.ChangeType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 34100%0 of 0n/a010101
static {...}34100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/ReferenceCounter.html b/docs/coverage/test/html/neureka.devices/ReferenceCounter.html index 36294f21e..b8ba4b0f6 100644 --- a/docs/coverage/test/html/neureka.devices/ReferenceCounter.html +++ b/docs/coverage/test/html/neureka.devices/ReferenceCounter.html @@ -1 +1 @@ -ReferenceCounter

ReferenceCounter

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 9183%3 of 862%3901605
fullDelete()52281%1375%130501
increment()52080%1150%120401
decrement()52080%1150%120401
ReferenceCounter(Consumer)11100%n/a010201
count()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ReferenceCounter

ReferenceCounter

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 9183%3 of 862%3901605
fullDelete()52281%1375%130501
increment()52080%1150%120401
decrement()52080%1150%120401
ReferenceCounter(Consumer)11100%n/a010201
count()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/ReferenceCounter.java.html b/docs/coverage/test/html/neureka.devices/ReferenceCounter.java.html index de28e8224..200485cb9 100644 --- a/docs/coverage/test/html/neureka.devices/ReferenceCounter.java.html +++ b/docs/coverage/test/html/neureka.devices/ReferenceCounter.java.html @@ -51,4 +51,4 @@ public int count() { return _count; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/index.html b/docs/coverage/test/html/neureka.devices/index.html index f61c0a358..16e3cdaa1 100644 --- a/docs/coverage/test/html/neureka.devices/index.html +++ b/docs/coverage/test/html/neureka.devices/index.html @@ -1 +1 @@ -neureka.devices

neureka.devices

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total76 of 1,34594%18 of 11484%221389222481017
ReferenceCounter157683%3562%390160501
AbstractDevice.1.new Device.Writer() {...}152360%n/a13131301
AbstractDevice1015493%32187%3211360901
Query837197%73984%73404101101
Device714595%3770%3130290801
CustomDeviceCleaner79993%1583%174240401
AbstractDevice.new Device.Access() {...}76390%n/a11011011001
AbstractDeviceData411196%8100%1131291901
ReferenceCounter.ChangeEvent1885%n/a14181401
Device.new Device.In() {...}67100%1583%15070201
AbstractDevice.1.new Device.Writer() {...}45100%n/a03030301
AbstractBaseDevice42100%6100%090100601
ReferenceCounter.ChangeType21100%n/a01010101
CustomDeviceCleaner.ReferenceWithCleanup12100%n/a02050201
DeviceCleaner9100%n/a02020201
Device.Writer7100%n/a01010101
Device.Access6100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices

neureka.devices

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total304 of 1,45879%53 of 12457%4814655251984017
AbstractDevice877746%18625%132119363901
Query5732284%202656%153444101101
Device569663%5550%51311291801
CustomDeviceCleaner4314276%41071%51415491701
ReferenceCounter157683%3562%390160501
AbstractDevice.1.new Device.Writer() {...}152360%n/a13131301
CustomDeviceCleaner.ReferenceWithCleanup151854%1150%13290201
AbstractDevice.new Device.Access() {...}76390%n/a11011011001
AbstractDeviceData411196%8100%1131291901
ReferenceCounter.ChangeEvent1885%n/a14181401
AbstractBaseDevice4095%1583%190100601
Device.new Device.In() {...}67100%1583%15070201
AbstractDevice.1.new Device.Writer() {...}45100%n/a03030301
ReferenceCounter.ChangeType34100%n/a01010101
DeviceCleaner9100%n/a02020201
Device.Writer7100%n/a01010101
Device.Access6100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.devices/index.source.html b/docs/coverage/test/html/neureka.devices/index.source.html index 9ee012585..0e6ff1102 100644 --- a/docs/coverage/test/html/neureka.devices/index.source.html +++ b/docs/coverage/test/html/neureka.devices/index.source.html @@ -1 +1 @@ -neureka.devices

neureka.devices

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total76 of 1,34594%18 of 11484%221389222481017
AbstractDevice.java3228589%32187%53734922504
ReferenceCounter.java1811586%3562%41412511003
Query.java837197%73984%73404101101
Device.java722596%41275%42003701204
CustomDeviceCleaner.java711194%1583%194290602
AbstractDeviceData.java411196%8100%1131291901
AbstractBaseDevice.java42100%6100%090100601
DeviceCleaner.java9100%n/a02020201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.devices

neureka.devices

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total304 of 1,45879%53 of 12457%4814655251984017
AbstractDevice.java10920865%18625%1537214952504
CustomDeviceCleaner.java5816073%51168%61717581902
Query.java5732284%202656%153444101101
Device.java5617675%61062%620113711204
ReferenceCounter.java1812887%3562%41412511003
AbstractDeviceData.java411196%8100%1131291901
AbstractBaseDevice.java4095%1583%190100601
DeviceCleaner.java9100%n/a02020201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/AbstractNumericType.html b/docs/coverage/test/html/neureka.dtype.custom/AbstractNumericType.html index 96cffedd6..04c549b81 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/AbstractNumericType.html +++ b/docs/coverage/test/html/neureka.dtype.custom/AbstractNumericType.html @@ -1 +1 @@ -AbstractNumericType

AbstractNumericType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 72100%0 of 2100%0501704
static {...}50100%n/a0101101
writeDataTo(DataOutput, Iterator)13100%2100%020401
getNumericTypeTarget()6100%n/a010101
AbstractNumericType()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractNumericType

AbstractNumericType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 72100%0 of 2100%0501704
static {...}50100%n/a0101101
writeDataTo(DataOutput, Iterator)13100%2100%020401
getNumericTypeTarget()6100%n/a010101
AbstractNumericType()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/AbstractNumericType.java.html b/docs/coverage/test/html/neureka.dtype.custom/AbstractNumericType.java.html index 9d5d43803..60b43244c 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/AbstractNumericType.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/AbstractNumericType.java.html @@ -56,4 +56,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/F32.html b/docs/coverage/test/html/neureka.dtype.custom/F32.html index ab77ffa58..1fcf4f81a 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/F32.html +++ b/docs/coverage/test/html/neureka.dtype.custom/F32.html @@ -1 +1 @@ -F32

F32

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total11 of 17793%1 of 1693%326336218
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
convertToHolder(Object)25896%11191%1711301
targetToForeignHolderBytes(Float)33100%n/a010201
readAndConvertForeignDataFrom(Iterator, int)20100%2100%020301
readForeignDataFrom(DataInput, int)17100%2100%020301
convertToTargetArray(Object)6100%n/a010101
foreignHolderBytesToTarget(byte[])5100%n/a010101
readAndConvertForeignDataFrom(DataInput, int)5100%n/a010101
readForeignDataFrom(Iterator, int)5100%n/a010101
F32()3100%n/a010201
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Float)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +F32

F32

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total11 of 17793%1 of 1693%326336218
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
convertToHolder(Object)25896%11191%1711301
targetToForeignHolderBytes(Float)33100%n/a010201
readAndConvertForeignDataFrom(Iterator, int)20100%2100%020301
readForeignDataFrom(DataInput, int)17100%2100%020301
convertToTargetArray(Object)6100%n/a010101
foreignHolderBytesToTarget(byte[])5100%n/a010101
readAndConvertForeignDataFrom(DataInput, int)5100%n/a010101
readForeignDataFrom(Iterator, int)5100%n/a010101
F32()3100%n/a010201
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Float)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/F32.java.html b/docs/coverage/test/html/neureka.dtype.custom/F32.java.html index c8009560c..63e9a15ba 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/F32.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/F32.java.html @@ -107,4 +107,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/F64.html b/docs/coverage/test/html/neureka.dtype.custom/F64.html index 93e466a80..de6fb640a 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/F64.html +++ b/docs/coverage/test/html/neureka.dtype.custom/F64.html @@ -1 +1 @@ -F64

F64

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total36 of 25185%3 of 1883%628738419
readAndConvertForeignDataFrom(Iterator, int)200%20%223311
readForeignDataFrom(DataInput, int)50%n/a111111
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
convertToHolder(Object)25896%11191%1711301
targetToForeignHolderBytes(Double)87100%n/a010201
readForeignDataFrom(Iterator, int)20100%2100%020301
_readFrom(DataInput, int)17100%2100%020301
convertToTargetArray(Object)6100%n/a010101
foreignHolderBytesToTarget(byte[])5100%n/a010101
readAndConvertForeignDataFrom(DataInput, int)5100%n/a010101
F64()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Double)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +F64

F64

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total36 of 25185%3 of 1883%628738419
readAndConvertForeignDataFrom(Iterator, int)200%20%223311
readForeignDataFrom(DataInput, int)50%n/a111111
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
convertToHolder(Object)25896%11191%1711301
targetToForeignHolderBytes(Double)87100%n/a010201
readForeignDataFrom(Iterator, int)20100%2100%020301
_readFrom(DataInput, int)17100%2100%020301
convertToTargetArray(Object)6100%n/a010101
foreignHolderBytesToTarget(byte[])5100%n/a010101
readAndConvertForeignDataFrom(DataInput, int)5100%n/a010101
F64()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Double)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/F64.java.html b/docs/coverage/test/html/neureka.dtype.custom/F64.java.html index 7f94b6d4e..4146e5972 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/F64.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/F64.java.html @@ -113,4 +113,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/I16.html b/docs/coverage/test/html/neureka.dtype.custom/I16.html index d3b7de7d3..67085c9f0 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/I16.html +++ b/docs/coverage/test/html/neureka.dtype.custom/I16.html @@ -1 +1 @@ -I16

I16

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 17690%1 of 1693%427439319
readForeignDataFrom(DataInput, int)50%n/a111111
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
convertToHolder(Object)25896%11191%1711301
readAndConvertForeignDataFrom(Iterator, int)20100%2100%020301
foreignHolderBytesToTarget(byte[])17100%n/a010301
_readData(DataInput, int)17100%2100%020301
targetToForeignHolderBytes(Short)11100%n/a010201
I16()7100%n/a010201
convertToTargetArray(Object)6100%n/a010101
readAndConvertForeignDataFrom(DataInput, int)5100%n/a010101
readForeignDataFrom(Iterator, int)5100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Short)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +I16

I16

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 17690%1 of 1693%427439319
readForeignDataFrom(DataInput, int)50%n/a111111
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
convertToHolder(Object)25896%11191%1711301
readAndConvertForeignDataFrom(Iterator, int)20100%2100%020301
foreignHolderBytesToTarget(byte[])17100%n/a010301
_readData(DataInput, int)17100%2100%020301
targetToForeignHolderBytes(Short)11100%n/a010201
I16()7100%n/a010201
convertToTargetArray(Object)6100%n/a010101
readAndConvertForeignDataFrom(DataInput, int)5100%n/a010101
readForeignDataFrom(Iterator, int)5100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Short)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/I16.java.html b/docs/coverage/test/html/neureka.dtype.custom/I16.java.html index e77b729a9..b17e1dfcb 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/I16.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/I16.java.html @@ -109,4 +109,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/I32.html b/docs/coverage/test/html/neureka.dtype.custom/I32.html index 9ba55d566..55c7c0a01 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/I32.html +++ b/docs/coverage/test/html/neureka.dtype.custom/I32.html @@ -1 +1 @@ -I32

I32

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 20392%1 of 1693%427442319
readForeignDataFrom(DataInput, int)50%n/a111111
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
convertToHolder(Object)26096%11191%1711301
targetToForeignHolderBytes(Integer)43100%n/a010501
_readData(DataInput, int)26100%2100%020601
readAndConvertForeignDataFrom(Iterator, int)20100%2100%020301
convertToTargetArray(Object)6100%n/a010101
foreignHolderBytesToTarget(byte[])5100%n/a010101
readAndConvertForeignDataFrom(DataInput, int)5100%n/a010101
readForeignDataFrom(Iterator, int)5100%n/a010101
I32()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Integer)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +I32

I32

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 20392%1 of 1693%427442319
readForeignDataFrom(DataInput, int)50%n/a111111
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
convertToHolder(Object)26096%11191%1711301
targetToForeignHolderBytes(Integer)43100%n/a010501
_readData(DataInput, int)26100%2100%020601
readAndConvertForeignDataFrom(Iterator, int)20100%2100%020301
convertToTargetArray(Object)6100%n/a010101
foreignHolderBytesToTarget(byte[])5100%n/a010101
readAndConvertForeignDataFrom(DataInput, int)5100%n/a010101
readForeignDataFrom(Iterator, int)5100%n/a010101
I32()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Integer)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/I32.java.html b/docs/coverage/test/html/neureka.dtype.custom/I32.java.html index 46adb5bf8..4e98de524 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/I32.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/I32.java.html @@ -113,4 +113,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/I64.html b/docs/coverage/test/html/neureka.dtype.custom/I64.html index 29179c857..7c7fa9544 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/I64.html +++ b/docs/coverage/test/html/neureka.dtype.custom/I64.html @@ -1 +1 @@ -I64

I64

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 11792%0 of 4100%220226218
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
readAndConvertForeignDataFrom(Iterator, int)20100%2100%020301
foreignHolderBytesToTarget(byte[])17100%n/a010301
readForeignDataFrom(DataInput, int)17100%2100%020301
targetToForeignHolderBytes(Long)11100%n/a010201
I64()7100%n/a010201
convertToHolder(Object)6100%n/a010101
convertToTargetArray(Object)6100%n/a010101
readAndConvertForeignDataFrom(DataInput, int)5100%n/a010101
readForeignDataFrom(Iterator, int)5100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Long)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +I64

I64

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 11792%0 of 4100%220226218
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
readAndConvertForeignDataFrom(Iterator, int)20100%2100%020301
foreignHolderBytesToTarget(byte[])17100%n/a010301
readForeignDataFrom(DataInput, int)17100%2100%020301
targetToForeignHolderBytes(Long)11100%n/a010201
I64()7100%n/a010201
convertToHolder(Object)6100%n/a010101
convertToTargetArray(Object)6100%n/a010101
readAndConvertForeignDataFrom(DataInput, int)5100%n/a010101
readForeignDataFrom(Iterator, int)5100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Long)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/I64.java.html b/docs/coverage/test/html/neureka.dtype.custom/I64.java.html index 4a399e364..7b4361ee7 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/I64.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/I64.java.html @@ -90,4 +90,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/I8.html b/docs/coverage/test/html/neureka.dtype.custom/I8.html index 32a4936ae..2dddfbc97 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/I8.html +++ b/docs/coverage/test/html/neureka.dtype.custom/I8.html @@ -1 +1 @@ -I8

I8

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 15786%1 of 1693%426636318
readForeignDataFrom(DataInput, int)100%n/a113311
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
convertToHolder(Object)25896%11191%1711301
readAndConvertForeignDataFrom(Iterator, int)20100%2100%020301
readAndConvertForeignDataFrom(DataInput, int)17100%2100%020301
targetToForeignHolderBytes(Byte)8100%n/a010101
convertToTargetArray(Object)6100%n/a010101
foreignHolderBytesToTarget(byte[])5100%n/a010101
readForeignDataFrom(Iterator, int)5100%n/a010101
I8()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Byte)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +I8

I8

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 15786%1 of 1693%426636318
readForeignDataFrom(DataInput, int)100%n/a113311
convertToHolderArray(Object)50%n/a111111
convertToTarget(Object)40%n/a111111
convertToHolder(Object)25896%11191%1711301
readAndConvertForeignDataFrom(Iterator, int)20100%2100%020301
readAndConvertForeignDataFrom(DataInput, int)17100%2100%020301
targetToForeignHolderBytes(Byte)8100%n/a010101
convertToTargetArray(Object)6100%n/a010101
foreignHolderBytesToTarget(byte[])5100%n/a010101
readForeignDataFrom(Iterator, int)5100%n/a010101
I8()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
holderType()2100%n/a010101
holderArrayType()2100%n/a010101
toTarget(Byte)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/I8.java.html b/docs/coverage/test/html/neureka.dtype.custom/I8.java.html index 295b142d1..5c9a996a8 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/I8.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/I8.java.html @@ -101,4 +101,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/UI16.html b/docs/coverage/test/html/neureka.dtype.custom/UI16.html index 58e7c20fd..39794e313 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/UI16.html +++ b/docs/coverage/test/html/neureka.dtype.custom/UI16.html @@ -1 +1 @@ -UI16

UI16

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total44 of 10156%2 of 20%9191326818
readAndConvertForeignDataFrom(DataInput, int)260%20%226611
readAndConvertForeignDataFrom(Iterator, int)30%n/a111111
readForeignDataFrom(DataInput, int)30%n/a111111
readForeignDataFrom(Iterator, int)30%n/a111111
convertToHolderArray(Object)30%n/a111111
holderType()20%n/a111111
holderArrayType()20%n/a111111
convertToTarget(Object)20%n/a111111
foreignHolderBytesToTarget(byte[])17100%n/a010201
targetToForeignHolderBytes(Integer)12100%n/a010301
convertToHolder(Object)6100%n/a010101
convertToTargetArray(Object)6100%n/a010101
toTarget(Short)5100%n/a010101
UI16()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +UI16

UI16

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total44 of 10156%2 of 20%9191326818
readAndConvertForeignDataFrom(DataInput, int)260%20%226611
readAndConvertForeignDataFrom(Iterator, int)30%n/a111111
readForeignDataFrom(DataInput, int)30%n/a111111
readForeignDataFrom(Iterator, int)30%n/a111111
convertToHolderArray(Object)30%n/a111111
holderType()20%n/a111111
holderArrayType()20%n/a111111
convertToTarget(Object)20%n/a111111
foreignHolderBytesToTarget(byte[])17100%n/a010201
targetToForeignHolderBytes(Integer)12100%n/a010301
convertToHolder(Object)6100%n/a010101
convertToTargetArray(Object)6100%n/a010101
toTarget(Short)5100%n/a010101
UI16()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/UI16.java.html b/docs/coverage/test/html/neureka.dtype.custom/UI16.java.html index a481aba4e..f0038aef7 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/UI16.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/UI16.java.html @@ -91,4 +91,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/UI32.html b/docs/coverage/test/html/neureka.dtype.custom/UI32.html index 2d212a911..2965ea176 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/UI32.html +++ b/docs/coverage/test/html/neureka.dtype.custom/UI32.html @@ -1 +1 @@ -UI32

UI32

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 12282%0 of 0n/a818822818
readAndConvertForeignDataFrom(DataInput, int)30%n/a111111
readAndConvertForeignDataFrom(Iterator, int)30%n/a111111
readForeignDataFrom(DataInput, int)30%n/a111111
readForeignDataFrom(Iterator, int)30%n/a111111
convertToHolderArray(Object)30%n/a111111
holderType()20%n/a111111
holderArrayType()20%n/a111111
convertToTarget(Object)20%n/a111111
targetToForeignHolderBytes(Long)38100%n/a010401
foreignHolderBytesToTarget(byte[])35100%n/a010201
convertToHolder(Object)6100%n/a010101
convertToTargetArray(Object)6100%n/a010101
toTarget(Integer)5100%n/a010101
UI32()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +UI32

UI32

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 12282%0 of 0n/a818822818
readAndConvertForeignDataFrom(DataInput, int)30%n/a111111
readAndConvertForeignDataFrom(Iterator, int)30%n/a111111
readForeignDataFrom(DataInput, int)30%n/a111111
readForeignDataFrom(Iterator, int)30%n/a111111
convertToHolderArray(Object)30%n/a111111
holderType()20%n/a111111
holderArrayType()20%n/a111111
convertToTarget(Object)20%n/a111111
targetToForeignHolderBytes(Long)38100%n/a010401
foreignHolderBytesToTarget(byte[])35100%n/a010201
convertToHolder(Object)6100%n/a010101
convertToTargetArray(Object)6100%n/a010101
toTarget(Integer)5100%n/a010101
UI32()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/UI32.java.html b/docs/coverage/test/html/neureka.dtype.custom/UI32.java.html index 73ab3148d..0060e9bbf 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/UI32.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/UI32.java.html @@ -83,4 +83,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/UI64.html b/docs/coverage/test/html/neureka.dtype.custom/UI64.html index 9aba47951..cdec65843 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/UI64.html +++ b/docs/coverage/test/html/neureka.dtype.custom/UI64.html @@ -1 +1 @@ -UI64

UI64

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 9277%0 of 0n/a818826818
readAndConvertForeignDataFrom(DataInput, int)30%n/a111111
readAndConvertForeignDataFrom(Iterator, int)30%n/a111111
readForeignDataFrom(DataInput, int)30%n/a111111
readForeignDataFrom(Iterator, int)30%n/a111111
convertToHolderArray(Object)30%n/a111111
holderType()20%n/a111111
holderArrayType()20%n/a111111
convertToTarget(Object)20%n/a111111
targetToForeignHolderBytes(BigInteger)22100%n/a010601
foreignHolderBytesToTarget(byte[])19100%n/a010401
toTarget(Long)7100%n/a010101
convertToHolder(Object)6100%n/a010101
convertToTargetArray(Object)6100%n/a010101
UI64()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +UI64

UI64

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total21 of 9277%0 of 0n/a818826818
readAndConvertForeignDataFrom(DataInput, int)30%n/a111111
readAndConvertForeignDataFrom(Iterator, int)30%n/a111111
readForeignDataFrom(DataInput, int)30%n/a111111
readForeignDataFrom(Iterator, int)30%n/a111111
convertToHolderArray(Object)30%n/a111111
holderType()20%n/a111111
holderArrayType()20%n/a111111
convertToTarget(Object)20%n/a111111
targetToForeignHolderBytes(BigInteger)22100%n/a010601
foreignHolderBytesToTarget(byte[])19100%n/a010401
toTarget(Long)7100%n/a010101
convertToHolder(Object)6100%n/a010101
convertToTargetArray(Object)6100%n/a010101
UI64()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/UI64.java.html b/docs/coverage/test/html/neureka.dtype.custom/UI64.java.html index 3402fe7b1..023623b8c 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/UI64.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/UI64.java.html @@ -92,4 +92,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/UI8.html b/docs/coverage/test/html/neureka.dtype.custom/UI8.html index 9cc166c8c..5559e3e24 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/UI8.html +++ b/docs/coverage/test/html/neureka.dtype.custom/UI8.html @@ -1 +1 @@ -UI8

UI8

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total98 of 17243%14 of 1612%14262440718
convertToTarget(Object)600%120%77131311
readForeignDataFrom(DataInput, int)250%20%226611
readAndConvertForeignDataFrom(Iterator, int)30%n/a111111
readForeignDataFrom(Iterator, int)30%n/a111111
convertToHolderArray(Object)30%n/a111111
holderType()20%n/a111111
holderArrayType()20%n/a111111
readAndConvertForeignDataFrom(DataInput, int)26100%2100%020601
targetToForeignHolderBytes(Short)11100%n/a010101
foreignHolderBytesToTarget(byte[])8100%n/a010101
toTarget(Byte)6100%n/a010101
convertToHolder(Object)6100%n/a010101
convertToTargetArray(Object)6100%n/a010101
UI8()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +UI8

UI8

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total98 of 17243%14 of 1612%14262440718
convertToTarget(Object)600%120%77131311
readForeignDataFrom(DataInput, int)250%20%226611
readAndConvertForeignDataFrom(Iterator, int)30%n/a111111
readForeignDataFrom(Iterator, int)30%n/a111111
convertToHolderArray(Object)30%n/a111111
holderType()20%n/a111111
holderArrayType()20%n/a111111
readAndConvertForeignDataFrom(DataInput, int)26100%2100%020601
targetToForeignHolderBytes(Short)11100%n/a010101
foreignHolderBytesToTarget(byte[])8100%n/a010101
toTarget(Byte)6100%n/a010101
convertToHolder(Object)6100%n/a010101
convertToTargetArray(Object)6100%n/a010101
UI8()3100%n/a010101
signed()2100%n/a010101
numberOfBytes()2100%n/a010101
targetType()2100%n/a010101
targetArrayType()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/UI8.java.html b/docs/coverage/test/html/neureka.dtype.custom/UI8.java.html index 055ba6e71..c7011afb9 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/UI8.java.html +++ b/docs/coverage/test/html/neureka.dtype.custom/UI8.java.html @@ -96,4 +96,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/index.html b/docs/coverage/test/html/neureka.dtype.custom/index.html index 6e8425d87..347fd9b2f 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/index.html +++ b/docs/coverage/test/html/neureka.dtype.custom/index.html @@ -1 +1 @@ -neureka.dtype.custom

neureka.dtype.custom

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total293 of 1,64082%23 of 10678%622407934848187011
UI8987443%14212%1426244071801
UI16445756%20%919132681801
F643621585%31583%62873841901
I82113686%11593%42663631801
UI322110182%n/a81882281801
UI64217177%n/a81882681801
I321618792%11593%42744231901
I161616090%11593%42743931901
F321116693%11593%32633621801
I64910892%4100%22022621801
AbstractNumericType72100%2100%050170401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.dtype.custom

neureka.dtype.custom

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total293 of 1,64082%23 of 10678%622407934848187011
UI8987443%14212%1426244071801
UI16445756%20%919132681801
F643621585%31583%62873841901
I82113686%11593%42663631801
UI322110182%n/a81882281801
UI64217177%n/a81882681801
I321618792%11593%42744231901
I161616090%11593%42743931901
F321116693%11593%32633621801
I64910892%4100%22022621801
AbstractNumericType72100%2100%050170401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype.custom/index.source.html b/docs/coverage/test/html/neureka.dtype.custom/index.source.html index 81b260d32..665e22618 100644 --- a/docs/coverage/test/html/neureka.dtype.custom/index.source.html +++ b/docs/coverage/test/html/neureka.dtype.custom/index.source.html @@ -1 +1 @@ -neureka.dtype.custom

neureka.dtype.custom

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total293 of 1,64082%23 of 10678%622407934848187011
UI8.java987443%14212%1426244071801
UI16.java445756%20%919132681801
F64.java3621585%31583%62873841901
I8.java2113686%11593%42663631801
UI32.java2110182%n/a81882281801
UI64.java217177%n/a81882681801
I32.java1618792%11593%42744231901
I16.java1616090%11593%42743931901
F32.java1116693%11593%32633621801
I64.java910892%4100%22022621801
AbstractNumericType.java72100%2100%050170401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.dtype.custom

neureka.dtype.custom

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total293 of 1,64082%23 of 10678%622407934848187011
UI8.java987443%14212%1426244071801
UI16.java445756%20%919132681801
F64.java3621585%31583%62873841901
I8.java2113686%11593%42663631801
UI32.java2110182%n/a81882281801
UI64.java217177%n/a81882681801
I32.java1618792%11593%42744231901
I16.java1616090%11593%42743931901
F32.java1116693%11593%32633621801
I64.java910892%4100%22022621801
AbstractNumericType.java72100%2100%050170401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype/DataType$1.html b/docs/coverage/test/html/neureka.dtype/DataType$1.html index 787c3e2ad..dab3e46ff 100644 --- a/docs/coverage/test/html/neureka.dtype/DataType$1.html +++ b/docs/coverage/test/html/neureka.dtype/DataType$1.html @@ -1 +1 @@ -DataType.new LinkedHashMap() {...}

DataType.new LinkedHashMap() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 1181%1 of 250%130202
removeEldestEntry(Map.Entry)2675%1150%120101
{...}3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DataType.new LinkedHashMap() {...}

DataType.new LinkedHashMap() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 1181%1 of 250%130202
removeEldestEntry(Map.Entry)2675%1150%120101
{...}3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype/DataType.html b/docs/coverage/test/html/neureka.dtype/DataType.html index 9018222be..1bb3f5cbc 100644 --- a/docs/coverage/test/html/neureka.dtype/DataType.html +++ b/docs/coverage/test/html/neureka.dtype/DataType.html @@ -1 +1 @@ -DataType

DataType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total38 of 31687%7 of 4684%835656112
getTypeClassInstance(Class)304761%3350%3451501
hashCode()60%n/a111111
equals(Object)21789%1375%130401
_numericTypeRepresentationOf(Class)69100%32187%3130801
toString()35100%2100%020201
of(Class)30100%2100%020701
dataArrayType()25100%6100%040701
DataType(Class)24100%n/a010501
getItemTypeClass()12100%2100%020301
typeClassImplements(Class)11100%n/a010201
static {...}5100%n/a010101
getRepresentativeType()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +DataType

DataType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total38 of 31687%7 of 4684%835656112
getTypeClassInstance(Class)304761%3350%3451501
hashCode()60%n/a111111
equals(Object)21789%1375%130401
_numericTypeRepresentationOf(Class)69100%32187%3130801
toString()35100%2100%020201
of(Class)30100%2100%020701
dataArrayType()25100%6100%040701
DataType(Class)24100%n/a010501
getItemTypeClass()12100%2100%020301
typeClassImplements(Class)11100%n/a010201
static {...}5100%n/a010101
getRepresentativeType()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype/DataType.java.html b/docs/coverage/test/html/neureka.dtype/DataType.java.html index af2ddd9b8..d7ceb685a 100644 --- a/docs/coverage/test/html/neureka.dtype/DataType.java.html +++ b/docs/coverage/test/html/neureka.dtype/DataType.java.html @@ -179,4 +179,4 @@ return _typeClass; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype/index.html b/docs/coverage/test/html/neureka.dtype/index.html index 9c280e60b..5aaa64485 100644 --- a/docs/coverage/test/html/neureka.dtype/index.html +++ b/docs/coverage/test/html/neureka.dtype/index.html @@ -1 +1 @@ -neureka.dtype

neureka.dtype

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total40 of 32787%8 of 4883%93865711402
DataType3827887%73984%83565611201
DataType.new LinkedHashMap() {...}981%1150%13020201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.dtype

neureka.dtype

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total40 of 32787%8 of 4883%93865711402
DataType3827887%73984%83565611201
DataType.new LinkedHashMap() {...}981%1150%13020201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.dtype/index.source.html b/docs/coverage/test/html/neureka.dtype/index.source.html index abb5d5e53..114f40286 100644 --- a/docs/coverage/test/html/neureka.dtype/index.source.html +++ b/docs/coverage/test/html/neureka.dtype/index.source.html @@ -1 +1 @@ -neureka.dtype

neureka.dtype

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total40 of 32787%8 of 4883%93865711402
DataType.java4028787%84083%93865711402
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.dtype

neureka.dtype

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total40 of 32787%8 of 4883%93865711402
DataType.java4028787%84083%93865711402
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.building.states/IterByOrIterFromOrAllTensor.html b/docs/coverage/test/html/neureka.fluent.building.states/IterByOrIterFromOrAllTensor.html index 74d4a24d3..3cdb89a11 100644 --- a/docs/coverage/test/html/neureka.fluent.building.states/IterByOrIterFromOrAllTensor.html +++ b/docs/coverage/test/html/neureka.fluent.building.states/IterByOrIterFromOrAllTensor.html @@ -1 +1 @@ -IterByOrIterFromOrAllTensor

IterByOrIterFromOrAllTensor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 5100%0 of 0n/a010101
andFill(List)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +IterByOrIterFromOrAllTensor

IterByOrIterFromOrAllTensor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 5100%0 of 0n/a010101
andFill(List)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.building.states/IterByOrIterFromOrAllTensor.java.html b/docs/coverage/test/html/neureka.fluent.building.states/IterByOrIterFromOrAllTensor.java.html index 0cf6d5c61..f321f437e 100644 --- a/docs/coverage/test/html/neureka.fluent.building.states/IterByOrIterFromOrAllTensor.java.html +++ b/docs/coverage/test/html/neureka.fluent.building.states/IterByOrIterFromOrAllTensor.java.html @@ -33,4 +33,4 @@ Tensor<V> andSeed(Object seed ); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.building.states/WithShapeOrScalarOrVectorTensor.html b/docs/coverage/test/html/neureka.fluent.building.states/WithShapeOrScalarOrVectorTensor.html index 7f48432d4..f40e9b6dd 100644 --- a/docs/coverage/test/html/neureka.fluent.building.states/WithShapeOrScalarOrVectorTensor.html +++ b/docs/coverage/test/html/neureka.fluent.building.states/WithShapeOrScalarOrVectorTensor.html @@ -1 +1 @@ -WithShapeOrScalarOrVectorTensor

WithShapeOrScalarOrVectorTensor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 4163%0 of 0n/a133713
vector(Iterable)150%n/a113311
withShape(List)18100%n/a010301
vector(List)8100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +WithShapeOrScalarOrVectorTensor

WithShapeOrScalarOrVectorTensor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total15 of 4163%0 of 0n/a133713
vector(Iterable)150%n/a113311
withShape(List)18100%n/a010301
vector(List)8100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.building.states/WithShapeOrScalarOrVectorTensor.java.html b/docs/coverage/test/html/neureka.fluent.building.states/WithShapeOrScalarOrVectorTensor.java.html index f26a41ca8..bdd5a9f23 100644 --- a/docs/coverage/test/html/neureka.fluent.building.states/WithShapeOrScalarOrVectorTensor.java.html +++ b/docs/coverage/test/html/neureka.fluent.building.states/WithShapeOrScalarOrVectorTensor.java.html @@ -41,4 +41,4 @@ Tensor<V> scalar(V value ); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.building.states/index.html b/docs/coverage/test/html/neureka.fluent.building.states/index.html index 9c01abaad..b4670666b 100644 --- a/docs/coverage/test/html/neureka.fluent.building.states/index.html +++ b/docs/coverage/test/html/neureka.fluent.building.states/index.html @@ -1 +1 @@ -neureka.fluent.building.states

neureka.fluent.building.states

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total15 of 4667%0 of 0n/a14381402
WithShapeOrScalarOrVectorTensor152663%n/a13371301
IterByOrIterFromOrAllTensor5100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.fluent.building.states

neureka.fluent.building.states

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total15 of 4667%0 of 0n/a14381402
WithShapeOrScalarOrVectorTensor152663%n/a13371301
IterByOrIterFromOrAllTensor5100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.building.states/index.source.html b/docs/coverage/test/html/neureka.fluent.building.states/index.source.html index 29c812522..5073892e9 100644 --- a/docs/coverage/test/html/neureka.fluent.building.states/index.source.html +++ b/docs/coverage/test/html/neureka.fluent.building.states/index.source.html @@ -1 +1 @@ -neureka.fluent.building.states

neureka.fluent.building.states

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total15 of 4667%0 of 0n/a14381402
WithShapeOrScalarOrVectorTensor.java152663%n/a13371301
IterByOrIterFromOrAllTensor.java5100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.fluent.building.states

neureka.fluent.building.states

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total15 of 4667%0 of 0n/a14381402
WithShapeOrScalarOrVectorTensor.java152663%n/a13371301
IterByOrIterFromOrAllTensor.java5100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.building/NdaBuilder.html b/docs/coverage/test/html/neureka.fluent.building/NdaBuilder.html index dcbcdbb32..1281ff85e 100644 --- a/docs/coverage/test/html/neureka.fluent.building/NdaBuilder.html +++ b/docs/coverage/test/html/neureka.fluent.building/NdaBuilder.html @@ -1 +1 @@ -NdaBuilder

NdaBuilder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total47 of 80094%14 of 8683%12667132023
scalar(Object)243458%1375%1341101
step(double)1328195%83078%62025701
_checked(Object)85487%31583%31001101
_isAllOne(Object[])4195%2880%261901
andSeed(Object)101100%8100%0501201
vector(Object[])34100%2100%020401
andFill(Object[])26100%2100%020301
withShape(int[])24100%2100%020501
_size()21100%2100%020301
NdaBuilder(Class)20100%n/a010501
_get(Object)19100%n/a010201
andFillFrom(Object)19100%n/a010301
on(Device)15100%n/a010301
andWhere(Filler)10100%n/a010101
lambda$step$5(List, int)9100%n/a010101
lambda$step$3(List, int)9100%n/a010101
lambda$step$1(List, int)9100%n/a010101
to(Object)7100%n/a010101
all(Object)4100%n/a010101
lambda$step$4(int)4100%n/a010101
lambda$step$2(int)4100%n/a010101
lambda$step$0(int)4100%n/a010101
static {...}4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NdaBuilder

NdaBuilder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total47 of 80094%14 of 8683%12667132023
scalar(Object)243458%1375%1341101
step(double)1328195%83078%62025701
_checked(Object)85487%31583%31001101
_isAllOne(Object[])4195%2880%261901
andSeed(Object)101100%8100%0501201
vector(Object[])34100%2100%020401
andFill(Object[])26100%2100%020301
withShape(int[])24100%2100%020501
_size()21100%2100%020301
NdaBuilder(Class)20100%n/a010501
_get(Object)19100%n/a010201
andFillFrom(Object)19100%n/a010301
on(Device)15100%n/a010301
andWhere(Filler)10100%n/a010101
lambda$step$5(List, int)9100%n/a010101
lambda$step$3(List, int)9100%n/a010101
lambda$step$1(List, int)9100%n/a010101
to(Object)7100%n/a010101
all(Object)4100%n/a010101
lambda$step$4(int)4100%n/a010101
lambda$step$2(int)4100%n/a010101
lambda$step$0(int)4100%n/a010101
static {...}4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.building/NdaBuilder.java.html b/docs/coverage/test/html/neureka.fluent.building/NdaBuilder.java.html index f9859a417..3775627ce 100644 --- a/docs/coverage/test/html/neureka.fluent.building/NdaBuilder.java.html +++ b/docs/coverage/test/html/neureka.fluent.building/NdaBuilder.java.html @@ -72,7 +72,7 @@ private Shape _shape; private V _from; private V _to; - private Device<V> _device = (Device<V>) CPU.get(); + private Device<? super V> _device = CPU.get(); /** * @param typeClass The type of the values which ought to be represented by the {@link Tensor} built by this {@link NdaBuilder}. @@ -299,10 +299,10 @@ } @Override - public WithShapeOrScalarOrVectorTensor<V> on(Device<V> device ) { + public WithShapeOrScalarOrVectorTensor<V> on(Device<? super V> device ) { LogUtil.nullArgCheck(device, "device", Device.class, "Cannot create a tensor with an undefined device!"); _device = device; return this; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.building/index.html b/docs/coverage/test/html/neureka.fluent.building/index.html index a84283f57..0985925a8 100644 --- a/docs/coverage/test/html/neureka.fluent.building/index.html +++ b/docs/coverage/test/html/neureka.fluent.building/index.html @@ -1 +1 @@ -neureka.fluent.building

neureka.fluent.building

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total47 of 80094%14 of 8683%1266713202301
NdaBuilder4775394%147283%1266713202301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.fluent.building

neureka.fluent.building

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total47 of 80094%14 of 8683%1266713202301
NdaBuilder4775394%147283%1266713202301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.building/index.source.html b/docs/coverage/test/html/neureka.fluent.building/index.source.html index 1ad6649f8..ce55a164c 100644 --- a/docs/coverage/test/html/neureka.fluent.building/index.source.html +++ b/docs/coverage/test/html/neureka.fluent.building/index.source.html @@ -1 +1 @@ -neureka.fluent.building

neureka.fluent.building

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total47 of 80094%14 of 8683%1266713202301
NdaBuilder.java4775394%147283%1266713202301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.fluent.building

neureka.fluent.building

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total47 of 80094%14 of 8683%1266713202301
NdaBuilder.java4775394%147283%1266713202301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.slicing/AxisSliceBuilder.html b/docs/coverage/test/html/neureka.fluent.slicing/AxisSliceBuilder.html index 79be0ee8d..3ba8f9bfb 100644 --- a/docs/coverage/test/html/neureka.fluent.slicing/AxisSliceBuilder.html +++ b/docs/coverage/test/html/neureka.fluent.slicing/AxisSliceBuilder.html @@ -1 +1 @@ -AxisSliceBuilder

AxisSliceBuilder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 9292%0 of 0n/a110121110
all()70%n/a111111
AxisSliceBuilder(int, AxisSliceBuilder.Resolution)17100%n/a010601
axis(int)12100%n/a010101
get()11100%n/a010101
detached()11100%n/a010101
resolve()11100%n/a010201
at(int)8100%n/a010301
from(int)5100%n/a010201
to(int)5100%n/a010201
step(int)5100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AxisSliceBuilder

AxisSliceBuilder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 9292%0 of 0n/a110121110
all()70%n/a111111
AxisSliceBuilder(int, AxisSliceBuilder.Resolution)17100%n/a010601
axis(int)12100%n/a010101
get()11100%n/a010101
detached()11100%n/a010101
resolve()11100%n/a010201
at(int)8100%n/a010301
from(int)5100%n/a010201
to(int)5100%n/a010201
step(int)5100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.slicing/AxisSliceBuilder.java.html b/docs/coverage/test/html/neureka.fluent.slicing/AxisSliceBuilder.java.html index dd7bbbb80..39988b440 100644 --- a/docs/coverage/test/html/neureka.fluent.slicing/AxisSliceBuilder.java.html +++ b/docs/coverage/test/html/neureka.fluent.slicing/AxisSliceBuilder.java.html @@ -111,4 +111,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.slicing/SliceBuilder.html b/docs/coverage/test/html/neureka.fluent.slicing/SliceBuilder.html index 6a795b2d7..77e8eb724 100644 --- a/docs/coverage/test/html/neureka.fluent.slicing/SliceBuilder.html +++ b/docs/coverage/test/html/neureka.fluent.slicing/SliceBuilder.html @@ -1 +1 @@ -SliceBuilder

SliceBuilder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 24393%4 of 2281%41804007
lambda$new$1(int[], int, int[], int[], int[], int, int, int)126283%31178%3801301
axis(int)51066%1150%120201
SliceBuilder(Tensor)62100%2100%0201201
lambda$new$0(Tensor, int[], int[], int[], boolean)49100%n/a010801
lambda$new$2(SliceBuilder.CreationCallback, int[], int[], int[], Boolean)29100%4100%030301
get()7100%n/a010101
detached()7100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SliceBuilder

SliceBuilder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 24393%4 of 2281%41804007
lambda$new$1(int[], int, int[], int[], int[], int, int, int)126283%31178%3801301
axis(int)51066%1150%120201
SliceBuilder(Tensor)62100%2100%0201201
lambda$new$0(Tensor, int[], int[], int[], boolean)49100%n/a010801
lambda$new$2(SliceBuilder.CreationCallback, int[], int[], int[], Boolean)29100%4100%030301
get()7100%n/a010101
detached()7100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.slicing/SliceBuilder.java.html b/docs/coverage/test/html/neureka.fluent.slicing/SliceBuilder.java.html index f58bdc3e7..b83326a57 100644 --- a/docs/coverage/test/html/neureka.fluent.slicing/SliceBuilder.java.html +++ b/docs/coverage/test/html/neureka.fluent.slicing/SliceBuilder.java.html @@ -121,4 +121,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.slicing/SmartSlicer.html b/docs/coverage/test/html/neureka.fluent.slicing/SmartSlicer.html index edd239050..cc9621a62 100644 --- a/docs/coverage/test/html/neureka.fluent.slicing/SmartSlicer.html +++ b/docs/coverage/test/html/neureka.fluent.slicing/SmartSlicer.html @@ -1 +1 @@ -SmartSlicer

SmartSlicer

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total53 of 47288%7 of 4484%82568113
slice(Object[], Tensor)5041589%73784%72357901
SmartSlicer()0%n/a111111
static {...}4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SmartSlicer

SmartSlicer

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total53 of 47288%7 of 4484%82568113
slice(Object[], Tensor)5041589%73784%72357901
SmartSlicer()0%n/a111111
static {...}4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.slicing/SmartSlicer.java.html b/docs/coverage/test/html/neureka.fluent.slicing/SmartSlicer.java.html index a46ed34c5..5855f43b7 100644 --- a/docs/coverage/test/html/neureka.fluent.slicing/SmartSlicer.java.html +++ b/docs/coverage/test/html/neureka.fluent.slicing/SmartSlicer.java.html @@ -137,4 +137,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.slicing/index.html b/docs/coverage/test/html/neureka.fluent.slicing/index.html index 9120addfe..4c85d328e 100644 --- a/docs/coverage/test/html/neureka.fluent.slicing/index.html +++ b/docs/coverage/test/html/neureka.fluent.slicing/index.html @@ -1 +1 @@ -neureka.fluent.slicing

neureka.fluent.slicing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total77 of 80790%11 of 6683%1353714222003
SmartSlicer5341988%73784%8256811301
SliceBuilder1722693%41881%4180400701
AxisSliceBuilder78592%n/a11012111001
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.fluent.slicing

neureka.fluent.slicing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total77 of 80790%11 of 6683%1353714222003
SmartSlicer5341988%73784%8256811301
SliceBuilder1722693%41881%4180400701
AxisSliceBuilder78592%n/a11012111001
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.fluent.slicing/index.source.html b/docs/coverage/test/html/neureka.fluent.slicing/index.source.html index dbee6361b..11aa90eda 100644 --- a/docs/coverage/test/html/neureka.fluent.slicing/index.source.html +++ b/docs/coverage/test/html/neureka.fluent.slicing/index.source.html @@ -1 +1 @@ -neureka.fluent.slicing

neureka.fluent.slicing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total77 of 80790%11 of 6683%1353714222003
SmartSlicer.java5341988%73784%8256811301
SliceBuilder.java1722693%41881%4180400701
AxisSliceBuilder.java78592%n/a11012111001
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.fluent.slicing

neureka.fluent.slicing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total77 of 80790%11 of 6683%1353714222003
SmartSlicer.java5341988%73784%8256811301
SliceBuilder.java1722693%41881%4180400701
AxisSliceBuilder.java78592%n/a11012111001
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing.fluent/AxisFrame$Builder.html b/docs/coverage/test/html/neureka.framing.fluent/AxisFrame$Builder.html index bdee9897b..d4cb36c26 100644 --- a/docs/coverage/test/html/neureka.framing.fluent/AxisFrame$Builder.html +++ b/docs/coverage/test/html/neureka.framing.fluent/AxisFrame$Builder.html @@ -1 +1 @@ -AxisFrame.Builder

AxisFrame.Builder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 43100%0 of 0n/a0701207
build()15100%n/a010101
getter(At)5100%n/a010201
setter(At)5100%n/a010201
replacer(Replace)5100%n/a010201
allAliasGetter(Supplier)5100%n/a010201
allAliasGetterFor(Function)5100%n/a010201
AxisFrame.Builder()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AxisFrame.Builder

AxisFrame.Builder

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 43100%0 of 0n/a0701207
build()15100%n/a010101
getter(At)5100%n/a010201
setter(At)5100%n/a010201
replacer(Replace)5100%n/a010201
allAliasGetter(Supplier)5100%n/a010201
allAliasGetterFor(Function)5100%n/a010201
AxisFrame.Builder()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing.fluent/AxisFrame.html b/docs/coverage/test/html/neureka.framing.fluent/AxisFrame.html index f192aaa59..3fde7c00d 100644 --- a/docs/coverage/test/html/neureka.framing.fluent/AxisFrame.html +++ b/docs/coverage/test/html/neureka.framing.fluent/AxisFrame.html @@ -1 +1 @@ -AxisFrame

AxisFrame

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 52100%0 of 0n/a0701307
AxisFrame(At, At, Replace, Supplier, Function)18100%n/a010701
getIndexAtAlias(Object)7100%n/a010101
getAllAliasesForIndex(int)7100%n/a010101
atIndexAlias(Object)6100%n/a010101
replace(Object)5100%n/a010101
getAllAliases()5100%n/a010101
builder()4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AxisFrame

AxisFrame

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 52100%0 of 0n/a0701307
AxisFrame(At, At, Replace, Supplier, Function)18100%n/a010701
getIndexAtAlias(Object)7100%n/a010101
getAllAliasesForIndex(int)7100%n/a010101
atIndexAlias(Object)6100%n/a010101
replace(Object)5100%n/a010101
getAllAliases()5100%n/a010101
builder()4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing.fluent/AxisFrame.java.html b/docs/coverage/test/html/neureka.framing.fluent/AxisFrame.java.html index 14e6b6dcd..968df35aa 100644 --- a/docs/coverage/test/html/neureka.framing.fluent/AxisFrame.java.html +++ b/docs/coverage/test/html/neureka.framing.fluent/AxisFrame.java.html @@ -113,4 +113,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing.fluent/index.html b/docs/coverage/test/html/neureka.framing.fluent/index.html index bafd84e17..2fcfa3498 100644 --- a/docs/coverage/test/html/neureka.framing.fluent/index.html +++ b/docs/coverage/test/html/neureka.framing.fluent/index.html @@ -1 +1 @@ -neureka.framing.fluent

neureka.framing.fluent

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 95100%0 of 0n/a01402501402
AxisFrame52100%n/a070130701
AxisFrame.Builder43100%n/a070120701
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.framing.fluent

neureka.framing.fluent

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 95100%0 of 0n/a01402501402
AxisFrame52100%n/a070130701
AxisFrame.Builder43100%n/a070120701
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing.fluent/index.source.html b/docs/coverage/test/html/neureka.framing.fluent/index.source.html index ccc257195..e38a3d5f9 100644 --- a/docs/coverage/test/html/neureka.framing.fluent/index.source.html +++ b/docs/coverage/test/html/neureka.framing.fluent/index.source.html @@ -1 +1 @@ -neureka.framing.fluent

neureka.framing.fluent

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 95100%0 of 0n/a01402501402
AxisFrame.java95100%n/a01402501402
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.framing.fluent

neureka.framing.fluent

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 95100%0 of 0n/a01402501402
AxisFrame.java95100%n/a01402501402
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing/NDFrame.html b/docs/coverage/test/html/neureka.framing/NDFrame.html index 1ec923569..b6e5ee009 100644 --- a/docs/coverage/test/html/neureka.framing/NDFrame.html +++ b/docs/coverage/test/html/neureka.framing/NDFrame.html @@ -1 +1 @@ -NDFrame

NDFrame

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total78 of 1,02092%15 of 8882%147814176234
get(Object[])500%60%449911
get(List)130%n/a112211
getState()65990%1583%1401101
lambda$atAxis$11(Object, Integer)42184%1150%121501
lambda$atAxis$2(Object, Object)31785%1150%121401
_paddedCentered(String, int)25996%1375%1311001
toString()182100%14100%0803301
lambda$new$0(int[], Tensor, Object, List)68100%6100%040901
NDFrame(Map, Tensor, String)67100%1375%1301201
_label(List)63100%1990%160701
lambda$toString$15(Object[], int[], StringBuilder, Object, Object)63100%2880%2601101
_initializeIndexMap(Object, Object, int)57100%6100%0401001
lambda$atAxis$9(Object)40100%6100%040601
atAxis(Object)29100%n/a010801
NDFrame(List, Map, String)22100%n/a010601
lambda$toString$13(StringBuilder, Object, Object)22100%2100%020601
lambda$atAxis$6(Object, Object, Object)22100%1150%120401
lambda$new$1(int[], Object, List)20100%n/a010301
hasLabelsForAxis(Object)16100%2100%020201
lambda$atAxis$4(Object, Object, int)14100%n/a010301
withAxesLabels(List)12100%n/a010101
lambda$toString$12(int, int, Integer)12100%2100%020101
lambda$toString$14(int[], Object[], Object, Integer)11100%2100%020201
NDFrame(List, Tensor, String)10100%n/a010301
withLabel(String)9100%n/a010101
lambda$atAxis$10(Integer, List, Object, Integer)9100%2100%020101
NDFrame(Tensor, String)6100%n/a010201
update(Component.OwnerChangeRequest)5100%n/a010201
lambda$atAxis$8(List, Object, Integer)5100%n/a010101
lambda$atAxis$7(Object, Object)5100%n/a010101
lambda$atAxis$5(Object, Object)5100%n/a010101
lambda$atAxis$3(Object, Object)5100%n/a010101
_mapping()4100%n/a010101
getLabel()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDFrame

NDFrame

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total78 of 1,02092%15 of 8882%147814176234
get(Object[])500%60%449911
get(List)130%n/a112211
getState()65990%1583%1401101
lambda$atAxis$11(Object, Integer)42184%1150%121501
lambda$atAxis$2(Object, Object)31785%1150%121401
_paddedCentered(String, int)25996%1375%1311001
toString()182100%14100%0803301
lambda$new$0(int[], Tensor, Object, List)68100%6100%040901
NDFrame(Map, Tensor, String)67100%1375%1301201
_label(List)63100%1990%160701
lambda$toString$15(Object[], int[], StringBuilder, Object, Object)63100%2880%2601101
_initializeIndexMap(Object, Object, int)57100%6100%0401001
lambda$atAxis$9(Object)40100%6100%040601
atAxis(Object)29100%n/a010801
NDFrame(List, Map, String)22100%n/a010601
lambda$toString$13(StringBuilder, Object, Object)22100%2100%020601
lambda$atAxis$6(Object, Object, Object)22100%1150%120401
lambda$new$1(int[], Object, List)20100%n/a010301
hasLabelsForAxis(Object)16100%2100%020201
lambda$atAxis$4(Object, Object, int)14100%n/a010301
withAxesLabels(List)12100%n/a010101
lambda$toString$12(int, int, Integer)12100%2100%020101
lambda$toString$14(int[], Object[], Object, Integer)11100%2100%020201
NDFrame(List, Tensor, String)10100%n/a010301
withLabel(String)9100%n/a010101
lambda$atAxis$10(Integer, List, Object, Integer)9100%2100%020101
NDFrame(Tensor, String)6100%n/a010201
update(Component.OwnerChangeRequest)5100%n/a010201
lambda$atAxis$8(List, Object, Integer)5100%n/a010101
lambda$atAxis$7(Object, Object)5100%n/a010101
lambda$atAxis$5(Object, Object)5100%n/a010101
lambda$atAxis$3(Object, Object)5100%n/a010101
_mapping()4100%n/a010101
getLabel()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing/NDFrame.java.html b/docs/coverage/test/html/neureka.framing/NDFrame.java.html index ceea29b3d..504922331 100644 --- a/docs/coverage/test/html/neureka.framing/NDFrame.java.html +++ b/docs/coverage/test/html/neureka.framing/NDFrame.java.html @@ -333,4 +333,4 @@ return _mainLabel; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing/Relation.html b/docs/coverage/test/html/neureka.framing/Relation.html index 594c97ea7..e6275cfe1 100644 --- a/docs/coverage/test/html/neureka.framing/Relation.html +++ b/docs/coverage/test/html/neureka.framing/Relation.html @@ -1 +1 @@ -Relation

Relation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total186 of 48861%34 of 6648%23493380116
removeChild(Tensor)1040%100%66191911
update(Component.OwnerChangeRequest)742122%15316%810131801
findRootTensor()22893%1583%140401
getPermuteRelationFor(Tensor)22692%2466%241501
childCount()21184%1150%120101
hasChildren()11090%2250%230101
lambda$childCount$0(WeakReference)1685%1150%120101
addChild(Tensor)74100%2100%0201201
getChildren()48100%2675%250901
addPermuteRelationFor(Tensor, int[])27100%6100%040501
toString()24100%n/a010101
hasParent()7100%2100%020101
Relation(Tensor)6100%n/a010101
newParentToChildren()5100%n/a010101
newChildToParent(Tensor)5100%n/a010101
getParent()4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Relation

Relation

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total188 of 48861%35 of 6646%24493380116
removeChild(Tensor)1040%100%66191911
update(Component.OwnerChangeRequest)742122%15316%810131801
findRootTensor()42686%2466%240401
getPermuteRelationFor(Tensor)22692%2466%241501
childCount()21184%1150%120101
hasChildren()11090%2250%230101
lambda$childCount$0(WeakReference)1685%1150%120101
addChild(Tensor)74100%2100%0201201
getChildren()48100%2675%250901
addPermuteRelationFor(Tensor, int[])27100%6100%040501
toString()24100%n/a010101
hasParent()7100%2100%020101
Relation(Tensor)6100%n/a010101
newParentToChildren()5100%n/a010101
newChildToParent(Tensor)5100%n/a010101
getParent()4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing/Relation.java.html b/docs/coverage/test/html/neureka.framing/Relation.java.html index 9f031130e..310d78644 100644 --- a/docs/coverage/test/html/neureka.framing/Relation.java.html +++ b/docs/coverage/test/html/neureka.framing/Relation.java.html @@ -228,7 +228,7 @@ */ public Optional<Tensor<V>> findRootTensor() { - if ( _parent == null ) return Optional.empty(); + if ( _parent == null ) return Optional.empty(); else if ( !_parent.has( Relation.class ) ) return Optional.empty(); else if ( !_parent.get( Relation.class ).hasParent() ) return Optional.of(_parent); else return _parent.get( Relation.class ).findRootTensor(); @@ -285,4 +285,4 @@ return Optional.ofNullable( _parent ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing/index.html b/docs/coverage/test/html/neureka.framing/index.html index db5799520..c51b157f1 100644 --- a/docs/coverage/test/html/neureka.framing/index.html +++ b/docs/coverage/test/html/neureka.framing/index.html @@ -1 +1 @@ -neureka.framing

neureka.framing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total264 of 1,50882%49 of 15468%371274725635002
Relation18630261%343248%2349338011601
NDFrame7894292%157382%14781417623401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.framing

neureka.framing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total266 of 1,50882%50 of 15467%381274725635002
Relation18830061%353146%2449338011601
NDFrame7894292%157382%14781417623401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.framing/index.source.html b/docs/coverage/test/html/neureka.framing/index.source.html index 39089f2ed..432dd698a 100644 --- a/docs/coverage/test/html/neureka.framing/index.source.html +++ b/docs/coverage/test/html/neureka.framing/index.source.html @@ -1 +1 @@ -neureka.framing

neureka.framing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total264 of 1,50882%49 of 15468%371274725635002
Relation.java18630261%343248%2349338011601
NDFrame.java7894292%157382%14781417623401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.framing

neureka.framing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total266 of 1,50882%50 of 15467%381274725635002
Relation.java18830061%353146%2449338011601
NDFrame.java7894292%157382%14781417623401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$Axis.html b/docs/coverage/test/html/neureka.math.args/Arg$Axis.html index b4dd920dc..ea3485968 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$Axis.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$Axis.html @@ -1 +1 @@ -Arg.Axis

Arg.Axis

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 10100%0 of 0n/a020202
of(int)5100%n/a010101
Arg.Axis(int)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.Axis

Arg.Axis

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 10100%0 of 0n/a020202
of(int)5100%n/a010101
Arg.Axis(int)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$DerivIdx.html b/docs/coverage/test/html/neureka.math.args/Arg$DerivIdx.html index 1b85cb473..0ade2e20b 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$DerivIdx.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$DerivIdx.html @@ -1 +1 @@ -Arg.DerivIdx

Arg.DerivIdx

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 10100%0 of 0n/a020202
of(int)5100%n/a010101
Arg.DerivIdx(int)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.DerivIdx

Arg.DerivIdx

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 10100%0 of 0n/a020202
of(int)5100%n/a010101
Arg.DerivIdx(int)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$Derivative.html b/docs/coverage/test/html/neureka.math.args/Arg$Derivative.html index 2597803ee..71dc8328f 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$Derivative.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$Derivative.html @@ -1 +1 @@ -Arg.Derivative

Arg.Derivative

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(Tensor)5100%n/a010101
Arg.Derivative(Tensor)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.Derivative

Arg.Derivative

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(Tensor)5100%n/a010101
Arg.Derivative(Tensor)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$Ends.html b/docs/coverage/test/html/neureka.math.args/Arg$Ends.html index 048f2264b..d4494c87e 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$Ends.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$Ends.html @@ -1 +1 @@ -Arg.Ends

Arg.Ends

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 90%0 of 0n/a222222
of(int[])50%n/a111111
Arg.Ends(int[])40%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.Ends

Arg.Ends

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total9 of 90%0 of 0n/a222222
of(int[])50%n/a111111
Arg.Ends(int[])40%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$Indices.html b/docs/coverage/test/html/neureka.math.args/Arg$Indices.html index d7b548b33..f89747377 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$Indices.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$Indices.html @@ -1 +1 @@ -Arg.Indices

Arg.Indices

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(int[])5100%n/a010101
Arg.Indices(int[])4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.Indices

Arg.Indices

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(int[])5100%n/a010101
Arg.Indices(int[])4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$Layout.html b/docs/coverage/test/html/neureka.math.args/Arg$Layout.html index f1909031a..8ec1c3c6f 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$Layout.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$Layout.html @@ -1 +1 @@ -Arg.Layout

Arg.Layout

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(NDConfiguration.Layout)5100%n/a010101
Arg.Layout(NDConfiguration.Layout)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.Layout

Arg.Layout

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(NDConfiguration.Layout)5100%n/a010101
Arg.Layout(NDConfiguration.Layout)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$MinRank.html b/docs/coverage/test/html/neureka.math.args/Arg$MinRank.html index 8d47d9c13..650422044 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$MinRank.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$MinRank.html @@ -1 +1 @@ -Arg.MinRank

Arg.MinRank

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 100%0 of 0n/a222222
of(int)50%n/a111111
Arg.MinRank(int)50%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.MinRank

Arg.MinRank

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 100%0 of 0n/a222222
of(int)50%n/a111111
Arg.MinRank(int)50%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$Offset.html b/docs/coverage/test/html/neureka.math.args/Arg$Offset.html index 1c932c919..474654ad2 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$Offset.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$Offset.html @@ -1 +1 @@ -Arg.Offset

Arg.Offset

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(int[])5100%n/a010101
Arg.Offset(int[])4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.Offset

Arg.Offset

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(int[])5100%n/a010101
Arg.Offset(int[])4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$Seed.html b/docs/coverage/test/html/neureka.math.args/Arg$Seed.html index c00cf1c39..b998b30cb 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$Seed.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$Seed.html @@ -1 +1 @@ -Arg.Seed

Arg.Seed

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 39100%0 of 2100%050704
_longStringHash(String)23100%2100%020401
of(String)6100%n/a010101
of(long)5100%n/a010101
Arg.Seed(long)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.Seed

Arg.Seed

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 39100%0 of 2100%050704
_longStringHash(String)23100%2100%020401
of(String)6100%n/a010101
of(long)5100%n/a010101
Arg.Seed(long)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$Shape.html b/docs/coverage/test/html/neureka.math.args/Arg$Shape.html index ba1a9e14c..ef3bb3ca2 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$Shape.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$Shape.html @@ -1 +1 @@ -Arg.Shape

Arg.Shape

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(int[])5100%n/a010101
Arg.Shape(int[])4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.Shape

Arg.Shape

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(int[])5100%n/a010101
Arg.Shape(int[])4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$Stride.html b/docs/coverage/test/html/neureka.math.args/Arg$Stride.html index 32391f51f..a9a8152e6 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$Stride.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$Stride.html @@ -1 +1 @@ -Arg.Stride

Arg.Stride

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(int[])5100%n/a010101
Arg.Stride(int[])4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.Stride

Arg.Stride

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(int[])5100%n/a010101
Arg.Stride(int[])4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$TargetDevice.html b/docs/coverage/test/html/neureka.math.args/Arg$TargetDevice.html index 2ad96be2b..d22db7648 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$TargetDevice.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$TargetDevice.html @@ -1 +1 @@ -Arg.TargetDevice

Arg.TargetDevice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(Device)5100%n/a010101
Arg.TargetDevice(Device)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.TargetDevice

Arg.TargetDevice

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 9100%0 of 0n/a020202
of(Device)5100%n/a010101
Arg.TargetDevice(Device)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg$VarIdx.html b/docs/coverage/test/html/neureka.math.args/Arg$VarIdx.html index 2d285514e..2915020f6 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg$VarIdx.html +++ b/docs/coverage/test/html/neureka.math.args/Arg$VarIdx.html @@ -1 +1 @@ -Arg.VarIdx

Arg.VarIdx

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 10100%0 of 0n/a020202
of(int)5100%n/a010101
Arg.VarIdx(int)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg.VarIdx

Arg.VarIdx

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 10100%0 of 0n/a020202
of(int)5100%n/a010101
Arg.VarIdx(int)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg.html b/docs/coverage/test/html/neureka.math.args/Arg.html index 3246daa60..7be8f9a3f 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg.html +++ b/docs/coverage/test/html/neureka.math.args/Arg.html @@ -1 +1 @@ -Arg

Arg

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total35 of 9964%7 of 1656%71201204
get()354053%7956%790901
toString()16100%n/a010101
Arg(Object)6100%n/a010101
update(Component.OwnerChangeRequest)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Arg

Arg

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total35 of 9964%7 of 1656%71201204
get()354053%7956%790901
toString()16100%n/a010101
Arg(Object)6100%n/a010101
update(Component.OwnerChangeRequest)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Arg.java.html b/docs/coverage/test/html/neureka.math.args/Arg.java.html index f35bc615a..04a34b065 100644 --- a/docs/coverage/test/html/neureka.math.args/Arg.java.html +++ b/docs/coverage/test/html/neureka.math.args/Arg.java.html @@ -131,4 +131,4 @@ public String toString() { return this.getClass().getSimpleName() + "[" + _value + "]"; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Args.html b/docs/coverage/test/html/neureka.math.args/Args.html index 122bbbc2b..1ab56240c 100644 --- a/docs/coverage/test/html/neureka.math.args/Args.html +++ b/docs/coverage/test/html/neureka.math.args/Args.html @@ -1 +1 @@ -Args

Args

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 5976%3 of 862%41031126
valOfOr(Class, Object)120%20%222211
_removeOrReject(Component)20%n/a111111
of(Arg[])28100%1375%130401
valOf(Class)12100%2100%020201
Args()3100%n/a010101
_setOrReject(Component)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Args

Args

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 5976%3 of 862%41031126
valOfOr(Class, Object)120%20%222211
_removeOrReject(Component)20%n/a111111
of(Arg[])28100%1375%130401
valOf(Class)12100%2100%020201
Args()3100%n/a010101
_setOrReject(Component)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/Args.java.html b/docs/coverage/test/html/neureka.math.args/Args.java.html index 2e805e66c..b0b9a8f86 100644 --- a/docs/coverage/test/html/neureka.math.args/Args.java.html +++ b/docs/coverage/test/html/neureka.math.args/Args.java.html @@ -33,4 +33,4 @@ return newComponent; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/index.html b/docs/coverage/test/html/neureka.math.args/index.html index 9d112406b..8b7ebabd4 100644 --- a/docs/coverage/test/html/neureka.math.args/index.html +++ b/docs/coverage/test/html/neureka.math.args/index.html @@ -1 +1 @@ -neureka.math.args

neureka.math.args

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total68 of 30977%10 of 2661%1551754638215
Arg356464%7956%7120120401
Args144576%3562%4103112601
Arg.MinRank100%n/a22222211
Arg.Ends90%n/a22222211
Arg.Seed39100%2100%05070401
Arg.VarIdx10100%n/a02020201
Arg.DerivIdx10100%n/a02020201
Arg.Axis10100%n/a02020201
Arg.Indices9100%n/a02020201
Arg.Layout9100%n/a02020201
Arg.Offset9100%n/a02020201
Arg.Derivative9100%n/a02020201
Arg.Stride9100%n/a02020201
Arg.Shape9100%n/a02020201
Arg.TargetDevice9100%n/a02020201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.math.args

neureka.math.args

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total68 of 30977%10 of 2661%1551754638215
Arg356464%7956%7120120401
Args144576%3562%4103112601
Arg.MinRank100%n/a22222211
Arg.Ends90%n/a22222211
Arg.Seed39100%2100%05070401
Arg.VarIdx10100%n/a02020201
Arg.DerivIdx10100%n/a02020201
Arg.Axis10100%n/a02020201
Arg.Indices9100%n/a02020201
Arg.Layout9100%n/a02020201
Arg.Offset9100%n/a02020201
Arg.Derivative9100%n/a02020201
Arg.Stride9100%n/a02020201
Arg.Shape9100%n/a02020201
Arg.TargetDevice9100%n/a02020201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.args/index.source.html b/docs/coverage/test/html/neureka.math.args/index.source.html index 7e0d9cdc9..ae06334b3 100644 --- a/docs/coverage/test/html/neureka.math.args/index.source.html +++ b/docs/coverage/test/html/neureka.math.args/index.source.html @@ -1 +1 @@ -neureka.math.args

neureka.math.args

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total68 of 30977%10 of 2661%1551754638215
Arg.java5419678%71161%1141443432214
Args.java144576%3562%4103112601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.math.args

neureka.math.args

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total68 of 30977%10 of 2661%1551754638215
Arg.java5419678%71161%1141443432214
Args.java144576%3562%4103112601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.implementations/FunctionConstant.html b/docs/coverage/test/html/neureka.math.implementations/FunctionConstant.html index 3befbe556..801ee2bc7 100644 --- a/docs/coverage/test/html/neureka.math.implementations/FunctionConstant.html +++ b/docs/coverage/test/html/neureka.math.implementations/FunctionConstant.html @@ -1 +1 @@ -FunctionConstant

FunctionConstant

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total11 of 12491%3 of 1681%721437413
getSubFunctions()40%n/a111111
getDerivative(int)30%n/a111111
isFlat()20%n/a111111
getOperation()20%n/a111111
FunctionConstant(String)54100%21083%2701301
execute(Args, Tensor[])41100%1375%1301301
toString()4100%n/a010101
value()3100%n/a010101
call(double[], int)3100%n/a010101
isDoingAD()2100%n/a010101
dependsOn(int)2100%n/a010101
derive(double[], int)2100%n/a010101
derive(double[], int, int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FunctionConstant

FunctionConstant

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total11 of 12491%3 of 1681%721437413
getSubFunctions()40%n/a111111
getDerivative(int)30%n/a111111
isFlat()20%n/a111111
getOperation()20%n/a111111
FunctionConstant(String)54100%21083%2701301
execute(Args, Tensor[])41100%1375%1301301
toString()4100%n/a010101
value()3100%n/a010101
call(double[], int)3100%n/a010101
isDoingAD()2100%n/a010101
dependsOn(int)2100%n/a010101
derive(double[], int)2100%n/a010101
derive(double[], int, int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.implementations/FunctionConstant.java.html b/docs/coverage/test/html/neureka.math.implementations/FunctionConstant.java.html index 2098a1f31..bec539f1b 100644 --- a/docs/coverage/test/html/neureka.math.implementations/FunctionConstant.java.html +++ b/docs/coverage/test/html/neureka.math.implementations/FunctionConstant.java.html @@ -88,4 +88,4 @@ @Override public String toString() { return String.valueOf( _value ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.implementations/FunctionInput.html b/docs/coverage/test/html/neureka.math.implementations/FunctionInput.html index 089a10ffb..169d5dbe2 100644 --- a/docs/coverage/test/html/neureka.math.implementations/FunctionInput.html +++ b/docs/coverage/test/html/neureka.math.implementations/FunctionInput.html @@ -1 +1 @@ -FunctionInput

FunctionInput

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 30896%3 of 4493%438148116
call(double[], int)51676%1375%130301
getDerivative(int)2880%1150%120101
isFlat()20%n/a111111
execute(Args, Tensor[])18198%1787%1501001
of(String, boolean)75100%10100%0601201
_extract(Tensor)35100%6100%040901
toString()21100%2100%020101
derive(double[], int, int)13100%4100%030301
index()12100%2100%020101
dependsOn(int)8100%2100%020101
derive(double[], int)8100%2100%020101
providesGradient()7100%2100%020101
FunctionInput(int)6100%n/a010101
getSubFunctions()4100%n/a010101
isDoingAD()2100%n/a010101
getOperation()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FunctionInput

FunctionInput

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 30896%3 of 4493%438148116
call(double[], int)51676%1375%130301
getDerivative(int)2880%1150%120101
isFlat()20%n/a111111
execute(Args, Tensor[])18198%1787%1501001
of(String, boolean)75100%10100%0601201
_extract(Tensor)35100%6100%040901
toString()21100%2100%020101
derive(double[], int, int)13100%4100%030301
index()12100%2100%020101
dependsOn(int)8100%2100%020101
derive(double[], int)8100%2100%020101
providesGradient()7100%2100%020101
FunctionInput(int)6100%n/a010101
getSubFunctions()4100%n/a010101
isDoingAD()2100%n/a010101
getOperation()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.implementations/FunctionInput.java.html b/docs/coverage/test/html/neureka.math.implementations/FunctionInput.java.html index 831f02cfd..586d5646e 100644 --- a/docs/coverage/test/html/neureka.math.implementations/FunctionInput.java.html +++ b/docs/coverage/test/html/neureka.math.implementations/FunctionInput.java.html @@ -131,4 +131,4 @@ public String toString() { return "I" + ( this.providesGradient() ? "g" : "" ) + "[" + index() + "]"; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.implementations/FunctionNode.html b/docs/coverage/test/html/neureka.math.implementations/FunctionNode.html index 173955eaa..1fc171981 100644 --- a/docs/coverage/test/html/neureka.math.implementations/FunctionNode.html +++ b/docs/coverage/test/html/neureka.math.implementations/FunctionNode.html @@ -1 +1 @@ -FunctionNode

FunctionNode

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 38293%3 of 6295%347258016
FunctionNode(Operation, List, boolean)2311783%22291%21322101
lambda$toString$0(Function)2571%1150%120101
_shareGuestDevice(Tensor[])81100%20100%0110901
_deviceFor(Tensor[])38100%10100%060501
execute(Args, Tensor[])28100%2100%020801
dependsOn(int)25100%4100%030301
toString()12100%n/a010401
call(double[], int)9100%n/a010101
derive(double[], int, int)9100%n/a010101
derive(double[], int)9100%n/a010101
getDerivative(int)8100%n/a010101
getSubFunctions()4100%n/a010101
getOperation()3100%n/a010101
isFlat()3100%n/a010101
isDoingAD()3100%n/a010101
lambda$toString$1(int)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FunctionNode

FunctionNode

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total34 of 38291%10 of 6283%947258016
FunctionNode(Operation, List, boolean)2311783%22291%21322101
_shareGuestDevice(Tensor[])97288%61470%5110901
lambda$toString$0(Function)2571%1150%120101
_deviceFor(Tensor[])38100%1990%160501
execute(Args, Tensor[])28100%2100%020801
dependsOn(int)25100%4100%030301
toString()12100%n/a010401
call(double[], int)9100%n/a010101
derive(double[], int, int)9100%n/a010101
derive(double[], int)9100%n/a010101
getDerivative(int)8100%n/a010101
getSubFunctions()4100%n/a010101
getOperation()3100%n/a010101
isFlat()3100%n/a010101
isDoingAD()3100%n/a010101
lambda$toString$1(int)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.implementations/FunctionNode.java.html b/docs/coverage/test/html/neureka.math.implementations/FunctionNode.java.html index 474ed5343..40f35fba3 100644 --- a/docs/coverage/test/html/neureka.math.implementations/FunctionNode.java.html +++ b/docs/coverage/test/html/neureka.math.implementations/FunctionNode.java.html @@ -112,7 +112,7 @@ Device<?> device = inputs[ 0 ].get( Device.class ); boolean onSameDevice = _shareGuestDevice( inputs ); boolean doAccel = !_operation.getOperator().equals(",") && onSameDevice; - return ( doAccel && device != null ? device : inputs[ 0 ].getDevice() ); + return ( doAccel && device != null ? device : inputs[ 0 ].getDevice() ); } /** @@ -127,12 +127,12 @@ if ( device != null ) { for ( Tensor<?> tensor : tensors ) { - onSameGuestDevice = ( !tensor.isVirtual() && device == tensor.get(Device.class) ) && onSameGuestDevice; + onSameGuestDevice = ( !tensor.isVirtual() && device == tensor.get(Device.class) ) && onSameGuestDevice; } } else onSameGuestDevice = false; - if ( device != null && tensors.length == 2 && tensors[ 1 ].size() == 1 ) onSameGuestDevice = true; + if ( device != null && tensors.length == 2 && tensors[ 1 ].size() == 1 ) onSameGuestDevice = true; return onSameGuestDevice; } @@ -161,4 +161,4 @@ public boolean isDoingAD() { return _isDoingAD; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.implementations/FunctionVariable.html b/docs/coverage/test/html/neureka.math.implementations/FunctionVariable.html index c07143226..7c51e9c4c 100644 --- a/docs/coverage/test/html/neureka.math.implementations/FunctionVariable.html +++ b/docs/coverage/test/html/neureka.math.implementations/FunctionVariable.html @@ -1 +1 @@ -FunctionVariable

FunctionVariable

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total42 of 17475%6 of 2272%924831413
call(double[], int)19624%3125%233501
execute(Args, Tensor[])128387%31178%3811401
getSubFunctions()40%n/a111111
getDerivative(int)30%n/a111111
isFlat()20%n/a111111
getOperation()20%n/a111111
toString()16100%2100%020101
derive(double[], int, int)10100%2100%020201
FunctionVariable(String)8100%n/a010101
providesGradient()3100%n/a010101
isDoingAD()2100%n/a010101
dependsOn(int)2100%n/a010101
derive(double[], int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FunctionVariable

FunctionVariable

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total42 of 17475%6 of 2272%924831413
call(double[], int)19624%3125%233501
execute(Args, Tensor[])128387%31178%3811401
getSubFunctions()40%n/a111111
getDerivative(int)30%n/a111111
isFlat()20%n/a111111
getOperation()20%n/a111111
toString()16100%2100%020101
derive(double[], int, int)10100%2100%020201
FunctionVariable(String)8100%n/a010101
providesGradient()3100%n/a010101
isDoingAD()2100%n/a010101
dependsOn(int)2100%n/a010101
derive(double[], int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.implementations/FunctionVariable.java.html b/docs/coverage/test/html/neureka.math.implementations/FunctionVariable.java.html index e88629272..0bf472ce0 100644 --- a/docs/coverage/test/html/neureka.math.implementations/FunctionVariable.java.html +++ b/docs/coverage/test/html/neureka.math.implementations/FunctionVariable.java.html @@ -92,4 +92,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.implementations/index.html b/docs/coverage/test/html/neureka.math.implementations/index.html index cb01a453e..b661437e3 100644 --- a/docs/coverage/test/html/neureka.math.implementations/index.html +++ b/docs/coverage/test/html/neureka.math.implementations/index.html @@ -1 +1 @@ -neureka.math.implementations

neureka.math.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total88 of 98891%15 of 14489%231301517495804
FunctionVariable4213275%61672%92483141301
FunctionNode2535793%35995%34725801601
FunctionConstant1111391%31381%72143741301
FunctionInput1029896%34193%43814811601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.math.implementations

neureka.math.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total97 of 98890%22 of 14484%291301517495804
FunctionVariable4213275%61672%92483141301
FunctionNode3434891%105283%94725801601
FunctionConstant1111391%31381%72143741301
FunctionInput1029896%34193%43814811601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.implementations/index.source.html b/docs/coverage/test/html/neureka.math.implementations/index.source.html index 0aa08e169..4381a062b 100644 --- a/docs/coverage/test/html/neureka.math.implementations/index.source.html +++ b/docs/coverage/test/html/neureka.math.implementations/index.source.html @@ -1 +1 @@ -neureka.math.implementations

neureka.math.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total88 of 98891%15 of 14489%231301517495804
FunctionVariable.java4213275%61672%92483141301
FunctionNode.java2535793%35995%34725801601
FunctionConstant.java1111391%31381%72143741301
FunctionInput.java1029896%34193%43814811601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.math.implementations

neureka.math.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total97 of 98890%22 of 14484%291301517495804
FunctionVariable.java4213275%61672%92483141301
FunctionNode.java3434891%105283%94725801601
FunctionConstant.java1111391%31381%72143741301
FunctionInput.java1029896%34193%43814811601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.parsing/FunctionParser.html b/docs/coverage/test/html/neureka.math.parsing/FunctionParser.html index 285086edb..233d28cdc 100644 --- a/docs/coverage/test/html/neureka.math.parsing/FunctionParser.html +++ b/docs/coverage/test/html/neureka.math.parsing/FunctionParser.html @@ -1 +1 @@ -FunctionParser

FunctionParser

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total60 of 92193%16 of 11085%16665172011
_buildFunction(String, boolean)2215587%22090%21222701
parse(String, boolean)165978%2880%2611201
parse(Operation, int, boolean)133371%1150%121701
_buildOperators(List, List, boolean)722696%62076%61403801
_parse(String, boolean)28699%44090%42317101
_groupAccordingToArity(int, List, String)58100%1583%1401001
static {...}19100%n/a010601
lambda$parse$1(int)11100%n/a010101
FunctionParser(BackendContext)6100%n/a010101
lambda$_groupAccordingToArity$2(int)4100%n/a010101
lambda$parse$0(int)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FunctionParser

FunctionParser

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total60 of 92193%16 of 11085%16665172011
_buildFunction(String, boolean)2215587%22090%21222701
parse(String, boolean)165978%2880%2611201
parse(Operation, int, boolean)133371%1150%121701
_buildOperators(List, List, boolean)722696%62076%61403801
_parse(String, boolean)28699%44090%42317101
_groupAccordingToArity(int, List, String)58100%1583%1401001
static {...}19100%n/a010601
lambda$parse$1(int)11100%n/a010101
FunctionParser(BackendContext)6100%n/a010101
lambda$_groupAccordingToArity$2(int)4100%n/a010101
lambda$parse$0(int)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.parsing/FunctionParser.java.html b/docs/coverage/test/html/neureka.math.parsing/FunctionParser.java.html index e3c8b8428..f03786c47 100644 --- a/docs/coverage/test/html/neureka.math.parsing/FunctionParser.java.html +++ b/docs/coverage/test/html/neureka.math.parsing/FunctionParser.java.html @@ -330,4 +330,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.parsing/ParseUtil.html b/docs/coverage/test/html/neureka.math.parsing/ParseUtil.html index bf114bacf..2df58301d 100644 --- a/docs/coverage/test/html/neureka.math.parsing/ParseUtil.html +++ b/docs/coverage/test/html/neureka.math.parsing/ParseUtil.html @@ -1 +1 @@ -ParseUtil

ParseUtil

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total95 of 1,08191%47 of 21678%431219187013
cleanedHeadAndTail(String)3815880%172761%152333301
unpackAndCorrect(String)2620988%53186%41954701
groupBy(String, String, String, String)212150%5337%451701
similarity(String, String)617896%21890%21103101
findComponentIn(String, int)211298%11794%11002101
findParametersIn(String, int)210698%12596%11402001
isForbiddenChar(char)52100%151753%15170101
assumptionBasedOn(String)48100%8100%050901
parsedOperation(String, int)32100%1787%150601
_isOperationComponent(String, int, int)29100%8100%050301
numberOfOperationsWithin(List)23100%4100%030501
isAnOperation(String)17100%4100%030301
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ParseUtil

ParseUtil

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total95 of 1,08191%47 of 21678%431219187013
cleanedHeadAndTail(String)3815880%172761%152333301
unpackAndCorrect(String)2620988%53186%41954701
groupBy(String, String, String, String)212150%5337%451701
similarity(String, String)617896%21890%21103101
findComponentIn(String, int)211298%11794%11002101
findParametersIn(String, int)210698%12596%11402001
isForbiddenChar(char)52100%151753%15170101
assumptionBasedOn(String)48100%8100%050901
parsedOperation(String, int)32100%1787%150601
_isOperationComponent(String, int, int)29100%8100%050301
numberOfOperationsWithin(List)23100%4100%030501
isAnOperation(String)17100%4100%030301
static {...}100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.parsing/ParseUtil.java.html b/docs/coverage/test/html/neureka.math.parsing/ParseUtil.java.html index 406e19cb9..c3a896938 100644 --- a/docs/coverage/test/html/neureka.math.parsing/ParseUtil.java.html +++ b/docs/coverage/test/html/neureka.math.parsing/ParseUtil.java.html @@ -306,4 +306,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.parsing/index.html b/docs/coverage/test/html/neureka.math.parsing/index.html index 2afbf2f5e..9432226de 100644 --- a/docs/coverage/test/html/neureka.math.parsing/index.html +++ b/docs/coverage/test/html/neureka.math.parsing/index.html @@ -1 +1 @@ -neureka.math.parsing

neureka.math.parsing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total155 of 2,00292%63 of 32680%591871435902402
ParseUtil9598691%4716978%43121918701301
FunctionParser6086193%169485%1666517201101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.math.parsing

neureka.math.parsing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total155 of 2,00292%63 of 32680%591871435902402
ParseUtil9598691%4716978%43121918701301
FunctionParser6086193%169485%1666517201101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math.parsing/index.source.html b/docs/coverage/test/html/neureka.math.parsing/index.source.html index 768ee369b..9067d0488 100644 --- a/docs/coverage/test/html/neureka.math.parsing/index.source.html +++ b/docs/coverage/test/html/neureka.math.parsing/index.source.html @@ -1 +1 @@ -neureka.math.parsing

neureka.math.parsing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total155 of 2,00292%63 of 32680%591871435902402
ParseUtil.java9598691%4716978%43121918701301
FunctionParser.java6086193%169485%1666517201101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.math.parsing

neureka.math.parsing

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total155 of 2,00292%63 of 32680%591871435902402
ParseUtil.java9598691%4716978%43121918701301
FunctionParser.java6086193%169485%1666517201101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math/Function$Callable.html b/docs/coverage/test/html/neureka.math/Function$Callable.html index d4b7253c7..870d994e1 100644 --- a/docs/coverage/test/html/neureka.math/Function$Callable.html +++ b/docs/coverage/test/html/neureka.math/Function$Callable.html @@ -1 +1 @@ -Function.Callable

Function.Callable

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 11100%0 of 0n/a020202
call(Tensor[])7100%n/a010101
invoke(Tensor[])4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Function.Callable

Function.Callable

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 11100%0 of 0n/a020202
call(Tensor[])7100%n/a010101
invoke(Tensor[])4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math/Function.html b/docs/coverage/test/html/neureka.math/Function.html index 1bfd581a4..0d88fb6ab 100644 --- a/docs/coverage/test/html/neureka.math/Function.html +++ b/docs/coverage/test/html/neureka.math/Function.html @@ -1 +1 @@ -Function

Function

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total100 of 39374%3 of 1478%144412581137
lambda$with$3(Args, Tensor[])266170%2675%250901
derive(Tensor[], int, int)110%n/a112211
derive(List, int, int)100%n/a111111
call(double)90%n/a111111
call(Args, Tensor[])90%n/a111111
call(Tensor[], int)80%n/a111111
invoke(double[], int)50%n/a111111
invoke(Args, Tensor[])50%n/a111111
invoke(Tensor[], int)50%n/a111111
invoke(double)40%n/a111111
invoke(double[])40%n/a111111
invoke(List)40%n/a111111
execute(Call)39100%1375%130501
getAllFunctions()27100%2100%020501
execute(Tensor[], int)18100%n/a010101
executeDerive(Tensor[], int, int)18100%n/a010101
numberOfArgs()13100%n/a010701
derive(Tensor[], int)10100%n/a010201
of(String, boolean)9100%n/a010101
call(Tensor)9100%n/a010101
derive(List, int)9100%n/a010101
call(List)8100%n/a010101
call(Call)7100%n/a010101
call(Tensor[])7100%n/a010101
executeDerive(Tensor[], int)6100%n/a010101
lambda$with$2(Args, Tensor[])6100%n/a010101
call(double[])5100%n/a010101
call(Call.Builder)5100%n/a010101
with(Arg[])5100%n/a010101
execute(Tensor[])5100%n/a010101
of(String)4100%n/a010101
invoke(Call.Builder)4100%n/a010101
with(Args)4100%n/a010101
invoke(Tensor)4100%n/a010101
invoke(Tensor[])4100%n/a010101
lambda$numberOfArgs$1(Function)3100%n/a010101
lambda$numberOfArgs$0(Function)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Function

Function

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total100 of 39374%3 of 1478%144412581137
lambda$with$3(Args, Tensor[])266170%2675%250901
derive(Tensor[], int, int)110%n/a112211
derive(List, int, int)100%n/a111111
call(double)90%n/a111111
call(Args, Tensor[])90%n/a111111
call(Tensor[], int)80%n/a111111
invoke(double[], int)50%n/a111111
invoke(Args, Tensor[])50%n/a111111
invoke(Tensor[], int)50%n/a111111
invoke(double)40%n/a111111
invoke(double[])40%n/a111111
invoke(List)40%n/a111111
execute(Call)39100%1375%130501
getAllFunctions()27100%2100%020501
execute(Tensor[], int)18100%n/a010101
executeDerive(Tensor[], int, int)18100%n/a010101
numberOfArgs()13100%n/a010701
derive(Tensor[], int)10100%n/a010201
of(String, boolean)9100%n/a010101
call(Tensor)9100%n/a010101
derive(List, int)9100%n/a010101
call(List)8100%n/a010101
call(Call)7100%n/a010101
call(Tensor[])7100%n/a010101
executeDerive(Tensor[], int)6100%n/a010101
lambda$with$2(Args, Tensor[])6100%n/a010101
call(double[])5100%n/a010101
call(Call.Builder)5100%n/a010101
with(Arg[])5100%n/a010101
execute(Tensor[])5100%n/a010101
of(String)4100%n/a010101
invoke(Call.Builder)4100%n/a010101
with(Args)4100%n/a010101
invoke(Tensor)4100%n/a010101
invoke(Tensor[])4100%n/a010101
lambda$numberOfArgs$1(Function)3100%n/a010101
lambda$numberOfArgs$0(Function)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math/Function.java.html b/docs/coverage/test/html/neureka.math/Function.java.html index 89f106054..8633cbb5c 100644 --- a/docs/coverage/test/html/neureka.math/Function.java.html +++ b/docs/coverage/test/html/neureka.math/Function.java.html @@ -578,4 +578,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math/FunctionCache$1.html b/docs/coverage/test/html/neureka.math/FunctionCache$1.html index ad81dd893..48a51f45a 100644 --- a/docs/coverage/test/html/neureka.math/FunctionCache$1.html +++ b/docs/coverage/test/html/neureka.math/FunctionCache$1.html @@ -1 +1 @@ -FunctionCache.new LinkedHashMap() {...}

FunctionCache.new LinkedHashMap() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 1485%1 of 250%130202
removeEldestEntry(Map.Entry)2675%1150%120101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FunctionCache.new LinkedHashMap() {...}

FunctionCache.new LinkedHashMap() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 1485%1 of 250%130202
removeEldestEntry(Map.Entry)2675%1150%120101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math/FunctionCache.html b/docs/coverage/test/html/neureka.math/FunctionCache.html index dc889c1c2..d094f6297 100644 --- a/docs/coverage/test/html/neureka.math/FunctionCache.html +++ b/docs/coverage/test/html/neureka.math/FunctionCache.html @@ -1 +1 @@ -FunctionCache

FunctionCache

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 11185%1 of 887%1921505
put(Function)162863%1375%132701
get(String, boolean)19100%2100%020201
has(String, boolean)18100%2100%020201
toString()17100%n/a010101
FunctionCache()13100%n/a010301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +FunctionCache

FunctionCache

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total16 of 11185%1 of 887%1921505
put(Function)162863%1375%132701
get(String, boolean)19100%2100%020201
has(String, boolean)18100%2100%020201
toString()17100%n/a010101
FunctionCache()13100%n/a010301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math/FunctionCache.java.html b/docs/coverage/test/html/neureka.math/FunctionCache.java.html index 3224b3d26..886ef70d3 100644 --- a/docs/coverage/test/html/neureka.math/FunctionCache.java.html +++ b/docs/coverage/test/html/neureka.math/FunctionCache.java.html @@ -76,4 +76,4 @@ public String toString() { return this.getClass().getSimpleName()+"[size="+_functionCache.size()+"]"; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math/Functions.html b/docs/coverage/test/html/neureka.math/Functions.html index 8e04d6fd5..d4c5926a4 100644 --- a/docs/coverage/test/html/neureka.math/Functions.html +++ b/docs/coverage/test/html/neureka.math/Functions.html @@ -1 +1 @@ -Functions

Functions

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total113 of 59781%0 of 0n/a341013615734101
lambda$toString$0(Field)111760%n/a012301
getReshape()30%n/a111111
getRelayout()30%n/a111111
getPermute()30%n/a111111
getDimTrim()30%n/a111111
getIdy()30%n/a111111
getConv()30%n/a111111
getPlus()30%n/a111111
getPlusAssign()30%n/a111111
getMinus()30%n/a111111
getMinusAssign()30%n/a111111
getDiv()30%n/a111111
getDivAssign()30%n/a111111
getPow()30%n/a111111
getPowAssign()30%n/a111111
powAssign()30%n/a111111
getMul()30%n/a111111
getMulAssign()30%n/a111111
getAdd()30%n/a111111
getAddAssign()30%n/a111111
addAssign()30%n/a111111
getMod()30%n/a111111
getModAssign()30%n/a111111
modAssign()30%n/a111111
getNeg()30%n/a111111
getMatMul()30%n/a111111
getDot()30%n/a111111
getTranspose2D()30%n/a111111
getRandom()30%n/a111111
getSum()30%n/a111111
getExp()30%n/a111111
getLog10()30%n/a111111
getSqrt()30%n/a111111
getCbrt()30%n/a111111
getConcat()30%n/a111111
Functions(boolean)248100%n/a0105101
toString()27100%n/a010501
reshape()3100%n/a010101
relayout()3100%n/a010101
permute()3100%n/a010101
dimTrim()3100%n/a010101
idy()3100%n/a010101
conv()3100%n/a010101
plus()3100%n/a010101
plusAssign()3100%n/a010101
minus()3100%n/a010101
minusAssign()3100%n/a010101
div()3100%n/a010101
divAssign()3100%n/a010101
pow()3100%n/a010101
mul()3100%n/a010101
mulAssign()3100%n/a010101
add()3100%n/a010101
mod()3100%n/a010101
neg()3100%n/a010101
matMul()3100%n/a010101
dot()3100%n/a010101
transpose2D()3100%n/a010101
random()3100%n/a010101
getTanh()3100%n/a010101
tanh()3100%n/a010101
getFastTanh()3100%n/a010101
fastTanh()3100%n/a010101
getSoftsign()3100%n/a010101
softsign()3100%n/a010101
getSigmoid()3100%n/a010101
sigmoid()3100%n/a010101
getGaus()3100%n/a010101
gaus()3100%n/a010101
getFastGaus()3100%n/a010101
fastGaus()3100%n/a010101
getLn()3100%n/a010101
ln()3100%n/a010101
getQuad()3100%n/a010101
quad()3100%n/a010101
getRelu()3100%n/a010101
relu()3100%n/a010101
getAbs()3100%n/a010101
abs()3100%n/a010101
getSin()3100%n/a010101
sin()3100%n/a010101
getCos()3100%n/a010101
cos()3100%n/a010101
getSoftplus()3100%n/a010101
softplus()3100%n/a010101
getSilu()3100%n/a010101
silu()3100%n/a010101
getGelu()3100%n/a010101
gelu()3100%n/a010101
getSelu()3100%n/a010101
selu()3100%n/a010101
getMin()3100%n/a010101
min()3100%n/a010101
getMax()3100%n/a010101
max()3100%n/a010101
sum()3100%n/a010101
exp()3100%n/a010101
log10()3100%n/a010101
sqrt()3100%n/a010101
cbrt()3100%n/a010101
concat()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Functions

Functions

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total113 of 59781%0 of 0n/a341013615734101
lambda$toString$0(Field)111760%n/a012301
getReshape()30%n/a111111
getRelayout()30%n/a111111
getPermute()30%n/a111111
getDimTrim()30%n/a111111
getIdy()30%n/a111111
getConv()30%n/a111111
getPlus()30%n/a111111
getPlusAssign()30%n/a111111
getMinus()30%n/a111111
getMinusAssign()30%n/a111111
getDiv()30%n/a111111
getDivAssign()30%n/a111111
getPow()30%n/a111111
getPowAssign()30%n/a111111
powAssign()30%n/a111111
getMul()30%n/a111111
getMulAssign()30%n/a111111
getAdd()30%n/a111111
getAddAssign()30%n/a111111
addAssign()30%n/a111111
getMod()30%n/a111111
getModAssign()30%n/a111111
modAssign()30%n/a111111
getNeg()30%n/a111111
getMatMul()30%n/a111111
getDot()30%n/a111111
getTranspose2D()30%n/a111111
getRandom()30%n/a111111
getSum()30%n/a111111
getExp()30%n/a111111
getLog10()30%n/a111111
getSqrt()30%n/a111111
getCbrt()30%n/a111111
getConcat()30%n/a111111
Functions(boolean)248100%n/a0105101
toString()27100%n/a010501
reshape()3100%n/a010101
relayout()3100%n/a010101
permute()3100%n/a010101
dimTrim()3100%n/a010101
idy()3100%n/a010101
conv()3100%n/a010101
plus()3100%n/a010101
plusAssign()3100%n/a010101
minus()3100%n/a010101
minusAssign()3100%n/a010101
div()3100%n/a010101
divAssign()3100%n/a010101
pow()3100%n/a010101
mul()3100%n/a010101
mulAssign()3100%n/a010101
add()3100%n/a010101
mod()3100%n/a010101
neg()3100%n/a010101
matMul()3100%n/a010101
dot()3100%n/a010101
transpose2D()3100%n/a010101
random()3100%n/a010101
getTanh()3100%n/a010101
tanh()3100%n/a010101
getFastTanh()3100%n/a010101
fastTanh()3100%n/a010101
getSoftsign()3100%n/a010101
softsign()3100%n/a010101
getSigmoid()3100%n/a010101
sigmoid()3100%n/a010101
getGaus()3100%n/a010101
gaus()3100%n/a010101
getFastGaus()3100%n/a010101
fastGaus()3100%n/a010101
getLn()3100%n/a010101
ln()3100%n/a010101
getQuad()3100%n/a010101
quad()3100%n/a010101
getRelu()3100%n/a010101
relu()3100%n/a010101
getAbs()3100%n/a010101
abs()3100%n/a010101
getSin()3100%n/a010101
sin()3100%n/a010101
getCos()3100%n/a010101
cos()3100%n/a010101
getSoftplus()3100%n/a010101
softplus()3100%n/a010101
getSilu()3100%n/a010101
silu()3100%n/a010101
getGelu()3100%n/a010101
gelu()3100%n/a010101
getSelu()3100%n/a010101
selu()3100%n/a010101
getMin()3100%n/a010101
min()3100%n/a010101
getMax()3100%n/a010101
max()3100%n/a010101
sum()3100%n/a010101
exp()3100%n/a010101
log10()3100%n/a010101
sqrt()3100%n/a010101
cbrt()3100%n/a010101
concat()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math/Functions.java.html b/docs/coverage/test/html/neureka.math/Functions.java.html index e34537e19..6d5b692f3 100644 --- a/docs/coverage/test/html/neureka.math/Functions.java.html +++ b/docs/coverage/test/html/neureka.math/Functions.java.html @@ -484,4 +484,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math/index.html b/docs/coverage/test/html/neureka.math/index.html index 3e6ab3f28..a5e2ea1ad 100644 --- a/docs/coverage/test/html/neureka.math/index.html +++ b/docs/coverage/test/html/neureka.math/index.html @@ -1 +1 @@ -neureka.math

neureka.math

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total231 of 1,12679%5 of 2479%50159502334514705
Functions11348481%n/a34101361573410101
Function10029374%31178%14441258113701
FunctionCache169585%1787%192150501
FunctionCache.new LinkedHashMap() {...}1285%1150%13020201
Function.Callable11100%n/a02020201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.math

neureka.math

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total231 of 1,12679%5 of 2479%50159502334514705
Functions11348481%n/a34101361573410101
Function10029374%31178%14441258113701
FunctionCache169585%1787%192150501
FunctionCache.new LinkedHashMap() {...}1285%1150%13020201
Function.Callable11100%n/a02020201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.math/index.source.html b/docs/coverage/test/html/neureka.math/index.source.html index 20ea65437..5d92fd04a 100644 --- a/docs/coverage/test/html/neureka.math/index.source.html +++ b/docs/coverage/test/html/neureka.math/index.source.html @@ -1 +1 @@ -neureka.math

neureka.math

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total231 of 1,12679%5 of 2479%50159502334514705
Functions.java11348481%n/a34101361573410101
Function.java10030475%31178%14461260113902
FunctionCache.java1810785%2880%2122160702
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.math

neureka.math

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total231 of 1,12679%5 of 2479%50159502334514705
Functions.java11348481%n/a34101361573410101
Function.java10030475%31178%14461260113902
FunctionCache.java1810785%2880%2122160702
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted1DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted1DConfiguration.html index e7b176119..6d7567e9d 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted1DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted1DConfiguration.html @@ -1 +1 @@ -Permuted1DConfiguration

Permuted1DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 12582%2 of 450%620424418
Permuted1DConfiguration(int, int, int)81869%2250%230701
indexOfIndices(int)50%n/a111111
shape(int)30%n/a111111
indicesMap(int)30%n/a111111
strides(int)30%n/a111111
construct(int[], int[], int[])15100%n/a010101
indicesOfIndex(int)10100%n/a010101
shape()8100%n/a010101
indicesMap()8100%n/a010101
strides()8100%n/a010101
indexOfIndex(int)8100%n/a010101
spread()7100%n/a010101
offset()7100%n/a010101
indexOfIndices(int[])7100%n/a010101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Permuted1DConfiguration

Permuted1DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total22 of 12582%2 of 450%620424418
Permuted1DConfiguration(int, int, int)81869%2250%230701
indexOfIndices(int)50%n/a111111
shape(int)30%n/a111111
indicesMap(int)30%n/a111111
strides(int)30%n/a111111
construct(int[], int[], int[])15100%n/a010101
indicesOfIndex(int)10100%n/a010101
shape()8100%n/a010101
indicesMap()8100%n/a010101
strides()8100%n/a010101
indexOfIndex(int)8100%n/a010101
spread()7100%n/a010101
offset()7100%n/a010101
indexOfIndices(int[])7100%n/a010101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted1DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted1DConfiguration.java.html index e8f0f377e..db0d641f2 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted1DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted1DConfiguration.java.html @@ -83,4 +83,4 @@ @Override public final int indexOfIndices( int d1 ) { return d1 * _stride; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted2DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted2DConfiguration.html index 1d7c089ff..a86ea321c 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted2DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted2DConfiguration.html @@ -1 +1 @@ -Permuted2DConfiguration

Permuted2DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 22296%2 of 666%220134117
indicesMap(int)80%20%221111
Permuted2DConfiguration(int[], int[], int[])33100%n/a010801
indicesOfIndex(int)30100%n/a010501
indexOfIndices(int[])22100%n/a010401
indexOfIndex(int)19100%n/a010101
indexOfIndices(int, int)18100%n/a010401
shape()13100%n/a010101
indicesMap()13100%n/a010101
strides()13100%n/a010101
spread()11100%n/a010101
offset()11100%n/a010101
construct(int[], int[], int[])9100%n/a010101
shape(int)8100%2100%020101
strides(int)8100%2100%020101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Permuted2DConfiguration

Permuted2DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 22296%2 of 666%220134117
indicesMap(int)80%20%221111
Permuted2DConfiguration(int[], int[], int[])33100%n/a010801
indicesOfIndex(int)30100%n/a010501
indexOfIndices(int[])22100%n/a010401
indexOfIndex(int)19100%n/a010101
indexOfIndices(int, int)18100%n/a010401
shape()13100%n/a010101
indicesMap()13100%n/a010101
strides()13100%n/a010101
spread()11100%n/a010101
offset()11100%n/a010101
construct(int[], int[], int[])9100%n/a010101
shape(int)8100%2100%020101
strides(int)8100%2100%020101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted2DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted2DConfiguration.java.html index 3d6fbb1dd..cb5ae2fb7 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted2DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted2DConfiguration.java.html @@ -106,4 +106,4 @@ return i; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted3DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted3DConfiguration.html index ad482590d..9b0a9f937 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted3DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted3DConfiguration.html @@ -1 +1 @@ -Permuted3DConfiguration

Permuted3DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 30590%8 of 1233%623237217
indicesMap(int)140%40%331111
strides(int)140%40%331111
Permuted3DConfiguration(int[], int[], int[])48100%n/a0101101
indexOfIndex(int)40100%n/a010601
indicesOfIndex(int)40100%n/a010601
indexOfIndices(int[])21100%n/a010101
shape()18100%n/a010101
indicesMap()18100%n/a010101
strides()18100%n/a010101
spread()15100%n/a010101
offset()15100%n/a010101
indexOfIndices(int, int, int)15100%n/a010101
shape(int)14100%4100%030101
construct(int[], int[], int[])9100%n/a010101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Permuted3DConfiguration

Permuted3DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 30590%8 of 1233%623237217
indicesMap(int)140%40%331111
strides(int)140%40%331111
Permuted3DConfiguration(int[], int[], int[])48100%n/a0101101
indexOfIndex(int)40100%n/a010601
indicesOfIndex(int)40100%n/a010601
indexOfIndices(int[])21100%n/a010101
shape()18100%n/a010101
indicesMap()18100%n/a010101
strides()18100%n/a010101
spread()15100%n/a010101
offset()15100%n/a010101
indexOfIndices(int, int, int)15100%n/a010101
shape(int)14100%4100%030101
construct(int[], int[], int[])9100%n/a010101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted3DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted3DConfiguration.java.html index bbe33b52a..d32ba0b7d 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted3DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.permuted/Permuted3DConfiguration.java.html @@ -119,4 +119,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.permuted/PermutedNDConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.permuted/PermutedNDConfiguration.html index a1e054520..b8657c05b 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.permuted/PermutedNDConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.permuted/PermutedNDConfiguration.html @@ -1 +1 @@ -PermutedNDConfiguration

PermutedNDConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 14496%0 of 4100%118131116
indicesMap(int)50%n/a111111
indicesOfIndex(int)34100%2100%020501
indexOfIndices(int[])24100%2100%020401
PermutedNDConfiguration(int[], int[], int[])15100%n/a010501
construct(int[], int[], int[])9100%n/a010101
spread()9100%n/a010301
offset()9100%n/a010301
indexOfIndex(int)6100%n/a010101
shape()5100%n/a010101
shape(int)5100%n/a010101
indicesMap()5100%n/a010101
strides()5100%n/a010101
strides(int)5100%n/a010101
rank()4100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +PermutedNDConfiguration

PermutedNDConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 14496%0 of 4100%118131116
indicesMap(int)50%n/a111111
indicesOfIndex(int)34100%2100%020501
indexOfIndices(int[])24100%2100%020401
PermutedNDConfiguration(int[], int[], int[])15100%n/a010501
construct(int[], int[], int[])9100%n/a010101
spread()9100%n/a010301
offset()9100%n/a010301
indexOfIndex(int)6100%n/a010101
shape()5100%n/a010101
shape(int)5100%n/a010101
indicesMap()5100%n/a010101
strides()5100%n/a010101
strides(int)5100%n/a010101
rank()4100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.permuted/PermutedNDConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.permuted/PermutedNDConfiguration.java.html index 6c1f8faf3..89ebef467 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.permuted/PermutedNDConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.permuted/PermutedNDConfiguration.java.html @@ -106,4 +106,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.permuted/index.html b/docs/coverage/test/html/neureka.ndim.config.types.permuted/index.html index cfa1771fa..b7f04647c 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.permuted/index.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.permuted/index.html @@ -1 +1 @@ -neureka.ndim.config.types.permuted

neureka.ndim.config.types.permuted

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total63 of 79692%12 of 2653%1581812686804
Permuted3DConfiguration2827790%8433%62323721701
Permuted1DConfiguration2210382%2250%62042441801
Permuted2DConfiguration821496%2466%22013411701
PermutedNDConfiguration513996%4100%11813111601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types.permuted

neureka.ndim.config.types.permuted

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total63 of 79692%12 of 2653%1581812686804
Permuted3DConfiguration2827790%8433%62323721701
Permuted1DConfiguration2210382%2250%62042441801
Permuted2DConfiguration821496%2466%22013411701
PermutedNDConfiguration513996%4100%11813111601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.permuted/index.source.html b/docs/coverage/test/html/neureka.ndim.config.types.permuted/index.source.html index 80af5c93f..9aa545ae2 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.permuted/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.permuted/index.source.html @@ -1 +1 @@ -neureka.ndim.config.types.permuted

neureka.ndim.config.types.permuted

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total63 of 79692%12 of 2653%1581812686804
Permuted3DConfiguration.java2827790%8433%62323721701
Permuted1DConfiguration.java2210382%2250%62042441801
Permuted2DConfiguration.java821496%2466%22013411701
PermutedNDConfiguration.java513996%4100%11813111601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types.permuted

neureka.ndim.config.types.permuted

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total63 of 79692%12 of 2653%1581812686804
Permuted3DConfiguration.java2827790%8433%62323721701
Permuted1DConfiguration.java2210382%2250%62042441801
Permuted2DConfiguration.java821496%2466%22013411701
PermutedNDConfiguration.java513996%4100%11813111601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple0DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple0DConfiguration.html index ab5386e8d..825afe0ee 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple0DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple0DConfiguration.html @@ -1 +1 @@ -Simple0DConfiguration

Simple0DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 66100%0 of 0n/a015015015
shape()7100%n/a010101
indicesMap()7100%n/a010101
strides()7100%n/a010101
spread()7100%n/a010101
offset()7100%n/a010101
indicesOfIndex(int)7100%n/a010101
construct()6100%n/a010101
indexOfIndices(int[])4100%n/a010101
rank()2100%n/a010101
shape(int)2100%n/a010101
indicesMap(int)2100%n/a010101
strides(int)2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
indexOfIndex(int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Simple0DConfiguration

Simple0DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 66100%0 of 0n/a015015015
shape()7100%n/a010101
indicesMap()7100%n/a010101
strides()7100%n/a010101
spread()7100%n/a010101
offset()7100%n/a010101
indicesOfIndex(int)7100%n/a010101
construct()6100%n/a010101
indexOfIndices(int[])4100%n/a010101
rank()2100%n/a010101
shape(int)2100%n/a010101
indicesMap(int)2100%n/a010101
strides(int)2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
indexOfIndex(int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple0DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple0DConfiguration.java.html index eac5d136c..1df95a7be 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple0DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple0DConfiguration.java.html @@ -54,4 +54,4 @@ @Override public int indexOfIndices(int[] indices) { return indices[ 0 ]; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple1DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple1DConfiguration.html index 2a9cd6d46..08e6fe440 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple1DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple1DConfiguration.html @@ -1 +1 @@ -Simple1DConfiguration

Simple1DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 98100%0 of 0n/a017020017
construct(int[], int[])12100%n/a010101
indicesOfIndex(int)10100%n/a010101
Simple1DConfiguration(int, int)9100%n/a010401
shape()8100%n/a010101
indicesMap()8100%n/a010101
strides()8100%n/a010101
spread()7100%n/a010101
offset()7100%n/a010101
indexOfIndices(int[])7100%n/a010101
indexOfIndices(int)5100%n/a010101
shape(int)3100%n/a010101
indicesMap(int)3100%n/a010101
strides(int)3100%n/a010101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
indexOfIndex(int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Simple1DConfiguration

Simple1DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 98100%0 of 0n/a017020017
construct(int[], int[])12100%n/a010101
indicesOfIndex(int)10100%n/a010101
Simple1DConfiguration(int, int)9100%n/a010401
shape()8100%n/a010101
indicesMap()8100%n/a010101
strides()8100%n/a010101
spread()7100%n/a010101
offset()7100%n/a010101
indexOfIndices(int[])7100%n/a010101
indexOfIndices(int)5100%n/a010101
shape(int)3100%n/a010101
indicesMap(int)3100%n/a010101
strides(int)3100%n/a010101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
indexOfIndex(int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple1DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple1DConfiguration.java.html index c5b8a792f..0c89eb003 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple1DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple1DConfiguration.java.html @@ -76,4 +76,4 @@ @Override public final int indexOfIndices( int d1 ) { return d1 * _stride_and_indicesMap; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple2DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple2DConfiguration.html index d93531712..b6e32c675 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple2DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple2DConfiguration.html @@ -1 +1 @@ -Simple2DConfiguration

Simple2DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 211100%0 of 6100%020032017
indicesOfIndex(int)30100%n/a010501
Simple2DConfiguration(int[], int[])23100%n/a010601
indexOfIndices(int[])22100%n/a010401
indexOfIndex(int)19100%n/a010101
indexOfIndices(int, int)18100%n/a010401
shape()13100%n/a010101
indicesMap()13100%n/a010101
strides()13100%n/a010101
spread()11100%n/a010101
offset()11100%n/a010101
construct(int[], int[])8100%n/a010101
shape(int)8100%2100%020101
indicesMap(int)8100%2100%020101
strides(int)8100%2100%020101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Simple2DConfiguration

Simple2DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 211100%0 of 6100%020032017
indicesOfIndex(int)30100%n/a010501
Simple2DConfiguration(int[], int[])23100%n/a010601
indexOfIndices(int[])22100%n/a010401
indexOfIndex(int)19100%n/a010101
indexOfIndices(int, int)18100%n/a010401
shape()13100%n/a010101
indicesMap()13100%n/a010101
strides()13100%n/a010101
spread()11100%n/a010101
offset()11100%n/a010101
construct(int[], int[])8100%n/a010101
shape(int)8100%2100%020101
indicesMap(int)8100%2100%020101
strides(int)8100%2100%020101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple2DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple2DConfiguration.java.html index d7d2dc6f8..e0bbe7f5a 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple2DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple2DConfiguration.java.html @@ -99,4 +99,4 @@ return i; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple3DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple3DConfiguration.html index 2d8e9a08b..8be3a0f7f 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple3DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple3DConfiguration.html @@ -1 +1 @@ -Simple3DConfiguration

Simple3DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 289100%0 of 12100%023034017
indexOfIndex(int)40100%n/a010601
indicesOfIndex(int)40100%n/a010601
Simple3DConfiguration(int[], int[])33100%n/a010801
indexOfIndices(int[])21100%n/a010101
shape()18100%n/a010101
indicesMap()18100%n/a010101
strides()18100%n/a010101
spread()15100%n/a010101
offset()15100%n/a010101
indexOfIndices(int, int, int)15100%n/a010101
shape(int)14100%4100%030101
indicesMap(int)14100%4100%030101
strides(int)14100%4100%030101
construct(int[], int[])8100%n/a010101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Simple3DConfiguration

Simple3DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 289100%0 of 12100%023034017
indexOfIndex(int)40100%n/a010601
indicesOfIndex(int)40100%n/a010601
Simple3DConfiguration(int[], int[])33100%n/a010801
indexOfIndices(int[])21100%n/a010101
shape()18100%n/a010101
indicesMap()18100%n/a010101
strides()18100%n/a010101
spread()15100%n/a010101
offset()15100%n/a010101
indexOfIndices(int, int, int)15100%n/a010101
shape(int)14100%4100%030101
indicesMap(int)14100%4100%030101
strides(int)14100%4100%030101
construct(int[], int[])8100%n/a010101
rank()2100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple3DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple3DConfiguration.java.html index 764e81f58..05c1be9c8 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple3DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/Simple3DConfiguration.java.html @@ -107,4 +107,4 @@ d3 * _stride3; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/SimpleNDConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/SimpleNDConfiguration.html index c6d674167..b787218a4 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/SimpleNDConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/SimpleNDConfiguration.html @@ -1 +1 @@ -SimpleNDConfiguration

SimpleNDConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 136100%0 of 4100%018027016
indicesOfIndex(int)34100%2100%020501
indexOfIndices(int[])24100%2100%020301
SimpleNDConfiguration(int[], int[])11100%n/a010401
spread()10100%n/a010301
construct(int[], int[])8100%n/a010101
indexOfIndex(int)6100%n/a010101
shape()5100%n/a010101
shape(int)5100%n/a010101
indicesMap()5100%n/a010101
indicesMap(int)5100%n/a010101
strides()5100%n/a010101
strides(int)5100%n/a010101
offset()5100%n/a010101
rank()4100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SimpleNDConfiguration

SimpleNDConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 136100%0 of 4100%018027016
indicesOfIndex(int)34100%2100%020501
indexOfIndices(int[])24100%2100%020301
SimpleNDConfiguration(int[], int[])11100%n/a010401
spread()10100%n/a010301
construct(int[], int[])8100%n/a010101
indexOfIndex(int)6100%n/a010101
shape()5100%n/a010101
shape(int)5100%n/a010101
indicesMap()5100%n/a010101
indicesMap(int)5100%n/a010101
strides()5100%n/a010101
strides(int)5100%n/a010101
offset()5100%n/a010101
rank()4100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/SimpleNDConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/SimpleNDConfiguration.java.html index 4fc437e1c..63323eb44 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/SimpleNDConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/SimpleNDConfiguration.java.html @@ -88,4 +88,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/index.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/index.html index 3400f8944..7b4701a88 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/index.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/index.html @@ -1 +1 @@ -neureka.ndim.config.types.simple

neureka.ndim.config.types.simple

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 800100%0 of 22100%093012808205
Simple3DConfiguration289100%12100%02303401701
Simple2DConfiguration211100%6100%02003201701
SimpleNDConfiguration136100%4100%01802701601
Simple1DConfiguration98100%n/a01702001701
Simple0DConfiguration66100%n/a01501501501
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types.simple

neureka.ndim.config.types.simple

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 800100%0 of 22100%093012808205
Simple3DConfiguration289100%12100%02303401701
Simple2DConfiguration211100%6100%02003201701
SimpleNDConfiguration136100%4100%01802701601
Simple1DConfiguration98100%n/a01702001701
Simple0DConfiguration66100%n/a01501501501
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.simple/index.source.html b/docs/coverage/test/html/neureka.ndim.config.types.simple/index.source.html index 1c681fad4..f28ab1a29 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.simple/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.simple/index.source.html @@ -1 +1 @@ -neureka.ndim.config.types.simple

neureka.ndim.config.types.simple

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 800100%0 of 22100%093012808205
Simple3DConfiguration.java289100%12100%02303401701
Simple2DConfiguration.java211100%6100%02003201701
SimpleNDConfiguration.java136100%4100%01802701601
Simple1DConfiguration.java98100%n/a01702001701
Simple0DConfiguration.java66100%n/a01501501501
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types.simple

neureka.ndim.config.types.simple

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 800100%0 of 22100%093012808205
Simple3DConfiguration.java289100%12100%02303401701
Simple2DConfiguration.java211100%6100%02003201701
SimpleNDConfiguration.java136100%4100%01802701601
Simple1DConfiguration.java98100%n/a01702001701
Simple0DConfiguration.java66100%n/a01501501501
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced0DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced0DConfiguration.html index f54d103ef..ac5223053 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced0DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced0DConfiguration.html @@ -1 +1 @@ -Sliced0DConfiguration

Sliced0DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 91100%0 of 0n/a016019016
construct(int[], int[])12100%n/a010101
Sliced0DConfiguration(int, int)9100%n/a010401
shape()8100%n/a010101
offset()8100%n/a010101
indicesMap()7100%n/a010101
strides()7100%n/a010101
spread()7100%n/a010101
indicesOfIndex(int)7100%n/a010101
indexOfIndices(int[])7100%n/a010101
indexOfIndex(int)5100%n/a010101
shape(int)3100%n/a010101
offset(int)3100%n/a010101
rank()2100%n/a010101
indicesMap(int)2100%n/a010101
strides(int)2100%n/a010101
spread(int)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sliced0DConfiguration

Sliced0DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 91100%0 of 0n/a016019016
construct(int[], int[])12100%n/a010101
Sliced0DConfiguration(int, int)9100%n/a010401
shape()8100%n/a010101
offset()8100%n/a010101
indicesMap()7100%n/a010101
strides()7100%n/a010101
spread()7100%n/a010101
indicesOfIndex(int)7100%n/a010101
indexOfIndices(int[])7100%n/a010101
indexOfIndex(int)5100%n/a010101
shape(int)3100%n/a010101
offset(int)3100%n/a010101
rank()2100%n/a010101
indicesMap(int)2100%n/a010101
strides(int)2100%n/a010101
spread(int)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced0DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced0DConfiguration.java.html index 15805d0a4..6ee8e2dec 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced0DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced0DConfiguration.java.html @@ -78,4 +78,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced1DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced1DConfiguration.html index d4fe721a5..1df2b4a17 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced1DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced1DConfiguration.html @@ -1 +1 @@ -Sliced1DConfiguration

Sliced1DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total12 of 16692%3 of 650%321027018
Sliced1DConfiguration(int, int, int, int, int)122769%3350%3401001
construct(int[], int[], int[], int[], int[])21100%n/a010101
indexOfIndex(int)14100%n/a010101
indexOfIndices(int[])13100%n/a010101
indexOfIndices(int)11100%n/a010101
indicesOfIndex(int)10100%n/a010101
shape()8100%n/a010101
indicesMap()8100%n/a010101
strides()8100%n/a010101
spread()8100%n/a010101
offset()8100%n/a010101
shape(int)3100%n/a010101
indicesMap(int)3100%n/a010101
strides(int)3100%n/a010101
spread(int)3100%n/a010101
offset(int)3100%n/a010101
rank()2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sliced1DConfiguration

Sliced1DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total12 of 16692%3 of 650%321027018
Sliced1DConfiguration(int, int, int, int, int)122769%3350%3401001
construct(int[], int[], int[], int[], int[])21100%n/a010101
indexOfIndex(int)14100%n/a010101
indexOfIndices(int[])13100%n/a010101
indexOfIndices(int)11100%n/a010101
indicesOfIndex(int)10100%n/a010101
shape()8100%n/a010101
indicesMap()8100%n/a010101
strides()8100%n/a010101
spread()8100%n/a010101
offset()8100%n/a010101
shape(int)3100%n/a010101
indicesMap(int)3100%n/a010101
strides(int)3100%n/a010101
spread(int)3100%n/a010101
offset(int)3100%n/a010101
rank()2100%n/a010101
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced1DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced1DConfiguration.java.html index 6f373613c..5cb998218 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced1DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced1DConfiguration.java.html @@ -99,4 +99,4 @@ @Override public final int indexOfIndices( int d1 ) { return ( d1 * _spread + _offset ) * _stride; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced2DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced2DConfiguration.html index f0f696d33..91c9c1f9c 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced2DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced2DConfiguration.html @@ -1 +1 @@ -Sliced2DConfiguration

Sliced2DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 29698%1 of 1090%122038017
indicesMap(int)3562%1150%120101
Sliced2DConfiguration(int[], int[], int[], int[], int[])53100%n/a0101201
indexOfIndices(int[])34100%n/a010401
indexOfIndex(int)31100%n/a010101
indicesOfIndex(int)30100%n/a010501
indexOfIndices(int, int)30100%n/a010401
shape()13100%n/a010101
indicesMap()13100%n/a010101
strides()13100%n/a010101
spread()13100%n/a010101
offset()13100%n/a010101
construct(int[], int[], int[], int[], int[])11100%n/a010101
shape(int)8100%2100%020101
strides(int)8100%2100%020101
spread(int)8100%2100%020101
offset(int)8100%2100%020101
rank()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sliced2DConfiguration

Sliced2DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 29698%1 of 1090%122038017
indicesMap(int)3562%1150%120101
Sliced2DConfiguration(int[], int[], int[], int[], int[])53100%n/a0101201
indexOfIndices(int[])34100%n/a010401
indexOfIndex(int)31100%n/a010101
indicesOfIndex(int)30100%n/a010501
indexOfIndices(int, int)30100%n/a010401
shape()13100%n/a010101
indicesMap()13100%n/a010101
strides()13100%n/a010101
spread()13100%n/a010101
offset()13100%n/a010101
construct(int[], int[], int[], int[], int[])11100%n/a010101
shape(int)8100%2100%020101
strides(int)8100%2100%020101
spread(int)8100%2100%020101
offset(int)8100%2100%020101
rank()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced2DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced2DConfiguration.java.html index 761fc1fa0..9923394fe 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced2DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced2DConfiguration.java.html @@ -124,4 +124,4 @@ return i; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced3DConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced3DConfiguration.html index b2a177698..e0ea3c0a3 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced3DConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced3DConfiguration.html @@ -1 +1 @@ -Sliced3DConfiguration

Sliced3DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 42193%8 of 2060%627243217
indicesMap(int)140%40%331111
strides(int)140%40%331111
Sliced3DConfiguration(int[], int[], int[], int[], int[])78100%n/a0101701
indexOfIndex(int)58100%n/a010601
indicesOfIndex(int)40100%n/a010601
indexOfIndices(int[])39100%n/a010101
indexOfIndices(int, int, int)33100%n/a010101
shape()18100%n/a010101
indicesMap()18100%n/a010101
strides()18100%n/a010101
spread()18100%n/a010101
offset()18100%n/a010101
shape(int)14100%4100%030101
spread(int)14100%4100%030101
offset(int)14100%4100%030101
construct(int[], int[], int[], int[], int[])11100%n/a010101
rank()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sliced3DConfiguration

Sliced3DConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 42193%8 of 2060%627243217
indicesMap(int)140%40%331111
strides(int)140%40%331111
Sliced3DConfiguration(int[], int[], int[], int[], int[])78100%n/a0101701
indexOfIndex(int)58100%n/a010601
indicesOfIndex(int)40100%n/a010601
indexOfIndices(int[])39100%n/a010101
indexOfIndices(int, int, int)33100%n/a010101
shape()18100%n/a010101
indicesMap()18100%n/a010101
strides()18100%n/a010101
spread()18100%n/a010101
offset()18100%n/a010101
shape(int)14100%4100%030101
spread(int)14100%4100%030101
offset(int)14100%4100%030101
construct(int[], int[], int[], int[], int[])11100%n/a010101
rank()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced3DConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced3DConfiguration.java.html index a27c5d712..013632c3d 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced3DConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/Sliced3DConfiguration.java.html @@ -147,4 +147,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/SlicedNDConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/SlicedNDConfiguration.html index e2759d853..12ab61353 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/SlicedNDConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/SlicedNDConfiguration.html @@ -1 +1 @@ -SlicedNDConfiguration

SlicedNDConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 16293%0 of 4100%218229216
indicesMap(int)50%n/a111111
strides(int)50%n/a111111
indicesOfIndex(int)34100%2100%020501
indexOfIndices(int[])34100%2100%020401
SlicedNDConfiguration(int[], int[], int[], int[], int[])23100%n/a010701
construct(int[], int[], int[], int[], int[])11100%n/a010101
indexOfIndex(int)6100%n/a010101
shape()5100%n/a010101
shape(int)5100%n/a010101
indicesMap()5100%n/a010101
strides()5100%n/a010101
spread()5100%n/a010101
spread(int)5100%n/a010101
offset()5100%n/a010101
offset(int)5100%n/a010101
rank()4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SlicedNDConfiguration

SlicedNDConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total10 of 16293%0 of 4100%218229216
indicesMap(int)50%n/a111111
strides(int)50%n/a111111
indicesOfIndex(int)34100%2100%020501
indexOfIndices(int[])34100%2100%020401
SlicedNDConfiguration(int[], int[], int[], int[], int[])23100%n/a010701
construct(int[], int[], int[], int[], int[])11100%n/a010101
indexOfIndex(int)6100%n/a010101
shape()5100%n/a010101
shape(int)5100%n/a010101
indicesMap()5100%n/a010101
strides()5100%n/a010101
spread()5100%n/a010101
spread(int)5100%n/a010101
offset()5100%n/a010101
offset(int)5100%n/a010101
rank()4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/SlicedNDConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/SlicedNDConfiguration.java.html index e496c6407..201a5ea5d 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/SlicedNDConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/SlicedNDConfiguration.java.html @@ -112,4 +112,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/index.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/index.html index d1f47bf7c..26d1f2381 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/index.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/index.html @@ -1 +1 @@ -neureka.ndim.config.types.sliced

neureka.ndim.config.types.sliced

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total53 of 1,13695%12 of 4070%12104415648405
Sliced3DConfiguration2839393%81260%62724321701
Sliced1DConfiguration1215492%3350%32102701801
SlicedNDConfiguration1015293%4100%21822921601
Sliced2DConfiguration29398%1990%12203801701
Sliced0DConfiguration91100%n/a01601901601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types.sliced

neureka.ndim.config.types.sliced

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total53 of 1,13695%12 of 4070%12104415648405
Sliced3DConfiguration2839393%81260%62724321701
Sliced1DConfiguration1215492%3350%32102701801
SlicedNDConfiguration1015293%4100%21822921601
Sliced2DConfiguration29398%1990%12203801701
Sliced0DConfiguration91100%n/a01601901601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.sliced/index.source.html b/docs/coverage/test/html/neureka.ndim.config.types.sliced/index.source.html index 4b98345d6..6d6fec583 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.sliced/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.sliced/index.source.html @@ -1 +1 @@ -neureka.ndim.config.types.sliced

neureka.ndim.config.types.sliced

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total53 of 1,13695%12 of 4070%12104415648405
Sliced3DConfiguration.java2839393%81260%62724321701
Sliced1DConfiguration.java1215492%3350%32102701801
SlicedNDConfiguration.java1015293%4100%21822921601
Sliced2DConfiguration.java29398%1990%12203801701
Sliced0DConfiguration.java91100%n/a01601901601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types.sliced

neureka.ndim.config.types.sliced

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total53 of 1,13695%12 of 4070%12104415648405
Sliced3DConfiguration.java2839393%81260%62724321701
Sliced1DConfiguration.java1215492%3350%32102701801
SlicedNDConfiguration.java1015293%4100%21822921601
Sliced2DConfiguration.java29398%1990%12203801701
Sliced0DConfiguration.java91100%n/a01601901601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/VirtualNDConfiguration.html b/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/VirtualNDConfiguration.html index 15d355ea1..a67effd0d 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/VirtualNDConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/VirtualNDConfiguration.html @@ -1 +1 @@ -VirtualNDConfiguration

VirtualNDConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 6093%0 of 0n/a217217217
indicesMap(int)20%n/a111111
strides(int)20%n/a111111
VirtualNDConfiguration(int[])7100%n/a010101
construct(int[])7100%n/a010101
shape(int)5100%n/a010101
rank()4100%n/a010101
indicesMap()4100%n/a010101
strides()4100%n/a010101
spread()4100%n/a010101
offset()4100%n/a010101
indicesOfIndex(int)4100%n/a010101
shape()3100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
indexOfIndex(int)2100%n/a010101
indexOfIndices(int[])2100%n/a010101
isVirtual()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +VirtualNDConfiguration

VirtualNDConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 6093%0 of 0n/a217217217
indicesMap(int)20%n/a111111
strides(int)20%n/a111111
VirtualNDConfiguration(int[])7100%n/a010101
construct(int[])7100%n/a010101
shape(int)5100%n/a010101
rank()4100%n/a010101
indicesMap()4100%n/a010101
strides()4100%n/a010101
spread()4100%n/a010101
offset()4100%n/a010101
indicesOfIndex(int)4100%n/a010101
shape()3100%n/a010101
spread(int)2100%n/a010101
offset(int)2100%n/a010101
indexOfIndex(int)2100%n/a010101
indexOfIndices(int[])2100%n/a010101
isVirtual()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/VirtualNDConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/VirtualNDConfiguration.java.html index 720da407b..81d34280e 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/VirtualNDConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/VirtualNDConfiguration.java.html @@ -68,4 +68,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/index.html b/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/index.html index d7b205341..8a8238273 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/index.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/index.html @@ -1 +1 @@ -neureka.ndim.config.types.views.virtual

neureka.ndim.config.types.views.virtual

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total4 of 6093%0 of 0n/a21721721701
VirtualNDConfiguration45693%n/a21721721701
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types.views.virtual

neureka.ndim.config.types.views.virtual

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total4 of 6093%0 of 0n/a21721721701
VirtualNDConfiguration45693%n/a21721721701
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/index.source.html b/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/index.source.html index 5f4d01cf2..cf0ea65c1 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.views.virtual/index.source.html @@ -1 +1 @@ -neureka.ndim.config.types.views.virtual

neureka.ndim.config.types.views.virtual

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total4 of 6093%0 of 0n/a21721721701
VirtualNDConfiguration.java45693%n/a21721721701
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types.views.virtual

neureka.ndim.config.types.views.virtual

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total4 of 6093%0 of 0n/a21721721701
VirtualNDConfiguration.java45693%n/a21721721701
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.views/SimpleReshapeView.html b/docs/coverage/test/html/neureka.ndim.config.types.views/SimpleReshapeView.html index e96a52a8c..92df083f4 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.views/SimpleReshapeView.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.views/SimpleReshapeView.html @@ -1 +1 @@ -SimpleReshapeView

SimpleReshapeView

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total20 of 19689%1 of 1291%423439317
SimpleReshapeView(int[], NDConfiguration)57093%1583%1411701
shape(int)50%n/a111111
indicesMap(int)50%n/a111111
strides(int)50%n/a111111
indicesOfIndex(int)34100%2100%020501
_rearrange(int[], int[], int[])22100%4100%030301
indexOfIndices(int[])12100%n/a010201
indexOfIndex(int)6100%n/a010101
spread(int)5100%n/a010101
offset(int)5100%n/a010101
rank()4100%n/a010101
shape()3100%n/a010101
indicesMap()3100%n/a010101
strides()3100%n/a010101
spread()3100%n/a010101
offset()3100%n/a010101
lambda$new$0(Integer)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SimpleReshapeView

SimpleReshapeView

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total20 of 19689%1 of 1291%423439317
SimpleReshapeView(int[], NDConfiguration)57093%1583%1411701
shape(int)50%n/a111111
indicesMap(int)50%n/a111111
strides(int)50%n/a111111
indicesOfIndex(int)34100%2100%020501
_rearrange(int[], int[], int[])22100%4100%030301
indexOfIndices(int[])12100%n/a010201
indexOfIndex(int)6100%n/a010101
spread(int)5100%n/a010101
offset(int)5100%n/a010101
rank()4100%n/a010101
shape()3100%n/a010101
indicesMap()3100%n/a010101
strides()3100%n/a010101
spread()3100%n/a010101
offset()3100%n/a010101
lambda$new$0(Integer)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.views/SimpleReshapeView.java.html b/docs/coverage/test/html/neureka.ndim.config.types.views/SimpleReshapeView.java.html index 540076bbf..93ca3077b 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.views/SimpleReshapeView.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.views/SimpleReshapeView.java.html @@ -107,4 +107,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.views/index.html b/docs/coverage/test/html/neureka.ndim.config.types.views/index.html index 65d741c1b..07e582028 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.views/index.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.views/index.html @@ -1 +1 @@ -neureka.ndim.config.types.views

neureka.ndim.config.types.views

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total20 of 19689%1 of 1291%42343931701
SimpleReshapeView2017689%11191%42343931701
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types.views

neureka.ndim.config.types.views

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total20 of 19689%1 of 1291%42343931701
SimpleReshapeView2017689%11191%42343931701
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types.views/index.source.html b/docs/coverage/test/html/neureka.ndim.config.types.views/index.source.html index 64180b66d..03ed84705 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types.views/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.config.types.views/index.source.html @@ -1 +1 @@ -neureka.ndim.config.types.views

neureka.ndim.config.types.views

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total20 of 19689%1 of 1291%42343931701
SimpleReshapeView.java2017689%11191%42343931701
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types.views

neureka.ndim.config.types.views

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total20 of 19689%1 of 1291%42343931701
SimpleReshapeView.java2017689%11191%42343931701
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types/D1C.html b/docs/coverage/test/html/neureka.ndim.config.types/D1C.html index c5580511e..1568e0086 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types/D1C.html +++ b/docs/coverage/test/html/neureka.ndim.config.types/D1C.html @@ -1 +1 @@ -D1C

D1C

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 3100%0 of 0n/a010101
D1C()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +D1C

D1C

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 3100%0 of 0n/a010101
D1C()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types/D1C.java.html b/docs/coverage/test/html/neureka.ndim.config.types/D1C.java.html index 172d5cd6a..0f1964d48 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types/D1C.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types/D1C.java.html @@ -10,4 +10,4 @@ { public abstract int indexOfIndices( int d1 ); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types/D2C.html b/docs/coverage/test/html/neureka.ndim.config.types/D2C.html index f3423b942..4ddac8f8c 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types/D2C.html +++ b/docs/coverage/test/html/neureka.ndim.config.types/D2C.html @@ -1 +1 @@ -D2C

D2C

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 3100%0 of 0n/a010101
D2C()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +D2C

D2C

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 3100%0 of 0n/a010101
D2C()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types/D2C.java.html b/docs/coverage/test/html/neureka.ndim.config.types/D2C.java.html index 98ec9bcde..4ae977e61 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types/D2C.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types/D2C.java.html @@ -10,4 +10,4 @@ { public abstract int indexOfIndices( int d1, int d2 ); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types/D3C.html b/docs/coverage/test/html/neureka.ndim.config.types/D3C.html index 9c1f84370..5d93762c9 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types/D3C.html +++ b/docs/coverage/test/html/neureka.ndim.config.types/D3C.html @@ -1 +1 @@ -D3C

D3C

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 3100%0 of 0n/a010101
D3C()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +D3C

D3C

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 3100%0 of 0n/a010101
D3C()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types/D3C.java.html b/docs/coverage/test/html/neureka.ndim.config.types/D3C.java.html index 19651eb6a..a581db9a2 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types/D3C.java.html +++ b/docs/coverage/test/html/neureka.ndim.config.types/D3C.java.html @@ -10,4 +10,4 @@ { public abstract int indexOfIndices( int d1, int d2, int d3 ); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types/index.html b/docs/coverage/test/html/neureka.ndim.config.types/index.html index 2658dcaac..258c59485 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types/index.html +++ b/docs/coverage/test/html/neureka.ndim.config.types/index.html @@ -1 +1 @@ -neureka.ndim.config.types

neureka.ndim.config.types

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 9100%0 of 0n/a03030303
D3C3100%n/a01010101
D1C3100%n/a01010101
D2C3100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types

neureka.ndim.config.types

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 9100%0 of 0n/a03030303
D3C3100%n/a01010101
D1C3100%n/a01010101
D2C3100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config.types/index.source.html b/docs/coverage/test/html/neureka.ndim.config.types/index.source.html index 95eb9dda6..f7c3e34f9 100644 --- a/docs/coverage/test/html/neureka.ndim.config.types/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.config.types/index.source.html @@ -1 +1 @@ -neureka.ndim.config.types

neureka.ndim.config.types

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 9100%0 of 0n/a03030303
D3C.java3100%n/a01010101
D1C.java3100%n/a01010101
D2C.java3100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config.types

neureka.ndim.config.types

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 9100%0 of 0n/a03030303
D3C.java3100%n/a01010101
D1C.java3100%n/a01010101
D2C.java3100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/AbstractNDC.html b/docs/coverage/test/html/neureka.ndim.config/AbstractNDC.html index f98690961..b0a06c775 100644 --- a/docs/coverage/test/html/neureka.ndim.config/AbstractNDC.html +++ b/docs/coverage/test/html/neureka.ndim.config/AbstractNDC.html @@ -1 +1 @@ -AbstractNDC

AbstractNDC

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total51 of 58591%11 of 8687%11565100013
construct(int[], int[], int[], int[], int[])4717378%33592%32053901
equals(Object)41780%2466%240501
_simpleReshape(int[], NDConfiguration)91100%21083%2701201
toString()56100%n/a010701
equals(NDConfiguration)50100%41275%490801
hashCode()47100%n/a010901
_isSimpleConfiguration(int[], int[], int[], int[], int[])33100%8100%050701
newReshaped(int[])22100%2100%020301
_isSimpleTransposedConfiguration(int[], int[], int[])21100%4100%030401
static {...}11100%n/a010301
_cacheArray(int[])5100%n/a010101
_cached(NDConfiguration)5100%n/a010101
AbstractNDC()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractNDC

AbstractNDC

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total51 of 58591%11 of 8687%11565100013
construct(int[], int[], int[], int[], int[])4717378%33592%32053901
equals(Object)41780%2466%240501
_simpleReshape(int[], NDConfiguration)91100%21083%2701201
toString()56100%n/a010701
equals(NDConfiguration)50100%41275%490801
hashCode()47100%n/a010901
_isSimpleConfiguration(int[], int[], int[], int[], int[])33100%8100%050701
newReshaped(int[])22100%2100%020301
_isSimpleTransposedConfiguration(int[], int[], int[])21100%4100%030401
static {...}11100%n/a010301
_cacheArray(int[])5100%n/a010101
_cached(NDConfiguration)5100%n/a010101
AbstractNDC()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/AbstractNDC.java.html b/docs/coverage/test/html/neureka.ndim.config/AbstractNDC.java.html index 08ca03a74..2a04354dc 100644 --- a/docs/coverage/test/html/neureka.ndim.config/AbstractNDC.java.html +++ b/docs/coverage/test/html/neureka.ndim.config/AbstractNDC.java.html @@ -250,4 +250,4 @@ } -
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\ No newline at end of file +
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\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/NDConfiguration$Layout.html b/docs/coverage/test/html/neureka.ndim.config/NDConfiguration$Layout.html index bcf4b0f99..459d9499f 100644 --- a/docs/coverage/test/html/neureka.ndim.config/NDConfiguration$Layout.html +++ b/docs/coverage/test/html/neureka.ndim.config/NDConfiguration$Layout.html @@ -1 +1 @@ -NDConfiguration.Layout

NDConfiguration.Layout

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 18595%5 of 3284%52012904
newStridesFor(int[])58994%11593%1911501
isCompatible(NDConfiguration.Layout)32086%3770%360301
rearrange(int[], int[], int[])41100%1583%140601
static {...}27100%n/a010501
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDConfiguration.Layout

NDConfiguration.Layout

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total8 of 20296%5 of 3284%52012904
newStridesFor(int[])58994%11593%1911501
isCompatible(NDConfiguration.Layout)32086%3770%360301
static {...}44100%n/a010501
rearrange(int[], int[], int[])41100%1583%140601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/NDConfiguration$Utility.html b/docs/coverage/test/html/neureka.ndim.config/NDConfiguration$Utility.html index 1ba899e05..c7bf97c30 100644 --- a/docs/coverage/test/html/neureka.ndim.config/NDConfiguration$Utility.html +++ b/docs/coverage/test/html/neureka.ndim.config/NDConfiguration$Utility.html @@ -1 +1 @@ -NDConfiguration.Utility

NDConfiguration.Utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 16696%5 of 2479%61933117
NDConfiguration.Utility()30%n/a111111
_incrementAt(int, int[], int[])13296%1375%131801
_decrementAt(int, int[], int[])13196%1375%131801
rearrange(int[], int[])38100%1583%140501
sizeOfShape(int[])24100%2100%020301
increment(int[], int[])18100%1375%130301
decrement(int[], int[])18100%1375%130301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDConfiguration.Utility

NDConfiguration.Utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 16696%5 of 2479%61933117
NDConfiguration.Utility()30%n/a111111
_incrementAt(int, int[], int[])13296%1375%131801
_decrementAt(int, int[], int[])13196%1375%131801
rearrange(int[], int[])38100%1583%140501
sizeOfShape(int[])24100%2100%020301
increment(int[], int[])18100%1375%130301
decrement(int[], int[])18100%1375%130301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/NDConfiguration.html b/docs/coverage/test/html/neureka.ndim.config/NDConfiguration.html index 3cf09d653..d566466f9 100644 --- a/docs/coverage/test/html/neureka.ndim.config/NDConfiguration.html +++ b/docs/coverage/test/html/neureka.ndim.config/NDConfiguration.html @@ -1 +1 @@ -NDConfiguration

NDConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 20897%0 of 34100%130132113
has(NDTrait)50%n/a111111
getLayout()58100%18100%01001101
asInlineArray()55100%n/a010801
isSimple()23100%6100%040401
isCompact()20100%4100%030301
lambda$isCompact$1(int)13100%4100%030101
lambda$isCompact$2(int)8100%2100%020101
of(int[], int[], int[], int[], int[])7100%n/a010101
size()7100%n/a010101
lambda$size$0(int, int)4100%n/a010101
getTraits()3100%n/a010101
getIndexToIndexAccessPattern()3100%n/a010101
isVirtual()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDConfiguration

NDConfiguration

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 21097%0 of 34100%131133114
has(NDTrait)50%n/a111111
getLayout()58100%18100%01001101
asInlineArray()55100%n/a010801
isSimple()23100%6100%040401
isCompact()20100%4100%030301
lambda$isCompact$1(int)13100%4100%030101
lambda$isCompact$2(int)8100%2100%020101
of(int[], int[], int[], int[], int[])7100%n/a010101
size()7100%n/a010101
lambda$size$0(int, int)4100%n/a010101
getTraits()3100%n/a010101
getIndexToIndexAccessPattern()3100%n/a010101
none()2100%n/a010101
isVirtual()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/NDConfiguration.java.html b/docs/coverage/test/html/neureka.ndim.config/NDConfiguration.java.html index a51cfc7c7..cf4deaffb 100644 --- a/docs/coverage/test/html/neureka.ndim.config/NDConfiguration.java.html +++ b/docs/coverage/test/html/neureka.ndim.config/NDConfiguration.java.html @@ -45,6 +45,11 @@ */ public interface NDConfiguration { + /** + * @return A {@link NDConfiguration} instance which represents the absence of a configuration. + */ + static NDConfiguration none() { return NoOpNDConfig.INSTANCE; } + static NDConfiguration of( int[] shape, // The shape of the tensor. int[] strides, // Strides are the distances between elements of a tensor in each dimension. @@ -52,7 +57,7 @@ int[] spread, int[] offset ) { - return AbstractNDC.construct(shape, strides, indicesMap, spread, offset); + return AbstractNDC.construct(shape, strides, indicesMap, spread, offset); } /** @@ -87,54 +92,54 @@ * </li> * </ul> */ - enum Layout + enum Layout { - ROW_MAJOR, - COLUMN_MAJOR, - SYMMETRIC, // Both row- and column-major compatible! - UNSPECIFIC; // Possibly a slice or something reshaped/permuted or whatnot... + ROW_MAJOR, + COLUMN_MAJOR, + SYMMETRIC, // Both row- and column-major compatible! + UNSPECIFIC; // Possibly a slice or something reshaped/permuted or whatnot... public boolean isCompatible(Layout other) { - if (this == UNSPECIFIC || other == UNSPECIFIC) return false; - if (this == SYMMETRIC || other == SYMMETRIC) return true; - return this == other; + if (this == UNSPECIFIC || other == UNSPECIFIC) return false; + if (this == SYMMETRIC || other == SYMMETRIC) return true; + return this == other; } public int[] newStridesFor(int[] shape) { - int[] order = new int[shape.length]; - for ( int i = 0; i < shape.length; i++ ) - order[i] = shape.length - 1 - i; + int[] order = new int[shape.length]; + for ( int i = 0; i < shape.length; i++ ) + order[i] = shape.length - 1 - i; - if ( this == COLUMN_MAJOR && shape.length > 1 ) { + if ( this == COLUMN_MAJOR && shape.length > 1 ) { // Swap the first two elements of the order array: - int tmp = order[0]; - order[0] = order[1]; - order[1] = tmp; + int tmp = order[0]; + order[0] = order[1]; + order[1] = tmp; } - int[] strides = new int[shape.length]; - int prod = 1; - if ( this == COLUMN_MAJOR || this == ROW_MAJOR || this == UNSPECIFIC || this == SYMMETRIC) { - for ( int i : order ) { - strides[i] = prod; - prod *= shape[i]; + int[] strides = new int[shape.length]; + int prod = 1; + if ( this == COLUMN_MAJOR || this == ROW_MAJOR || this == UNSPECIFIC || this == SYMMETRIC) { + for ( int i : order ) { + strides[i] = prod; + prod *= shape[i]; } } else - throw new IllegalStateException("Unknown data layout!"); + throw new IllegalStateException("Unknown data layout!"); - return strides; + return strides; } public int[] rearrange(int[] tln, int[] shape, int[] newForm) { - int[] shpTln = this.newStridesFor(shape); - int[] newTln = new int[newForm.length]; - for (int i = 0; i < newForm.length; i++) { - if (newForm[i] < 0) newTln[i] = shpTln[i]; - else if (newForm[i] >= 0) newTln[i] = tln[newForm[i]]; + int[] shpTln = this.newStridesFor(shape); + int[] newTln = new int[newForm.length]; + for (int i = 0; i < newForm.length; i++) { + if (newForm[i] < 0) newTln[i] = shpTln[i]; + else if (newForm[i] >= 0) newTln[i] = tln[newForm[i]]; } - return newTln; + return newTln; } } @@ -150,26 +155,26 @@ * @return The layout of the underlying data array of a tensor. */ default Layout getLayout() { - if ( !this.isCompact() ) // Non-compact tensors have at least 1 step/spread greater than 1 AND at least 1 offset greater than 0! - return Layout.UNSPECIFIC; + if ( !this.isCompact() ) // Non-compact tensors have at least 1 step/spread greater than 1 AND at least 1 offset greater than 0! + return Layout.UNSPECIFIC; else { - int[] stridesRM = Layout.ROW_MAJOR.newStridesFor(this.shape()); - boolean hasRMIndices = Arrays.equals(stridesRM, indicesMap()); - boolean isRM = (Arrays.equals(stridesRM, strides()) && hasRMIndices); + int[] stridesRM = Layout.ROW_MAJOR.newStridesFor(this.shape()); + boolean hasRMIndices = Arrays.equals(stridesRM, indicesMap()); + boolean isRM = (Arrays.equals(stridesRM, strides()) && hasRMIndices); - int[] stridesCM = Layout.COLUMN_MAJOR.newStridesFor(this.shape()); - boolean isCM = (Arrays.equals(stridesCM, strides()) && hasRMIndices); + int[] stridesCM = Layout.COLUMN_MAJOR.newStridesFor(this.shape()); + boolean isCM = (Arrays.equals(stridesCM, strides()) && hasRMIndices); - if ( isRM && isCM ) return Layout.SYMMETRIC; - if ( isRM ) return Layout.ROW_MAJOR; - if ( isCM ) return Layout.COLUMN_MAJOR; + if ( isRM && isCM ) return Layout.SYMMETRIC; + if ( isRM ) return Layout.ROW_MAJOR; + if ( isCM ) return Layout.COLUMN_MAJOR; } - return Layout.UNSPECIFIC; + return Layout.UNSPECIFIC; } - default List<NDTrait> getTraits() { return NDTrait.traitsOf(this); } + default List<NDTrait> getTraits() { return NDTrait.traitsOf(this); } - default boolean has( NDTrait trait ) { return NDTrait.traitsOf(this).contains(trait); } + default boolean has( NDTrait trait ) { return NDTrait.traitsOf(this).contains(trait); } /** * This method returns the number of axis of @@ -180,7 +185,7 @@ */ int rank(); - default int size() { return Arrays.stream(shape()).reduce(1, (a, b) -> a * b); } + default int size() { return Arrays.stream(shape()).reduce(1, (a, b) -> a * b); } /** * This method returns an array of axis sizes. @@ -328,15 +333,15 @@ * @return An array of flattened arrays which define this nd-configuration in a compact manner. */ default int[] asInlineArray() { - int rank = rank(); - int[] inline = new int[rank * 5]; + int rank = rank(); + int[] inline = new int[rank * 5]; //config format: [ shape | translation | indicesMap | offsets | spreads ] - System.arraycopy(shape(), 0, inline, rank * 0, rank); //=> SHAPE - System.arraycopy(strides(), 0, inline, rank * 1, rank); //=> TRANSLATION (translates n-dimensional indices to an index) - System.arraycopy(indicesMap(), 0, inline, rank * 2, rank); //=> INDICES MAP (translates scalar to n-dimensional index) - System.arraycopy(offset(), 0, inline, rank * 3, rank); //=> SPREAD / STRIDES (step size for dimensions in underlying parent tensor) - System.arraycopy(spread(), 0, inline, rank * 4, rank); //=> OFFSET (nd-position inside underlying parent tensor) - return inline; + System.arraycopy(shape(), 0, inline, rank * 0, rank); //=> SHAPE + System.arraycopy(strides(), 0, inline, rank * 1, rank); //=> TRANSLATION (translates n-dimensional indices to an index) + System.arraycopy(indicesMap(), 0, inline, rank * 2, rank); //=> INDICES MAP (translates scalar to n-dimensional index) + System.arraycopy(offset(), 0, inline, rank * 3, rank); //=> SPREAD / STRIDES (step size for dimensions in underlying parent tensor) + System.arraycopy(spread(), 0, inline, rank * 4, rank); //=> OFFSET (nd-position inside underlying parent tensor) + return inline; } int hashCode(); @@ -369,12 +374,12 @@ * @return The truth value determining if this configuration is not modeling more complex indices like permuted views or slices... */ default boolean isSimple() { - int[] simpleTranslation = this.getLayout().newStridesFor(this.shape()); - return Arrays.equals(this.strides(), simpleTranslation) + int[] simpleTranslation = this.getLayout().newStridesFor(this.shape()); + return Arrays.equals(this.strides(), simpleTranslation) && - Arrays.equals(this.indicesMap(), simpleTranslation) + Arrays.equals(this.indicesMap(), simpleTranslation) && - isCompact(); + isCompact(); } /** @@ -385,22 +390,22 @@ * @return The truth value determining if this configuration has no offset and spread/steps larger than 1. */ default boolean isCompact() { - return - IntStream.range(0, this.rank()).allMatch( i -> this.spread(i) == 1 || this.spread(i) == 0 ) + return + IntStream.range(0, this.rank()).allMatch( i -> this.spread(i) == 1 || this.spread(i) == 0 ) && - IntStream.range(0, this.rank()).allMatch(i -> this.offset(i) == 0); + IntStream.range(0, this.rank()).allMatch(i -> this.offset(i) == 0); } /** * @return The truth value determining if this {@link NDConfiguration} * represents virtual tensors (see {@link Tensor#isVirtual()}). */ - default boolean isVirtual() { return false; } + default boolean isVirtual() { return false; } /** * @return A function which can map tensor indices to the indices of its data array. */ - default IndexToIndexFunction getIndexToIndexAccessPattern() { return this::indexOfIndex; } + default IndexToIndexFunction getIndexToIndexAccessPattern() { return this::indexOfIndex; } /** * Implementations of this are produced and returned by the {@link #getIndexToIndexAccessPattern()} @@ -416,62 +421,62 @@ * for nd-configuration related operations like reshaping, * incrementing or decrementing index arrays... */ - class Utility { + class Utility { public static int[] rearrange(int[] array, int[] pointers) { - int[] newShp = new int[pointers.length]; - for (int i = 0; i < pointers.length; i++) { - if (pointers[i] < 0) newShp[i] = Math.abs(pointers[i]); - else if (pointers[i] >= 0) newShp[i] = array[pointers[i]]; + int[] newShp = new int[pointers.length]; + for (int i = 0; i < pointers.length; i++) { + if (pointers[i] < 0) newShp[i] = Math.abs(pointers[i]); + else if (pointers[i] >= 0) newShp[i] = array[pointers[i]]; } - return newShp; + return newShp; } public static void increment(int[] indices, int[] shape) { - int i = shape.length - 1; - while (i >= 0 && i < shape.length) i = _incrementAt(i, indices, shape); - } + int i = shape.length - 1; + while (i >= 0 && i < shape.length) i = _incrementAt(i, indices, shape); + } private static int _incrementAt(int i, int[] indices, int[] shape) { - if (indices[i] < shape[i]) { - indices[i]++; - if (indices[i] == shape[i]) { - indices[i] = 0; - i--; - } else i = -1; - } else i--; - return i; + if (indices[i] < shape[i]) { + indices[i]++; + if (indices[i] == shape[i]) { + indices[i] = 0; + i--; + } else i = -1; + } else i--; + return i; } public static void decrement(int[] indices, int[] shape) { - int i = shape.length - 1; - while (i >= 0 && i < shape.length) i = _decrementAt(i, indices, shape); - } + int i = shape.length - 1; + while (i >= 0 && i < shape.length) i = _decrementAt(i, indices, shape); + } private static int _decrementAt(int i, int[] indices, int[] shape) { - if (indices[i] >= 0) { - indices[i]--; - if (indices[i] == -1) { - indices[i] = shape[i] - 1; - i--; - } else i = -1; - } else i--; - return i; + if (indices[i] >= 0) { + indices[i]--; + if (indices[i] == -1) { + indices[i] = shape[i] - 1; + i--; + } else i = -1; + } else i--; + return i; } public static int sizeOfShape( int[] shape ) { - int size = 1; - for (int i : shape) size *= i; - return size; + int size = 1; + for (int i : shape) size *= i; + return size; } } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/NDTrait.html b/docs/coverage/test/html/neureka.ndim.config/NDTrait.html index 8cc079a83..82f000eb4 100644 --- a/docs/coverage/test/html/neureka.ndim.config/NDTrait.html +++ b/docs/coverage/test/html/neureka.ndim.config/NDTrait.html @@ -1 +1 @@ -NDTrait

NDTrait

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total36 of 40191%28 of 7663%2752159014
_isOffsetMatrix(NDConfiguration)83380%4660%461801
_isMatrix(NDConfiguration)53587%4450%450701
lambda$_isCompact$0(NDConfiguration, int)5861%3125%230101
_isContinuousMatrix(NDConfiguration)43288%3562%350601
_isRM(NDConfiguration)33892%3770%360501
_isCM(NDConfiguration)33892%3770%360501
_last2DimensionsAreNotPermuted(NDConfiguration)33090%2466%240501
_rightSpreadPadding(NDConfiguration)31583%2250%230401
_isCompact(NDConfiguration)11995%2250%230301
lambda$_isCompact$1(NDConfiguration, int)1787%1150%120101
static {...}45100%n/a010701
traitsOf(NDConfiguration)32100%4100%030401
_isSimple(NDConfiguration)23100%1583%140401
NDTrait(String, int, Predicate)10100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDTrait

NDTrait

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total36 of 42691%28 of 7663%2752159014
_isOffsetMatrix(NDConfiguration)83380%4660%461801
_isMatrix(NDConfiguration)53587%4450%450701
lambda$_isCompact$0(NDConfiguration, int)5861%3125%230101
_isContinuousMatrix(NDConfiguration)43288%3562%350601
_isRM(NDConfiguration)33892%3770%360501
_isCM(NDConfiguration)33892%3770%360501
_last2DimensionsAreNotPermuted(NDConfiguration)33090%2466%240501
_rightSpreadPadding(NDConfiguration)31583%2250%230401
_isCompact(NDConfiguration)11995%2250%230301
lambda$_isCompact$1(NDConfiguration, int)1787%1150%120101
static {...}70100%n/a010701
traitsOf(NDConfiguration)32100%4100%030401
_isSimple(NDConfiguration)23100%1583%140401
NDTrait(String, int, Predicate)10100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/NDTrait.java.html b/docs/coverage/test/html/neureka.ndim.config/NDTrait.java.html index 1860c0d64..92620d2a7 100644 --- a/docs/coverage/test/html/neureka.ndim.config/NDTrait.java.html +++ b/docs/coverage/test/html/neureka.ndim.config/NDTrait.java.html @@ -139,4 +139,4 @@ return spreadCol == 1 && spreadRow == 1; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/NoOpNDConfig.html b/docs/coverage/test/html/neureka.ndim.config/NoOpNDConfig.html new file mode 100644 index 000000000..7c914e14d --- /dev/null +++ b/docs/coverage/test/html/neureka.ndim.config/NoOpNDConfig.html @@ -0,0 +1 @@ +NoOpNDConfig

NoOpNDConfig

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total38 of 4311%0 of 0n/a161716171617
shape()30%n/a111111
indicesMap()30%n/a111111
strides()30%n/a111111
spread()30%n/a111111
offset()30%n/a111111
indicesOfIndex(int)30%n/a111111
rank()20%n/a111111
shape(int)20%n/a111111
indicesMap(int)20%n/a111111
strides(int)20%n/a111111
spread(int)20%n/a111111
offset(int)20%n/a111111
indexOfIndex(int)20%n/a111111
indexOfIndices(int[])20%n/a111111
equals(NDConfiguration)20%n/a111111
newReshaped(int[])20%n/a111111
static {...}5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/NoOpNDConfig.java.html b/docs/coverage/test/html/neureka.ndim.config/NoOpNDConfig.java.html new file mode 100644 index 000000000..e5f72e9eb --- /dev/null +++ b/docs/coverage/test/html/neureka.ndim.config/NoOpNDConfig.java.html @@ -0,0 +1,90 @@ +NoOpNDConfig.java

NoOpNDConfig.java

package neureka.ndim.config;
+
+final class NoOpNDConfig implements NDConfiguration
+{
+
+    static final NoOpNDConfig INSTANCE = new NoOpNDConfig();
+
+    private NoOpNDConfig() {}
+
+    @Override
+    public int rank() {
+        return 0;
+    }
+
+    @Override
+    public int[] shape() {
+        return new int[0];
+    }
+
+    @Override
+    public int shape(int i) {
+        return 0;
+    }
+
+    @Override
+    public int[] indicesMap() {
+        return new int[0];
+    }
+
+    @Override
+    public int indicesMap(int i) {
+        return 0;
+    }
+
+    @Override
+    public int[] strides() {
+        return new int[0];
+    }
+
+    @Override
+    public int strides(int i) {
+        return 0;
+    }
+
+    @Override
+    public int[] spread() {
+        return new int[0];
+    }
+
+    @Override
+    public int spread(int i) {
+        return 0;
+    }
+
+    @Override
+    public int[] offset() {
+        return new int[0];
+    }
+
+    @Override
+    public int offset(int i) {
+        return 0;
+    }
+
+    @Override
+    public int indexOfIndex(int index) {
+        return 0;
+    }
+
+    @Override
+    public int[] indicesOfIndex(int index) {
+        return new int[0];
+    }
+
+    @Override
+    public int indexOfIndices(int[] indices) {
+        return 0;
+    }
+
+    @Override
+    public boolean equals(NDConfiguration ndc) {
+        return false;
+    }
+
+    @Override
+    public NDConfiguration newReshaped(int[] newForm) {
+        return this;
+    }
+}
+
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/index.html b/docs/coverage/test/html/neureka.ndim.config/index.html index 43f0d1cba..09aa67162 100644 --- a/docs/coverage/test/html/neureka.ndim.config/index.html +++ b/docs/coverage/test/html/neureka.ndim.config/index.html @@ -1 +1 @@ -neureka.ndim.config

neureka.ndim.config

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total105 of 1,54593%49 of 25280%501771125125105
AbstractNDC5153491%117587%1156510001301
NDTrait3636591%284863%275215901401
NDConfiguration.Layout817795%52784%5201290401
NDConfiguration520397%34100%13013211301
NDConfiguration.Utility516196%51979%6193311701
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config

neureka.ndim.config

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total143 of 1,63291%49 of 25280%6619527269186906
AbstractNDC5153491%117587%1156510001301
NoOpNDConfig38511%n/a16171617161701
NDTrait3639091%284863%275215901401
NDConfiguration.Layout819496%52784%5201290401
NDConfiguration520597%34100%13113311401
NDConfiguration.Utility516196%51979%6193311701
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.config/index.source.html b/docs/coverage/test/html/neureka.ndim.config/index.source.html index 17782046b..5e9a149ad 100644 --- a/docs/coverage/test/html/neureka.ndim.config/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.config/index.source.html @@ -1 +1 @@ -neureka.ndim.config

neureka.ndim.config

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total105 of 1,54593%49 of 25280%501771125125105
AbstractNDC.java5153491%117587%1156510001301
NDTrait.java3636591%284863%275215901401
NDConfiguration.java1854196%108088%126959222403
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.config

neureka.ndim.config

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total143 of 1,63291%49 of 25280%6619527269186906
AbstractNDC.java5153491%117587%1156510001301
NoOpNDConfig.java38511%n/a16171617161701
NDTrait.java3639091%284863%275215901401
NDConfiguration.java1856096%108088%127059322503
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted2DCIterator.html b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted2DCIterator.html index f81393047..5ce8b87e6 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted2DCIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted2DCIterator.html @@ -1 +1 @@ -Permuted2DCIterator

Permuted2DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 10883%4 of 850%41242328
set(int, int)100%20%223311
get(int)80%20%221111
decrement()23100%2100%020501
increment()21100%2100%020501
Permuted2DCIterator(Permuted2DConfiguration)15100%n/a010401
get()13100%n/a010101
set(int[])11100%n/a010301
i()7100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Permuted2DCIterator

Permuted2DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total18 of 10883%4 of 850%41242328
set(int, int)100%20%223311
get(int)80%20%221111
decrement()23100%2100%020501
increment()21100%2100%020501
Permuted2DCIterator(Permuted2DConfiguration)15100%n/a010401
get()13100%n/a010101
set(int[])11100%n/a010301
i()7100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted2DCIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted2DCIterator.java.html index b9b8518a2..f43fff6b1 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted2DCIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted2DCIterator.java.html @@ -52,4 +52,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted3DCIterator.html b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted3DCIterator.html index 4c41a623c..490c6ad64 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted3DCIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted3DCIterator.html @@ -1 +1 @@ -Permuted3DCIterator

Permuted3DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 166100%0 of 16100%01603208
decrement()39100%4100%030801
increment()35100%4100%030801
Permuted3DCIterator(Permuted3DConfiguration)18100%n/a010501
get()18100%n/a010101
set(int, int)17100%4100%030401
set(int[])16100%n/a010401
get(int)14100%4100%030101
i()9100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Permuted3DCIterator

Permuted3DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 166100%0 of 16100%01603208
decrement()39100%4100%030801
increment()35100%4100%030801
Permuted3DCIterator(Permuted3DConfiguration)18100%n/a010501
get()18100%n/a010101
set(int, int)17100%4100%030401
set(int[])16100%n/a010401
get(int)14100%4100%030101
i()9100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted3DCIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted3DCIterator.java.html index 33c40ba55..6fb693b97 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted3DCIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/Permuted3DCIterator.java.html @@ -64,4 +64,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/index.html b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/index.html index bbd18258f..8237e38aa 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/index.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/index.html @@ -1 +1 @@ -neureka.ndim.iterator.types.permuted

neureka.ndim.iterator.types.permuted

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total18 of 27493%4 of 2483%42845521602
Permuted2DCIterator189083%4450%4124232801
Permuted3DCIterator166100%16100%0160320801
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.iterator.types.permuted

neureka.ndim.iterator.types.permuted

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total18 of 27493%4 of 2483%42845521602
Permuted2DCIterator189083%4450%4124232801
Permuted3DCIterator166100%16100%0160320801
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/index.source.html b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/index.source.html index 9475c8211..8ed5d2c54 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.permuted/index.source.html @@ -1 +1 @@ -neureka.ndim.iterator.types.permuted

neureka.ndim.iterator.types.permuted

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total18 of 27493%4 of 2483%42845521602
Permuted2DCIterator.java189083%4450%4124232801
Permuted3DCIterator.java166100%16100%0160320801
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.iterator.types.permuted

neureka.ndim.iterator.types.permuted

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total18 of 27493%4 of 2483%42845521602
Permuted2DCIterator.java189083%4450%4124232801
Permuted3DCIterator.java166100%16100%0160320801
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple1DCIterator.html b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple1DCIterator.html index a17a219bb..d64c892bd 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple1DCIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple1DCIterator.html @@ -1 +1 @@ -Simple1DCIterator

Simple1DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 49100%0 of 0n/a080908
Simple1DCIterator(Simple1DConfiguration)9100%n/a010201
get()8100%n/a010101
increment()7100%n/a010101
decrement()7100%n/a010101
set(int[])6100%n/a010101
i()5100%n/a010101
set(int, int)4100%n/a010101
get(int)3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Simple1DCIterator

Simple1DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 49100%0 of 0n/a080908
Simple1DCIterator(Simple1DConfiguration)9100%n/a010201
get()8100%n/a010101
increment()7100%n/a010101
decrement()7100%n/a010101
set(int[])6100%n/a010101
i()5100%n/a010101
set(int, int)4100%n/a010101
get(int)3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple1DCIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple1DCIterator.java.html index 763a62452..90ea01e6d 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple1DCIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple1DCIterator.java.html @@ -35,4 +35,4 @@ @Override public final void set( int[] indices) { _d1 = indices[0]; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple2DCIterator.html b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple2DCIterator.html index de4428b76..2c4a291bb 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple2DCIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple2DCIterator.html @@ -1 +1 @@ -Simple2DCIterator

Simple2DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 106100%0 of 8100%01202308
decrement()23100%2100%020501
increment()21100%2100%020501
Simple2DCIterator(Simple2DConfiguration)13100%n/a010401
get()13100%n/a010101
set(int[])11100%n/a010301
set(int, int)10100%2100%020301
get(int)8100%2100%020101
i()7100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Simple2DCIterator

Simple2DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 106100%0 of 8100%01202308
decrement()23100%2100%020501
increment()21100%2100%020501
Simple2DCIterator(Simple2DConfiguration)13100%n/a010401
get()13100%n/a010101
set(int[])11100%n/a010301
set(int, int)10100%2100%020301
get(int)8100%2100%020101
i()7100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple2DCIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple2DCIterator.java.html index 77e60c0aa..64174c47f 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple2DCIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple2DCIterator.java.html @@ -52,4 +52,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple3DCIterator.html b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple3DCIterator.html index d763a773e..501586bfe 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple3DCIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple3DCIterator.html @@ -1 +1 @@ -Simple3DCIterator

Simple3DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 164100%0 of 16100%01603208
decrement()39100%4100%030801
increment()35100%4100%030801
get()18100%n/a010101
set(int, int)17100%4100%030401
Simple3DCIterator(Simple3DConfiguration)16100%n/a010501
set(int[])16100%n/a010401
get(int)14100%4100%030101
i()9100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Simple3DCIterator

Simple3DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 164100%0 of 16100%01603208
decrement()39100%4100%030801
increment()35100%4100%030801
get()18100%n/a010101
set(int, int)17100%4100%030401
Simple3DCIterator(Simple3DConfiguration)16100%n/a010501
set(int[])16100%n/a010401
get(int)14100%4100%030101
i()9100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple3DCIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple3DCIterator.java.html index 2476777e4..e63d1b035 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple3DCIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/Simple3DCIterator.java.html @@ -70,4 +70,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/index.html b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/index.html index e560eb69e..24264422f 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/index.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/index.html @@ -1 +1 @@ -neureka.ndim.iterator.types.simple

neureka.ndim.iterator.types.simple

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 319100%0 of 24100%03606402403
Simple3DCIterator164100%16100%0160320801
Simple2DCIterator106100%8100%0120230801
Simple1DCIterator49100%n/a08090801
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.iterator.types.simple

neureka.ndim.iterator.types.simple

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 319100%0 of 24100%03606402403
Simple3DCIterator164100%16100%0160320801
Simple2DCIterator106100%8100%0120230801
Simple1DCIterator49100%n/a08090801
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/index.source.html b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/index.source.html index b095b61bf..ffab7bdd6 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.simple/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.simple/index.source.html @@ -1 +1 @@ -neureka.ndim.iterator.types.simple

neureka.ndim.iterator.types.simple

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 319100%0 of 24100%03606402403
Simple3DCIterator.java164100%16100%0160320801
Simple2DCIterator.java106100%8100%0120230801
Simple1DCIterator.java49100%n/a08090801
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.iterator.types.simple

neureka.ndim.iterator.types.simple

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 319100%0 of 24100%03606402403
Simple3DCIterator.java164100%16100%0160320801
Simple2DCIterator.java106100%8100%0120230801
Simple1DCIterator.java49100%n/a08090801
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced1DCIterator.html b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced1DCIterator.html index 414104238..ffa65d3ac 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced1DCIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced1DCIterator.html @@ -1 +1 @@ -Sliced1DCIterator

Sliced1DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 5887%0 of 0n/a282928
set(int, int)40%n/a111111
get(int)30%n/a111111
Sliced1DCIterator(Sliced1DConfiguration)18100%n/a010201
get()8100%n/a010101
increment()7100%n/a010101
decrement()7100%n/a010101
set(int[])6100%n/a010101
i()5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sliced1DCIterator

Sliced1DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total7 of 5887%0 of 0n/a282928
set(int, int)40%n/a111111
get(int)30%n/a111111
Sliced1DCIterator(Sliced1DConfiguration)18100%n/a010201
get()8100%n/a010101
increment()7100%n/a010101
decrement()7100%n/a010101
set(int[])6100%n/a010101
i()5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced1DCIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced1DCIterator.java.html index 80f5ddc52..26b75e729 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced1DCIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced1DCIterator.java.html @@ -33,4 +33,4 @@ @Override public final void set( int[] indices ) { _d1 = indices[0]; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced2DCIterator.html b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced2DCIterator.html index 177bbf769..0976be399 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced2DCIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced2DCIterator.html @@ -1 +1 @@ -Sliced2DCIterator

Sliced2DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 112100%0 of 8100%01202308
decrement()23100%2100%020501
increment()21100%2100%020501
Sliced2DCIterator(Sliced2DConfiguration)19100%n/a010401
get()13100%n/a010101
set(int[])11100%n/a010301
set(int, int)10100%2100%020301
get(int)8100%2100%020101
i()7100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sliced2DCIterator

Sliced2DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 112100%0 of 8100%01202308
decrement()23100%2100%020501
increment()21100%2100%020501
Sliced2DCIterator(Sliced2DConfiguration)19100%n/a010401
get()13100%n/a010101
set(int[])11100%n/a010301
set(int, int)10100%2100%020301
get(int)8100%2100%020101
i()7100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced2DCIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced2DCIterator.java.html index bd5c34f7a..39d9f9c94 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced2DCIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced2DCIterator.java.html @@ -53,4 +53,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced3DCIterator.html b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced3DCIterator.html index fd0b21c31..8d21ae677 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced3DCIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced3DCIterator.html @@ -1 +1 @@ -Sliced3DCIterator

Sliced3DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total31 of 17081%8 of 1650%61653228
set(int, int)170%40%334411
get(int)140%40%331111
decrement()39100%4100%030801
increment()35100%4100%030801
Sliced3DCIterator(Sliced3DConfiguration)22100%n/a010501
get()18100%n/a010101
set(int[])16100%n/a010401
i()9100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Sliced3DCIterator

Sliced3DCIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total31 of 17081%8 of 1650%61653228
set(int, int)170%40%334411
get(int)140%40%331111
decrement()39100%4100%030801
increment()35100%4100%030801
Sliced3DCIterator(Sliced3DConfiguration)22100%n/a010501
get()18100%n/a010101
set(int[])16100%n/a010401
i()9100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced3DCIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced3DCIterator.java.html index 8113f8b93..29ad3da12 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced3DCIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/Sliced3DCIterator.java.html @@ -64,4 +64,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/SlicedNDIterator.html b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/SlicedNDIterator.html index 526399afe..8e19d8412 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/SlicedNDIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/SlicedNDIterator.html @@ -1 +1 @@ -SlicedNDIterator

SlicedNDIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 6995%0 of 0n/a111115111
shape()30%n/a111111
SlicedNDIterator(NDConfiguration)16100%n/a010501
set(int[])9100%n/a010101
increment()6100%n/a010101
decrement()6100%n/a010101
i()6100%n/a010101
set(int, int)6100%n/a010101
shape(int)5100%n/a010101
get(int)5100%n/a010101
rank()4100%n/a010101
get()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +SlicedNDIterator

SlicedNDIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 6995%0 of 0n/a111115111
shape()30%n/a111111
SlicedNDIterator(NDConfiguration)16100%n/a010501
set(int[])9100%n/a010101
increment()6100%n/a010101
decrement()6100%n/a010101
i()6100%n/a010101
set(int, int)6100%n/a010101
shape(int)5100%n/a010101
get(int)5100%n/a010101
rank()4100%n/a010101
get()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/SlicedNDIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/SlicedNDIterator.java.html index 53d432fef..471e7169d 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/SlicedNDIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/SlicedNDIterator.java.html @@ -48,4 +48,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/index.html b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/index.html index d0e7d821c..1544254bb 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/index.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/index.html @@ -1 +1 @@ -neureka.ndim.iterator.types.sliced

neureka.ndim.iterator.types.sliced

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total41 of 40989%8 of 2466%94787953504
Sliced3DCIterator3113981%8850%6165322801
Sliced1DCIterator75187%n/a28292801
SlicedNDIterator36695%n/a11111511101
Sliced2DCIterator112100%8100%0120230801
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.iterator.types.sliced

neureka.ndim.iterator.types.sliced

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total41 of 40989%8 of 2466%94787953504
Sliced3DCIterator3113981%8850%6165322801
Sliced1DCIterator75187%n/a28292801
SlicedNDIterator36695%n/a11111511101
Sliced2DCIterator112100%8100%0120230801
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/index.source.html b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/index.source.html index ad9eb6a16..50160bfaf 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.sliced/index.source.html @@ -1 +1 @@ -neureka.ndim.iterator.types.sliced

neureka.ndim.iterator.types.sliced

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total41 of 40989%8 of 2466%94787953504
Sliced3DCIterator.java3113981%8850%6165322801
Sliced1DCIterator.java75187%n/a28292801
SlicedNDIterator.java36695%n/a11111511101
Sliced2DCIterator.java112100%8100%0120230801
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.iterator.types.sliced

neureka.ndim.iterator.types.sliced

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total41 of 40989%8 of 2466%94787953504
Sliced3DCIterator.java3113981%8850%6165322801
Sliced1DCIterator.java75187%n/a28292801
SlicedNDIterator.java36695%n/a11111511101
Sliced2DCIterator.java112100%8100%0120230801
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/VirtualNDIterator.html b/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/VirtualNDIterator.html index 1942dd00c..73fcaf206 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/VirtualNDIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/VirtualNDIterator.html @@ -1 +1 @@ -VirtualNDIterator

VirtualNDIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 3528%0 of 0n/a711711711
shape(int)50%n/a111111
get(int)50%n/a111111
get()50%n/a111111
shape()40%n/a111111
rank()40%n/a111111
decrement()10%n/a111111
set(int, int)10%n/a111111
VirtualNDIterator(VirtualNDConfiguration)6100%n/a010101
i()2100%n/a010101
increment()1100%n/a010101
set(int[])1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +VirtualNDIterator

VirtualNDIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 3528%0 of 0n/a711711711
shape(int)50%n/a111111
get(int)50%n/a111111
get()50%n/a111111
shape()40%n/a111111
rank()40%n/a111111
decrement()10%n/a111111
set(int, int)10%n/a111111
VirtualNDIterator(VirtualNDConfiguration)6100%n/a010101
i()2100%n/a010101
increment()1100%n/a010101
set(int[])1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/VirtualNDIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/VirtualNDIterator.java.html index 6864e67ec..cbc05b49a 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/VirtualNDIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/VirtualNDIterator.java.html @@ -61,4 +61,4 @@ @Override public final int rank() { return _conf.rank(); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/index.html b/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/index.html index dd743c47d..c3047c8a2 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/index.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/index.html @@ -1 +1 @@ -neureka.ndim.iterator.types.virtual

neureka.ndim.iterator.types.virtual

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total25 of 3528%0 of 0n/a71171171101
VirtualNDIterator251028%n/a71171171101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.iterator.types.virtual

neureka.ndim.iterator.types.virtual

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total25 of 3528%0 of 0n/a71171171101
VirtualNDIterator251028%n/a71171171101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/index.source.html b/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/index.source.html index 4c92b51a4..377eb2ad9 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.iterator.types.virtual/index.source.html @@ -1 +1 @@ -neureka.ndim.iterator.types.virtual

neureka.ndim.iterator.types.virtual

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total25 of 3528%0 of 0n/a71171171101
VirtualNDIterator.java251028%n/a71171171101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.iterator.types.virtual

neureka.ndim.iterator.types.virtual

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total25 of 3528%0 of 0n/a71171171101
VirtualNDIterator.java251028%n/a71171171101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator/NDIterator$NonVirtual.html b/docs/coverage/test/html/neureka.ndim.iterator/NDIterator$NonVirtual.html index e7fc2fa1a..e3c1d4ada 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator/NDIterator$NonVirtual.html +++ b/docs/coverage/test/html/neureka.ndim.iterator/NDIterator$NonVirtual.html @@ -1 +1 @@ -NDIterator.NonVirtual

NDIterator.NonVirtual

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 15100%0 of 0n/a010101
static {...}15100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDIterator.NonVirtual

NDIterator.NonVirtual

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 24100%0 of 0n/a010101
static {...}24100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator/NDIterator.html b/docs/coverage/test/html/neureka.ndim.iterator/NDIterator.html index 85383f4a8..2503fb025 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator/NDIterator.html +++ b/docs/coverage/test/html/neureka.ndim.iterator/NDIterator.html @@ -1 +1 @@ -NDIterator

NDIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 105100%0 of 20100%01401604
of(NDConfiguration, NDIterator.NonVirtual)89100%20100%01101101
getIndexAndIncrement()7100%n/a010301
of(Tensor, NDIterator.NonVirtual)5100%n/a010101
of(Tensor)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDIterator

NDIterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 105100%0 of 20100%01401604
of(NDConfiguration, NDIterator.NonVirtual)89100%20100%01101101
getIndexAndIncrement()7100%n/a010301
of(Tensor, NDIterator.NonVirtual)5100%n/a010101
of(Tensor)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator/NDIterator.java.html b/docs/coverage/test/html/neureka.ndim.iterator/NDIterator.java.html index dcd3d5095..5818e1e2e 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator/NDIterator.java.html +++ b/docs/coverage/test/html/neureka.ndim.iterator/NDIterator.java.html @@ -149,4 +149,4 @@ int rank(); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator/index.html b/docs/coverage/test/html/neureka.ndim.iterator/index.html index da973cf93..f7ffecb58 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator/index.html +++ b/docs/coverage/test/html/neureka.ndim.iterator/index.html @@ -1 +1 @@ -neureka.ndim.iterator

neureka.ndim.iterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 120100%0 of 20100%0150170502
NDIterator105100%20100%0140160401
NDIterator.NonVirtual15100%n/a01010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.iterator

neureka.ndim.iterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 129100%0 of 20100%0150170502
NDIterator105100%20100%0140160401
NDIterator.NonVirtual24100%n/a01010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim.iterator/index.source.html b/docs/coverage/test/html/neureka.ndim.iterator/index.source.html index 3fa4d0a84..98dae68dc 100644 --- a/docs/coverage/test/html/neureka.ndim.iterator/index.source.html +++ b/docs/coverage/test/html/neureka.ndim.iterator/index.source.html @@ -1 +1 @@ -neureka.ndim.iterator

neureka.ndim.iterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 120100%0 of 20100%0150170502
NDIterator.java120100%20100%0150170502
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim.iterator

neureka.ndim.iterator

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total0 of 129100%0 of 20100%0150170502
NDIterator.java129100%20100%0150170502
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim/NDConstructor$1.html b/docs/coverage/test/html/neureka.ndim/NDConstructor$1.html index 8509ca04c..36e2c762a 100644 --- a/docs/coverage/test/html/neureka.ndim/NDConstructor$1.html +++ b/docs/coverage/test/html/neureka.ndim/NDConstructor$1.html @@ -1 +1 @@ -NDConstructor.new NDConstructor() {...}

NDConstructor.new NDConstructor() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 5952%5 of 837%6831124
produceNDC(boolean)201948%5337%451601
getSize()40%n/a111111
getShape()40%n/a111111
{...}12100%n/a010301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDConstructor.new NDConstructor() {...}

NDConstructor.new NDConstructor() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total28 of 5952%5 of 837%6831124
produceNDC(boolean)201948%5337%451601
getSize()40%n/a111111
getShape()40%n/a111111
{...}12100%n/a010301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim/NDConstructor$2.html b/docs/coverage/test/html/neureka.ndim/NDConstructor$2.html index 3e3138814..31c1b8fa3 100644 --- a/docs/coverage/test/html/neureka.ndim/NDConstructor$2.html +++ b/docs/coverage/test/html/neureka.ndim/NDConstructor$2.html @@ -1 +1 @@ -NDConstructor.new NDConstructor() {...}

NDConstructor.new NDConstructor() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total13 of 8484%4 of 850%3811704
produceNDC(boolean)134878%4450%3511201
{...}15100%n/a010301
getShape()5100%n/a010101
getSize()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDConstructor.new NDConstructor() {...}

NDConstructor.new NDConstructor() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total13 of 8484%4 of 850%3811704
produceNDC(boolean)134878%4450%3511201
{...}15100%n/a010301
getShape()5100%n/a010101
getSize()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim/NDConstructor.html b/docs/coverage/test/html/neureka.ndim/NDConstructor.html index 4f2b1754a..8f5258e1f 100644 --- a/docs/coverage/test/html/neureka.ndim/NDConstructor.html +++ b/docs/coverage/test/html/neureka.ndim/NDConstructor.html @@ -1 +1 @@ -NDConstructor

NDConstructor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 5352%1 of 250%153904
of(int[])251130%1150%123601
of(int[], int[], int[], int[], int[])8100%n/a010101
of(NDConfiguration)5100%n/a010101
of(Shape)4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDConstructor

NDConstructor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total25 of 5352%1 of 250%153904
of(int[])251130%1150%123601
of(int[], int[], int[], int[], int[])8100%n/a010101
of(NDConfiguration)5100%n/a010101
of(Shape)4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim/NDConstructor.java.html b/docs/coverage/test/html/neureka.ndim/NDConstructor.java.html index 1193068b4..4b0133f4d 100644 --- a/docs/coverage/test/html/neureka.ndim/NDConstructor.java.html +++ b/docs/coverage/test/html/neureka.ndim/NDConstructor.java.html @@ -98,4 +98,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim/NDUtil.html b/docs/coverage/test/html/neureka.ndim/NDUtil.html index 2fa621bca..d92e9d4c6 100644 --- a/docs/coverage/test/html/neureka.ndim/NDUtil.html +++ b/docs/coverage/test/html/neureka.ndim/NDUtil.html @@ -1 +1 @@ -NDUtil

NDUtil

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 7295%0 of 6100%161813
NDUtil()30%n/a111111
shapeString(int[])39100%4100%030401
asList(int[])30100%2100%020301
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDUtil

NDUtil

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 7295%0 of 6100%161813
NDUtil()30%n/a111111
shapeString(int[])39100%4100%030401
asList(int[])30100%2100%020301
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim/NDUtil.java.html b/docs/coverage/test/html/neureka.ndim/NDUtil.java.html index b958fec45..c1c79f485 100644 --- a/docs/coverage/test/html/neureka.ndim/NDUtil.java.html +++ b/docs/coverage/test/html/neureka.ndim/NDUtil.java.html @@ -25,4 +25,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim/NDimensional.html b/docs/coverage/test/html/neureka.ndim/NDimensional.html index 691230f4c..2cb551652 100644 --- a/docs/coverage/test/html/neureka.ndim/NDimensional.html +++ b/docs/coverage/test/html/neureka.ndim/NDimensional.html @@ -1 +1 @@ -NDimensional

NDimensional

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 63100%0 of 0n/a014014014
shape()5100%n/a010101
indicesMap()5100%n/a010101
strides()5100%n/a010101
spread()5100%n/a010101
offset()5100%n/a010101
shape(int)5100%n/a010101
size()5100%n/a010101
indexOfIndex(int)5100%n/a010101
indicesOfIndex(int)5100%n/a010101
indexOfIndices(int[])5100%n/a010101
rank()4100%n/a010101
getRank()3100%n/a010101
getShape()3100%n/a010101
getSize()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDimensional

NDimensional

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 63100%0 of 0n/a014014014
shape()5100%n/a010101
indicesMap()5100%n/a010101
strides()5100%n/a010101
spread()5100%n/a010101
offset()5100%n/a010101
shape(int)5100%n/a010101
size()5100%n/a010101
indexOfIndex(int)5100%n/a010101
indicesOfIndex(int)5100%n/a010101
indexOfIndices(int[])5100%n/a010101
rank()4100%n/a010101
getRank()3100%n/a010101
getShape()3100%n/a010101
getSize()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim/NDimensional.java.html b/docs/coverage/test/html/neureka.ndim/NDimensional.java.html index ff78a6d61..eaca36dc5 100644 --- a/docs/coverage/test/html/neureka.ndim/NDimensional.java.html +++ b/docs/coverage/test/html/neureka.ndim/NDimensional.java.html @@ -117,4 +117,4 @@ default int indexOfIndices( int[] indices ) { return getNDConf().indexOfIndices( indices ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim/index.html b/docs/coverage/test/html/neureka.ndim/index.html index dc02c4c2b..f257420c2 100644 --- a/docs/coverage/test/html/neureka.ndim/index.html +++ b/docs/coverage/test/html/neureka.ndim/index.html @@ -1 +1 @@ -neureka.ndim

neureka.ndim

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total69 of 33179%10 of 2458%114185732905
NDConstructor.new NDConstructor() {...}283152%5337%683112401
NDConstructor252852%1150%15390401
NDConstructor.new NDConstructor() {...}137184%4450%381170401
NDUtil36995%6100%16181301
NDimensional63100%n/a01401401401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim

neureka.ndim

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total69 of 33179%10 of 2458%114185732905
NDConstructor.new NDConstructor() {...}283152%5337%683112401
NDConstructor252852%1150%15390401
NDConstructor.new NDConstructor() {...}137184%4450%381170401
NDUtil36995%6100%16181301
NDimensional63100%n/a01401401401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.ndim/index.source.html b/docs/coverage/test/html/neureka.ndim/index.source.html index 52538a1ce..fe3dbff65 100644 --- a/docs/coverage/test/html/neureka.ndim/index.source.html +++ b/docs/coverage/test/html/neureka.ndim/index.source.html @@ -1 +1 @@ -neureka.ndim

neureka.ndim

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total69 of 33179%10 of 2458%114185732905
NDConstructor.java6613066%10844%102173521203
NDUtil.java36995%6100%16181301
NDimensional.java63100%n/a01401401401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.ndim

neureka.ndim

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total69 of 33179%10 of 2458%114185732905
NDConstructor.java6613066%10844%102173521203
NDUtil.java36995%6100%16181301
NDimensional.java63100%n/a01401401401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.optimization.implementations/ADAM.html b/docs/coverage/test/html/neureka.optimization.implementations/ADAM.html index c79318985..2b5f76dca 100644 --- a/docs/coverage/test/html/neureka.optimization.implementations/ADAM.html +++ b/docs/coverage/test/html/neureka.optimization.implementations/ADAM.html @@ -1 +1 @@ -ADAM

ADAM

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 25193%0 of 0n/a5843158
lambda$optimize$0()50%n/a111111
getMomentum()30%n/a111111
getVelocity()30%n/a111111
getTime()30%n/a111111
getLearningRate()30%n/a111111
optimize(Tensor)188100%n/a0101501
ADAM(long, double, Tensor, Tensor)27100%n/a010801
ADAM(long, double, Tensor)19100%n/a010401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ADAM

ADAM

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total17 of 25193%0 of 0n/a5843158
lambda$optimize$0()50%n/a111111
getMomentum()30%n/a111111
getVelocity()30%n/a111111
getTime()30%n/a111111
getLearningRate()30%n/a111111
optimize(Tensor)188100%n/a0101501
ADAM(long, double, Tensor, Tensor)27100%n/a010801
ADAM(long, double, Tensor)19100%n/a010401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.optimization.implementations/ADAM.java.html b/docs/coverage/test/html/neureka.optimization.implementations/ADAM.java.html index fb31e5fc8..e9a416061 100644 --- a/docs/coverage/test/html/neureka.optimization.implementations/ADAM.java.html +++ b/docs/coverage/test/html/neureka.optimization.implementations/ADAM.java.html @@ -106,4 +106,4 @@ public final double getLearningRate() { return lr; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.optimization.implementations/ADAMFactory.html b/docs/coverage/test/html/neureka.optimization.implementations/ADAMFactory.html index 79aac8f1e..5cf7e0b5b 100644 --- a/docs/coverage/test/html/neureka.optimization.implementations/ADAMFactory.html +++ b/docs/coverage/test/html/neureka.optimization.implementations/ADAMFactory.html @@ -1 +1 @@ -ADAMFactory

ADAMFactory

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 5648%1 of 250%4731036
create(Tensor, Tensor)100%n/a111111
withLearningRate(double)70%n/a111111
withTime(long)70%n/a111111
ADAMFactory(double, long)51372%1150%120501
create(Tensor)9100%n/a010101
ADAMFactory()5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +ADAMFactory

ADAMFactory

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 5648%1 of 250%4731036
create(Tensor, Tensor)100%n/a111111
withLearningRate(double)70%n/a111111
withTime(long)70%n/a111111
ADAMFactory(double, long)51372%1150%120501
create(Tensor)9100%n/a010101
ADAMFactory()5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.optimization.implementations/ADAMFactory.java.html b/docs/coverage/test/html/neureka.optimization.implementations/ADAMFactory.java.html index 5a3aba2f3..d5badd551 100644 --- a/docs/coverage/test/html/neureka.optimization.implementations/ADAMFactory.java.html +++ b/docs/coverage/test/html/neureka.optimization.implementations/ADAMFactory.java.html @@ -33,4 +33,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.optimization.implementations/AdaGrad.html b/docs/coverage/test/html/neureka.optimization.implementations/AdaGrad.html index b6b920ad9..fc853372e 100644 --- a/docs/coverage/test/html/neureka.optimization.implementations/AdaGrad.html +++ b/docs/coverage/test/html/neureka.optimization.implementations/AdaGrad.html @@ -1 +1 @@ -AdaGrad

AdaGrad

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 84100%0 of 0n/a0201002
optimize(Tensor)58100%n/a010401
AdaGrad(Tensor, double)26100%n/a010601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AdaGrad

AdaGrad

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 84100%0 of 0n/a0201002
optimize(Tensor)58100%n/a010401
AdaGrad(Tensor, double)26100%n/a010601
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Created with JaCoCo 0.8.11.202310140853
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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\ No newline at end of file +RMSPropFactory

RMSPropFactory

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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RMSPropFactory()9100%n/a010401
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Created with JaCoCo 0.8.11.202310140853
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SGD

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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.optimization.implementations/SGD.java.html b/docs/coverage/test/html/neureka.optimization.implementations/SGD.java.html index 301c6c55e..316e67b32 100644 --- a/docs/coverage/test/html/neureka.optimization.implementations/SGD.java.html +++ b/docs/coverage/test/html/neureka.optimization.implementations/SGD.java.html @@ -68,4 +68,4 @@ public double learningRate() { return _lr; } } -
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\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.optimization.implementations/SGDFactory.html b/docs/coverage/test/html/neureka.optimization.implementations/SGDFactory.html index 317c4f031..ebcf267b6 100644 --- a/docs/coverage/test/html/neureka.optimization.implementations/SGDFactory.html +++ b/docs/coverage/test/html/neureka.optimization.implementations/SGDFactory.html @@ -1 +1 @@ -SGDFactory

SGDFactory

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total95 of 73487%1 of 250%1743191151642010
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ADAM1723493%n/a584315801
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total95 of 73487%1 of 250%1743191151642010
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ADAM1723493%n/a584315801
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neureka.optimization.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total95 of 73487%1 of 250%1743191151642010
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MomentumFactory.java231843%n/a356113501
ADAM.java1723493%n/a584315801
AdaGradFactory.java111354%n/a24462401
SGD.java83280%n/a24172401
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RMSProp.java96100%n/a020110201
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neureka.optimization.implementations

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total95 of 73487%1 of 250%1743191151642010
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MomentumFactory.java231843%n/a356113501
ADAM.java1723493%n/a584315801
AdaGradFactory.java111354%n/a24462401
SGD.java83280%n/a24172401
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RMSProp.java96100%n/a020110201
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Created with JaCoCo 0.8.11.202310140853
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
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Created with JaCoCo 0.8.11.202310140853
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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Optimizer.new Optimizer() {...}51777%n/a14031401
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ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
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Optimizer.java234365%n/a51031151013
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.view/NDPrintSettings.html b/docs/coverage/test/html/neureka.view/NDPrintSettings.html index 92fcaf7e4..706853226 100644 --- a/docs/coverage/test/html/neureka.view/NDPrintSettings.html +++ b/docs/coverage/test/html/neureka.view/NDPrintSettings.html @@ -1 +1 @@ -NDPrintSettings

NDPrintSettings

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total34 of 44692%19 of 4658%19591113036
with(String)127486%8850%8911701
setCellSize(int)21184%1150%120301
setRowLimit(int)21184%1150%120301
setIsMultiline(boolean)21184%1150%120301
setHasValue(boolean)21184%1150%120301
setHasShape(boolean)21184%1150%120301
setHasRecursiveGraph(boolean)21184%1150%120301
setHasDerivatives(boolean)21184%1150%120301
setIsCellBound(boolean)21184%1150%120301
setPrefix(String)21184%1150%120301
setPostfix(String)21184%1150%120301
setIndent(String)21184%1150%120301
_imposeOn(NDPrintSettings)57100%n/a0101501
NDPrintSettings(Supplier)45100%n/a0101601
setHasGradient(boolean)13100%2100%020301
setIsScientific(boolean)13100%2100%020301
setHasSlimNumbers(boolean)13100%2100%020301
setIsLegacy(boolean)13100%2100%020301
clone()10100%n/a010301
with(NDPrintSettings)5100%n/a010201
getCellSize()3100%n/a010101
getRowLimit()3100%n/a010101
getHasGradient()3100%n/a010101
getIsScientific()3100%n/a010101
getIsMultiline()3100%n/a010101
getHasSlimNumbers()3100%n/a010101
getHasValue()3100%n/a010101
getHasShape()3100%n/a010101
getHasRecursiveGraph()3100%n/a010101
getHasDerivatives()3100%n/a010101
getIsCellBound()3100%n/a010101
getPrefix()3100%n/a010101
getPostfix()3100%n/a010101
getIndent()3100%n/a010101
getIsLegacy()3100%n/a010101
lambda$clone$0()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NDPrintSettings

NDPrintSettings

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total34 of 44692%19 of 4658%19591113036
with(String)127486%8850%8911701
setCellSize(int)21184%1150%120301
setRowLimit(int)21184%1150%120301
setIsMultiline(boolean)21184%1150%120301
setHasValue(boolean)21184%1150%120301
setHasShape(boolean)21184%1150%120301
setHasRecursiveGraph(boolean)21184%1150%120301
setHasDerivatives(boolean)21184%1150%120301
setIsCellBound(boolean)21184%1150%120301
setPrefix(String)21184%1150%120301
setPostfix(String)21184%1150%120301
setIndent(String)21184%1150%120301
_imposeOn(NDPrintSettings)57100%n/a0101501
NDPrintSettings(Supplier)45100%n/a0101601
setHasGradient(boolean)13100%2100%020301
setIsScientific(boolean)13100%2100%020301
setHasSlimNumbers(boolean)13100%2100%020301
setIsLegacy(boolean)13100%2100%020301
clone()10100%n/a010301
with(NDPrintSettings)5100%n/a010201
getCellSize()3100%n/a010101
getRowLimit()3100%n/a010101
getHasGradient()3100%n/a010101
getIsScientific()3100%n/a010101
getIsMultiline()3100%n/a010101
getHasSlimNumbers()3100%n/a010101
getHasValue()3100%n/a010101
getHasShape()3100%n/a010101
getHasRecursiveGraph()3100%n/a010101
getHasDerivatives()3100%n/a010101
getIsCellBound()3100%n/a010101
getPrefix()3100%n/a010101
getPostfix()3100%n/a010101
getIndent()3100%n/a010101
getIsLegacy()3100%n/a010101
lambda$clone$0()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.view/NDPrintSettings.java.html b/docs/coverage/test/html/neureka.view/NDPrintSettings.java.html index 0899db540..6d45decb3 100644 --- a/docs/coverage/test/html/neureka.view/NDPrintSettings.java.html +++ b/docs/coverage/test/html/neureka.view/NDPrintSettings.java.html @@ -373,4 +373,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.view/NdaAsString$1.html b/docs/coverage/test/html/neureka.view/NdaAsString$1.html index 03b7a33ea..9e8490f39 100644 --- a/docs/coverage/test/html/neureka.view/NdaAsString$1.html +++ b/docs/coverage/test/html/neureka.view/NdaAsString$1.html @@ -1 +1 @@ -NdaAsString.new NdaAsString.Builder() {...}

NdaAsString.new NdaAsString.Builder() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 31100%0 of 0n/a040404
withConfig(String)10100%n/a010101
withConfig(NDPrintSettings)8100%n/a010101
byDefaults()7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NdaAsString.new NdaAsString.Builder() {...}

NdaAsString.new NdaAsString.Builder() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 31100%0 of 0n/a040404
withConfig(String)10100%n/a010101
withConfig(NDPrintSettings)8100%n/a010101
byDefaults()7100%n/a010101
{...}6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.view/NdaAsString$Util.html b/docs/coverage/test/html/neureka.view/NdaAsString$Util.html index abc7b6e1c..9cd2000b6 100644 --- a/docs/coverage/test/html/neureka.view/NdaAsString$Util.html +++ b/docs/coverage/test/html/neureka.view/NdaAsString$Util.html @@ -1 +1 @@ -NdaAsString.Util

NdaAsString.Util

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 4192%0 of 0n/a151515
NdaAsString.Util()30%n/a111111
pad(int, String)13100%n/a010101
pad(String, int)13100%n/a010101
indent(int)6100%n/a010101
spaces(int)6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NdaAsString.Util

NdaAsString.Util

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 4192%0 of 0n/a151515
NdaAsString.Util()30%n/a111111
pad(int, String)13100%n/a010101
pad(String, int)13100%n/a010101
indent(int)6100%n/a010101
spaces(int)6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.view/NdaAsString.html b/docs/coverage/test/html/neureka.view/NdaAsString.html index cb8adceb5..0200e8bd9 100644 --- a/docs/coverage/test/html/neureka.view/NdaAsString.html +++ b/docs/coverage/test/html/neureka.view/NdaAsString.html @@ -1 +1 @@ -NdaAsString

NdaAsString

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total70 of 1,75196%19 of 23691%201555255137
toString()1020095%54189%52403101
_recursiveFormatting(int[], int)843598%46293%43426001
lambda$_createStringItemFilter$2(String)82676%1583%140301
lambda$_createStringItemFilter$3(String)81869%1375%130201
lambda$_createBasicStringifierFor$10(boolean, Function, Object, int)81361%1150%121301
lambda$_createStringItemFilter$4(String)8642%1150%120101
_createBasicStringifierFor(Object, boolean)56893%11794%11012001
lambda$toString$16(String, String, String, GraphNode, ADAction)56392%1150%120501
NdaAsString(Nda, NDPrintSettings)410496%1150%1212501
lambda$_recursiveFormatting$19(NdaAsString.ValStringifier, int[])40%n/a111111
_buildSingleLabel(NDFrame, int, int[])7797%3975%3701001
formatFP(double)97100%12100%0701401
_stringifyAllValues()80100%12100%0701201
_buildRow(int, int, int, int[], NdaAsString.NDValStringifier, String)61100%10100%060701
_strShape()47100%8100%050701
_createStringItemFilter()35100%12100%0701001
lambda$_createValStringifierAndFormatter$0(NdaAsString.ValStringifier, int, int)35100%2100%020701
lambda$toString$17(GraphNode, ADAction)34100%2100%020301
_createValStringifierAndFormatter(Object)32100%4100%030801
_postProcessed(String)30100%2100%020501
lambda$_createValStringifierAndFormatter$1(NdaAsString.ValStringifier, int, int)29100%2100%020501
lambda$_createBasicStringifierFor$9(boolean, Function, Object, int)21100%2100%020301
lambda$_createBasicStringifierFor$8(boolean, Function, Object, int)21100%2100%020301
lambda$_createBasicStringifierFor$7(boolean, Function, Object, int)21100%2100%020301
lambda$_createBasicStringifierFor$6(boolean, Function, Object, int)21100%2100%020301
lambda$_createBasicStringifierFor$5(boolean, Function, Object, int)20100%2100%020301
lambda$toString$14(NDPrintSettings)14100%n/a010401
_breakAndIndent()10100%n/a010101
lambda$toString$15(Tensor)10100%n/a010101
lambda$_recursiveFormatting$18(NdaAsString.ValStringifier, int[])9100%n/a010101
_$(String)7100%n/a010101
_$(int)7100%n/a010101
lambda$_recursiveFormatting$20(NdaAsString.ValStringifier, int[])7100%n/a010101
lambda$_createBasicStringifierFor$13(Object, int)6100%n/a010101
lambda$_createBasicStringifierFor$12(Object, int)6100%n/a010101
lambda$_createBasicStringifierFor$11(Object, int)6100%n/a010101
representing(Nda)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +NdaAsString

NdaAsString

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total70 of 1,75196%19 of 23691%201555255137
toString()1020095%54189%52403101
_recursiveFormatting(int[], int)843598%46293%43426001
lambda$_createStringItemFilter$2(String)82676%1583%140301
lambda$_createStringItemFilter$3(String)81869%1375%130201
lambda$_createBasicStringifierFor$10(boolean, Function, Object, int)81361%1150%121301
lambda$_createStringItemFilter$4(String)8642%1150%120101
_createBasicStringifierFor(Object, boolean)56893%11794%11012001
lambda$toString$16(String, String, String, GraphNode, ADAction)56392%1150%120501
NdaAsString(Nda, NDPrintSettings)410496%1150%1212501
lambda$_recursiveFormatting$19(NdaAsString.ValStringifier, int[])40%n/a111111
_buildSingleLabel(NDFrame, int, int[])7797%3975%3701001
formatFP(double)97100%12100%0701401
_stringifyAllValues()80100%12100%0701201
_buildRow(int, int, int, int[], NdaAsString.NDValStringifier, String)61100%10100%060701
_strShape()47100%8100%050701
_createStringItemFilter()35100%12100%0701001
lambda$_createValStringifierAndFormatter$0(NdaAsString.ValStringifier, int, int)35100%2100%020701
lambda$toString$17(GraphNode, ADAction)34100%2100%020301
_createValStringifierAndFormatter(Object)32100%4100%030801
_postProcessed(String)30100%2100%020501
lambda$_createValStringifierAndFormatter$1(NdaAsString.ValStringifier, int, int)29100%2100%020501
lambda$_createBasicStringifierFor$9(boolean, Function, Object, int)21100%2100%020301
lambda$_createBasicStringifierFor$8(boolean, Function, Object, int)21100%2100%020301
lambda$_createBasicStringifierFor$7(boolean, Function, Object, int)21100%2100%020301
lambda$_createBasicStringifierFor$6(boolean, Function, Object, int)21100%2100%020301
lambda$_createBasicStringifierFor$5(boolean, Function, Object, int)20100%2100%020301
lambda$toString$14(NDPrintSettings)14100%n/a010401
_breakAndIndent()10100%n/a010101
lambda$toString$15(Tensor)10100%n/a010101
lambda$_recursiveFormatting$18(NdaAsString.ValStringifier, int[])9100%n/a010101
_$(String)7100%n/a010101
_$(int)7100%n/a010101
lambda$_recursiveFormatting$20(NdaAsString.ValStringifier, int[])7100%n/a010101
lambda$_createBasicStringifierFor$13(Object, int)6100%n/a010101
lambda$_createBasicStringifierFor$12(Object, int)6100%n/a010101
lambda$_createBasicStringifierFor$11(Object, int)6100%n/a010101
representing(Nda)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.view/NdaAsString.java.html b/docs/coverage/test/html/neureka.view/NdaAsString.java.html index d05a7e029..925e1997c 100644 --- a/docs/coverage/test/html/neureka.view/NdaAsString.java.html +++ b/docs/coverage/test/html/neureka.view/NdaAsString.java.html @@ -608,4 +608,4 @@ } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.view/index.html b/docs/coverage/test/html/neureka.view/index.html index 5fd2a7bcb..808d663a6 100644 --- a/docs/coverage/test/html/neureka.view/index.html +++ b/docs/coverage/test/html/neureka.view/index.html @@ -1 +1 @@ -neureka.view

neureka.view

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total107 of 2,26995%38 of 28286%40223737628204
NdaAsString701,68196%1921791%20155525513701
NDPrintSettings3441292%192758%1959111303601
NdaAsString.Util3892%n/a15151501
NdaAsString.new NdaAsString.Builder() {...}31100%n/a04040401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.view

neureka.view

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total107 of 2,26995%38 of 28286%40223737628204
NdaAsString701,68196%1921791%20155525513701
NDPrintSettings3441292%192758%1959111303601
NdaAsString.Util3892%n/a15151501
NdaAsString.new NdaAsString.Builder() {...}31100%n/a04040401
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka.view/index.source.html b/docs/coverage/test/html/neureka.view/index.source.html index 1895d4896..0f5a479cc 100644 --- a/docs/coverage/test/html/neureka.view/index.source.html +++ b/docs/coverage/test/html/neureka.view/index.source.html @@ -1 +1 @@ -neureka.view

neureka.view

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total107 of 2,26995%38 of 28286%40223737628204
NdaAsString.java731,75095%1921791%21164626324603
NDPrintSettings.java3441292%192758%1959111303601
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka.view

neureka.view

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total107 of 2,26995%38 of 28286%40223737628204
NdaAsString.java731,75095%1921791%21164626324603
NDPrintSettings.java3441292%192758%1959111303601
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/AbstractNda.html b/docs/coverage/test/html/neureka/AbstractNda.html index f2ac23666..e2e93db7e 100644 --- a/docs/coverage/test/html/neureka/AbstractNda.html +++ b/docs/coverage/test/html/neureka/AbstractNda.html @@ -1 +1 @@ -AbstractNda

AbstractNda

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total36 of 63694%11 of 8286%12748101133
_allocateVirtual()140%n/a116611
_setData(Data)74285%21285%281801
_convertedDataOfType(Class)72376%2250%231701
_setNDConf(NDConfiguration)43589%1583%140701
_getRawData()2777%1150%120101
getItemType()1694%2250%230101
getRepresentativeItemClass()1694%2250%230101
_initDataArrayFrom(Filler)121100%16100%0902001
_setDataAndCountUsage(Data)21100%6100%040601
lambda$_initDataArrayFrom$7(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$6(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$5(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$4(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$3(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$2(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$1(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$0(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$8(Object, Filler, int, int)19100%2100%020301
getDataType()16100%1375%130101
_guardGet(String)13100%n/a010101
_guardSet(String)13100%n/a010101
_guardMod(String)13100%n/a010101
_guard(String)12100%2100%020401
is(Class)12100%2100%020201
constructFor(AbstractNda, Device, NDConstructor)10100%n/a010101
_virtualize()8100%n/a010101
_actualize()8100%n/a010101
AbstractNda()6100%n/a010201
getNDConf()6100%n/a010101
_getData()6100%n/a010101
lambda$_setNDConf$10(int, int)4100%n/a010101
lambda$_setNDConf$9(int, int)4100%n/a010101
static {...}100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +AbstractNda

AbstractNda

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total48 of 60292%14 of 8082%13701097130
_setData(Data)212857%5964%383801
_allocateVirtual()140%n/a116611
_convertedDataOfType(Class)72376%2250%231701
is(Class)21083%1150%120201
_getRawData()2777%1150%120101
getItemType()1694%2250%230101
getRepresentativeItemClass()1694%2250%230101
_initDataArrayFrom(Filler)121100%16100%0902001
_setDataAndCountUsage(Data)25100%8100%050601
lambda$_initDataArrayFrom$7(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$6(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$5(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$4(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$3(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$2(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$1(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$0(Object, Filler, int, int)21100%2100%020301
lambda$_initDataArrayFrom$8(Object, Filler, int, int)19100%2100%020301
getDataType()16100%1375%130101
_guardGet(String)13100%n/a010101
_guardSet(String)13100%n/a010101
_guardMod(String)13100%n/a010101
_guard(String)12100%2100%020401
_setNDConf(NDConfiguration)11100%2100%020301
constructFor(Device, NDConstructor)9100%n/a010101
_virtualize()8100%n/a010101
_actualize()8100%n/a010101
AbstractNda()6100%n/a010201
getNDConf()6100%n/a010101
_getData()6100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/AbstractNda.java.html b/docs/coverage/test/html/neureka/AbstractNda.java.html index cdd3b1ae8..930a086ea 100644 --- a/docs/coverage/test/html/neureka/AbstractNda.java.html +++ b/docs/coverage/test/html/neureka/AbstractNda.java.html @@ -153,9 +153,9 @@ _guardSet( "data object" ); Object data = newData == null ? null : newData.getOrNull(); // Note: If the data is null, this might mean the tensor is outsourced (data is somewhere else) - if ( _data != null && _data.getOrNull() != data && data != null && _data.getOrNull() != null ) { - boolean isProbablyDeviceTransfer = ( _data.getOrNull().getClass().isArray() != data.getClass().isArray() ); - if ( !isProbablyDeviceTransfer) + if ( _data != null && _data.getOrNull() != data && data != null && _data.getOrNull() != null ) { + boolean isProbablyDeviceTransfer = ( _data.getOrNull().getClass().isArray() != data.getClass().isArray() ); + if ( !isProbablyDeviceTransfer) _version++; // Autograd must be warned! } _setDataAndCountUsage( newData ); @@ -167,7 +167,7 @@ if ( newData instanceof DeviceData ) ( (DeviceData<?>) newData ).incrementUsageCount(); - _data = newData; // This must be the only place where the data is set!!! + _data = ( newData != null ? newData : (Data<V>) Data.none()); // This must be the only place where the data is set!!! } protected <T> void _initDataArrayFrom( Filler<T> filler ) @@ -254,17 +254,13 @@ * * @return An {@link TensorConstructor} exposing a simple API for configuring a new {@link Tensor} instance. */ - protected static TensorConstructor constructFor(AbstractNda<?, ?> nda, Device<?> targetDevice, NDConstructor ndConstructor ) + protected static TensorConstructor constructFor( Device<?> targetDevice, NDConstructor ndConstructor ) { return - new TensorConstructor( - targetDevice, ndConstructor, - new TensorConstructor.API() { - @Override public void setConf( NDConfiguration conf ) { nda.mut().setNDConf( conf ); } - @Override public void setData( Data o ) { nda._setData( o ); } - @Override public void setIsVirtual( boolean isVirtual ) { nda._setIsVirtual( isVirtual ); } - } - ); + new TensorConstructor( + targetDevice, ndConstructor, + new TensorConstructor.Args() + ); } /** @@ -276,7 +272,7 @@ * It would be unreasonable to allocate an arrays filled entirely with one and the same value item! * <br> */ - protected final void _virtualize() { _setDataAndCountUsage(getDevice().access(this).virtualize()); } + protected final void _virtualize() { _setDataAndCountUsage(getDevice().access(this).virtualize()); } /** * An actual NDArray (tensor) is the opposite to a virtual one. <br> @@ -288,21 +284,21 @@ * This method turns the data of a virtual NDArray into a newly allocated data array matching the * size of the nd-array type... <br> */ - protected final void _actualize() { _setDataAndCountUsage(getDevice().access(this).actualize()); } + protected final void _actualize() { _setDataAndCountUsage(getDevice().access(this).actualize()); } protected Object _convertedDataOfType( Class<?> typeClass ) { - DataType<?> newDT = DataType.of( typeClass ); - if ( - newDT.typeClassImplements( NumericType.class ) + DataType<?> newDT = DataType.of( typeClass ); + if ( + newDT.typeClassImplements( NumericType.class ) && - getDataType().typeClassImplements( NumericType.class ) + getDataType().typeClassImplements( NumericType.class ) ) { - NumericType<?,Object, ?, Object> targetType = (NumericType<?, Object,?, Object>) newDT.getTypeClassInstance(NumericType.class); - return targetType.readForeignDataFrom( iterator(), this.size() ); + NumericType<?,Object, ?, Object> targetType = (NumericType<?, Object,?, Object>) newDT.getTypeClassInstance(NumericType.class); + return targetType.readForeignDataFrom( iterator(), this.size() ); } else - return DataConverter.get().convert( _getRawData(), newDT.getRepresentativeType() ); + return DataConverter.get().convert( _getRawData(), newDT.getRepresentativeType() ); } /** @@ -310,8 +306,8 @@ */ @Override public boolean is( Class<?> typeClass ) { - DataType<?> type = DataType.of( typeClass ); - return type == _getData().dataType(); + DataType<?> type = DataType.of( typeClass ); + return type == _getData().dataType(); } /** @@ -322,14 +318,9 @@ */ protected void _setNDConf( NDConfiguration ndConfiguration ) { - _guardSet( "ND-Configuration" ); - if ( _NDConf != null && ndConfiguration != null ) { - int s1 = Arrays.stream( _NDConf.shape() ).map( Math::abs ).reduce( 1, ( a, b ) -> a * b ); - int s2 = Arrays.stream( ndConfiguration.shape() ).map( Math::abs ).reduce( 1, ( a, b ) -> a * b ); - assert s1 == s2; - } - _NDConf = ndConfiguration; - } + _guardSet( "ND-Configuration" ); + _NDConf = ( ndConfiguration != null ? ndConfiguration : NDConfiguration.none() ); + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Data.html b/docs/coverage/test/html/neureka/Data.html index 502e477c7..4c558014b 100644 --- a/docs/coverage/test/html/neureka/Data.html +++ b/docs/coverage/test/html/neureka/Data.html @@ -1 +1 @@ -Data

Data

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total54 of 9543%3 of 650%915717612
as(Class)191442%2250%231401
get()5758%1150%120301
of(Class, Object[])50%n/a111111
of(byte[])50%n/a111111
of(short[])50%n/a111111
of(boolean[])50%n/a111111
of(char[])50%n/a111111
of(String[])50%n/a111111
of(float[])5100%n/a010101
of(double[])5100%n/a010101
of(int[])5100%n/a010101
of(long[])5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Data

Data

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total54 of 9744%3 of 650%916718613
as(Class)191442%2250%231401
get()5758%1150%120301
of(Class, Object[])50%n/a111111
of(byte[])50%n/a111111
of(short[])50%n/a111111
of(boolean[])50%n/a111111
of(char[])50%n/a111111
of(String[])50%n/a111111
of(float[])5100%n/a010101
of(double[])5100%n/a010101
of(int[])5100%n/a010101
of(long[])5100%n/a010101
none()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Data.java.html b/docs/coverage/test/html/neureka/Data.java.html index 0ca98622c..434327a35 100644 --- a/docs/coverage/test/html/neureka/Data.java.html +++ b/docs/coverage/test/html/neureka/Data.java.html @@ -16,25 +16,36 @@ */ public interface Data<V> { - static <V> Data<V> of( Class<V> type, V... data ) { return CPU.get().allocate( type, data ); } + /** + * This is a static factory method which returns a {@link Data} object + * which does not contain any data. It is a sort of no-operation null object + * which can be used to represent the absence of data. + * A deleted tensor will typically have a {@link Data} object which does not contain any data. + * + * @return A {@link Data} object which does not contain any data. + */ + static Data<Void> none() { return NoOpData.INSTANCE; } + + + static <V> Data<V> of( Class<V> type, V... data ) { return CPU.get().allocate( type, data ); } - static Data<Float> of( float... items ) { return CPU.get().allocate( Float.class, items ); } + static Data<Float> of( float... items ) { return CPU.get().allocate( Float.class, items ); } - static Data<Double> of( double... items ) { return CPU.get().allocate( Double.class, items ); } + static Data<Double> of( double... items ) { return CPU.get().allocate( Double.class, items ); } - static Data<Integer> of( int... items ) { return CPU.get().allocate( Integer.class, items ); } + static Data<Integer> of( int... items ) { return CPU.get().allocate( Integer.class, items ); } - static Data<Long> of( long... items ) { return CPU.get().allocate( Long.class, items ); } + static Data<Long> of( long... items ) { return CPU.get().allocate( Long.class, items ); } - static Data<Byte> of( byte... items ) { return CPU.get().allocate( Byte.class, items ); } + static Data<Byte> of( byte... items ) { return CPU.get().allocate( Byte.class, items ); } - static Data<Short> of( short... items ) { return CPU.get().allocate( Short.class, items ); } + static Data<Short> of( short... items ) { return CPU.get().allocate( Short.class, items ); } - static Data<Boolean> of( boolean... items ) { return CPU.get().allocate( Boolean.class, items ); } + static Data<Boolean> of( boolean... items ) { return CPU.get().allocate( Boolean.class, items ); } - static Data<Character> of( char... items ) { return CPU.get().allocate( Character.class, items ); } + static Data<Character> of( char... items ) { return CPU.get().allocate( Character.class, items ); } - static Data<String> of( String... items ) { return CPU.get().allocate( String.class, items ); } + static Data<String> of( String... items ) { return CPU.get().allocate( String.class, items ); } /** * @return The owner of this data array wrapper (the device which allocated the memory). @@ -63,9 +74,9 @@ * @return The raw data object underlying a nd-array/tensor. */ default Object get() { - Object data = getOrNull(); - if ( data == null ) throw new NullPointerException("The data reference is missing!"); - return data; + Object data = getOrNull(); + if ( data == null ) throw new NullPointerException("The data reference is missing!"); + return data; } /** @@ -78,10 +89,10 @@ * @return The raw data object underlying a nd-array/tensor. */ default <D> D as( Class<D> dataType ) { - Object data = getOrNull(); - if ( data != null && !dataType.isAssignableFrom(data.getClass()) ) - throw new IllegalArgumentException("Provided data type '"+dataType+"' is not assignable from '"+data.getClass()+"'."); - return dataType.cast(data); + Object data = getOrNull(); + if ( data != null && !dataType.isAssignableFrom(data.getClass()) ) + throw new IllegalArgumentException("Provided data type '"+dataType+"' is not assignable from '"+data.getClass()+"'."); + return dataType.cast(data); } /** @@ -100,4 +111,4 @@ */ int usages(); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/MutateNda.html b/docs/coverage/test/html/neureka/MutateNda.html index 4dfcd26b5..c4bc88a3d 100644 --- a/docs/coverage/test/html/neureka/MutateNda.html +++ b/docs/coverage/test/html/neureka/MutateNda.html @@ -1 +1 @@ -MutateNda

MutateNda

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total32 of 320%0 of 0n/a222222
set(int, int, int, Object)180%n/a111111
set(int, int, Object)140%n/a111111
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +MutateNda

MutateNda

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total32 of 320%0 of 0n/a222222
set(int, int, int, Object)180%n/a111111
set(int, int, Object)140%n/a111111
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/MutateNda.java.html b/docs/coverage/test/html/neureka/MutateNda.java.html index 044d0c0a9..b2c0e8824 100644 --- a/docs/coverage/test/html/neureka/MutateNda.java.html +++ b/docs/coverage/test/html/neureka/MutateNda.java.html @@ -283,4 +283,4 @@ void set( V value ); } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/MutateTensor.html b/docs/coverage/test/html/neureka/MutateTensor.html index 505a4f542..c41077c40 100644 --- a/docs/coverage/test/html/neureka/MutateTensor.html +++ b/docs/coverage/test/html/neureka/MutateTensor.html @@ -1 +1 @@ -MutateTensor

MutateTensor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 9770%3 of 650%3831305
getDataForWriting(Class)292647%3350%343901
set(int, int, int, Object)18100%n/a010101
set(int, int, Object)14100%n/a010101
set(int[], Object)5100%n/a010101
set(int, Object)5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +MutateTensor

MutateTensor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total29 of 9770%3 of 650%3831305
getDataForWriting(Class)292647%3350%343901
set(int, int, int, Object)18100%n/a010101
set(int, int, Object)14100%n/a010101
set(int[], Object)5100%n/a010101
set(int, Object)5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/MutateTensor.java.html b/docs/coverage/test/html/neureka/MutateTensor.java.html index 699c06142..681f29e2e 100644 --- a/docs/coverage/test/html/neureka/MutateTensor.java.html +++ b/docs/coverage/test/html/neureka/MutateTensor.java.html @@ -328,4 +328,4 @@ Tensor<T> setIsVirtual(boolean isVirtual ); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Nda$Item.html b/docs/coverage/test/html/neureka/Nda$Item.html index 0743ad982..4c015e3c2 100644 --- a/docs/coverage/test/html/neureka/Nda$Item.html +++ b/docs/coverage/test/html/neureka/Nda$Item.html @@ -1 +1 @@ -Nda.Item

Nda.Item

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 6478%4 of 1471%41321416
map(Function)120%20%222211
exists()1685%1150%120101
doesNotExist()1685%1150%120101
orElse(Object)16100%4100%030401
get()12100%2100%020401
toOptional()10100%2100%020201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Nda.Item

Nda.Item

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total14 of 6478%4 of 1471%41321416
map(Function)120%20%222211
exists()1685%1150%120101
doesNotExist()1685%1150%120101
orElse(Object)16100%4100%030401
get()12100%2100%020401
toOptional()10100%2100%020201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Nda.html b/docs/coverage/test/html/neureka/Nda.html index 779f79db5..9a6caf13d 100644 --- a/docs/coverage/test/html/neureka/Nda.html +++ b/docs/coverage/test/html/neureka/Nda.html @@ -1 +1 @@ -Nda

Nda

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total152 of 62075%15 of 5070%289019991665
of(float[])120%n/a111111
of(byte[])120%n/a111111
of(long[])120%n/a111111
of(short[])120%n/a111111
count(Predicate)101152%3125%230201
lambda$flatMap$5(Function, Object)91359%3125%233601
none(Predicate)9950%3125%230201
of(Shape, Object[])60%n/a111111
getDataAs(Class)60%n/a111111
lambda$shaped$6(List, List)60%n/a111111
item(int[])55691%1583%140801
any(Predicate)5964%1150%120201
of(Shape, double[])50%n/a111111
of(Shape, float[])50%n/a111111
of(Shape, byte[])50%n/a111111
of(Shape, int[])50%n/a111111
of(Shape, long[])50%n/a111111
of(Shape, short[])50%n/a111111
of(Shape, boolean[])50%n/a111111
minItem(Comparator)31178%1150%120201
maxItem(Comparator)31178%1150%120201
of(Iterable)30%n/a111111
mut()30%n/a111111
isFullSlice()1990%2250%230101
stream()110100%20100%01102001
toString(Consumer)15100%n/a010301
every(Predicate)14100%2100%020201
of(double)12100%n/a010101
of(double[])12100%n/a010101
of(int[])12100%n/a010101
of(boolean[])12100%n/a010101
getLabel()10100%n/a010101
shaped(int[])9100%n/a010101
flatMap(Function)6100%n/a010101
getItems()6100%n/a010101
item(int)6100%n/a010101
getItemsAs(Class)6100%n/a010101
toString(NDPrintSettings)6100%n/a010101
lambda$stream$4(Object, int)6100%n/a010101
lambda$stream$3(Object, int)6100%n/a010101
lambda$stream$2(Object, int)6100%n/a010101
lambda$stream$1(Object, int)6100%n/a010101
lambda$stream$0(Object, int)6100%n/a010101
of(Class)5100%n/a010101
filter(Predicate)5100%n/a010101
lambda$shaped$7(int[], List)5100%n/a010101
item()4100%n/a010101
ofStrings()3100%n/a010101
ofInts()3100%n/a010101
ofDoubles()3100%n/a010101
ofFloats()3100%n/a010101
ofLongs()3100%n/a010101
ofBooleans()3100%n/a010101
ofChars()3100%n/a010101
ofBytes()3100%n/a010101
ofShorts()3100%n/a010101
ofObjects()3100%n/a010101
ofNumbers()3100%n/a010101
ofBigDecimals()3100%n/a010101
of(Object[])3100%n/a010101
of(List)3100%n/a010101
label()3100%n/a010101
itemType()3100%n/a010101
items()3100%n/a010101
getItem()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Nda

Nda

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total158 of 62074%17 of 5066%309019991665
of(float[])120%n/a111111
of(byte[])120%n/a111111
of(long[])120%n/a111111
of(short[])120%n/a111111
count(Predicate)101152%3125%230201
lambda$flatMap$5(Function, Object)91359%3125%233601
none(Predicate)9950%3125%230201
stream()610494%21890%21102001
of(Shape, Object[])60%n/a111111
getDataAs(Class)60%n/a111111
lambda$shaped$6(List, List)60%n/a111111
item(int[])55691%1583%140801
any(Predicate)5964%1150%120201
of(Shape, double[])50%n/a111111
of(Shape, float[])50%n/a111111
of(Shape, byte[])50%n/a111111
of(Shape, int[])50%n/a111111
of(Shape, long[])50%n/a111111
of(Shape, short[])50%n/a111111
of(Shape, boolean[])50%n/a111111
minItem(Comparator)31178%1150%120201
maxItem(Comparator)31178%1150%120201
of(Iterable)30%n/a111111
mut()30%n/a111111
isFullSlice()1990%2250%230101
toString(Consumer)15100%n/a010301
every(Predicate)14100%2100%020201
of(double)12100%n/a010101
of(double[])12100%n/a010101
of(int[])12100%n/a010101
of(boolean[])12100%n/a010101
getLabel()10100%n/a010101
shaped(int[])9100%n/a010101
flatMap(Function)6100%n/a010101
getItems()6100%n/a010101
item(int)6100%n/a010101
getItemsAs(Class)6100%n/a010101
toString(NDPrintSettings)6100%n/a010101
lambda$stream$4(Object, int)6100%n/a010101
lambda$stream$3(Object, int)6100%n/a010101
lambda$stream$2(Object, int)6100%n/a010101
lambda$stream$1(Object, int)6100%n/a010101
lambda$stream$0(Object, int)6100%n/a010101
of(Class)5100%n/a010101
filter(Predicate)5100%n/a010101
lambda$shaped$7(int[], List)5100%n/a010101
item()4100%n/a010101
ofStrings()3100%n/a010101
ofInts()3100%n/a010101
ofDoubles()3100%n/a010101
ofFloats()3100%n/a010101
ofLongs()3100%n/a010101
ofBooleans()3100%n/a010101
ofChars()3100%n/a010101
ofBytes()3100%n/a010101
ofShorts()3100%n/a010101
ofObjects()3100%n/a010101
ofNumbers()3100%n/a010101
ofBigDecimals()3100%n/a010101
of(Object[])3100%n/a010101
of(List)3100%n/a010101
label()3100%n/a010101
itemType()3100%n/a010101
items()3100%n/a010101
getItem()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Nda.java.html b/docs/coverage/test/html/neureka/Nda.java.html index 8be774bd0..502f5ee76 100644 --- a/docs/coverage/test/html/neureka/Nda.java.html +++ b/docs/coverage/test/html/neureka/Nda.java.html @@ -504,8 +504,8 @@ else stream = (Stream<V>) Arrays.stream( (Object[]) rawItems ); - boolean executeInParallel = ( this.size() > 1_000 ); - return executeInParallel ? stream.parallel() : stream; + boolean executeInParallel = ( this.size() > 1_000 ); + return executeInParallel ? stream.parallel() : stream; } /** @@ -1178,4 +1178,4 @@ String toString(); } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Neureka$Settings$AutoGrad.html b/docs/coverage/test/html/neureka/Neureka$Settings$AutoGrad.html index 2f86021eb..1d0c9ff7f 100644 --- a/docs/coverage/test/html/neureka/Neureka$Settings$AutoGrad.html +++ b/docs/coverage/test/html/neureka/Neureka$Settings$AutoGrad.html @@ -1 +1 @@ -Neureka.Settings.AutoGrad

Neureka.Settings.AutoGrad

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 108100%3 of 1478%317026010
toString()27100%n/a010501
Neureka.Settings.AutoGrad(Neureka.Settings)18100%n/a010501
setIsPreventingInlineOperations(boolean)14100%1375%130301
setIsRetainingPendingErrorForJITProp(boolean)14100%1375%130301
setIsApplyingGradientWhenTensorIsUsed(boolean)14100%1375%130301
setIsApplyingGradientWhenRequested(boolean)9100%2100%020301
isPreventingInlineOperations()3100%n/a010101
isRetainingPendingErrorForJITProp()3100%n/a010101
isApplyingGradientWhenTensorIsUsed()3100%n/a010101
isApplyingGradientWhenRequested()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Neureka.Settings.AutoGrad

Neureka.Settings.AutoGrad

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 108100%3 of 1478%317026010
toString()27100%n/a010501
Neureka.Settings.AutoGrad(Neureka.Settings)18100%n/a010501
setIsPreventingInlineOperations(boolean)14100%1375%130301
setIsRetainingPendingErrorForJITProp(boolean)14100%1375%130301
setIsApplyingGradientWhenTensorIsUsed(boolean)14100%1375%130301
setIsApplyingGradientWhenRequested(boolean)9100%2100%020301
isPreventingInlineOperations()3100%n/a010101
isRetainingPendingErrorForJITProp()3100%n/a010101
isApplyingGradientWhenTensorIsUsed()3100%n/a010101
isApplyingGradientWhenRequested()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Neureka$Settings$DType.html b/docs/coverage/test/html/neureka/Neureka$Settings$DType.html index fbe1068f6..8457ab028 100644 --- a/docs/coverage/test/html/neureka/Neureka$Settings$DType.html +++ b/docs/coverage/test/html/neureka/Neureka$Settings$DType.html @@ -1 +1 @@ -Neureka.Settings.DType

Neureka.Settings.DType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 5796%2 of 450%2901407
setDefaultDataTypeClass(Class)1888%1150%120301
setIsAutoConvertingExternalDataToJVMTypes(boolean)1888%1150%120301
toString()17100%n/a010201
Neureka.Settings.DType(Neureka.Settings)12100%n/a010301
getDefaultDataType()4100%n/a010101
getDefaultDataTypeClass()3100%n/a010101
getIsAutoConvertingExternalDataToJVMTypes()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Neureka.Settings.DType

Neureka.Settings.DType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 5796%2 of 450%2901407
setDefaultDataTypeClass(Class)1888%1150%120301
setIsAutoConvertingExternalDataToJVMTypes(boolean)1888%1150%120301
toString()17100%n/a010201
Neureka.Settings.DType(Neureka.Settings)12100%n/a010301
getDefaultDataType()4100%n/a010101
getDefaultDataTypeClass()3100%n/a010101
getIsAutoConvertingExternalDataToJVMTypes()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Neureka$Settings$Debug.html b/docs/coverage/test/html/neureka/Neureka$Settings$Debug.html index 708d7e9eb..74daec89c 100644 --- a/docs/coverage/test/html/neureka/Neureka$Settings$Debug.html +++ b/docs/coverage/test/html/neureka/Neureka$Settings$Debug.html @@ -1 +1 @@ -Neureka.Settings.Debug

Neureka.Settings.Debug

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total1 of 4897%1 of 475%1801306
setIsDeletingIntermediateTensors(boolean)1888%1150%120301
Neureka.Settings.Debug(Neureka.Settings)12100%n/a010301
toString()12100%n/a010201
setIsKeepingDerivativeTargetPayloads(boolean)9100%2100%020301
isKeepingDerivativeTargetPayloads()3100%n/a010101
isDeletingIntermediateTensors()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Neureka.Settings.Debug

Neureka.Settings.Debug

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total1 of 4897%1 of 475%1801306
setIsDeletingIntermediateTensors(boolean)1888%1150%120301
Neureka.Settings.Debug(Neureka.Settings)12100%n/a010301
toString()12100%n/a010201
setIsKeepingDerivativeTargetPayloads(boolean)9100%2100%020301
isKeepingDerivativeTargetPayloads()3100%n/a010101
isDeletingIntermediateTensors()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Neureka$Settings$NDim.html b/docs/coverage/test/html/neureka/Neureka$Settings$NDim.html index 52d2cff03..5bcf51b8a 100644 --- a/docs/coverage/test/html/neureka/Neureka$Settings$NDim.html +++ b/docs/coverage/test/html/neureka/Neureka$Settings$NDim.html @@ -1 +1 @@ -Neureka.Settings.NDim

Neureka.Settings.NDim

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 33100%0 of 2100%050804
toString()12100%n/a010201
Neureka.Settings.NDim(Neureka.Settings)9100%n/a010201
setIsOnlyUsingDefaultNDConfiguration(boolean)9100%2100%020301
isOnlyUsingDefaultNDConfiguration()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Neureka.Settings.NDim

Neureka.Settings.NDim

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 33100%0 of 2100%050804
toString()12100%n/a010201
Neureka.Settings.NDim(Neureka.Settings)9100%n/a010201
setIsOnlyUsingDefaultNDConfiguration(boolean)9100%2100%020301
isOnlyUsingDefaultNDConfiguration()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Neureka$Settings$View.html b/docs/coverage/test/html/neureka/Neureka$Settings$View.html index 57b5d13f4..5a758e09b 100644 --- a/docs/coverage/test/html/neureka/Neureka$Settings$View.html +++ b/docs/coverage/test/html/neureka/Neureka$Settings$View.html @@ -1 +1 @@ -Neureka.Settings.View

Neureka.Settings.View

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 37100%0 of 0n/a050505
Neureka.Settings.View(Neureka.Settings)13100%n/a010101
toString()12100%n/a010201
ndArrays(Consumer)5100%n/a010101
lambda$new$0(Neureka.Settings)4100%n/a010101
getNDPrintSettings()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Neureka.Settings.View

Neureka.Settings.View

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 37100%0 of 0n/a050505
Neureka.Settings.View(Neureka.Settings)13100%n/a010101
toString()12100%n/a010201
ndArrays(Consumer)5100%n/a010101
lambda$new$0(Neureka.Settings)4100%n/a010101
getNDPrintSettings()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Neureka$Settings.html b/docs/coverage/test/html/neureka/Neureka$Settings.html index 800615a5b..1cecc2575 100644 --- a/docs/coverage/test/html/neureka/Neureka$Settings.html +++ b/docs/coverage/test/html/neureka/Neureka$Settings.html @@ -1 +1 @@ -Neureka.Settings

Neureka.Settings

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 15998%2 of 666%218133015
notModifiable()31885%2466%241601
Neureka.Settings(Neureka)39100%n/a010801
toString()37100%n/a010201
debug(Object)8100%n/a010201
autograd(Object)8100%n/a010201
view(Object)8100%n/a010201
ndim(Object)8100%n/a010201
dtype(Object)8100%n/a010201
setIsLocked(boolean)4100%n/a010101
debug()3100%n/a010101
autograd()3100%n/a010101
view()3100%n/a010101
ndim()3100%n/a010101
dtype()3100%n/a010101
isLocked()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Neureka.Settings

Neureka.Settings

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total3 of 15998%2 of 666%218133015
notModifiable()31885%2466%241601
Neureka.Settings(Neureka)39100%n/a010801
toString()37100%n/a010201
debug(Object)8100%n/a010201
autograd(Object)8100%n/a010201
view(Object)8100%n/a010201
ndim(Object)8100%n/a010201
dtype(Object)8100%n/a010201
setIsLocked(boolean)4100%n/a010101
debug()3100%n/a010101
autograd()3100%n/a010101
view()3100%n/a010101
ndim()3100%n/a010101
dtype()3100%n/a010101
isLocked()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Neureka$Utility.html b/docs/coverage/test/html/neureka/Neureka$Utility.html index e20c958d1..29fb79d67 100644 --- a/docs/coverage/test/html/neureka/Neureka$Utility.html +++ b/docs/coverage/test/html/neureka/Neureka$Utility.html @@ -1 +1 @@ -Neureka.Utility

Neureka.Utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total53 of 11654%2 of 875%2772403
readResource(String)293957%1583%1441301
isPresent(String, Supplier)242146%1150%1231001
Neureka.Utility()3100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Neureka.Utility

Neureka.Utility

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total34 of 11670%2 of 875%2762403
readResource(String)293957%1583%1441301
isPresent(String, Supplier)54088%1150%1221001
Neureka.Utility()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Neureka.html b/docs/coverage/test/html/neureka/Neureka.html index 3b37e5355..d755780e3 100644 --- a/docs/coverage/test/html/neureka/Neureka.html +++ b/docs/coverage/test/html/neureka/Neureka.html @@ -1 +1 @@ -Neureka

Neureka

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total101 of 36372%15 of 3658%16391681121
backend()307370%5964%5852301
reset()21725%n/a015901
get()152865%1375%1321101
set(Neureka)15937%1150%122501
_illegalStateFor(String)140%n/a112211
_currentThreadIsNotAuthorized()2777%1150%120101
canAccessOpenCLDevice()11894%2250%230501
canAccessOpenCL()11794%2250%230501
lambda$canAccessOpenCLDevice$2(CLBackend)1787%1150%120101
lambda$canAccessOpenCL$1(CLBackend)1787%1150%120101
toString()22100%n/a010101
Neureka()15100%n/a010401
static {...}14100%n/a010501
configure(Object)12100%1150%120301
settings(Object)8100%n/a010201
setBackend(BackendContext)4100%n/a010101
settings()3100%n/a010101
utility()3100%n/a010101
getBackend()3100%n/a010101
lambda$static$0()3100%n/a010101
version()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Neureka

Neureka

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total146 of 36359%19 of 3647%18392281321
backend()356866%5964%5852301
reset()21725%n/a015901
get()152865%1375%1321101
set(Neureka)15937%1150%122501
canAccessOpenCLDevice()14526%3125%233501
canAccessOpenCL()14422%3125%233501
_illegalStateFor(String)140%n/a112211
lambda$canAccessOpenCLDevice$2(CLBackend)80%20%221111
lambda$canAccessOpenCL$1(CLBackend)80%20%221111
_currentThreadIsNotAuthorized()2777%1150%120101
toString()22100%n/a010101
Neureka()15100%n/a010401
static {...}14100%n/a010501
configure(Object)12100%1150%120301
settings(Object)8100%n/a010201
setBackend(BackendContext)4100%n/a010101
settings()3100%n/a010101
utility()3100%n/a010101
getBackend()3100%n/a010101
lambda$static$0()3100%n/a010101
version()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Neureka.java.html b/docs/coverage/test/html/neureka/Neureka.java.html index dcb130b14..d025d4d73 100644 --- a/docs/coverage/test/html/neureka/Neureka.java.html +++ b/docs/coverage/test/html/neureka/Neureka.java.html @@ -78,7 +78,7 @@ /** * The current semantic version of this library build. */ - private static String _VERSION = "1.0.0"; + private static String _VERSION = "1.0.1"; /** * The truth value determining if OpenCL is available or not. @@ -139,9 +139,9 @@ _backend.set( new CPUBackend() ); // CPU (JVM) is always available! if ( _OPENCL_AVAILABLE ) - _backend.set( new CLBackend() ); // OpenCL is available if the jocl dependency can find OpenCL drivers! + _backend.set( new CLBackend() ); // OpenCL is available if the jocl dependency can find OpenCL drivers! else - _LOG.debug( Messages.clContextCreationFailed() ); + _LOG.debug( Messages.clContextCreationFailed() ); } return _backend; } @@ -217,10 +217,10 @@ */ public boolean canAccessOpenCL() { return _OPENCL_AVAILABLE && - get().backend() - .find(CLBackend.class) - .map( it -> it.getTotalNumberOfDevices() > 0 ) - .orElse(false); + get().backend() + .find(CLBackend.class) + .map( it -> it.getTotalNumberOfDevices() > 0 ) + .orElse(false); } /** @@ -228,10 +228,10 @@ */ public boolean canAccessOpenCLDevice() { return canAccessOpenCL() && - get().backend() - .find(CLBackend.class) - .map( it -> it.getTotalNumberOfDevices() > 0 ) - .orElse(false); + get().backend() + .find(CLBackend.class) + .map( it -> it.getTotalNumberOfDevices() > 0 ) + .orElse(false); } /** @@ -772,14 +772,14 @@ boolean found = false; String cause = " unknown "; try { - Class.forName( className ); - found = true; - } catch ( Throwable ex ) {// Class or one of its dependencies is not present... - cause = ex.getMessage(); + Class.forName( className ); + found = true; + } catch ( Throwable ex ) {// Class or one of its dependencies is not present... + cause = ex.getMessage(); } finally { String tipMessage = tip.get().replace("\n", "\n "+" ").trim(); if ( !found ) - _LOG.debug( + _LOG.debug( "Neureka:\n" + " info: Failed to load class '" + className + "'!" + "\n" + " cause: " + cause + "\n" + @@ -792,4 +792,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/NoOpData.html b/docs/coverage/test/html/neureka/NoOpData.html new file mode 100644 index 000000000..51cf14ca7 --- /dev/null +++ b/docs/coverage/test/html/neureka/NoOpData.html @@ -0,0 +1 @@ +NoOpData

NoOpData

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 1464%0 of 0n/a252525
dataType()30%n/a111111
usages()20%n/a111111
static {...}5100%n/a010101
owner()2100%n/a010101
getOrNull()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/NoOpData.java.html b/docs/coverage/test/html/neureka/NoOpData.java.html new file mode 100644 index 000000000..1026170e3 --- /dev/null +++ b/docs/coverage/test/html/neureka/NoOpData.java.html @@ -0,0 +1,33 @@ +NoOpData.java

NoOpData.java

package neureka;
+
+import neureka.devices.Device;
+import neureka.devices.host.CPU;
+import neureka.dtype.DataType;
+
+final class NoOpData implements Data<Void>
+{
+    static final NoOpData INSTANCE = new NoOpData();
+
+    private NoOpData() {}
+
+    @Override
+    public Device<Void> owner() {
+        return (Device) CPU.get();
+    }
+
+    @Override
+    public Object getOrNull() {
+        return null;
+    }
+
+    @Override
+    public DataType<Void> dataType() {
+        return DataType.of(Void.class);
+    }
+
+    @Override
+    public int usages() {
+        return 0;
+    }
+}
+
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Shape$1.html b/docs/coverage/test/html/neureka/Shape$1.html index 558d15c38..d22b053b9 100644 --- a/docs/coverage/test/html/neureka/Shape$1.html +++ b/docs/coverage/test/html/neureka/Shape$1.html @@ -1 +1 @@ -Shape.new Shape() {...}

Shape.new Shape() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 10198%2 of 1888%21502006
equals(Object)26296%21487%2901301
hashCode()18100%2100%020301
{...}6100%n/a010101
get(int)5100%n/a010101
size()4100%n/a010101
toString()4100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Shape.new Shape() {...}

Shape.new Shape() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 10198%2 of 1888%21502006
equals(Object)26296%21487%2901301
hashCode()18100%2100%020301
{...}6100%n/a010101
get(int)5100%n/a010101
size()4100%n/a010101
toString()4100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Shape$2.html b/docs/coverage/test/html/neureka/Shape$2.html index 416b6fefb..5d3b5291f 100644 --- a/docs/coverage/test/html/neureka/Shape$2.html +++ b/docs/coverage/test/html/neureka/Shape$2.html @@ -1 +1 @@ -Shape.new Iterator() {...}

Shape.new Iterator() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 31100%0 of 2100%040403
next()12100%n/a010101
hasNext()10100%2100%020101
{...}9100%n/a010201
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Shape.new Iterator() {...}

Shape.new Iterator() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 31100%0 of 2100%040403
next()12100%n/a010101
hasNext()10100%2100%020101
{...}9100%n/a010201
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Shape.html b/docs/coverage/test/html/neureka/Shape.html index a822e67d0..e087ee770 100644 --- a/docs/coverage/test/html/neureka/Shape.html +++ b/docs/coverage/test/html/neureka/Shape.html @@ -1 +1 @@ -Shape

Shape

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total49 of 25080%10 of 3066%1031838216
filter(Predicate)330%40%335511
slice(int)60%n/a111111
slice(int, int)43288%3562%350401
every(Predicate)41578%2250%231301
any(Predicate)21789%1375%131301
map(Function)26100%2100%020301
toIntArray()20100%2100%020301
count(Predicate)20100%4100%030401
elements()18100%2100%020301
of(Iterable)11100%n/a010301
of(int[])9100%n/a010201
stream()8100%n/a010101
of(List)7100%n/a010101
lambda$of$0(List, Number)7100%n/a010101
of(Stream)6100%n/a010101
iterator()5100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Shape

Shape

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total49 of 25080%10 of 3066%1031838216
filter(Predicate)330%40%335511
slice(int)60%n/a111111
slice(int, int)43288%3562%350401
every(Predicate)41578%2250%231301
any(Predicate)21789%1375%131301
map(Function)26100%2100%020301
toIntArray()20100%2100%020301
count(Predicate)20100%4100%030401
elements()18100%2100%020301
of(Iterable)11100%n/a010301
of(int[])9100%n/a010201
stream()8100%n/a010101
of(List)7100%n/a010101
lambda$of$0(List, Number)7100%n/a010101
of(Stream)6100%n/a010101
iterator()5100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Shape.java.html b/docs/coverage/test/html/neureka/Shape.java.html index cc6d5a03b..f48f11035 100644 --- a/docs/coverage/test/html/neureka/Shape.java.html +++ b/docs/coverage/test/html/neureka/Shape.java.html @@ -207,4 +207,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Tensor$ImageType.html b/docs/coverage/test/html/neureka/Tensor$ImageType.html index 918f08d8f..db57af31c 100644 --- a/docs/coverage/test/html/neureka/Tensor$ImageType.html +++ b/docs/coverage/test/html/neureka/Tensor$ImageType.html @@ -1 +1 @@ -Tensor.ImageType

Tensor.ImageType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 117100%0 of 0n/a0201702
static {...}102100%n/a0101201
Tensor.ImageType(String, int, int, Class, int)15100%n/a010501
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Tensor.ImageType

Tensor.ImageType

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 162100%0 of 0n/a0201702
static {...}147100%n/a0101201
Tensor.ImageType(String, int, int, Class, int)15100%n/a010501
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Tensor.html b/docs/coverage/test/html/neureka/Tensor.html index a8c86de2b..a4f84a23d 100644 --- a/docs/coverage/test/html/neureka/Tensor.html +++ b/docs/coverage/test/html/neureka/Tensor.html @@ -1 +1 @@ -Tensor

Tensor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total246 of 2,91991%37 of 15676%462782842910200
lambda$mapTo$31(Class, Function)2826490%63485%52165601
of(Tensor, char, Tensor, char, Tensor)260%n/a111111
xor(Tensor)260%n/a112211
matMul(Tensor)203462%2250%232501
of(Tensor, char, Tensor)170%n/a111111
sum()151346%1150%122601
min()151346%1150%122601
max()151346%1150%122601
xor(double)100%n/a111111
of(Iterable)90%n/a112211
get(Object)90%n/a111111
relu()70%n/a111111
toString(String)61571%3350%340401
toString(NDPrintSettings)61368%3350%340401
of(Class, Shape, List)60%n/a111111
lambda$shaped$5(List, List)60%n/a111111
update(Component.OwnerChangeRequest)53487%2466%241801
mapTo(Class, Function)51575%1150%121501
get(Number)40%n/a111111
convDot(Tensor)10798%11392%1801501
T()5996%1787%150701
shallowCopy()1083%2250%230201
shallowClone()1083%2250%230201
isUndefined()981%2250%230101
lambda$of$1(Object)990%1150%120101
of(String, Tensor, String, Tensor, String, Tensor, String)102100%n/a010801
concatAt(int, Nda, Nda[])59100%n/a010701
lambda$applyGradient$17(Tensor)50100%2250%230701
of(Class, List)47100%4100%0301401
mean()42100%1150%120501
of(List, List)37100%1150%120701
of(DataType, Shape, List)36100%n/a010401
sum(int)33100%4100%030601
transpose(int, int)28100%2100%020501
multiply(double)28100%1150%120501
of(List, String)27100%2100%020301
plus(Tensor)26100%n/a010201
minus(Tensor)26100%n/a010201
dot(Tensor)26100%n/a010201
conv(Tensor)26100%n/a010201
multiply(Tensor)26100%n/a010201
div(Tensor)26100%n/a010201
mod(Tensor)26100%n/a010201
power(Tensor)26100%n/a010201
of(String, Tensor, char, Tensor, String)25100%n/a010101
concatAt(int, Nda)25100%n/a010601
minus(Object)24100%n/a010501
sum(int[])24100%2100%020401
multiply(Object)24100%n/a010501
getDevice()23100%6100%040601
reshape(int[])20100%n/a010601
permute(int[])20100%n/a010601
power(Object)20100%n/a010201
of(List, Object)18100%n/a010301
of(Shape, Object)18100%n/a010301
backward(Tensor)18100%n/a010301
isLeave()17100%1375%130101
of(String, Tensor, String)16100%n/a010101
of(Shape, List)16100%1150%120301
applyGradient()16100%n/a010401
times(Object)15100%n/a010201
isShallowCopy()14100%n/a010501
isPartialSlice()14100%n/a010501
contains(Tensor)14100%n/a010201
times(Tensor)14100%n/a010201
isEmpty()13100%1375%130101
of(double[])12100%n/a010101
of(double)12100%n/a010101
of(float[])12100%n/a010101
of(float)12100%n/a010101
of(byte[])12100%n/a010101
of(byte)12100%n/a010101
of(int[])12100%n/a010101
of(int)12100%n/a010101
of(long[])12100%n/a010101
of(long)12100%n/a010101
of(short[])12100%n/a010101
of(short)12100%n/a010101
of(boolean[])12100%n/a010101
of(DataType, List, Filler)12100%n/a010201
of(DataType, Shape, Filler)12100%n/a010201
of(String, Number[])12100%n/a010101
like(Tensor)12100%n/a010301
detached()12100%2100%020301
isSlice()11100%n/a010101
sliceCount()11100%n/a010101
isSliceParent()11100%n/a010101
lambda$concatAt$19(int)11100%n/a010101
to(String)10100%n/a010101
mod(int)10100%n/a010101
getAt(Number)10100%n/a010101
lambda$isPartialSlice$12(Relation)10100%n/a010301
lambda$isPartialSlice$11(Tensor)10100%2100%020101
lambda$isShallowCopy$9(Relation)10100%n/a010301
of(Object[])9100%n/a010201
shaped(Shape)9100%n/a010101
backward(double)9100%n/a010201
plus(Object)9100%n/a010101
div(Object)9100%n/a010101
softmax(int)9100%n/a010201
toString(Consumer)9100%2100%020201
lambda$mapTo$24(Function, byte[], Integer)9100%n/a010101
lambda$mapTo$23(Function, short[], Integer)9100%n/a010101
lambda$mapTo$22(Function, float[], Integer)9100%n/a010101
lambda$mapTo$21(Function, double[], Integer)9100%n/a010101
lambda$mapTo$20(Function, int[], Integer)9100%n/a010101
of(Shape, Data)8100%n/a010101
ofRandom(Class, int[])8100%n/a010301
isOutsourced()8100%2100%020101
set(OptimizerFactory)8100%n/a010201
softmax(int[])8100%n/a010101
lambda$mapTo$29(Function, byte[], Integer)8100%n/a010101
lambda$mapTo$28(Function, short[], Integer)8100%n/a010101
lambda$mapTo$27(Function, float[], Integer)8100%n/a010101
lambda$mapTo$26(Function, double[], Integer)8100%n/a010101
lambda$mapTo$25(Function, int[], Integer)8100%n/a010101
lambda$backward$14(double)8100%n/a010101
lambda$of$3(Number)8100%n/a010101
of(Class, List, Object)7100%n/a010101
of(Class, List, List)7100%n/a010101
of(DataType, List, List)7100%n/a010101
of(DataType, Shape, Object)7100%n/a010101
of(DataType, Device, Shape, Object)7100%n/a010101
isBranch()7100%2100%020101
negative()7100%n/a010101
dimtrim()7100%n/a010101
sig()7100%n/a010101
tanh()7100%n/a010101
sin()7100%n/a010101
cos()7100%n/a010101
ln()7100%n/a010101
softplus()7100%n/a010101
exp()7100%n/a010101
sqrt()7100%n/a010101
log10()7100%n/a010101
cbrt()7100%n/a010101
abs()7100%n/a010101
neg()7100%n/a010101
softmax()7100%n/a010101
sigmoid()7100%n/a010101
getAt(Object[])7100%n/a010201
lambda$mapTo$30(Function, Object[], Integer)7100%n/a010101
lambda$isShallowCopy$8(Tensor)7100%n/a010101
of(Class, Shape, Arg.Seed)6100%n/a010101
of(Shape, double)6100%n/a010101
of(Shape, float)6100%n/a010101
of(Class, Shape, Object)6100%n/a010101
of(Class, Shape, Number)6100%n/a010101
ofAny(Class, Shape, Object)6100%n/a010101
of(Class, Shape, Filler)6100%n/a010101
of(String, List)6100%n/a010101
of(String, boolean, List)6100%n/a010101
of(String, Tensor)6100%n/a010101
of(String, Tensor[])6100%n/a010101
of(String, boolean, Tensor[])6100%n/a010101
backward()6100%n/a010201
getGradient()6100%n/a010101
getGraphNode()6100%n/a010101
getAt(int)6100%n/a010101
map(Function)6100%n/a010101
of(List)5100%n/a010101
of(Class)5100%n/a010101
of(Shape, double[])5100%n/a010101
of(Shape, int[])5100%n/a010101
of(Shape, byte[])5100%n/a010101
of(Shape, long[])5100%n/a010101
of(Shape, short[])5100%n/a010101
of(Shape, float[])5100%n/a010101
of(Shape, boolean[])5100%n/a010101
of(DataType, Shape)5100%n/a010101
of(DataType, NDConstructor, Data)5100%n/a010101
lambda$of$2(DataConverter, Class, Object)5100%n/a010101
newInstance()4100%n/a010101
shaped(int[])4100%n/a010101
belongsToGraph()4100%n/a010101
hasGradient()4100%n/a010101
getFrame()4100%n/a010101
rem(int)4100%n/a010101
get(int[])4100%n/a010101
get(Object[])4100%n/a010101
get(int)4100%n/a010101
lambda$applyGradient$16(Optimizer)4100%n/a010101
lambda$shaped$6(Shape, List)4100%n/a010101
ofDoubles()3100%n/a010101
ofFloats()3100%n/a010101
ofInts()3100%n/a010101
ofShorts()3100%n/a010101
ofBytes()3100%n/a010101
mut()3100%n/a010101
gradient()3100%n/a010101
graphNode()3100%n/a010101
frame()3100%n/a010101
getT()3100%n/a010101
lambda$of$4(int)3100%n/a010101
lambda$getGraphNode$18(GraphNode)100%n/a010101
lambda$getGradient$15(Tensor)100%n/a010101
lambda$backward$13(Tensor)100%n/a010101
lambda$isPartialSlice$10(Relation)100%n/a010101
lambda$isShallowCopy$7(Relation)100%n/a010101
lambda$of$0(Object)100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +Tensor

Tensor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total248 of 2,91991%38 of 15675%472782842910200
lambda$mapTo$31(Class, Function)2826490%63485%52165601
of(Tensor, char, Tensor, char, Tensor)260%n/a111111
xor(Tensor)260%n/a112211
matMul(Tensor)203462%2250%232501
of(Tensor, char, Tensor)170%n/a111111
sum()151346%1150%122601
min()151346%1150%122601
max()151346%1150%122601
xor(double)100%n/a111111
of(Iterable)90%n/a112211
get(Object)90%n/a111111
relu()70%n/a111111
toString(String)61571%3350%340401
toString(NDPrintSettings)61368%3350%340401
of(Class, Shape, List)60%n/a111111
lambda$shaped$5(List, List)60%n/a111111
update(Component.OwnerChangeRequest)53487%2466%241801
mapTo(Class, Function)51575%1150%121501
get(Number)40%n/a111111
convDot(Tensor)10798%11392%1801501
T()5996%1787%150701
shallowCopy()1083%2250%230201
shallowClone()1083%2250%230201
isUndefined()981%2250%230101
toString(Consumer)777%1150%120201
lambda$of$1(Object)990%1150%120101
of(String, Tensor, String, Tensor, String, Tensor, String)102100%n/a010801
concatAt(int, Nda, Nda[])59100%n/a010701
lambda$applyGradient$17(Tensor)50100%2250%230701
of(Class, List)47100%4100%0301401
mean()42100%1150%120501
of(List, List)37100%1150%120701
of(DataType, Shape, List)36100%n/a010401
sum(int)33100%4100%030601
transpose(int, int)28100%2100%020501
multiply(double)28100%1150%120501
of(List, String)27100%2100%020301
plus(Tensor)26100%n/a010201
minus(Tensor)26100%n/a010201
dot(Tensor)26100%n/a010201
conv(Tensor)26100%n/a010201
multiply(Tensor)26100%n/a010201
div(Tensor)26100%n/a010201
mod(Tensor)26100%n/a010201
power(Tensor)26100%n/a010201
of(String, Tensor, char, Tensor, String)25100%n/a010101
concatAt(int, Nda)25100%n/a010601
minus(Object)24100%n/a010501
sum(int[])24100%2100%020401
multiply(Object)24100%n/a010501
getDevice()23100%6100%040601
reshape(int[])20100%n/a010601
permute(int[])20100%n/a010601
power(Object)20100%n/a010201
of(List, Object)18100%n/a010301
of(Shape, Object)18100%n/a010301
backward(Tensor)18100%n/a010301
isLeave()17100%1375%130101
of(String, Tensor, String)16100%n/a010101
of(Shape, List)16100%1150%120301
applyGradient()16100%n/a010401
times(Object)15100%n/a010201
isShallowCopy()14100%n/a010501
isPartialSlice()14100%n/a010501
contains(Tensor)14100%n/a010201
times(Tensor)14100%n/a010201
isEmpty()13100%1375%130101
of(double[])12100%n/a010101
of(double)12100%n/a010101
of(float[])12100%n/a010101
of(float)12100%n/a010101
of(byte[])12100%n/a010101
of(byte)12100%n/a010101
of(int[])12100%n/a010101
of(int)12100%n/a010101
of(long[])12100%n/a010101
of(long)12100%n/a010101
of(short[])12100%n/a010101
of(short)12100%n/a010101
of(boolean[])12100%n/a010101
of(DataType, List, Filler)12100%n/a010201
of(DataType, Shape, Filler)12100%n/a010201
of(String, Number[])12100%n/a010101
like(Tensor)12100%n/a010301
detached()12100%2100%020301
isSlice()11100%n/a010101
sliceCount()11100%n/a010101
isSliceParent()11100%n/a010101
lambda$concatAt$19(int)11100%n/a010101
to(String)10100%n/a010101
mod(int)10100%n/a010101
getAt(Number)10100%n/a010101
lambda$isPartialSlice$12(Relation)10100%n/a010301
lambda$isPartialSlice$11(Tensor)10100%2100%020101
lambda$isShallowCopy$9(Relation)10100%n/a010301
of(Object[])9100%n/a010201
shaped(Shape)9100%n/a010101
backward(double)9100%n/a010201
plus(Object)9100%n/a010101
div(Object)9100%n/a010101
softmax(int)9100%n/a010201
lambda$mapTo$24(Function, byte[], Integer)9100%n/a010101
lambda$mapTo$23(Function, short[], Integer)9100%n/a010101
lambda$mapTo$22(Function, float[], Integer)9100%n/a010101
lambda$mapTo$21(Function, double[], Integer)9100%n/a010101
lambda$mapTo$20(Function, int[], Integer)9100%n/a010101
of(Shape, Data)8100%n/a010101
ofRandom(Class, int[])8100%n/a010301
isOutsourced()8100%2100%020101
set(OptimizerFactory)8100%n/a010201
softmax(int[])8100%n/a010101
lambda$mapTo$29(Function, byte[], Integer)8100%n/a010101
lambda$mapTo$28(Function, short[], Integer)8100%n/a010101
lambda$mapTo$27(Function, float[], Integer)8100%n/a010101
lambda$mapTo$26(Function, double[], Integer)8100%n/a010101
lambda$mapTo$25(Function, int[], Integer)8100%n/a010101
lambda$backward$14(double)8100%n/a010101
lambda$of$3(Number)8100%n/a010101
of(Class, List, Object)7100%n/a010101
of(Class, List, List)7100%n/a010101
of(DataType, List, List)7100%n/a010101
of(DataType, Shape, Object)7100%n/a010101
of(DataType, Device, Shape, Object)7100%n/a010101
isBranch()7100%2100%020101
negative()7100%n/a010101
dimtrim()7100%n/a010101
sig()7100%n/a010101
tanh()7100%n/a010101
sin()7100%n/a010101
cos()7100%n/a010101
ln()7100%n/a010101
softplus()7100%n/a010101
exp()7100%n/a010101
sqrt()7100%n/a010101
log10()7100%n/a010101
cbrt()7100%n/a010101
abs()7100%n/a010101
neg()7100%n/a010101
softmax()7100%n/a010101
sigmoid()7100%n/a010101
getAt(Object[])7100%n/a010201
lambda$mapTo$30(Function, Object[], Integer)7100%n/a010101
lambda$isShallowCopy$8(Tensor)7100%n/a010101
of(Class, Shape, Arg.Seed)6100%n/a010101
of(Shape, double)6100%n/a010101
of(Shape, float)6100%n/a010101
of(Class, Shape, Object)6100%n/a010101
of(Class, Shape, Number)6100%n/a010101
ofAny(Class, Shape, Object)6100%n/a010101
of(Class, Shape, Filler)6100%n/a010101
of(String, List)6100%n/a010101
of(String, boolean, List)6100%n/a010101
of(String, Tensor)6100%n/a010101
of(String, Tensor[])6100%n/a010101
of(String, boolean, Tensor[])6100%n/a010101
backward()6100%n/a010201
getGradient()6100%n/a010101
getGraphNode()6100%n/a010101
getAt(int)6100%n/a010101
map(Function)6100%n/a010101
of(List)5100%n/a010101
of(Class)5100%n/a010101
of(Shape, double[])5100%n/a010101
of(Shape, int[])5100%n/a010101
of(Shape, byte[])5100%n/a010101
of(Shape, long[])5100%n/a010101
of(Shape, short[])5100%n/a010101
of(Shape, float[])5100%n/a010101
of(Shape, boolean[])5100%n/a010101
of(DataType, Shape)5100%n/a010101
of(DataType, NDConstructor, Data)5100%n/a010101
lambda$of$2(DataConverter, Class, Object)5100%n/a010101
newInstance()4100%n/a010101
shaped(int[])4100%n/a010101
belongsToGraph()4100%n/a010101
hasGradient()4100%n/a010101
getFrame()4100%n/a010101
rem(int)4100%n/a010101
get(int[])4100%n/a010101
get(Object[])4100%n/a010101
get(int)4100%n/a010101
lambda$applyGradient$16(Optimizer)4100%n/a010101
lambda$shaped$6(Shape, List)4100%n/a010101
ofDoubles()3100%n/a010101
ofFloats()3100%n/a010101
ofInts()3100%n/a010101
ofShorts()3100%n/a010101
ofBytes()3100%n/a010101
mut()3100%n/a010101
gradient()3100%n/a010101
graphNode()3100%n/a010101
frame()3100%n/a010101
getT()3100%n/a010101
lambda$of$4(int)3100%n/a010101
lambda$getGraphNode$18(GraphNode)100%n/a010101
lambda$getGradient$15(Tensor)100%n/a010101
lambda$backward$13(Tensor)100%n/a010101
lambda$isPartialSlice$10(Relation)100%n/a010101
lambda$isShallowCopy$7(Relation)100%n/a010101
lambda$of$0(Object)100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/Tensor.java.html b/docs/coverage/test/html/neureka/Tensor.java.html index 39baf3e15..5cacea591 100644 --- a/docs/coverage/test/html/neureka/Tensor.java.html +++ b/docs/coverage/test/html/neureka/Tensor.java.html @@ -2642,7 +2642,7 @@ /** {@inheritDoc} */ @Override default String toString( Consumer<NDPrintSettings> configurator ) { - if ( this.isDeleted() ) return "deleted"; + if ( this.isDeleted() ) return "deleted"; return Nda.super.toString( configurator ); } @@ -2704,4 +2704,4 @@ } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/TensorConstructor$Args.html b/docs/coverage/test/html/neureka/TensorConstructor$Args.html new file mode 100644 index 000000000..b6f221a7e --- /dev/null +++ b/docs/coverage/test/html/neureka/TensorConstructor$Args.html @@ -0,0 +1 @@ +TensorConstructor.Args

TensorConstructor.Args

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 25100%0 of 0n/a070707
setIsVirtual(boolean)5100%n/a010101
setConf(NDConfiguration)4100%n/a010101
setData(Data)4100%n/a010101
TensorConstructor.Args()3100%n/a010101
getConf()3100%n/a010101
getData()3100%n/a010101
isVirtual()3100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/TensorConstructor.html b/docs/coverage/test/html/neureka/TensorConstructor.html index df6639e80..740c843fe 100644 --- a/docs/coverage/test/html/neureka/TensorConstructor.html +++ b/docs/coverage/test/html/neureka/TensorConstructor.html @@ -1 +1 @@ -TensorConstructor

TensorConstructor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total5 of 25498%3 of 2286%31805007
newSeeded(Class, Arg.Seed)43589%1150%120801
newPopulatedFromOne(Object, Class)14897%1583%140801
tryConstructing(DataType, Object)97100%11191%1701801
TensorConstructor(Device, NDConstructor, TensorConstructor.API)32100%n/a010701
unpopulated(boolean, boolean, DataType)24100%2100%020501
constructTrusted(Data)12100%n/a010301
static {...}1100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +TensorConstructor

TensorConstructor

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 26398%2 of 2090%21705307
newSeeded(Class, Arg.Seed)43790%1150%120801
tryConstructing(DataType, Object)103100%11191%1702001
newPopulatedFromOne(Object, Class)46100%4100%030801
TensorConstructor(Device, NDConstructor, TensorConstructor.Args)32100%n/a010701
unpopulated(boolean, boolean, DataType)26100%2100%020601
constructTrusted(Data)14100%n/a010301
static {...}1100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/TensorConstructor.java.html b/docs/coverage/test/html/neureka/TensorConstructor.java.html index 9063c30e6..7c24e4af5 100644 --- a/docs/coverage/test/html/neureka/TensorConstructor.java.html +++ b/docs/coverage/test/html/neureka/TensorConstructor.java.html @@ -23,17 +23,7 @@ */ final class TensorConstructor { - /** - * An interface defining methods for configuring a {@link Tensor} - * in the making... - */ - public interface API { - void setConf( NDConfiguration conf ); - void setData( Data<?> o ); - void setIsVirtual( boolean isVirtual ); - } - - private final API _API; + private final Args _Args; private final Device<Object> _targetDevice; private final NDConstructor _ndConstructor; @@ -41,15 +31,15 @@ * * @param targetDevice The {@link Device} to be used for the construction of the {@link Tensor} * @param ndConstructor A producer of the {@link NDConfiguration} interface implementation. - * @param API An implementation of the {@link API} interface. + * @param Args An implementation of the {@link Args} interface. */ - public TensorConstructor(Device<?> targetDevice, NDConstructor ndConstructor, API API ) { - LogUtil.nullArgCheck( targetDevice, "targetDevice", Device.class, "Cannot construct a tensor without target device." ); - LogUtil.nullArgCheck( ndConstructor, "ndConstructor", NDConstructor.class, "Cannot construct tensor without shape information." ); - _targetDevice = (Device<Object>) targetDevice; - _ndConstructor = ndConstructor; - _API = API; - } + public TensorConstructor(Device<?> targetDevice, NDConstructor ndConstructor, Args Args) { + LogUtil.nullArgCheck( targetDevice, "targetDevice", Device.class, "Cannot construct a tensor without target device." ); + LogUtil.nullArgCheck( ndConstructor, "ndConstructor", NDConstructor.class, "Cannot construct tensor without shape information." ); + _targetDevice = (Device<Object>) targetDevice; + _ndConstructor = ndConstructor; + _Args = Args; + } /** * Constructs the tensor without any initial (filled) {@link Data}. @@ -57,73 +47,103 @@ * @param makeVirtual A flag determining if the tensor should be actual or virtual (not fully allocated). * @param autoAllocate Determines if the underlying data array should be allocated or not. */ - void unpopulated( + Args unpopulated( boolean makeVirtual, boolean autoAllocate, DataType<?> type ) { - NDConfiguration ndc = _ndConstructor.produceNDC( makeVirtual ); - _API.setIsVirtual( makeVirtual ); - _API.setConf( ndc ); - if ( autoAllocate ) _API.setData( _targetDevice.allocate( type, ndc ) ); - } - - public void constructTrusted( Data<?> data ) { - _API.setConf( _ndConstructor.produceNDC( false ) ); - _API.setData( data ); - } - - public void tryConstructing( + NDConfiguration ndc = _ndConstructor.produceNDC( makeVirtual ); + _Args.setIsVirtual( makeVirtual ); + _Args.setConf( ndc ); + if ( autoAllocate ) + _Args.setData( _targetDevice.allocate( type, ndc ) ); + return _Args; + } + + public Args constructTrusted(Data<?> data ) { + _Args.setConf( _ndConstructor.produceNDC( false ) ); + _Args.setData( data ); + return _Args; + } + + public Args tryConstructing( DataType<?> dataType, Object data ) { - LogUtil.nullArgCheck( _ndConstructor, "ndConstructor", NDConstructor.class ); - LogUtil.nullArgCheck( _ndConstructor.getShape(), "shape", int[].class ); - LogUtil.nullArgCheck( dataType, "dataType", DataType.class ); - LogUtil.nullArgCheck( data, "data", Object.class ); - - int size = _ndConstructor.getSize(); - if ( data instanceof Object[] ) - data = CPU.get().allocate( dataType.getItemTypeClass(), size, data ).getOrNull(); + LogUtil.nullArgCheck( _ndConstructor, "ndConstructor", NDConstructor.class ); + LogUtil.nullArgCheck( _ndConstructor.getShape(), "shape", int[].class ); + LogUtil.nullArgCheck( dataType, "dataType", DataType.class ); + LogUtil.nullArgCheck( data, "data", Object.class ); + + int size = _ndConstructor.getSize(); + if ( data instanceof Object[] ) + data = CPU.get().allocate( dataType.getItemTypeClass(), size, data ).getOrNull(); else { - boolean isDefinitelyScalarValue = ( dataType == DataType.of(data.getClass()) ); + boolean isDefinitelyScalarValue = ( dataType == DataType.of(data.getClass()) ); - if ( data instanceof Number && !isDefinitelyScalarValue ) { - data = DataConverter.get().convert( data, dataType.getItemTypeClass() ); - isDefinitelyScalarValue = true; + if ( data instanceof Number && !isDefinitelyScalarValue ) { + data = DataConverter.get().convert( data, dataType.getItemTypeClass() ); + isDefinitelyScalarValue = true; } - if ( isDefinitelyScalarValue ) // This means that "data" is a single value! - if ( newPopulatedFromOne( data, dataType.getItemTypeClass() ) ) return; + if ( isDefinitelyScalarValue ) { // This means that "data" is a single value! + newPopulatedFromOne( data, dataType.getItemTypeClass() ); + if ( data != null ) + return _Args; + } } - NDConfiguration ndc = _ndConstructor.produceNDC( false ); - _API.setIsVirtual( false ); - _API.setConf( ndc ); - _API.setData( _targetDevice.allocateFromAll( dataType, ndc, data) ); - } + NDConfiguration ndc = _ndConstructor.produceNDC( false ); + _Args.setIsVirtual( false ); + _Args.setConf( ndc ); + _Args.setData( _targetDevice.allocateFromAll( dataType, ndc, data) ); + return _Args; + } - public boolean newPopulatedFromOne( Object singleItem, Class<?> type ) + public Args newPopulatedFromOne(Object singleItem, Class<?> type ) { - int size = _ndConstructor.getSize(); - NDConfiguration ndc = _ndConstructor.produceNDC(_ndConstructor.getSize() > 1); - DataType<Object> dataType = (DataType<Object>) DataType.of( type ); - Data<?> array = _targetDevice.allocateFromOne( dataType, ndc, singleItem ); - _API.setIsVirtual( size > 1 ); - _API.setConf( ndc ); - _API.setData( array ); - return singleItem != null; + int size = _ndConstructor.getSize(); + NDConfiguration ndc = _ndConstructor.produceNDC(_ndConstructor.getSize() > 1); + DataType<Object> dataType = (DataType<Object>) DataType.of( type ); + Data<?> array = _targetDevice.allocateFromOne( dataType, ndc, singleItem ); + _Args.setIsVirtual( size > 1 ); + _Args.setConf( ndc ); + _Args.setData( array ); + return _Args; } - public <V> void newSeeded( Class<V> valueType, Arg.Seed seed ) + public <V> Args newSeeded(Class<V> valueType, Arg.Seed seed ) { - NDConfiguration ndc = _ndConstructor.produceNDC( false ); - Data<?> data = _targetDevice.allocate( DataType.of( valueType ), ndc ); - Object out = CPURandomization.fillRandomly( data.getOrNull(), seed ); - assert out == data.getOrNull(); - _API.setIsVirtual( false ); - _API.setConf( ndc ); - _API.setData( data ); - } + NDConfiguration ndc = _ndConstructor.produceNDC( false ); + Data<?> data = _targetDevice.allocate( DataType.of( valueType ), ndc ); + Object out = CPURandomization.fillRandomly( data.getOrNull(), seed ); + assert out == data.getOrNull(); + _Args.setIsVirtual( false ); + _Args.setConf( ndc ); + _Args.setData( data ); + return _Args; + } + + /** + * An interface defining methods for configuring a {@link Tensor} + * in the making... + */ + static class Args { + private NDConfiguration _conf; + private Data<?> _data; + private Boolean _isVirtual; + + public void setConf( NDConfiguration conf ) { _conf = conf; } + + public void setData( Data<?> o ) { _data = o; } + + public void setIsVirtual( boolean isVirtual ) { _isVirtual = isVirtual; } + + public NDConfiguration getConf() { return _conf; } + + public Data<?> getData() { return _data; } + + public Boolean isVirtual() { return _isVirtual; } + } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/TensorImpl$1.html b/docs/coverage/test/html/neureka/TensorImpl$1.html index 895266498..df0dcafb8 100644 --- a/docs/coverage/test/html/neureka/TensorImpl$1.html +++ b/docs/coverage/test/html/neureka/TensorImpl$1.html @@ -1 +1 @@ -TensorImpl.new Data() {...}

TensorImpl.new Data() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total2 of 1788%0 of 0n/a151515
usages()20%n/a111111
{...}6100%n/a010101
dataType()5100%n/a010101
owner()2100%n/a010101
getOrNull()2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +TensorImpl.new Data() {...}

TensorImpl.new Data() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total4 of 1776%0 of 0n/a252525
owner()20%n/a111111
usages()20%n/a111111
{...}6100%n/a010101
dataType()5100%n/a010101
getOrNull()2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/TensorImpl$2.html b/docs/coverage/test/html/neureka/TensorImpl$2.html index 2c1576405..535d5b6a4 100644 --- a/docs/coverage/test/html/neureka/TensorImpl$2.html +++ b/docs/coverage/test/html/neureka/TensorImpl$2.html @@ -1 +1 @@ -TensorImpl.new MutateNda.Item() {...}

TensorImpl.new MutateNda.Item() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total20 of 6368%5 of 837%61021026
hashCode()100%20%221111
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toString()40%n/a111111
{...}9100%n/a010101
set(Object)9100%n/a010101
orElseNull()6100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +TensorImpl.new MutateNda.Item() {...}

TensorImpl.new MutateNda.Item() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total20 of 6368%5 of 837%61021026
hashCode()100%20%221111
equals(Object)61976%3350%340501
toString()40%n/a111111
{...}9100%n/a010101
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Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/TensorImpl$3.html b/docs/coverage/test/html/neureka/TensorImpl$3.html index d95b6d243..da426ab57 100644 --- a/docs/coverage/test/html/neureka/TensorImpl$3.html +++ b/docs/coverage/test/html/neureka/TensorImpl$3.html @@ -1 +1 @@ -TensorImpl.new Iterator() {...}

TensorImpl.new Iterator() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 44100%0 of 2100%040803
next()18100%n/a010401
{...}17100%n/a010301
hasNext()9100%2100%020101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +TensorImpl.new Iterator() {...}

TensorImpl.new Iterator() {...}

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total0 of 44100%0 of 2100%040803
next()18100%n/a010401
{...}17100%n/a010301
hasNext()9100%2100%020101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/TensorImpl.html b/docs/coverage/test/html/neureka/TensorImpl.html index 82d5554f6..12cd68909 100644 --- a/docs/coverage/test/html/neureka/TensorImpl.html +++ b/docs/coverage/test/html/neureka/TensorImpl.html @@ -1 +1 @@ -TensorImpl

TensorImpl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total346 of 2,94888%42 of 28685%51258535119114
_of(Object[])4516178%62278%61553501
modAssign(Tensor)260%n/a112211
assign(Object)240%n/a115511
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putAt(int[], Object)196276%3975%3741501
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lambda$addToGradient$19(Tensor, Device)7646%n/a013601
lambda$setIsVirtual$3(Relation)60%n/a111111
lambda$setIsVirtual$2(Relation)60%n/a111111
_clone(boolean)55591%1375%1311101
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lambda$_toType$21(Class, TensorImpl)50%n/a111111
lambda$setIsVirtual$4(Tensor)50%n/a111111
toString()41680%2466%240401
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_checkRankForImageConversion(Tensor.ImageType, Class, int[])119100%10100%0601601
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label(String)35100%2100%020501
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_setRqsGradient(boolean)22100%4100%030401
_setIsVirtual(boolean)22100%4100%030401
isCase(Tensor)22100%n/a010401
timesAssign(Object)20100%n/a010201
getAt(Map)19100%n/a010201
lambda$_toType$20(Class, DataType)18100%n/a010401
getAt(int[])17100%n/a010201
lambda$addToGradient$17(Tensor, Tensor)16100%n/a010501
toType(Class)14100%n/a010201
_putAtCheckFor(Tensor)14100%2100%020501
setItemAt(int, Object)14100%n/a010401
lambda$_delete$6(GraphNode)14100%2100%020501
TensorImpl()12100%n/a010401
incrementVersion(ExecutionCall)11100%n/a010301
lambda$addToGradient$18(Tensor, Tensor)11100%n/a010201
rqsGradient()10100%2100%020101
isIntermediate()10100%2100%020101
isVirtual()10100%2100%020101
isDeleted()10100%2100%020101
gradientApplyRequested()10100%2100%020101
_writeImgData(DataBuffer, BufferedImage)10100%n/a010301
setDataAt(int, Object)9100%n/a010301
iterator()9100%n/a010201
to(Device)9100%2100%020201
lambda$isCase$12(Tensor, Relation)8100%n/a010101
lambda$_backward$9(LazyRef)8100%n/a010101
getDataAs(Class)7100%n/a010101
getDataAt(int)7100%n/a010101
putAt(int, Object)7100%n/a010101
getRawData()7100%n/a010201
at(int[])6100%n/a010101
toLayout(NDConfiguration.Layout)6100%n/a010201
detach()6100%n/a010101
lambda$getAt$13(Object)6100%2100%020101
lambda$_backward$10(LazyRef, GraphNode)6100%n/a010101
getMut()5100%n/a010201
setNDConf(NDConfiguration)5100%n/a010101
setData(Data)5100%n/a010201
slice()5100%n/a010101
lambda$_delete$8(TensorImpl)5100%n/a010101
lambda$_delete$7(Device)5100%n/a010101
setIsIntermediate(boolean)4100%n/a010101
deepCopy()4100%n/a010101
deepClone()4100%n/a010101
lambda$putAt$15(Object)4100%n/a010101
lambda$isCase$11(Tensor, Tensor)4100%n/a010101
static {...}4100%n/a010201
getVersion()3100%n/a010101
delete()3100%n/a010101
getData()3100%n/a010101
lambda$_checkRankForImageConversion$16(Integer)3100%n/a010101
lambda$putAt$14(Object)3100%n/a010101
_setOrReject(Component)2100%n/a010101
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +TensorImpl

TensorImpl

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethods
Total380 of 2,98887%53 of 29882%612666052010116
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_putAt(Tensor, Tensor)322644%4233%34101601
modAssign(Tensor)260%n/a112211
assign(Object)240%n/a115511
_of(NDConstructor, Device, DataType, Object)204066%3770%3641201
putAt(int[], Object)196276%3975%3741501
upcast(Class)191340%1150%121401
_removeOrReject(Component)19520%3125%237901
setItems(Object)164573%2466%2421201
asImage(Tensor.ImageType)1414491%1375%1411701
_setDataAt(int, Object)142058%1375%131501
withLabel(String)141348%1150%121401
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lambda$setIsVirtual$1(Object)110%n/a113311
putAt(List, Object)101661%1150%121301
putAt(List, Nda)83581%4450%451701
setIsVirtual(boolean)75488%2880%2611801
lambda$addToGradient$19(Tensor, Device)7646%n/a013601
lambda$setIsVirtual$3(Relation)60%n/a111111
lambda$setIsVirtual$2(Relation)60%n/a111111
_clone(boolean)55591%1375%1311101
setRqsGradient(boolean)53587%1787%151801
_of(NDConstructor, DataType, Data)51777%1150%121301
lambda$_toType$21(Class, TensorImpl)50%n/a111111
lambda$_delete$7(Device)50%n/a111111
lambda$setIsVirtual$4(Tensor)50%n/a111111
toString()41680%2466%240401
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_setState(TensorConstructor.Args)32388%3350%3411001
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getAt(List)152100%12796%11501901
_checkRankForImageConversion(Tensor.ImageType, Class, int[])119100%10100%0601601
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labelAxes(String[][])82100%11191%1701401
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labelAxes(List)35100%2100%020501
label(String)35100%2100%020501
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labelAxes(Map)33100%1375%130501
TensorImpl(TensorConstructor.Args)31100%1583%1401201
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withLabels(String[][])27100%2100%020401
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withLabels(Map)27100%2100%020401
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timesAssign(Tensor)26100%n/a010201
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plusAssign(Tensor)26100%n/a010201
minusAssign(Tensor)26100%n/a010201
_toType(Class)26100%2100%020501
minusAssign(Object)24100%n/a010501
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_setIsIntermediate(boolean)23100%4100%030401
_setRqsGradient(boolean)22100%4100%030401
_setIsVirtual(boolean)22100%4100%030401
isCase(Tensor)22100%n/a010401
_of(List)21100%n/a010501
timesAssign(Object)20100%n/a010201
getAt(Map)19100%n/a010201
lambda$_toType$20(Class, DataType)18100%n/a010401
getAt(int[])17100%n/a010201
lambda$addToGradient$17(Tensor, Tensor)16100%n/a010501
toType(Class)14100%n/a010201
_putAtCheckFor(Tensor)14100%2100%020501
setItemAt(int, Object)14100%n/a010401
lambda$_delete$6(GraphNode)14100%2100%020501
TensorImpl()12100%n/a010401
lambda$addToGradient$18(Tensor, Tensor)12100%n/a010201
incrementVersion(ExecutionCall)11100%n/a010301
rqsGradient()10100%2100%020101
isIntermediate()10100%2100%020101
isVirtual()10100%2100%020101
isDeleted()10100%2100%020101
gradientApplyRequested()10100%2100%020101
_writeImgData(DataBuffer, BufferedImage)10100%n/a010301
setDataAt(int, Object)9100%n/a010301
iterator()9100%n/a010201
to(Device)9100%2100%020201
lambda$isCase$12(Tensor, Relation)8100%n/a010101
lambda$_backward$9(LazyRef)8100%n/a010101
getDataAs(Class)7100%n/a010101
getDataAt(int)7100%n/a010101
putAt(int, Object)7100%n/a010101
getRawData()7100%n/a010201
at(int[])6100%n/a010101
toLayout(NDConfiguration.Layout)6100%n/a010201
detach()6100%n/a010101
lambda$getAt$13(Object)6100%2100%020101
lambda$_backward$10(LazyRef, GraphNode)6100%n/a010101
getMut()5100%n/a010201
setNDConf(NDConfiguration)5100%n/a010101
setData(Data)5100%n/a010201
slice()5100%n/a010101
lambda$_delete$8(TensorImpl)5100%n/a010101
setIsIntermediate(boolean)4100%n/a010101
deepCopy()4100%n/a010101
deepClone()4100%n/a010101
lambda$putAt$15(Object)4100%n/a010101
lambda$isCase$11(Tensor, Tensor)4100%n/a010101
static {...}4100%n/a010201
getVersion()3100%n/a010101
delete()3100%n/a010101
getData()3100%n/a010101
lambda$_checkRankForImageConversion$16(Integer)3100%n/a010101
lambda$putAt$14(Object)3100%n/a010101
_setOrReject(Component)2100%n/a010101
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/TensorImpl.java.html b/docs/coverage/test/html/neureka/TensorImpl.java.html index d2950289c..8d16d2dbf 100644 --- a/docs/coverage/test/html/neureka/TensorImpl.java.html +++ b/docs/coverage/test/html/neureka/TensorImpl.java.html @@ -114,25 +114,22 @@ { if ( args == null || args.length == 0 ) return new TensorImpl<>(); if ( args.length == 1 ) { - TensorImpl<T> t = new TensorImpl<>(); - boolean success = constructFor(t, CPU.get(), NDConstructor.of(1)).newPopulatedFromOne( args[ 0 ], args[ 0 ].getClass() ); - if ( !success ) { - String message = "Cannot create tensor from argument of type '" + args[ 0 ].getClass().getName() + "'!"; - _LOG.error( message ); - throw new IllegalArgumentException( message ); + TensorImpl<T> t = new TensorImpl<>(constructFor(CPU.get(), NDConstructor.of(1)).newPopulatedFromOne( args[ 0 ], args[ 0 ].getClass() )); + if ( args[ 0 ] == null ) { + String message = "Cannot create tensor from argument of type '" + args[ 0 ].getClass().getName() + "'!"; + _LOG.error( message ); + throw new IllegalArgumentException( message ); } - return t; + return t; } - Class<?> commonType = _extractCommonType(args); - if ( commonType != null ) { - TensorImpl<T> t = new TensorImpl<>(); - constructFor(t, CPU.get(), NDConstructor.of( args.length )) - .tryConstructing( - DataType.of(commonType), - args - ); - return t; + Class<?> commonType = _extractCommonType(args); + if ( commonType != null ) { + return new TensorImpl<>(constructFor(CPU.get(), NDConstructor.of( args.length )) + .tryConstructing( + DataType.of(commonType), + args + )); } /* EXPRESSION BASED CONSTRUCTION: @@ -142,50 +139,47 @@ Tensor<?> t = Tensor.of( "tanh(", x, ") * 7 **", y ); */ - boolean containsString = false; - int numberOfTensors = 0; - for ( Object o : args ) { - containsString = ( o instanceof String ) || containsString; - if ( o instanceof TensorImpl) - numberOfTensors++; + boolean containsString = false; + int numberOfTensors = 0; + for ( Object o : args ) { + containsString = ( o instanceof String ) || containsString; + if ( o instanceof TensorImpl) + numberOfTensors++; } - TensorImpl<T>[] tensors = new TensorImpl[ numberOfTensors ]; - StringBuilder f = new StringBuilder(); - int ti = 0; - for ( Object o : args ) { - if ( o instanceof TensorImpl) { - tensors[ ti ] = ( (TensorImpl<T>) o ); - f.append( "I[" ).append( ti ).append( "]" ); - ti++; + TensorImpl<T>[] tensors = new TensorImpl[ numberOfTensors ]; + StringBuilder f = new StringBuilder(); + int ti = 0; + for ( Object o : args ) { + if ( o instanceof TensorImpl) { + tensors[ ti ] = ( (TensorImpl<T>) o ); + f.append( "I[" ).append( ti ).append( "]" ); + ti++; } - else if ( o instanceof String ) f.append( (String) o ); + else if ( o instanceof String ) f.append( (String) o ); else - _LOG.debug( - "Unexpected tensor construction argument of type '"+o.getClass().getSimpleName()+"'" + _LOG.debug( + "Unexpected tensor construction argument of type '"+o.getClass().getSimpleName()+"'" ); } - if ( tensors.length == 0 || tensors[0] == null) return new TensorImpl<>(); - return Function.of( f.toString(), true ).call( tensors ); + if ( tensors.length == 0 || tensors[0] == null) return new TensorImpl<>(); + return Function.of( f.toString(), true ).call( tensors ); } static <T> Tensor<T> _of( Iterable<T> iterable ) { - List<T> list = new ArrayList<>(); - iterable.forEach( list::add ); - return _of( list ); + List<T> list = new ArrayList<>(); + iterable.forEach( list::add ); + return _of( list ); } static <T> Tensor<T> _of( List<T> list ) { - TensorImpl<T> t = new TensorImpl<>(); - Class<?> commonType = _extractCommonType( list.toArray() ); - // We construct the tensor: - constructFor(t, CPU.get(), NDConstructor.of( list.size() )) - .tryConstructing( - DataType.of(commonType), - list.toArray() - ); - return t; + return new TensorImpl<>( + constructFor(CPU.get(), NDConstructor.of( list.size() )) + .tryConstructing( + DataType.of(_extractCommonType( list.toArray() )), + list.toArray() + )); } @@ -194,14 +188,14 @@ * @return A common type or null if they are not all of the same type. */ private static Class<?> _extractCommonType( Object... args ) { - Class<?> commonType = null; - for ( Object o : args ) - if ( o != null ) { - if ( commonType == null ) commonType = o.getClass(); - else if ( !commonType.equals(o.getClass()) ) return null; + Class<?> commonType = null; + for ( Object o : args ) + if ( o != null ) { + if ( commonType == null ) commonType = o.getClass(); + else if ( !commonType.equals(o.getClass()) ) return null; } - return commonType; + return commonType; } // Constructors: @@ -215,80 +209,83 @@ * second parameter into this {@link Tensor} instance. * This constructor will be called by the {@link Tensor#newInstance()} factory method. */ - TensorImpl() { - _setData(new Data<V>() { - @Override public Device<V> owner() { return (Device<V>) CPU.get(); } - @Override public Object getOrNull() { return null;} + TensorImpl() { + _setData(new Data<V>() { + @Override public Device<V> owner() { return (Device<V>) CPU.get(); } + @Override public Object getOrNull() { return null;} @Override public DataType<V> dataType() { - return (DataType<V>) Neureka.get().settings().dtype().getDefaultDataType(); + return (DataType<V>) Neureka.get().settings().dtype().getDefaultDataType(); } - @Override public int usages() { return 1; } + @Override public int usages() { return 1; } }); - } + } + + TensorImpl( TensorConstructor.Args args ) { + NDConfiguration ndc = args.getConf(); + Boolean isVirtual = args.isVirtual(); + Data<V> data = (Data<V>) args.getData(); + if ( isVirtual != null ) + _setIsVirtual( isVirtual ); + if ( ndc != null ) + _setNDConf( ndc ); + if ( data != null ) + _setData( data ); + } public static <V> TensorImpl<V> _of( NDConstructor ndConstructor, Device device, DataType<V> dataType, Object value ) { - Object data = value; - if ( List.class.isAssignableFrom( dataType.getItemTypeClass() ) ) - data = new Object[]{ value }; // Make a nd-array of lists possible - if ( Object[].class.isAssignableFrom( dataType.getItemTypeClass() ) ) - data = new Object[]{ value }; // Make a nd-array of arrays possible - if ( Object.class == dataType.getItemTypeClass() ) { - if ( value.getClass() != Object[].class ) - data = new Object[]{ value }; + Object data = value; + if ( List.class.isAssignableFrom( dataType.getItemTypeClass() ) ) + data = new Object[]{ value }; // Make a nd-array of lists possible + if ( Object[].class.isAssignableFrom( dataType.getItemTypeClass() ) ) + data = new Object[]{ value }; // Make a nd-array of arrays possible + if ( Object.class == dataType.getItemTypeClass() ) { + if ( value.getClass() != Object[].class ) + data = new Object[]{ value }; } - if ( data instanceof List<?> ) { - List<?> range = (List<?>) data; - data = range.toArray();// TODO: This is probably wrong! + if ( data instanceof List<?> ) { + List<?> range = (List<?>) data; + data = range.toArray();// TODO: This is probably wrong! } - TensorImpl<V> t = new TensorImpl<>(); - constructFor(t, device, ndConstructor).tryConstructing( dataType, data ); - return t; + return new TensorImpl<>(constructFor(device, ndConstructor).tryConstructing( dataType, data )); } static <V> TensorImpl<V> _of( NDConstructor ndConstructor, DataType<V> dataType, Data<V> data ) { // We check if the type of the data is compatible with the type of the tensor: - if ( !dataType.getItemTypeClass().isAssignableFrom( data.dataType().getItemTypeClass() ) ) - throw new IllegalArgumentException( + if ( !dataType.getItemTypeClass().isAssignableFrom( data.dataType().getItemTypeClass() ) ) + throw new IllegalArgumentException( "The data type of the data is not compatible with the data type of the tensor!" ); - TensorImpl<V> t = new TensorImpl<>(); - constructFor(t, data.owner(), ndConstructor).constructTrusted( data ); - return t; + return new TensorImpl<>(constructFor(data.owner(), ndConstructor).constructTrusted( data )); } /** * see {@link Tensor#of(DataType, Shape, Filler)} */ static <V> TensorImpl<V> _of( NDConstructor ndConstructor, DataType<V> type, Filler<V> filler ) { - LogUtil.nullArgCheck(ndConstructor, "ndcProducer", NDConstructor.class ); - LogUtil.nullArgCheck( type, "type", DataType.class ); - LogUtil.nullArgCheck( type, "filler", Filler.class ); - TensorImpl<V> t = new TensorImpl<>(); - constructFor(t, CPU.get(), ndConstructor).unpopulated( false, true, type ); - t._initDataArrayFrom( filler ); - return t; + LogUtil.nullArgCheck(ndConstructor, "ndcProducer", NDConstructor.class ); + LogUtil.nullArgCheck( type, "type", DataType.class ); + LogUtil.nullArgCheck( type, "filler", Filler.class ); + TensorImpl<V> t = new TensorImpl<>(constructFor(CPU.get(), ndConstructor).unpopulated( false, true, type )); + t._initDataArrayFrom( filler ); + return t; } /** * See {@link Tensor#of(Class, Shape, neureka.math.args.Arg.Seed)} and {@link #of(List, String)} */ static <V> TensorImpl<V> _of( Class<V> valueType, NDConstructor ndConstructor, Arg.Seed seed ) { - LogUtil.nullArgCheck( valueType, "valueType", Class.class ); - LogUtil.nullArgCheck(ndConstructor, "ndcProducer", NDConstructor.class ); - LogUtil.nullArgCheck( seed, "seed", Arg.Seed.class ); - TensorImpl<V> t = new TensorImpl<>(); - constructFor(t, CPU.get(), ndConstructor).newSeeded( valueType, seed ); - return t; + LogUtil.nullArgCheck( valueType, "valueType", Class.class ); + LogUtil.nullArgCheck(ndConstructor, "ndcProducer", NDConstructor.class ); + LogUtil.nullArgCheck( seed, "seed", Arg.Seed.class ); + return new TensorImpl<>(constructFor(CPU.get(), ndConstructor).newSeeded( valueType, seed )); } static <V> TensorImpl<V> _of( NDConstructor ndConstructor, DataType<?> type ) { - LogUtil.nullArgCheck(ndConstructor, "ndcProducer", NDConstructor.class ); - LogUtil.nullArgCheck( type, "type", DataType.class ); - TensorImpl<V> t = new TensorImpl<>(); - constructFor(t, CPU.get(), ndConstructor).unpopulated( true, true, type ); - return t; + LogUtil.nullArgCheck(ndConstructor, "ndcProducer", NDConstructor.class ); + LogUtil.nullArgCheck( type, "type", DataType.class ); + return new TensorImpl<>(constructFor(CPU.get(), ndConstructor).unpopulated( true, true, type )); } /*================================================================================================================== @@ -300,34 +297,34 @@ /** {@inheritDoc} */ @Override public Tensor<V> setRqsGradient( boolean rqsGradient ) { - if ( rqsGradient() != rqsGradient ) { - if ( !rqsGradient ) this.remove( TensorImpl.class ); - else if ( has(GraphNode.class) ) { - if ( getGraphNode().map( n -> n.getMode() == 0 ).orElse(false) ) - remove(GraphNode.class); + if ( rqsGradient() != rqsGradient ) { + if ( !rqsGradient ) this.remove( TensorImpl.class ); + else if ( has(GraphNode.class) ) { + if ( getGraphNode().map( n -> n.getMode() == 0 ).orElse(false) ) + remove(GraphNode.class); else - throw new IllegalArgumentException( + throw new IllegalArgumentException( "This tensor is already part of a gradient dependent graph as " + "branch node and therefore cannot be removed from it." ); } } - _setRqsGradient( rqsGradient ); - return this; + _setRqsGradient( rqsGradient ); + return this; } /** {@inheritDoc} */ - @Override public boolean rqsGradient() { return ( _flags & RQS_GRADIENT_MASK ) == RQS_GRADIENT_MASK; } + @Override public boolean rqsGradient() { return ( _flags & RQS_GRADIENT_MASK ) == RQS_GRADIENT_MASK; } private void _setRqsGradient( boolean rqsGradient ) { - if ( rqsGradient() != rqsGradient ) { - if ( rqsGradient ) _flags += RQS_GRADIENT_MASK; - else _flags -= RQS_GRADIENT_MASK; + if ( rqsGradient() != rqsGradient ) { + if ( rqsGradient ) _flags += RQS_GRADIENT_MASK; + else _flags -= RQS_GRADIENT_MASK; } - } + } /** {@inheritDoc} */ - @Override public boolean isIntermediate() { return ( _flags & IS_INTERMEDIATE_MASK ) == IS_INTERMEDIATE_MASK; } + @Override public boolean isIntermediate() { return ( _flags & IS_INTERMEDIATE_MASK ) == IS_INTERMEDIATE_MASK; } /** * Intermediate tensors are internal non-user tensors which may be eligible @@ -338,52 +335,66 @@ * tensor which may be eligible for deletion by {@link Function}s consuming it. */ private Tensor<V> _setIsIntermediate( boolean isIntermediate ) { - if ( isIntermediate() != isIntermediate ) { - if ( isIntermediate ) _flags += IS_INTERMEDIATE_MASK; - else _flags -= IS_INTERMEDIATE_MASK; + if ( isIntermediate() != isIntermediate ) { + if ( isIntermediate ) _flags += IS_INTERMEDIATE_MASK; + else _flags -= IS_INTERMEDIATE_MASK; } - return this; + return this; } /** {@inheritDoc} */ @Override - public boolean isVirtual() { return ( _flags & IS_VIRTUAL_MASK ) == IS_VIRTUAL_MASK; } + public boolean isVirtual() { return ( _flags & IS_VIRTUAL_MASK ) == IS_VIRTUAL_MASK; } /** {@inheritDoc} */ @Override public Tensor<V> setIsVirtual(boolean isVirtual ) { - if ( getNDConf() == null ) - throw new IllegalStateException( + if ( getNDConf() == null ) + throw new IllegalStateException( "Cannot set the virtual flag of a tensor which has not been constructed yet!" ); - if ( isVirtual() != isVirtual ) + if ( isVirtual() != isVirtual ) { - if ( isVirtual ) - _virtualize(); + if ( isVirtual ) + _virtualize(); else - _actualize(); + _actualize(); // Virtual and actual tensors require a different mapping from a given index to the underlying data.. // Therefore, we need to re-initialize the NDConfiguration object: - constructFor(this, getDevice(),NDConstructor.of(getNDConf().shape())).unpopulated( isVirtual, false, getDataType() ); - if ( isVirtual ) - this.find( Relation.class ) - .ifPresent( r -> - r.getChildren().forEach(c -> { - ((TensorImpl<V>)c)._setData( _getData() ); - ((TensorImpl<V>)c).setIsVirtual( true ); - }) + TensorConstructor.Args args = constructFor(getDevice(),NDConstructor.of(getNDConf().shape())).unpopulated( isVirtual, false, getDataType() ); + _setState( args ); + + if ( isVirtual ) + this.find( Relation.class ) + .ifPresent( r -> + r.getChildren().forEach(c -> { + ((TensorImpl<V>)c)._setData( _getData() ); + ((TensorImpl<V>)c).setIsVirtual( true ); + }) ); else - this.find(Relation.class) - .map( relation -> ((Relation<V>)relation).getParent().orElse(null) ) - .map( parent -> parent.get(Relation.class) ) - .ifPresent( parentRelation -> parentRelation.removeChild( this ) ); - } - else if ( isVirtual ) _allocateVirtual(); //> Only a single value representing the rest. - return this; - } + this.find(Relation.class) + .map( relation -> ((Relation<V>)relation).getParent().orElse(null) ) + .map( parent -> parent.get(Relation.class) ) + .ifPresent( parentRelation -> parentRelation.removeChild( this ) ); + } + else if ( isVirtual ) _allocateVirtual(); //> Only a single value representing the rest. + return this; + } + + private void _setState(TensorConstructor.Args args) { + Boolean isVirtual = args.isVirtual(); + NDConfiguration ndc = args.getConf(); + Data<V> data = (Data<V>) args.getData(); + if ( isVirtual != null ) + _setIsVirtual( isVirtual ); + if ( ndc != null ) + _setNDConf( ndc ); + if ( data != null ) + _setData( data ); + } /** * This method is the inner counterpart to the public "{@link MutateTensor#setIsVirtual}" method. @@ -393,36 +404,36 @@ */ @Override protected void _setIsVirtual( boolean isVirtual ) { - if ( isVirtual() != isVirtual ) { - if ( isVirtual ) _flags += IS_VIRTUAL_MASK; - else _flags -= IS_VIRTUAL_MASK; + if ( isVirtual() != isVirtual ) { + if ( isVirtual ) _flags += IS_VIRTUAL_MASK; + else _flags -= IS_VIRTUAL_MASK; } - } + } /** {@inheritDoc} */ @Override - public boolean isDeleted() { return ( _flags & IS_DELETED_MASK ) == IS_DELETED_MASK; } + public boolean isDeleted() { return ( _flags & IS_DELETED_MASK ) == IS_DELETED_MASK; } /** {@inheritDoc} */ @Override - public boolean gradientApplyRequested() { return ( _flags & GRADIENT_APPLY_RQD_MASK ) == GRADIENT_APPLY_RQD_MASK; } + public boolean gradientApplyRequested() { return ( _flags & GRADIENT_APPLY_RQD_MASK ) == GRADIENT_APPLY_RQD_MASK; } /** {@inheritDoc} */ @Override public Tensor<V> setGradientApplyRequested(boolean applyRequested ) { - if ( gradientApplyRequested() != applyRequested ) { - if ( applyRequested ) { + if ( gradientApplyRequested() != applyRequested ) { + if ( applyRequested ) { if ( - Neureka.get().settings().autograd().isApplyingGradientWhenRequested() && - !Neureka.get().settings().autograd().isApplyingGradientWhenTensorIsUsed() + Neureka.get().settings().autograd().isApplyingGradientWhenRequested() && + !Neureka.get().settings().autograd().isApplyingGradientWhenTensorIsUsed() ) - this.applyGradient(); + this.applyGradient(); else - _flags += GRADIENT_APPLY_RQD_MASK; + _flags += GRADIENT_APPLY_RQD_MASK; } - else _flags -= GRADIENT_APPLY_RQD_MASK; + else _flags -= GRADIENT_APPLY_RQD_MASK; } - return this; + return this; } /** @@ -438,22 +449,22 @@ */ private Tensor<V> _delete() { - if ( isDeleted() ) return this; - getGraphNode().ifPresent( n -> { - if ( !n.canBeDeleted() ) { - String message = "Cannot delete a tensor which is used as derivative by the AD computation graph!"; - _LOG.error( message ); - throw new IllegalStateException( message ); + if ( isDeleted() ) return this; + getGraphNode().ifPresent( n -> { + if ( !n.canBeDeleted() ) { + String message = "Cannot delete a tensor which is used as derivative by the AD computation graph!"; + _LOG.error( message ); + throw new IllegalStateException( message ); } - }); - this.find( Device.class ).ifPresent( device -> device.free( this ) ); - _setData( null ); - _setNDConf( null ); - _flags = 0; - this.find( TensorImpl.class ).ifPresent(t -> t.mut().delete() ); - _deleteComponents(); - _flags += IS_DELETED_MASK; - return this; + }); + this.find( Device.class ).ifPresent( device -> device.free( this ) ); + _setData( null ); + _setNDConf( null ); + _flags = 0; + this.find( TensorImpl.class ).ifPresent(t -> t.mut().delete() ); + _deleteComponents(); + _flags += IS_DELETED_MASK; + return this; } /*================================================================================================================== @@ -465,14 +476,14 @@ /** {@inheritDoc} */ @Override public <T extends Component<?>> T get( Class<T> componentClass ) { - LogUtil.nullArgCheck( componentClass, "componentClass", Class.class ); + LogUtil.nullArgCheck( componentClass, "componentClass", Class.class ); - if ( GraphNode.class.isAssignableFrom(componentClass) ) - _guardGet(componentClass.getSimpleName()); - else if ( NDFrame.class.isAssignableFrom(componentClass) ) - _guardGet(componentClass.getSimpleName()); + if ( GraphNode.class.isAssignableFrom(componentClass) ) + _guardGet(componentClass.getSimpleName()); + else if ( NDFrame.class.isAssignableFrom(componentClass) ) + _guardGet(componentClass.getSimpleName()); - return super.get(componentClass); + return super.get(componentClass); } /** @@ -485,7 +496,7 @@ * @return The unchanged object or maybe in future versions: null (component rejected) */ @Override - protected < T extends Component<Tensor<V>> > T _setOrReject(T newComponent ) { return newComponent; } + protected < T extends Component<Tensor<V>> > T _setOrReject(T newComponent ) { return newComponent; } /** * This method is executed when a component is being removed from the tensor. @@ -499,28 +510,28 @@ @Override protected <T extends Component<Tensor<V>>> T _removeOrReject(T newComponent ) { - if ( newComponent instanceof Device ) { - Device<V> device = (Device<V>) newComponent; + if ( newComponent instanceof Device ) { + Device<V> device = (Device<V>) newComponent; /* The following seems like a redundant check, however often times a tensor will be removed from a Device implementation inside the "restore" method when the tensor has already been removed from the device... Without the condition below a stack overflow would occur! */ - if ( device.has( this ) ) { + if ( device.has( this ) ) { try { - device.restore( this ); - } catch ( Exception exception ) { - _LOG.error( + device.restore( this ); + } catch ( Exception exception ) { + _LOG.error( "Removing device from tensor / tensor from device failed.\n" + "Restoring tensor from device threw exception.\n", exception ); - throw exception; - } + throw exception; + } } } - return newComponent; + return newComponent; } @@ -534,7 +545,7 @@ * {@inheritDoc} */ @Override - public int getVersion() { return _version; } + public int getVersion() { return _version; } /*================================================================================================================== @@ -553,58 +564,58 @@ * @param call The context object containing all relevant information that defines a call for tensor execution. */ private void _incrementVersionBecauseOf( ExecutionCall<?> call ) { - if ( Neureka.get().settings().autograd().isPreventingInlineOperations() ) { - _version++; // Autograd must be warned! - GraphNode<?> node = get( GraphNode.class ); - if ( node != null && node.getPayloadReferenceVersion() != _version ) { - if ( node.usesAD() || node.isUsedAsDerivative() ) { - String error = "Inline operation occurred on tensor which is part of a computation graph node with autograd support!\n" + - "The following OperationType caused an internal version mismatch: '"+call.getOperation().getIdentifier()+"'"; - _LOG.error( error ); - throw new IllegalStateException( error ); + if ( Neureka.get().settings().autograd().isPreventingInlineOperations() ) { + _version++; // Autograd must be warned! + GraphNode<?> node = get( GraphNode.class ); + if ( node != null && node.getPayloadReferenceVersion() != _version ) { + if ( node.usesAD() || node.isUsedAsDerivative() ) { + String error = "Inline operation occurred on tensor which is part of a computation graph node with autograd support!\n" + + "The following OperationType caused an internal version mismatch: '"+call.getOperation().getIdentifier()+"'"; + _LOG.error( error ); + throw new IllegalStateException( error ); } } } - } + } /** * {@inheritDoc} */ @Override public MutateTensor<V> getMut() { - _guardGet("unsafe API"); - return this; + _guardGet("unsafe API"); + return this; } /** {@inheritDoc} */ @Override public MutateNda.Item<V> at(int... indices ) { - return new MutateNda.Item<V>() { - @Override public V orElseNull() { return item( indices ); } - @Override public void set( V value ) { getMut().putAt( indices, value ); } + return new MutateNda.Item<V>() { + @Override public V orElseNull() { return item( indices ); } + @Override public void set( V value ) { getMut().putAt( indices, value ); } @Override public boolean equals( Object o ) { - if ( o == null ) return false; - if ( o == this ) return true; - if ( o.getClass() != this.getClass() ) return false; - Nda.Item<V> other = (Nda.Item<V>) o; - return this.get().equals( other.get() ); + if ( o == null ) return false; + if ( o == this ) return true; + if ( o.getClass() != this.getClass() ) return false; + Nda.Item<V> other = (Nda.Item<V>) o; + return this.get().equals( other.get() ); } - @Override public int hashCode() { V item = get(); return ( item == null ? 0 : item.hashCode() ); } - @Override public String toString() { return String.valueOf( get() ); } + @Override public int hashCode() { V item = get(); return ( item == null ? 0 : item.hashCode() ); } + @Override public String toString() { return String.valueOf( get() ); } }; } /** * {@inheritDoc} */ @Override - public Tensor<V> setNDConf(NDConfiguration configuration ) { TensorImpl.this._setNDConf( configuration ); return TensorImpl.this; } + public Tensor<V> setNDConf(NDConfiguration configuration ) { TensorImpl.this._setNDConf( configuration ); return TensorImpl.this; } /** * {@inheritDoc} */ @Override public <V> Tensor<V> toType(Class<V> typeClass ) { - LogUtil.nullArgCheck( typeClass, "typeClass", Class.class, "Cannot convert tensor to 'null' data type." ); - return TensorImpl.this._toType( typeClass ); + LogUtil.nullArgCheck( typeClass, "typeClass", Class.class, "Cannot convert tensor to 'null' data type." ); + return TensorImpl.this._toType( typeClass ); } /** @@ -612,11 +623,11 @@ */ @Override public <U> Tensor<U> upcast(Class<U> superType ) { - LogUtil.nullArgCheck( superType, "superType", Class.class ); - if ( superType.isAssignableFrom(TensorImpl.this.itemType()) ) - return (Tensor<U>) TensorImpl.this; + LogUtil.nullArgCheck( superType, "superType", Class.class ); + if ( superType.isAssignableFrom(TensorImpl.this.itemType()) ) + return (Tensor<U>) TensorImpl.this; else - throw new IllegalArgumentException("Provided type '"+superType+"' is not a super type of '"+ TensorImpl.this.itemType()+"'."); + throw new IllegalArgumentException("Provided type '"+superType+"' is not a super type of '"+ TensorImpl.this.itemType()+"'."); } /** @@ -624,8 +635,8 @@ */ @Override public Tensor<V> toLayout(NDConfiguration.Layout layout ) { - ReLayout.toLayout( this, layout ); - return TensorImpl.this; + ReLayout.toLayout( this, layout ); + return TensorImpl.this; } /** @@ -633,33 +644,33 @@ */ @Override public Tensor<V> incrementVersion(ExecutionCall<?> call ) { - LogUtil.nullArgCheck( call, "call", ExecutionCall.class ); - _incrementVersionBecauseOf( call ); - return TensorImpl.this; + LogUtil.nullArgCheck( call, "call", ExecutionCall.class ); + _incrementVersionBecauseOf( call ); + return TensorImpl.this; } /** * {@inheritDoc} */ @Override - public Tensor<V> setIsIntermediate(boolean isIntermediate ) { return _setIsIntermediate( isIntermediate ); } + public Tensor<V> setIsIntermediate(boolean isIntermediate ) { return _setIsIntermediate( isIntermediate ); } /** * {@inheritDoc} */ - @Override public Tensor<V> delete() { return TensorImpl.this._delete(); } + @Override public Tensor<V> delete() { return TensorImpl.this._delete(); } /** * {@inheritDoc} */ - @Override public Data<V> getData() { return _getData(); } + @Override public Data<V> getData() { return _getData(); } /** * {@inheritDoc} */ @Override public <A> A getDataAs( Class<A> arrayTypeClass ) { - return DataConverter.get().convert( _getData(false), arrayTypeClass ); + return DataConverter.get().convert( _getData(false), arrayTypeClass ); } /** @@ -667,9 +678,9 @@ */ @Override public Tensor<V> setDataAt(int i, V o ) { - _guardMod("data object"); - _setDataAt( i, o ); - return TensorImpl.this; + _guardMod("data object"); + _setDataAt( i, o ); + return TensorImpl.this; } /** @@ -677,131 +688,131 @@ */ @Override public Tensor<V> setData(Data<V> data ) { - TensorImpl.this._setData( data ); - return TensorImpl.this; + TensorImpl.this._setData( data ); + return TensorImpl.this; } /** * {@inheritDoc} */ - @Override public Tensor<V> detach() { TensorImpl.this.remove( GraphNode.class ); return TensorImpl.this; } + @Override public Tensor<V> detach() { TensorImpl.this.remove( GraphNode.class ); return TensorImpl.this; } /** {@inheritDoc} */ @Override public Tensor<V> timesAssign(Tensor<V> other ) { - LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot multiply-assign 'null' to a tensor!"); - return Neureka.get().backend().getFunction().mulAssign().call( TensorImpl.this, other ); + LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot multiply-assign 'null' to a tensor!"); + return Neureka.get().backend().getFunction().mulAssign().call( TensorImpl.this, other ); } /** {@inheritDoc} */ @Override public Tensor<V> timesAssign(V other ) { - LogUtil.nullArgCheck(other, "other", TensorImpl.this.getItemType(), "Cannot multiply-assign 'null' to a tensor!"); - return this.timesAssign( Tensor.of( getItemType(), this.shape(), other ) ); + LogUtil.nullArgCheck(other, "other", TensorImpl.this.getItemType(), "Cannot multiply-assign 'null' to a tensor!"); + return this.timesAssign( Tensor.of( getItemType(), this.shape(), other ) ); } /** {@inheritDoc} */ @Override public Tensor<V> divAssign(Tensor<V> other ) { - LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot divide-assign a tensor by 'null' (In any sense of the word)!"); - return Neureka.get().backend().getFunction().divAssign().call( TensorImpl.this, other ); + LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot divide-assign a tensor by 'null' (In any sense of the word)!"); + return Neureka.get().backend().getFunction().divAssign().call( TensorImpl.this, other ); } /** {@inheritDoc} */ @Override public Tensor<V> modAssign(Tensor<V> other ) { - LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot perform tensor modulo 'null'!"); - return Neureka.get().backend().getFunction().modAssign().call( TensorImpl.this, other ); + LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot perform tensor modulo 'null'!"); + return Neureka.get().backend().getFunction().modAssign().call( TensorImpl.this, other ); } /** {@inheritDoc} */ @Override public Tensor<V> plusAssign(Tensor<V> other ) { - LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot add-assign 'null' to a tensor!"); - return Neureka.get().backend().getFunction().plusAssign().call( TensorImpl.this, other ); + LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot add-assign 'null' to a tensor!"); + return Neureka.get().backend().getFunction().plusAssign().call( TensorImpl.this, other ); } /** {@inheritDoc} */ @Override public Tensor<V> minusAssign(Tensor<V> other ) { - LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot subtract-assign 'null' from a tensor!"); - return Neureka.get().backend().getFunction().minusAssign().call( TensorImpl.this, other ); + LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot subtract-assign 'null' from a tensor!"); + return Neureka.get().backend().getFunction().minusAssign().call( TensorImpl.this, other ); } /** {@inheritDoc} */ @Override public Tensor<V> minusAssign(V other ) { - LogUtil.nullArgCheck(other, "other", TensorImpl.this.getItemType(), "Cannot subtract-assign 'null' from a tensor!"); - return minusAssign( - Tensor.of( TensorImpl.this.getDataType().getItemTypeClass() ) - .withShape(TensorImpl.this.getNDConf().shape()) - .all(other) + LogUtil.nullArgCheck(other, "other", TensorImpl.this.getItemType(), "Cannot subtract-assign 'null' from a tensor!"); + return minusAssign( + Tensor.of( TensorImpl.this.getDataType().getItemTypeClass() ) + .withShape(TensorImpl.this.getNDConf().shape()) + .all(other) ); } @Override public Tensor<V> assign(V other ) { - LogUtil.nullArgCheck(other, "other", TensorImpl.this.getItemType(), "Cannot subtract-assign 'null' from a tensor!"); - return assign( - Tensor.of( TensorImpl.this.getDataType().getItemTypeClass() ) - .withShape(TensorImpl.this.getNDConf().shape()) - .all(other) + LogUtil.nullArgCheck(other, "other", TensorImpl.this.getItemType(), "Cannot subtract-assign 'null' from a tensor!"); + return assign( + Tensor.of( TensorImpl.this.getDataType().getItemTypeClass() ) + .withShape(TensorImpl.this.getNDConf().shape()) + .all(other) ); } @Override public Tensor<V> assign(Nda<V> other ) { - LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot assign 'null' to a tensor!"); - return Neureka.get().backend().getFunction().idy().call( TensorImpl.this, (Tensor<V>) other ); + LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot assign 'null' to a tensor!"); + return Neureka.get().backend().getFunction().idy().call( TensorImpl.this, (Tensor<V>) other ); } @Override public Tensor<V> labelAxes(String[]... labels ) { - LogUtil.nullArgCheck(labels, "labels", String[][].class, "Tensors cannot be labeled 'null'!"); - if ( labels.length > this.rank() ) - throw new IllegalArgumentException( + LogUtil.nullArgCheck(labels, "labels", String[][].class, "Tensors cannot be labeled 'null'!"); + if ( labels.length > this.rank() ) + throw new IllegalArgumentException( "Number of the provided axes labels is larger than the total number of axes (rank) of the nd-array." ); - NDFrame<V> frame = get( NDFrame.class ); - if ( frame == null ) { - frame = new NDFrame<>( this, null); - this.set(frame); + NDFrame<V> frame = get( NDFrame.class ); + if ( frame == null ) { + frame = new NDFrame<>( this, null); + this.set(frame); } - for ( int i = 0; i < labels.length; i++ ) { - if ( labels[ i ] != null ) { - AxisFrame<Integer, V> atAxis = frame.atAxis( i ); - for ( int ii = 0; ii < labels[ i ].length; ii++ ) { - if ( labels[ i ][ ii ] != null ) - atAxis.atIndexAlias( labels[ i ][ ii ] ).setIndex( ii ); + for ( int i = 0; i < labels.length; i++ ) { + if ( labels[ i ] != null ) { + AxisFrame<Integer, V> atAxis = frame.atAxis( i ); + for ( int ii = 0; ii < labels[ i ].length; ii++ ) { + if ( labels[ i ][ ii ] != null ) + atAxis.atIndexAlias( labels[ i ][ ii ] ).setIndex( ii ); } } } - return this; + return this; } /** {@inheritDoc} */ @Override public Tensor<V> labelAxes(List<List<Object>> labels ) { - LogUtil.nullArgCheck(labels, "labels", List.class, "Tensors cannot be labeled 'null'!"); - NDFrame<V> frame = get( NDFrame.class ); - if ( frame == null ) set( new NDFrame<>( labels, this, null ) ); - else set( frame.withAxesLabels( labels ) ); - return TensorImpl.this; + LogUtil.nullArgCheck(labels, "labels", List.class, "Tensors cannot be labeled 'null'!"); + NDFrame<V> frame = get( NDFrame.class ); + if ( frame == null ) set( new NDFrame<>( labels, this, null ) ); + else set( frame.withAxesLabels( labels ) ); + return TensorImpl.this; } /** {@inheritDoc} */ @Override public Tensor<V> label(String label ) { - LogUtil.nullArgCheck( label, "label", List.class, "Tensors cannot be labeled 'null'!" ); - NDFrame<V> frame = get( NDFrame.class ); - if ( frame == null ) set( new NDFrame<>( Collections.emptyList(), this, label ) ); - else set( frame.withLabel(label) ); - return TensorImpl.this; + LogUtil.nullArgCheck( label, "label", List.class, "Tensors cannot be labeled 'null'!" ); + NDFrame<V> frame = get( NDFrame.class ); + if ( frame == null ) set( new NDFrame<>( Collections.emptyList(), this, label ) ); + else set( frame.withLabel(label) ); + return TensorImpl.this; } /** {@inheritDoc} */ @Override public Tensor<V> labelAxes(Map<Object, List<Object>> labels ) { - LogUtil.nullArgCheck(labels, "labels", Map.class, "Tensors cannot be labeled 'null'!"); - String label = getLabel(); - label = label == null || label.isEmpty() ? null : label; - TensorImpl.this.set( new NDFrame<>( labels, TensorImpl.this, label ) ); - return TensorImpl.this; + LogUtil.nullArgCheck(labels, "labels", Map.class, "Tensors cannot be labeled 'null'!"); + String label = getLabel(); + label = label == null || label.isEmpty() ? null : label; + TensorImpl.this.set( new NDFrame<>( labels, TensorImpl.this, label ) ); + return TensorImpl.this; } /*================================================================================================================== @@ -819,20 +830,20 @@ @Override public Iterator<V> iterator() { - NDIterator _ndi = NDIterator.of( this ); - return new Iterator<V>() - { - private final int _size = TensorImpl.this.size(); - private int _count = 0; + NDIterator _ndi = NDIterator.of( this ); + return new Iterator<V>() + { + private final int _size = TensorImpl.this.size(); + private int _count = 0; - @Override public boolean hasNext() { return _count != _size; } + @Override public boolean hasNext() { return _count != _size; } @Override public V next() { - V value = TensorImpl.this.getDataAt( _ndi.i() ); - _ndi.increment(); - _count ++; - return value; + V value = TensorImpl.this.getDataAt( _ndi.i() ); + _ndi.increment(); + _count ++; + return value; } }; } @@ -849,8 +860,8 @@ */ @Override public Tensor<V> to(Device<?> device ){ - if ( this.getDevice() != device ) super._set( device ); - return this; + if ( this.getDevice() != device ) super._set( device ); + return this; } /** @@ -859,50 +870,50 @@ * This is to avoid unnecessary allocations and computations. */ void _backward( LazyRef<Tensor<V>> error ) { - LogUtil.nullArgCheck(error, "error", Tensor.class, "Cannot back-propagate 'null'!"); - LazyRef<Tensor<V>> errorRef = this.isOutsourced() - ? LazyRef.of(()->error.get().deepCopy().to(this.getDevice())) - : error; + LogUtil.nullArgCheck(error, "error", Tensor.class, "Cannot back-propagate 'null'!"); + LazyRef<Tensor<V>> errorRef = this.isOutsourced() + ? LazyRef.of(()->error.get().deepCopy().to(this.getDevice())) + : error; - find( GraphNode.class ).ifPresent( node -> node.backward(errorRef.get()) ); + find( GraphNode.class ).ifPresent( node -> node.backward(errorRef.get()) ); - if ( this.rqsGradient() ) - mut().addToGradient( errorRef.get() ); - } + if ( this.rqsGradient() ) + mut().addToGradient( errorRef.get() ); + } @Override public Tensor<V> withLabel(String label ) { - Tensor<V> copy = this.shallowCopy(); - if ( copy.label().endsWith(":slice") ) // We remove the slice postfix if it exists... - copy = copy.shallowClone().mut().label( copy.label().substring(0, copy.label().length()-6) ); - return copy.mut().label( label ); + Tensor<V> copy = this.shallowCopy(); + if ( copy.label().endsWith(":slice") ) // We remove the slice postfix if it exists... + copy = copy.shallowClone().mut().label( copy.label().substring(0, copy.label().length()-6) ); + return copy.mut().label( label ); } /** {@inheritDoc} */ @Override public Tensor<V> withLabels(String[]... labels ) { - Tensor<V> copy = this.shallowCopy(); - if ( copy.label().endsWith(":slice") ) // We remove the slice postfix if it exists... - copy = copy.shallowClone().mut().label( copy.label().substring(0, copy.label().length()-6) ); - return copy.mut().labelAxes( labels ); + Tensor<V> copy = this.shallowCopy(); + if ( copy.label().endsWith(":slice") ) // We remove the slice postfix if it exists... + copy = copy.shallowClone().mut().label( copy.label().substring(0, copy.label().length()-6) ); + return copy.mut().labelAxes( labels ); } /** {@inheritDoc} */ @Override public Tensor<V> withLabels(List<List<Object>> labels ) { - Tensor<V> copy = this.shallowCopy(); - if ( copy.label().endsWith(":slice") ) // We remove the slice postfix if it exists... - copy = copy.shallowClone().mut().label( copy.label().substring(0, copy.label().length()-6) ); - return copy.getMut().labelAxes( labels ); + Tensor<V> copy = this.shallowCopy(); + if ( copy.label().endsWith(":slice") ) // We remove the slice postfix if it exists... + copy = copy.shallowClone().mut().label( copy.label().substring(0, copy.label().length()-6) ); + return copy.getMut().labelAxes( labels ); } /** {@inheritDoc} */ @Override public Tensor<V> withLabels(Map<Object, List<Object>> labels ) { - Tensor<V> copy = this.shallowCopy(); - if ( copy.label().endsWith(":slice") ) // We remove the slice postfix if it exists... - copy = copy.shallowClone().mut().label( copy.label().substring(0, copy.label().length()-6) ); - return copy.getMut().labelAxes( labels ); + Tensor<V> copy = this.shallowCopy(); + if ( copy.label().endsWith(":slice") ) // We remove the slice postfix if it exists... + copy = copy.shallowClone().mut().label( copy.label().substring(0, copy.label().length()-6) ); + return copy.getMut().labelAxes( labels ); } /*================================================================================================================== @@ -915,10 +926,10 @@ /** {@inheritDoc} */ @Override public boolean isCase( Tensor<V> other ) { - LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot perform 'is case' operation when second operand is 'null'!"); - return this.find( Relation.class ) - .map( r -> ((Relation<?>)r).getChildren().stream().anyMatch( (Tensor<?> c) -> c.equals(other) )) - .orElse(false); + LogUtil.nullArgCheck(other, "other", Tensor.class, "Cannot perform 'is case' operation when second operand is 'null'!"); + return this.find( Relation.class ) + .map( r -> ((Relation<?>)r).getChildren().stream().anyMatch( (Tensor<?> c) -> c.equals(other) )) + .orElse(false); } /*================================================================================================================== @@ -931,16 +942,16 @@ /** {@inheritDoc} */ @Override public Tensor<V> getAt(int... indices ) { - LogUtil.nullArgCheck(indices, "indices", int[].class, "Indices array must not be 'null'!"); - return getAt( Arrays.stream( indices ).boxed().toArray() ); + LogUtil.nullArgCheck(indices, "indices", int[].class, "Indices array must not be 'null'!"); + return getAt( Arrays.stream( indices ).boxed().toArray() ); } /** {@inheritDoc} */ @Override public Tensor<V> getAt(Map<?,Integer> rangToSteps) { - LogUtil.nullArgCheck(rangToSteps, "rankToSteps", Map.class, "Rank-to-steps map must not be 'null'!"); + LogUtil.nullArgCheck(rangToSteps, "rankToSteps", Map.class, "Rank-to-steps map must not be 'null'!"); // ...not a simple slice... Advanced: - return SmartSlicer.slice(new Object[]{rangToSteps}, this); + return SmartSlicer.slice(new Object[]{rangToSteps}, this); } /** @@ -948,35 +959,35 @@ */ @Override public Tensor<V> getAt(List<?> key ) { - LogUtil.nullArgCheck( key, "key", List.class ); - if ( key.stream().anyMatch( i -> i == null ) ) - throw new IllegalArgumentException("List of indices/ranges may not contain entries which are null!"); - if ( key.isEmpty() ) { + LogUtil.nullArgCheck( key, "key", List.class ); + if ( key.stream().anyMatch( i -> i == null ) ) + throw new IllegalArgumentException("List of indices/ranges may not contain entries which are null!"); + if ( key.isEmpty() ) { /* An empty List instance is being interpreted as the request to create an identical slice, meaning that the resulting tensor views the same data as its parent while not being the same instance. (In a sense, its a shallow copy!) */ - return shallowCopy(); + return shallowCopy(); } - Object[] indices = key.toArray(); - - boolean allInt = true; - for ( Object o : indices ) allInt = allInt && o instanceof Integer; - if ( allInt && indices.length == rank() ) { - int[] newOffset = DataConverter.get().convert(indices, int[].class); - for ( int i = 0; i < this.rank(); i++ ) - newOffset[ i ] = ( newOffset[ i ] < 0 ) ? getNDConf().shape( i ) + newOffset[ i ] : newOffset[ i ]; - for ( int i = 0; i < this.rank(); i++ ) - indices[ i ] = newOffset[ i ]; - allInt = false; + Object[] indices = key.toArray(); + + boolean allInt = true; + for ( Object o : indices ) allInt = allInt && o instanceof Integer; + if ( allInt && indices.length == rank() ) { + int[] newOffset = DataConverter.get().convert(indices, int[].class); + for ( int i = 0; i < this.rank(); i++ ) + newOffset[ i ] = ( newOffset[ i ] < 0 ) ? getNDConf().shape( i ) + newOffset[ i ] : newOffset[ i ]; + for ( int i = 0; i < this.rank(); i++ ) + indices[ i ] = newOffset[ i ]; + allInt = false; } - boolean hasScale = false; - for ( Object o : indices ) hasScale = hasScale || o instanceof Map; - return SmartSlicer.slice( - ( allInt ? new Object[]{ DataConverter.get().convert(indices, int[].class) } : indices ), + boolean hasScale = false; + for ( Object o : indices ) hasScale = hasScale || o instanceof Map; + return SmartSlicer.slice( + ( allInt ? new Object[]{ DataConverter.get().convert(indices, int[].class) } : indices ), this ); } @@ -984,50 +995,50 @@ /** {@inheritDoc} */ @Override public TensorImpl<V> deepCopy() { - return _clone( false ); + return _clone( false ); } /** {@inheritDoc} */ @Override public Tensor<V> deepClone() { - return _clone( true ); + return _clone( true ); } private TensorImpl<V> _clone(boolean autograd) { - Function cloner = autograd ? Neureka.get().backend().getAutogradFunction().idy() : Neureka.get().backend().getFunction().idy(); - boolean thisIsIntermediate = this.isIntermediate(); - _setIsIntermediate( false ); - Tensor<V> clone = Tensor.like( this ) - .all( (V) Double.valueOf(0.0) ); + Function cloner = autograd ? Neureka.get().backend().getAutogradFunction().idy() : Neureka.get().backend().getFunction().idy(); + boolean thisIsIntermediate = this.isIntermediate(); + _setIsIntermediate( false ); + Tensor<V> clone = Tensor.like( this ) + .all( (V) Double.valueOf(0.0) ); - if ( clone.itemType() != this.itemType() ) - throw new IllegalStateException("Item type of clone must be the same as the item type of the original!"); + if ( clone.itemType() != this.itemType() ) + throw new IllegalStateException("Item type of clone must be the same as the item type of the original!"); - clone = cloner.call( clone, this ); - clone.getMut().setIsIntermediate( thisIsIntermediate ); - _setIsIntermediate( thisIsIntermediate ); - return (TensorImpl<V>) clone; + clone = cloner.call( clone, this ); + clone.getMut().setIsIntermediate( thisIsIntermediate ); + _setIsIntermediate( thisIsIntermediate ); + return (TensorImpl<V>) clone; } /** * {@inheritDoc} */ @Override - public AxisOrGetTensor<V> slice() { return new SliceBuilder<>( this ); } + public AxisOrGetTensor<V> slice() { return new SliceBuilder<>( this ); } /** * {@inheritDoc} */ @Override public Tensor<V> putAt(List<?> key, Nda<V> value ) { - _putAtCheckFor( (Tensor<?>) value ); - Tensor<V> slice = ( key == null ) ? this : getAt( key ); - Data<V> thisData = this.getMut().getData(); - Object thisDataRef = ( thisData != null ? thisData.getOrNull() : null ); - if ( thisDataRef != null && !thisDataRef.equals(slice.getMut().getData().getOrNull()) ) - throw new IllegalStateException("Failed to isolate slice for inline assignment!"); + _putAtCheckFor( (Tensor<?>) value ); + Tensor<V> slice = ( key == null ) ? this : getAt( key ); + Data<V> thisData = this.getMut().getData(); + Object thisDataRef = ( thisData != null ? thisData.getOrNull() : null ); + if ( thisDataRef != null && !thisDataRef.equals(slice.getMut().getData().getOrNull()) ) + throw new IllegalStateException("Failed to isolate slice for inline assignment!"); - return _putAt( slice, (Tensor<V>) value ); + return _putAt( slice, (Tensor<V>) value ); } /** @@ -1035,22 +1046,22 @@ */ @Override public Tensor<V> putAt(int[] indices, V item ) { - if ( indices == null ) - throw new IllegalArgumentException( "Provided indices are null!" ); - if ( indices.length > this.rank() ) { - int[] correct = new int[rank()]; - System.arraycopy( indices, 0, correct, 0, indices.length ); - indices = correct; - } else if ( indices.length < rank() ) { - int[] correct = new int[rank()]; - System.arraycopy( indices, 0, correct, 0, indices.length ); - for ( int i = indices.length; i < rank(); i++ ) correct[i] = 0; - indices = correct; + if ( indices == null ) + throw new IllegalArgumentException( "Provided indices are null!" ); + if ( indices.length > this.rank() ) { + int[] correct = new int[rank()]; + System.arraycopy( indices, 0, correct, 0, indices.length ); + indices = correct; + } else if ( indices.length < rank() ) { + int[] correct = new int[rank()]; + System.arraycopy( indices, 0, correct, 0, indices.length ); + for ( int i = indices.length; i < rank(); i++ ) correct[i] = 0; + indices = correct; } - if ( this.isVirtual() && this.size() > 1 ) this.setIsVirtual( false ); - int i = getNDConf().indexOfIndices(indices); - this.getMut().setDataAt( i, item ); - return this; + if ( this.isVirtual() && this.size() > 1 ) this.setIsVirtual( false ); + int i = getNDConf().indexOfIndices(indices); + this.getMut().setDataAt( i, item ); + return this; } /** @@ -1058,77 +1069,77 @@ */ @Override public Tensor<V> putAt(Map<?,Integer> key, Nda<V> value ) { - _putAtCheckFor((Tensor<?>) value); - Tensor<V> slice = ( key == null ) ? this : getAt( key ); - return _putAt( slice, (Tensor<V>) value); + _putAtCheckFor((Tensor<?>) value); + Tensor<V> slice = ( key == null ) ? this : getAt( key ); + return _putAt( slice, (Tensor<V>) value); } private void _putAtCheckFor( Tensor<?> value ) { - if ( value.isEmpty() ) { - String message = "Provided tensor is empty! Empty tensors cannot be injected."; - _LOG.error( message ); - throw new IllegalArgumentException( message ); + if ( value.isEmpty() ) { + String message = "Provided tensor is empty! Empty tensors cannot be injected."; + _LOG.error( message ); + throw new IllegalArgumentException( message ); } - } + } private Tensor<V> _putAt(Tensor<V> slice, Tensor<V> value ) { - boolean valueIsDeviceVisitor = false; - if ( slice.isOutsourced() && !value.isOutsourced() ) { - Device<V> device = slice.getDevice(); + boolean valueIsDeviceVisitor = false; + if ( slice.isOutsourced() && !value.isOutsourced() ) { + Device<V> device = slice.getDevice(); try { - device.store( value ); - } catch ( Exception e ) { - _LOG.error( "Trying to migrate target slice tensor to device failed.", e ); - throw e; - } - valueIsDeviceVisitor = true; + device.store( value ); + } catch ( Exception e ) { + _LOG.error( "Trying to migrate target slice tensor to device failed.", e ); + throw e; + } + valueIsDeviceVisitor = true; } - Neureka.get().backend().getFunction().idy().call( slice, value ); + Neureka.get().backend().getFunction().idy().call( slice, value ); try { - if ( valueIsDeviceVisitor ) value.getDevice().restore( value ); - } catch ( Exception exception ) { - _LOG.error( "Trying to migrate source tensor back to original location failed.", exception ); - throw exception; - } - return this; + if ( valueIsDeviceVisitor ) value.getDevice().restore( value ); + } catch ( Exception exception ) { + _LOG.error( "Trying to migrate source tensor back to original location failed.", exception ); + throw exception; + } + return this; } /** * {@inheritDoc} */ @Override - public V getDataAt( int i ) { return getDevice().access( this ).readAt( i ); } + public V getDataAt( int i ) { return getDevice().access( this ).readAt( i ); } /** * {@inheritDoc} */ @Override public Tensor<V> setItemAt(int i, V o ) { - _guardMod("data object"); - NDConfiguration ndc = this.getNDConf(); - _setDataAt( ndc.indexOfIndex( i ), o ); - return this; + _guardMod("data object"); + NDConfiguration ndc = this.getNDConf(); + _setDataAt( ndc.indexOfIndex( i ), o ); + return this; } /** {@inheritDoc} */ @Override public Tensor<V> putAt(List<?> indices, V value ) { - if ( indices.stream().allMatch( i -> i instanceof Number ) ) - return setItemAt( indexOfIndices(indices.stream().mapToInt( i -> ((Number)i).intValue() ).toArray()), value ); + if ( indices.stream().allMatch( i -> i instanceof Number ) ) + return setItemAt( indexOfIndices(indices.stream().mapToInt( i -> ((Number)i).intValue() ).toArray()), value ); else - return this.putAt( indices, Tensor.ofAny( this.getItemType(), shape(), value ) ); + return this.putAt( indices, Tensor.ofAny( this.getItemType(), shape(), value ) ); } /** {@inheritDoc} */ - @Override public Tensor<V> putAt(int index, V value ) { return putAt( indicesOfIndex(index), value ); } + @Override public Tensor<V> putAt(int index, V value ) { return putAt( indicesOfIndex(index), value ); } private void _setDataAt( int i, V o ) { - if ( this.isVirtual() && i > 0 ) - throw new IllegalArgumentException("There is no data item at index "+i+" for this virtual tensor!"); + if ( this.isVirtual() && i > 0 ) + throw new IllegalArgumentException("There is no data item at index "+i+" for this virtual tensor!"); - getDevice().access( this ).write( o ).at( i ); - _version++; // Autograd must be warned! - } + getDevice().access( this ).write( o ).at( i ); + _version++; // Autograd must be warned! + } /** * {@inheritDoc} @@ -1136,22 +1147,22 @@ @Override public Tensor<V> setItems(Object value ) { - LogUtil.nullArgCheck( value, "value", Object.class ); - boolean success = true; - if ( Number.class.isAssignableFrom(value.getClass()) ) { // A virtual tensor! - this.setIsVirtual( true ); - value = DataConverter.get().convert( value, this.itemType() ); - this.getMut().setDataAt( 0, (V) value ); + LogUtil.nullArgCheck( value, "value", Object.class ); + boolean success = true; + if ( Number.class.isAssignableFrom(value.getClass()) ) { // A virtual tensor! + this.setIsVirtual( true ); + value = DataConverter.get().convert( value, this.itemType() ); + this.getMut().setDataAt( 0, (V) value ); } - else if ( value.getClass().isArray() ) - getDevice().access(this).writeFrom( value ); + else if ( value.getClass().isArray() ) + getDevice().access(this).writeFrom( value ); - else success = false; + else success = false; - if ( !success ) - _LOG.warn( "Failed to set value of type '"+value.getClass().getSimpleName()+"'!" ); + if ( !success ) + _LOG.warn( "Failed to set value of type '"+value.getClass().getSimpleName()+"'!" ); - return this; + return this; } /** @@ -1159,14 +1170,14 @@ */ @Override public Object getRawData() { - _guardGet("data object"); - return _getData( true ); + _guardGet("data object"); + return _getData( true ); } private Object _getData( boolean clone ) { - Device<V> device = this.getDevice(); - if ( device == null ) return null; - else return device.access( this ).readAll( clone ); + Device<V> device = this.getDevice(); + if ( device == null ) return null; + else return device.access( this ).readAll( clone ); } /** @@ -1174,11 +1185,11 @@ */ @Override public Object getRawItems() { - _guardGet("value object"); - if ( this.getNDConf().isSimple() && !this.isSlice() ) - return getDevice().access(this).readAll(!this.isOutsourced()); + _guardGet("value object"); + if ( this.getNDConf().isSimple() && !this.isSlice() ) + return getDevice().access(this).readAll(!this.isOutsourced()); else - return getDevice().access( this.deepCopy().setIsVirtual( false ) ).readAll(false); + return getDevice().access( this.deepCopy().setIsVirtual( false ) ).readAll(false); } /*================================================================================================================== @@ -1194,98 +1205,98 @@ @Override public BufferedImage asImage( ImageType type ) { - switch ( type.bufferType ) + switch ( type.bufferType ) { case BufferedImage.TYPE_3BYTE_BGR: { - _checkRankForImageConversion(type, Number.class, 0, 0, 3); + _checkRankForImageConversion(type, Number.class, 0, 0, 3); // We expect a tensor of shape (height x width x 3)! - BufferedImage image = new BufferedImage(shape(1), shape(0), type.bufferType); - byte[] data = DataConverter.get().convert( _getRawData(), byte[].class); - _writeImgData(new DataBufferByte(data, data.length), image); - return image; + BufferedImage image = new BufferedImage(shape(1), shape(0), type.bufferType); + byte[] data = DataConverter.get().convert( _getRawData(), byte[].class); + _writeImgData(new DataBufferByte(data, data.length), image); + return image; } case BufferedImage.TYPE_4BYTE_ABGR: case BufferedImage.TYPE_4BYTE_ABGR_PRE: { - _checkRankForImageConversion(type, Number.class, 0, 0, 4); - BufferedImage image = new BufferedImage(shape(1), shape(0), type.bufferType); - byte[] data = DataConverter.get().convert( _getRawData(), byte[].class); - _writeImgData(new DataBufferByte(data, data.length), image); - return image; + _checkRankForImageConversion(type, Number.class, 0, 0, 4); + BufferedImage image = new BufferedImage(shape(1), shape(0), type.bufferType); + byte[] data = DataConverter.get().convert( _getRawData(), byte[].class); + _writeImgData(new DataBufferByte(data, data.length), image); + return image; } case BufferedImage.TYPE_INT_ARGB: { - _checkRankForImageConversion(type, Number.class, 0, 0, 1); - BufferedImage image = new BufferedImage(shape(1), shape(0), type.bufferType); - int[] data = DataConverter.get().convert( _getRawData(), int[].class); - _writeImgData(new DataBufferInt(data, data.length), image); - return image; + _checkRankForImageConversion(type, Number.class, 0, 0, 1); + BufferedImage image = new BufferedImage(shape(1), shape(0), type.bufferType); + int[] data = DataConverter.get().convert( _getRawData(), int[].class); + _writeImgData(new DataBufferInt(data, data.length), image); + return image; } } - throw new IllegalArgumentException("Image type '"+type+"' not supported."); + throw new IllegalArgumentException("Image type '"+type+"' not supported."); } private void _checkRankForImageConversion( ImageType type, Class<?> dataType, int... pattern ) { - int rank = pattern.length; // The expected rank! - if ( this.rank() != rank ) { - throw new IllegalArgumentException( - "Cannot create image of type '" + type.name() + "' from tensor of rank " + this.rank() + ". " + + int rank = pattern.length; // The expected rank! + if ( this.rank() != rank ) { + throw new IllegalArgumentException( + "Cannot create image of type '" + type.name() + "' from tensor of rank " + this.rank() + ". " + "Expected to receive tensor of rank " + rank + "." ); } - for ( int i = 0; i < pattern.length; i++ ) { - int axisSize = pattern[ i ]; // The expected axis size! - if ( axisSize > 0 ) { - if ( axisSize != this.shape(i) ) { - String shape = this.shape().stream().map( a -> a.toString() ).collect(Collectors.joining("x")); - throw new IllegalArgumentException( - "Cannot create image of type '" + type.name() + "' from tensor with shape (" + shape + "). " + + for ( int i = 0; i < pattern.length; i++ ) { + int axisSize = pattern[ i ]; // The expected axis size! + if ( axisSize > 0 ) { + if ( axisSize != this.shape(i) ) { + String shape = this.shape().stream().map( a -> a.toString() ).collect(Collectors.joining("x")); + throw new IllegalArgumentException( + "Cannot create image of type '" + type.name() + "' from tensor with shape (" + shape + "). " + "Axis " + i + " is expected to be of size " + axisSize + "." ); } } } - if ( !dataType.isAssignableFrom(this.getItemType()) ) - throw new IllegalArgumentException( - "Cannot create image of type '" + type.name() + "' from tensor of type '" + this.getItemType().getSimpleName() + ". " + - "Expected to receive a tensor whose type is at least a sub-type of '" + dataType.getSimpleName() + "'." + if ( !dataType.isAssignableFrom(this.getItemType()) ) + throw new IllegalArgumentException( + "Cannot create image of type '" + type.name() + "' from tensor of type '" + this.getItemType().getSimpleName() + ". " + + "Expected to receive a tensor whose type is at least a sub-type of '" + dataType.getSimpleName() + "'." ); - } + } private static void _writeImgData( DataBuffer data, BufferedImage target ) { - target.setData( - Raster.createRaster( target.getSampleModel(), data, new Point() ) + target.setData( + Raster.createRaster( target.getSampleModel(), data, new Point() ) ); - } + } /** * {@inheritDoc} */ @Override public Tensor<V> addToGradient( Tensor<V> error ) { - _guardSet("gradient"); - Optional<Tensor> grad = this.find( Tensor.class ); - grad.ifPresent( gradient -> - this.set( - MemUtil.keep( gradient, error, () -> - Neureka.get() - .backend() - .getFunction() - .plusAssign() - .call(gradient, error) + _guardSet("gradient"); + Optional<Tensor> grad = this.find( Tensor.class ); + grad.ifPresent( gradient -> + this.set( + MemUtil.keep( gradient, error, () -> + Neureka.get() + .backend() + .getFunction() + .plusAssign() + .call(gradient, error) ) )); - if ( !grad.isPresent() ) { - this.set( error ); - this.find( Device.class ).ifPresent( device -> { + if ( !grad.isPresent() ) { + this.set( error ); + this.find( Device.class ).ifPresent( device -> { try { - device.store( error ) ; - } catch ( Exception exception ) { - _LOG.error( "Failed trying to store a given error to a device for gradient accumulation.", exception ); - throw exception; - } - }); + device.store( error ) ; + } catch ( Exception exception ) { + _LOG.error( "Failed trying to store a given error to a device for gradient accumulation.", exception ); + throw exception; + } + }); } - return this; + return this; } /** @@ -1294,16 +1305,16 @@ @Override public <T> T asType( Class<T> typeClass ) { - LogUtil.nullArgCheck( typeClass, "typeClass", Class.class ); - if ( typeClass == Tensor.class ) return (T) this; - if ( Number.class.isAssignableFrom( this.itemType()) && Number.class.isAssignableFrom(typeClass) ) { - DataConverter converter = DataConverter.get(); - return converter.convert( mean().at(0).get(), typeClass ); + LogUtil.nullArgCheck( typeClass, "typeClass", Class.class ); + if ( typeClass == Tensor.class ) return (T) this; + if ( Number.class.isAssignableFrom( this.itemType()) && Number.class.isAssignableFrom(typeClass) ) { + DataConverter converter = DataConverter.get(); + return converter.convert( mean().at(0).get(), typeClass ); } - if ( typeClass == String.class ) - return (T) this.toString(); + if ( typeClass == String.class ) + return (T) this.toString(); - throw new IllegalArgumentException("Failed to convert this tensor of type '"+getDataType()+"' to '"+typeClass+"'!"); + throw new IllegalArgumentException("Failed to convert this tensor of type '"+getDataType()+"' to '"+typeClass+"'!"); } /** @@ -1329,52 +1340,52 @@ */ private <T> Tensor<T> _toType( Class<T> typeClass ) { - DataType<V> newDataType = (DataType<V>) DataType.of( typeClass ); - if ( newDataType != this.getDataType() ) { - CPU.get().borrow((Tensor<Object>) this).in(()->{ - Object newData = _convertedDataOfType(typeClass); - _setData( null ); - _setData( getDevice().allocateFromAll( newDataType, this.getNDConf(), newData) ); - return null; + DataType<V> newDataType = (DataType<V>) DataType.of( typeClass ); + if ( newDataType != this.getDataType() ) { + CPU.get().borrow((Tensor<Object>) this).in(()->{ + Object newData = _convertedDataOfType(typeClass); + _setData( null ); + _setData( getDevice().allocateFromAll( newDataType, this.getNDConf(), newData) ); + return null; }); } - this.find( TensorImpl.class ).ifPresent(gradient -> gradient._toType( typeClass ) ); - return (Tensor<T>) this; + this.find( TensorImpl.class ).ifPresent(gradient -> gradient._toType( typeClass ) ); + return (Tensor<T>) this; } @Override public String toString() { - if ( this.isDeleted() ) return "deleted"; - else if ( this.isEmpty() ) return "empty"; - else if ( this.isUndefined() ) return "undefined"; - return NdaAsString.representing( this ).byDefaults().toString(); + if ( this.isDeleted() ) return "deleted"; + else if ( this.isEmpty() ) return "empty"; + else if ( this.isUndefined() ) return "undefined"; + return NdaAsString.representing( this ).byDefaults().toString(); } static int[][] makeFit( int[] sA, int[] sB ) { - int lastIndexOfA = 0; - for ( int i = sA.length-1; i >= 0; i-- ) { - if ( sA[ i ] != 1 ) { - lastIndexOfA = i; - break; + int lastIndexOfA = 0; + for ( int i = sA.length-1; i >= 0; i-- ) { + if ( sA[ i ] != 1 ) { + lastIndexOfA = i; + break; } } - int firstIndexOfB = 0; - for ( int i = 0; i < sB.length; i++ ) { - if ( sB[ i ] != 1 ) { - firstIndexOfB = i; - break; + int firstIndexOfB = 0; + for ( int i = 0; i < sB.length; i++ ) { + if ( sB[ i ] != 1 ) { + firstIndexOfB = i; + break; } } - int newSize = lastIndexOfA + sB.length - firstIndexOfB; - int[] rsA = new int[ newSize ]; - int[] rsB = new int[ newSize ]; - for( int i = 0; i <newSize; i++ ) { - if ( i <= lastIndexOfA ) rsA[ i ] = i; else rsA[ i ] = -1; - if ( i >= lastIndexOfA ) rsB[ i ] = i - lastIndexOfA+firstIndexOfB; else rsB[ i ] = -1; + int newSize = lastIndexOfA + sB.length - firstIndexOfB; + int[] rsA = new int[ newSize ]; + int[] rsB = new int[ newSize ]; + for( int i = 0; i <newSize; i++ ) { + if ( i <= lastIndexOfA ) rsA[ i ] = i; else rsA[ i ] = -1; + if ( i >= lastIndexOfA ) rsB[ i ] = i - lastIndexOfA+firstIndexOfB; else rsB[ i ] = -1; } - return new int[][]{ rsA, rsB }; + return new int[][]{ rsA, rsB }; } } -
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/index.html b/docs/coverage/test/html/neureka/index.html index dd0f5bcb8..02f523a70 100644 --- a/docs/coverage/test/html/neureka/index.html +++ b/docs/coverage/test/html/neureka/index.html @@ -1 +1 @@ -neureka

neureka

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total1,147 of 9,23287%160 of 75678%2039391571,54251560125
TensorImpl3462,60288%4224485%5125853511911401
Tensor2462,67391%3711976%46278284291020001
Nda15246875%153570%28901999166501
Neureka10126272%152158%1639168112101
Data544143%3350%91571761201
Neureka.Utility536354%675%277240301
Shape4920180%102066%103183821601
AbstractNda3660094%117186%1274810113301
MutateNda320%n/a22222211
MutateTensor296870%3350%383130501
TensorImpl.new MutateNda.Item() {...}4368%5337%6102102601
Nda.Item5078%41071%4132141601
TensorConstructor24998%31986%3180500701
Neureka.Settings15698%466%21813301501
Shape.new Shape() {...}9998%1688%2150200601
Neureka.Settings.DType5596%50%290140701
TensorImpl.new Data() {...}88%n/a15151501
Neureka.Settings.Debug4797%375%180130601
Tensor.ImageType117100%n/a020170201
Neureka.Settings.AutoGrad108100%31178%31702601001
TensorImpl.new Iterator() {...}44100%100%04080301
Neureka.Settings.View37100%n/a05050501
Neureka.Settings.NDim33100%100%05080401
Shape.new Iterator() {...}31100%100%04040301
AbstractNda.new TensorConstructor.API() {...}100%n/a04040401
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka

neureka

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total1,233 of 9,31086%180 of 76476%2219511741,55957568126
TensorImpl3802,60887%5324582%61266605201011601
Tensor2482,67191%3811875%47278284291020001
Nda15846274%173366%30901999166501
Neureka14621759%191747%1839228132101
Data544344%3350%91671861301
Shape4920180%102066%103183821601
AbstractNda4855492%146682%1370109713001
Neureka.Utility348270%675%276240301
MutateNda320%n/a22222211
MutateTensor296870%3350%383130501
TensorImpl.new MutateNda.Item() {...}4368%5337%6102102601
Nda.Item5078%41071%4132141601
NoOpData64%n/a25252501
TensorConstructor25998%1890%2170530701
TensorImpl.new Data() {...}76%n/a25252501
Neureka.Settings15698%466%21813301501
Shape.new Shape() {...}9998%1688%2150200601
Neureka.Settings.DType5596%50%290140701
Neureka.Settings.Debug4797%375%180130601
Tensor.ImageType162100%n/a020170201
Neureka.Settings.AutoGrad108100%31178%31702601001
TensorImpl.new Iterator() {...}44100%100%04080301
Neureka.Settings.View37100%n/a05050501
Neureka.Settings.NDim33100%100%05080401
Shape.new Iterator() {...}31100%100%04040301
TensorConstructor.Args25100%n/a07070701
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/html/neureka/index.source.html b/docs/coverage/test/html/neureka/index.source.html index 5dda8e5d9..5fb274068 100644 --- a/docs/coverage/test/html/neureka/index.source.html +++ b/docs/coverage/test/html/neureka/index.source.html @@ -1 +1 @@ -neureka

neureka

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total1,147 of 9,23287%160 of 75678%2039391571,54251560125
TensorImpl.java3682,70488%4724984%58277565321212804
Tensor.java2462,79091%3711976%46280284461020202
Nda.java16651875%194570%3210321113177102
Neureka.java16076182%254966%261082420417108
Data.java544143%3350%91571761201
Shape.java5133186%123876%125086022503
AbstractNda.java3662394%117186%1278810513702
MutateNda.java320%n/a22222211
MutateTensor.java296870%3350%383130501
TensorConstructor.java24998%31986%3180500701
Created with JaCoCo 0.8.9.202303310957
\ No newline at end of file +neureka

neureka

ElementMissed InstructionsCov.Missed BranchesCov.MissedCxtyMissedLinesMissedMethodsMissedClasses
Total1,233 of 9,31086%180 of 76476%2219511741,55957568126
TensorImpl.java4042,70887%5825081%69285645411413004
Tensor.java2482,83391%3811875%47280284461020202
Neureka.java18673579%294560%281082920437108
Nda.java17251274%214367%3410321113177102
Data.java544344%3350%91671861301
Shape.java5133186%123876%125086022503
AbstractNda.java4855492%146682%1370109713001
MutateNda.java320%n/a22222211
MutateTensor.java296870%3350%383130501
NoOpData.java64%n/a25252501
TensorConstructor.java28498%1890%22406001402
Created with JaCoCo 0.8.11.202310140853
\ No newline at end of file diff --git a/docs/coverage/test/jacocoTestReport.xml b/docs/coverage/test/jacocoTestReport.xml index 524138c05..a739e4a62 100644 --- a/docs/coverage/test/jacocoTestReport.xml +++ b/docs/coverage/test/jacocoTestReport.xml @@ -1 +1 @@ - \ No newline at end of file + \ No newline at end of file diff --git a/docs/jdocs/allclasses-frame.html b/docs/jdocs/allclasses-frame.html new file mode 100644 index 000000000..b8e3302f1 --- /dev/null +++ b/docs/jdocs/allclasses-frame.html @@ -0,0 +1,438 @@ + + + + + +All Classes (neureka 1.0.1 API) + + + + +

All Classes

+
+ +
+ + diff --git a/docs/jdocs/allclasses-index.html b/docs/jdocs/allclasses-index.html deleted file mode 100644 index 67f77acbb..000000000 --- a/docs/jdocs/allclasses-index.html +++ /dev/null @@ -1,1418 +0,0 @@ - - - - -All Classes and Interfaces (neureka 1.0.0 API) - - - - - - - - - - - - - - -
- -
-
-
-

All Classes and Interfaces

-
-
-
-
-
-
Class
-
Description
-
Absolute
-
 
-
AbstractBaseDevice<V>
-
 
-
AbstractComponentOwner<C>
-
-
Together with the Component interface, this class defines a simple - component system in which implementations of the Component interface - are managed by extensions of this AbstractComponentOwner.
-
-
AbstractCPUConvolution
-
 
-
AbstractDevice<V>
-
-
This is the abstract precursor class providing - some useful implementations for core concepts which are most likely - applicable to most concrete implementations of the Device interface.
-
-
AbstractDeviceAlgorithm<C extends DeviceAlgorithm<C>>
-
-
This is a partial implementation of the Algorithm interface which implements - the component system for implementation instances of the ImplementationFor interface.
-
-
AbstractDeviceData<T>
-
 
-
AbstractFunAlgorithm
-
 
-
AbstractFunDeviceAlgorithm<C extends DeviceAlgorithm<C>>
-
-
This is the base class for implementations of the Algorithm interface.
-
-
AbstractImplementationFor<D extends Device<?>>
-
 
-
AbstractNDC
-
-
The following is an abstract implementation of the NDConfiguration which offers a basis for - instantiation and caching of concrete implementations extending this abstract class.
-
-
AbstractOperation
-
-
This abstract Operation implementation is a useful template for creating new operations.
-
-
ADAction
-
-
This interface is the declaration for - lambda actions for both the ADAction.act(ADTarget) method of the ADAction interface.
-
-
ADActionSupplier
-
-
Implementations of this functional interface ought to return a new instance - of the ADAction class responsible for performing automatic differentiation - both for forward and backward mode differentiation.
-
-
AdaGrad<V extends Number>
-
-
Adaptive Gradients, or AdaGrad for short, is an extension of the gradient descent optimization - algorithm that adjusts the step size for each parameter based on the squared gradients - seen over the course of previous optimization steps.
-
-
AdaGradFactory
-
 
-
ADAM<V extends Number>
-
-
ADAM (short for Adaptive Moment Estimation) is an adaptive learning rate optimization algorithm that utilises both - momentum and scaling, combining the benefits of RMSProp and SGD with respect to Momentum.
-
-
ADAMFactory
-
 
-
Addition
-
 
-
ADSupportPredicate
-
-
A ADSupportPredicate lambda checks which auto differentiation mode - can be performed for a given ExecutionCall.
-
-
ADTarget<V>
-
-
This is simply a wrapper for useful information needed by implementations of - the ADAction and ADAction interfaces to perform error propagation.
-
-
Algorithm
-
-
This class is the middle layer of the 3 tier compositional architecture of this backend, which - consists of Operations, Algorithms and - in case of a DeviceAlgorithm also ImplementationFor.
-
-
AndBackward<F>
-
 
-
Arg<T>
-
-
Extend this class to define additional meta arguments for Functions.
-
-
Arg.Axis
-
 
-
Arg.Derivative<V>
-
 
-
Arg.DerivIdx
-
-
This is an import argument whose - role might not be clear at first : - An operation can have multiple inputs, however - when calculating the derivative for a forward or backward pass - then one must know which derivative ought to be calculated.
-
-
Arg.Ends
-
 
-
Arg.Indices
-
 
-
Arg.Layout
-
 
-
Arg.MinRank
-
 
-
Arg.Offset
-
 
-
Arg.Seed
-
 
-
Arg.Shape
-
 
-
Arg.Stride
-
 
-
Arg.TargetDevice
-
 
-
Arg.VarIdx
-
-
The following argument is relevant for a particular type of operation, namely: an "indexer".
-
-
Args
-
 
-
AssignLeft
-
 
-
At<K,R>
-
 
-
AutoDiffMode
-
 
-
AxisFrame<G,V>
-
-
This class represents the labeled axis of an NDFrame.
-
-
AxisFrame.Builder<SetType,GetType,ValueType>
-
 
-
AxisFrame.Set<V>
-
 
-
AxisOrGet<V>
-
-
This is the starting point of the call transition graph exposed by the slice builder API.
-
-
AxisOrGetTensor<V>
-
 
-
AxisSliceBuilder<V>
-
 
-
AXPY
-
-
The ?axpy routines perform a vector-vector operation defined as y := a*x + y where: a is a scalar x and y - are vectors each with a number of elements that equals n.
-
-
BackendContext
-
-
Instances of this class are execution contexts hosting Operation instances which receive Tensor - instances for execution.
-
-
BackendContext.Runner
-
-
This is a very simple class with a single purpose, namely - it exposes methods which receive lambda instances in order to then execute them - in a given BackendContext, just to then switch back to the original context again.
-
-
BackendExtension
-
-
Implementations of this might introduce CUDA or ROCM to Neureka.
-
-
BackendExtension.DeviceOption
-
-
This class describes an available Device implementation found for a given BackendExtension.
-
-
BackendLoader
-
 
-
BackendRegistry
-
 
-
BasicMachine
-
-
How much memory, and how many threads share that memory.
-
-
BiElementwise
-
 
-
BiScalarBroadcast
-
 
-
Broadcast
-
 
-
Cache<O>
-
-
This is a simple, fixed size cache for immutable objects which are - shared throughout the library runtime...
-
-
Cache.LazyEntry<K,V>
-
-
Lazy cache entries are entries whose values will be calculated - only when the entry is being stored in the cache.
-
-
Call<D>
-
-
Instances of this class model simple execution calls to the backend.
-
-
Call.Builder<V,T extends Device<V>>
-
 
-
Call.DeviceCondition
-
 
-
Call.Else<T>
-
 
-
Call.OperationCondition
-
 
-
Call.TensorCompare
-
 
-
Call.TensorCondition
-
 
-
Call.TensorProperty
-
 
-
Call.TensorsCondition
-
 
-
Cat
-
 
-
Cbrt
-
 
-
CLBackend
-
-
This is an OpenCL context component for any given BackendContext which - extends a given backend context instance for additional functionality, which in - this case is the OpenCL backend storing platform and device information.
-
-
CLBiElementwise
-
 
-
CLBiElementwiseAddition
-
 
-
CLBiElementwiseDivision
-
 
-
CLBiElementwiseModulo
-
 
-
CLBiElementwiseMultiplication
-
 
-
CLBiElementwisePower
-
 
-
CLBiElementwiseSubtraction
-
 
-
CLBroadcast
-
 
-
CLBroadcastAddition
-
 
-
CLBroadcastDivision
-
 
-
CLBroadcastModulo
-
 
-
CLBroadcastMultiplication
-
 
-
CLBroadcastPower
-
 
-
CLBroadcastSubtraction
-
 
-
CLConvolution
-
 
-
CLDot
-
-
Performs a dot product on two vectors using OpenCL.
-
-
CLElementwiseFunction
-
 
-
CLFunctionCompiler
-
-
Turns a Function into OpenCL kernel code to make - optimized just in time compilation possible.
-
-
CLGEMM
-
 
-
CLImplementation
-
-
This class is the ExecutorFor < OpenCLDevice > implementation - used to properly call an OpenCLDevice instance via the - ExecutionOn < OpenCLDevice > lambda implementation - receiving an instance of the ExecutionCall class.
-
-
CLMatMul
-
 
-
CLRandomization
-
 
-
CLReduce
-
 
-
CLReduce.Type
-
 
-
CLScalarBroadcast
-
 
-
CLScalarBroadcastAddition
-
 
-
CLScalarBroadcastDivision
-
 
-
CLScalarBroadcastIdentity
-
 
-
CLScalarBroadcastModulo
-
 
-
CLScalarBroadcastMultiplication
-
 
-
CLScalarBroadcastPower
-
 
-
CLScalarBroadcastSubtraction
-
 
-
CLScalarFunction
-
 
-
CLSettings
-
-
OpenCL related settings for the CLBackend extension.
-
-
CLSum
-
 
-
CommonMachine
-
-
Stuff common to Hardware and ConcreteMachine.
-
-
Component<O>
-
-
This interface alongside the AbstractComponentOwner class define a simple component system.
-
-
Component.IsBeing
-
-
Entries of this enum represent events describing updates to the state - of the owner of a given Component instance.
-
-
Component.OwnerChangeRequest<O>
-
-
Component.OwnerChangeRequest implementation instances will be passed to - the Component.update(OwnerChangeRequest) method which inform a - given component about a state change related to said component.
-
-
ComponentOwner<C>
-
-
A component owner is a thing holding components which can be accessed by their type class.
-
-
ConcreteMachine
-
 
-
Convolution
-
 
-
ConvUtil
-
 
-
COPY
-
-
The ?copy routines perform a vector-vector operation defined as y = x, where x and y are vectors.
-
-
Cosinus
-
 
-
CPU
-
-
The CPU class, one of many implementations of the Device interface, - is simply supposed to be an API for dispatching threaded workloads onto the CPU - as well as reading from or writing to tensors it stores.
-
-
CPU.IndexedWorkload
-
 
-
CPU.JVMExecutor
-
-
The CPU.JVMExecutor offers a similar functionality as the parallel stream API, - however it differs in that the CPU.JVMExecutor is processing CPU.RangeWorkload lambdas - instead of simply exposing a single index or concrete elements for a given workload size.
-
-
CPU.RangeWorkload
-
-
A simple functional interface for executing a range whose implementations will - either be executed sequentially or they are being dispatched to - a thread-pool, given that the provided workload is large enough.
-
-
CPUBackend
-
-
This class loads the CPU operations into the Neureka library context.
-
-
CPUBiElementWise
-
 
-
CPUBiElementWiseAddition
-
 
-
CPUBiElementWiseDivision
-
 
-
CPUBiElementWiseModulo
-
 
-
CPUBiElementWiseMultiplication
-
 
-
CPUBiElementWisePower
-
 
-
CPUBiElementWiseSubtraction
-
 
-
CPUBiFun
-
 
-
CPUBroadcast
-
 
-
CPUBroadcastAddition
-
 
-
CPUBroadcastDivision
-
 
-
CPUBroadcastModulo
-
 
-
CPUBroadcastMultiplication
-
 
-
CPUBroadcastPower
-
 
-
CPUBroadcastSubtraction
-
 
-
CPUBroadcastSummation
-
 
-
CPUConvolution
-
 
-
CPUDot
-
 
-
CPUElementwiseAssignFun
-
 
-
CPUElementwiseFunction
-
 
-
CPUFun
-
 
-
CPUImplementation
-
-
This class is a wrapper class for the ImplementationFor<CPU> interface - which enables a functional style of implementing the backend API!
- It is used merely as a simple formality and implementation type specification.
-
-
CPUImplementation.AndImplementation
-
-
This is represents the second step in the simple builder API for CPUImplementation instances.
-
-
CPUMatMul
-
-
This is a library internal class, do not depend on this.
-
-
CPURandomization
-
 
-
CPUReduce
-
-
An implementation of the min and max algorithm running on the CPU.
-
-
CPUReduce.Type
-
 
-
CPUScalaBroadcastPower
-
 
-
CPUScalarBroadcast
-
 
-
CPUScalarBroadcastAddition
-
 
-
CPUScalarBroadcastDivision
-
 
-
CPUScalarBroadcastFunction
-
 
-
CPUScalarBroadcastIdentity
-
 
-
CPUScalarBroadcastModulo
-
 
-
CPUScalarBroadcastMultiplication
-
 
-
CPUScalarBroadcastSubtraction
-
 
-
CPUScalarFunction
-
 
-
CPUSum
-
-
An implementation of the sum and may algorithm running on the CPU.
-
-
CSVHandle
-
-
This class is one of many extensions of the AbstractFileHandle which - is therefore ultimately an implementation of the FileHandle interface.
-
-
D1C
-
-
An abstract class for NDConfigurations which are representing - tensors of rank 1, meaning the name of this class translates to "Dimension-1-Configuration".
-
-
D2C
-
-
An abstract class for NDConfigurations which are representing - tensors of rank 2, meaning the name of this class translates to "Dimension-^2-Configuration".
-
-
D3C
-
-
An abstract class for NDConfigurations which are representing - tensors of rank 3, meaning the name of this class translates to "Dimension-3-Configuration".
-
-
Data<V>
-
-
A wrapper type for the raw data array of a tensor/nd-array, - which is typically provided by implementations of the Device interface.
-
-
DataConverter
-
-
This class is a singleton.
-
-
DataConverter.ForTensor
-
-
This is a stateful and parallelized converter for converting the internal data array of a tensor - to another data array based on a provided lambda.
-
-
DataConverter.Utility
-
-
This is a static utility class containing the actual conversion logic - which is usually referenced by the Converter lambdas via method signatures...
-
-
DataType<T>
-
-
This class is a Multiton implementation for wrapping and representing type classes.
-
-
Device<V>
-
-
Implementations of this represent computational - devices for storing tensors (instances of the Tensor<V> class), which may - also expose a useful API for executing operations on tensors (used in backend operations).
-
-
Device.Access<V>
-
-
Implementations of this represent the access to tensors stored on a device - in order to read from or write to said tensor.
-
-
Device.In
-
-
The second part of the method chain of the fluent API for executing - tensors on this Device temporarily.
-
-
Device.Writer
-
-
Instances of this complete a request for writing to an accessed tensor stored on a device.
-
-
DeviceAlgorithm<C extends DeviceAlgorithm<C>>
-
-
A DeviceAlgorithm is an advanced form of Algorithm which - delegates the execution to implementations of ImplementationFor specific Device types.
-
-
DeviceCleaner
-
 
-
DeviceData<V>
-
-
A sub-interface of the Data interface providing - more device specific methods.
-
-
DeviceQuery
-
-
A program that queries and prints information about all - available devices.
-
-
DimFit
-
 
-
DimTrim
-
 
-
Division
-
 
-
DOT
-
-
The ?dot routines perform a vector-vector reduction operation defined as Equation where xi and yi are - elements of vectors x and y.
-
-
DotProduct
-
 
-
DotProductAlgorithm
-
 
-
ElementwiseAlgorithm
-
-
This is lambda based Algorithm implementation - providing some basic functionality for implementing custom - activation functions.
-
-
ElemWiseUtil
-
-
Methods inside this utility class execute only some ExecutionCall arguments - in groups if their total number exceeds the arity of an operation.
-
-
Execution
-
-
Implementations of this functional interface - is supposed to be the final execution procedure responsible for dispatching - the execution further into the backend.
-
-
ExecutionCall<D extends Device<?>>
-
-
This class is a simple container holding references to a targeted - Device, Operation and maybe some case specific - meta Args needed to execute - an array of input tensors which are also wrapped by this.
-
-
ExecutionCall.Builder<D extends Device<?>>
-
 
-
ExecutionPreparation
-
-
An Algorithm will typically produce a result when executing an ExecutionCall.
-
-
Exp
-
 
-
Extensions
-
-
This is an internal class for managing the extension of any given BackendContext class.
-
-
F32
-
 
-
F64
-
 
-
FallbackAlgorithm
-
 
-
FileDevice
-
-
The FileDevice is a Device implementation - responsible for reading tensors from and or writing them to a given directory.
-
-
FileHandle<FinalType,ValType>
-
 
-
Filler<T>
-
-
Implementations of this ought to map the index of a - tensor entry to a value which should be placed at that entry position.
-
-
FinalExecutor
-
 
-
FromOrAt<V>
-
-
This is the starting point for defining the slice range of a specified axis within - the call transition graph exposed by the slice builder API.
-
-
FromOrAtTensor<V>
-
 
-
FunAlgorithm
-
 
-
Function
-
-
Besides the Tensor class, which is the core class of Neureka, this interface and its implementations - represents the second most important feature of this library.
-
-
Function.Callable
-
-
An API for calling a Function after having specified - a set of Arg instances through the Function.with(Args) - method.
-
-
FunctionCache
-
-
This class is part of a given BackendContext instance - responsible for caching Function references based on - their String representation generated by Object.toString() - as well as caching of results for active functions.
-
-
FunctionConstant
-
-
Instances of this implementation of the Function interface - are leave nodes within the abstract syntax tree of a function, representing constant numeric values to a function.
-
-
FunctionInput
-
-
Instances of this implementation of the Function interface - are leave nodes within the abstract syntax tree of a function, representing inputs to a function.
-
-
FunctionNode
-
-
The most common type of Function which references other Functions to - form an abstract syntax tree.
-
-
FunctionParser
-
-
The FunctionParser takes a BackendContext instance based on which - it builds Function implementation instances, usually by parsing Strings.
-
-
Functions
-
 
-
FunctionVariable
-
-
Instances of this implementation of the Function interface - are leave nodes within the abstract syntax tree of a function, representing indexed inputs to a function.
-
-
FunDeviceAlgorithm
-
 
-
GaSU
-
-
The Self Gated Softsign Unit is based on the Softsign function - (a computationally cheap non-exponential quasi Tanh) - making it a polynomially based version of the GaTU function which - is itself based on the Tanh function.
-
-
GaTU
-
-
The Self Gated Tanh Unit is based on the Tanh - making it an exponentiation based version of the GaSU function which - is itself based on the Softsign function - (a computationally cheap non-exponential quasi Tanh).
-
-
Gaussian
-
 
-
GaussianFast
-
 
-
GeLU
-
-
The GELU activation function is based on the standard Gaussian cumulative distribution function - and is defined as x Φ( x ) and implemented as x * sigmoid(x * 1.702).
-
-
GEMM
-
-
A collection of primitive sub-routines for matrix multiplication performed on - continuous arrays which are designed so that they can be vectorized by the - JVMs JIT compiler (AVX instructions).
-
-
GEMM.VectorOperationF32
-
 
-
GEMM.VectorOperationF64
-
 
-
Get<ValueType>
-
 
-
GraphNode<V>
-
-
Instances of the GraphNode class are components of tensors (Tensor instances) - which model and record computations / operations between them.
-
-
GraphNode.Print
-
 
-
Hardware
-
-
This models the cache levels and threads of a CPU - using an array of where each entry represents a memory level.
-
-
I16
-
 
-
I32
-
 
-
I64
-
 
-
I8
-
-
The following abstract class implements some basic logic which - is applicable across all final concrete classes extending this abstract one.
-
-
IAXPY
-
-
The ?axpy routines perform a vector-vector operation defined as y := a*x + y where: a is a scalar x and y - are vectors each with a number of elements that equals n.
-
-
Identity
-
 
-
IDOT
-
-
The ?dot routines perform a vector-vector reduction operation defined as Equation where xi and yi are - elements of vectors x and y.
-
-
IDXHandle
-
-
This class is one of many extensions of the AbstractFileHandle which - is therefore ultimately an implementation of the FileHandle interface.
-
-
IGEMM
-
-
A collection of primitive sub-routines for matrix multiplication performed on - continuous arrays which are designed so that they can be vectorized by the - JVMs JIT compiler (AVX instructions).
-
-
IGEMM.VectorOperationI32
-
 
-
IGEMM.VectorOperationI64
-
 
-
ImplementationFor<D extends Device<?>>
-
-
Generally speaking, this interface describes the functionality of an implementation - of an execution procedure tailored to a specific Device (interface) instance - and Algorithm (interface) instance! - Instances of implementations of the ImplementationFor interface are components - of instances of implementations of the Algorithm interface, - which themselves are components of Operation implementation instances.
-
-
ImplementationReceiver
-
 
-
IterByOrIterFromOrAll<V>
-
 
-
IterByOrIterFromOrAllTensor<V>
-
 
-
JITProp<V>
-
-
This class keeps track of graph nodes which require - back-propagation in order - to be able to continue the process at a later point in time - (based on some configurable conditions).
-
-
KernelCache
-
-
A fixed sized cache for ad-hoc (just in time compiled) OpenCLDevice kernels.
-
-
KernelCaller
-
-
Instances of this class are utility factories provided by OpenCLDevice instances.
-
-
KernelCode
-
 
-
KernelSource
-
-
Provides kernel source code for a provided ExecutionCall.
-
-
LazyRef<V>
-
-
This will simply fetch a variable from a lambda once and then continuously - return this one value.
-
-
ListReader
-
-
This is a simple utility class which traverses nested data structures and converts them into - information which can be used to instantiate a tensor, - namely: A flat data array, a shape array and a type class.
-
-
ListReader.Result
-
 
-
LoadingContext
-
 
-
Log10
-
 
-
Logarithm
-
 
-
LogUtil
-
-
A utility class for message formatting.
-
-
MatMul
-
 
-
MatMulAlgorithm
-
 
-
Max
-
 
-
MemUtil
-
-
Utility methods for deleting tensors or preventing thereof.
-
-
MemValidator
-
-
This class validates the states of tensors with respect to memory management - before and after a lambda executes a function or some kind of algorithm on said tensors.
-
-
Messages
-
 
-
Messages.Tips
-
 
-
Min
-
 
-
Modulo
-
 
-
Momentum<V extends Number>
-
 
-
MomentumFactory
-
 
-
Multiplication
-
 
-
MutateNda<T>
-
-
Nd-arrays should be used as immutable data structures mostly, however sometimes it - is important to mutate their state for performance reasons.
-
-
MutateNda.Item<V>
-
-
Instances of this are being returned by the Nda.at(int...) method, - and they allow you to get or set individual nd-array items
-
-
MutateTensor<T>
-
-
Tensors should be considered immutable, however sometimes it - is important to mutate their state for performance reasons.
-
-
Nda<V>
-
-
Nda, which is an abbreviation of 'N-Dimensional-Array', represents - a multidimensional, homogeneously filled fixed-size array of items.
-
-
Nda.Item<V>
-
-
Instances of this are being returned by the Nda.at(int...) method, - and they allow you to get individual nd-array items
-
-
NdaAsString
-
-
This class is in essence a simple wrapper class for a tensor and a StringBuilder - Methods in this class use the builder in order to construct a String representation - for said tensor.
-
-
NdaAsString.Builder
-
-
A builder interface providing multiple different options for building - a NdaAsString instance in a fluent way.
-
-
NdaAsString.Util
-
-
This class is a simple utility class which contains - a collection of static and stateless methods containing - useful functionalities for tensor stringification.
-
-
NdaBuilder<V>
-
-
This is the implementation of the fluent builder API for creating Nda/Tensor instances.
-
-
NDConfiguration
-
-
This interface represents the access pattern configuration for the data array of a tensor.
-
-
NDConfiguration.IndexToIndexFunction
-
-
Implementations of this are produced and returned by the NDConfiguration.getIndexToIndexAccessPattern() - and their purpose is to translate the item index of a tensor to the index of the - item within the underlying data array of said tensor.
-
-
NDConfiguration.Layout
-
-
Types of common data layouts:
- - ROW_MAJOR
-
-
NDConfiguration.Utility
-
-
This utility class provides static methods which are helpful - for nd-configuration related operations like reshaping, - incrementing or decrementing index arrays...
-
-
NDConstructor
-
 
-
NDConvolution
-
 
-
NDFrame<V>
-
-
Instances of this class are components of tensors, which store aliases for the indices of the tensor.
-
-
NDimensional
-
-
This interface defines the most essential methods - of the nd-array/tensor API, which describe them - with respect to their dimensionality.
-
-
NDIterator
-
-
An NDIterator is used to iterate over n-dimensional arrays.
-
-
NDIterator.NonVirtual
-
-
Defines if a new NDIterator is allowed to be a VirtualNDIterator.
-
-
NDPrintSettings
-
-
This is simply a mutable container for configuring how Tensor - instances ought to be converted to Strings.
-
-
NDTrait
-
 
-
NDUtil
-
-
Static utility methods for the NDArray.
-
-
Neureka
-
-
Neureka is the key access point for thread local / global library settings ( seeNeureka.Settings) - as well as execution contexts (see BackendContext) - and pre-instantiated Functions.
-
-
Neureka.Utility
-
 
-
NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
-
-
This interface enables "Polymorphic" utility by defining common functionalities - used for handling various numeric types.
-
-
OpenCLDevice
-
-
This class models OpenCL supporting accelerator hardware like GPUs or FPGAs - for storing tensors and executing operations on them.
-
-
OpenCLDevice.Query
-
 
-
OpenCLDevice.Type
-
 
-
OpenCLPlatform
-
-
This class models the OpenCL concept of platforms, which refer to device - vendors / or vendor OpenCL runtime drivers.
-
-
Operation
-
-
This interface is part of the backend API, and it embodies the top layer of the 3 tier backend architecture.
-
-
OperationBuilder
-
-
This builder class builds instances of the Operation interface.
-
-
OperationBuilder.Derivation
-
 
-
OperationBuilder.Stringifier
-
 
-
Optimization<V>
-
 
-
Optimizer<V>
-
-
Optimizers are tensor components which implement the Optimization (functional) - interface applying various optimization algorithms to the gradients of tensors.
-
-
OptimizerFactory
-
 
-
Parallelism
-
-
A set of standard levels of parallelism derived from the number of available cores and optionally capped by - reserving a specified amount of memory per thread.
-
-
ParsedCLImplementation
-
 
-
ParseUtil
-
-
Utility for parsing function expressions.
-
-
Permute
-
 
-
Permuted1DConfiguration
-
 
-
Permuted2DCIterator
-
 
-
Permuted2DConfiguration
-
 
-
Permuted3DCIterator
-
 
-
Permuted3DConfiguration
-
 
-
PermutedNDConfiguration
-
 
-
Power
-
 
-
Product
-
-
This type of operation belongs to the same species as the - Summation operation.
-
-
Quadratic
-
 
-
Randomization
-
-
This Operation takes an optional user seed, - the shape of its input tensor, and - the indices of individual elements within said tensor to generate - floats or doubles with a gaussian distribution where the mean - is 0 and the standard deviation is 1.
-
-
ReceiveForDevice<D extends Device<?>>
-
 
-
ReceiveForOperation<D extends Device<?>>
-
 
-
ReferenceCounter
-
 
-
ReferenceCounter.ChangeEvent
-
 
-
ReferenceCounter.ChangeType
-
 
-
Relation<V>
-
-
This class is an important tensor component responsible for - managing the relationships between slices and the tensors from which - they have been derived.
-
-
ReLayout
-
 
-
ReLU
-
 
-
Replace<ValueType,ReplacementType,ReturnType>
-
 
-
Reshape
-
 
-
Result
-
-
An immutable wrapper for a tensor as a result of anb Execution - as well as an ADActionSupplier for providing auto-differentiation support.
-
-
RMSProp<V extends Number>
-
-
Root Mean Squared Propagation, or RMSProp, - is an extension of gradient descent and the AdaGrad version of gradient - descent that uses a decaying average of partial gradients in the adaptation of the - step size for each parameter.
-
-
RMSPropFactory
-
 
-
ScalarAbsolute
-
 
-
ScalarAlgorithm
-
 
-
ScalarBroadcast
-
 
-
ScalarCbrt
-
 
-
ScalarCosinus
-
 
-
ScalarExp
-
 
-
ScalarFun
-
 
-
ScalarGaSU
-
-
The Self Gated ScalarSoftsign Unit is based on the ScalarSoftsign function - (a computationally cheap non-exponential quasi ScalarTanh) - making it a polynomially based version of the ScalarGaTU function which - is itself based on the ScalarTanh function.
-
-
ScalarGaTU
-
-
The Self Gated ScalarTanh Unit is based on the ScalarTanh - making it an exponentiation based version of the ScalarGaSU function which - is itself based on the ScalarSoftsign function - (a computationally cheap non-exponential quasi ScalarTanh).
-
-
ScalarGaussian
-
 
-
ScalarGaussianFast
-
 
-
ScalarGeLU
-
-
The GELU activation function is based on the standard Gaussian cumulative distribution function - and is defined as x Φ( x ) and implemented as x * sigmoid(x * 1.702).
-
-
ScalarIdentity
-
 
-
ScalarLog10
-
 
-
ScalarLogarithm
-
 
-
ScalarQuadratic
-
 
-
ScalarReLU
-
 
-
ScalarSeLU
-
-
The Scaled Exponential Linear Unit, or SELU, is an activation - function that induces self-normalizing properties.
-
-
ScalarSigmoid
-
 
-
ScalarSiLU
-
-
The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x).
-
-
ScalarSinus
-
 
-
ScalarSoftplus
-
-
SoftPlus is a smooth approximation to the ReLU function and can be used - to constrain the output of a machine to always be positive.
-
-
ScalarSoftsign
-
-
The softsign function, defined as x / ( 1 + Math.abs( x ) ), - is a computationally cheap 0 centered activation function - which rescales the inputs between -1 and 1, very much like the ScalarTanh function.
-
-
ScalarSqrt
-
 
-
ScalarSumAlgorithm
-
 
-
ScalarTanh
-
 
-
ScalarTanhFast
-
 
-
SeLU
-
-
The Scaled Exponential Linear Unit, or SELU, is an activation - functions that induce self-normalizing properties.
-
-
Set<V>
-
 
-
SettingsLoader
-
-
This class is a helper class for Neureka instances (Thread local singletons).
-
-
SGD<V>
-
-
Stochastic Gradient Descent is an iterative optimization technique - that uses the gradient of a weight variable to adjust said variable, - in order to reduce the error used to calculate said gradient.
-
-
SGDFactory
-
 
-
Shape
-
-
Basically a tuple of integers which is used to describe the shape of an array.
-
-
Sigmoid
-
 
-
SiLU
-
-
The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x).
-
-
Simple0DConfiguration
-
 
-
Simple1DCIterator
-
 
-
Simple1DConfiguration
-
 
-
Simple2DCIterator
-
 
-
Simple2DConfiguration
-
 
-
Simple3DCIterator
-
 
-
Simple3DConfiguration
-
 
-
SimpleCLImplementation
-
 
-
SimpleNDConfiguration
-
 
-
SimpleReshapeView
-
 
-
Sinus
-
 
-
Slice
-
 
-
SliceBuilder<V>
-
-
This class is the heart of the slice builder API, collecting range configurations by - exposing an API consisting of multiple interfaces which form a call state transition graph.
-
-
Sliced0DConfiguration
-
 
-
Sliced1DCIterator
-
 
-
Sliced1DConfiguration
-
 
-
Sliced2DCIterator
-
 
-
Sliced2DConfiguration
-
 
-
Sliced3DCIterator
-
 
-
Sliced3DConfiguration
-
 
-
SlicedNDConfiguration
-
 
-
SlicedNDIterator
-
 
-
SmartSlicer
-
-
This class is responsible for receiving any input and trying to interpret it so that a - slice can be formed.
-
-
Softplus
-
-
SoftPlus is a smooth approximation to the ReLU function and can be used - to constrain the output of a machine to always be positive.
-
-
Softsign
-
-
The softsign function, defined as x / ( 1 + Math.abs( x ) ), - is a computationally cheap 0 centered activation function - which rescales the inputs between -1 and 1, very much like the Tanh function.
-
-
Sqrt
-
 
-
StaticKernelSource
-
 
-
Step<V>
-
-
This interface defines the last step in the call transition graph of the fluent builder API when - building a Tensor instance populated based on the values within a defined range.
-
-
StepForTensor<V>
-
 
-
StepsOrAxisOrGet<V>
-
-
This interface extends the AxisOrGet interface which provides the option to either continue - slicing another axis or simply trigger the creation and return of a slice instance based on the - already provided slice configuration.
-
-
StepsOrAxisOrGetTensor<V>
-
 
-
Storage<V>
-
-
This is an abstract interface which simply describes "a thing that stores tensors".
-
-
Subtraction
-
 
-
SuitabilityPredicate
-
-
The SuitabilityPredicate checks if a given instance of an ExecutionCall is - suitable to be executed in ImplementationFor - residing in this Algorithm as components.
-
-
Sum
-
 
-
SumAlgorithm
-
 
-
Summation
-
-
This type of operation belongs to the same species as the - Product operation.
-
-
Tanh
-
 
-
TanhFast
-
 
-
Tensor<V>
-
-
A Tensor is a mathematical concept and type of multidimensional - data-structure with certain transformation properties.
-
-
Tensor.ImageType
-
-
Use this enum as argument for the Tensor.asImage(Tensor.ImageType) method to - specify the type of image that should be returned.
-
-
To<V>
-
-
This step in the call transition graph of the fluent builder API is a followup call - from the IterByOrIterFromOrAll.andFillFrom(Object) method which - expects a range to be specified whose values will be used to populate the Tensor instance.
-
-
To<V>
-
-
This is the second part for defining the slice range of a specified axis within - the call transition graph exposed by the slice builder API.
-
-
ToForTensor<V>
-
 
-
ToForTensor<V>
-
 
-
UI16
-
 
-
UI32
-
 
-
UI64
-
 
-
UI8
-
 
-
Util
-
 
-
VirtualNDConfiguration
-
-
VirtualNDConfigurations represent tensors which - are filled homogeneously with a single value exclusively, - like for example a tensor filled with only zeros.
-
-
VirtualNDIterator
-
 
-
With<ValueType,TargetType>
-
 
-
WithForward<F>
-
 
-
WithShapeOrScalarOrVector<V>
-
 
-
WithShapeOrScalarOrVectorOnDevice<V>
-
 
-
WithShapeOrScalarOrVectorTensor<V>
-
 
-
WorkScheduler
-
-
An API for registering workloads which will be divided into smaller workloads so that they can - be executed efficiently by a thread pool...
-
-
WorkScheduler.Divider
-
-
Divides workloads until they can be processed efficiently - and then submits them to a thread pool for execution...
-
-
XConvLeft
-
 
-
XConvRight
-
 
-
-
-
-
-
-
- - diff --git a/docs/jdocs/allclasses.html b/docs/jdocs/allclasses-noframe.html similarity index 96% rename from docs/jdocs/allclasses.html rename to docs/jdocs/allclasses-noframe.html index 5da1d2a23..db92e8e0d 100644 --- a/docs/jdocs/allclasses.html +++ b/docs/jdocs/allclasses-noframe.html @@ -1,24 +1,15 @@ - + -All Classes (neureka 0.21.0 API) - +All Classes (neureka 1.0.1 API) - - - - - -

All Classes

-
+
-
+ diff --git a/docs/jdocs/allpackages-index.html b/docs/jdocs/allpackages-index.html deleted file mode 100644 index 7e2872b77..000000000 --- a/docs/jdocs/allpackages-index.html +++ /dev/null @@ -1,261 +0,0 @@ - - - - -All Packages (neureka 1.0.0 API) - - - - - - - - - - - - - - -
- -
-
-
-

All Packages

-
-
Package Summary
-
-
Package
-
Description
-
neureka
-
 
-
neureka.autograd
-
 
-
neureka.backend.api
-
 
-
neureka.backend.api.fun
-
 
-
neureka.backend.api.ini
-
 
-
neureka.backend.api.template.algorithms
-
 
-
neureka.backend.api.template.implementations
-
 
-
neureka.backend.api.template.operations
-
 
-
neureka.backend.cpu
-
 
-
neureka.backend.main.algorithms
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.backend.main.algorithms.internal
-
 
-
neureka.backend.main.implementations
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.backend.main.implementations.broadcast
-
 
-
neureka.backend.main.implementations.convolution
-
 
-
neureka.backend.main.implementations.elementwise
-
 
-
neureka.backend.main.implementations.fun
-
 
-
neureka.backend.main.implementations.fun.api
-
 
-
neureka.backend.main.implementations.linear
-
 
-
neureka.backend.main.implementations.matmul
-
 
-
neureka.backend.main.implementations.scalar
-
 
-
neureka.backend.main.internal
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.backend.main.memory
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.backend.main.operations
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.backend.main.operations.functions
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.backend.main.operations.indexer
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.backend.main.operations.linear
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.backend.main.operations.linear.internal.blas
-
-
Everything in this package should be considered library-private! - DO NOT USE CLASSES INSIDE THIS PACKAGE!
-
-
neureka.backend.main.operations.linear.internal.opencl
-
 
-
neureka.backend.main.operations.operator
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.backend.main.operations.other
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.backend.main.operations.other.internal
-
 
-
neureka.backend.ocl
-
 
-
neureka.common.composition
-
 
-
neureka.common.utility
-
 
-
neureka.devices
-
 
-
neureka.devices.file
-
 
-
neureka.devices.host
-
 
-
neureka.devices.host.concurrent
-
-
Everything in this package should be considered library-private! - DO NOT USE CLASSES INSIDE THIS PACKAGE!
-
-
neureka.devices.host.machine
-
-
Everything in this package should be considered library-private! - DO NOT USE CLASSES INSIDE THIS PACKAGE!
-
-
neureka.devices.opencl
-
 
-
neureka.devices.opencl.utility
-
 
-
neureka.dtype
-
 
-
neureka.dtype.custom
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.fluent.building
-
 
-
neureka.fluent.building.states
-
 
-
neureka.fluent.slicing
-
 
-
neureka.fluent.slicing.states
-
 
-
neureka.framing
-
 
-
neureka.framing.fluent
-
 
-
neureka.math
-
 
-
neureka.math.args
-
 
-
neureka.math.implementations
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.math.parsing
-
-
Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
-
-
neureka.ndim
-
 
-
neureka.ndim.config
-
 
-
neureka.ndim.config.types
-
 
-
neureka.ndim.config.types.permuted
-
 
-
neureka.ndim.config.types.simple
-
 
-
neureka.ndim.config.types.sliced
-
 
-
neureka.ndim.config.types.views
-
 
-
neureka.ndim.config.types.views.virtual
-
 
-
neureka.ndim.iterator
-
 
-
neureka.ndim.iterator.types.permuted
-
 
-
neureka.ndim.iterator.types.simple
-
 
-
neureka.ndim.iterator.types.sliced
-
 
-
neureka.ndim.iterator.types.virtual
-
 
-
neureka.optimization
-
 
-
neureka.optimization.implementations
-
 
-
neureka.view
-
 
-
-
-
-
- - diff --git a/docs/jdocs/constant-values.html b/docs/jdocs/constant-values.html index 4ab02cace..d27e6c3db 100644 --- a/docs/jdocs/constant-values.html +++ b/docs/jdocs/constant-values.html @@ -1,160 +1,300 @@ - + + - -Constant Field Values (neureka 1.0.0 API) - - - - + +Constant Field Values (neureka 1.0.1 API) - - - - - - + + -
- -
-

Constant Field Values

-

Contents

-
-
+
+ +

neureka.backend.*

-
    -
  • -
    neureka.backend.api.fun.SuitabilityPredicate
    -
    -
    Modifier and Type
    -
    Constant Field
    -
    Value
    -
    public static final float
    -
    BAD
    -
    0.25f
    -
    public static final float
    -
    EXCELLENT
    -
    0.875f
    -
    public static final float
    -
    GOOD
    -
    0.625f
    -
    public static final float
    -
    NOT_GOOD
    -
    0.375f
    -
    public static final float
    -
    OKAY
    -
    0.5f
    -
    public static final float
    -
    PERFECT
    -
    1.0f
    -
    public static final float
    -
    TERRIBLE
    -
    0.125f
    -
    public static final float
    -
    UNSUITABLE
    -
    0.0f
    -
    public static final float
    -
    VERY_GOOD
    -
    0.75f
    -
    +
      +
    • + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      neureka.backend.api.fun.SuitabilityPredicate 
      Modifier and TypeConstant FieldValue
      + +public static final floatBAD0.25f
      + +public static final floatEXCELLENT0.875f
      + +public static final floatGOOD0.625f
      + +public static final floatNOT_GOOD0.375f
      + +public static final floatOKAY0.5f
      + +public static final floatPERFECT1.0f
      + +public static final floatTERRIBLE0.125f
      + +public static final floatUNSUITABLE0.0f
      + +public static final floatVERY_GOOD0.75f
    -
      -
    • -
      neureka.backend.main.implementations.broadcast.CLScalarBroadcast
      -
      -
      Modifier and Type
      -
      Constant Field
      -
      Value
      -
      protected static final String
      -
      TYPE
      -
      "#DATA_TYPE#"
      -
      +
        +
      • + + + + + + + + + + + + + + +
        neureka.backend.main.implementations.broadcast.CLScalarBroadcast 
        Modifier and TypeConstant FieldValue
        + +protected static final java.lang.StringTYPE"#DATA_TYPE#"
      -
-
+ + +

neureka.devices.*

-
    -
  • -
    neureka.devices.host.CPU
    -
    -
    Modifier and Type
    -
    Constant Field
    -
    Value
    -
    public static final int
    -
    PARALLELIZATION_THRESHOLD
    -
    32
    -
    public static final String
    -
    THREAD_PREFIX
    -
    "neureka-daemon"
    -
    + -
      -
    • -
      neureka.devices.host.machine.CommonMachine
      -
      -
      Modifier and Type
      -
      Constant Field
      -
      Value
      -
      protected static final long
      -
      K
      -
      1024L
      -
      +
        +
      • + + + + + + + + + + + + + + +
        neureka.devices.host.machine.CommonMachine 
        Modifier and TypeConstant FieldValue
        + +protected static final longK1024L
        • -
      • -
        neureka.devices.host.machine.Hardware
        -
        -
        Modifier and Type
        -
        Constant Field
        -
        Value
        -
        public static final long
        -
        OS_MEMORY_PAGE_SIZE
        -
        4096L
        -
        +
      • + + + + + + + + + + + + + + +
        neureka.devices.host.machine.Hardware 
        Modifier and TypeConstant FieldValue
        + +public static final longOS_MEMORY_PAGE_SIZE4096L
      -
-
+ +
+ + +
Skip navigation links
+ + + + +
+
+ + + +
+
+ + +
+ diff --git a/docs/jdocs/deprecated-list.html b/docs/jdocs/deprecated-list.html index ee94d58e2..1b6cd5bc5 100644 --- a/docs/jdocs/deprecated-list.html +++ b/docs/jdocs/deprecated-list.html @@ -1,74 +1,143 @@ - + + - -Deprecated List (neureka 1.0.0 API) - - - - + +Deprecated List (neureka 1.0.1 API) - - - - - - + + -
- -
-

Deprecated API

Contents

-
+ +
+ + +
Skip navigation links
+ + + + +
+
+
    +
  • Prev
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    • +
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+ diff --git a/docs/jdocs/help-doc.html b/docs/jdocs/help-doc.html index 43586fd99..44f0adfd1 100644 --- a/docs/jdocs/help-doc.html +++ b/docs/jdocs/help-doc.html @@ -1,186 +1,222 @@ - + + - -API Help (neureka 1.0.0 API) - - - - + +API Help (neureka 1.0.1 API) - - - - - - + + -
- -
-
-

JavaDoc Help

-
    -
  • Navigation: -
      -
    • Search
      • + - -
    • Kinds of Pages: - -
      -
      -

      Navigation

      -Starting from the Overview page, you can browse the documentation using the links in each page, and in the navigation bar at the top of each page. The Index and Search box allow you to navigate to specific declarations and summary pages, including: All Packages, All Classes and Interfaces - +
      + +
      + + +
      + +
      +

      How This API Document Is Organized

      +
      This API (Application Programming Interface) document has pages corresponding to the items in the navigation bar, described as follows.
      -
      -
      -

      Kinds of Pages

      -The following sections describe the different kinds of pages in this collection. -
      -

      Overview

      -

      The Overview page is the front page of this API document and provides a list of all packages with a summary for each. This page can also contain an overall description of the set of packages.

      -
      -
      -

      Package

      -

      Each package has a page that contains a list of its classes and interfaces, with a summary for each. These pages may contain the following categories:

      -
        -
      • Interfaces
        • +
        +
          +
        • +

          Overview

          +

          The Overview page is the front page of this API document and provides a list of all packages with a summary for each. This page can also contain an overall description of the set of packages.

          +
          • +
        • +

          Package

          +

          Each package has a page that contains a list of its classes and interfaces, with a summary for each. This page can contain six categories:

          +
            +
          • Interfaces (italic)
          • Classes
            • -
          • Enum Classes
            • +
          • Enums
          • Exceptions
          • Errors
            • -
          • Annotation Interfaces
            • -
          -
      -
      -

      Class or Interface

      -

      Each class, interface, nested class and nested interface has its own separate page. Each of these pages has three sections consisting of a declaration and description, member summary tables, and detailed member descriptions. Entries in each of these sections are omitted if they are empty or not applicable.

      -
        -
      • Class Inheritance Diagram
        • +
      • Annotation Types
        • +
      +
      • +
    • +

      Class/Interface

      +

      Each class, interface, nested class and nested interface has its own separate page. Each of these pages has three sections consisting of a class/interface description, summary tables, and detailed member descriptions:

      +
        +
      • Class inheritance diagram
      • Direct Subclasses
      • All Known Subinterfaces
      • All Known Implementing Classes
        • -
      • Class or Interface Declaration
        • -
      • Class or Interface Description
        • +
      • Class/interface declaration
        • +
      • Class/interface description
      -
      -
        +
        • Nested Class Summary
          • -
        • Enum Constant Summary
        • Field Summary
          • -
        • Property Summary
        • Constructor Summary
        • Method Summary
          • +
        +
          +
        • Field Detail
          • +
        • Constructor Detail
          • +
        • Method Detail
          • +
        +

        Each summary entry contains the first sentence from the detailed description for that item. The summary entries are alphabetical, while the detailed descriptions are in the order they appear in the source code. This preserves the logical groupings established by the programmer.

        + +
      • +

        Annotation Type

        +

        Each annotation type has its own separate page with the following sections:

        +
          +
        • Annotation Type declaration
          • +
        • Annotation Type description
        • Required Element Summary
        • Optional Element Summary
          • +
        • Element Detail
        -
        -
          -
        • Enum Constant Details
          • -
        • Field Details
          • -
        • Property Details
          • -
        • Constructor Details
          • -
        • Method Details
          • -
        • Element Details
          • -
        -

        Note: Annotation interfaces have required and optional elements, but not methods. Only enum classes have enum constants. The components of a record class are displayed as part of the declaration of the record class. Properties are a feature of JavaFX.

        -

        The summary entries are alphabetical, while the detailed descriptions are in the order they appear in the source code. This preserves the logical groupings established by the programmer.

        - -
        -

        Other Files

        -

        Packages and modules may contain pages with additional information related to the declarations nearby.

        -
        -
        -

        Tree (Class Hierarchy)

        -

        There is a Class Hierarchy page for all packages, plus a hierarchy for each package. Each hierarchy page contains a list of classes and a list of interfaces. Classes are organized by inheritance structure starting with java.lang.Object. Interfaces do not inherit from java.lang.Object.

        -
          -
        • When viewing the Overview page, clicking on TREE displays the hierarchy for all packages.
          • -
        • When viewing a particular package, class or interface page, clicking on TREE displays the hierarchy for only that package.
          • -
        -
        -
        -

        Deprecated API

        -

        The Deprecated API page lists all of the API that have been deprecated. A deprecated API is not recommended for use, generally due to shortcomings, and a replacement API is usually given. Deprecated APIs may be removed in future implementations.

        -
        -
        -

        Constant Field Values

        +
        • +
      • +

        Enum

        +

        Each enum has its own separate page with the following sections:

        +
          +
        • Enum declaration
          • +
        • Enum description
          • +
        • Enum Constant Summary
          • +
        • Enum Constant Detail
          • +
        +
        • +
      • +

        Tree (Class Hierarchy)

        +

        There is a Class Hierarchy page for all packages, plus a hierarchy for each package. Each hierarchy page contains a list of classes and a list of interfaces. The classes are organized by inheritance structure starting with java.lang.Object. The interfaces do not inherit from java.lang.Object.

        +
          +
        • When viewing the Overview page, clicking on "Tree" displays the hierarchy for all packages.
          • +
        • When viewing a particular package, class or interface page, clicking "Tree" displays the hierarchy for only that package.
          • +
        +
        • +
      • +

        Deprecated API

        +

        The Deprecated API page lists all of the API that have been deprecated. A deprecated API is not recommended for use, generally due to improvements, and a replacement API is usually given. Deprecated APIs may be removed in future implementations.

        +
        • +
      • +

        Index

        +

        The Index contains an alphabetic list of all classes, interfaces, constructors, methods, and fields.

        +
        • +
      • +

        Prev/Next

        +

        These links take you to the next or previous class, interface, package, or related page.

        +
        • +
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        Frames/No Frames

        +

        These links show and hide the HTML frames. All pages are available with or without frames.

        +
        • +
      • +

        All Classes

        +

        The All Classes link shows all classes and interfaces except non-static nested types.

        +
        • +
      • +

        Serialized Form

        +

        Each serializable or externalizable class has a description of its serialization fields and methods. This information is of interest to re-implementors, not to developers using the API. While there is no link in the navigation bar, you can get to this information by going to any serialized class and clicking "Serialized Form" in the "See also" section of the class description.

        +
        • +
      • +

        Constant Field Values

        The Constant Field Values page lists the static final fields and their values.

        - -
        -

        All Packages

        -

        The All Packages page contains an alphabetic index of all packages contained in the documentation.

        -
        -
        -

        All Classes and Interfaces

        -

        The All Classes and Interfaces page contains an alphabetic index of all classes and interfaces contained in the documentation, including annotation interfaces, enum classes, and record classes.

        -
        -
        -

        Index

        -

        The Index contains an alphabetic index of all classes, interfaces, constructors, methods, and fields in the documentation, as well as summary pages such as All Packages, All Classes and Interfaces.

        -
        -
-
-This help file applies to API documentation generated by the standard doclet. + + +This help file applies to API documentation generated using the standard doclet.
+ +
+ + +
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+ diff --git a/docs/jdocs/index-all.html b/docs/jdocs/index-all.html index 35df86c86..fcc508198 100644 --- a/docs/jdocs/index-all.html +++ b/docs/jdocs/index-all.html @@ -1,491 +1,500 @@ - + + - -Index (neureka 1.0.0 API) - - - - + +Index (neureka 1.0.1 API) - - - - - - + + -
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Index

-
-A B C D E F G H I J K L M N O P Q R S T U V W X _ 
All Classes and Interfaces|All Packages|Constant Field Values -

A

-
-
ABGR_4BYTE - Enum constant in enum class neureka.Tensor.ImageType
-
 
-
ABGR_PRE_4BYTE - Enum constant in enum class neureka.Tensor.ImageType
+
A B C D E F G H I J K L M N O P Q R S T U V W X _  + + +

A

+
+
abs() - Method in class neureka.math.Functions
 
-
abs() - Method in class neureka.math.Functions
-
 
-
abs() - Method in interface neureka.Tensor
+
abs() - Method in interface neureka.Tensor
This method is a functionally identical to the following alternatives:
-
Absolute - Class in neureka.backend.main.operations.functions
+
ABSOLUTE - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
Absolute() - Constructor for class neureka.backend.main.operations.functions.Absolute
+
Absolute - Class in neureka.backend.main.operations.functions
 
-
ABSOLUTE - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
Absolute() - Constructor for class neureka.backend.main.operations.functions.Absolute
 
-
AbstractBaseDevice<V> - Class in neureka.devices
+
AbstractBaseDevice<V> - Class in neureka.devices
 
-
AbstractBaseDevice() - Constructor for class neureka.devices.AbstractBaseDevice
+
AbstractBaseDevice() - Constructor for class neureka.devices.AbstractBaseDevice
 
-
AbstractComponentOwner<C> - Class in neureka.common.composition
+
AbstractComponentOwner<C> - Class in neureka.common.composition
Together with the Component interface, this class defines a simple component system in which implementations of the Component interface are managed by extensions of this AbstractComponentOwner.
-
AbstractComponentOwner() - Constructor for class neureka.common.composition.AbstractComponentOwner
+
AbstractComponentOwner() - Constructor for class neureka.common.composition.AbstractComponentOwner
 
-
AbstractCPUConvolution - Class in neureka.backend.main.implementations.convolution
+
AbstractCPUConvolution - Class in neureka.backend.main.implementations.convolution
 
-
AbstractCPUConvolution() - Constructor for class neureka.backend.main.implementations.convolution.AbstractCPUConvolution
+
AbstractCPUConvolution() - Constructor for class neureka.backend.main.implementations.convolution.AbstractCPUConvolution
 
-
AbstractDevice<V> - Class in neureka.devices
+
AbstractDevice<V> - Class in neureka.devices
This is the abstract precursor class providing some useful implementations for core concepts which are most likely applicable to most concrete implementations of the Device interface.
-
AbstractDevice() - Constructor for class neureka.devices.AbstractDevice
+
AbstractDevice() - Constructor for class neureka.devices.AbstractDevice
 
-
AbstractDeviceAlgorithm<C extends DeviceAlgorithm<C>> - Class in neureka.backend.api.template.algorithms
+
AbstractDeviceAlgorithm<C extends DeviceAlgorithm<C>> - Class in neureka.backend.api.template.algorithms
This is a partial implementation of the Algorithm interface which implements the component system for implementation instances of the ImplementationFor interface.
-
AbstractDeviceAlgorithm(String) - Constructor for class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
AbstractDeviceAlgorithm(String) - Constructor for class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
AbstractDeviceData<T> - Class in neureka.devices
+
AbstractDeviceData<T> - Class in neureka.devices
 
-
AbstractDeviceData(AbstractBaseDevice<?>, Object, DataType<T>, Runnable) - Constructor for class neureka.devices.AbstractDeviceData
+
AbstractDeviceData(AbstractBaseDevice<?>, Object, DataType<T>, Runnable) - Constructor for class neureka.devices.AbstractDeviceData
 
-
AbstractFunAlgorithm - Class in neureka.backend.api.template.algorithms
+
AbstractFunAlgorithm - Class in neureka.backend.api.template.algorithms
 
-
AbstractFunAlgorithm(String) - Constructor for class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
+
AbstractFunAlgorithm(String) - Constructor for class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
 
-
AbstractFunDeviceAlgorithm<C extends DeviceAlgorithm<C>> - Class in neureka.backend.api.template.algorithms
+
AbstractFunDeviceAlgorithm<C extends DeviceAlgorithm<C>> - Class in neureka.backend.api.template.algorithms
This is the base class for implementations of the Algorithm interface.
-
AbstractFunDeviceAlgorithm(String) - Constructor for class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
AbstractFunDeviceAlgorithm(String) - Constructor for class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
 
-
AbstractImplementationFor<D extends Device<?>> - Class in neureka.backend.api.template.implementations
+
AbstractImplementationFor<D extends Device<?>> - Class in neureka.backend.api.template.implementations
 
-
AbstractImplementationFor(ImplementationFor<D>, int) - Constructor for class neureka.backend.api.template.implementations.AbstractImplementationFor
+
AbstractImplementationFor(ImplementationFor<D>, int) - Constructor for class neureka.backend.api.template.implementations.AbstractImplementationFor
 
-
AbstractNDC - Class in neureka.ndim.config
+
AbstractNDC - Class in neureka.ndim.config
The following is an abstract implementation of the NDConfiguration which offers a basis for instantiation and caching of concrete implementations extending this abstract class.
-
AbstractNDC() - Constructor for class neureka.ndim.config.AbstractNDC
+
AbstractNDC() - Constructor for class neureka.ndim.config.AbstractNDC
 
-
AbstractOperation - Class in neureka.backend.api.template.operations
+
AbstractOperation - Class in neureka.backend.api.template.operations
This abstract Operation implementation is a useful template for creating new operations.
-
AbstractOperation(OperationBuilder) - Constructor for class neureka.backend.api.template.operations.AbstractOperation
-
 
-
ACCELERATOR - Enum constant in enum class neureka.devices.opencl.OpenCLDevice.Type
+
AbstractOperation(OperationBuilder) - Constructor for class neureka.backend.api.template.operations.AbstractOperation
 
-
accept(Class<? extends Operation>, Class<? extends DeviceAlgorithm>, Class<? extends D>, Function<LoadingContext, ImplementationFor<D>>) - Method in interface neureka.backend.api.ini.ImplementationReceiver
+
accept(Class<? extends Operation>, Class<? extends DeviceAlgorithm>, Class<? extends D>, Function<LoadingContext, ImplementationFor<D>>) - Method in interface neureka.backend.api.ini.ImplementationReceiver
 
-
access(Tensor<T>) - Method in class neureka.devices.AbstractDevice
+
access(Tensor<T>) - Method in class neureka.devices.AbstractDevice
This method exposes the tensor access API for reading from or writing to a tensor stored on this device.
-
access(Tensor<T>) - Method in interface neureka.devices.Device
+
access(Tensor<T>) - Method in interface neureka.devices.Device
This method exposes the tensor access API for reading from or writing to a tensor stored on this device.
-
access(Tensor<T>) - Method in class neureka.devices.file.FileDevice
+
access(Tensor<T>) - Method in class neureka.devices.file.FileDevice
 
-
act(ADTarget<?>) - Method in interface neureka.autograd.ADAction
+
act(ADTarget<?>) - Method in interface neureka.autograd.ADAction
The auto-differentiation forward or backward pass of an ADAction propagate partial differentiations forward along the computation graph.
-
activationCode() - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
activationCode() - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarAbsolute
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarAbsolute
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarCbrt
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarCbrt
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarCosinus
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarCosinus
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarExp
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarExp
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaSU
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaSU
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaTU
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaTU
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaussian
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaussian
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaussianFast
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaussianFast
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGeLU
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGeLU
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarIdentity
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarIdentity
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarLog10
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarLog10
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarLogarithm
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarLogarithm
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarQuadratic
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarQuadratic
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarReLU
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarReLU
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSeLU
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSeLU
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSigmoid
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSigmoid
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSiLU
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSiLU
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSinus
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSinus
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSoftplus
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSoftplus
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSoftsign
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSoftsign
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSqrt
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSqrt
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarTanh
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarTanh
 
-
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarTanhFast
+
activationCode() - Method in class neureka.backend.main.implementations.fun.ScalarTanhFast
 
-
actualize() - Method in interface neureka.devices.Device.Access
+
actualize() - Method in interface neureka.devices.Device.Access
 
-
ADAction - Interface in neureka.autograd
+
ADAction - Interface in neureka.autograd
This interface is the declaration for - lambda actions for both the ADAction.act(ADTarget) method of the ADAction interface.
+ lambda actions for both the ADAction.act(ADTarget) method of the ADAction interface.
-
ADAction(Function, ExecutionCall<? extends Device<?>>) - Static method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
+
ADAction(Function, ExecutionCall<? extends Device<?>>) - Static method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
 
-
ADActionSupplier - Interface in neureka.backend.api.fun
+
ADActionSupplier - Interface in neureka.backend.api.fun
Implementations of this functional interface ought to return a new instance of the ADAction class responsible for performing automatic differentiation both for forward and backward mode differentiation.
-
AdaGrad<V extends Number> - Class in neureka.optimization.implementations
+
AdaGrad<V extends java.lang.Number> - Class in neureka.optimization.implementations
Adaptive Gradients, or AdaGrad for short, is an extension of the gradient descent optimization algorithm that adjusts the step size for each parameter based on the squared gradients seen over the course of previous optimization steps.
-
AdaGrad - Static variable in interface neureka.optimization.Optimizer
+
AdaGrad - Static variable in interface neureka.optimization.Optimizer
 
-
AdaGradFactory - Class in neureka.optimization.implementations
+
AdaGradFactory - Class in neureka.optimization.implementations
 
-
AdaGradFactory() - Constructor for class neureka.optimization.implementations.AdaGradFactory
+
AdaGradFactory() - Constructor for class neureka.optimization.implementations.AdaGradFactory
 
-
ADAM<V extends Number> - Class in neureka.optimization.implementations
+
ADAM<V extends java.lang.Number> - Class in neureka.optimization.implementations
ADAM (short for Adaptive Moment Estimation) is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of RMSProp and SGD with respect to Momentum.
-
ADAM - Static variable in interface neureka.optimization.Optimizer
+
ADAM - Static variable in interface neureka.optimization.Optimizer
 
-
ADAMFactory - Class in neureka.optimization.implementations
+
ADAMFactory - Class in neureka.optimization.implementations
 
-
ADAMFactory() - Constructor for class neureka.optimization.implementations.ADAMFactory
+
ADAMFactory() - Constructor for class neureka.optimization.implementations.ADAMFactory
 
-
add() - Method in class neureka.math.Functions
+
add() - Method in class neureka.math.Functions
 
-
addAssign() - Method in class neureka.math.Functions
+
addAssign() - Method in class neureka.math.Functions
 
-
addChild(Tensor<V>) - Method in class neureka.framing.Relation
+
addChild(Tensor<V>) - Method in class neureka.framing.Relation
 
-
ADDED - Enum constant in enum class neureka.common.composition.Component.IsBeing
+
Addition - Class in neureka.backend.main.operations.operator
 
-
Addition - Class in neureka.backend.main.operations.operator
+
Addition() - Constructor for class neureka.backend.main.operations.operator.Addition
 
-
Addition() - Constructor for class neureka.backend.main.operations.operator.Addition
-
 
-
addOperation(Operation) - Method in class neureka.backend.api.BackendContext
+
addOperation(Operation) - Method in class neureka.backend.api.BackendContext
This method registers Operation implementation instances in this BackendContext which is the thread local execution context receiving and processing Tensor instances...
-
addPending(Set<GraphNode<V>>) - Method in class neureka.autograd.JITProp
+
addPending(Set<GraphNode<V>>) - Method in class neureka.autograd.JITProp
 
-
addPermuteRelationFor(Tensor<V>, int[]) - Method in class neureka.framing.Relation
+
addPermuteRelationFor(Tensor<V>, int[]) - Method in class neureka.framing.Relation
When creating permuted versions of slices then there must be a translation between the shape configuration between this new slice and the original parent tensor from which both slices have been derived.
-
addToGradient(Tensor<T>) - Method in interface neureka.MutateTensor
+
addToGradient(Tensor<T>) - Method in interface neureka.MutateTensor
This method takes the provided Tensor instance and adds its contents to the contents of the Tensor which is set as gradient of this very Tensor.
-
ADSupportPredicate - Interface in neureka.backend.api.fun
+
ADSupportPredicate - Interface in neureka.backend.api.fun
A ADSupportPredicate lambda checks which auto differentiation mode can be performed for a given ExecutionCall.
-
ADTarget<V> - Class in neureka.autograd
+
ADTarget<V> - Class in neureka.autograd
This is simply a wrapper for useful information needed by implementations of the ADAction and ADAction interfaces to perform error propagation.
-
Algorithm - Interface in neureka.backend.api
+
Algorithm - Interface in neureka.backend.api
This class is the middle layer of the 3 tier compositional architecture of this backend, which consists of Operations, Algorithms and in case of a DeviceAlgorithm also ImplementationFor.
-
all() - Method in class neureka.fluent.slicing.AxisSliceBuilder
-
 
-
all() - Method in interface neureka.fluent.slicing.states.FromOrAt
-
-
This is a convenience method replacing "from(0).to(axisSize-1)", meaning that - it simply slices the whole current axis from the original tensor.
-
-
all() - Method in interface neureka.fluent.slicing.states.FromOrAtTensor
-
-
This is a convenience method replacing "from(0).to(axisSize-1)", meaning that - it simply slices the whole current axis from the original tensor.
-
-
all(Call.TensorCompare) - Method in class neureka.backend.api.Call.Validator
+
all(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
 
-
all(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
+
all(Call.TensorCompare) - Method in class neureka.backend.api.Call.Validator
 
-
all(V) - Method in class neureka.fluent.building.NdaBuilder
+
all(V) - Method in class neureka.fluent.building.NdaBuilder
 
-
all(V) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
+
all(V) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
This method creates and return a Tensor instance which will be homogeneously filled by the the provided value irrespective of the previously defined shape.
-
all(V) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
+
all(V) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
This method creates and return a Tensor instance which will be homogeneously filled by the the provided value irrespective of the previously defined shape.
-
ALL - Enum constant in enum class neureka.devices.opencl.OpenCLDevice.Type
+
all() - Method in class neureka.fluent.slicing.AxisSliceBuilder
+
 
+
all() - Method in interface neureka.fluent.slicing.states.FromOrAt
+
+
This is a convenience method replacing "from(0).to(axisSize-1)", meaning that + it simply slices the whole current axis from the original tensor.
+
+
all() - Method in interface neureka.fluent.slicing.states.FromOrAtTensor
+
+
This is a convenience method replacing "from(0).to(axisSize-1)", meaning that + it simply slices the whole current axis from the original tensor.
+
+
allAliasGetter(Supplier<List<Object>>) - Method in class neureka.framing.fluent.AxisFrame.Builder
 
-
allAliasGetter(Supplier<List<Object>>) - Method in class neureka.framing.fluent.AxisFrame.Builder
+
allAliasGetterFor(Function<Integer, List<Object>>) - Method in class neureka.framing.fluent.AxisFrame.Builder
 
-
allAliasGetterFor(Function<Integer, List<Object>>) - Method in class neureka.framing.fluent.AxisFrame.Builder
+
allMetaArgs() - Method in class neureka.backend.api.Call
 
-
allMetaArgs() - Method in class neureka.backend.api.Call
+
allNotNull(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
 
-
allNotNull(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
+
allNotNullHaveSame(Call.TensorProperty) - Method in class neureka.backend.api.Call.Validator
 
-
allNotNullHaveSame(Call.TensorProperty) - Method in class neureka.backend.api.Call.Validator
+
allocate(DataType<T>, NDConfiguration) - Method in interface neureka.devices.Device
 
-
allocate(Class<T>, int, Object) - Method in class neureka.devices.host.CPU
+
allocate(DataType<T>, int) - Method in interface neureka.devices.Device
 
-
allocate(Class<T>, Object) - Method in class neureka.devices.host.CPU
+
allocate(DataType<V>, NDConfiguration) - Method in class neureka.devices.file.FileDevice
 
-
allocate(DataType<T>, int) - Method in interface neureka.devices.Device
+
allocate(Class<T>, Object) - Method in class neureka.devices.host.CPU
 
-
allocate(DataType<T>, NDConfiguration) - Method in interface neureka.devices.Device
+
allocate(Class<T>, int, Object) - Method in class neureka.devices.host.CPU
 
-
allocate(DataType<T>, NDConfiguration) - Method in class neureka.devices.host.CPU
+
allocate(DataType<T>, NDConfiguration) - Method in class neureka.devices.host.CPU
 
-
allocate(DataType<T>, NDConfiguration) - Method in class neureka.devices.opencl.OpenCLDevice
+
allocate(DataType<T>, NDConfiguration) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
allocate(DataType<V>, NDConfiguration) - Method in class neureka.devices.file.FileDevice
+
allocateFromAll(DataType<T>, NDConfiguration, Object) - Method in interface neureka.devices.Device
 
-
allocateFromAll(DataType<T>, NDConfiguration, Object) - Method in interface neureka.devices.Device
+
allocateFromAll(DataType<T>, NDConfiguration, Object) - Method in class neureka.devices.file.FileDevice
 
-
allocateFromAll(DataType<T>, NDConfiguration, Object) - Method in class neureka.devices.file.FileDevice
+
allocateFromAll(DataType<T>, NDConfiguration, Object) - Method in class neureka.devices.host.CPU
 
-
allocateFromAll(DataType<T>, NDConfiguration, Object) - Method in class neureka.devices.host.CPU
+
allocateFromAll(DataType<T>, NDConfiguration, Object) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
allocateFromAll(DataType<T>, NDConfiguration, Object) - Method in class neureka.devices.opencl.OpenCLDevice
+
allocateFromOne(DataType<T>, NDConfiguration, T) - Method in interface neureka.devices.Device
 
-
allocateFromOne(DataType<T>, NDConfiguration, T) - Method in interface neureka.devices.Device
+
allocateFromOne(DataType<V>, NDConfiguration, V) - Method in class neureka.devices.file.FileDevice
 
-
allocateFromOne(DataType<T>, NDConfiguration, T) - Method in class neureka.devices.host.CPU
+
allocateFromOne(DataType<T>, NDConfiguration, T) - Method in class neureka.devices.host.CPU
 
-
allocateFromOne(DataType<T>, NDConfiguration, T) - Method in class neureka.devices.opencl.OpenCLDevice
+
allocateFromOne(DataType<T>, NDConfiguration, T) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
allocateFromOne(DataType<V>, NDConfiguration, V) - Method in class neureka.devices.file.FileDevice
+
allowsBackward() - Method in enum neureka.backend.api.AutoDiffMode
 
-
allowsBackward() - Method in enum class neureka.backend.api.AutoDiffMode
+
allowsForward() - Method in enum neureka.backend.api.AutoDiffMode
 
-
allowsForward() - Method in enum class neureka.backend.api.AutoDiffMode
+
allShare(Function<Tensor<?>, T>) - Method in class neureka.backend.api.Call.Validator
 
-
allShare(Function<Tensor<?>, T>) - Method in class neureka.backend.api.Call.Validator
+
and(F) - Method in interface neureka.backend.main.algorithms.internal.AndBackward
 
-
and(F) - Method in interface neureka.backend.main.algorithms.internal.AndBackward
+
andArgs(List<Arg>) - Method in class neureka.backend.api.Call.Builder
 
-
andArgs(List<Arg>) - Method in class neureka.backend.api.Call.Builder
+
andArgs(Arg<?>...) - Method in class neureka.backend.api.Call.Builder
 
-
andArgs(List<Arg>) - Method in class neureka.backend.api.ExecutionCall.Builder
+
andArgs(List<Arg>) - Method in class neureka.backend.api.ExecutionCall.Builder
 
-
andArgs(Arg<?>...) - Method in class neureka.backend.api.Call.Builder
+
andArgs(Arg<?>...) - Method in class neureka.backend.api.ExecutionCall.Builder
 
-
andArgs(Arg<?>...) - Method in class neureka.backend.api.ExecutionCall.Builder
+
AndBackward<F> - Interface in neureka.backend.main.algorithms.internal
 
-
AndBackward<F> - Interface in neureka.backend.main.algorithms.internal
+
andFill(V...) - Method in class neureka.fluent.building.NdaBuilder
 
-
andFill(List<V>) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
+
andFill(V...) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
-
Provide a list of values which will be used to fill +
Provide an array of values which will be used to fill the Tensor instance returned by this last fluent builder method.
-
andFill(List<V>) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
+
andFill(List<V>) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
Provide a list of values which will be used to fill the Tensor instance returned by this last fluent builder method.
-
andFill(V...) - Method in class neureka.fluent.building.NdaBuilder
-
 
-
andFill(V...) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
+
andFill(V...) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
Provide an array of values which will be used to fill the Tensor instance returned by this last fluent builder method.
-
andFill(V...) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
+
andFill(List<V>) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
-
Provide an array of values which will be used to fill +
Provide a list of values which will be used to fill the Tensor instance returned by this last fluent builder method.
-
andFillFrom(V) - Method in class neureka.fluent.building.NdaBuilder
+
andFillFrom(V) - Method in class neureka.fluent.building.NdaBuilder
 
-
andFillFrom(V) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
+
andFillFrom(V) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
This part of the builder API allows for specifying a range which starts from the provided value and will end at the value specified in the next builder step returned by this method.
-
andFillFrom(V) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
+
andFillFrom(V) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
This part of the builder API allows for specifying a range which starts from the provided value and will end at the value specified in the next builder step returned by this method.
-
andImplementation(ImplementationFor<CPU>) - Method in interface neureka.backend.main.implementations.CPUImplementation.AndImplementation
+
andImplementation(ImplementationFor<CPU>) - Method in interface neureka.backend.main.implementations.CPUImplementation.AndImplementation
 
-
andSeed(Object) - Method in class neureka.fluent.building.NdaBuilder
+
andSeed(Object) - Method in class neureka.fluent.building.NdaBuilder
 
-
andSeed(Object) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
+
andSeed(Object) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
This method creates and return a Tensor instance which will be filled based on the provided seed object.
-
andSeed(Object) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
+
andSeed(Object) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
This method creates and return a Tensor instance which will be filled based on the provided seed object.
-
andWhere(Filler<V>) - Method in class neureka.fluent.building.NdaBuilder
+
andWhere(Filler<V>) - Method in class neureka.fluent.building.NdaBuilder
This method receives an Filler lambda which will be used to populate the Tensor instance produced by this API with values.
-
andWhere(Filler<V>) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
+
andWhere(Filler<V>) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAll
Pass a lambda to this method which will be used to populate the Tensor built by this fluent builder API based on the indices of the tensor.
-
andWhere(Filler<V>) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
+
andWhere(Filler<V>) - Method in interface neureka.fluent.building.states.IterByOrIterFromOrAllTensor
Pass a lambda to this method which will be used to populate the Tensor built by this fluent builder API based on the indices of the tensor.
-
any(String...) - Static method in interface neureka.devices.Device
+
any(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
+
 
+
any(String...) - Static method in interface neureka.devices.Device
This method returns Device instances matching the given search parameter.
-
any(Predicate<Integer>) - Method in interface neureka.Shape
-
 
-
any(Predicate<V>) - Method in interface neureka.Nda
+
any(Predicate<V>) - Method in interface neureka.Nda
Iterates over every element of this nd-array, and checks whether any element matches the provided lambda.
-
any(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
+
any(Predicate<Integer>) - Method in interface neureka.Shape
 
-
anyNotNull(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
+
anyNotNull(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
 
-
applyGradient() - Method in interface neureka.Tensor
+
applyGradient() - Method in interface neureka.Tensor
If this tensor owns a gradient tensor as component, then it can be applied by this method.
-
approve(ExecutionCall<? extends Device<?>>) - Method in class neureka.devices.AbstractDevice
+
approve(ExecutionCall<? extends Device<?>>) - Method in class neureka.devices.AbstractDevice
This method plays an important role in approving a provided ExecutionCall. When implementing custom operations or such for the backend of this library, then one may use this in order to check if the provided call is suitable for this Device.
-
approve(ExecutionCall<? extends Device<?>>) - Method in interface neureka.devices.Device
+
approve(ExecutionCall<? extends Device<?>>) - Method in interface neureka.devices.Device
This method is used internally to give Device implementations the opportunity to perform some exception handling before the ExecutionCall will be dispatched.
-
approve(ExecutionCall<? extends Device<?>>) - Method in class neureka.devices.file.FileDevice
+
approve(ExecutionCall<? extends Device<?>>) - Method in class neureka.devices.file.FileDevice
 
-
architecture - Variable in class neureka.devices.host.machine.CommonMachine
+
architecture - Variable in class neureka.devices.host.machine.CommonMachine
 
-
Arg<T> - Class in neureka.math.args
+
Arg<T> - Class in neureka.math.args
Extend this class to define additional meta arguments for Functions.
-
Arg(T) - Constructor for class neureka.math.args.Arg
+
Arg(T) - Constructor for class neureka.math.args.Arg
 
-
Arg.Axis - Class in neureka.math.args
+
Arg.Axis - Class in neureka.math.args
 
-
Arg.Derivative<V> - Class in neureka.math.args
+
Arg.Derivative<V> - Class in neureka.math.args
 
-
Arg.DerivIdx - Class in neureka.math.args
+
Arg.DerivIdx - Class in neureka.math.args
This is an import argument whose role might not be clear at first : @@ -493,3617 +502,3564 @@

A

when calculating the derivative for a forward or backward pass then one must know which derivative ought to be calculated.
-
Arg.Ends - Class in neureka.math.args
+
Arg.Ends - Class in neureka.math.args
 
-
Arg.Indices - Class in neureka.math.args
+
Arg.Indices - Class in neureka.math.args
 
-
Arg.Layout - Class in neureka.math.args
+
Arg.Layout - Class in neureka.math.args
 
-
Arg.MinRank - Class in neureka.math.args
+
Arg.MinRank - Class in neureka.math.args
 
-
Arg.Offset - Class in neureka.math.args
+
Arg.Offset - Class in neureka.math.args
 
-
Arg.Seed - Class in neureka.math.args
+
Arg.Seed - Class in neureka.math.args
 
-
Arg.Shape - Class in neureka.math.args
+
Arg.Shape - Class in neureka.math.args
 
-
Arg.Stride - Class in neureka.math.args
+
Arg.Stride - Class in neureka.math.args
 
-
Arg.TargetDevice - Class in neureka.math.args
+
Arg.TargetDevice - Class in neureka.math.args
 
-
Arg.VarIdx - Class in neureka.math.args
+
Arg.VarIdx - Class in neureka.math.args
The following argument is relevant for a particular type of operation, namely: an "indexer".
-
ARGB_1INT - Enum constant in enum class neureka.Tensor.ImageType
-
 
-
ARGB_PRE_1INT - Enum constant in enum class neureka.Tensor.ImageType
-
 
-
Args - Class in neureka.math.args
+
Args - Class in neureka.math.args
 
-
Args() - Constructor for class neureka.math.args.Args
+
Args() - Constructor for class neureka.math.args.Args
 
-
arity() - Method in class neureka.backend.api.Call
+
arity() - Method in class neureka.backend.api.Call
 
-
arity(int) - Method in class neureka.backend.api.template.operations.OperationBuilder
+
arity(int) - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
as(Class<D>) - Method in interface neureka.Data
+
as(Class<D>) - Method in interface neureka.Data
This returns the underlying raw data object of a nd-array or tensor.
-
asDerivative(Function[], int) - Method in interface neureka.backend.api.Operation
+
asDerivative(Function[], int) - Method in interface neureka.backend.api.Operation
Operation implementations and Function implementations are in a tight relationship where the Function describes an abstract syntax tree based on the syntactic information provided - by the Operation (through methods like Operation.getOperator() or Operation.getIdentifier()).
+ by the Operation (through methods like Operation.getOperator() or Operation.getIdentifier()).
-
asDerivative(Function[], int) - Method in class neureka.backend.api.template.operations.AbstractOperation
+
asDerivative(Function[], int) - Method in class neureka.backend.api.template.operations.AbstractOperation
Operation implementations and Function implementations are in a tight relationship where the Function describes an abstract syntax tree based on the syntactic information provided - by the Operation (through methods like Operation.getOperator() or Operation.getIdentifier()).
+ by the Operation (through methods like Operation.getOperator() or Operation.getIdentifier()).
-
asDerivative(Function[], int) - Method in interface neureka.backend.api.template.operations.OperationBuilder.Derivation
+
asDerivative(Function[], int) - Method in interface neureka.backend.api.template.operations.OperationBuilder.Derivation
 
-
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.functions.Logarithm
+
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.functions.Logarithm
 
-
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Addition
+
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Addition
 
-
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Division
+
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Division
 
-
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Modulo
+
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Modulo
 
-
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Multiplication
+
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Multiplication
 
-
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Power
+
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Power
 
-
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Subtraction
+
asDerivative(Function[], int) - Method in class neureka.backend.main.operations.operator.Subtraction
 
-
asImage(Tensor.ImageType) - Method in interface neureka.Tensor
+
asImage(Tensor.ImageType) - Method in interface neureka.Tensor
-
Turns this tensor into a BufferedImage based on the provided - Tensor.ImageType formatting choice.
+
Turns this tensor into a BufferedImage based on the provided + Tensor.ImageType formatting choice.
-
asInlineArray() - Method in interface neureka.ndim.config.NDConfiguration
+
asInlineArray() - Method in interface neureka.ndim.config.NDConfiguration
This method returns an array of flattened arrays which define this nd-configuration in a compact manner.
-
assign(Nda<T>) - Method in interface neureka.MutateNda
+
assign(T) - Method in interface neureka.MutateNda
-
Use this to assign the provided nd-array to this nd-array! +
Use this to assign the provided item to all elements of this nd-array! This method is an inline operation which changes the underlying data of the nd-array.
-
assign(Nda<T>) - Method in interface neureka.MutateTensor
-
 
-
assign(T) - Method in interface neureka.MutateNda
+
assign(Nda<T>) - Method in interface neureka.MutateNda
-
Use this to assign the provided item to all elements of this nd-array! +
Use this to assign the provided nd-array to this nd-array! This method is an inline operation which changes the underlying data of the nd-array.
-
assign(T) - Method in interface neureka.MutateTensor
+
assign(T) - Method in interface neureka.MutateTensor
+
 
+
assign(Nda<T>) - Method in interface neureka.MutateTensor
 
-
AssignLeft - Class in neureka.backend.main.operations.other
+
AssignLeft - Class in neureka.backend.main.operations.other
 
-
AssignLeft() - Constructor for class neureka.backend.main.operations.other.AssignLeft
+
AssignLeft() - Constructor for class neureka.backend.main.operations.other.AssignLeft
 
-
assumptionBasedOn(String) - Static method in class neureka.math.parsing.ParseUtil
+
assumptionBasedOn(String) - Static method in class neureka.math.parsing.ParseUtil
-
This method tries to find the next best operation String the user might have meant.
+
This method tries to find the next best operation String the user might have meant.
-
asType(Class<T>) - Method in interface neureka.Tensor
+
asType(Class<T>) - Method in interface neureka.Tensor
 
-
at(int) - Method in interface neureka.devices.Device.Writer
+
at(int) - Method in interface neureka.devices.Device.Writer
Writes whatever kind of data was previously specified, to the tensors' data at the position targeted by the provided index.
-
at(int) - Method in class neureka.fluent.slicing.AxisSliceBuilder
+
at(String) - Static method in class neureka.devices.file.FileDevice
+
 
+
at(int) - Method in class neureka.fluent.slicing.AxisSliceBuilder
This method returns an instance of this very AxisSliceBuilder instance disguised by the AxisOrGet interface.
-
at(int) - Method in interface neureka.fluent.slicing.states.FromOrAt
+
at(int) - Method in interface neureka.fluent.slicing.states.FromOrAt
This is a convenience method replacing "from(i).to(i)", meaning that it simply slices a single axis from the original tensor at the specified index.
-
at(int) - Method in interface neureka.fluent.slicing.states.FromOrAtTensor
+
at(int) - Method in interface neureka.fluent.slicing.states.FromOrAtTensor
This is a convenience method replacing "from(i).to(i)", meaning that it simply slices a single axis from the original tensor at the specified index.
-
at(int...) - Method in interface neureka.MutateNda
+
At<K,R> - Interface in neureka.framing.fluent
+
 
+
at(K) - Method in interface neureka.framing.fluent.At
+
 
+
at(int...) - Method in interface neureka.MutateNda
Exposes the MutateNda.Item interface which allows you to get or set individual nd-array items.
-
at(int...) - Method in interface neureka.Nda
+
at(int...) - Method in interface neureka.Nda
This method exposes the Nda.Item API which allows you to get or set individual items within this nd-array targeted by an array of provided indices.
-
at(String) - Static method in class neureka.devices.file.FileDevice
-
 
-
at(K) - Method in interface neureka.framing.fluent.At
-
 
-
At<K,R> - Interface in neureka.framing.fluent
-
 
-
atAxis(Object) - Method in class neureka.framing.NDFrame
+
atAxis(Object) - Method in class neureka.framing.NDFrame
A NDFrame exposes aliases for axes as well as aliases for individual positions within an axis.
-
atIndexAlias(Object) - Method in class neureka.framing.fluent.AxisFrame
+
atIndexAlias(Object) - Method in class neureka.framing.fluent.AxisFrame
 
-
autoDelete(Tensor<?>...) - Static method in class neureka.backend.main.memory.MemUtil
+
autoDelete(Tensor<?>...) - Static method in class neureka.backend.main.memory.MemUtil
This method will try to delete the provided array of tensors if the tensors are not important computation graph components (like derivatives for example).
-
AutoDiffMode - Enum Class in neureka.backend.api
+
AutoDiffMode - Enum in neureka.backend.api
 
-
autoDiffModeFrom(ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.api.fun.ADSupportPredicate
+
autoDiffModeFrom(ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.api.fun.ADSupportPredicate
Implementations of this ought to check which auto differentiation mode can be performed for a given ExecutionCall.
-
autoDiffModeFrom(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
+
autoDiffModeFrom(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
 
-
autoDiffModeFrom(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
autoDiffModeFrom(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
 
-
autoDiffModeFrom(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
+
autoDiffModeFrom(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
 
-
autograd() - Method in class neureka.Neureka.Settings
+
autograd() - Method in class neureka.Neureka.Settings
 
-
autograd(Object) - Method in class neureka.Neureka.Settings
+
autograd(Object) - Method in class neureka.Neureka.Settings
This allows you to configure Neureka using a Groovy DSL.
-
AutoGrad() - Constructor for class neureka.Neureka.Settings.AutoGrad
+
AutoGrad() - Constructor for class neureka.Neureka.Settings.AutoGrad
 
-
autogradMode() - Method in class neureka.backend.api.ExecutionCall
+
autogradMode() - Method in class neureka.backend.api.ExecutionCall
This method queries the underlying Operation for a suitable Algorithm for this ExecutionCall to see what kind of auto differentiation can be performed.
-
axis(int) - Method in class neureka.fluent.slicing.AxisSliceBuilder
+
axis(int) - Method in class neureka.fluent.slicing.AxisSliceBuilder
This method returns an instance of the AxisSliceBuilder targeted by the provided index.
-
axis(int) - Method in class neureka.fluent.slicing.SliceBuilder
+
axis(int) - Method in class neureka.fluent.slicing.SliceBuilder
This method returns an instance of the AxisSliceBuilder disguised by the FromOrAt interface.
-
axis(int) - Method in interface neureka.fluent.slicing.states.AxisOrGet
+
axis(int) - Method in interface neureka.fluent.slicing.states.AxisOrGet
Slicing a tensor ultimately means slicing one or more of its axes! This method allows one to specify which axis should be sliced next.
-
axis(int) - Method in interface neureka.fluent.slicing.states.AxisOrGetTensor
+
axis(int) - Method in interface neureka.fluent.slicing.states.AxisOrGetTensor
Slicing a tensor ultimately means slicing one or more of its axes! This method allows one to specify which axis should be sliced next.
-
AxisFrame<G,V> - Class in neureka.framing.fluent
+
AxisFrame<G,V> - Class in neureka.framing.fluent
This class represents the labeled axis of an NDFrame.
-
AxisFrame.Builder<SetType,GetType,ValueType> - Class in neureka.framing.fluent
+
AxisFrame.Builder<SetType,GetType,ValueType> - Class in neureka.framing.fluent
 
-
AxisFrame.Set<V> - Interface in neureka.framing.fluent
+
AxisFrame.Set<V> - Interface in neureka.framing.fluent
 
-
AxisOrGet<V> - Interface in neureka.fluent.slicing.states
+
AxisOrGet<V> - Interface in neureka.fluent.slicing.states
This is the starting point of the call transition graph exposed by the slice builder API.
-
AxisOrGetTensor<V> - Interface in neureka.fluent.slicing.states
+
AxisOrGetTensor<V> - Interface in neureka.fluent.slicing.states
 
-
AxisSliceBuilder<V> - Class in neureka.fluent.slicing
+
AxisSliceBuilder<V> - Class in neureka.fluent.slicing
 
-
AXPY - Class in neureka.backend.main.operations.linear.internal.blas
+
AXPY - Class in neureka.backend.main.operations.linear.internal.blas
The ?axpy routines perform a vector-vector operation defined as y := a*x + y where: a is a scalar x and y are vectors each with a number of elements that equals n.
-
AXPY() - Constructor for class neureka.backend.main.operations.linear.internal.blas.AXPY
+
AXPY() - Constructor for class neureka.backend.main.operations.linear.internal.blas.AXPY
 
-

B

-
-
backend() - Method in class neureka.Neureka
+ + + +

B

+
+
backend() - Method in class neureka.Neureka
 
-
BackendContext - Class in neureka.backend.api
+
BackendContext - Class in neureka.backend.api
Instances of this class are execution contexts hosting Operation instances which receive Tensor instances for execution.
-
BackendContext() - Constructor for class neureka.backend.api.BackendContext
+
BackendContext() - Constructor for class neureka.backend.api.BackendContext
This creates a new context which is completely void of any Operation implementation instances.
-
BackendContext.Runner - Class in neureka.backend.api
+
BackendContext.Runner - Class in neureka.backend.api
This is a very simple class with a single purpose, namely it exposes methods which receive lambda instances in order to then execute them in a given BackendContext, just to then switch back to the original context again.
-
BackendExtension - Interface in neureka.backend.api
+
BackendExtension - Interface in neureka.backend.api
Implementations of this might introduce CUDA or ROCM to Neureka.
-
BackendExtension.DeviceOption - Class in neureka.backend.api
+
BackendExtension.DeviceOption - Class in neureka.backend.api
This class describes an available Device implementation found for a given BackendExtension.
-
BackendLoader - Interface in neureka.backend.api.ini
+
BackendLoader - Interface in neureka.backend.api.ini
 
-
BackendRegistry - Class in neureka.backend.api.ini
+
BackendRegistry - Class in neureka.backend.api.ini
 
-
backward() - Method in interface neureka.Tensor
+
backward(Tensor<V>) - Method in class neureka.autograd.GraphNode
-
Use this to back-propagate an error signal of 1.0 through the recorded computation graph.
+
This method is the entry-point for the back-propagation process.
-
backward(double) - Method in interface neureka.Tensor
+
backward(Tensor<V>) - Method in interface neureka.Tensor
Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
-
backward(Tensor<V>) - Method in class neureka.autograd.GraphNode
+
backward(double) - Method in interface neureka.Tensor
-
This method is the entry-point for the back-propagation process.
+
Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
-
backward(Tensor<V>) - Method in interface neureka.Tensor
+
backward() - Method in interface neureka.Tensor
-
Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
+
Use this to back-propagate an error signal of 1.0 through the recorded computation graph.
-
BACKWARD_ONLY - Enum constant in enum class neureka.backend.api.AutoDiffMode
-
 
-
backwardJIT(Tensor<V>) - Method in class neureka.autograd.GraphNode
+
backwardJIT(Tensor<V>) - Method in class neureka.autograd.GraphNode
This method is called only when JITProp is active.
-
BAD - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
+
BAD - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
 
-
badIfAll(Call.TensorCompare) - Method in class neureka.backend.api.Call.Validator.Estimator
+
badIfAll(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
 
-
badIfAll(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
+
badIfAll(Call.TensorCompare) - Method in class neureka.backend.api.Call.Validator.Estimator
 
-
badIfAny(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
+
badIfAny(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
 
-
badIfAnyNonNull(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
+
badIfAnyNonNull(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
 
-
BasicMachine - Class in neureka.devices.host.machine
+
BasicMachine - Class in neureka.devices.host.machine
How much memory, and how many threads share that memory.
-
BasicMachine(long, int) - Constructor for class neureka.devices.host.machine.BasicMachine
+
BasicMachine(long, int) - Constructor for class neureka.devices.host.machine.BasicMachine
 
-
basicSuitability() - Method in class neureka.backend.api.Call.Validator
+
basicSuitability() - Method in class neureka.backend.api.Call.Validator
The validity as float being >0/true and 0/false.
-
belongsToGraph() - Method in interface neureka.Tensor
+
belongsToGraph() - Method in interface neureka.Tensor
Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
-
BGR_1INT - Enum constant in enum class neureka.Tensor.ImageType
-
 
-
BGR_3BYTE - Enum constant in enum class neureka.Tensor.ImageType
+
BiElementwise - Class in neureka.backend.main.algorithms
 
-
BiElementwise - Class in neureka.backend.main.algorithms
+
BiElementwise() - Constructor for class neureka.backend.main.algorithms.BiElementwise
 
-
BiElementwise() - Constructor for class neureka.backend.main.algorithms.BiElementwise
+
BiScalarBroadcast - Class in neureka.backend.main.algorithms
 
-
BiScalarBroadcast - Class in neureka.backend.main.algorithms
+
BiScalarBroadcast() - Constructor for class neureka.backend.main.algorithms.BiScalarBroadcast
 
-
BiScalarBroadcast() - Constructor for class neureka.backend.main.algorithms.BiScalarBroadcast
+
boolToDouble(boolean[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
boolToDouble(boolean[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
boolToFloat(boolean[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
boolToFloat(boolean[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
bootstrapTip() - Method in enum neureka.devices.opencl.utility.Messages.Tips
 
-
bootstrapTip() - Method in enum class neureka.devices.opencl.utility.Messages.Tips
-
 
-
borrow(Tensor<V>, Tensor<V>...) - Method in interface neureka.devices.Device
+
borrow(Tensor<V>, Tensor<V>...) - Method in interface neureka.devices.Device
This is a very simple fluent API for temporarily storing a number of tensors on this Device, executing a provided lambda action, and then migrating all the tensors back to their original devices.
-
Broadcast - Class in neureka.backend.main.algorithms
+
Broadcast - Class in neureka.backend.main.algorithms
 
-
Broadcast() - Constructor for class neureka.backend.main.algorithms.Broadcast
+
Broadcast() - Constructor for class neureka.backend.main.algorithms.Broadcast
 
-
bufferType - Variable in enum class neureka.Tensor.ImageType
+
bufferType - Variable in enum neureka.Tensor.ImageType
 
-
build() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
build() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
build() - Method in class neureka.framing.fluent.AxisFrame.Builder
+
build() - Method in class neureka.framing.fluent.AxisFrame.Builder
 
-
builder() - Static method in interface neureka.backend.api.Operation
+
builder() - Static method in interface neureka.backend.api.Operation
 
-
builder() - Static method in class neureka.framing.fluent.AxisFrame
+
builder() - Static method in class neureka.framing.fluent.AxisFrame
 
-
buildFunAlgorithm() - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
+
buildFunAlgorithm() - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
 
-
buildFunAlgorithm() - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
buildFunAlgorithm() - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
 
-
byDefaults() - Method in interface neureka.view.NdaAsString.Builder
+
byDefaults() - Method in interface neureka.view.NdaAsString.Builder
 
-
byteToBigInteger(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
byteToBigInteger(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
byteToDouble(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
byteToDouble(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
byteToFloat(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
byteToFloat(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
byteToInt(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
byteToInt(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
byteToLong(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
byteToLong(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
byteToShort(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
byteToShort(byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-

C

-
-
cache - Variable in class neureka.devices.host.machine.CommonMachine
-
-
The size of one top level (L3 or L2) cache unit in bytes.
-
-
Cache<O> - Class in neureka.common.utility
+ + + +

C

+
+
Cache<O> - Class in neureka.common.utility
This is a simple, fixed size cache for immutable objects which are shared throughout the library runtime...
-
Cache(int) - Constructor for class neureka.common.utility.Cache
+
Cache(int) - Constructor for class neureka.common.utility.Cache
 
-
Cache.LazyEntry<K,V> - Class in neureka.common.utility
+
cache - Variable in class neureka.devices.host.machine.CommonMachine
+
+
The size of one top level (L3 or L2) cache unit in bytes.
+
+
Cache.LazyEntry<K,V> - Class in neureka.common.utility
Lazy cache entries are entries whose values will be calculated only when the entry is being stored in the cache.
-
calculate(double[], int, int, Function[]) - Method in interface neureka.backend.api.Operation
+
calculate(double[], int, int, Function[]) - Method in interface neureka.backend.api.Operation
This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of Function instances...
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.functions.SiLU
-
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.indexer.Product
+
calculate(double, boolean) - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.indexer.Summation
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.indexer.Product
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.linear.Convolution
+
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.indexer.Product
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.linear.DotProduct
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.indexer.Summation
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.linear.MatMul
+
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.indexer.Summation
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.linear.XConvLeft
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.linear.Convolution
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.linear.XConvRight
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.linear.DotProduct
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Addition
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.linear.MatMul
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Division
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.linear.XConvLeft
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Modulo
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.linear.XConvRight
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Multiplication
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Addition
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Power
+
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Addition
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Subtraction
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Division
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.AssignLeft
+
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Division
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Cat
+
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Modulo
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.DimFit
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Modulo
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.DimTrim
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Multiplication
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Max
+
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Multiplication
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Min
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Power
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Permute
+
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Power
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Randomization
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.operator.Subtraction
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.ReLayout
+
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Subtraction
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Reshape
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.AssignLeft
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Slice
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Cat
 
-
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Sum
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.DimFit
 
-
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.indexer.Product
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.DimTrim
 
-
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.indexer.Summation
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Max
 
-
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Addition
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Min
 
-
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Division
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Permute
 
-
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Modulo
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Randomization
 
-
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Multiplication
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.ReLayout
 
-
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Power
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Reshape
 
-
calculate(double[], int, Function[]) - Static method in class neureka.backend.main.operations.operator.Subtraction
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Slice
 
-
calculate(double, boolean) - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
calculate(double[], int, int, Function[]) - Method in class neureka.backend.main.operations.other.Sum
 
-
call(double) - Method in interface neureka.math.Function
+
call(Supplier<T>) - Method in class neureka.backend.api.BackendContext.Runner
-
Invokes this Function with the provided scalar as a single input and returns the scalar result.
-
-
call(double...) - Method in interface neureka.math.Function
-
-
Invokes this Function with the provided array of inputs.
+
Use this method to supply a lambda which will be executed in the BackendContext + which produced this very BackendContext.Runner instance.
-
call(double[], int) - Method in interface neureka.math.Function
+
Call<D> - Class in neureka.backend.api
-
Invokes this Function with the provided array of inputs ad an index for input dependent indexing.
+
Instances of this class model simple execution calls to the backend.
-
call(double[], int) - Method in class neureka.math.implementations.FunctionConstant
+
Call(Tensor<?>[], D, List<Arg>) - Constructor for class neureka.backend.api.Call
 
-
call(double[], int) - Method in class neureka.math.implementations.FunctionInput
+
call(int) - Method in class neureka.devices.opencl.KernelCaller
 
-
call(double[], int) - Method in class neureka.math.implementations.FunctionNode
-
 
-
call(double[], int) - Method in class neureka.math.implementations.FunctionVariable
-
 
-
call(int) - Method in class neureka.devices.opencl.KernelCaller
-
 
-
call(long[], long[]) - Method in class neureka.devices.opencl.KernelCaller
+
call(long[], long[]) - Method in class neureka.devices.opencl.KernelCaller
Use this to call the kernel with 2 long arrays defining how the kernel should be indexed and parallelized.
-
call(Supplier<T>) - Method in class neureka.backend.api.BackendContext.Runner
+
call(double) - Method in interface neureka.math.Function
-
Use this method to supply a lambda which will be executed in the BackendContext - which produced this very BackendContext.Runner instance.
+
Invokes this Function with the provided scalar as a single input and returns the scalar result.
-
call(List<Tensor<T>>) - Method in interface neureka.math.Function
-
 
-
call(Call.Builder<T, D>) - Method in interface neureka.math.Function
+
call(double[], int) - Method in interface neureka.math.Function
+
+
Invokes this Function with the provided array of inputs ad an index for input dependent indexing.
+
+
call(double...) - Method in interface neureka.math.Function
+
+
Invokes this Function with the provided array of inputs.
+
+
call(Call.Builder<T, D>) - Method in interface neureka.math.Function
Use this for more control over the execution, which is especially useful when interfacing with more complex types of operations, requiring more context information.
-
call(Call<D>) - Method in interface neureka.math.Function
+
call(Call<D>) - Method in interface neureka.math.Function
Use this for more control over the execution, which is very helpful when interfacing with more complex types of operations, requiring more context information.
-
call(Args, Tensor<T>...) - Method in interface neureka.math.Function
+
call(Args, Tensor<T>...) - Method in interface neureka.math.Function
Use this to call this Function alongside with some additional meta-arguments which will be passed to the underlying Operation(s).
-
call(Tensor<T>) - Method in interface neureka.math.Function
+
call(Tensor<T>) - Method in interface neureka.math.Function
 
-
call(Tensor<T>...) - Method in interface neureka.math.Function
+
call(List<Tensor<T>>) - Method in interface neureka.math.Function
 
-
call(Tensor<T>...) - Method in interface neureka.math.Function.Callable
+
call(Tensor<T>[], int) - Method in interface neureka.math.Function
 
-
call(Tensor<T>[], int) - Method in interface neureka.math.Function
+
call(Tensor<T>...) - Method in interface neureka.math.Function
 
-
Call<D> - Class in neureka.backend.api
-
-
Instances of this class model simple execution calls to the backend.
-
-
Call(Tensor<?>[], D, List<Arg>) - Constructor for class neureka.backend.api.Call
+
call(Tensor<T>...) - Method in interface neureka.math.Function.Callable
 
-
Call.Builder<V,T extends Device<V>> - Class in neureka.backend.api
+
call(double[], int) - Method in class neureka.math.implementations.FunctionConstant
 
-
Call.DeviceCondition - Interface in neureka.backend.api
+
call(double[], int) - Method in class neureka.math.implementations.FunctionInput
 
-
Call.Else<T> - Interface in neureka.backend.api
+
call(double[], int) - Method in class neureka.math.implementations.FunctionNode
 
-
Call.OperationCondition - Interface in neureka.backend.api
+
call(double[], int) - Method in class neureka.math.implementations.FunctionVariable
 
-
Call.TensorCompare - Interface in neureka.backend.api
+
Call.Builder<V,T extends Device<V>> - Class in neureka.backend.api
 
-
Call.TensorCondition - Interface in neureka.backend.api
+
Call.DeviceCondition - Interface in neureka.backend.api
 
-
Call.TensorProperty - Interface in neureka.backend.api
+
Call.Else<T> - Interface in neureka.backend.api
 
-
Call.TensorsCondition - Interface in neureka.backend.api
+
Call.OperationCondition - Interface in neureka.backend.api
 
-
Call.Validator - Class in neureka.backend.api
+
Call.TensorCompare - Interface in neureka.backend.api
+
 
+
Call.TensorCondition - Interface in neureka.backend.api
+
 
+
Call.TensorProperty - Interface in neureka.backend.api
+
 
+
Call.TensorsCondition - Interface in neureka.backend.api
+
 
+
Call.Validator - Class in neureka.backend.api
This is a simple nested class offering various lambda based methods for validating the tensor arguments stored inside this ExecutionCall.
-
Call.Validator.Estimator - Class in neureka.backend.api
+
Call.Validator.Estimator - Class in neureka.backend.api
 
-
canAccessOpenCL() - Method in class neureka.Neureka
+
canAccessOpenCL() - Method in class neureka.Neureka
 
-
canAccessOpenCLDevice() - Method in class neureka.Neureka
+
canAccessOpenCLDevice() - Method in class neureka.Neureka
 
-
canBeDeleted() - Method in class neureka.autograd.GraphNode
+
canBeDeleted() - Method in class neureka.autograd.GraphNode
 
-
Cat - Class in neureka.backend.main.operations.other
+
Cat - Class in neureka.backend.main.operations.other
 
-
Cat() - Constructor for class neureka.backend.main.operations.other.Cat
+
Cat() - Constructor for class neureka.backend.main.operations.other.Cat
 
-
cbrt() - Method in class neureka.math.Functions
+
CBRT - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
cbrt() - Method in interface neureka.Tensor
-
-
This method is a functionally identical to the following alternatives:
-
-
Cbrt - Class in neureka.backend.main.operations.functions
+
Cbrt - Class in neureka.backend.main.operations.functions
 
-
Cbrt() - Constructor for class neureka.backend.main.operations.functions.Cbrt
+
Cbrt() - Constructor for class neureka.backend.main.operations.functions.Cbrt
 
-
CBRT - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
cbrt() - Method in class neureka.math.Functions
 
-
change() - Method in class neureka.devices.ReferenceCounter.ChangeEvent
+
cbrt() - Method in interface neureka.Tensor
+
+
This method is a functionally identical to the following alternatives:
+
+
change() - Method in class neureka.devices.ReferenceCounter.ChangeEvent
 
-
ChangeEvent(ReferenceCounter.ChangeType, int, int) - Constructor for class neureka.devices.ReferenceCounter.ChangeEvent
+
ChangeEvent(ReferenceCounter.ChangeType, int, int) - Constructor for class neureka.devices.ReferenceCounter.ChangeEvent
 
-
check(Operation) - Method in interface neureka.backend.api.Call.OperationCondition
+
check(Device<?>) - Method in interface neureka.backend.api.Call.DeviceCondition
 
-
check(Device<?>) - Method in interface neureka.backend.api.Call.DeviceCondition
+
check(Operation) - Method in interface neureka.backend.api.Call.OperationCondition
 
-
check(Tensor<?>) - Method in interface neureka.backend.api.Call.TensorCondition
+
check(Tensor<?>, Tensor<?>) - Method in interface neureka.backend.api.Call.TensorCompare
 
-
check(Tensor<?>[]) - Method in interface neureka.backend.api.Call.TensorsCondition
+
check(Tensor<?>) - Method in interface neureka.backend.api.Call.TensorCondition
 
-
check(Tensor<?>, Tensor<?>) - Method in interface neureka.backend.api.Call.TensorCompare
+
check(Tensor<?>[]) - Method in interface neureka.backend.api.Call.TensorsCondition
 
-
checkArity() - Method in class neureka.backend.api.ExecutionCall
+
checkArity() - Method in class neureka.backend.api.ExecutionCall
 
-
childCount() - Method in class neureka.framing.Relation
+
childCount() - Method in class neureka.framing.Relation
 
-
CLBackend - Class in neureka.backend.ocl
+
CLBackend - Class in neureka.backend.ocl
This is an OpenCL context component for any given BackendContext which extends a given backend context instance for additional functionality, which in this case is the OpenCL backend storing platform and device information.
-
CLBackend() - Constructor for class neureka.backend.ocl.CLBackend
+
CLBackend() - Constructor for class neureka.backend.ocl.CLBackend
Use this constructor if you want to create a new OpenCL world in which there are unique OpenCLPlatform and OpenCLDevice instances.
-
CLBiElementwise - Class in neureka.backend.main.implementations.elementwise
+
CLBiElementwise - Class in neureka.backend.main.implementations.elementwise
 
-
CLBiElementwise(String, String, String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwise
+
CLBiElementwise(String, String, String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwise
 
-
CLBiElementwiseAddition - Class in neureka.backend.main.implementations.elementwise
+
CLBiElementwiseAddition - Class in neureka.backend.main.implementations.elementwise
 
-
CLBiElementwiseAddition(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwiseAddition
+
CLBiElementwiseAddition(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwiseAddition
 
-
CLBiElementwiseDivision - Class in neureka.backend.main.implementations.elementwise
+
CLBiElementwiseDivision - Class in neureka.backend.main.implementations.elementwise
 
-
CLBiElementwiseDivision(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwiseDivision
+
CLBiElementwiseDivision(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwiseDivision
 
-
CLBiElementwiseModulo - Class in neureka.backend.main.implementations.elementwise
+
CLBiElementwiseModulo - Class in neureka.backend.main.implementations.elementwise
 
-
CLBiElementwiseModulo(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwiseModulo
+
CLBiElementwiseModulo(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwiseModulo
 
-
CLBiElementwiseMultiplication - Class in neureka.backend.main.implementations.elementwise
+
CLBiElementwiseMultiplication - Class in neureka.backend.main.implementations.elementwise
 
-
CLBiElementwiseMultiplication(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwiseMultiplication
+
CLBiElementwiseMultiplication(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwiseMultiplication
 
-
CLBiElementwisePower - Class in neureka.backend.main.implementations.elementwise
+
CLBiElementwisePower - Class in neureka.backend.main.implementations.elementwise
 
-
CLBiElementwisePower(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwisePower
+
CLBiElementwisePower(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwisePower
 
-
CLBiElementwiseSubtraction - Class in neureka.backend.main.implementations.elementwise
+
CLBiElementwiseSubtraction - Class in neureka.backend.main.implementations.elementwise
 
-
CLBiElementwiseSubtraction(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwiseSubtraction
+
CLBiElementwiseSubtraction(String) - Constructor for class neureka.backend.main.implementations.elementwise.CLBiElementwiseSubtraction
 
-
CLBroadcast - Class in neureka.backend.main.implementations.broadcast
+
CLBroadcast - Class in neureka.backend.main.implementations.broadcast
 
-
CLBroadcast(String, String, String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcast
+
CLBroadcast(String, String, String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcast
 
-
CLBroadcastAddition - Class in neureka.backend.main.implementations.broadcast
+
CLBroadcastAddition - Class in neureka.backend.main.implementations.broadcast
 
-
CLBroadcastAddition(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastAddition
+
CLBroadcastAddition(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastAddition
 
-
CLBroadcastDivision - Class in neureka.backend.main.implementations.broadcast
+
CLBroadcastDivision - Class in neureka.backend.main.implementations.broadcast
 
-
CLBroadcastDivision(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastDivision
+
CLBroadcastDivision(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastDivision
 
-
CLBroadcastModulo - Class in neureka.backend.main.implementations.broadcast
+
CLBroadcastModulo - Class in neureka.backend.main.implementations.broadcast
 
-
CLBroadcastModulo(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastModulo
+
CLBroadcastModulo(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastModulo
 
-
CLBroadcastMultiplication - Class in neureka.backend.main.implementations.broadcast
+
CLBroadcastMultiplication - Class in neureka.backend.main.implementations.broadcast
 
-
CLBroadcastMultiplication(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastMultiplication
+
CLBroadcastMultiplication(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastMultiplication
 
-
CLBroadcastPower - Class in neureka.backend.main.implementations.broadcast
+
CLBroadcastPower - Class in neureka.backend.main.implementations.broadcast
 
-
CLBroadcastPower(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastPower
+
CLBroadcastPower(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastPower
 
-
CLBroadcastSubtraction - Class in neureka.backend.main.implementations.broadcast
+
CLBroadcastSubtraction - Class in neureka.backend.main.implementations.broadcast
 
-
CLBroadcastSubtraction(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastSubtraction
+
CLBroadcastSubtraction(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLBroadcastSubtraction
 
-
clConfigOf(NDConfiguration) - Method in class neureka.devices.opencl.OpenCLDevice
+
clConfigOf(Tensor<?>) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
clConfigOf(Tensor<?>) - Method in class neureka.devices.opencl.OpenCLDevice
+
clConfigOf(NDConfiguration) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
clContextCouldNotFindAnyDevices() - Static method in class neureka.devices.opencl.utility.Messages
+
clContextCouldNotFindAnyDevices() - Static method in class neureka.devices.opencl.utility.Messages
 
-
clContextCreationFailed() - Static method in class neureka.devices.opencl.utility.Messages
+
clContextCreationFailed() - Static method in class neureka.devices.opencl.utility.Messages
 
-
CLConvolution - Class in neureka.backend.main.implementations.convolution
+
CLConvolution - Class in neureka.backend.main.implementations.convolution
 
-
CLConvolution(String) - Constructor for class neureka.backend.main.implementations.convolution.CLConvolution
+
CLConvolution(String) - Constructor for class neureka.backend.main.implementations.convolution.CLConvolution
 
-
CLDot - Class in neureka.backend.main.implementations.linear
+
CLDot - Class in neureka.backend.main.implementations.linear
Performs a dot product on two vectors using OpenCL.
-
CLDot() - Constructor for class neureka.backend.main.implementations.linear.CLDot
+
CLDot() - Constructor for class neureka.backend.main.implementations.linear.CLDot
 
-
cleanedHeadAndTail(String) - Static method in class neureka.math.parsing.ParseUtil
+
cleanedHeadAndTail(String) - Static method in class neureka.math.parsing.ParseUtil
 
-
cleanup(Runnable) - Method in interface neureka.devices.Device.Access
+
cleanup(Runnable) - Method in interface neureka.devices.Device.Access
Use this to perform some custom memory cleanup for when the accessed Tensor gets garbage collected.
-
CLElementwiseFunction - Class in neureka.backend.main.implementations.elementwise
+
CLElementwiseFunction - Class in neureka.backend.main.implementations.elementwise
 
-
CLElementwiseFunction(ScalarFun) - Constructor for class neureka.backend.main.implementations.elementwise.CLElementwiseFunction
+
CLElementwiseFunction(ScalarFun) - Constructor for class neureka.backend.main.implementations.elementwise.CLElementwiseFunction
 
-
CLFunctionCompiler - Class in neureka.devices.opencl.utility
+
CLFunctionCompiler - Class in neureka.devices.opencl.utility
Turns a Function into OpenCL kernel code to make optimized just in time compilation possible.
-
CLFunctionCompiler(OpenCLDevice, Function, String) - Constructor for class neureka.devices.opencl.utility.CLFunctionCompiler
+
CLFunctionCompiler(OpenCLDevice, Function, String) - Constructor for class neureka.devices.opencl.utility.CLFunctionCompiler
 
-
CLGEMM - Class in neureka.backend.main.operations.linear.internal.opencl
+
CLGEMM - Class in neureka.backend.main.operations.linear.internal.opencl
 
-
CLGEMM() - Constructor for class neureka.backend.main.operations.linear.internal.opencl.CLGEMM
+
CLGEMM() - Constructor for class neureka.backend.main.operations.linear.internal.opencl.CLGEMM
 
-
CLImplementation - Class in neureka.backend.main.implementations
+
CLImplementation - Class in neureka.backend.main.implementations
This class is the ExecutorFor < OpenCLDevice > implementation used to properly call an OpenCLDevice instance via the ExecutionOn < OpenCLDevice > lambda implementation receiving an instance of the ExecutionCall class.
-
CLImplementation(ImplementationFor<OpenCLDevice>, int) - Constructor for class neureka.backend.main.implementations.CLImplementation
+
CLImplementation(ImplementationFor<OpenCLDevice>, int) - Constructor for class neureka.backend.main.implementations.CLImplementation
 
-
CLMatMul - Class in neureka.backend.main.implementations.matmul
+
CLMatMul - Class in neureka.backend.main.implementations.matmul
 
-
CLMatMul() - Constructor for class neureka.backend.main.implementations.matmul.CLMatMul
+
CLMatMul() - Constructor for class neureka.backend.main.implementations.matmul.CLMatMul
 
-
clone() - Method in class neureka.backend.api.BackendContext
+
clone() - Method in class neureka.backend.api.BackendContext
This method produces a shallow copy of this BackendContext.
-
clone() - Method in class neureka.view.NDPrintSettings
+
clone() - Method in class neureka.view.NDPrintSettings
 
-
CLRandomization - Class in neureka.backend.main.implementations.elementwise
+
CLRandomization - Class in neureka.backend.main.implementations.elementwise
 
-
CLRandomization() - Constructor for class neureka.backend.main.implementations.elementwise.CLRandomization
+
CLRandomization() - Constructor for class neureka.backend.main.implementations.elementwise.CLRandomization
 
-
CLReduce - Class in neureka.backend.main.operations.linear.internal.opencl
+
CLReduce - Class in neureka.backend.main.operations.linear.internal.opencl
 
-
CLReduce(CLReduce.Type) - Constructor for class neureka.backend.main.operations.linear.internal.opencl.CLReduce
+
CLReduce(CLReduce.Type) - Constructor for class neureka.backend.main.operations.linear.internal.opencl.CLReduce
 
-
CLReduce.Type - Enum Class in neureka.backend.main.operations.linear.internal.opencl
+
CLReduce.Type - Enum in neureka.backend.main.operations.linear.internal.opencl
 
-
CLScalarBroadcast - Class in neureka.backend.main.implementations.broadcast
+
CLScalarBroadcast - Class in neureka.backend.main.implementations.broadcast
 
-
CLScalarBroadcast(String, String, String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcast
+
CLScalarBroadcast(String, String, String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcast
 
-
CLScalarBroadcastAddition - Class in neureka.backend.main.implementations.broadcast
+
CLScalarBroadcastAddition - Class in neureka.backend.main.implementations.broadcast
 
-
CLScalarBroadcastAddition(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastAddition
+
CLScalarBroadcastAddition(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastAddition
 
-
CLScalarBroadcastDivision - Class in neureka.backend.main.implementations.broadcast
+
CLScalarBroadcastDivision - Class in neureka.backend.main.implementations.broadcast
 
-
CLScalarBroadcastDivision(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastDivision
+
CLScalarBroadcastDivision(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastDivision
 
-
CLScalarBroadcastIdentity - Class in neureka.backend.main.implementations.broadcast
+
CLScalarBroadcastIdentity - Class in neureka.backend.main.implementations.broadcast
 
-
CLScalarBroadcastIdentity(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastIdentity
+
CLScalarBroadcastIdentity(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastIdentity
 
-
CLScalarBroadcastModulo - Class in neureka.backend.main.implementations.broadcast
+
CLScalarBroadcastModulo - Class in neureka.backend.main.implementations.broadcast
 
-
CLScalarBroadcastModulo(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastModulo
+
CLScalarBroadcastModulo(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastModulo
 
-
CLScalarBroadcastMultiplication - Class in neureka.backend.main.implementations.broadcast
+
CLScalarBroadcastMultiplication - Class in neureka.backend.main.implementations.broadcast
 
-
CLScalarBroadcastMultiplication(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastMultiplication
+
CLScalarBroadcastMultiplication(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastMultiplication
 
-
CLScalarBroadcastPower - Class in neureka.backend.main.implementations.broadcast
+
CLScalarBroadcastPower - Class in neureka.backend.main.implementations.broadcast
 
-
CLScalarBroadcastPower(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastPower
+
CLScalarBroadcastPower(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastPower
 
-
CLScalarBroadcastSubtraction - Class in neureka.backend.main.implementations.broadcast
+
CLScalarBroadcastSubtraction - Class in neureka.backend.main.implementations.broadcast
 
-
CLScalarBroadcastSubtraction(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastSubtraction
+
CLScalarBroadcastSubtraction(String) - Constructor for class neureka.backend.main.implementations.broadcast.CLScalarBroadcastSubtraction
 
-
CLScalarFunction - Class in neureka.backend.main.implementations.scalar
+
CLScalarFunction - Class in neureka.backend.main.implementations.scalar
 
-
CLScalarFunction(ScalarFun) - Constructor for class neureka.backend.main.implementations.scalar.CLScalarFunction
+
CLScalarFunction(ScalarFun) - Constructor for class neureka.backend.main.implementations.scalar.CLScalarFunction
 
-
CLSettings - Class in neureka.backend.ocl
+
CLSettings - Class in neureka.backend.ocl
OpenCL related settings for the CLBackend extension.
-
CLSettings() - Constructor for class neureka.backend.ocl.CLSettings
-
 
-
CLSum - Class in neureka.backend.main.operations.linear.internal.opencl
-
 
-
CLSum() - Constructor for class neureka.backend.main.operations.linear.internal.opencl.CLSum
+
CLSettings() - Constructor for class neureka.backend.ocl.CLSettings
 
-
COL_MAJOR - Enum constant in enum class neureka.ndim.config.NDTrait
+
CLSum - Class in neureka.backend.main.operations.linear.internal.opencl
 
-
COLUMN_MAJOR - Enum constant in enum class neureka.ndim.config.NDConfiguration.Layout
+
CLSum() - Constructor for class neureka.backend.main.operations.linear.internal.opencl.CLSum
 
-
CommonMachine - Class in neureka.devices.host.machine
+
CommonMachine - Class in neureka.devices.host.machine
Stuff common to Hardware and ConcreteMachine.
-
CommonMachine(String, BasicMachine[]) - Constructor for class neureka.devices.host.machine.CommonMachine
+
CommonMachine(Hardware, Runtime) - Constructor for class neureka.devices.host.machine.CommonMachine
+
 
+
CommonMachine(String, BasicMachine[]) - Constructor for class neureka.devices.host.machine.CommonMachine
new MemoryThreads[] { SYSTEM, L3, L2, L1 } or new MemoryThreads[] { SYSTEM, L2, L1 } or in worst case new MemoryThreads[] { SYSTEM, L1 }
-
CommonMachine(Hardware, Runtime) - Constructor for class neureka.devices.host.machine.CommonMachine
-
 
-
COMPACT - Enum constant in enum class neureka.autograd.GraphNode.Print
-
 
-
COMPACT - Enum constant in enum class neureka.ndim.config.NDTrait
-
 
-
compareTo(Hardware) - Method in class neureka.devices.host.machine.Hardware
+
compareTo(Hardware) - Method in class neureka.devices.host.machine.Hardware
 
-
compileAdHocKernel(String, String) - Method in class neureka.devices.opencl.OpenCLDevice
+
compileAdHocKernel(String, String) - Method in class neureka.devices.opencl.OpenCLDevice
This method compiles so called "ad hoc" kernel.
-
compileAndGetAdHocKernel(String, String) - Method in class neureka.devices.opencl.OpenCLDevice
+
compileAndGetAdHocKernel(String, String) - Method in class neureka.devices.opencl.OpenCLDevice
This method compiles and returns the KernelCaller for a so called "ad hoc" kernel.
-
Component<O> - Interface in neureka.common.composition
+
Component<O> - Interface in neureka.common.composition
This interface alongside the AbstractComponentOwner class define a simple component system.
-
Component.IsBeing - Enum Class in neureka.common.composition
+
Component.IsBeing - Enum in neureka.common.composition
Entries of this enum represent events describing updates to the state of the owner of a given Component instance.
-
Component.OwnerChangeRequest<O> - Interface in neureka.common.composition
+
Component.OwnerChangeRequest<O> - Interface in neureka.common.composition
Component.OwnerChangeRequest implementation instances will be passed to - the Component.update(OwnerChangeRequest) method which inform a + the Component.update(OwnerChangeRequest) method which inform a given component about a state change related to said component.
-
ComponentOwner<C> - Interface in neureka.common.composition
+
ComponentOwner<C> - Interface in neureka.common.composition
A component owner is a thing holding components which can be accessed by their type class.
-
concat() - Method in class neureka.math.Functions
+
concat() - Method in class neureka.math.Functions
 
-
concatAt(int, Nda<V>) - Method in interface neureka.Nda
+
concatAt(int, Nda<V>, Nda<V>...) - Method in interface neureka.Nda
-
This method concatenates the provided nd-array together with this nd-array along a specified axis.
+
This method concatenates the provided nd-arrays together with this nd-array along a specified axis.
-
concatAt(int, Nda<V>) - Method in interface neureka.Tensor
+
concatAt(int, Nda<V>) - Method in interface neureka.Nda
This method concatenates the provided nd-array together with this nd-array along a specified axis.
-
concatAt(int, Nda<V>, Nda<V>...) - Method in interface neureka.Nda
+
concatAt(int, Nda<V>, Nda<V>...) - Method in interface neureka.Tensor
This method concatenates the provided nd-arrays together with this nd-array along a specified axis.
-
concatAt(int, Nda<V>, Nda<V>...) - Method in interface neureka.Tensor
+
concatAt(int, Nda<V>) - Method in interface neureka.Tensor
-
This method concatenates the provided nd-arrays together with this nd-array along a specified axis.
+
This method concatenates the provided nd-array together with this nd-array along a specified axis.
-
ConcreteMachine - Class in neureka.devices.host.machine
+
ConcreteMachine - Class in neureka.devices.host.machine
 
-
confidence() - Method in class neureka.backend.api.BackendExtension.DeviceOption
+
confidence() - Method in class neureka.backend.api.BackendExtension.DeviceOption
 
-
configure(Object) - Static method in class neureka.Neureka
+
configure(Object) - Static method in class neureka.Neureka
This allows you to configure Neureka using a Groovy DSL.
-
construct() - Static method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
construct(int[], int[], int[]) - Static method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
 
-
construct(int[]) - Static method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
construct(int[], int[], int[]) - Static method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
 
-
construct(int[], int[]) - Static method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
construct(int[], int[], int[]) - Static method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
 
-
construct(int[], int[]) - Static method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
construct(int[], int[], int[]) - Static method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
 
-
construct(int[], int[]) - Static method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
construct() - Static method in class neureka.ndim.config.types.simple.Simple0DConfiguration
 
-
construct(int[], int[]) - Static method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
construct(int[], int[]) - Static method in class neureka.ndim.config.types.simple.Simple1DConfiguration
 
-
construct(int[], int[]) - Static method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
construct(int[], int[]) - Static method in class neureka.ndim.config.types.simple.Simple2DConfiguration
 
-
construct(int[], int[], int[]) - Static method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
construct(int[], int[]) - Static method in class neureka.ndim.config.types.simple.Simple3DConfiguration
 
-
construct(int[], int[], int[]) - Static method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
construct(int[], int[]) - Static method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
 
-
construct(int[], int[], int[]) - Static method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
construct(int[], int[]) - Static method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
 
-
construct(int[], int[], int[]) - Static method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
construct(int[], int[], int[], int[], int[]) - Static method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
 
-
construct(int[], int[], int[], int[], int[]) - Static method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
construct(int[], int[], int[], int[], int[]) - Static method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
 
-
construct(int[], int[], int[], int[], int[]) - Static method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
construct(int[], int[], int[], int[], int[]) - Static method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
 
-
construct(int[], int[], int[], int[], int[]) - Static method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
construct(int[], int[], int[], int[], int[]) - Static method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
 
-
construct(int[], int[], int[], int[], int[]) - Static method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
construct(int[]) - Static method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
 
-
contains(Tensor<V>) - Method in class neureka.devices.AbstractBaseDevice
+
contains(Tensor<V>) - Method in class neureka.devices.AbstractBaseDevice
 
-
contains(Tensor<Number>) - Method in class neureka.devices.file.IDXHandle
+
contains(Tensor<V>) - Method in interface neureka.devices.Storage
 
-
contains(Tensor<V>) - Method in interface neureka.devices.Storage
-
 
-
contains(Tensor<V>) - Method in interface neureka.Tensor
+
contains(Tensor<V>) - Method in interface neureka.Tensor
This method name translates to the "in" keyword in Kotlin! The same is true for the "isCase" method in Groovy.
-
CONTINUOUS_MATRIX - Enum constant in enum class neureka.ndim.config.NDTrait
-
 
-
conv() - Method in class neureka.math.Functions
+
conv() - Method in class neureka.math.Functions
 
-
conv(Tensor<V>) - Method in interface neureka.Tensor
+
conv(Tensor<V>) - Method in interface neureka.Tensor
This method performs convolution between this tensor and the one passed as argument.
-
convDot(Tensor<V>) - Method in interface neureka.Tensor
+
convDot(Tensor<V>) - Method in interface neureka.Tensor
This method performs a convolutional based dot product between the last dimension of this tensor and the first dimension of the passed tensor.
-
convert(Object, Class<T>) - Method in class neureka.common.utility.DataConverter
+
convert(Object, Class<T>) - Method in class neureka.common.utility.DataConverter
This method embodies the purpose of this class.
-
convertToHolder(Object) - Method in class neureka.dtype.custom.F32
+
convertToHolder(Object) - Method in class neureka.dtype.custom.F32
 
-
convertToHolder(Object) - Method in class neureka.dtype.custom.F64
+
convertToHolder(Object) - Method in class neureka.dtype.custom.F64
 
-
convertToHolder(Object) - Method in class neureka.dtype.custom.I16
+
convertToHolder(Object) - Method in class neureka.dtype.custom.I16
 
-
convertToHolder(Object) - Method in class neureka.dtype.custom.I32
+
convertToHolder(Object) - Method in class neureka.dtype.custom.I32
 
-
convertToHolder(Object) - Method in class neureka.dtype.custom.I64
+
convertToHolder(Object) - Method in class neureka.dtype.custom.I64
 
-
convertToHolder(Object) - Method in class neureka.dtype.custom.I8
+
convertToHolder(Object) - Method in class neureka.dtype.custom.I8
 
-
convertToHolder(Object) - Method in class neureka.dtype.custom.UI16
+
convertToHolder(Object) - Method in class neureka.dtype.custom.UI16
 
-
convertToHolder(Object) - Method in class neureka.dtype.custom.UI32
+
convertToHolder(Object) - Method in class neureka.dtype.custom.UI32
 
-
convertToHolder(Object) - Method in class neureka.dtype.custom.UI64
+
convertToHolder(Object) - Method in class neureka.dtype.custom.UI64
 
-
convertToHolder(Object) - Method in class neureka.dtype.custom.UI8
+
convertToHolder(Object) - Method in class neureka.dtype.custom.UI8
 
-
convertToHolder(Object) - Method in interface neureka.dtype.NumericType
+
convertToHolder(Object) - Method in interface neureka.dtype.NumericType
This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
-
convertToHolderArray(Object) - Method in class neureka.dtype.custom.F32
+
convertToHolderArray(Object) - Method in class neureka.dtype.custom.F32
 
-
convertToHolderArray(Object) - Method in class neureka.dtype.custom.F64
+
convertToHolderArray(Object) - Method in class neureka.dtype.custom.F64
 
-
convertToHolderArray(Object) - Method in class neureka.dtype.custom.I16
+
convertToHolderArray(Object) - Method in class neureka.dtype.custom.I16
 
-
convertToHolderArray(Object) - Method in class neureka.dtype.custom.I32
+
convertToHolderArray(Object) - Method in class neureka.dtype.custom.I32
 
-
convertToHolderArray(Object) - Method in class neureka.dtype.custom.I64
+
convertToHolderArray(Object) - Method in class neureka.dtype.custom.I64
 
-
convertToHolderArray(Object) - Method in class neureka.dtype.custom.I8
+
convertToHolderArray(Object) - Method in class neureka.dtype.custom.I8
 
-
convertToHolderArray(Object) - Method in class neureka.dtype.custom.UI16
+
convertToHolderArray(Object) - Method in class neureka.dtype.custom.UI16
 
-
convertToHolderArray(Object) - Method in class neureka.dtype.custom.UI32
+
convertToHolderArray(Object) - Method in class neureka.dtype.custom.UI32
 
-
convertToHolderArray(Object) - Method in class neureka.dtype.custom.UI64
+
convertToHolderArray(Object) - Method in class neureka.dtype.custom.UI64
 
-
convertToHolderArray(Object) - Method in class neureka.dtype.custom.UI8
+
convertToHolderArray(Object) - Method in class neureka.dtype.custom.UI8
 
-
convertToHolderArray(Object) - Method in interface neureka.dtype.NumericType
+
convertToHolderArray(Object) - Method in interface neureka.dtype.NumericType
This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
-
convertToTarget(Object) - Method in class neureka.dtype.custom.F32
+
convertToTarget(Object) - Method in class neureka.dtype.custom.F32
 
-
convertToTarget(Object) - Method in class neureka.dtype.custom.F64
+
convertToTarget(Object) - Method in class neureka.dtype.custom.F64
 
-
convertToTarget(Object) - Method in class neureka.dtype.custom.I16
+
convertToTarget(Object) - Method in class neureka.dtype.custom.I16
 
-
convertToTarget(Object) - Method in class neureka.dtype.custom.I32
+
convertToTarget(Object) - Method in class neureka.dtype.custom.I32
 
-
convertToTarget(Object) - Method in class neureka.dtype.custom.I64
+
convertToTarget(Object) - Method in class neureka.dtype.custom.I64
 
-
convertToTarget(Object) - Method in class neureka.dtype.custom.I8
+
convertToTarget(Object) - Method in class neureka.dtype.custom.I8
 
-
convertToTarget(Object) - Method in class neureka.dtype.custom.UI16
+
convertToTarget(Object) - Method in class neureka.dtype.custom.UI16
 
-
convertToTarget(Object) - Method in class neureka.dtype.custom.UI32
+
convertToTarget(Object) - Method in class neureka.dtype.custom.UI32
 
-
convertToTarget(Object) - Method in class neureka.dtype.custom.UI64
+
convertToTarget(Object) - Method in class neureka.dtype.custom.UI64
 
-
convertToTarget(Object) - Method in class neureka.dtype.custom.UI8
+
convertToTarget(Object) - Method in class neureka.dtype.custom.UI8
 
-
convertToTarget(Object) - Method in interface neureka.dtype.NumericType
+
convertToTarget(Object) - Method in interface neureka.dtype.NumericType
This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
-
convertToTargetArray(Object) - Method in class neureka.dtype.custom.F32
+
convertToTargetArray(Object) - Method in class neureka.dtype.custom.F32
 
-
convertToTargetArray(Object) - Method in class neureka.dtype.custom.F64
+
convertToTargetArray(Object) - Method in class neureka.dtype.custom.F64
 
-
convertToTargetArray(Object) - Method in class neureka.dtype.custom.I16
+
convertToTargetArray(Object) - Method in class neureka.dtype.custom.I16
 
-
convertToTargetArray(Object) - Method in class neureka.dtype.custom.I32
+
convertToTargetArray(Object) - Method in class neureka.dtype.custom.I32
 
-
convertToTargetArray(Object) - Method in class neureka.dtype.custom.I64
+
convertToTargetArray(Object) - Method in class neureka.dtype.custom.I64
 
-
convertToTargetArray(Object) - Method in class neureka.dtype.custom.I8
+
convertToTargetArray(Object) - Method in class neureka.dtype.custom.I8
 
-
convertToTargetArray(Object) - Method in class neureka.dtype.custom.UI16
+
convertToTargetArray(Object) - Method in class neureka.dtype.custom.UI16
 
-
convertToTargetArray(Object) - Method in class neureka.dtype.custom.UI32
+
convertToTargetArray(Object) - Method in class neureka.dtype.custom.UI32
 
-
convertToTargetArray(Object) - Method in class neureka.dtype.custom.UI64
+
convertToTargetArray(Object) - Method in class neureka.dtype.custom.UI64
 
-
convertToTargetArray(Object) - Method in class neureka.dtype.custom.UI8
+
convertToTargetArray(Object) - Method in class neureka.dtype.custom.UI8
 
-
convertToTargetArray(Object) - Method in interface neureka.dtype.NumericType
+
convertToTargetArray(Object) - Method in interface neureka.dtype.NumericType
This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
-
Convolution - Class in neureka.backend.main.operations.linear
+
Convolution - Class in neureka.backend.main.operations.linear
 
-
Convolution() - Constructor for class neureka.backend.main.operations.linear.Convolution
+
Convolution() - Constructor for class neureka.backend.main.operations.linear.Convolution
 
-
ConvUtil - Class in neureka.backend.main.operations
+
ConvUtil - Class in neureka.backend.main.operations
 
-
ConvUtil() - Constructor for class neureka.backend.main.operations.ConvUtil
+
ConvUtil() - Constructor for class neureka.backend.main.operations.ConvUtil
 
-
COPY - Class in neureka.backend.main.operations.linear.internal.blas
+
COPY - Class in neureka.backend.main.operations.linear.internal.blas
The ?copy routines perform a vector-vector operation defined as y = x, where x and y are vectors.
-
COPY() - Constructor for class neureka.backend.main.operations.linear.internal.blas.COPY
+
COPY() - Constructor for class neureka.backend.main.operations.linear.internal.blas.COPY
 
-
copyOf(T[]) - Static method in class neureka.backend.main.operations.linear.internal.blas.COPY
+
copyOf(T[]) - Static method in class neureka.backend.main.operations.linear.internal.blas.COPY
 
-
cores - Variable in class neureka.devices.host.machine.CommonMachine
+
cores - Variable in class neureka.devices.host.machine.CommonMachine
The total number of processor cores.
-
CORES - Enum constant in enum class neureka.devices.host.concurrent.Parallelism
-
-
The number of CPU cores
-
-
cos() - Method in class neureka.math.Functions
+
cos() - Method in class neureka.math.Functions
 
-
cos() - Method in interface neureka.Tensor
+
cos() - Method in interface neureka.Tensor
This method is a functionally identical to the following alternatives:
-
Cosinus - Class in neureka.backend.main.operations.functions
+
COSINUS - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
Cosinus() - Constructor for class neureka.backend.main.operations.functions.Cosinus
+
Cosinus - Class in neureka.backend.main.operations.functions
 
-
COSINUS - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
Cosinus() - Constructor for class neureka.backend.main.operations.functions.Cosinus
 
-
count() - Method in class neureka.devices.ReferenceCounter
+
count() - Method in class neureka.devices.ReferenceCounter
 
-
count(Predicate<Integer>) - Method in interface neureka.Shape
-
 
-
count(Predicate<V>) - Method in interface neureka.Nda
+
count(Predicate<V>) - Method in interface neureka.Nda
Iterates over every element of this nd-array, and counts the number of times the provided lambda matches the items of this array.
-
CPU - Class in neureka.devices.host
+
count(Predicate<Integer>) - Method in interface neureka.Shape
+
 
+
CPU - Class in neureka.devices.host
The CPU class, one of many implementations of the Device interface, is simply supposed to be an API for dispatching threaded workloads onto the CPU as well as reading from or writing to tensors it stores.
-
CPU - Enum constant in enum class neureka.devices.opencl.OpenCLDevice.Type
+
CPU.IndexedWorkload - Interface in neureka.devices.host
 
-
CPU.IndexedWorkload - Interface in neureka.devices.host
-
 
-
CPU.JVMExecutor - Class in neureka.devices.host
+
CPU.JVMExecutor - Class in neureka.devices.host
The CPU.JVMExecutor offers a similar functionality as the parallel stream API, however it differs in that the CPU.JVMExecutor is processing CPU.RangeWorkload lambdas instead of simply exposing a single index or concrete elements for a given workload size.
-
CPU.RangeWorkload - Interface in neureka.devices.host
+
CPU.RangeWorkload - Interface in neureka.devices.host
A simple functional interface for executing a range whose implementations will either be executed sequentially or they are being dispatched to a thread-pool, given that the provided workload is large enough.
-
CPUBackend - Class in neureka.backend.cpu
+
CPUBackend - Class in neureka.backend.cpu
This class loads the CPU operations into the Neureka library context.
-
CPUBackend() - Constructor for class neureka.backend.cpu.CPUBackend
+
CPUBackend() - Constructor for class neureka.backend.cpu.CPUBackend
 
-
CPUBiElementWise - Class in neureka.backend.main.implementations.elementwise
+
CPUBiElementWise - Class in neureka.backend.main.implementations.elementwise
 
-
CPUBiElementWise() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWise
+
CPUBiElementWise() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWise
 
-
CPUBiElementWiseAddition - Class in neureka.backend.main.implementations.elementwise
+
CPUBiElementWiseAddition - Class in neureka.backend.main.implementations.elementwise
 
-
CPUBiElementWiseAddition() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWiseAddition
+
CPUBiElementWiseAddition() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWiseAddition
 
-
CPUBiElementWiseDivision - Class in neureka.backend.main.implementations.elementwise
+
CPUBiElementWiseDivision - Class in neureka.backend.main.implementations.elementwise
 
-
CPUBiElementWiseDivision() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWiseDivision
+
CPUBiElementWiseDivision() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWiseDivision
 
-
CPUBiElementWiseModulo - Class in neureka.backend.main.implementations.elementwise
+
CPUBiElementWiseModulo - Class in neureka.backend.main.implementations.elementwise
 
-
CPUBiElementWiseModulo() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWiseModulo
+
CPUBiElementWiseModulo() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWiseModulo
 
-
CPUBiElementWiseMultiplication - Class in neureka.backend.main.implementations.elementwise
+
CPUBiElementWiseMultiplication - Class in neureka.backend.main.implementations.elementwise
 
-
CPUBiElementWiseMultiplication() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWiseMultiplication
+
CPUBiElementWiseMultiplication() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWiseMultiplication
 
-
CPUBiElementWisePower - Class in neureka.backend.main.implementations.elementwise
+
CPUBiElementWisePower - Class in neureka.backend.main.implementations.elementwise
 
-
CPUBiElementWisePower() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWisePower
+
CPUBiElementWisePower() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWisePower
 
-
CPUBiElementWiseSubtraction - Class in neureka.backend.main.implementations.elementwise
+
CPUBiElementWiseSubtraction - Class in neureka.backend.main.implementations.elementwise
 
-
CPUBiElementWiseSubtraction() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWiseSubtraction
+
CPUBiElementWiseSubtraction() - Constructor for class neureka.backend.main.implementations.elementwise.CPUBiElementWiseSubtraction
 
-
CPUBiFun - Interface in neureka.backend.main.implementations.fun.api
+
CPUBiFun - Interface in neureka.backend.main.implementations.fun.api
 
-
CPUBroadcast - Class in neureka.backend.main.implementations.broadcast
+
CPUBroadcast - Class in neureka.backend.main.implementations.broadcast
 
-
CPUBroadcast() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcast
+
CPUBroadcast() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcast
 
-
CPUBroadcastAddition - Class in neureka.backend.main.implementations.broadcast
+
CPUBroadcastAddition - Class in neureka.backend.main.implementations.broadcast
 
-
CPUBroadcastAddition() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastAddition
+
CPUBroadcastAddition() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastAddition
 
-
CPUBroadcastDivision - Class in neureka.backend.main.implementations.broadcast
+
CPUBroadcastDivision - Class in neureka.backend.main.implementations.broadcast
 
-
CPUBroadcastDivision() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastDivision
+
CPUBroadcastDivision() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastDivision
 
-
CPUBroadcastModulo - Class in neureka.backend.main.implementations.broadcast
+
CPUBroadcastModulo - Class in neureka.backend.main.implementations.broadcast
 
-
CPUBroadcastModulo() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastModulo
+
CPUBroadcastModulo() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastModulo
 
-
CPUBroadcastMultiplication - Class in neureka.backend.main.implementations.broadcast
+
CPUBroadcastMultiplication - Class in neureka.backend.main.implementations.broadcast
 
-
CPUBroadcastMultiplication() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastMultiplication
+
CPUBroadcastMultiplication() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastMultiplication
 
-
CPUBroadcastPower - Class in neureka.backend.main.implementations.broadcast
+
CPUBroadcastPower - Class in neureka.backend.main.implementations.broadcast
 
-
CPUBroadcastPower() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastPower
+
CPUBroadcastPower() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastPower
 
-
CPUBroadcastSubtraction - Class in neureka.backend.main.implementations.broadcast
+
CPUBroadcastSubtraction - Class in neureka.backend.main.implementations.broadcast
 
-
CPUBroadcastSubtraction() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastSubtraction
+
CPUBroadcastSubtraction() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastSubtraction
 
-
CPUBroadcastSummation - Class in neureka.backend.main.implementations.broadcast
+
CPUBroadcastSummation - Class in neureka.backend.main.implementations.broadcast
 
-
CPUBroadcastSummation() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastSummation
+
CPUBroadcastSummation() - Constructor for class neureka.backend.main.implementations.broadcast.CPUBroadcastSummation
 
-
CPUConvolution - Class in neureka.backend.main.implementations.convolution
+
CPUConvolution - Class in neureka.backend.main.implementations.convolution
 
-
CPUConvolution() - Constructor for class neureka.backend.main.implementations.convolution.CPUConvolution
+
CPUConvolution() - Constructor for class neureka.backend.main.implementations.convolution.CPUConvolution
 
-
CPUDot - Class in neureka.backend.main.implementations.linear
+
CPUDot - Class in neureka.backend.main.implementations.linear
 
-
CPUDot() - Constructor for class neureka.backend.main.implementations.linear.CPUDot
+
CPUDot() - Constructor for class neureka.backend.main.implementations.linear.CPUDot
 
-
CPUElementwiseAssignFun - Class in neureka.backend.main.implementations.elementwise
+
CPUElementwiseAssignFun - Class in neureka.backend.main.implementations.elementwise
 
-
CPUElementwiseAssignFun() - Constructor for class neureka.backend.main.implementations.elementwise.CPUElementwiseAssignFun
+
CPUElementwiseAssignFun() - Constructor for class neureka.backend.main.implementations.elementwise.CPUElementwiseAssignFun
 
-
CPUElementwiseFunction - Class in neureka.backend.main.implementations.elementwise
+
CPUElementwiseFunction - Class in neureka.backend.main.implementations.elementwise
 
-
CPUElementwiseFunction(ScalarFun) - Constructor for class neureka.backend.main.implementations.elementwise.CPUElementwiseFunction
+
CPUElementwiseFunction(ScalarFun) - Constructor for class neureka.backend.main.implementations.elementwise.CPUElementwiseFunction
 
-
CPUFun - Interface in neureka.backend.main.implementations.fun.api
+
CPUFun - Interface in neureka.backend.main.implementations.fun.api
 
-
CPUImplementation - Class in neureka.backend.main.implementations
+
CPUImplementation - Class in neureka.backend.main.implementations
This class is a wrapper class for the ImplementationFor<CPU> interface which enables a functional style of implementing the backend API!
It is used merely as a simple formality and implementation type specification.
-
CPUImplementation.AndImplementation - Interface in neureka.backend.main.implementations
+
CPUImplementation.AndImplementation - Interface in neureka.backend.main.implementations
This is represents the second step in the simple builder API for CPUImplementation instances.
-
CPUMatMul - Class in neureka.backend.main.implementations.matmul
+
CPUMatMul - Class in neureka.backend.main.implementations.matmul
This is a library internal class, do not depend on this.
-
CPUMatMul() - Constructor for class neureka.backend.main.implementations.matmul.CPUMatMul
+
CPUMatMul() - Constructor for class neureka.backend.main.implementations.matmul.CPUMatMul
 
-
CPURandomization - Class in neureka.backend.main.implementations.elementwise
+
CPURandomization - Class in neureka.backend.main.implementations.elementwise
 
-
CPURandomization() - Constructor for class neureka.backend.main.implementations.elementwise.CPURandomization
+
CPURandomization() - Constructor for class neureka.backend.main.implementations.elementwise.CPURandomization
 
-
CPUReduce - Class in neureka.backend.main.operations.other.internal
+
CPUReduce - Class in neureka.backend.main.operations.other.internal
An implementation of the min and max algorithm running on the CPU.
-
CPUReduce(CPUReduce.Type) - Constructor for class neureka.backend.main.operations.other.internal.CPUReduce
+
CPUReduce(CPUReduce.Type) - Constructor for class neureka.backend.main.operations.other.internal.CPUReduce
 
-
CPUReduce.Type - Enum Class in neureka.backend.main.operations.other.internal
+
CPUReduce.Type - Enum in neureka.backend.main.operations.other.internal
 
-
CPUScalaBroadcastPower - Class in neureka.backend.main.implementations.broadcast
+
CPUScalaBroadcastPower - Class in neureka.backend.main.implementations.broadcast
 
-
CPUScalaBroadcastPower() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalaBroadcastPower
+
CPUScalaBroadcastPower() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalaBroadcastPower
 
-
CPUScalarBroadcast - Class in neureka.backend.main.implementations.broadcast
+
CPUScalarBroadcast - Class in neureka.backend.main.implementations.broadcast
 
-
CPUScalarBroadcast() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
+
CPUScalarBroadcast() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
 
-
CPUScalarBroadcastAddition - Class in neureka.backend.main.implementations.broadcast
+
CPUScalarBroadcastAddition - Class in neureka.backend.main.implementations.broadcast
 
-
CPUScalarBroadcastAddition() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
+
CPUScalarBroadcastAddition() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
 
-
CPUScalarBroadcastDivision - Class in neureka.backend.main.implementations.broadcast
+
CPUScalarBroadcastDivision - Class in neureka.backend.main.implementations.broadcast
 
-
CPUScalarBroadcastDivision() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastDivision
+
CPUScalarBroadcastDivision() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastDivision
 
-
CPUScalarBroadcastFunction - Class in neureka.backend.main.implementations.scalar
+
CPUScalarBroadcastFunction - Class in neureka.backend.main.implementations.scalar
 
-
CPUScalarBroadcastFunction(ScalarFun) - Constructor for class neureka.backend.main.implementations.scalar.CPUScalarBroadcastFunction
+
CPUScalarBroadcastFunction(ScalarFun) - Constructor for class neureka.backend.main.implementations.scalar.CPUScalarBroadcastFunction
 
-
CPUScalarBroadcastIdentity - Class in neureka.backend.main.implementations.broadcast
+
CPUScalarBroadcastIdentity - Class in neureka.backend.main.implementations.broadcast
 
-
CPUScalarBroadcastIdentity() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastIdentity
+
CPUScalarBroadcastIdentity() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastIdentity
 
-
CPUScalarBroadcastModulo - Class in neureka.backend.main.implementations.broadcast
+
CPUScalarBroadcastModulo - Class in neureka.backend.main.implementations.broadcast
 
-
CPUScalarBroadcastModulo() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastModulo
+
CPUScalarBroadcastModulo() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastModulo
 
-
CPUScalarBroadcastMultiplication - Class in neureka.backend.main.implementations.broadcast
+
CPUScalarBroadcastMultiplication - Class in neureka.backend.main.implementations.broadcast
 
-
CPUScalarBroadcastMultiplication() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
+
CPUScalarBroadcastMultiplication() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
 
-
CPUScalarBroadcastSubtraction - Class in neureka.backend.main.implementations.broadcast
+
CPUScalarBroadcastSubtraction - Class in neureka.backend.main.implementations.broadcast
 
-
CPUScalarBroadcastSubtraction() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastSubtraction
+
CPUScalarBroadcastSubtraction() - Constructor for class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastSubtraction
 
-
CPUScalarFunction - Class in neureka.backend.main.implementations.scalar
+
CPUScalarFunction - Class in neureka.backend.main.implementations.scalar
 
-
CPUScalarFunction(ScalarFun) - Constructor for class neureka.backend.main.implementations.scalar.CPUScalarFunction
+
CPUScalarFunction(ScalarFun) - Constructor for class neureka.backend.main.implementations.scalar.CPUScalarFunction
 
-
CPUSum - Class in neureka.backend.main.operations.other.internal
+
CPUSum - Class in neureka.backend.main.operations.other.internal
An implementation of the sum and may algorithm running on the CPU.
-
CPUSum() - Constructor for class neureka.backend.main.operations.other.internal.CPUSum
+
CPUSum() - Constructor for class neureka.backend.main.operations.other.internal.CPUSum
 
-
create(Tensor<V>) - Method in class neureka.optimization.implementations.AdaGradFactory
+
create(Tensor<V>) - Method in class neureka.optimization.implementations.AdaGradFactory
 
-
create(Tensor<V>) - Method in class neureka.optimization.implementations.ADAMFactory
+
create(Tensor<V>) - Method in class neureka.optimization.implementations.ADAMFactory
 
-
create(Tensor<V>) - Method in class neureka.optimization.implementations.MomentumFactory
+
create(Tensor<V>, Tensor<V>) - Method in class neureka.optimization.implementations.ADAMFactory
 
-
create(Tensor<V>) - Method in class neureka.optimization.implementations.RMSPropFactory
+
create(Tensor<V>) - Method in class neureka.optimization.implementations.MomentumFactory
 
-
create(Tensor<V>) - Method in class neureka.optimization.implementations.SGDFactory
+
create(Tensor<V>) - Method in class neureka.optimization.implementations.RMSPropFactory
 
-
create(Tensor<V>) - Method in interface neureka.optimization.OptimizerFactory
+
create(Tensor<V>) - Method in class neureka.optimization.implementations.SGDFactory
 
-
create(Tensor<V>, Tensor<V>) - Method in class neureka.optimization.implementations.ADAMFactory
+
create(Tensor<V>) - Method in interface neureka.optimization.OptimizerFactory
 
-
createDeconvolutionFor(String) - Static method in class neureka.backend.main.operations.ConvUtil
+
createDeconvolutionFor(String) - Static method in class neureka.backend.main.operations.ConvUtil
 
-
CSVHandle - Class in neureka.devices.file
+
CSVHandle - Class in neureka.devices.file
This class is one of many extensions of the AbstractFileHandle which is therefore ultimately an implementation of the FileHandle interface.
-
CSVHandle(String, Map<String, Object>) - Constructor for class neureka.devices.file.CSVHandle
-
 
-
currentCount() - Method in class neureka.devices.ReferenceCounter.ChangeEvent
+
CSVHandle(String, Map<String, Object>) - Constructor for class neureka.devices.file.CSVHandle
 
-
CUSTOM - Enum constant in enum class neureka.devices.opencl.OpenCLDevice.Type
+
currentCount() - Method in class neureka.devices.ReferenceCounter.ChangeEvent
 
-

D

-
-
D1C - Class in neureka.ndim.config.types
+ + + +

D

+
+
D1C - Class in neureka.ndim.config.types
An abstract class for NDConfigurations which are representing tensors of rank 1, meaning the name of this class translates to "Dimension-1-Configuration".
-
D1C() - Constructor for class neureka.ndim.config.types.D1C
+
D1C() - Constructor for class neureka.ndim.config.types.D1C
 
-
D2C - Class in neureka.ndim.config.types
+
D2C - Class in neureka.ndim.config.types
An abstract class for NDConfigurations which are representing tensors of rank 2, meaning the name of this class translates to "Dimension-^2-Configuration".
-
D2C() - Constructor for class neureka.ndim.config.types.D2C
+
D2C() - Constructor for class neureka.ndim.config.types.D2C
 
-
D3C - Class in neureka.ndim.config.types
+
D3C - Class in neureka.ndim.config.types
An abstract class for NDConfigurations which are representing tensors of rank 3, meaning the name of this class translates to "Dimension-3-Configuration".
-
D3C() - Constructor for class neureka.ndim.config.types.D3C
+
D3C() - Constructor for class neureka.ndim.config.types.D3C
 
-
Data<V> - Interface in neureka
+
Data<V> - Interface in neureka
A wrapper type for the raw data array of a tensor/nd-array, which is typically provided by implementations of the Device interface.
-
dataArrayType() - Method in class neureka.dtype.DataType
+
dataArrayType() - Method in class neureka.dtype.DataType
 
-
DataConverter - Class in neureka.common.utility
+
DataConverter - Class in neureka.common.utility
This class is a singleton.
-
DataConverter.ForTensor - Class in neureka.common.utility
+
DataConverter.ForTensor - Class in neureka.common.utility
This is a stateful and parallelized converter for converting the internal data array of a tensor to another data array based on a provided lambda.
-
DataConverter.Utility - Class in neureka.common.utility
+
DataConverter.Utility - Class in neureka.common.utility
This is a static utility class containing the actual conversion logic which is usually referenced by the Converter lambdas via method signatures...
-
dataType - Variable in enum class neureka.Tensor.ImageType
+
dataType() - Method in interface neureka.Data
 
-
dataType() - Method in interface neureka.Data
+
dataType() - Method in class neureka.devices.AbstractDeviceData
 
-
dataType() - Method in class neureka.devices.AbstractDeviceData
-
 
-
DataType<T> - Class in neureka.dtype
+
DataType<T> - Class in neureka.dtype
This class is a Multiton implementation for wrapping and representing type classes.
-
debug() - Method in class neureka.Neureka.Settings
+
dataType - Variable in enum neureka.Tensor.ImageType
+
 
+
debug() - Method in class neureka.Neureka.Settings
 
-
debug(Object) - Method in class neureka.Neureka.Settings
+
debug(Object) - Method in class neureka.Neureka.Settings
This allows you to configure Neureka using a Groovy DSL.
-
Debug() - Constructor for class neureka.Neureka.Settings.Debug
+
Debug() - Constructor for class neureka.Neureka.Settings.Debug
 
-
decrement() - Method in class neureka.devices.ReferenceCounter
+
decrement() - Method in class neureka.devices.ReferenceCounter
 
-
decrement() - Method in interface neureka.ndim.iterator.NDIterator
+
decrement(int[], int[]) - Static method in class neureka.ndim.config.NDConfiguration.Utility
 
-
decrement() - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
-
decrement() - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
-
decrement() - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
-
decrement() - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
-
decrement() - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
-
decrement() - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
-
decrement() - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
-
decrement() - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
-
decrement() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
-
decrement() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
decrement() - Method in interface neureka.ndim.iterator.NDIterator
 
-
decrement(int[], int[]) - Static method in class neureka.ndim.config.NDConfiguration.Utility
+
decrement() - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
+
decrement() - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
+
decrement() - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
+
decrement() - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
+
decrement() - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
+
decrement() - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
+
decrement() - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
+
decrement() - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
+
decrement() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
decrement() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
 
-
DECREMENT - Enum constant in enum class neureka.devices.ReferenceCounter.ChangeType
+
decrementUsageCount() - Method in class neureka.devices.AbstractDeviceData
 
-
decrementUsageCount() - Method in class neureka.devices.AbstractDeviceData
+
decrementUsageCount() - Method in interface neureka.devices.DeviceData
 
-
decrementUsageCount() - Method in interface neureka.devices.DeviceData
-
 
-
deepClone() - Method in interface neureka.Tensor
+
deepClone() - Method in interface neureka.Tensor
-
This is almost identical to the Tensor.deepCopy() method except that +
This is almost identical to the Tensor.deepCopy() method except that the returned tensor will have autograd support, meaning that the cloning will be part of the autograd computation graph, and backpropagation will traverse the cloned tensor as well.
-
deepCopy() - Method in interface neureka.Nda
+
deepCopy() - Method in interface neureka.Nda
This method creates and returns a new nd-array instance which is not only a copy of the configuration of this nd-array but also a copy of the underlying data array.
-
deepCopy() - Method in interface neureka.Tensor
+
deepCopy() - Method in interface neureka.Tensor
This method creates and returns a new nd-array instance which is not only a copy of the configuration of this nd-array but also a copy of the underlying data array.
-
DEFAULT - Enum constant in enum class neureka.devices.opencl.OpenCLDevice.Type
-
 
-
delete() - Method in interface neureka.MutateTensor
+
delete() - Method in interface neureka.MutateTensor
Although tensors will be garbage collected when they are not strongly referenced, there is also the option to manually free up the tensor and its associated data in a native environment.
-
dependsOn(int) - Method in interface neureka.math.Function
+
dependsOn(int) - Method in interface neureka.math.Function
Use this to determine if this function directly or indirectly references an input with the provided index.
-
dependsOn(int) - Method in class neureka.math.implementations.FunctionConstant
+
dependsOn(int) - Method in class neureka.math.implementations.FunctionConstant
 
-
dependsOn(int) - Method in class neureka.math.implementations.FunctionInput
+
dependsOn(int) - Method in class neureka.math.implementations.FunctionInput
 
-
dependsOn(int) - Method in class neureka.math.implementations.FunctionNode
+
dependsOn(int) - Method in class neureka.math.implementations.FunctionNode
 
-
dependsOn(int) - Method in class neureka.math.implementations.FunctionVariable
+
dependsOn(int) - Method in class neureka.math.implementations.FunctionVariable
 
-
derivationCode() - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
derivationCode() - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarAbsolute
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarAbsolute
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarCbrt
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarCbrt
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarCosinus
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarCosinus
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarExp
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarExp
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaSU
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaSU
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaTU
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaTU
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaussian
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaussian
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaussianFast
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGaussianFast
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGeLU
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarGeLU
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarIdentity
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarIdentity
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarLog10
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarLog10
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarLogarithm
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarLogarithm
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarQuadratic
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarQuadratic
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarReLU
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarReLU
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSeLU
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSeLU
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSigmoid
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSigmoid
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSiLU
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSiLU
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSinus
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSinus
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSoftplus
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSoftplus
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSoftsign
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSoftsign
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSqrt
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarSqrt
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarTanh
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarTanh
 
-
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarTanhFast
+
derivationCode() - Method in class neureka.backend.main.implementations.fun.ScalarTanhFast
 
-
derive(double[], int) - Method in interface neureka.math.Function
+
derive(int[], Tensor[], Function<Integer, Tensor<?>>) - Static method in class neureka.backend.main.operations.operator.Multiplication
+
 
+
derive(double[], int, int) - Method in interface neureka.math.Function
+
+
Calculates the derivative of a particular input with respect to the output of this Function + based on the provided array of inputs, an index targeting the input to be derived + and an index for input dependent indexing.
+
+
derive(double[], int) - Method in interface neureka.math.Function
Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs and an index targeting the input to be derived.
-
derive(double[], int) - Method in class neureka.math.implementations.FunctionConstant
+
derive(Tensor<T>[], int, int) - Method in interface neureka.math.Function
 
-
derive(double[], int) - Method in class neureka.math.implementations.FunctionInput
+
derive(Tensor<T>[], int) - Method in interface neureka.math.Function
 
-
derive(double[], int) - Method in class neureka.math.implementations.FunctionNode
+
derive(List<Tensor<T>>, int, int) - Method in interface neureka.math.Function
 
-
derive(double[], int) - Method in class neureka.math.implementations.FunctionVariable
-
 
-
derive(double[], int, int) - Method in interface neureka.math.Function
-
-
Calculates the derivative of a particular input with respect to the output of this Function - based on the provided array of inputs, an index targeting the input to be derived - and an index for input dependent indexing.
-
-
derive(double[], int, int) - Method in class neureka.math.implementations.FunctionConstant
+
derive(List<Tensor<T>>, int) - Method in interface neureka.math.Function
 
-
derive(double[], int, int) - Method in class neureka.math.implementations.FunctionInput
+
derive(double[], int) - Method in class neureka.math.implementations.FunctionConstant
 
-
derive(double[], int, int) - Method in class neureka.math.implementations.FunctionNode
+
derive(double[], int, int) - Method in class neureka.math.implementations.FunctionConstant
 
-
derive(double[], int, int) - Method in class neureka.math.implementations.FunctionVariable
+
derive(double[], int) - Method in class neureka.math.implementations.FunctionInput
 
-
derive(int[], Tensor[], Function<Integer, Tensor<?>>) - Static method in class neureka.backend.main.operations.operator.Multiplication
+
derive(double[], int, int) - Method in class neureka.math.implementations.FunctionInput
 
-
derive(List<Tensor<T>>, int) - Method in interface neureka.math.Function
+
derive(double[], int, int) - Method in class neureka.math.implementations.FunctionNode
 
-
derive(List<Tensor<T>>, int, int) - Method in interface neureka.math.Function
+
derive(double[], int) - Method in class neureka.math.implementations.FunctionNode
 
-
derive(Tensor<T>[], int) - Method in interface neureka.math.Function
+
derive(double[], int) - Method in class neureka.math.implementations.FunctionVariable
 
-
derive(Tensor<T>[], int, int) - Method in interface neureka.math.Function
+
derive(double[], int, int) - Method in class neureka.math.implementations.FunctionVariable
 
-
detach() - Method in interface neureka.MutateTensor
+
detach() - Method in interface neureka.MutateTensor
This method detaches this tensor from its underlying computation-graph or simply does nothing if no graph is present.
Nodes within a computation graph are instances of the "GraphNode" class which are also simple components of the tensors they represent in the graph.
-
detached() - Method in class neureka.fluent.slicing.AxisSliceBuilder
+
detached() - Method in class neureka.fluent.slicing.AxisSliceBuilder
 
-
detached() - Method in class neureka.fluent.slicing.SliceBuilder
+
detached() - Method in class neureka.fluent.slicing.SliceBuilder
 
-
detached() - Method in interface neureka.fluent.slicing.states.AxisOrGet
+
detached() - Method in interface neureka.fluent.slicing.states.AxisOrGet
This method concludes the slicing API by performing the actual slicing and returning the resulting Tensor instance based on the previously specified slice configuration...
-
detached() - Method in interface neureka.fluent.slicing.states.AxisOrGetTensor
+
detached() - Method in interface neureka.fluent.slicing.states.AxisOrGetTensor
This method concludes the slicing API by performing the actual slicing and returning the resulting Tensor instance based on the previously specified slice configuration...
-
detached() - Method in interface neureka.fluent.slicing.states.StepsOrAxisOrGetTensor
+
detached() - Method in interface neureka.fluent.slicing.states.StepsOrAxisOrGetTensor
This method concludes the slicing API by performing the actual slicing and returning the resulting Tensor instance based on the previously specified slice configuration...
-
detached() - Method in interface neureka.Tensor
+
detached() - Method in interface neureka.Tensor
This method returns a new tensor detached from any underlying computation-graph or simply does nothing if no graph is present.
Nodes within a computation graph are instances of the "GraphNode" class which are also simple components of the tensors they represent in the graph.
-
device() - Method in class neureka.backend.api.BackendExtension.DeviceOption
+
device() - Method in class neureka.backend.api.BackendExtension.DeviceOption
 
-
Device<V> - Interface in neureka.devices
+
Device<V> - Interface in neureka.devices
Implementations of this represent computational devices for storing tensors (instances of the Tensor<V> class), which may also expose a useful API for executing operations on tensors (used in backend operations).
-
Device.Access<V> - Interface in neureka.devices
+
Device.Access<V> - Interface in neureka.devices
Implementations of this represent the access to tensors stored on a device in order to read from or write to said tensor.
-
Device.In - Interface in neureka.devices
+
Device.In - Interface in neureka.devices
The second part of the method chain of the fluent API for executing tensors on this Device temporarily.
-
Device.Writer - Interface in neureka.devices
+
Device.Writer - Interface in neureka.devices
Instances of this complete a request for writing to an accessed tensor stored on a device.
-
DeviceAlgorithm<C extends DeviceAlgorithm<C>> - Interface in neureka.backend.api
+
DeviceAlgorithm<C extends DeviceAlgorithm<C>> - Interface in neureka.backend.api
A DeviceAlgorithm is an advanced form of Algorithm which delegates the execution to implementations of ImplementationFor specific Device types.
-
DeviceCleaner - Interface in neureka.devices
+
DeviceCleaner - Interface in neureka.devices
 
-
DeviceData<V> - Interface in neureka.devices
+
DeviceData<V> - Interface in neureka.devices
A sub-interface of the Data interface providing more device specific methods.
-
DeviceOption(Device<?>, double) - Constructor for class neureka.backend.api.BackendExtension.DeviceOption
+
DeviceOption(Device<?>, double) - Constructor for class neureka.backend.api.BackendExtension.DeviceOption
 
-
DeviceQuery - Class in neureka.devices.opencl.utility
+
DeviceQuery - Class in neureka.devices.opencl.utility
A program that queries and prints information about all available devices.
-
DimFit - Class in neureka.backend.main.operations.other
+
DimFit - Class in neureka.backend.main.operations.other
 
-
DimFit() - Constructor for class neureka.backend.main.operations.other.DimFit
+
DimFit() - Constructor for class neureka.backend.main.operations.other.DimFit
 
-
dimtrim() - Method in interface neureka.Tensor
-
-
This creates a new tensor with the same underlying Data and whose shape is trimmed.
-
-
dimTrim() - Method in class neureka.math.Functions
+
DimTrim - Class in neureka.backend.main.operations.other
 
-
DimTrim - Class in neureka.backend.main.operations.other
+
DimTrim() - Constructor for class neureka.backend.main.operations.other.DimTrim
 
-
DimTrim() - Constructor for class neureka.backend.main.operations.other.DimTrim
+
dimTrim() - Method in class neureka.math.Functions
 
-
dispatch(Function, ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
+
dimtrim() - Method in interface neureka.Tensor
+
+
This creates a new tensor with the same underlying Data and whose shape is trimmed.
+
+
dispatch(Function, ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
 
-
dispose() - Method in interface neureka.backend.api.BackendExtension
+
dispose() - Method in interface neureka.backend.api.BackendExtension
Tells this extension to dispose itself.
-
dispose() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
dispose() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
dispose() - Method in class neureka.backend.cpu.CPUBackend
+
dispose() - Method in class neureka.backend.cpu.CPUBackend
 
-
dispose() - Method in class neureka.backend.ocl.CLBackend
+
dispose() - Method in class neureka.backend.ocl.CLBackend
This method will free all the resources occupied by this context, meaning that all platforms and their devices will be disposed.
-
dispose() - Method in interface neureka.devices.Device
+
dispose() - Method in interface neureka.devices.Device
This method signals the device to get ready for garbage collection.
-
dispose() - Method in class neureka.devices.file.FileDevice
+
dispose() - Method in class neureka.devices.file.FileDevice
 
-
dispose() - Method in class neureka.devices.host.CPU
+
dispose() - Method in class neureka.devices.host.CPU
This method will shut down the internal thread-pool used by this class to execute JVM/CPU based operations in parallel.
-
dispose() - Method in class neureka.devices.opencl.OpenCLDevice
+
dispose() - Method in class neureka.devices.opencl.OpenCLDevice
This method tells the to restore all tensors stored on it and release all resources.
-
dispose() - Method in class neureka.devices.opencl.OpenCLPlatform
+
dispose() - Method in class neureka.devices.opencl.OpenCLPlatform
 
-
div() - Method in class neureka.math.Functions
+
div() - Method in class neureka.math.Functions
 
-
div(Tensor<V>) - Method in interface neureka.Tensor
+
div(Tensor<V>) - Method in interface neureka.Tensor
This method will produce the quotient of two tensors with the same rank (or two ranks which can be made compatible with padding ones), where the left operand is this Tensor instance and the right operand is the tensor passed to the method.
-
div(V) - Method in interface neureka.Tensor
+
div(V) - Method in interface neureka.Tensor
 
-
divAssign() - Method in class neureka.math.Functions
+
divAssign() - Method in class neureka.math.Functions
 
-
divAssign(Tensor<T>) - Method in interface neureka.MutateTensor
+
divAssign(Tensor<T>) - Method in interface neureka.MutateTensor
 
-
divide(int, int, CPU.RangeWorkload) - Method in class neureka.devices.host.concurrent.WorkScheduler.Divider
+
divide(int, CPU.RangeWorkload) - Method in class neureka.devices.host.concurrent.WorkScheduler.Divider
 
-
divide(int, CPU.RangeWorkload) - Method in class neureka.devices.host.concurrent.WorkScheduler.Divider
+
divide(int, int, CPU.RangeWorkload) - Method in class neureka.devices.host.concurrent.WorkScheduler.Divider
 
-
Divider(ExecutorService) - Constructor for class neureka.devices.host.concurrent.WorkScheduler.Divider
+
Divider(ExecutorService) - Constructor for class neureka.devices.host.concurrent.WorkScheduler.Divider
 
-
Division - Class in neureka.backend.main.operations.operator
+
Division - Class in neureka.backend.main.operations.operator
 
-
Division() - Constructor for class neureka.backend.main.operations.operator.Division
+
Division() - Constructor for class neureka.backend.main.operations.operator.Division
 
-
doesNotExist() - Method in interface neureka.Nda.Item
+
doesNotExist() - Method in interface neureka.Nda.Item
 
-
dot() - Method in class neureka.math.Functions
-
 
-
dot(Tensor<V>) - Method in interface neureka.Tensor
-
-
Performs a dot product between the last dimension of this tensor - and the first dimension of the provided tensor.
-
-
DOT - Class in neureka.backend.main.operations.linear.internal.blas
+
DOT - Class in neureka.backend.main.operations.linear.internal.blas
The ?dot routines perform a vector-vector reduction operation defined as Equation where xi and yi are elements of vectors x and y.
-
DOT() - Constructor for class neureka.backend.main.operations.linear.internal.blas.DOT
+
DOT() - Constructor for class neureka.backend.main.operations.linear.internal.blas.DOT
+
 
+
dot() - Method in class neureka.math.Functions
 
-
DotProduct - Class in neureka.backend.main.operations.linear
+
dot(Tensor<V>) - Method in interface neureka.Tensor
+
+
Performs a dot product between the last dimension of this tensor + and the first dimension of the provided tensor.
+
+
DotProduct - Class in neureka.backend.main.operations.linear
 
-
DotProduct() - Constructor for class neureka.backend.main.operations.linear.DotProduct
+
DotProduct() - Constructor for class neureka.backend.main.operations.linear.DotProduct
 
-
DotProductAlgorithm - Class in neureka.backend.main.algorithms
+
DotProductAlgorithm - Class in neureka.backend.main.algorithms
 
-
DotProductAlgorithm() - Constructor for class neureka.backend.main.algorithms.DotProductAlgorithm
+
DotProductAlgorithm() - Constructor for class neureka.backend.main.algorithms.DotProductAlgorithm
 
-
doubleToBigInteger(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
doubleToBigInteger(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
doubleToBool(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
doubleToBool(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
doubleToByte(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
doubleToByte(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
doubleToFloat(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
doubleToFloat(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
doubleToInt(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
doubleToInt(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
doubleToLong(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
doubleToLong(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
doubleToShort(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
doubleToShort(double[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
dtype() - Method in class neureka.Neureka.Settings
+
dtype() - Method in class neureka.Neureka.Settings
 
-
dtype(Object) - Method in class neureka.Neureka.Settings
+
dtype(Object) - Method in class neureka.Neureka.Settings
This allows you to configure Neureka using a Groovy DSL.
-
DType() - Constructor for class neureka.Neureka.Settings.DType
+
DType() - Constructor for class neureka.Neureka.Settings.DType
 
-

E

-
-
EIGHT - Enum constant in enum class neureka.devices.host.concurrent.Parallelism
-
-
8
-
-
elements() - Method in interface neureka.Shape
+ + + +

E

+
+
elements() - Method in interface neureka.Shape
 
-
ElementwiseAlgorithm - Class in neureka.backend.main.algorithms
+
ElementwiseAlgorithm - Class in neureka.backend.main.algorithms
This is lambda based Algorithm implementation providing some basic functionality for implementing custom activation functions.
-
ElementwiseAlgorithm() - Constructor for class neureka.backend.main.algorithms.ElementwiseAlgorithm
+
ElementwiseAlgorithm() - Constructor for class neureka.backend.main.algorithms.ElementwiseAlgorithm
 
-
ElemWiseUtil - Class in neureka.backend.main.operations
+
ElemWiseUtil - Class in neureka.backend.main.operations
Methods inside this utility class execute only some ExecutionCall arguments in groups if their total number exceeds the arity of an operation.
-
ElemWiseUtil() - Constructor for class neureka.backend.main.operations.ElemWiseUtil
+
ElemWiseUtil() - Constructor for class neureka.backend.main.operations.ElemWiseUtil
 
-
endsFrom(int[]) - Static method in class neureka.backend.main.operations.other.DimTrim
+
endsFrom(int[]) - Static method in class neureka.backend.main.operations.other.DimTrim
 
-
ENVIRONMENT - Static variable in class neureka.devices.host.machine.ConcreteMachine
+
ENVIRONMENT - Static variable in class neureka.devices.host.machine.ConcreteMachine
 
-
equals(Object) - Method in class neureka.common.utility.Cache.LazyEntry
+
equals(Object) - Method in class neureka.common.utility.Cache.LazyEntry
 
-
equals(Object) - Method in class neureka.devices.host.machine.BasicMachine
+
equals(Object) - Method in class neureka.devices.host.machine.BasicMachine
 
-
equals(Object) - Method in class neureka.devices.host.machine.CommonMachine
+
equals(Object) - Method in class neureka.devices.host.machine.CommonMachine
 
-
equals(Object) - Method in class neureka.devices.host.machine.ConcreteMachine
+
equals(Object) - Method in class neureka.devices.host.machine.ConcreteMachine
 
-
equals(Object) - Method in class neureka.devices.host.machine.Hardware
+
equals(Object) - Method in class neureka.devices.host.machine.Hardware
 
-
equals(Object) - Method in class neureka.devices.opencl.KernelCode
+
equals(Object) - Method in class neureka.devices.opencl.KernelCode
 
-
equals(Object) - Method in class neureka.dtype.DataType
+
equals(Object) - Method in class neureka.dtype.DataType
 
-
equals(Object) - Method in class neureka.ndim.config.AbstractNDC
+
equals(Object) - Method in class neureka.ndim.config.AbstractNDC
 
-
equals(NDConfiguration) - Method in class neureka.ndim.config.AbstractNDC
+
equals(NDConfiguration) - Method in class neureka.ndim.config.AbstractNDC
 
-
equals(NDConfiguration) - Method in interface neureka.ndim.config.NDConfiguration
+
equals(NDConfiguration) - Method in interface neureka.ndim.config.NDConfiguration
 
-
error() - Method in class neureka.autograd.ADTarget
+
error() - Method in class neureka.autograd.ADTarget
 
-
errorCorrectionSupport() - Method in class neureka.devices.opencl.OpenCLDevice
+
errorCorrectionSupport() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
Estimator(boolean) - Constructor for class neureka.backend.api.Call.Validator.Estimator
+
Estimator(boolean) - Constructor for class neureka.backend.api.Call.Validator.Estimator
 
-
every(Predicate<Integer>) - Method in interface neureka.Shape
-
 
-
every(Predicate<V>) - Method in interface neureka.Nda
+
every(Predicate<V>) - Method in interface neureka.Nda
Iterates over every element of this nd-array, and checks whether all elements are true according to the provided lambda.
-
EXCELLENT - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
+
every(Predicate<Integer>) - Method in interface neureka.Shape
+
 
+
EXCELLENT - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
 
-
execute() - Method in class neureka.autograd.JITProp
+
execute() - Method in class neureka.autograd.JITProp
This method triggers the continuation of the back-propagation which has been put on hold by saving the pending graph nodes inside this class.
-
execute(boolean, double[], double[], double[], int, int, int) - Static method in class neureka.backend.main.implementations.matmul.CPUMatMul
+
execute(Function, ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.api.fun.Execution
 
-
execute(boolean, float[], float[], float[], int, int, int) - Static method in class neureka.backend.main.implementations.matmul.CPUMatMul
+
execute(Function, ExecutionCall<?>) - Method in interface neureka.backend.api.Operation
 
-
execute(boolean, int[], int[], int[], int, int, int) - Static method in class neureka.backend.main.implementations.matmul.CPUMatMul
-
 
-
execute(boolean, long[], long[], long[], int, int, int) - Static method in class neureka.backend.main.implementations.matmul.CPUMatMul
-
 
-
execute(int) - Method in interface neureka.devices.host.CPU.IndexedWorkload
-
 
-
execute(int, int) - Method in interface neureka.devices.host.CPU.RangeWorkload
-
 
-
execute(Call<?>) - Method in interface neureka.math.Function
-
-
Warning: Tensors returned by this method are eligible for deletion when consumed by other functions. - Use this to pass more context information for execution of input tensors.
-
-
execute(ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.main.internal.FinalExecutor
+
execute(Function, ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
 
-
execute(Args, Tensor<?>...) - Method in interface neureka.math.Function
-
-
Warning: Tensors returned by this method are eligible for deletion when consumed by other functions. -
- Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
-
-
execute(Args, Tensor<?>...) - Method in class neureka.math.implementations.FunctionConstant
+
execute(Function, ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
 
-
execute(Args, Tensor<?>...) - Method in class neureka.math.implementations.FunctionInput
+
execute(Function, ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
 
-
execute(Args, Tensor<?>...) - Method in class neureka.math.implementations.FunctionNode
+
execute(boolean, double[], double[], double[], int, int, int) - Static method in class neureka.backend.main.implementations.matmul.CPUMatMul
 
-
execute(Args, Tensor<?>...) - Method in class neureka.math.implementations.FunctionVariable
+
execute(boolean, float[], float[], float[], int, int, int) - Static method in class neureka.backend.main.implementations.matmul.CPUMatMul
 
-
execute(Function, ExecutionCall<?>) - Method in interface neureka.backend.api.Operation
+
execute(boolean, long[], long[], long[], int, int, int) - Static method in class neureka.backend.main.implementations.matmul.CPUMatMul
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.functions.SiLU
+
execute(boolean, int[], int[], int[], int, int, int) - Static method in class neureka.backend.main.implementations.matmul.CPUMatMul
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.indexer.Product
+
execute(ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.main.internal.FinalExecutor
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.indexer.Summation
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.indexer.Product
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.linear.Convolution
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.indexer.Summation
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Addition
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.linear.Convolution
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Division
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.linear.MatMul
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Modulo
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Addition
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Multiplication
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Division
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Power
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Modulo
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Subtraction
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Multiplication
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.other.AssignLeft
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Power
 
-
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.other.Cat
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.operator.Subtraction
 
-
execute(Function, ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.api.fun.Execution
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.other.AssignLeft
 
-
execute(Function, ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
+
execute(Function, ExecutionCall<?>) - Method in class neureka.backend.main.operations.other.Cat
 
-
execute(Function, ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
execute(int) - Method in interface neureka.devices.host.CPU.IndexedWorkload
 
-
execute(Function, ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
+
execute(int, int) - Method in interface neureka.devices.host.CPU.RangeWorkload
 
-
execute(Tensor<?>...) - Method in interface neureka.math.Function.Callable
+
execute(Tensor<?>...) - Method in interface neureka.math.Function.Callable
Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
-
execute(Tensor<?>...) - Method in interface neureka.math.Function
+
execute(Call<?>) - Method in interface neureka.math.Function
+
+
Warning: Tensors returned by this method are eligible for deletion when consumed by other functions. + Use this to pass more context information for execution of input tensors.
+
+
execute(Args, Tensor<?>...) - Method in interface neureka.math.Function
+
+
Warning: Tensors returned by this method are eligible for deletion when consumed by other functions. +
+ Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
+
+
execute(Tensor<?>...) - Method in interface neureka.math.Function
Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
-
execute(Tensor<?>[], int) - Method in interface neureka.math.Function
+
execute(Tensor<?>[], int) - Method in interface neureka.math.Function
Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
-
executeChange() - Method in interface neureka.common.composition.Component.OwnerChangeRequest
+
execute(Args, Tensor<?>...) - Method in class neureka.math.implementations.FunctionConstant
+
 
+
execute(Args, Tensor<?>...) - Method in class neureka.math.implementations.FunctionInput
+
 
+
execute(Args, Tensor<?>...) - Method in class neureka.math.implementations.FunctionNode
+
 
+
execute(Args, Tensor<?>...) - Method in class neureka.math.implementations.FunctionVariable
+
 
+
executeChange() - Method in interface neureka.common.composition.Component.OwnerChangeRequest
-
This method will trigger the actual state change identified by the Component.IsBeing - constant returned by the Component.OwnerChangeRequest.type() method.
+
This method will trigger the actual state change identified by the Component.IsBeing + constant returned by the Component.OwnerChangeRequest.type() method.
-
executeDerive(Tensor<?>[], int) - Method in interface neureka.math.Function
+
executeDerive(Tensor<?>[], int, int) - Method in interface neureka.math.Function
Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
-
executeDerive(Tensor<?>[], int, int) - Method in interface neureka.math.Function
+
executeDerive(Tensor<?>[], int) - Method in interface neureka.math.Function
Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
-
executeDeviceAlgorithm(ExecutionCall<? extends Device<?>>) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
executeDeviceAlgorithm(ExecutionCall<? extends Device<?>>) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
executeFor(Function, ExecutionCall<? extends Device<?>>, FinalExecutor) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
executeFor(Function, ExecutionCall<? extends Device<?>>, FinalExecutor) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
executeOnCommonDevice(ExecutionCall<?>, Supplier<R>) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
executeOnCommonDevice(ExecutionCall<?>, Supplier<R>) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
executeRecursively(String, ExecutionCall<? extends Device<?>>) - Static method in class neureka.backend.main.operations.ConvUtil
+
executeRecursively(String, ExecutionCall<? extends Device<?>>) - Static method in class neureka.backend.main.operations.ConvUtil
 
-
Execution - Interface in neureka.backend.api.fun
+
Execution - Interface in neureka.backend.api.fun
Implementations of this functional interface is supposed to be the final execution procedure responsible for dispatching the execution further into the backend.
-
ExecutionCall<D extends Device<?>> - Class in neureka.backend.api
+
ExecutionCall<D extends Device<?>> - Class in neureka.backend.api
This class is a simple container holding references to a targeted Device, Operation and maybe some case specific meta Args needed to execute an array of input tensors which are also wrapped by this.
-
ExecutionCall.Builder<D extends Device<?>> - Class in neureka.backend.api
+
ExecutionCall.Builder<D extends Device<?>> - Class in neureka.backend.api
 
-
ExecutionPreparation - Interface in neureka.backend.api.fun
+
ExecutionPreparation - Interface in neureka.backend.api.fun
An Algorithm will typically produce a result when executing an ExecutionCall.
-
exists() - Method in interface neureka.Nda.Item
+
exists() - Method in interface neureka.Nda.Item
 
-
exp() - Method in class neureka.math.Functions
-
 
-
exp() - Method in interface neureka.Tensor
-
-
This method is a functionally identical to the following alternatives:
-
-
Exp - Class in neureka.backend.main.operations.functions
+
EXP - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
Exp() - Constructor for class neureka.backend.main.operations.functions.Exp
+
Exp - Class in neureka.backend.main.operations.functions
 
-
EXP - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
Exp() - Constructor for class neureka.backend.main.operations.functions.Exp
 
-
extension() - Method in class neureka.devices.file.IDXHandle
+
exp() - Method in class neureka.math.Functions
 
-
extension() - Method in interface neureka.devices.file.FileHandle
+
exp() - Method in interface neureka.Tensor
+
+
This method is a functionally identical to the following alternatives:
+
+
extension() - Method in interface neureka.devices.file.FileHandle
The file ending which comes after the '.' character...
-
Extensions - Class in neureka.backend.api
+
Extensions - Class in neureka.backend.api
This is an internal class for managing the extension of any given BackendContext class.
-
Extensions() - Constructor for class neureka.backend.api.Extensions
+
Extensions() - Constructor for class neureka.backend.api.Extensions
 
-

F

-
-
F32 - Class in neureka.dtype.custom
-
 
-
F32() - Constructor for class neureka.dtype.custom.F32
+ + + +

F

+
+
F32 - Class in neureka.dtype.custom
 
-
F64 - Class in neureka.dtype.custom
+
F32() - Constructor for class neureka.dtype.custom.F32
 
-
F64() - Constructor for class neureka.dtype.custom.F64
+
F64 - Class in neureka.dtype.custom
 
-
FACTORY - Static variable in interface neureka.devices.file.FileHandle
+
F64() - Constructor for class neureka.dtype.custom.F64
 
-
FallbackAlgorithm - Class in neureka.backend.api.template.algorithms
+
FACTORY - Static variable in interface neureka.devices.file.FileHandle
 
-
FallbackAlgorithm(String, int, Operation) - Constructor for class neureka.backend.api.template.algorithms.FallbackAlgorithm
+
FallbackAlgorithm - Class in neureka.backend.api.template.algorithms
 
-
FALSE - Enum constant in enum class neureka.ndim.iterator.NDIterator.NonVirtual
+
FallbackAlgorithm(String, int, Operation) - Constructor for class neureka.backend.api.template.algorithms.FallbackAlgorithm
 
-
FANCY - Enum constant in enum class neureka.autograd.GraphNode.Print
+
fastGaus() - Method in class neureka.math.Functions
 
-
fastGaus() - Method in class neureka.math.Functions
+
fastTanh() - Method in class neureka.math.Functions
 
-
fastTanh() - Method in class neureka.math.Functions
-
 
-
FEDORA - Enum constant in enum class neureka.devices.opencl.utility.Messages.Tips
-
 
-
FileDevice - Class in neureka.devices.file
+
FileDevice - Class in neureka.devices.file
The FileDevice is a Device implementation responsible for reading tensors from and or writing them to a given directory.
-
FileHandle<FinalType,ValType> - Interface in neureka.devices.file
+
FileHandle<FinalType,ValType> - Interface in neureka.devices.file
 
-
fileHandleOf(Tensor<?>) - Method in class neureka.devices.file.FileDevice
+
fileHandleOf(Tensor<?>) - Method in class neureka.devices.file.FileDevice
 
-
Filler<T> - Interface in neureka.ndim
+
Filler<T> - Interface in neureka.ndim
Implementations of this ought to map the index of a tensor entry to a value which should be placed at that entry position.
-
fillRandomly(T, long) - Static method in class neureka.backend.main.implementations.elementwise.CPURandomization
+
fillRandomly(T, Arg.Seed) - Static method in class neureka.backend.main.implementations.elementwise.CPURandomization
 
-
fillRandomly(T, String) - Static method in class neureka.backend.main.implementations.elementwise.CPURandomization
+
fillRandomly(T, String) - Static method in class neureka.backend.main.implementations.elementwise.CPURandomization
 
-
fillRandomly(T, Arg.Seed) - Static method in class neureka.backend.main.implementations.elementwise.CPURandomization
+
fillRandomly(T, long) - Static method in class neureka.backend.main.implementations.elementwise.CPURandomization
 
-
filter(Predicate<Integer>) - Method in interface neureka.Shape
-
 
-
filter(Predicate<V>) - Method in interface neureka.Nda
+
filter(Predicate<V>) - Method in interface neureka.Nda
A convenience method for stream().filter( predicate ).
-
FinalExecutor - Interface in neureka.backend.main.internal
+
filter(Predicate<Integer>) - Method in interface neureka.Shape
+
 
+
FinalExecutor - Interface in neureka.backend.main.internal
 
-
find(Class<D>, String...) - Static method in interface neureka.devices.Device
+
find(Class<E>) - Method in class neureka.backend.api.BackendContext
-
This method returns Device instances matching - the given search parameters.
+
Returns an Optional instance of the provided BackendExtension type + or an empty Optional if no extension of that type was found.
-
find(Class<E>) - Method in class neureka.backend.api.BackendContext
+
find(String) - Method in interface neureka.backend.api.BackendExtension
-
Returns an Optional instance of the provided BackendExtension type - or an empty Optional if no extension of that type was found.
+
The BackendContext does not handle Device instances directly.
-
find(Class<T>) - Method in class neureka.common.composition.AbstractComponentOwner
+
find(String) - Method in class neureka.backend.cpu.CPUBackend
+
 
+
find(String) - Method in class neureka.backend.ocl.CLBackend
+
 
+
find(Class<T>) - Method in class neureka.common.composition.AbstractComponentOwner
This method finds a component of the given type/class - and returns it as an Optional which may or may not be empty.
+ and returns it as an Optional which may or may not be empty.
-
find(Class<T>) - Method in interface neureka.common.composition.ComponentOwner
+
find(Class<T>) - Method in interface neureka.common.composition.ComponentOwner
This method finds a component of the given type/class - and returns it as an Optional which may or may not be empty.
+ and returns it as an Optional which may or may not be empty.
-
find(String) - Method in interface neureka.backend.api.BackendExtension
+
find(String...) - Static method in interface neureka.devices.Device
-
The BackendContext does not handle Device instances directly.
+
This method returns Device instances matching + the given search parameter.
-
find(String) - Method in class neureka.backend.cpu.CPUBackend
-
 
-
find(String) - Method in class neureka.backend.ocl.CLBackend
-
 
-
find(String...) - Static method in interface neureka.devices.Device
+
find(Class<D>, String...) - Static method in interface neureka.devices.Device
This method returns Device instances matching - the given search parameter.
+ the given search parameters.
-
findAdHocKernel(String) - Method in class neureka.devices.opencl.OpenCLDevice
+
findAdHocKernel(String) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
findCaptured() - Method in interface neureka.autograd.ADAction
+
findCaptured() - Method in interface neureka.autograd.ADAction
Finds captured Tensor instances in this current action using reflection (This is usually a partial derivative).
-
findComponentIn(String, int) - Static method in class neureka.math.parsing.ParseUtil
+
findComponentIn(String, int) - Static method in class neureka.math.parsing.ParseUtil
 
-
findOrCompileAdHocKernel(String, Supplier<String>) - Method in class neureka.devices.opencl.OpenCLDevice
+
findOrCompileAdHocKernel(String, Supplier<String>) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
findParametersIn(String, int) - Static method in class neureka.math.parsing.ParseUtil
+
findParametersIn(String, int) - Static method in class neureka.math.parsing.ParseUtil
 
-
findRootTensor() - Method in class neureka.framing.Relation
+
findRootTensor() - Method in class neureka.framing.Relation
This method tries to find the root data ancestor of this tensor.
-
findTip() - Static method in class neureka.devices.opencl.utility.Messages
+
findTip() - Static method in class neureka.devices.opencl.utility.Messages
 
-
finishedCount() - Method in class neureka.autograd.JITProp
+
finishedCount() - Method in class neureka.autograd.JITProp
 
-
first(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
+
first(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
 
-
flatMap(Function<V, Stream<R>>) - Method in interface neureka.Nda
+
flatMap(Function<V, Stream<R>>) - Method in interface neureka.Nda
A convenience method for nda.stream().flatMap( mapper ), - which turns this Nda into a Stream of its items.
+ which turns this Nda into a Stream of its items.
-
flatten(Function, ExecutionCall<D>) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
flatten(Function, ExecutionCall<D>) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
flattenForIndexer(Function, ExecutionCall<D>) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
flattenForIndexer(Function, ExecutionCall<D>) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
floatToBigInteger(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
floatToBigInteger(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
floatToByte(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
floatToByte(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
floatToDouble(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
floatToDouble(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
floatToInt(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
floatToInt(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
floatToLong(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
floatToLong(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
floatToShort(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
floatToShort(float[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
forDevice(Class<? extends D>) - Method in class neureka.backend.api.ini.BackendRegistry
+
forDevice(Class<? extends D>) - Method in class neureka.backend.api.ini.BackendRegistry
 
-
forEachDerivative(BiConsumer<GraphNode<V>, ADAction>) - Method in class neureka.autograd.GraphNode
+
forEachDerivative(BiConsumer<GraphNode<V>, ADAction>) - Method in class neureka.autograd.GraphNode
 
-
forEachTarget(Consumer<GraphNode<V>>) - Method in class neureka.autograd.GraphNode
+
forEachTarget(Consumer<GraphNode<V>>) - Method in class neureka.autograd.GraphNode
 
-
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.F32
+
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.F32
 
-
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.F64
+
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.F64
 
-
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.I16
+
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.I16
 
-
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.I32
+
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.I32
 
-
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.I64
+
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.I64
 
-
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.I8
+
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.I8
 
-
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.UI16
+
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.UI16
 
-
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.UI32
+
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.UI32
 
-
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.UI64
+
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.UI64
 
-
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.UI8
+
foreignHolderBytesToTarget(byte[]) - Method in class neureka.dtype.custom.UI8
 
-
foreignHolderBytesToTarget(byte[]) - Method in interface neureka.dtype.NumericType
+
foreignHolderBytesToTarget(byte[]) - Method in interface neureka.dtype.NumericType
 
-
forInputs(Tensor<?>[], Supplier<Result>) - Static method in class neureka.backend.main.memory.MemValidator
+
forInputs(Tensor<?>[], Supplier<Result>) - Static method in class neureka.backend.main.memory.MemValidator
 
-
format(String, Object...) - Static method in class neureka.common.utility.LogUtil
+
format(String, Object...) - Static method in class neureka.common.utility.LogUtil
 
-
forOperation(Class<? extends Operation>) - Method in interface neureka.backend.api.ini.ReceiveForDevice
+
forOperation(Class<? extends Operation>) - Method in interface neureka.backend.api.ini.ReceiveForDevice
 
-
ForTensor(Tensor<?>) - Constructor for class neureka.common.utility.DataConverter.ForTensor
+
ForTensor(Tensor<?>) - Constructor for class neureka.common.utility.DataConverter.ForTensor
 
-
FORWARD_AND_BACKWARD - Enum constant in enum class neureka.backend.api.AutoDiffMode
-
 
-
FORWARD_ONLY - Enum constant in enum class neureka.backend.api.AutoDiffMode
-
 
-
FOUR - Enum constant in enum class neureka.devices.host.concurrent.Parallelism
+
frame() - Method in interface neureka.Tensor
-
4
+
This is a functionally identical alternative to Tensor.getFrame().
-
frame() - Method in interface neureka.Tensor
+
free(Tensor<T>) - Method in interface neureka.devices.Device
-
This is a functionally identical alternative to Tensor.getFrame().
+
Use this to remove the provided tensor from this Device!

-
free() - Method in class neureka.devices.file.IDXHandle
+
free(Tensor<T>) - Method in class neureka.devices.file.FileDevice
 
-
free() - Method in interface neureka.devices.file.FileHandle
+
free() - Method in interface neureka.devices.file.FileHandle
An implementation of this method ought to "free" up the memory used to store a tensor.
-
free(Tensor<T>) - Method in interface neureka.devices.Device
-
-
Use this to remove the provided tensor from this Device!

-
-
free(Tensor<T>) - Method in class neureka.devices.file.FileDevice
+
free(Tensor<T>) - Method in class neureka.devices.host.CPU
 
-
free(Tensor<T>) - Method in class neureka.devices.host.CPU
+
free(Tensor<T>) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
free(Tensor<T>) - Method in class neureka.devices.opencl.OpenCLDevice
-
 
-
from(int) - Method in class neureka.fluent.slicing.AxisSliceBuilder
+
from(int) - Method in class neureka.fluent.slicing.AxisSliceBuilder
This method returns an instance of this very AxisSliceBuilder instance disguised by the To interface.
-
from(int) - Method in interface neureka.fluent.slicing.states.FromOrAt
+
from(int) - Method in interface neureka.fluent.slicing.states.FromOrAt
This is the starting point for defining the slice range of a specified axis within the method chain/graph exposed by the slice builder API.
-
from(int) - Method in interface neureka.fluent.slicing.states.FromOrAtTensor
+
from(int) - Method in interface neureka.fluent.slicing.states.FromOrAtTensor
This is the starting point for defining the slice range of a specified axis within the method chain/graph exposed by the slice builder API.
-
FromOrAt<V> - Interface in neureka.fluent.slicing.states
+
FromOrAt<V> - Interface in neureka.fluent.slicing.states
This is the starting point for defining the slice range of a specified axis within the call transition graph exposed by the slice builder API.
-
FromOrAtTensor<V> - Interface in neureka.fluent.slicing.states
-
 
-
FULL_DELETE - Enum constant in enum class neureka.devices.ReferenceCounter.ChangeType
+
FromOrAtTensor<V> - Interface in neureka.fluent.slicing.states
 
-
fullDelete() - Method in class neureka.devices.ReferenceCounter
+
fullDelete() - Method in class neureka.devices.ReferenceCounter
 
-
fully() - Method in interface neureka.devices.Device.Writer
+
fully() - Method in interface neureka.devices.Device.Writer
A convenience method for specifying that the entire data array of the accessed tensor should be written to.
-
FunAlgorithm - Class in neureka.backend.api.template.algorithms
+
FunAlgorithm - Class in neureka.backend.api.template.algorithms
 
-
FunAlgorithm(String) - Constructor for class neureka.backend.api.template.algorithms.FunAlgorithm
+
FunAlgorithm(String) - Constructor for class neureka.backend.api.template.algorithms.FunAlgorithm
 
-
Function - Interface in neureka.math
+
Function - Interface in neureka.math
Besides the Tensor class, which is the core class of Neureka, this interface and its implementations represents the second most important feature of this library.
-
Function.Callable - Interface in neureka.math
+
Function.Callable - Interface in neureka.math
An API for calling a Function after having specified - a set of Arg instances through the Function.with(Args) + a set of Arg instances through the Function.with(Args) method.
-
FunctionCache - Class in neureka.math
+
FunctionCache - Class in neureka.math
This class is part of a given BackendContext instance responsible for caching Function references based on - their String representation generated by Object.toString() + their String representation generated by Object.toString() as well as caching of results for active functions.
-
FunctionCache() - Constructor for class neureka.math.FunctionCache
+
FunctionCache() - Constructor for class neureka.math.FunctionCache
 
-
FunctionConstant - Class in neureka.math.implementations
+
FunctionConstant - Class in neureka.math.implementations
Instances of this implementation of the Function interface are leave nodes within the abstract syntax tree of a function, representing constant numeric values to a function.
-
FunctionConstant(String) - Constructor for class neureka.math.implementations.FunctionConstant
+
FunctionConstant(String) - Constructor for class neureka.math.implementations.FunctionConstant
 
-
FunctionInput - Class in neureka.math.implementations
+
FunctionInput - Class in neureka.math.implementations
Instances of this implementation of the Function interface are leave nodes within the abstract syntax tree of a function, representing inputs to a function.
-
FunctionNode - Class in neureka.math.implementations
+
FunctionNode - Class in neureka.math.implementations
The most common type of Function which references other Functions to form an abstract syntax tree.
-
FunctionNode(Operation, List<Function>, boolean) - Constructor for class neureka.math.implementations.FunctionNode
+
FunctionNode(Operation, List<Function>, boolean) - Constructor for class neureka.math.implementations.FunctionNode
 
-
FunctionParser - Class in neureka.math.parsing
+
FunctionParser - Class in neureka.math.parsing
The FunctionParser takes a BackendContext instance based on which - it builds Function implementation instances, usually by parsing Strings.
+ it builds Function implementation instances, usually by parsing Strings.
-
FunctionParser(BackendContext) - Constructor for class neureka.math.parsing.FunctionParser
+
FunctionParser(BackendContext) - Constructor for class neureka.math.parsing.FunctionParser
 
-
Functions - Class in neureka.math
+
Functions - Class in neureka.math
 
-
Functions(boolean) - Constructor for class neureka.math.Functions
+
Functions(boolean) - Constructor for class neureka.math.Functions
 
-
FunctionVariable - Class in neureka.math.implementations
+
FunctionVariable - Class in neureka.math.implementations
Instances of this implementation of the Function interface are leave nodes within the abstract syntax tree of a function, representing indexed inputs to a function.
-
FunctionVariable(String) - Constructor for class neureka.math.implementations.FunctionVariable
+
FunctionVariable(String) - Constructor for class neureka.math.implementations.FunctionVariable
 
-
FunDeviceAlgorithm - Class in neureka.backend.api.template.algorithms
+
FunDeviceAlgorithm - Class in neureka.backend.api.template.algorithms
 
-
FunDeviceAlgorithm(String) - Constructor for class neureka.backend.api.template.algorithms.FunDeviceAlgorithm
+
FunDeviceAlgorithm(String) - Constructor for class neureka.backend.api.template.algorithms.FunDeviceAlgorithm
 
-

G

-
-
GaSU - Class in neureka.backend.main.operations.functions
+ + + +

G

+
+
GASU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
 
+
GaSU - Class in neureka.backend.main.operations.functions
The Self Gated Softsign Unit is based on the Softsign function (a computationally cheap non-exponential quasi Tanh) making it a polynomially based version of the GaTU function which is itself based on the Tanh function.
-
GaSU() - Constructor for class neureka.backend.main.operations.functions.GaSU
+
GaSU() - Constructor for class neureka.backend.main.operations.functions.GaSU
 
-
GASU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
GATU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
GaTU - Class in neureka.backend.main.operations.functions
+
GaTU - Class in neureka.backend.main.operations.functions
The Self Gated Tanh Unit is based on the Tanh making it an exponentiation based version of the GaSU function which is itself based on the Softsign function (a computationally cheap non-exponential quasi Tanh).
-
GaTU() - Constructor for class neureka.backend.main.operations.functions.GaTU
-
 
-
GATU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
GaTU() - Constructor for class neureka.backend.main.operations.functions.GaTU
 
-
gaus() - Method in class neureka.math.Functions
+
gaus() - Method in class neureka.math.Functions
 
-
Gaussian - Class in neureka.backend.main.operations.functions
+
GAUSSIAN - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
Gaussian() - Constructor for class neureka.backend.main.operations.functions.Gaussian
+
Gaussian - Class in neureka.backend.main.operations.functions
 
-
GAUSSIAN - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
Gaussian() - Constructor for class neureka.backend.main.operations.functions.Gaussian
 
-
GAUSSIAN_FAST - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
GAUSSIAN_FAST - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
GaussianFast - Class in neureka.backend.main.operations.functions
+
GaussianFast - Class in neureka.backend.main.operations.functions
 
-
GaussianFast() - Constructor for class neureka.backend.main.operations.functions.GaussianFast
+
GaussianFast() - Constructor for class neureka.backend.main.operations.functions.GaussianFast
 
-
gaussianFrom(long, double[]) - Static method in class neureka.backend.main.implementations.elementwise.CPURandomization
+
gaussianFrom(long, double[]) - Static method in class neureka.backend.main.implementations.elementwise.CPURandomization
 
-
gelu() - Method in class neureka.math.Functions
+
GELU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
gelu(double) - Static method in class neureka.backend.main.implementations.fun.ScalarGeLU
+
gelu(double) - Static method in class neureka.backend.main.implementations.fun.ScalarGeLU
 
-
GeLU - Class in neureka.backend.main.operations.functions
+
GeLU - Class in neureka.backend.main.operations.functions
The GELU activation function is based on the standard Gaussian cumulative distribution function and is defined as x Φ( x ) and implemented as x * sigmoid(x * 1.702).
-
GeLU() - Constructor for class neureka.backend.main.operations.functions.GeLU
+
GeLU() - Constructor for class neureka.backend.main.operations.functions.GeLU
 
-
GELU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
gelu() - Method in class neureka.math.Functions
 
-
GEMM - Class in neureka.backend.main.operations.linear.internal.blas
+
GEMM - Class in neureka.backend.main.operations.linear.internal.blas
A collection of primitive sub-routines for matrix multiplication performed on continuous arrays which are designed so that they can be vectorized by the JVMs JIT compiler (AVX instructions).
-
GEMM() - Constructor for class neureka.backend.main.operations.linear.internal.blas.GEMM
+
GEMM() - Constructor for class neureka.backend.main.operations.linear.internal.blas.GEMM
 
-
GEMM.VectorOperationF32 - Interface in neureka.backend.main.operations.linear.internal.blas
+
GEMM.VectorOperationF32 - Interface in neureka.backend.main.operations.linear.internal.blas
 
-
GEMM.VectorOperationF64 - Interface in neureka.backend.main.operations.linear.internal.blas
+
GEMM.VectorOperationF64 - Interface in neureka.backend.main.operations.linear.internal.blas
 
-
get() - Method in class neureka.backend.api.Call.Builder
+
get() - Method in class neureka.backend.api.Call.Builder
 
-
get() - Method in class neureka.backend.api.LazyRef
+
get(Class<T>) - Method in class neureka.backend.api.Call
 
-
get() - Method in class neureka.backend.api.Result
+
get() - Method in class neureka.backend.api.LazyRef
 
-
get() - Static method in class neureka.common.utility.DataConverter
+
get() - Method in class neureka.backend.api.Result
+
 
+
get(Class<T>) - Method in class neureka.common.composition.AbstractComponentOwner
+
+
This method tries to find a component inside the internal + component array whose class matches the one provided.
+
+
get(Class<T>) - Method in interface neureka.common.composition.ComponentOwner
+
+
Use this to get the component of the specified component type class.
+
+
get() - Static method in class neureka.common.utility.DataConverter
This method returns the singleton.
-
get() - Method in interface neureka.Data
+
get() - Method in interface neureka.Data
This returns the underlying raw data object of a nd-array or tensor of a backend specific type (e.g.
-
get() - Static method in class neureka.devices.host.CPU
+
get(String...) - Static method in interface neureka.devices.Device
+
+
This method returns Device instances matching + the given search parameter.
+
+
get(Class<D>, String...) - Static method in interface neureka.devices.Device
+
+
This method returns Device instances matching + the given search parameters.
+
+
get() - Static method in class neureka.devices.host.CPU
Use this method to access the singleton instance of this CPU class, which is a Device type and default location for freshly instantiated Tensor instances.
-
get() - Method in class neureka.fluent.slicing.AxisSliceBuilder
+
get(String) - Method in class neureka.devices.opencl.KernelCache
+
 
+
get(cl_device_id) - Method in class neureka.devices.opencl.OpenCLPlatform
+
 
+
get() - Method in class neureka.fluent.slicing.AxisSliceBuilder
 
-
get() - Method in class neureka.fluent.slicing.SliceBuilder
+
get() - Method in class neureka.fluent.slicing.SliceBuilder
This method will create and return a new slice tensor based on the - provided configuration through methods like AxisSliceBuilder.from(int), - AxisSliceBuilder.to(int) and AxisSliceBuilder.at(int)...
+ provided configuration through methods like AxisSliceBuilder.from(int), + AxisSliceBuilder.to(int) and AxisSliceBuilder.at(int)...
-
get() - Method in interface neureka.fluent.slicing.states.AxisOrGet
+
get() - Method in interface neureka.fluent.slicing.states.AxisOrGet
This method concludes the slicing API by performing the actual slicing and returning the resulting Tensor instance based on the previously specified slice configuration...
-
get() - Method in interface neureka.fluent.slicing.states.AxisOrGetTensor
+
get() - Method in interface neureka.fluent.slicing.states.AxisOrGetTensor
This method concludes the slicing API by performing the actual slicing and returning the resulting Tensor instance based on the previously specified slice configuration...
-
get() - Method in interface neureka.fluent.slicing.states.StepsOrAxisOrGetTensor
+
get() - Method in interface neureka.fluent.slicing.states.StepsOrAxisOrGetTensor
This method concludes the slicing API by performing the actual slicing and returning the resulting Tensor instance based on the previously specified slice configuration...
-
get() - Method in interface neureka.framing.fluent.Get
+
Get<ValueType> - Interface in neureka.framing.fluent
 
-
get() - Method in class neureka.math.args.Arg
+
get() - Method in interface neureka.framing.fluent.Get
 
-
get() - Method in interface neureka.Nda.Item
-
-
Get the value at the targeted position or throw an exception if the item does not exist.
-
-
get() - Method in interface neureka.ndim.iterator.NDIterator
+
get(List<Object>) - Method in class neureka.framing.NDFrame
+
 
+
get(Object...) - Method in class neureka.framing.NDFrame
 
-
get() - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
-
get() - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
-
get() - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
-
get() - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
-
get() - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
-
get() - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
-
get() - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
-
get() - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
-
get() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
get() - Method in class neureka.math.args.Arg
 
-
get() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
get(String, boolean) - Method in class neureka.math.FunctionCache
 
-
get() - Static method in class neureka.Neureka
+
get(int...) - Method in interface neureka.Nda
-
The Neureka class represents the configuration of this library.
+
The following method enables access to specific scalar elements within the nd-array.
-
get(int) - Method in interface neureka.Nda
+
get(Object...) - Method in interface neureka.Nda
-
This getter method creates and returns a slice of the original nd-array.
+
The following method enables the creation of nd-array slices which access + the same underlying data (possibly from a different view).
-
get(int) - Method in interface neureka.ndim.iterator.NDIterator
-
 
-
get(int) - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
-
get(int) - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
-
get(int) - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
-
get(int) - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
-
get(int) - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
-
get(int) - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
-
get(int) - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
-
get(int) - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
-
get(int) - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
-
get(int) - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
-
 
-
get(int) - Method in interface neureka.Shape
-
 
-
get(int) - Method in interface neureka.Tensor
+
get(int) - Method in interface neureka.Nda
This getter method creates and returns a slice of the original nd-array.
-
get(int...) - Method in interface neureka.Nda
+
get(Number) - Method in interface neureka.Nda
-
The following method enables access to specific scalar elements within the nd-array.
+
This getter method creates and returns a slice of the original nd-array.
-
get(int...) - Method in interface neureka.Tensor
+
get(Object) - Method in interface neureka.Nda
-
The following method enables access to specific scalar elements within the nd-array.
+
This method enables nd-array slicing! + It takes a key of various types and configures a slice + nd-array which shares the same underlying data as the original nd-array.
-
get(Class<D>, String...) - Static method in interface neureka.devices.Device
+
get() - Method in interface neureka.Nda.Item
-
This method returns Device instances matching - the given search parameters.
+
Get the value at the targeted position or throw an exception if the item does not exist.
-
get(Class<T>) - Method in class neureka.backend.api.Call
+
get(int) - Method in interface neureka.ndim.iterator.NDIterator
 
-
get(Class<T>) - Method in class neureka.common.composition.AbstractComponentOwner
-
-
This method tries to find a component inside the internal - component array whose class matches the one provided.
+
get() - Method in interface neureka.ndim.iterator.NDIterator
+
 
+
get(int) - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
+
get() - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
+
get(int) - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
+
get() - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
+
get(int) - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
+
get() - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
+
get(int) - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
+
get() - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
+
get(int) - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
+
get() - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
+
get(int) - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
+
get() - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
+
get(int) - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
+
get() - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
+
get(int) - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
+
get() - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
+
get(int) - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
get() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
 
+
get(int) - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
 
+
get() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
 
+
get() - Static method in class neureka.Neureka
+
+
The Neureka class represents the configuration of this library.
-
get(Class<T>) - Method in interface neureka.common.composition.ComponentOwner
+
get(int) - Method in interface neureka.Shape
+
 
+
get(int...) - Method in interface neureka.Tensor
-
Use this to get the component of the specified component type class.
+
The following method enables access to specific scalar elements within the nd-array.
-
get(Number) - Method in interface neureka.Nda
+
get(Object...) - Method in interface neureka.Tensor
-
This getter method creates and returns a slice of the original nd-array.
+
The following method enables the creation of nd-array slices which access + the same underlying data (possibly from a different view).
-
get(Number) - Method in interface neureka.Tensor
+
get(int) - Method in interface neureka.Tensor
This getter method creates and returns a slice of the original nd-array.
-
get(Object) - Method in interface neureka.Nda
+
get(Number) - Method in interface neureka.Tensor
-
This method enables nd-array slicing! - It takes a key of various types and configures a slice - nd-array which shares the same underlying data as the original nd-array.
+
This getter method creates and returns a slice of the original nd-array.
-
get(Object) - Method in interface neureka.Tensor
+
get(Object) - Method in interface neureka.Tensor
This method enables nd-array slicing! It takes a key of various types and configures a slice nd-array which shares the same underlying data as the original nd-array.
-
get(Object...) - Method in class neureka.framing.NDFrame
-
 
-
get(Object...) - Method in interface neureka.Nda
-
-
The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view).
-
-
get(Object...) - Method in interface neureka.Tensor
-
-
The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view).
-
-
get(String) - Method in class neureka.devices.opencl.KernelCache
-
 
-
get(String...) - Static method in interface neureka.devices.Device
-
-
This method returns Device instances matching - the given search parameter.
-
-
get(String, boolean) - Method in class neureka.math.FunctionCache
-
 
-
get(List<Object>) - Method in class neureka.framing.NDFrame
-
 
-
get(cl_device_id) - Method in class neureka.devices.opencl.OpenCLPlatform
+
getAbs() - Method in class neureka.math.Functions
 
-
Get<ValueType> - Interface in neureka.framing.fluent
+
getActivation() - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
getAbs() - Method in class neureka.math.Functions
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarAbsolute
 
-
getActivation() - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarCbrt
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarAbsolute
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarCosinus
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarCbrt
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarExp
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarCosinus
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarGaSU
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarExp
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarGaTU
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarGaSU
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarGaussian
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarGaTU
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarGaussianFast
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarGaussian
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarGeLU
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarGaussianFast
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarIdentity
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarGeLU
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarLog10
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarIdentity
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarLogarithm
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarLog10
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarQuadratic
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarLogarithm
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarReLU
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarQuadratic
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSeLU
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarReLU
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSigmoid
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSeLU
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSiLU
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSigmoid
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSinus
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSiLU
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSoftplus
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSinus
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSoftsign
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSoftplus
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSqrt
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSoftsign
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarTanh
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarSqrt
+
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarTanhFast
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarTanh
-
 
-
getActivation() - Method in class neureka.backend.main.implementations.fun.ScalarTanhFast
-
 
-
getActiveThreadCount() - Method in class neureka.devices.host.CPU.JVMExecutor
+
getActiveThreadCount() - Method in class neureka.devices.host.CPU.JVMExecutor
Returns the approximate number of threads that are actively executing tasks.
-
getAdd() - Method in class neureka.math.Functions
+
getAdd() - Method in class neureka.math.Functions
 
-
getAddAssign() - Method in class neureka.math.Functions
+
getAddAssign() - Method in class neureka.math.Functions
 
-
getAdHocKernel(String) - Method in class neureka.devices.opencl.OpenCLDevice
+
getAdHocKernel(String) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
getAgentSupplier() - Method in class neureka.backend.api.Result
+
getAgentSupplier() - Method in class neureka.backend.api.Result
 
-
getAlgorithm() - Method in class neureka.backend.api.ExecutionCall
+
getAlgorithm() - Method in class neureka.backend.api.ExecutionCall
An ExecutionCall will either already have a targeted Algorithm defined at instantiation or otherwise it will query the associated Operation for an Algorithm best suitable for the state of this ExecutionCall.
-
getAlgorithm(Class<T>) - Method in interface neureka.backend.api.Operation
+
getAlgorithm(Class<T>) - Method in interface neureka.backend.api.Operation
Operation implementations embody a component system hosting unique Algorithm instances.
-
getAlgorithm(Class<T>) - Method in class neureka.backend.api.template.operations.AbstractOperation
+
getAlgorithm(Class<T>) - Method in class neureka.backend.api.template.operations.AbstractOperation
Operation implementations embody a component system hosting unique Algorithm instances.
-
getAlgorithmFor(ExecutionCall<?>) - Method in interface neureka.backend.api.Operation
+
getAlgorithmFor(ExecutionCall<?>) - Method in interface neureka.backend.api.Operation
Alongside a component system made up of Algorithm instances, implementations of this interface also ought to express a routing mechanism which finds the best Algorithm for a given ExecutionCall instance.
-
getAlgorithmFor(ExecutionCall<?>) - Method in class neureka.backend.api.template.operations.AbstractOperation
+
getAlgorithmFor(ExecutionCall<?>) - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
getAlgorithmName() - Method in interface neureka.backend.api.ini.LoadingContext
+
getAlgorithmName() - Method in interface neureka.backend.api.ini.LoadingContext
 
-
getAll(Class<T>) - Method in class neureka.common.composition.AbstractComponentOwner
+
getAll(Class<T>) - Method in class neureka.common.composition.AbstractComponentOwner
This method tries to find all components inside the internal component array whose classes are sub types of the one provided.
-
getAll(Class<T>) - Method in interface neureka.common.composition.ComponentOwner
+
getAll(Class<T>) - Method in interface neureka.common.composition.ComponentOwner
Use this to get all components of the specified component type class.
-
getAllAlgorithms() - Method in interface neureka.backend.api.Operation
+
getAllAlgorithms() - Method in interface neureka.backend.api.Operation
 
-
getAllAlgorithms() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
getAllAlgorithms() - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
getAllAliases() - Method in class neureka.framing.fluent.AxisFrame
+
getAllAliases() - Method in class neureka.framing.fluent.AxisFrame
 
-
getAllAliasesForIndex(int) - Method in class neureka.framing.fluent.AxisFrame
+
getAllAliasesForIndex(int) - Method in class neureka.framing.fluent.AxisFrame
 
-
getAllFunctions() - Method in interface neureka.math.Function
+
getAllFunctions() - Method in interface neureka.math.Function
 
-
getAndRemovePendingError() - Method in class neureka.autograd.GraphNode
+
getAndRemovePendingError() - Method in class neureka.autograd.GraphNode
This method is called by the JITProp component.
-
getArchitecture() - Static method in class neureka.devices.host.machine.ConcreteMachine
+
getArchitecture() - Static method in class neureka.devices.host.machine.ConcreteMachine
 
-
getArity() - Method in interface neureka.backend.api.Operation
+
getArity() - Method in interface neureka.backend.api.Operation
Arity is the number of arguments or operands that this function or operation takes.
-
getArity() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
getArity() - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
getArity() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
getArity() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
getAsInt() - Method in enum class neureka.devices.host.concurrent.Parallelism
+
getAsInt() - Method in enum neureka.devices.host.concurrent.Parallelism
 
-
getAt(int) - Method in interface neureka.Nda
+
getAt(int...) - Method in interface neureka.Nda
-
This getter method creates and returns a slice of the original nd-array.
+
The following method enables access to specific scalar elements within the nd-array.
-
getAt(int) - Method in interface neureka.Tensor
+
getAt(Number) - Method in interface neureka.Nda
This getter method creates and returns a slice of the original nd-array.
-
getAt(int...) - Method in interface neureka.Nda
+
getAt(Object...) - Method in interface neureka.Nda
-
The following method enables access to specific scalar elements within the nd-array.
+
The following method enables the creation of nd-array slices which access + the same underlying data (possibly from a different view).
-
getAt(int...) - Method in interface neureka.Tensor
+
getAt(int) - Method in interface neureka.Nda
-
The following method enables access to specific scalar elements within the nd-array.
+
This getter method creates and returns a slice of the original nd-array.
-
getAt(Number) - Method in interface neureka.Nda
+
getAt(Map<?, Integer>) - Method in interface neureka.Nda
-
This getter method creates and returns a slice of the original nd-array.
+
This method is most useful when used in Groovy + where defining maps is done through square brackets, + making it possible to slice nd-arrays like so:
-
getAt(Number) - Method in interface neureka.Tensor
+
getAt(List<?>) - Method in interface neureka.Nda
-
This getter method creates and returns a slice of the original nd-array.
+
This method enables nd-array slicing! + It takes a key of various types and configures a slice + nd-array which shares the same underlying data as the original nd-array.
-
getAt(Object...) - Method in interface neureka.Nda
+
getAt(int...) - Method in interface neureka.Tensor
-
The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view).
+
The following method enables access to specific scalar elements within the nd-array.
-
getAt(Object...) - Method in interface neureka.Tensor
+
getAt(Number) - Method in interface neureka.Tensor
-
The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view).
+
This getter method creates and returns a slice of the original nd-array.
-
getAt(List<?>) - Method in interface neureka.Nda
+
getAt(Object...) - Method in interface neureka.Tensor
-
This method enables nd-array slicing! - It takes a key of various types and configures a slice - nd-array which shares the same underlying data as the original nd-array.
+
The following method enables the creation of nd-array slices which access + the same underlying data (possibly from a different view).
-
getAt(List<?>) - Method in interface neureka.Tensor
+
getAt(int) - Method in interface neureka.Tensor
-
This method enables nd-array slicing! - It takes a key of various types and configures a slice - nd-array which shares the same underlying data as the original nd-array.
+
This getter method creates and returns a slice of the original nd-array.
-
getAt(Map<?, Integer>) - Method in interface neureka.Nda
+
getAt(Map<?, Integer>) - Method in interface neureka.Tensor
This method is most useful when used in Groovy where defining maps is done through square brackets, making it possible to slice nd-arrays like so:
-
getAt(Map<?, Integer>) - Method in interface neureka.Tensor
+
getAt(List<?>) - Method in interface neureka.Tensor
-
This method is most useful when used in Groovy - where defining maps is done through square brackets, - making it possible to slice nd-arrays like so:
+
This method enables nd-array slicing! + It takes a key of various types and configures a slice + nd-array which shares the same underlying data as the original nd-array.
-
getAutogradFunction() - Method in class neureka.backend.api.BackendContext
+
getAutogradFunction() - Method in class neureka.backend.api.BackendContext
This method returns a Functions instance which wraps pre-instantiated Function instances which are configured to track their computational history.
-
getBackend() - Method in class neureka.Neureka
+
getBackend() - Method in class neureka.Neureka
 
-
getCbrt() - Method in class neureka.math.Functions
+
getCbrt() - Method in class neureka.math.Functions
 
-
getCellSize() - Method in class neureka.view.NDPrintSettings
+
getCellSize() - Method in class neureka.view.NDPrintSettings
A cell size refers to the number of characters reserved to - the String representation of a single element.
+ the String representation of a single element.
-
getChildren() - Method in class neureka.autograd.GraphNode
+
getChildren() - Method in class neureka.autograd.GraphNode
The children are GraphNode instances which represent computations involving the payload of this very GraphNode instance.
-
getChildren() - Method in class neureka.framing.Relation
+
getChildren() - Method in class neureka.framing.Relation
 
-
getCode() - Method in class neureka.devices.opencl.KernelCode
+
getCode() - Method in class neureka.devices.opencl.KernelCode
 
-
getColLabels() - Method in class neureka.devices.file.CSVHandle
+
getColLabels() - Method in class neureka.devices.file.CSVHandle
 
-
getCompletedTaskCount() - Method in class neureka.devices.host.CPU.JVMExecutor
+
getCompletedTaskCount() - Method in class neureka.devices.host.CPU.JVMExecutor
Returns the approximate total number of tasks that have completed execution.
-
getConcat() - Method in class neureka.math.Functions
+
getConcat() - Method in class neureka.math.Functions
 
-
getContext() - Method in class neureka.devices.opencl.OpenCLPlatform
+
getContext() - Method in class neureka.devices.opencl.OpenCLPlatform
 
-
getConv() - Method in class neureka.math.Functions
+
getConv() - Method in class neureka.math.Functions
 
-
getCoreCount() - Method in class neureka.devices.host.CPU
+
getCoreCount() - Method in class neureka.devices.host.CPU
Returns the number of CPU cores available to the Java virtual machine.
-
getCorePoolSize() - Method in class neureka.devices.host.CPU.JVMExecutor
+
getCorePoolSize() - Method in class neureka.devices.host.CPU.JVMExecutor
Returns the core number of threads.
-
getCos() - Method in class neureka.math.Functions
+
getCos() - Method in class neureka.math.Functions
 
-
getData() - Method in class neureka.common.utility.ListReader.Result
+
getData() - Method in class neureka.common.utility.ListReader.Result
 
-
getData() - Method in interface neureka.MutateNda
+
getData() - Method in interface neureka.MutateNda
At the heart of every tensor is the Data object, which holds the actual data array, a sequence of values of the same type.
-
getDataAs(Class<A>) - Method in interface neureka.MutateNda
+
getDataAs(Class<A>) - Method in interface neureka.MutateNda
This method returns the data of this nd-array as a Java array of the specified type.
-
getDataAs(Class<A>) - Method in interface neureka.Nda
+
getDataAs(Class<A>) - Method in interface neureka.Nda
Use this to get the items of the underlying Data buffer of this nd-array as a primitive array of the specified type.
-
getDataAt(int) - Method in interface neureka.Nda
+
getDataAt(int) - Method in interface neureka.Nda
Use this to access elements of the underlying data array without any index transformation applied to it.
-
getDataForWriting(Class<A>) - Method in interface neureka.MutateTensor
+
getDataForWriting(Class<A>) - Method in interface neureka.MutateTensor
Use this to access the underlying writable data of this tensor if you want to modify it.
-
getDataSize() - Method in interface neureka.devices.Device.Access
+
getDataSize() - Method in interface neureka.devices.Device.Access
 
-
getDataSize() - Method in class neureka.devices.file.CSVHandle
+
getDataSize() - Method in class neureka.devices.file.CSVHandle
 
-
getDataSize() - Method in interface neureka.devices.file.FileHandle
+
getDataSize() - Method in interface neureka.devices.file.FileHandle
This method returns the byte size of the data which is stored in the tensor of the file which is managed by this FileHandle.
-
getDataSize() - Method in class neureka.devices.file.IDXHandle
+
getDataSize() - Method in class neureka.devices.file.IDXHandle
 
-
getDataType() - Method in class neureka.devices.file.CSVHandle
+
getDataType() - Method in class neureka.devices.file.CSVHandle
 
-
getDataType() - Method in interface neureka.devices.file.FileHandle
+
getDataType() - Method in interface neureka.devices.file.FileHandle
 
-
getDataType() - Method in class neureka.devices.file.IDXHandle
+
getDataType() - Method in class neureka.devices.file.IDXHandle
 
-
getDataType() - Method in class neureka.devices.opencl.KernelCode
+
getDataType() - Method in class neureka.devices.opencl.KernelCode
 
-
getDataType() - Method in interface neureka.Tensor
+
getDataType() - Method in interface neureka.Tensor
This method returns the DataType instance of this Tensor, which is a wrapper object for the actual type class representing the value items stored inside the underlying data array of this tensor.
-
getDefaultAlgorithm() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
getDefaultAlgorithm() - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
getDefaultDataType() - Method in class neureka.Neureka.Settings.DType
+
getDefaultDataType() - Method in class neureka.Neureka.Settings.DType
 
-
getDefaultDataTypeClass() - Method in class neureka.Neureka.Settings.DType
+
getDefaultDataTypeClass() - Method in class neureka.Neureka.Settings.DType
The default data type is not relevant most of the time.
-
getDelimiter() - Method in class neureka.devices.file.CSVHandle
+
getDelimiter() - Method in class neureka.devices.file.CSVHandle
 
-
getDerivative() - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
getDerivative() - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarAbsolute
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarAbsolute
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarCbrt
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarCbrt
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarCosinus
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarCosinus
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarExp
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarExp
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarGaSU
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarGaSU
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarGaTU
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarGaTU
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarGaussian
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarGaussian
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarGaussianFast
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarGaussianFast
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarGeLU
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarGeLU
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarIdentity
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarIdentity
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarLog10
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarLog10
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarLogarithm
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarLogarithm
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarQuadratic
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarQuadratic
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarReLU
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarReLU
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSeLU
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSeLU
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSigmoid
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSigmoid
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSiLU
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSiLU
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSinus
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSinus
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSoftplus
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSoftplus
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSoftsign
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSoftsign
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSqrt
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarSqrt
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarTanh
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarTanh
 
-
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarTanhFast
+
getDerivative() - Method in class neureka.backend.main.implementations.fun.ScalarTanhFast
 
-
getDerivative(int) - Method in interface neureka.math.Function
+
getDerivative(int) - Method in interface neureka.math.Function
This method builds a new Function which is the derivative of this Function with respect to the provided input index.
-
getDerivative(int) - Method in class neureka.math.implementations.FunctionConstant
+
getDerivative(int) - Method in class neureka.math.implementations.FunctionConstant
 
-
getDerivative(int) - Method in class neureka.math.implementations.FunctionInput
+
getDerivative(int) - Method in class neureka.math.implementations.FunctionInput
 
-
getDerivative(int) - Method in class neureka.math.implementations.FunctionNode
+
getDerivative(int) - Method in class neureka.math.implementations.FunctionNode
 
-
getDerivative(int) - Method in class neureka.math.implementations.FunctionVariable
+
getDerivative(int) - Method in class neureka.math.implementations.FunctionVariable
 
-
getDerivativeIndex() - Method in class neureka.backend.api.Call
+
getDerivativeIndex() - Method in class neureka.backend.api.Call
 
-
getDerivator() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
getDerivator() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
getDevice() - Method in class neureka.backend.api.Call
+
getDevice() - Method in class neureka.backend.api.Call
 
-
getDevice() - Method in interface neureka.Tensor
+
getDevice() - Method in interface neureka.Tensor
 
-
getDeviceFor(Class<T>) - Method in class neureka.backend.api.Call
+
getDeviceFor(Class<T>) - Method in class neureka.backend.api.Call
 
-
getDevices() - Method in class neureka.devices.opencl.OpenCLPlatform
+
getDevices() - Method in class neureka.devices.opencl.OpenCLPlatform
 
-
getDimTrim() - Method in class neureka.math.Functions
+
getDimTrim() - Method in class neureka.math.Functions
 
-
getDirectory() - Method in class neureka.devices.file.FileDevice
+
getDirectory() - Method in class neureka.devices.file.FileDevice
 
-
getDiv() - Method in class neureka.math.Functions
+
getDiv() - Method in class neureka.math.Functions
 
-
getDivAssign() - Method in class neureka.math.Functions
+
getDivAssign() - Method in class neureka.math.Functions
 
-
getDot() - Method in class neureka.math.Functions
+
getDot() - Method in class neureka.math.Functions
 
-
getEstimation() - Method in class neureka.backend.api.Call.Validator.Estimator
+
getEstimation() - Method in class neureka.backend.api.Call.Validator.Estimator
 
-
getEstimator() - Method in class neureka.backend.api.Call.Validator
+
getEstimator() - Method in class neureka.backend.api.Call.Validator
 
-
getExecutor() - Method in class neureka.devices.host.CPU
+
getExecutor() - Method in class neureka.devices.host.CPU
The CPU.JVMExecutor offers a similar functionality as the parallel stream API, however it differs in that the CPU.JVMExecutor is processing CPU.RangeWorkload lambdas instead of simply exposing a single index or concrete elements for a given workload size.
-
getExp() - Method in class neureka.math.Functions
+
getExp() - Method in class neureka.math.Functions
 
-
getExtensions() - Method in class neureka.backend.api.BackendContext
+
getExtensions() - Method in class neureka.backend.api.BackendContext
 
-
getFastGaus() - Method in class neureka.math.Functions
+
getFastGaus() - Method in class neureka.math.Functions
 
-
getFastTanh() - Method in class neureka.math.Functions
+
getFastTanh() - Method in class neureka.math.Functions
 
-
getFileName() - Method in class neureka.devices.file.IDXHandle
+
getFileName() - Method in interface neureka.devices.file.FileHandle
 
-
getFileName() - Method in interface neureka.devices.file.FileHandle
+
getFrame() - Method in interface neureka.Tensor
 
-
getFrame() - Method in interface neureka.Tensor
-
 
-
getFunction() - Method in class neureka.autograd.GraphNode
+
getFunction() - Method in class neureka.autograd.GraphNode
Recorded Function which produced this GraphNode.
-
getFunction() - Method in class neureka.backend.api.BackendContext
+
getFunction() - Method in class neureka.backend.api.BackendContext
This method returns a Functions instance which wraps pre-instantiated Function instances which are configured to not track their computational history.
-
getFunctionCache() - Method in class neureka.backend.api.BackendContext
+
getFunctionCache() - Method in class neureka.backend.api.BackendContext
 
-
getGaus() - Method in class neureka.math.Functions
+
getGaus() - Method in class neureka.math.Functions
 
-
getGelu() - Method in class neureka.math.Functions
+
getGelu() - Method in class neureka.math.Functions
 
-
getGradient() - Method in interface neureka.Tensor
+
getGradient() - Method in interface neureka.Tensor
 
-
getGraphNode() - Method in interface neureka.Tensor
+
getGraphNode() - Method in interface neureka.Tensor
 
-
getHasDerivatives() - Method in class neureka.view.NDPrintSettings
+
getHasDerivatives() - Method in class neureka.view.NDPrintSettings
 
-
getHasGradient() - Method in class neureka.view.NDPrintSettings
+
getHasGradient() - Method in class neureka.view.NDPrintSettings
 
-
getHasRecursiveGraph() - Method in class neureka.view.NDPrintSettings
+
getHasRecursiveGraph() - Method in class neureka.view.NDPrintSettings
 
-
getHasShape() - Method in class neureka.view.NDPrintSettings
+
getHasShape() - Method in class neureka.view.NDPrintSettings
 
-
getHasSlimNumbers() - Method in class neureka.view.NDPrintSettings
+
getHasSlimNumbers() - Method in class neureka.view.NDPrintSettings
 
-
getHasValue() - Method in class neureka.view.NDPrintSettings
+
getHasValue() - Method in class neureka.view.NDPrintSettings
 
-
getId() - Method in class neureka.devices.opencl.OpenCLDevice
+
getId() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
getId() - Method in class neureka.devices.opencl.OpenCLPlatform
+
getId() - Method in class neureka.devices.opencl.OpenCLPlatform
 
-
getIdentifier() - Method in interface neureka.backend.api.Operation
+
getIdentifier() - Method in interface neureka.backend.api.Operation
Concrete Operation types ought to be representable by a function name.
-
getIdentifier() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
getIdentifier() - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
getIdentifier() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
getIdentifier() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
getIdy() - Method in class neureka.math.Functions
+
getIdy() - Method in class neureka.math.Functions
 
-
getImplementationFor(D) - Method in interface neureka.backend.api.DeviceAlgorithm
+
getImplementationFor(Class<D>) - Method in interface neureka.backend.api.DeviceAlgorithm
-
An ImplementationFor a specific Device can be accessed by passing +
An ImplementationFor a specific Device can be accessed by passing the class of the Device for which an implementation should be returned.
-
getImplementationFor(Class<D>) - Method in interface neureka.backend.api.DeviceAlgorithm
+
getImplementationFor(D) - Method in interface neureka.backend.api.DeviceAlgorithm
-
An ImplementationFor a specific Device can be accessed by passing the class of +
An ImplementationFor a specific Device can be accessed by passing the Device for which an implementation should be returned.
-
getImplementationFor(Class<D>) - Method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
getImplementationFor(Class<D>) - Method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
getIndent() - Method in class neureka.view.NDPrintSettings
+
getIndent() - Method in class neureka.view.NDPrintSettings
 
-
getIndexAndIncrement() - Method in interface neureka.ndim.iterator.NDIterator
+
getIndexAndIncrement() - Method in interface neureka.ndim.iterator.NDIterator
 
-
getIndexAtAlias(Object) - Method in class neureka.framing.fluent.AxisFrame
+
getIndexAtAlias(Object) - Method in class neureka.framing.fluent.AxisFrame
 
-
getIndexToIndexAccessPattern() - Method in interface neureka.ndim.config.NDConfiguration
+
getIndexToIndexAccessPattern() - Method in interface neureka.ndim.config.NDConfiguration
 
-
getInt(cl_device_id, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
+
getInt(cl_device_id, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
Returns the value of the device info parameter with the given name
-
getInts(cl_device_id, int, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
+
getInts(cl_device_id, int, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
Returns the values of the device info parameter with the given name
-
getIsAutoConvertingExternalDataToJVMTypes() - Method in class neureka.Neureka.Settings.DType
+
getIsAutoConvertingExternalDataToJVMTypes() - Method in class neureka.Neureka.Settings.DType
This flag will determine if foreign data types will be converted into the next best fit (in terms of bits) or if it should be converted into something that does not mess with the representation of the data.
-
getIsCellBound() - Method in class neureka.view.NDPrintSettings
+
getIsCellBound() - Method in class neureka.view.NDPrintSettings
 
-
getIsDifferentiable() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
getIsDifferentiable() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
getIsIndexer() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
getIsIndexer() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
getIsInline() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
getIsInline() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
getIsLegacy() - Method in class neureka.view.NDPrintSettings
+
getIsLegacy() - Method in class neureka.view.NDPrintSettings
This flag determines the usage of bracket types, where "[1x3]:(1, 2, 3)" would be the legacy version of "(1x3):[1, 2, 3]".
-
getIsMultiline() - Method in class neureka.view.NDPrintSettings
+
getIsMultiline() - Method in class neureka.view.NDPrintSettings
 
-
getIsOperator() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
getIsOperator() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
getIsScientific() - Method in class neureka.view.NDPrintSettings
+
getIsScientific() - Method in class neureka.view.NDPrintSettings
 
-
getItem() - Method in interface neureka.Nda
+
getItem() - Method in interface neureka.Nda
-
Equivalent to the #item(0) and Nda.item().
+
Equivalent to the #item(0) and Nda.item().
-
getItems() - Method in interface neureka.Nda
+
getItems() - Method in interface neureka.Nda
-
A more verbose version of the Nda.items() method (best used by JVM languages with property support).
+
A more verbose version of the Nda.items() method (best used by JVM languages with property support).
-
getItemsAs(Class<A>) - Method in interface neureka.Nda
+
getItemsAs(Class<A>) - Method in interface neureka.Nda
Use this to get the items of this nd-array as a primitive array of the specified type.
-
getItemType() - Method in interface neureka.Nda
+
getItemType() - Method in interface neureka.Nda
 
-
getItemTypeClass() - Method in class neureka.dtype.DataType
+
getItemTypeClass() - Method in class neureka.dtype.DataType
 
-
getKernel(String) - Method in class neureka.devices.opencl.OpenCLDevice
+
getKernel(ExecutionCall<OpenCLDevice>) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
getKernel(String) - Method in class neureka.devices.opencl.OpenCLPlatform
+
getKernel(String) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
getKernel(ExecutionCall<OpenCLDevice>) - Method in class neureka.devices.opencl.OpenCLDevice
+
getKernel(String) - Method in class neureka.devices.opencl.OpenCLPlatform
 
-
getKernelCode() - Method in class neureka.backend.main.implementations.ParsedCLImplementation
+
getKernelCode() - Method in class neureka.backend.main.implementations.ParsedCLImplementation
 
-
getKernelCode() - Method in class neureka.backend.main.implementations.SimpleCLImplementation
+
getKernelCode() - Method in class neureka.backend.main.implementations.SimpleCLImplementation
 
-
getKernelCode() - Method in interface neureka.devices.opencl.StaticKernelSource
+
getKernelCode() - Method in interface neureka.devices.opencl.StaticKernelSource
 
-
getKernelFor(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.ParsedCLImplementation
+
getKernelFor(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.ParsedCLImplementation
 
-
getKernelFor(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.SimpleCLImplementation
+
getKernelFor(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.SimpleCLImplementation
 
-
getKernelFor(ExecutionCall<OpenCLDevice>) - Method in interface neureka.devices.opencl.KernelSource
+
getKernelFor(ExecutionCall<OpenCLDevice>) - Method in interface neureka.devices.opencl.KernelSource
 
-
getLabel() - Method in class neureka.framing.NDFrame
+
getLabel() - Method in class neureka.framing.NDFrame
 
-
getLabel() - Method in interface neureka.Nda
+
getLabel() - Method in interface neureka.Nda
A nd-array can have a label.
-
getLayout() - Method in interface neureka.ndim.config.NDConfiguration
+
getLayout() - Method in interface neureka.ndim.config.NDConfiguration
The layout of most tensors is either row major or column major.
-
getLearningRate() - Method in class neureka.optimization.implementations.ADAM
-
 
-
getLn() - Method in class neureka.math.Functions
+
getLearningRate() - Method in class neureka.optimization.implementations.ADAM
 
-
getLoadable() - Method in class neureka.devices.file.FileDevice
+
getLn() - Method in class neureka.math.Functions
 
-
getLoaded() - Method in class neureka.devices.file.FileDevice
+
getLoadable() - Method in class neureka.devices.file.FileDevice
 
-
getLoader() - Method in interface neureka.backend.api.BackendExtension
+
getLoaded() - Method in class neureka.devices.file.FileDevice
 
-
getLoader() - Method in class neureka.backend.cpu.CPUBackend
+
getLoader() - Method in interface neureka.backend.api.BackendExtension
 
-
getLoader() - Method in class neureka.backend.ocl.CLBackend
+
getLoader() - Method in class neureka.backend.cpu.CPUBackend
 
-
getLocation() - Method in class neureka.devices.file.IDXHandle
+
getLoader() - Method in class neureka.backend.ocl.CLBackend
 
-
getLocation() - Method in interface neureka.devices.file.FileHandle
+
getLocation() - Method in interface neureka.devices.file.FileHandle
 
-
getLog10() - Method in class neureka.math.Functions
+
getLog10() - Method in class neureka.math.Functions
 
-
getLong(cl_device_id, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
+
getLong(cl_device_id, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
Returns the value of the device info parameter with the given name
-
getLongs(int, ByteBuffer, long[]) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
-
 
-
getLongs(cl_device_id, int, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
+
getLongs(cl_device_id, int, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
Returns the values of the device info parameter with the given name
-
getMatMul() - Method in class neureka.math.Functions
+
getLongs(int, ByteBuffer, long[]) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
+
 
+
getMatMul() - Method in class neureka.math.Functions
 
-
getMax() - Method in class neureka.math.Functions
+
getMax() - Method in class neureka.math.Functions
 
-
getMemory() - Static method in class neureka.devices.host.machine.ConcreteMachine
+
getMemory() - Static method in class neureka.devices.host.machine.ConcreteMachine
 
-
getMin() - Method in class neureka.math.Functions
+
getMin() - Method in class neureka.math.Functions
 
-
getMinus() - Method in class neureka.math.Functions
+
getMinus() - Method in class neureka.math.Functions
 
-
getMinusAssign() - Method in class neureka.math.Functions
+
getMinusAssign() - Method in class neureka.math.Functions
 
-
getMod() - Method in class neureka.math.Functions
+
getMod() - Method in class neureka.math.Functions
 
-
getModAssign() - Method in class neureka.math.Functions
+
getModAssign() - Method in class neureka.math.Functions
 
-
getMode() - Method in class neureka.autograd.GraphNode
+
getMode() - Method in class neureka.autograd.GraphNode
This is the getter for an important GraphNode property which holds the auto-differentiation mode used by this instance to decide if a given error should be forward propagated backward propagated or not propagated at all.
-
getMomentum() - Method in class neureka.optimization.implementations.ADAM
+
getMomentum() - Method in class neureka.optimization.implementations.ADAM
 
-
getMul() - Method in class neureka.math.Functions
+
getMul() - Method in class neureka.math.Functions
 
-
getMulAssign() - Method in class neureka.math.Functions
+
getMulAssign() - Method in class neureka.math.Functions
 
-
getMut() - Method in interface neureka.Nda
+
getMut() - Method in interface neureka.Nda
This method exposes an API for mutating the state of this tensor.
-
getMut() - Method in interface neureka.Tensor
+
getMut() - Method in interface neureka.Tensor
This method exposes an API for mutating the state of this tensor.
-
getName() - Method in interface neureka.backend.api.Algorithm
+
getName() - Method in interface neureka.backend.api.Algorithm
The name of an Algorithm may be used for OpenCL kernel compilation or simply for debugging purposes to identify which type of algorithm is being executed at any given time...
-
getName() - Method in class neureka.backend.main.algorithms.DotProductAlgorithm
-
-
This method returns the name of this Algorithm - which may be used as variable names in OpenCL kernels or other backends.
-
-
getName() - Method in class neureka.devices.opencl.KernelCode
+
getName() - Method in class neureka.devices.opencl.KernelCode
 
-
getNDConf() - Method in interface neureka.ndim.NDimensional
+
getNDConf() - Method in interface neureka.ndim.NDimensional
 
-
getNDPrintSettings() - Method in class neureka.Neureka.Settings.View
+
getNDPrintSettings() - Method in class neureka.Neureka.Settings.View
-
Settings for configuring how tensors should be converted to String representations.
+
Settings for configuring how tensors should be converted to String representations.
-
getNeg() - Method in class neureka.math.Functions
+
getNeg() - Method in class neureka.math.Functions
 
-
getNewInstance() - Static method in interface neureka.devices.DeviceCleaner
+
getNewInstance() - Static method in interface neureka.devices.DeviceCleaner
 
-
getNewOwner() - Method in interface neureka.common.composition.Component.OwnerChangeRequest
+
getNewOwner() - Method in interface neureka.common.composition.Component.OwnerChangeRequest
 
-
getNumberOfColumns() - Method in class neureka.devices.file.CSVHandle
+
getNumberOfColumns() - Method in class neureka.devices.file.CSVHandle
 
-
getNumberOfRows() - Method in class neureka.devices.file.CSVHandle
+
getNumberOfRows() - Method in class neureka.devices.file.CSVHandle
 
-
getNumericTypeTarget() - Method in class neureka.dtype.custom.I8
-
 
-
getNumericTypeTarget() - Method in interface neureka.dtype.NumericType
+
getNumericTypeTarget() - Method in interface neureka.dtype.NumericType
This method returns the NumericType representation of the target type of this class.
-
getOldOwner() - Method in interface neureka.common.composition.Component.OwnerChangeRequest
-
 
-
getOperation() - Method in class neureka.backend.api.ExecutionCall
-
-
This returns the operation which will ultimately process this execution call.
-
-
getOperation() - Method in interface neureka.math.Function
-
 
-
getOperation() - Method in class neureka.math.implementations.FunctionConstant
-
 
-
getOperation() - Method in class neureka.math.implementations.FunctionInput
-
 
-
getOperation() - Method in class neureka.math.implementations.FunctionNode
+
getOldOwner() - Method in interface neureka.common.composition.Component.OwnerChangeRequest
 
-
getOperation() - Method in class neureka.math.implementations.FunctionVariable
-
 
-
getOperation(int) - Method in class neureka.backend.api.BackendContext
+
getOperation(int) - Method in class neureka.backend.api.BackendContext
This method queries the operations in this BackendContext by a provided index integer targeting an entry in the list of Operation implementation instances sitting in this execution context.
-
getOperation(String) - Method in class neureka.backend.api.BackendContext
+
getOperation(String) - Method in class neureka.backend.api.BackendContext
This method queries the operations in this BackendContext by a provided identifier which has to match the name of an existing operation.
-
getOperationIdentidier() - Method in interface neureka.backend.api.ini.LoadingContext
+
getOperation() - Method in class neureka.backend.api.ExecutionCall
+
+
This returns the operation which will ultimately process this execution call.
+
+
getOperation() - Method in interface neureka.math.Function
+
 
+
getOperation() - Method in class neureka.math.implementations.FunctionConstant
+
 
+
getOperation() - Method in class neureka.math.implementations.FunctionInput
+
 
+
getOperation() - Method in class neureka.math.implementations.FunctionNode
+
 
+
getOperation() - Method in class neureka.math.implementations.FunctionVariable
 
-
getOperationLookupMap() - Method in class neureka.backend.api.BackendContext
+
getOperationIdentidier() - Method in interface neureka.backend.api.ini.LoadingContext
+
 
+
getOperationLookupMap() - Method in class neureka.backend.api.BackendContext
-
This method returns an unmodifiable view of the mapping between the Operation.getIdentifier() / Operation.getOperator() properties +
This method returns an unmodifiable view of the mapping between the Operation.getIdentifier() / Operation.getOperator() properties and the Operation implementation instances to which they belong.
-
getOperations() - Method in class neureka.backend.api.BackendContext
+
getOperations() - Method in class neureka.backend.api.BackendContext
This method returns an unmodifiable view of the list of Operation implementation instances managed by this context.
-
getOperator() - Method in interface neureka.backend.api.Operation
+
getOperator() - Method in interface neureka.backend.api.Operation
 
-
getOperator() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
getOperator() - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
getOperator() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
getOperator() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
getOrNull() - Method in interface neureka.Data
+
getOrNull() - Method in interface neureka.Data
This returns the underlying raw data object of a nd-array or tensor of a backend specific type (e.g.
-
getOrNull() - Method in class neureka.devices.AbstractDeviceData
+
getOrNull() - Method in class neureka.devices.AbstractDeviceData
 
-
getParent() - Method in class neureka.framing.Relation
+
getParent() - Method in class neureka.framing.Relation
 
-
getParents() - Method in class neureka.autograd.GraphNode
+
getParents() - Method in class neureka.autograd.GraphNode
 
-
getPayload() - Method in class neureka.autograd.GraphNode
+
getPayload() - Method in class neureka.autograd.GraphNode
The value of a graph node is the tensor to which it belongs (is a component of).
-
getPayloadDataType() - Method in class neureka.autograd.GraphNode
+
getPayloadDataType() - Method in class neureka.autograd.GraphNode
 
-
getPayloadReferenceVersion() - Method in class neureka.autograd.GraphNode
+
getPayloadReferenceVersion() - Method in class neureka.autograd.GraphNode
This variable holds a copy of the version of the payload tensor recorded when this GraphNode instance is instantiated.
-
getPayloadShape() - Method in class neureka.autograd.GraphNode
+
getPayloadShape() - Method in class neureka.autograd.GraphNode
Note: This method will never return null even if the actual payload tensor was garbage collected.
-
getPendingError() - Method in class neureka.autograd.GraphNode
+
getPendingError() - Method in class neureka.autograd.GraphNode
Used by the Just-In-Time back-prop component.
-
getPermute() - Method in class neureka.math.Functions
+
getPermute() - Method in class neureka.math.Functions
 
-
getPermuteRelationFor(Tensor<V>) - Method in class neureka.framing.Relation
+
getPermuteRelationFor(Tensor<V>) - Method in class neureka.framing.Relation
When creating permuted versions of slices then there must be a translation between the shape configuration between this new slice and the original parent tensor from which both slices have been derived.
-
getPlatform() - Method in class neureka.devices.opencl.OpenCLDevice
+
getPlatform() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
getPlatforms() - Method in class neureka.backend.ocl.CLBackend
+
getPlatforms() - Method in class neureka.backend.ocl.CLBackend
 
-
getPlus() - Method in class neureka.math.Functions
+
getPlus() - Method in class neureka.math.Functions
 
-
getPlusAssign() - Method in class neureka.math.Functions
+
getPlusAssign() - Method in class neureka.math.Functions
 
-
getPostfix() - Method in class neureka.view.NDPrintSettings
+
getPostfix() - Method in class neureka.view.NDPrintSettings
 
-
getPow() - Method in class neureka.math.Functions
+
getPow() - Method in class neureka.math.Functions
 
-
getPowAssign() - Method in class neureka.math.Functions
+
getPowAssign() - Method in class neureka.math.Functions
 
-
getPrefix() - Method in class neureka.view.NDPrintSettings
+
getPrefix() - Method in class neureka.view.NDPrintSettings
 
-
getQuad() - Method in class neureka.math.Functions
+
getQuad() - Method in class neureka.math.Functions
 
-
getRandom() - Method in class neureka.math.Functions
+
getRandom() - Method in class neureka.math.Functions
 
-
getRank() - Method in interface neureka.ndim.NDimensional
+
getRank() - Method in interface neureka.ndim.NDimensional
 
-
getRawData() - Method in interface neureka.Nda
+
getRawData() - Method in interface neureka.Nda
This returns an unprocessed version of the underlying data of this nd-array.
-
getRawItems() - Method in interface neureka.Nda
+
getRawItems() - Method in interface neureka.Nda
 
-
getRelayout() - Method in class neureka.math.Functions
+
getRelayout() - Method in class neureka.math.Functions
 
-
getRelu() - Method in class neureka.math.Functions
+
getRelu() - Method in class neureka.math.Functions
 
-
getRepresentativeItemClass() - Method in interface neureka.Tensor
+
getRepresentativeItemClass() - Method in interface neureka.Tensor
-
The Class returned by this method is the representative Class of the - value items of a concrete AbstractNda but not necessarily the actual Class of +
The Class returned by this method is the representative Class of the + value items of a concrete AbstractNda but not necessarily the actual Class of a given value item, this is especially true for numeric types, which are represented by implementations of the NumericType interface.
-
getRepresentativeType() - Method in class neureka.dtype.DataType
+
getRepresentativeType() - Method in class neureka.dtype.DataType
 
-
getReshape() - Method in class neureka.math.Functions
+
getReshape() - Method in class neureka.math.Functions
 
-
getResult() - Method in class neureka.backend.main.memory.MemValidator
+
getResult() - Method in class neureka.backend.main.memory.MemValidator
 
-
getRowLabels() - Method in class neureka.devices.file.CSVHandle
+
getRowLabels() - Method in class neureka.devices.file.CSVHandle
 
-
getRowLimit() - Method in class neureka.view.NDPrintSettings
+
getRowLimit() - Method in class neureka.view.NDPrintSettings
Very large tensors with a rank larger than 1 might take a lot - of vertical space when converted to a String.
+ of vertical space when converted to a String.
-
getSelu() - Method in class neureka.math.Functions
+
getSelu() - Method in class neureka.math.Functions
The Scaled Exponential Linear Unit, or SELU, is an activation functions that induce self-normalizing properties.
-
getSettings() - Method in class neureka.backend.ocl.CLBackend
+
getSettings() - Method in class neureka.backend.ocl.CLBackend
 
-
getShape() - Method in class neureka.common.utility.ListReader.Result
+
getShape() - Method in class neureka.common.utility.ListReader.Result
 
-
getShape() - Method in class neureka.devices.file.CSVHandle
+
getShape() - Method in class neureka.devices.file.CSVHandle
 
-
getShape() - Method in interface neureka.devices.file.FileHandle
+
getShape() - Method in interface neureka.devices.file.FileHandle
 
-
getShape() - Method in class neureka.devices.file.IDXHandle
+
getShape() - Method in class neureka.devices.file.IDXHandle
 
-
getShape() - Method in interface neureka.ndim.NDConstructor
+
getShape() - Method in interface neureka.ndim.NDConstructor
 
-
getShape() - Method in interface neureka.ndim.NDimensional
+
getShape() - Method in interface neureka.ndim.NDimensional
 
-
getSigmoid() - Method in class neureka.math.Functions
+
getSigmoid() - Method in class neureka.math.Functions
 
-
getSilu() - Method in class neureka.math.Functions
+
getSilu() - Method in class neureka.math.Functions
The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x).
-
getSin() - Method in class neureka.math.Functions
-
 
-
getSize() - Method in interface neureka.ndim.NDConstructor
+
getSin() - Method in class neureka.math.Functions
 
-
getSize() - Method in interface neureka.ndim.NDimensional
-
 
-
getSize(cl_device_id, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
+
getSize(cl_device_id, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
Returns the value of the device info parameter with the given name
-
getSizes(cl_device_id, int, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
+
getSize() - Method in interface neureka.ndim.NDConstructor
+
 
+
getSize() - Method in interface neureka.ndim.NDimensional
+
 
+
getSizes(cl_device_id, int, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
Returns the values of the device info parameter with the given name
-
getSoftplus() - Method in class neureka.math.Functions
+
getSoftplus() - Method in class neureka.math.Functions
SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.
-
getSoftsign() - Method in class neureka.math.Functions
+
getSoftsign() - Method in class neureka.math.Functions
The softsign function, defined as x / ( 1 + Math.abs( x ) ), is a computationally cheap 0 centered activation function which rescales the inputs between -1 and 1, very much like the Tanh function.
-
getSqrt() - Method in class neureka.math.Functions
+
getSqrt() - Method in class neureka.math.Functions
 
-
getState() - Method in class neureka.framing.NDFrame
+
getState() - Method in class neureka.framing.NDFrame
 
-
getString(cl_device_id, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
+
getString(cl_device_id, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
Returns the value of the device info parameter with the given name
-
getString(cl_platform_id, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
+
getString(cl_platform_id, int) - Static method in class neureka.devices.opencl.OpenCLDevice.Query
Returns the value of the platform info parameter with the given name
-
getStringifier() - Method in class neureka.backend.api.template.operations.OperationBuilder
+
getStringifier() - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
getSubFunctions() - Method in interface neureka.math.Function
+
getSubFunctions() - Method in interface neureka.math.Function
 
-
getSubFunctions() - Method in class neureka.math.implementations.FunctionConstant
+
getSubFunctions() - Method in class neureka.math.implementations.FunctionConstant
 
-
getSubFunctions() - Method in class neureka.math.implementations.FunctionInput
+
getSubFunctions() - Method in class neureka.math.implementations.FunctionInput
 
-
getSubFunctions() - Method in class neureka.math.implementations.FunctionNode
+
getSubFunctions() - Method in class neureka.math.implementations.FunctionNode
 
-
getSubFunctions() - Method in class neureka.math.implementations.FunctionVariable
+
getSubFunctions() - Method in class neureka.math.implementations.FunctionVariable
 
-
getSum() - Method in class neureka.math.Functions
+
getSum() - Method in class neureka.math.Functions
 
-
getT() - Method in interface neureka.Tensor
+
getT() - Method in interface neureka.Tensor
A method which returns a new Tensor instance which is a transposed twin of this instance.
- This is an alternative to the functionally identical Tensor.T() method.
+ This is an alternative to the functionally identical Tensor.T() method.
-
getTanh() - Method in class neureka.math.Functions
+
getTanh() - Method in class neureka.math.Functions
 
-
getter(At<Object, Get<GetType>>) - Method in class neureka.framing.fluent.AxisFrame.Builder
+
getter(At<Object, Get<GetType>>) - Method in class neureka.framing.fluent.AxisFrame.Builder
 
-
getThreads() - Static method in class neureka.devices.host.machine.ConcreteMachine
+
getThreads() - Static method in class neureka.devices.host.machine.ConcreteMachine
 
-
getTime() - Method in class neureka.optimization.implementations.ADAM
+
getTime() - Method in class neureka.optimization.implementations.ADAM
 
-
getTotalNumberOfDevices() - Method in class neureka.backend.ocl.CLBackend
+
getTotalNumberOfDevices() - Method in class neureka.backend.ocl.CLBackend
 
-
getTotalSize() - Method in class neureka.devices.file.CSVHandle
+
getTotalSize() - Method in class neureka.devices.file.CSVHandle
 
-
getTotalSize() - Method in interface neureka.devices.file.FileHandle
+
getTotalSize() - Method in interface neureka.devices.file.FileHandle
This method returns the number of bytes which are used to store the tensor in the file whose access is being managed by an implementation of th FileHandle interface.
-
getTotalSize() - Method in class neureka.devices.file.IDXHandle
+
getTotalSize() - Method in class neureka.devices.file.IDXHandle
 
-
getTraits() - Method in interface neureka.ndim.config.NDConfiguration
+
getTraits() - Method in interface neureka.ndim.config.NDConfiguration
 
-
getTranspose2D() - Method in class neureka.math.Functions
+
getTranspose2D() - Method in class neureka.math.Functions
 
-
getType() - Method in class neureka.common.utility.ListReader.Result
+
getType() - Method in class neureka.common.utility.ListReader.Result
 
-
getTypeClassInstance(Class<T>) - Method in class neureka.dtype.DataType
+
getTypeClassInstance(Class<T>) - Method in class neureka.dtype.DataType
 
-
getValOf(Class<T>) - Method in class neureka.backend.api.Call
+
getValOf(Class<T>) - Method in class neureka.backend.api.Call
 
-
getValue() - Method in class neureka.common.utility.Cache.LazyEntry
+
getValue() - Method in class neureka.common.utility.Cache.LazyEntry
 
-
getValueSize() - Method in class neureka.devices.file.CSVHandle
+
getValueSize() - Method in class neureka.devices.file.CSVHandle
 
-
getValueSize() - Method in interface neureka.devices.file.FileHandle
+
getValueSize() - Method in interface neureka.devices.file.FileHandle
This method return the size of the value which is stored in the tensor of the file which is managed by this FileHandle.
-
getValueSize() - Method in class neureka.devices.file.IDXHandle
+
getValueSize() - Method in class neureka.devices.file.IDXHandle
 
-
getVelocity() - Method in class neureka.optimization.implementations.ADAM
+
getVelocity() - Method in class neureka.optimization.implementations.ADAM
 
-
getVersion() - Method in interface neureka.Tensor
+
getVersion() - Method in interface neureka.Tensor
The version number is tracking how often this tensor has been mutated.
-
globalMemSize() - Method in class neureka.devices.opencl.OpenCLDevice
-
 
-
GOOD - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
+
globalMemSize() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
goodIfAll(Call.TensorCompare) - Method in class neureka.backend.api.Call.Validator.Estimator
+
GOOD - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
 
-
goodIfAll(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
+
goodIfAll(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
 
-
goodIfAny(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
+
goodIfAll(Call.TensorCompare) - Method in class neureka.backend.api.Call.Validator.Estimator
 
-
goodIfAnyNonNull(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
+
goodIfAny(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
 
-
GPU - Enum constant in enum class neureka.devices.opencl.OpenCLDevice.Type
+
goodIfAnyNonNull(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator.Estimator
 
-
gradient() - Method in interface neureka.Tensor
+
gradient() - Method in interface neureka.Tensor
-
This is a functionally identical alternative to the Tensor.getGradient() method.
+
This is a functionally identical alternative to the Tensor.getGradient() method.
-
gradientApplyRequested() - Method in interface neureka.Tensor
+
gradientApplyRequested() - Method in interface neureka.Tensor
This flag works alongside two autograd features which can be enabled inside the library settings.
-
graphNode() - Method in interface neureka.Tensor
-
-
This is a functionally identical alternative to Tensor.getGraphNode().
-
-
GraphNode<V> - Class in neureka.autograd
+
GraphNode<V> - Class in neureka.autograd
Instances of the GraphNode class are components of tensors (Tensor instances) which model and record computations / operations between them.
-
GraphNode(Function, ExecutionCall<Device<?>>, Supplier<Result>) - Constructor for class neureka.autograd.GraphNode
+
GraphNode(Function, ExecutionCall<Device<?>>, Supplier<Result>) - Constructor for class neureka.autograd.GraphNode
 
-
GraphNode.Print - Enum Class in neureka.autograd
-
 
-
GRAY_BYTE - Enum constant in enum class neureka.Tensor.ImageType
-
 
-
GRAY_USHORT - Enum constant in enum class neureka.Tensor.ImageType
+
graphNode() - Method in interface neureka.Tensor
+
+
This is a functionally identical alternative to Tensor.getGraphNode().
+
+
GraphNode.Print - Enum in neureka.autograd
 
-
groupBy(String, String, String, String) - Static method in class neureka.math.parsing.ParseUtil
+
groupBy(String, String, String, String) - Static method in class neureka.math.parsing.ParseUtil
 
-

H

-
-
Hardware - Class in neureka.devices.host.machine
+ + + +

H

+
+
Hardware - Class in neureka.devices.host.machine
This models the cache levels and threads of a CPU using an array of where each entry represents a memory level.
-
Hardware(String, BasicMachine[]) - Constructor for class neureka.devices.host.machine.Hardware
+
Hardware(String, BasicMachine[]) - Constructor for class neureka.devices.host.machine.Hardware
new BasicMachine[] { SYSTEM, L3, L2, L1 } or new BasicMachine[] { SYSTEM, L2, L1 } or in worst case new BasicMachine[] { SYSTEM, L1 }
-
has(Class<E>) - Method in class neureka.backend.api.BackendContext
+
has(GraphNode<V>) - Method in class neureka.autograd.GraphNode
+
+
This method checks if a given graph node is an AD target of this node.
+
+
has(Class<E>) - Method in class neureka.backend.api.BackendContext
Checks if this context has an instance of the provided BackendExtension type.
-
has(Class<T>) - Method in class neureka.common.composition.AbstractComponentOwner
+
has(Class<T>) - Method in class neureka.common.composition.AbstractComponentOwner
-
This method checks if a component identified by the passed Class +
This method checks if a component identified by the passed Class instance is present inside the stored component collection.
-
has(Class<T>) - Method in interface neureka.common.composition.ComponentOwner
+
has(Class<T>) - Method in interface neureka.common.composition.ComponentOwner
Use this to check if a component of the specified component type class is present.
-
has(String) - Method in class neureka.devices.opencl.KernelCache
-
 
-
has(String, boolean) - Method in class neureka.math.FunctionCache
-
 
-
has(GraphNode<V>) - Method in class neureka.autograd.GraphNode
-
-
This method checks if a given graph node is an AD target of this node.
-
-
has(NDTrait) - Method in interface neureka.ndim.config.NDConfiguration
+
has(O) - Method in class neureka.common.utility.Cache
 
-
has(Tensor<T>) - Method in class neureka.devices.AbstractBaseDevice
+
has(Tensor<T>) - Method in class neureka.devices.AbstractBaseDevice
This method checks if the passed tensor is stored on this Device instance.
-
has(Tensor<T>) - Method in interface neureka.devices.Device
+
has(Tensor<T>) - Method in interface neureka.devices.Device
Use this to check if a tensor is stored on this Device!

-
has(Tensor<T>) - Method in class neureka.devices.file.FileDevice
+
has(Tensor<T>) - Method in class neureka.devices.file.FileDevice
 
-
has(O) - Method in class neureka.common.utility.Cache
+
has(String) - Method in class neureka.devices.opencl.KernelCache
 
-
has(cl_device_id) - Method in class neureka.devices.opencl.OpenCLPlatform
+
has(cl_device_id) - Method in class neureka.devices.opencl.OpenCLPlatform
 
-
hasAdHocKernel(String) - Method in class neureka.devices.opencl.OpenCLDevice
+
has(String, boolean) - Method in class neureka.math.FunctionCache
 
-
hasChildren() - Method in class neureka.framing.Relation
+
has(NDTrait) - Method in interface neureka.ndim.config.NDConfiguration
 
-
hasDerivatives() - Method in class neureka.autograd.GraphNode
+
hasAdHocKernel(String) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
hasGradient() - Method in interface neureka.Tensor
+
hasChildren() - Method in class neureka.framing.Relation
+
 
+
hasDerivatives() - Method in class neureka.autograd.GraphNode
+
 
+
hasGradient() - Method in interface neureka.Tensor
Tensors can be components of other tensors which makes the implicitly their gradients.
-
hashCode() - Method in class neureka.common.utility.Cache.LazyEntry
+
hashCode() - Method in class neureka.common.utility.Cache.LazyEntry
 
-
hashCode() - Method in class neureka.devices.host.machine.BasicMachine
+
hashCode() - Method in class neureka.devices.host.machine.BasicMachine
 
-
hashCode() - Method in class neureka.devices.host.machine.CommonMachine
+
hashCode() - Method in class neureka.devices.host.machine.CommonMachine
 
-
hashCode() - Method in class neureka.devices.host.machine.ConcreteMachine
+
hashCode() - Method in class neureka.devices.host.machine.ConcreteMachine
 
-
hashCode() - Method in class neureka.devices.host.machine.Hardware
+
hashCode() - Method in class neureka.devices.host.machine.Hardware
 
-
hashCode() - Method in class neureka.devices.opencl.KernelCode
+
hashCode() - Method in class neureka.devices.opencl.KernelCode
 
-
hashCode() - Method in class neureka.dtype.DataType
+
hashCode() - Method in class neureka.dtype.DataType
 
-
hashCode() - Method in class neureka.ndim.config.AbstractNDC
+
hashCode() - Method in class neureka.ndim.config.AbstractNDC
 
-
hashCode() - Method in interface neureka.ndim.config.NDConfiguration
+
hashCode() - Method in interface neureka.ndim.config.NDConfiguration
 
-
hasImplementationFor(D) - Method in interface neureka.backend.api.DeviceAlgorithm
+
hasImplementationFor(D) - Method in interface neureka.backend.api.DeviceAlgorithm
 
-
hasKernel(String) - Method in class neureka.devices.opencl.OpenCLPlatform
+
hasKernel(String) - Method in class neureka.devices.opencl.OpenCLPlatform
 
-
hasLabelsForAxis(Object) - Method in class neureka.framing.NDFrame
+
hasLabelsForAxis(Object) - Method in class neureka.framing.NDFrame
 
-
hasOperation(String) - Method in class neureka.backend.api.BackendContext
+
hasOperation(Operation) - Method in class neureka.backend.api.BackendContext
 
-
hasOperation(Operation) - Method in class neureka.backend.api.BackendContext
+
hasOperation(String) - Method in class neureka.backend.api.BackendContext
 
-
hasParent() - Method in class neureka.framing.Relation
+
hasParent() - Method in class neureka.framing.Relation
 
-
holderArrayType() - Method in class neureka.dtype.custom.F32
+
holderArrayType() - Method in class neureka.dtype.custom.F32
 
-
holderArrayType() - Method in class neureka.dtype.custom.F64
+
holderArrayType() - Method in class neureka.dtype.custom.F64
 
-
holderArrayType() - Method in class neureka.dtype.custom.I16
+
holderArrayType() - Method in class neureka.dtype.custom.I16
 
-
holderArrayType() - Method in class neureka.dtype.custom.I32
+
holderArrayType() - Method in class neureka.dtype.custom.I32
 
-
holderArrayType() - Method in class neureka.dtype.custom.I64
+
holderArrayType() - Method in class neureka.dtype.custom.I64
 
-
holderArrayType() - Method in class neureka.dtype.custom.I8
+
holderArrayType() - Method in class neureka.dtype.custom.I8
 
-
holderArrayType() - Method in class neureka.dtype.custom.UI16
+
holderArrayType() - Method in class neureka.dtype.custom.UI16
 
-
holderArrayType() - Method in class neureka.dtype.custom.UI32
+
holderArrayType() - Method in class neureka.dtype.custom.UI32
 
-
holderArrayType() - Method in class neureka.dtype.custom.UI64
+
holderArrayType() - Method in class neureka.dtype.custom.UI64
 
-
holderArrayType() - Method in class neureka.dtype.custom.UI8
+
holderArrayType() - Method in class neureka.dtype.custom.UI8
 
-
holderArrayType() - Method in interface neureka.dtype.NumericType
+
holderArrayType() - Method in interface neureka.dtype.NumericType
The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]).
-
holderType() - Method in class neureka.dtype.custom.F32
+
holderType() - Method in class neureka.dtype.custom.F32
 
-
holderType() - Method in class neureka.dtype.custom.F64
+
holderType() - Method in class neureka.dtype.custom.F64
 
-
holderType() - Method in class neureka.dtype.custom.I16
+
holderType() - Method in class neureka.dtype.custom.I16
 
-
holderType() - Method in class neureka.dtype.custom.I32
+
holderType() - Method in class neureka.dtype.custom.I32
 
-
holderType() - Method in class neureka.dtype.custom.I64
+
holderType() - Method in class neureka.dtype.custom.I64
 
-
holderType() - Method in class neureka.dtype.custom.I8
+
holderType() - Method in class neureka.dtype.custom.I8
 
-
holderType() - Method in class neureka.dtype.custom.UI16
+
holderType() - Method in class neureka.dtype.custom.UI16
 
-
holderType() - Method in class neureka.dtype.custom.UI32
+
holderType() - Method in class neureka.dtype.custom.UI32
 
-
holderType() - Method in class neureka.dtype.custom.UI64
+
holderType() - Method in class neureka.dtype.custom.UI64
 
-
holderType() - Method in class neureka.dtype.custom.UI8
+
holderType() - Method in class neureka.dtype.custom.UI8
 
-
holderType() - Method in interface neureka.dtype.NumericType
+
holderType() - Method in interface neureka.dtype.NumericType
The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint).
-
HOW_TO_INSTALL_OPENCL - Variable in enum class neureka.devices.opencl.utility.Messages.Tips
+
HOW_TO_INSTALL_OPENCL - Variable in enum neureka.devices.opencl.utility.Messages.Tips
 
-
HOW_TO_INSTALL_OPENCL_DRIVERS - Variable in enum class neureka.devices.opencl.utility.Messages.Tips
+
HOW_TO_INSTALL_OPENCL_DRIVERS - Variable in enum neureka.devices.opencl.utility.Messages.Tips
 
-

I

-
-
i() - Method in interface neureka.ndim.iterator.NDIterator
+ + + +

I

+
+
i() - Method in interface neureka.ndim.iterator.NDIterator
 
-
i() - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
-
i() - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
-
i() - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
-
i() - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
-
i() - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
-
i() - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
-
i() - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
-
i() - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
-
i() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
-
i() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
i() - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
+
i() - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
+
i() - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
+
i() - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
+
i() - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
+
i() - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
+
i() - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
+
i() - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
+
i() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
i() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
 
-
I16 - Class in neureka.dtype.custom
+
I16 - Class in neureka.dtype.custom
 
-
I16() - Constructor for class neureka.dtype.custom.I16
+
I16() - Constructor for class neureka.dtype.custom.I16
 
-
I32 - Class in neureka.dtype.custom
+
I32 - Class in neureka.dtype.custom
 
-
I32() - Constructor for class neureka.dtype.custom.I32
+
I32() - Constructor for class neureka.dtype.custom.I32
 
-
I64 - Class in neureka.dtype.custom
+
I64 - Class in neureka.dtype.custom
 
-
I64() - Constructor for class neureka.dtype.custom.I64
+
I64() - Constructor for class neureka.dtype.custom.I64
 
-
I8 - Class in neureka.dtype.custom
+
I8 - Class in neureka.dtype.custom
The following abstract class implements some basic logic which is applicable across all final concrete classes extending this abstract one.
-
I8() - Constructor for class neureka.dtype.custom.I8
+
I8() - Constructor for class neureka.dtype.custom.I8
 
-
IAXPY - Class in neureka.backend.main.operations.linear.internal.blas
+
IAXPY - Class in neureka.backend.main.operations.linear.internal.blas
The ?axpy routines perform a vector-vector operation defined as y := a*x + y where: a is a scalar x and y are vectors each with a number of elements that equals n.
-
IAXPY() - Constructor for class neureka.backend.main.operations.linear.internal.blas.IAXPY
+
IAXPY() - Constructor for class neureka.backend.main.operations.linear.internal.blas.IAXPY
 
-
id() - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
id() - Method in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarAbsolute
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarAbsolute
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarCbrt
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarCbrt
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarCosinus
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarCosinus
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarExp
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarExp
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarGaSU
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarGaSU
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarGaTU
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarGaTU
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarGaussian
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarGaussian
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarGaussianFast
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarGaussianFast
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarGeLU
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarGeLU
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarIdentity
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarIdentity
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarLog10
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarLog10
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarLogarithm
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarLogarithm
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarQuadratic
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarQuadratic
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarReLU
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarReLU
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarSeLU
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarSeLU
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarSigmoid
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarSigmoid
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarSiLU
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarSiLU
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarSinus
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarSinus
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarSoftplus
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarSoftplus
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarSoftsign
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarSoftsign
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarSqrt
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarSqrt
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarTanh
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarTanh
 
-
id() - Method in class neureka.backend.main.implementations.fun.ScalarTanhFast
+
id() - Method in class neureka.backend.main.implementations.fun.ScalarTanhFast
 
-
identifier(String) - Method in class neureka.backend.api.template.operations.OperationBuilder
+
identifier(String) - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
Identity - Class in neureka.backend.main.operations.functions
+
IDENTITY - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
Identity() - Constructor for class neureka.backend.main.operations.functions.Identity
+
Identity - Class in neureka.backend.main.operations.functions
 
-
IDENTITY - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
Identity() - Constructor for class neureka.backend.main.operations.functions.Identity
 
-
IDOT - Class in neureka.backend.main.operations.linear.internal.blas
+
IDOT - Class in neureka.backend.main.operations.linear.internal.blas
The ?dot routines perform a vector-vector reduction operation defined as Equation where xi and yi are elements of vectors x and y.
-
IDOT() - Constructor for class neureka.backend.main.operations.linear.internal.blas.IDOT
+
IDOT() - Constructor for class neureka.backend.main.operations.linear.internal.blas.IDOT
 
-
IDXHandle - Class in neureka.devices.file
+
IDXHandle - Class in neureka.devices.file
This class is one of many extensions of the AbstractFileHandle which is therefore ultimately an implementation of the FileHandle interface.
-
IDXHandle(String) - Constructor for class neureka.devices.file.IDXHandle
+
IDXHandle(String) - Constructor for class neureka.devices.file.IDXHandle
 
-
IDXHandle(Tensor<Number>, String) - Constructor for class neureka.devices.file.IDXHandle
+
IDXHandle(Tensor<Number>, String) - Constructor for class neureka.devices.file.IDXHandle
 
-
idy() - Method in class neureka.math.Functions
+
idy() - Method in class neureka.math.Functions
 
-
ifValid(T) - Method in class neureka.backend.api.Call.Validator
+
ifValid(T) - Method in class neureka.backend.api.Call.Validator
 
-
IGEMM - Class in neureka.backend.main.operations.linear.internal.blas
+
IGEMM - Class in neureka.backend.main.operations.linear.internal.blas
A collection of primitive sub-routines for matrix multiplication performed on continuous arrays which are designed so that they can be vectorized by the JVMs JIT compiler (AVX instructions).
-
IGEMM() - Constructor for class neureka.backend.main.operations.linear.internal.blas.IGEMM
+
IGEMM() - Constructor for class neureka.backend.main.operations.linear.internal.blas.IGEMM
 
-
IGEMM.VectorOperationI32 - Interface in neureka.backend.main.operations.linear.internal.blas
+
IGEMM.VectorOperationI32 - Interface in neureka.backend.main.operations.linear.internal.blas
 
-
IGEMM.VectorOperationI64 - Interface in neureka.backend.main.operations.linear.internal.blas
+
IGEMM.VectorOperationI64 - Interface in neureka.backend.main.operations.linear.internal.blas
 
-
image2DMaxHeight() - Method in class neureka.devices.opencl.OpenCLDevice
+
image2DMaxHeight() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
image2DMaxWidth() - Method in class neureka.devices.opencl.OpenCLDevice
+
image2DMaxWidth() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
image3DMaxDepth() - Method in class neureka.devices.opencl.OpenCLDevice
+
image3DMaxDepth() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
image3DMaxHeight() - Method in class neureka.devices.opencl.OpenCLDevice
+
image3DMaxHeight() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
image3DMaxWidth() - Method in class neureka.devices.opencl.OpenCLDevice
+
image3DMaxWidth() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
imageSupport() - Method in class neureka.devices.opencl.OpenCLDevice
+
imageSupport() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
ImplementationFor<D extends Device<?>> - Interface in neureka.backend.api
+
ImplementationFor<D extends Device<?>> - Interface in neureka.backend.api
Generally speaking, this interface describes the functionality of an implementation of an execution procedure tailored to a specific Device (interface) instance @@ -4112,879 +4068,875 @@

I

of instances of implementations of the Algorithm interface, which themselves are components of Operation implementation instances.
-
ImplementationReceiver - Interface in neureka.backend.api.ini
+
ImplementationReceiver - Interface in neureka.backend.api.ini
 
-
in(Supplier<R>) - Method in interface neureka.devices.Device.In
+
in(Supplier<R>) - Method in interface neureka.devices.Device.In
 
-
increment() - Method in class neureka.devices.ReferenceCounter
+
increment() - Method in class neureka.devices.ReferenceCounter
 
-
increment() - Method in interface neureka.ndim.iterator.NDIterator
+
increment(int[], int[]) - Static method in class neureka.ndim.config.NDConfiguration.Utility
 
-
increment() - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
-
increment() - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
-
increment() - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
-
increment() - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
-
increment() - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
-
increment() - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
-
increment() - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
-
increment() - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
-
increment() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
-
increment() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
increment() - Method in interface neureka.ndim.iterator.NDIterator
 
-
increment(int[], int[]) - Static method in class neureka.ndim.config.NDConfiguration.Utility
+
increment() - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
+
increment() - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
+
increment() - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
+
increment() - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
+
increment() - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
+
increment() - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
+
increment() - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
+
increment() - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
+
increment() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
increment() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
 
-
INCREMENT - Enum constant in enum class neureka.devices.ReferenceCounter.ChangeType
+
incrementUsageCount() - Method in class neureka.devices.AbstractDeviceData
 
-
incrementUsageCount() - Method in class neureka.devices.AbstractDeviceData
+
incrementUsageCount() - Method in interface neureka.devices.DeviceData
 
-
incrementUsageCount() - Method in interface neureka.devices.DeviceData
-
 
-
incrementVersion(ExecutionCall<?>) - Method in interface neureka.MutateTensor
+
incrementVersion(ExecutionCall<?>) - Method in interface neureka.MutateTensor
This method is responsible for incrementing the "_version" field variable which represents the version of the data of this tensor.
-
indent(int) - Static method in class neureka.view.NdaAsString.Util
+
indent(int) - Static method in class neureka.view.NdaAsString.Util
 
-
index() - Method in class neureka.math.implementations.FunctionInput
+
index() - Method in class neureka.math.implementations.FunctionInput
 
-
indexOfIndex(int) - Method in interface neureka.ndim.config.NDConfiguration
+
indexOfIndex(int) - Method in interface neureka.ndim.config.NDConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
indexOfIndex(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
-
indexOfIndex(int) - Method in interface neureka.ndim.NDimensional
+
indexOfIndex(int) - Method in interface neureka.ndim.NDimensional
This is a convenience method identical to ndArray.getNDConf().indexOfIndex(i).
-
indexOfIndices(int) - Method in class neureka.ndim.config.types.D1C
-
 
-
indexOfIndices(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
-
indexOfIndices(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
-
indexOfIndices(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
-
indexOfIndices(int[]) - Method in interface neureka.ndim.config.NDConfiguration
+
indexOfIndices(int[]) - Method in interface neureka.ndim.config.NDConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
indexOfIndices(int) - Method in class neureka.ndim.config.types.D1C
+
 
+
indexOfIndices(int, int) - Method in class neureka.ndim.config.types.D2C
+
 
+
indexOfIndices(int, int, int) - Method in class neureka.ndim.config.types.D3C
+
 
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
indexOfIndices(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
indexOfIndices(int, int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
indexOfIndices(int, int, int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
indexOfIndices(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
indexOfIndices(int, int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
indexOfIndices(int, int, int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
indexOfIndices(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
indexOfIndices(int, int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
indexOfIndices(int, int, int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
indexOfIndices(int[]) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
The following method calculates the true index for an element in the data array based on a provided index array.
-
indexOfIndices(int[]) - Method in interface neureka.ndim.NDimensional
+
indexOfIndices(int[]) - Method in interface neureka.ndim.NDimensional
This is a convenience method identical to ndArray.getNDConf().indexOfIndices(indices).
-
indexOfIndices(int, int) - Method in class neureka.ndim.config.types.D2C
-
 
-
indexOfIndices(int, int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
-
indexOfIndices(int, int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
-
indexOfIndices(int, int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
-
indexOfIndices(int, int, int) - Method in class neureka.ndim.config.types.D3C
+
INDICES_MAPPER_ID - Static variable in class neureka.backend.main.operations.linear.internal.opencl.CLReduce
 
-
indexOfIndices(int, int, int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
-
indexOfIndices(int, int, int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
-
indexOfIndices(int, int, int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
-
INDICES_MAPPER_ID - Static variable in class neureka.backend.main.operations.linear.internal.opencl.CLReduce
-
 
-
indicesMap() - Method in interface neureka.ndim.config.NDConfiguration
+
indicesMap() - Method in interface neureka.ndim.config.NDConfiguration
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices for every axis of the tensor represented by this NDConfiguration.
-
indicesMap() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
indicesMap(int) - Method in interface neureka.ndim.config.NDConfiguration
-
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() - is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
+
This method receives an axis index and return the + indices mapping value of said axis to enable readable access to the indices map + of this configuration.
-
indicesMap() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices for every axis of the tensor represented by this NDConfiguration.
-
indicesMap() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
-
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() - is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
+
This method receives an axis index and return the + indices mapping value of said axis to enable readable access to the indices map + of this configuration.
-
indicesMap() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices for every axis of the tensor represented by this NDConfiguration.
-
indicesMap() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
-
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() - is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
+
This method receives an axis index and return the + indices mapping value of said axis to enable readable access to the indices map + of this configuration.
-
indicesMap() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices for every axis of the tensor represented by this NDConfiguration.
-
indicesMap() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
-
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() - is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
+
This method receives an axis index and return the + indices mapping value of said axis to enable readable access to the indices map + of this configuration.
-
indicesMap() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices for every axis of the tensor represented by this NDConfiguration.
-
indicesMap() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
-
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() - is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
+
This method receives an axis index and return the + indices mapping value of said axis to enable readable access to the indices map + of this configuration.
-
indicesMap() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices for every axis of the tensor represented by this NDConfiguration.
-
indicesMap() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
-
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() - is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
+
This method receives an axis index and return the + indices mapping value of said axis to enable readable access to the indices map + of this configuration.
-
indicesMap() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices for every axis of the tensor represented by this NDConfiguration.
-
indicesMap() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
-
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() - is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
+
This method receives an axis index and return the + indices mapping value of said axis to enable readable access to the indices map + of this configuration.
-
indicesMap() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices for every axis of the tensor represented by this NDConfiguration.
-
indicesMap() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
indicesMap(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
-
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() - is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
+
This method receives an axis index and return the + indices mapping value of said axis to enable readable access to the indices map + of this configuration.
-
indicesMap() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices for every axis of the tensor represented by this NDConfiguration.
-
indicesMap() - Method in interface neureka.ndim.NDimensional
-
 
-
indicesMap(int) - Method in interface neureka.ndim.config.NDConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
-
This method receives an axis index and return the - indices mapping value of said axis to enable readable access to the indices map - of this configuration.
+
If one wants to for example access the fourth last item of all items + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + is needed as a basis for translating said scalar index x to an array of indices + for every axis of the tensor represented by this NDConfiguration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
-
This method receives an axis index and return the - indices mapping value of said axis to enable readable access to the indices map - of this configuration.
+
If one wants to for example access the fourth last item of all items + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + is needed as a basis for translating said scalar index x to an array of indices + for every axis of the tensor represented by this NDConfiguration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
 
+
indicesMap() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
-
This method receives an axis index and return the - indices mapping value of said axis to enable readable access to the indices map - of this configuration.
+
If one wants to for example access the fourth last item of all items + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + is needed as a basis for translating said scalar index x to an array of indices + for every axis of the tensor represented by this NDConfiguration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
-
This method receives an axis index and return the - indices mapping value of said axis to enable readable access to the indices map - of this configuration.
+
If one wants to for example access the fourth last item of all items + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + is needed as a basis for translating said scalar index x to an array of indices + for every axis of the tensor represented by this NDConfiguration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
-
This method receives an axis index and return the - indices mapping value of said axis to enable readable access to the indices map - of this configuration.
+
If one wants to for example access the fourth last item of all items + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + is needed as a basis for translating said scalar index x to an array of indices + for every axis of the tensor represented by this NDConfiguration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
-
 
-
indicesMap(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
-
This method receives an axis index and return the - indices mapping value of said axis to enable readable access to the indices map - of this configuration.
+
If one wants to for example access the fourth last item of all items + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + is needed as a basis for translating said scalar index x to an array of indices + for every axis of the tensor represented by this NDConfiguration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
indicesMap() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
-
This method receives an axis index and return the - indices mapping value of said axis to enable readable access to the indices map - of this configuration.
+
If one wants to for example access the fourth last item of all items + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + is needed as a basis for translating said scalar index x to an array of indices + for every axis of the tensor represented by this NDConfiguration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
indicesMap() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
-
This method receives an axis index and return the - indices mapping value of said axis to enable readable access to the indices map - of this configuration.
+
If one wants to for example access the fourth last item of all items + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + is needed as a basis for translating said scalar index x to an array of indices + for every axis of the tensor represented by this NDConfiguration.
-
indicesMap(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
indicesMap(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
-
indicesOfIndex(int) - Method in interface neureka.ndim.config.NDConfiguration
+
indicesMap() - Method in interface neureka.ndim.NDimensional
+
 
+
indicesOfIndex(int) - Method in interface neureka.ndim.config.NDConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
indicesOfIndex(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
-
indicesOfIndex(int) - Method in interface neureka.ndim.NDimensional
+
indicesOfIndex(int) - Method in interface neureka.ndim.NDimensional
This is a convenience method identical to ndArray.getNDConf().IndicesOfIndex(i).
-
init(int, int[]) - Method in interface neureka.ndim.Filler
+
init(int, int[]) - Method in interface neureka.ndim.Filler
 
-
initialScramble(long) - Static method in class neureka.backend.main.implementations.elementwise.CPURandomization
+
initialScramble(long) - Static method in class neureka.backend.main.implementations.elementwise.CPURandomization
 
-
input(int) - Method in class neureka.backend.api.Call
+
input(int) - Method in class neureka.backend.api.Call
 
-
input(Class<V>, int) - Method in class neureka.backend.api.Call
+
input(Class<V>, int) - Method in class neureka.backend.api.Call
 
-
inputIndex() - Method in class neureka.autograd.ADTarget
+
inputIndex() - Method in class neureka.autograd.ADTarget
 
-
inputs() - Method in class neureka.backend.api.Call
+
inputs() - Method in class neureka.backend.api.Call
 
-
INSTANCE - Static variable in interface neureka.devices.DeviceCleaner
+
INSTANCE - Static variable in interface neureka.devices.DeviceCleaner
 
-
intoRange(int, int) - Method in interface neureka.devices.Device.Writer
+
intoRange(int, int) - Method in interface neureka.devices.Device.Writer
Writes whatever kind of data was previously specified, to the tensors' data into the range targeted by the provided start and limit.
-
intStream(int, int) - Static method in class neureka.common.utility.DataConverter.Utility
+
intStream(int, int) - Static method in class neureka.common.utility.DataConverter.Utility
-
Use this to create a range based IntStream +
Use this to create a range based IntStream which is only parallel if the provided threshold smaller than the provided workload size.
-
intToBigInteger(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
intToBigInteger(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
intToByte(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
intToByte(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
intToDouble(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
intToDouble(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
intToFloat(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
intToFloat(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
intToLong(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
intToLong(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
intToShort(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
intToShort(int[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
invert(int[]) - Static method in class neureka.backend.main.operations.other.Permute
+
invert(int[]) - Static method in class neureka.backend.main.operations.other.Permute
 
-
invoke(boolean) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
+
invoke(Supplier<T>) - Method in class neureka.backend.api.BackendContext.Runner
+
+
Use this method to supply a lambda which will be executed in the BackendContext + which produced this very BackendContext.Runner instance.
+
+
invoke(double, double) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
 
-
invoke(boolean, boolean) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
+
invoke(float, float) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
 
-
invoke(byte) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
+
invoke(int, int) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
 
-
invoke(byte, byte) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
+
invoke(long, long) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
 
-
invoke(char) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
+
invoke(byte, byte) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
 
-
invoke(char, char) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
+
invoke(short, short) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
 
-
invoke(double) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
+
invoke(boolean, boolean) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
 
-
invoke(double) - Method in interface neureka.math.Function
-
-
Invokes this Function with the provided scalar as a single input and returns the scalar result.
-
-
invoke(double...) - Method in interface neureka.math.Function
-
-
Invokes this Function with the provided array of inputs.
-
-
invoke(double[], double[], int, double[]) - Method in interface neureka.backend.main.operations.linear.internal.blas.GEMM.VectorOperationF64
+
invoke(char, char) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
 
-
invoke(double[], int) - Method in interface neureka.math.Function
-
-
Invokes this Function with the provided array of inputs ad an index for input dependent indexing.
-
-
invoke(double[], int, double[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.DOT
+
invoke(Object, Object) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
 
-
invoke(double[], int, double, double[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.AXPY
+
invoke(double) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
 
-
invoke(double, double) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
+
invoke(float) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
 
-
invoke(float) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
+
invoke(int) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
 
-
invoke(float[], float[], int, float[]) - Method in interface neureka.backend.main.operations.linear.internal.blas.GEMM.VectorOperationF32
+
invoke(long) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
 
-
invoke(float[], int, float[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.DOT
+
invoke(byte) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
 
-
invoke(float[], int, float, float[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.AXPY
+
invoke(short) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
 
-
invoke(float, float) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
+
invoke(boolean) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
 
-
invoke(int) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
+
invoke(char) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
 
-
invoke(int[], int[], int, int[]) - Method in interface neureka.backend.main.operations.linear.internal.blas.IGEMM.VectorOperationI32
+
invoke(Object) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
 
-
invoke(int[], int, int[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.DOT
+
invoke(double[], int, double, double[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.AXPY
 
-
invoke(int[], int, int[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.IDOT
+
invoke(float[], int, float, float[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.AXPY
 
-
invoke(int[], int, int, int[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.IAXPY
+
invoke(double[], int, double[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.DOT
 
-
invoke(int, int) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
+
invoke(float[], int, float[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.DOT
 
-
invoke(long) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
+
invoke(long[], int, long[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.DOT
 
-
invoke(long[], int, long[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.DOT
+
invoke(int[], int, int[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.DOT
 
-
invoke(long[], int, long[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.IDOT
+
invoke(float[], float[], int, float[]) - Method in interface neureka.backend.main.operations.linear.internal.blas.GEMM.VectorOperationF32
 
-
invoke(long[], int, long, long[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.IAXPY
+
invoke(double[], double[], int, double[]) - Method in interface neureka.backend.main.operations.linear.internal.blas.GEMM.VectorOperationF64
 
-
invoke(long[], long[], int, long[]) - Method in interface neureka.backend.main.operations.linear.internal.blas.IGEMM.VectorOperationI64
+
invoke(long[], int, long, long[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.IAXPY
 
-
invoke(long, long) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
+
invoke(int[], int, int, int[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.IAXPY
 
-
invoke(short) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
+
invoke(long[], int, long[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.IDOT
 
-
invoke(short, short) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
+
invoke(int[], int, int[], int, int, int) - Static method in class neureka.backend.main.operations.linear.internal.blas.IDOT
 
-
invoke(Object) - Method in interface neureka.backend.main.implementations.fun.api.CPUFun
+
invoke(int[], int[], int, int[]) - Method in interface neureka.backend.main.operations.linear.internal.blas.IGEMM.VectorOperationI32
 
-
invoke(Object, Object) - Method in interface neureka.backend.main.implementations.fun.api.CPUBiFun
+
invoke(long[], long[], int, long[]) - Method in interface neureka.backend.main.operations.linear.internal.blas.IGEMM.VectorOperationI64
 
-
invoke(ExecutorService, int, int, int) - Method in class neureka.devices.host.concurrent.WorkScheduler
+
invoke(ExecutorService, int, int, int) - Method in class neureka.devices.host.concurrent.WorkScheduler
Synchronous execution - wait until it's finished.
-
invoke(Supplier<T>) - Method in class neureka.backend.api.BackendContext.Runner
+
invoke(Tensor<T>...) - Method in interface neureka.math.Function.Callable
-
Use this method to supply a lambda which will be executed in the BackendContext - which produced this very BackendContext.Runner instance.
+
This method is functionally identically to Function.Callable.call(Tensor[]), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
-
invoke(List<Tensor<T>>) - Method in interface neureka.math.Function
+
invoke(double) - Method in interface neureka.math.Function
-
This method is functionally identically to Function.call(List), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
+
Invokes this Function with the provided scalar as a single input and returns the scalar result.
+
+
invoke(double[], int) - Method in interface neureka.math.Function
+
+
Invokes this Function with the provided array of inputs ad an index for input dependent indexing.
+
+
invoke(double...) - Method in interface neureka.math.Function
+
+
Invokes this Function with the provided array of inputs.
-
invoke(Call.Builder<T, D>) - Method in interface neureka.math.Function
+
invoke(Call.Builder<T, D>) - Method in interface neureka.math.Function
Use this to pass more context information for execution of input tensors.
-
invoke(Args, Tensor<T>...) - Method in interface neureka.math.Function
+
invoke(Args, Tensor<T>...) - Method in interface neureka.math.Function
Use this to call this Function alongside with some additional meta-arguments which will be passed to the underlying Operation(s).
-
invoke(Tensor<T>) - Method in interface neureka.math.Function
+
invoke(Tensor<T>) - Method in interface neureka.math.Function
-
This method is functionally identically to Function.call(Tensor), however it is best used +
This method is functionally identically to Function.call(Tensor), however it is best used in Kotlin, where one can omit the function name entirely and call this Function directly!
-
invoke(Tensor<T>...) - Method in interface neureka.math.Function.Callable
+
invoke(List<Tensor<T>>) - Method in interface neureka.math.Function
-
This method is functionally identically to Function.Callable.call(Tensor[]), however it is best used +
This method is functionally identically to Function.call(List), however it is best used in Kotlin, where one can omit the function name entirely and call this Function directly!
-
invoke(Tensor<T>...) - Method in interface neureka.math.Function
+
invoke(Tensor<T>[], int) - Method in interface neureka.math.Function
-
This method is functionally identically to Function.call(Tensor[]), however it is best used +
This method is functionally identically to Function.call(Tensor[], int), however it is best used in Kotlin, where one can omit the function name entirely and call this Function directly!
-
invoke(Tensor<T>[], int) - Method in interface neureka.math.Function
+
invoke(Tensor<T>...) - Method in interface neureka.math.Function
-
This method is functionally identically to Function.call(Tensor[], int), however it is best used +
This method is functionally identically to Function.call(Tensor[]), however it is best used in Kotlin, where one can omit the function name entirely and call this Function directly!
-
is(Class<?>) - Method in interface neureka.Tensor
+
is(Class<?>) - Method in interface neureka.Tensor
This method compares the passed class with the underlying data-type of this NDArray.
-
isAnOperation(String) - Static method in class neureka.math.parsing.ParseUtil
+
isAnOperation(String) - Static method in class neureka.math.parsing.ParseUtil
 
-
isApplyingGradientWhenRequested() - Method in class neureka.Neureka.Settings.AutoGrad
+
isApplyingGradientWhenRequested() - Method in class neureka.Neureka.Settings.AutoGrad
Gradients will only be applied if requested.
-
isApplyingGradientWhenTensorIsUsed() - Method in class neureka.Neureka.Settings.AutoGrad
+
isApplyingGradientWhenTensorIsUsed() - Method in class neureka.Neureka.Settings.AutoGrad
Gradients will automatically be applied (or JITed) to tensors as soon as they are being used for calculation (GraphNode instantiation).
-
isAutoConvertToFloat() - Method in class neureka.backend.ocl.CLSettings
+
isAutoConvertToFloat() - Method in class neureka.backend.ocl.CLSettings
 
-
isBranch() - Method in interface neureka.Tensor
+
isBranch() - Method in interface neureka.Tensor
Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
-
isCase(Tensor<V>) - Method in interface neureka.Tensor
+
isCase(Tensor<V>) - Method in interface neureka.Tensor
This method name translates to the "in" keyword in Groovy! The same is true for the "contains" method in Kotlin.
-
isCompact() - Method in interface neureka.ndim.config.NDConfiguration
+
isCompact() - Method in interface neureka.ndim.config.NDConfiguration
NDConfiguration instance where this flag is true will most likely not be slices because they have no offset (all 0) and a compact spread / step array (all 1).
-
isCompatible(NDConfiguration.Layout) - Method in enum class neureka.ndim.config.NDConfiguration.Layout
+
isCompatible(NDConfiguration.Layout) - Method in enum neureka.ndim.config.NDConfiguration.Layout
 
-
isDeleted() - Method in interface neureka.Tensor
+
isDeleted() - Method in interface neureka.Tensor
-
This will check if the MutateTensor.delete() method was previously called on this tensor.
+
This will check if the MutateTensor.delete() method was previously called on this tensor.
-
isDeletingIntermediateTensors() - Method in class neureka.Neureka.Settings.Debug
+
isDeletingIntermediateTensors() - Method in class neureka.Neureka.Settings.Debug
Function instances will produce hidden intermediate results when executing an array of inputs.
-
isDifferentiable() - Method in interface neureka.backend.api.Operation
+
isDifferentiable() - Method in interface neureka.backend.api.Operation
-
Deprecated.
+
Deprecated.
-
isDifferentiable() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
isDifferentiable() - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
isDifferentiable(boolean) - Method in class neureka.backend.api.template.operations.OperationBuilder
+
isDifferentiable(boolean) - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
isDoingAD() - Method in interface neureka.math.Function
+
isDoingAD() - Method in interface neureka.math.Function
Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions - whose Function.isFlat() flag is set to false!
+ whose Function.isFlat() flag is set to false!
-
isDoingAD() - Method in class neureka.math.implementations.FunctionConstant
+
isDoingAD() - Method in class neureka.math.implementations.FunctionConstant
 
-
isDoingAD() - Method in class neureka.math.implementations.FunctionInput
+
isDoingAD() - Method in class neureka.math.implementations.FunctionInput
 
-
isDoingAD() - Method in class neureka.math.implementations.FunctionNode
+
isDoingAD() - Method in class neureka.math.implementations.FunctionNode
 
-
isDoingAD() - Method in class neureka.math.implementations.FunctionVariable
+
isDoingAD() - Method in class neureka.math.implementations.FunctionVariable
 
-
isDone() - Method in class neureka.autograd.JITProp
+
isDone() - Method in class neureka.autograd.JITProp
 
-
isEmpty() - Method in class neureka.devices.AbstractBaseDevice
+
isEmpty() - Method in class neureka.devices.AbstractBaseDevice
A device is empty if there are no tensors stored on it.
-
isEmpty() - Method in class neureka.devices.file.IDXHandle
-
 
-
isEmpty() - Method in interface neureka.devices.Storage
+
isEmpty() - Method in interface neureka.devices.Storage
 
-
isEmpty() - Method in interface neureka.Tensor
+
isEmpty() - Method in interface neureka.Tensor
A tensor is empty if it's Data storage is null.
-
isFirstColIsIndex() - Method in class neureka.devices.file.CSVHandle
+
isFirstColIsIndex() - Method in class neureka.devices.file.CSVHandle
 
-
isFirstRowIsLabels() - Method in class neureka.devices.file.CSVHandle
+
isFirstRowIsLabels() - Method in class neureka.devices.file.CSVHandle
 
-
isFlat() - Method in interface neureka.math.Function
+
isFlat() - Method in interface neureka.math.Function
 
-
isFlat() - Method in class neureka.math.implementations.FunctionConstant
+
isFlat() - Method in class neureka.math.implementations.FunctionConstant
 
-
isFlat() - Method in class neureka.math.implementations.FunctionInput
+
isFlat() - Method in class neureka.math.implementations.FunctionInput
 
-
isFlat() - Method in class neureka.math.implementations.FunctionNode
+
isFlat() - Method in class neureka.math.implementations.FunctionNode
 
-
isFlat() - Method in class neureka.math.implementations.FunctionVariable
+
isFlat() - Method in class neureka.math.implementations.FunctionVariable
 
-
isFullSlice() - Method in interface neureka.Nda
+
isFullSlice() - Method in interface neureka.Nda
If this nd-array is a full slice of a parent nd-array then this method will yield true.
-
isGraphLeave() - Method in class neureka.autograd.GraphNode
+
isGraphLeave() - Method in class neureka.autograd.GraphNode
 
-
isIndexer() - Method in interface neureka.backend.api.Operation
+
isIndexer() - Method in interface neureka.backend.api.Operation
This boolean property tell the Function implementations that this Operation ought to be viewed as something to be indexed.
-
isIndexer() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
isIndexer() - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
isIndexer(boolean) - Method in class neureka.backend.api.template.operations.OperationBuilder
+
isIndexer(boolean) - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
isInline() - Method in interface neureka.backend.api.Operation
+
isInline() - Method in interface neureka.backend.api.Operation
This flag indicates that the implementation of this Operation performs an operation which modifies the inputs to that operation.
-
isInline() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
isInline() - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
isInline(boolean) - Method in class neureka.backend.api.template.operations.OperationBuilder
+
isInline(boolean) - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
isIntermediate() - Method in interface neureka.Tensor
+
isIntermediate() - Method in interface neureka.Tensor
Intermediate tensors are internal non-user tensors which may be eligible for deletion when further consumed by a Function.
-
isKeepingDerivativeTargetPayloads() - Method in class neureka.Neureka.Settings.Debug
+
isKeepingDerivativeTargetPayloads() - Method in class neureka.Neureka.Settings.Debug
Every derivative is calculated with respect to some graph node.
-
isL2Specified() - Method in class neureka.devices.host.machine.Hardware
+
isL2Specified() - Method in class neureka.devices.host.machine.Hardware
 
-
isL3Specified() - Method in class neureka.devices.host.machine.Hardware
+
isL3Specified() - Method in class neureka.devices.host.machine.Hardware
 
-
isLeave() - Method in class neureka.autograd.GraphNode
+
isLeave() - Method in class neureka.autograd.GraphNode
This node (and the corresponding tensor) was not created by a function! (it's a leave tensor)
-
isLeave() - Method in interface neureka.Tensor
+
isLeave() - Method in interface neureka.Tensor
Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
-
isLocked() - Method in class neureka.Neureka.Settings
+
isLocked() - Method in class neureka.Neureka.Settings
Locked settings can only be read but not written to.
-
isOnlyUsingDefaultNDConfiguration() - Method in class neureka.Neureka.Settings.NDim
+
isOnlyUsingDefaultNDConfiguration() - Method in class neureka.Neureka.Settings.NDim
This flag determines which NDConfiguration implementations should be used for nd-arrays/tensors.
-
isOperator() - Method in interface neureka.backend.api.Operation
+
isOperator() - Method in interface neureka.backend.api.Operation
An operator is an alternative to a function like "sum()" or "prod()".
-
isOperator() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
isOperator() - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
isOperator(boolean) - Method in class neureka.backend.api.template.operations.OperationBuilder
+
isOperator(boolean) - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
isOutsourced() - Method in interface neureka.Tensor
+
isOutsourced() - Method in interface neureka.Tensor
Outsourced means that the tensor is stored on a Device implementation instance which is not the CPU.
-
isPartialSlice() - Method in interface neureka.Nda
+
isPartialSlice() - Method in interface neureka.Nda
If this nd-array is a partial slice of a parent nd-array then this method will yield true.
-
isPartialSlice() - Method in interface neureka.Tensor
+
isPartialSlice() - Method in interface neureka.Tensor
If this nd-array is a partial slice of a parent nd-array then this method will yield true.
-
isPreventingInlineOperations() - Method in class neureka.Neureka.Settings.AutoGrad
+
isPreventingInlineOperations() - Method in class neureka.Neureka.Settings.AutoGrad
Inline operations are operations where the data of a tensor passed into an operation is being modified.
-
isReliesOnJustInTimeProp() - Method in class neureka.autograd.GraphNode
+
isReliesOnJustInTimeProp() - Method in class neureka.autograd.GraphNode
This flag is used for a performance optimization feature namely 'Just In Time Propagation'.
-
isRetainingPendingErrorForJITProp() - Method in class neureka.Neureka.Settings.AutoGrad
+
isRetainingPendingErrorForJITProp() - Method in class neureka.Neureka.Settings.AutoGrad
This flag enables an optimization technique which only propagates error values to gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them @@ -4992,37 +4944,37 @@

I

If the flag is set to true
then error values will accumulate at such junction nodes.
-
isShallowCopy() - Method in interface neureka.Nda
+
isShallowCopy() - Method in interface neureka.Nda
If this nd-array is a shallow copy of a parent nd-array then this method will yield true.
-
isShallowCopy() - Method in interface neureka.Tensor
+
isShallowCopy() - Method in interface neureka.Tensor
If this nd-array is a shallow copy of a parent nd-array then this method will yield true.
-
isSimple() - Method in interface neureka.ndim.config.NDConfiguration
+
isSimple() - Method in interface neureka.ndim.config.NDConfiguration
The boolean returned by this method simply reports if this configuration is the most basic form of configuration possible for the given shape represented by this instance.
-
isSlice() - Method in interface neureka.Nda
+
isSlice() - Method in interface neureka.Nda
If this nd-array is a slice of a parent nd-array then this method will yield true.
-
isSlice() - Method in interface neureka.Tensor
+
isSlice() - Method in interface neureka.Tensor
If this nd-array is a slice of a parent nd-array then this method will yield true.
-
isSliceParent() - Method in interface neureka.Nda
+
isSliceParent() - Method in interface neureka.Nda
If slices have been derived from this nd-array then it is a "slice parent".
-
isSliceParent() - Method in interface neureka.Tensor
+
isSliceParent() - Method in interface neureka.Tensor
If slices have been derived from this nd-array then it is a "slice parent".
-
isSuitableFor(ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.api.fun.SuitabilityPredicate
+
isSuitableFor(ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.api.fun.SuitabilityPredicate
When an ExecutionCall instance has been formed then it will be routed by
the given Operation instance to their components, namely :
@@ -5031,27 +4983,27 @@

I

The ability to decide which algorithm is suitable for a given ExecutionCall instance
is being granted by implementations of the following method.
-
isSuitableFor(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
+
isSuitableFor(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
The SuitabilityPredicate checks if a given instance of an ExecutionCall is suitable to be executed in ImplementationFor residing in this Algorithm as components.
-
isSuitableFor(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
isSuitableFor(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
The SuitabilityPredicate checks if a given instance of an ExecutionCall is suitable to be executed in ImplementationFor residing in this Algorithm as components.
-
isSuitableFor(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
+
isSuitableFor(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
 
-
isUndefined() - Method in interface neureka.Tensor
+
isUndefined() - Method in interface neureka.Tensor
A tensor is "undefined" if it has either no NDConfiguration implementation instance or this instance does not have a shape set for this Tensor which is needed for a tensor to also have a rank and dimensionality...
-
isUsedAsDerivative() - Method in class neureka.autograd.GraphNode
+
isUsedAsDerivative() - Method in class neureka.autograd.GraphNode
The chain-rule states that the derivative of f(x) = h(g(x)) with respect to x is: g'(x) * h'(g(x)) An example would be: @@ -5059,603 +5011,610 @@

I

f'(x) = (1*y) * (1*z) = z*y The values z,y or z*y must not be deleted as they are needed for back-propagation!
-
isValid() - Method in class neureka.backend.api.Call.Validator
+
isValid() - Method in class neureka.backend.api.Call.Validator
 
-
isVirtual() - Method in interface neureka.ndim.config.NDConfiguration
+
isVirtual() - Method in interface neureka.ndim.config.NDConfiguration
 
-
isVirtual() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
-
isVirtual() - Method in interface neureka.Tensor
+
isVirtual() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
isVirtual() - Method in interface neureka.Tensor
A Virtual tensor is a tensor whose underlying data array is of size 1, holding only a single value.
-
isWronglyIntermediate() - Method in class neureka.backend.main.memory.MemValidator
+
isWronglyIntermediate() - Method in class neureka.backend.main.memory.MemValidator
 
-
isWronglyNonIntermediate() - Method in class neureka.backend.main.memory.MemValidator
+
isWronglyNonIntermediate() - Method in class neureka.backend.main.memory.MemValidator
 
-
item() - Method in interface neureka.Nda
-
-
Equivalent to the #item(0) and Nda.getItem().
-
-
item(int) - Method in interface neureka.Nda
+
item(int) - Method in interface neureka.Nda
The following method returns a single item within this nd-array targeted by the provided integer index.
-
item(int...) - Method in interface neureka.Nda
+
item(int...) - Method in interface neureka.Nda
This method returns a raw value item within this nd-array targeted by an index array which is expected to hold an index for every dimension of the shape of this nd-array.
-
items() - Method in interface neureka.Nda
+
item() - Method in interface neureka.Nda
-
A more concise version of the Nda.getItems() method.
+
Equivalent to the #item(0) and Nda.getItem().
-
itemType() - Method in interface neureka.Nda
+
items() - Method in interface neureka.Nda
+
+
A more concise version of the Nda.getItems() method.
+
+
itemType() - Method in interface neureka.Nda
 
-
iterator() - Method in interface neureka.Shape
+
iterator() - Method in interface neureka.Shape
 
-
IterByOrIterFromOrAll<V> - Interface in neureka.fluent.building.states
+
IterByOrIterFromOrAll<V> - Interface in neureka.fluent.building.states
 
-
IterByOrIterFromOrAllTensor<V> - Interface in neureka.fluent.building.states
+
IterByOrIterFromOrAllTensor<V> - Interface in neureka.fluent.building.states
 
-

J

-
-
JITProp<V> - Class in neureka.autograd
+ + + +

J

+
+
JITProp<V> - Class in neureka.autograd
This class keeps track of graph nodes which require back-propagation in order to be able to continue the process at a later point in time (based on some configurable conditions).
-
JITProp(Set<GraphNode<V>>) - Constructor for class neureka.autograd.JITProp
+
JITProp(Set<GraphNode<V>>) - Constructor for class neureka.autograd.JITProp
 
-
JVMExecutor() - Constructor for class neureka.devices.host.CPU.JVMExecutor
+
JVMExecutor() - Constructor for class neureka.devices.host.CPU.JVMExecutor
 
-

K

-
-
K - Static variable in class neureka.devices.host.machine.CommonMachine
+ + + +

K

+
+
K - Static variable in class neureka.devices.host.machine.CommonMachine
 
-
keep(Tensor<?>[], Supplier<T>) - Static method in class neureka.backend.main.memory.MemUtil
+
keep(Tensor<?>[], Supplier<T>) - Static method in class neureka.backend.main.memory.MemUtil
This method makes sure that the provided tensors do not get deleted - by setting the Tensor.isIntermediate() flag to off - during the execution of the provided Supplier lambda! + by setting the Tensor.isIntermediate() flag to off + during the execution of the provided Supplier lambda! In said lambda the supplied thing will ultimately be returned by this method...
-
keep(Tensor<?>, Tensor<?>, Supplier<T>) - Static method in class neureka.backend.main.memory.MemUtil
+
keep(Tensor<?>, Tensor<?>, Supplier<T>) - Static method in class neureka.backend.main.memory.MemUtil
This method makes sure that the provided tensors do not get deleted - by setting the Tensor.isIntermediate() flag to off - during the execution of the provided Supplier lambda! + by setting the Tensor.isIntermediate() flag to off + during the execution of the provided Supplier lambda! In said lambda the supplied thing will ultimately be returned by this method...
-
KernelCache - Class in neureka.devices.opencl
+
KernelCache - Class in neureka.devices.opencl
A fixed sized cache for ad-hoc (just in time compiled) OpenCLDevice kernels.
-
KernelCache() - Constructor for class neureka.devices.opencl.KernelCache
+
KernelCache() - Constructor for class neureka.devices.opencl.KernelCache
 
-
KernelCaller - Class in neureka.devices.opencl
+
KernelCaller - Class in neureka.devices.opencl
Instances of this class are utility factories provided by OpenCLDevice instances.
-
KernelCaller(cl_kernel, cl_command_queue) - Constructor for class neureka.devices.opencl.KernelCaller
+
KernelCaller(cl_kernel, cl_command_queue) - Constructor for class neureka.devices.opencl.KernelCaller
 
-
KernelCode - Class in neureka.devices.opencl
+
KernelCode - Class in neureka.devices.opencl
 
-
KernelCode(String, String) - Constructor for class neureka.devices.opencl.KernelCode
+
KernelCode(String, String) - Constructor for class neureka.devices.opencl.KernelCode
 
-
KernelCode(String, String, DataType<?>) - Constructor for class neureka.devices.opencl.KernelCode
+
KernelCode(String, String, DataType<?>) - Constructor for class neureka.devices.opencl.KernelCode
 
-
KernelSource - Interface in neureka.devices.opencl
+
KernelSource - Interface in neureka.devices.opencl
Provides kernel source code for a provided ExecutionCall.
-

L

-
-
label() - Method in interface neureka.Nda
+ + + +

L

+
+
label(String) - Method in interface neureka.MutateNda
-
A nd-array can have a label.
+
Sets the label of this nd-array.
-
label(String) - Method in interface neureka.MutateNda
+
label(String) - Method in interface neureka.MutateTensor
Sets the label of this nd-array.
-
label(String) - Method in interface neureka.MutateTensor
+
label() - Method in interface neureka.Nda
-
Sets the label of this nd-array.
-
-
labelAxes(String[]...) - Method in interface neureka.MutateNda
-
-
This method receives a nested String array which - ought to contain a label for the index of this tensor.
-
-
labelAxes(String[]...) - Method in interface neureka.MutateTensor
-
-
This method receives a label for this tensor and a - nested String array which ought to contain a - label for the index of this tensor.
+
A nd-array can have a label.
-
labelAxes(List<List<Object>>) - Method in interface neureka.MutateNda
+
labelAxes(String[]...) - Method in interface neureka.MutateNda
-
This method receives a nested String list which +
This method receives a nested String array which ought to contain a label for the index of this tensor.
-
labelAxes(List<List<Object>>) - Method in interface neureka.MutateTensor
+
labelAxes(List<List<Object>>) - Method in interface neureka.MutateNda
-
This method receives a nested String list which +
This method receives a nested String list which ought to contain a label for the index of this tensor.
-
labelAxes(Map<Object, List<Object>>) - Method in interface neureka.MutateNda
+
labelAxes(Map<Object, List<Object>>) - Method in interface neureka.MutateNda
This method provides the ability to label not only the indices of the shape of this tensor, but also the dimension of the shape.
-
labelAxes(Map<Object, List<Object>>) - Method in interface neureka.MutateTensor
+
labelAxes(String[]...) - Method in interface neureka.MutateTensor
+
+
This method receives a label for this tensor and a + nested String array which ought to contain a + label for the index of this tensor.
+
+
labelAxes(List<List<Object>>) - Method in interface neureka.MutateTensor
+
+
This method receives a nested String list which + ought to contain a label for the index of this tensor.
+
+
labelAxes(Map<Object, List<Object>>) - Method in interface neureka.MutateTensor
This method provides the ability to label not only the indices of the shape of this tensor, but also the dimension of the shape.
-
last(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
+
last(Call.TensorCondition) - Method in class neureka.backend.api.Call.Validator
 
-
LazyEntry(K, Function<K, V>) - Constructor for class neureka.common.utility.Cache.LazyEntry
+
LazyEntry(K, Function<K, V>) - Constructor for class neureka.common.utility.Cache.LazyEntry
 
-
LazyRef<V> - Class in neureka.backend.api
+
LazyRef<V> - Class in neureka.backend.api
This will simply fetch a variable from a lambda once and then continuously return this one value.
-
learningRate() - Method in class neureka.optimization.implementations.SGD
+
learningRate() - Method in class neureka.optimization.implementations.SGD
 
-
like(Tensor<V>) - Static method in interface neureka.Tensor
+
like(Tensor<V>) - Static method in interface neureka.Tensor
Use this factory method to instantiate a new tensor with the same data type, shape and memory location (Device instance) as the provided template tensor.
-
ListReader - Class in neureka.common.utility
+
ListReader - Class in neureka.common.utility
This is a simple utility class which traverses nested data structures and converts them into information which can be used to instantiate a tensor, namely: A flat data array, a shape array and a type class.
-
ListReader.Result - Class in neureka.common.utility
+
ListReader.Result - Class in neureka.common.utility
 
-
ln() - Method in class neureka.math.Functions
+
ln() - Method in class neureka.math.Functions
 
-
ln() - Method in interface neureka.Tensor
+
ln() - Method in interface neureka.Tensor
This method is a functionally identical to the following alternatives:
-
load() - Method in class neureka.devices.file.CSVHandle
+
load(BackendRegistry) - Method in interface neureka.backend.api.ini.BackendLoader
+
 
+
load() - Method in class neureka.devices.file.CSVHandle
+
 
+
load(String) - Method in class neureka.devices.file.FileDevice
 
-
load() - Method in interface neureka.devices.file.FileHandle
+
load(String, Map<String, Object>) - Method in class neureka.devices.file.FileDevice
+
 
+
load() - Method in interface neureka.devices.file.FileHandle
An implementation of this method ought to create a new tensor instance containing the data which is stored in the file whose access this FileHandle manages.
-
load() - Method in class neureka.devices.file.IDXHandle
+
load() - Method in class neureka.devices.file.IDXHandle
 
-
load(String) - Method in class neureka.devices.file.FileDevice
+
LoadingContext - Interface in neureka.backend.api.ini
 
-
load(String, Map<String, Object>) - Method in class neureka.devices.file.FileDevice
+
loadProperties(Neureka) - Static method in class neureka.common.utility.SettingsLoader
 
-
load(BackendRegistry) - Method in interface neureka.backend.api.ini.BackendLoader
+
localMemSize() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
LoadingContext - Interface in neureka.backend.api.ini
+
localMemType() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
loadProperties(Neureka) - Static method in class neureka.common.utility.SettingsLoader
+
LOG10 - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
localMemSize() - Method in class neureka.devices.opencl.OpenCLDevice
+
Log10 - Class in neureka.backend.main.operations.functions
 
-
localMemType() - Method in class neureka.devices.opencl.OpenCLDevice
+
Log10() - Constructor for class neureka.backend.main.operations.functions.Log10
 
-
log10() - Method in class neureka.math.Functions
+
log10() - Method in class neureka.math.Functions
 
-
log10() - Method in interface neureka.Tensor
+
log10() - Method in interface neureka.Tensor
This method is a functionally identical to the following alternatives:
-
Log10 - Class in neureka.backend.main.operations.functions
-
 
-
Log10() - Constructor for class neureka.backend.main.operations.functions.Log10
-
 
-
LOG10 - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
LOGARITHM - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
Logarithm - Class in neureka.backend.main.operations.functions
+
Logarithm - Class in neureka.backend.main.operations.functions
 
-
Logarithm() - Constructor for class neureka.backend.main.operations.functions.Logarithm
+
Logarithm() - Constructor for class neureka.backend.main.operations.functions.Logarithm
 
-
LOGARITHM - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
-
 
-
LogUtil - Class in neureka.common.utility
+
LogUtil - Class in neureka.common.utility
A utility class for message formatting.
-
LogUtil() - Constructor for class neureka.common.utility.LogUtil
+
LogUtil() - Constructor for class neureka.common.utility.LogUtil
 
-
longToBigInteger(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
longToBigInteger(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
longToByte(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
longToByte(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
longToDouble(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
longToDouble(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
longToFloat(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
longToFloat(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
longToInt(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
longToInt(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
longToShort(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
longToShort(long[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-

M

-
-
makeFit(Tensor<?>[], boolean) - Static method in class neureka.backend.main.operations.other.Permute
-
 
-
makeSimple() - Static method in class neureka.devices.host.machine.Hardware
+ + + +

M

+
+
makeFit(Tensor<?>[], boolean) - Static method in class neureka.backend.main.operations.other.Permute
 
-
makeSimple(String, long, int) - Static method in class neureka.devices.host.machine.Hardware
+
makeSimple() - Static method in class neureka.devices.host.machine.Hardware
 
-
map(int) - Method in interface neureka.ndim.config.NDConfiguration.IndexToIndexFunction
+
makeSimple(String, long, int) - Static method in class neureka.devices.host.machine.Hardware
 
-
map(Function<Integer, Integer>) - Method in interface neureka.Shape
-
-
This method is used to transform a Shape into another Shape - by applying a function to it.
-
-
map(Function<V, V>) - Method in interface neureka.Nda.Item
+
map(Function<V, V>) - Method in interface neureka.Nda.Item
Maps this item to an optional value based on the provided lambda.
-
map(Function<V, V>) - Method in interface neureka.Nda
+
map(Function<V, V>) - Method in interface neureka.Nda
This method is a convenience method for mapping the items of this nd-array to another nd-array of the same type based on the provided lambda function, which will be applied to all items of this nd-array individually (element-wise).
-
map(Function<V, V>) - Method in interface neureka.Tensor
+
map(int) - Method in interface neureka.ndim.config.NDConfiguration.IndexToIndexFunction
+
 
+
map(Function<Integer, Integer>) - Method in interface neureka.Shape
+
+
This method is used to transform a Shape into another Shape + by applying a function to it.
+
+
map(Function<V, V>) - Method in interface neureka.Tensor
This method is a convenience method for mapping the items of this nd-array to another nd-array of the same type based on the provided lambda function, which will be applied to all items of this nd-array individually (element-wise).
-
mapTo(Class<T>, Function<V, T>) - Method in interface neureka.Nda
+
mapTo(Class<T>, Function<V, T>) - Method in interface neureka.Nda
This is a convenience method for mapping a nd-array to a nd-array of new type based on a provided target item type and mapping lambda.
-
mapTo(Class<T>, Function<V, T>) - Method in interface neureka.Tensor
+
mapTo(Class<T>, Function<V, T>) - Method in interface neureka.Tensor
This is a convenience method for mapping a nd-array to a nd-array of new type based on a provided target item type and mapping lambda.
-
matMul() - Method in class neureka.math.Functions
+
MatMul - Class in neureka.backend.main.operations.linear
+
 
+
MatMul() - Constructor for class neureka.backend.main.operations.linear.MatMul
 
-
matMul(Tensor<V>) - Method in interface neureka.Tensor
+
matMul() - Method in class neureka.math.Functions
+
 
+
matMul(Tensor<V>) - Method in interface neureka.Tensor
This will produce the matrix product of two tensors with rank 2 (matrices), where the left operand is this Tensor instance and the right operand is the argument passed to the method.
-
MatMul - Class in neureka.backend.main.operations.linear
+
MatMulAlgorithm - Class in neureka.backend.main.algorithms
 
-
MatMul() - Constructor for class neureka.backend.main.operations.linear.MatMul
+
MatMulAlgorithm() - Constructor for class neureka.backend.main.algorithms.MatMulAlgorithm
 
-
MatMulAlgorithm - Class in neureka.backend.main.algorithms
+
Max - Class in neureka.backend.main.operations.other
 
-
MatMulAlgorithm() - Constructor for class neureka.backend.main.algorithms.MatMulAlgorithm
+
Max() - Constructor for class neureka.backend.main.operations.other.Max
 
-
max() - Method in class neureka.math.Functions
+
max() - Method in class neureka.math.Functions
 
-
max() - Method in interface neureka.Tensor
+
max() - Method in interface neureka.Tensor
Calculate the max value of all values within this tensor and returns it in the form of a scalar tensor.
-
Max - Class in neureka.backend.main.operations.other
-
 
-
Max() - Constructor for class neureka.backend.main.operations.other.Max
+
maxAddressBits() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
MAX - Enum constant in enum class neureka.backend.main.operations.linear.internal.opencl.CLReduce.Type
+
maxClockFrequenzy() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
MAX - Enum constant in enum class neureka.backend.main.operations.other.internal.CPUReduce.Type
+
maxComputeUnits() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
maxAddressBits() - Method in class neureka.devices.opencl.OpenCLDevice
+
maxConstantBufferSize() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
maxClockFrequenzy() - Method in class neureka.devices.opencl.OpenCLDevice
+
maxConstantBufferSizeKB() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
maxComputeUnits() - Method in class neureka.devices.opencl.OpenCLDevice
-
 
-
maxConstantBufferSize() - Method in class neureka.devices.opencl.OpenCLDevice
-
 
-
maxConstantBufferSizeKB() - Method in class neureka.devices.opencl.OpenCLDevice
-
 
-
maxItem(Comparator<V>) - Method in interface neureka.Nda
+
maxItem(Comparator<V>) - Method in interface neureka.Nda
Returns the maximum item of this nd-array according to the provided - Comparator.
+ Comparator.
-
maxMemAllocSize() - Method in class neureka.devices.opencl.OpenCLDevice
+
maxMemAllocSize() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
maxReadImageArgs() - Method in class neureka.devices.opencl.OpenCLDevice
+
maxReadImageArgs() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
maxWorkGroupSize() - Method in class neureka.devices.opencl.OpenCLDevice
+
maxWorkGroupSize() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
maxWorkItemSimensions() - Method in class neureka.devices.opencl.OpenCLDevice
+
maxWorkItemSimensions() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
maxWorkItemSizes() - Method in class neureka.devices.opencl.OpenCLDevice
+
maxWorkItemSizes() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
maxWriteImageArgs() - Method in class neureka.devices.opencl.OpenCLDevice
+
maxWriteImageArgs() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
mean() - Method in interface neureka.Tensor
+
mean() - Method in interface neureka.Tensor
Calculate the mean value of all values within this tensor and returns it in the form of a scalar tensor.
-
memory - Variable in class neureka.devices.host.machine.BasicMachine
+
memory - Variable in class neureka.devices.host.machine.BasicMachine
 
-
MemUtil - Class in neureka.backend.main.memory
+
MemUtil - Class in neureka.backend.main.memory
Utility methods for deleting tensors or preventing thereof.
-
MemValidator - Class in neureka.backend.main.memory
+
MemValidator - Class in neureka.backend.main.memory
This class validates the states of tensors with respect to memory management before and after a lambda executes a function or some kind of algorithm on said tensors.
-
Messages - Class in neureka.devices.opencl.utility
+
Messages - Class in neureka.devices.opencl.utility
+
 
+
Messages.Tips - Enum in neureka.devices.opencl.utility
+
 
+
Min - Class in neureka.backend.main.operations.other
 
-
Messages.Tips - Enum Class in neureka.devices.opencl.utility
+
Min() - Constructor for class neureka.backend.main.operations.other.Min
 
-
min() - Method in class neureka.math.Functions
+
min() - Method in class neureka.math.Functions
 
-
min() - Method in interface neureka.Tensor
+
min() - Method in interface neureka.Tensor
Calculate the min value of all values within this tensor and returns it in the form of a scalar tensor.
-
Min - Class in neureka.backend.main.operations.other
-
 
-
Min() - Constructor for class neureka.backend.main.operations.other.Min
-
 
-
MIN - Enum constant in enum class neureka.backend.main.operations.linear.internal.opencl.CLReduce.Type
-
 
-
MIN - Enum constant in enum class neureka.backend.main.operations.other.internal.CPUReduce.Type
-
 
-
minItem(Comparator<V>) - Method in interface neureka.Nda
+
minItem(Comparator<V>) - Method in interface neureka.Nda
Returns the minimum item of this nd-array according to the provided - Comparator.
+ Comparator.
-
minus() - Method in class neureka.math.Functions
+
minus() - Method in class neureka.math.Functions
 
-
minus(Tensor<V>) - Method in interface neureka.Tensor
+
minus(Tensor<V>) - Method in interface neureka.Tensor
Performs subtraction on two tensors with the same rank (or two ranks which can be made compatible with padding ones), where the left operand is this Tensor instance and the right operand is the tensor passed to the method.
-
minus(V) - Method in interface neureka.Tensor
+
minus(V) - Method in interface neureka.Tensor
This method will create a new Tensor with the provided item subtracted from all elements of this Tensor.
-
minusAssign() - Method in class neureka.math.Functions
+
minusAssign() - Method in class neureka.math.Functions
 
-
minusAssign(Tensor<T>) - Method in interface neureka.MutateTensor
+
minusAssign(Tensor<T>) - Method in interface neureka.MutateTensor
 
-
minusAssign(T) - Method in interface neureka.MutateTensor
+
minusAssign(T) - Method in interface neureka.MutateTensor
 
-
mod() - Method in class neureka.math.Functions
+
mod() - Method in class neureka.math.Functions
 
-
mod(int) - Method in interface neureka.Tensor
-
 
-
mod(Tensor<V>) - Method in interface neureka.Tensor
+
mod(Tensor<V>) - Method in interface neureka.Tensor
Produces the modulus of two tensors with the same rank (or two ranks which can be made compatible with padding ones), where the left operand is this Tensor instance and the right operand is the tensor passed to the method.
-
modAssign() - Method in class neureka.math.Functions
+
mod(int) - Method in interface neureka.Tensor
 
-
modAssign(Tensor<T>) - Method in interface neureka.MutateTensor
+
modAssign() - Method in class neureka.math.Functions
 
-
Modulo - Class in neureka.backend.main.operations.operator
+
modAssign(Tensor<T>) - Method in interface neureka.MutateTensor
 
-
Modulo() - Constructor for class neureka.backend.main.operations.operator.Modulo
+
Modulo - Class in neureka.backend.main.operations.operator
 
-
Momentum<V extends Number> - Class in neureka.optimization.implementations
+
Modulo() - Constructor for class neureka.backend.main.operations.operator.Modulo
 
-
Momentum - Static variable in interface neureka.optimization.Optimizer
+
Momentum<V extends java.lang.Number> - Class in neureka.optimization.implementations
 
-
MomentumFactory - Class in neureka.optimization.implementations
+
Momentum - Static variable in interface neureka.optimization.Optimizer
 
-
MomentumFactory() - Constructor for class neureka.optimization.implementations.MomentumFactory
+
MomentumFactory - Class in neureka.optimization.implementations
 
-
mul() - Method in class neureka.math.Functions
+
MomentumFactory() - Constructor for class neureka.optimization.implementations.MomentumFactory
 
-
mulAssign() - Method in class neureka.math.Functions
+
mul() - Method in class neureka.math.Functions
 
-
Multiplication - Class in neureka.backend.main.operations.operator
+
mulAssign() - Method in class neureka.math.Functions
 
-
Multiplication() - Constructor for class neureka.backend.main.operations.operator.Multiplication
+
Multiplication - Class in neureka.backend.main.operations.operator
 
-
multiply(double) - Method in interface neureka.Tensor
+
Multiplication() - Constructor for class neureka.backend.main.operations.operator.Multiplication
 
-
multiply(Tensor<V>) - Method in interface neureka.Tensor
+
multiply(Tensor<V>) - Method in interface neureka.Tensor
-
This method is synonymous to the Tensor.times(Tensor) method.
+
This method is synonymous to the Tensor.times(Tensor) method.
-
multiply(V) - Method in interface neureka.Tensor
+
multiply(V) - Method in interface neureka.Tensor
+
 
+
multiply(double) - Method in interface neureka.Tensor
 
-
mut() - Method in interface neureka.Nda
+
mut() - Method in interface neureka.Nda
This method exposes an API for mutating the state of this tensor.
-
mut() - Method in interface neureka.Tensor
+
mut() - Method in interface neureka.Tensor
This method exposes an API for mutating the state of this tensor.
-
MutateNda<T> - Interface in neureka
+
MutateNda<T> - Interface in neureka
Nd-arrays should be used as immutable data structures mostly, however sometimes it is important to mutate their state for performance reasons.
-
MutateNda.Item<V> - Interface in neureka
+
MutateNda.Item<V> - Interface in neureka
-
Instances of this are being returned by the Nda.at(int...) method, +
Instances of this are being returned by the Nda.at(int...) method, and they allow you to get or set individual nd-array items
-
MutateTensor<T> - Interface in neureka
+
MutateTensor<T> - Interface in neureka
Tensors should be considered immutable, however sometimes it is important to mutate their state for performance reasons.
-

N

-
-
name() - Method in class neureka.devices.opencl.OpenCLDevice
+ + + +

N

+
+
name() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
Nda<V> - Interface in neureka
+
Nda<V> - Interface in neureka
Nda, which is an abbreviation of 'N-Dimensional-Array', represents a multidimensional, homogeneously filled fixed-size array of items.
-
Nda.Item<V> - Interface in neureka
+
Nda.Item<V> - Interface in neureka
-
Instances of this are being returned by the Nda.at(int...) method, +
Instances of this are being returned by the Nda.at(int...) method, and they allow you to get individual nd-array items
-
NdaAsString - Class in neureka.view
+
NdaAsString - Class in neureka.view
This class is in essence a simple wrapper class for a tensor and a StringBuilder Methods in this class use the builder in order to construct a String representation for said tensor.
-
NdaAsString.Builder - Interface in neureka.view
+
NdaAsString.Builder - Interface in neureka.view
A builder interface providing multiple different options for building a NdaAsString instance in a fluent way.
-
NdaAsString.Util - Class in neureka.view
+
NdaAsString.Util - Class in neureka.view
This class is a simple utility class which contains a collection of static and stateless methods containing useful functionalities for tensor stringification.
-
NdaBuilder<V> - Class in neureka.fluent.building
+
NdaBuilder<V> - Class in neureka.fluent.building
This is the implementation of the fluent builder API for creating Nda/Tensor instances.
-
NdaBuilder(Class<V>) - Constructor for class neureka.fluent.building.NdaBuilder
+
NdaBuilder(Class<V>) - Constructor for class neureka.fluent.building.NdaBuilder
 
-
ndArrays(Consumer<NDPrintSettings>) - Method in class neureka.Neureka.Settings.View
+
ndArrays(Consumer<NDPrintSettings>) - Method in class neureka.Neureka.Settings.View
This allows you to provide a lambda to configure how tensors should be - converted to String instances.
+ converted to String instances.
-
NDConfiguration - Interface in neureka.ndim.config
+
NDConfiguration - Interface in neureka.ndim.config
This interface represents the access pattern configuration for the data array of a tensor.
-
NDConfiguration.IndexToIndexFunction - Interface in neureka.ndim.config
+
NDConfiguration.IndexToIndexFunction - Interface in neureka.ndim.config
-
Implementations of this are produced and returned by the NDConfiguration.getIndexToIndexAccessPattern() +
Implementations of this are produced and returned by the NDConfiguration.getIndexToIndexAccessPattern() and their purpose is to translate the item index of a tensor to the index of the item within the underlying data array of said tensor.
-
NDConfiguration.Layout - Enum Class in neureka.ndim.config
+
NDConfiguration.Layout - Enum in neureka.ndim.config
Types of common data layouts:
ROW_MAJOR
-
NDConfiguration.Utility - Class in neureka.ndim.config
+
NDConfiguration.Utility - Class in neureka.ndim.config
This utility class provides static methods which are helpful for nd-configuration related operations like reshaping, incrementing or decrementing index arrays...
-
NDConstructor - Interface in neureka.ndim
+
NDConstructor - Interface in neureka.ndim
 
-
NDConvolution - Class in neureka.backend.main.algorithms
+
NDConvolution - Class in neureka.backend.main.algorithms
 
-
NDConvolution() - Constructor for class neureka.backend.main.algorithms.NDConvolution
+
NDConvolution() - Constructor for class neureka.backend.main.algorithms.NDConvolution
 
-
NDFrame<V> - Class in neureka.framing
+
NDFrame<V> - Class in neureka.framing
Instances of this class are components of tensors, which store aliases for the indices of the tensor.
-
NDFrame(List<List<Object>>, Tensor<V>, String) - Constructor for class neureka.framing.NDFrame
+
NDFrame(List<List<Object>>, Tensor<V>, String) - Constructor for class neureka.framing.NDFrame
 
-
NDFrame(Map<Object, List<Object>>, Tensor<V>, String) - Constructor for class neureka.framing.NDFrame
+
NDFrame(Tensor<V>, String) - Constructor for class neureka.framing.NDFrame
 
-
NDFrame(Tensor<V>, String) - Constructor for class neureka.framing.NDFrame
+
NDFrame(Map<Object, List<Object>>, Tensor<V>, String) - Constructor for class neureka.framing.NDFrame
 
-
ndim() - Method in class neureka.Neureka.Settings
+
ndim() - Method in class neureka.Neureka.Settings
 
-
ndim(Object) - Method in class neureka.Neureka.Settings
+
ndim(Object) - Method in class neureka.Neureka.Settings
This allows you to configure Neureka using a Groovy DSL.
-
NDim() - Constructor for class neureka.Neureka.Settings.NDim
+
NDim() - Constructor for class neureka.Neureka.Settings.NDim
 
-
NDimensional - Interface in neureka.ndim
+
NDimensional - Interface in neureka.ndim
This interface defines the most essential methods of the nd-array/tensor API, which describe them with respect to their dimensionality.
-
NDIterator - Interface in neureka.ndim.iterator
+
NDIterator - Interface in neureka.ndim.iterator
An NDIterator is used to iterate over n-dimensional arrays.
-
NDIterator.NonVirtual - Enum Class in neureka.ndim.iterator
+
NDIterator.NonVirtual - Enum in neureka.ndim.iterator
Defines if a new NDIterator is allowed to be a VirtualNDIterator.
-
NDPrintSettings - Class in neureka.view
+
NDPrintSettings - Class in neureka.view
This is simply a mutable container for configuring how Tensor - instances ought to be converted to Strings.
+ instances ought to be converted to Strings.
-
NDPrintSettings(Supplier<Boolean>) - Constructor for class neureka.view.NDPrintSettings
+
NDPrintSettings(Supplier<Boolean>) - Constructor for class neureka.view.NDPrintSettings
 
-
NDTrait - Enum Class in neureka.ndim.config
+
NDTrait - Enum in neureka.ndim.config
 
-
NDUtil - Class in neureka.ndim
+
NDUtil - Class in neureka.ndim
Static utility methods for the NDArray.
-
NDUtil() - Constructor for class neureka.ndim.NDUtil
+
NDUtil() - Constructor for class neureka.ndim.NDUtil
 
-
neg() - Method in class neureka.math.Functions
+
neg() - Method in class neureka.math.Functions
 
-
neg() - Method in interface neureka.Tensor
+
neg() - Method in interface neureka.Tensor
This method is a functionally identical to the following alternatives:
-
negative() - Method in interface neureka.Tensor
+
negative() - Method in interface neureka.Tensor
 
neureka - package neureka
 
-
Neureka - Class in neureka
+
Neureka - Class in neureka
Neureka is the key access point for thread local / global library settings ( seeNeureka.Settings) as well as execution contexts (see BackendContext) @@ -5856,1271 +5815,1280 @@

N

 
neureka.optimization.implementations - package neureka.optimization.implementations
 
-
Neureka.Settings - Class in neureka
+
Neureka.Settings - Class in neureka
This class hosts the settings of the Neureka instance which will be used throughout the library.
-
Neureka.Settings.AutoGrad - Class in neureka
+
Neureka.Settings.AutoGrad - Class in neureka
This class contains settings which are related to the automatic differentiation of tensors.
-
Neureka.Settings.Debug - Class in neureka
+
Neureka.Settings.Debug - Class in neureka
 
-
Neureka.Settings.DType - Class in neureka
+
Neureka.Settings.DType - Class in neureka
 
-
Neureka.Settings.NDim - Class in neureka
+
Neureka.Settings.NDim - Class in neureka
Settings for configuring the access pattern of nd-arrays/tensors.
-
Neureka.Settings.View - Class in neureka
+
Neureka.Settings.View - Class in neureka
-
Settings for configuring how objects should be converted to String representations.
+
Settings for configuring how objects should be converted to String representations.
-
Neureka.Utility - Class in neureka
+
Neureka.Utility - Class in neureka
 
neureka.view - package neureka.view
 
-
newChildToParent(Tensor<T>) - Static method in class neureka.framing.Relation
+
newChildToParent(Tensor<T>) - Static method in class neureka.framing.Relation
 
-
newInstance() - Static method in interface neureka.Tensor
+
newInstance() - Static method in interface neureka.Tensor
This static factory method creates and return a completely empty and undefined tensor which is void of any contents and meaning.
-
newParentToChildren() - Static method in class neureka.framing.Relation
+
newParentToChildren() - Static method in class neureka.framing.Relation
 
-
newReshaped(int[]) - Method in class neureka.ndim.config.AbstractNDC
+
newReshaped(int[]) - Method in class neureka.ndim.config.AbstractNDC
 
-
newReshaped(int[]) - Method in interface neureka.ndim.config.NDConfiguration
+
newReshaped(int[]) - Method in interface neureka.ndim.config.NDConfiguration
This method enables reshaping for NDConfiguration implementation instances.
-
newStridesFor(int[]) - Method in enum class neureka.ndim.config.NDConfiguration.Layout
+
newStridesFor(int[]) - Method in enum neureka.ndim.config.NDConfiguration.Layout
 
-
newTensorLike(Class<V>, Shape, boolean, Device<Object>, double) - Static method in class neureka.backend.main.operations.ElemWiseUtil
+
newTensorLike(Tensor<V>, double) - Static method in class neureka.backend.main.operations.ElemWiseUtil
 
-
newTensorLike(Tensor<V>, double) - Static method in class neureka.backend.main.operations.ElemWiseUtil
+
newTensorLike(Class<V>, Shape, boolean, Device<Object>, double) - Static method in class neureka.backend.main.operations.ElemWiseUtil
 
-
node() - Method in class neureka.autograd.ADTarget
+
node() - Method in class neureka.autograd.ADTarget
 
-
none(Predicate<V>) - Method in interface neureka.Nda
+
none() - Static method in interface neureka.Data
+
+
This is a static factory method which returns a Data object + which does not contain any data.
+
+
none(Predicate<V>) - Method in interface neureka.Nda
Iterates over every element of this nd-array, and checks whether none of the elements match the provided lambda.
-
NOT_GOOD - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
+
none() - Static method in interface neureka.ndim.config.NDConfiguration
 
-
NOT_SUPPORTED - Enum constant in enum class neureka.backend.api.AutoDiffMode
+
NOT_GOOD - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
 
-
noteFinished(GraphNode<V>) - Method in class neureka.autograd.JITProp
+
noteFinished(GraphNode<V>) - Method in class neureka.autograd.JITProp
 
-
nullArgCheck(T, String, Class<?>, String...) - Static method in class neureka.common.utility.LogUtil
+
nullArgCheck(T, String, Class<?>, String...) - Static method in class neureka.common.utility.LogUtil
 
-
numberOfArgs() - Method in interface neureka.math.Function
+
numberOfArgs() - Method in interface neureka.math.Function
 
-
numberOfBytes() - Method in class neureka.dtype.custom.F32
+
numberOfBytes() - Method in class neureka.dtype.custom.F32
 
-
numberOfBytes() - Method in class neureka.dtype.custom.F64
+
numberOfBytes() - Method in class neureka.dtype.custom.F64
 
-
numberOfBytes() - Method in class neureka.dtype.custom.I16
+
numberOfBytes() - Method in class neureka.dtype.custom.I16
 
-
numberOfBytes() - Method in class neureka.dtype.custom.I32
+
numberOfBytes() - Method in class neureka.dtype.custom.I32
 
-
numberOfBytes() - Method in class neureka.dtype.custom.I64
+
numberOfBytes() - Method in class neureka.dtype.custom.I64
 
-
numberOfBytes() - Method in class neureka.dtype.custom.I8
+
numberOfBytes() - Method in class neureka.dtype.custom.I8
 
-
numberOfBytes() - Method in class neureka.dtype.custom.UI16
+
numberOfBytes() - Method in class neureka.dtype.custom.UI16
 
-
numberOfBytes() - Method in class neureka.dtype.custom.UI32
+
numberOfBytes() - Method in class neureka.dtype.custom.UI32
 
-
numberOfBytes() - Method in class neureka.dtype.custom.UI64
+
numberOfBytes() - Method in class neureka.dtype.custom.UI64
 
-
numberOfBytes() - Method in class neureka.dtype.custom.UI8
+
numberOfBytes() - Method in class neureka.dtype.custom.UI8
 
-
numberOfBytes() - Method in interface neureka.dtype.NumericType
+
numberOfBytes() - Method in interface neureka.dtype.NumericType
 
-
numberOfChannels - Variable in enum class neureka.Tensor.ImageType
+
numberOfChannels - Variable in enum neureka.Tensor.ImageType
 
-
numberOfDataObjects() - Method in class neureka.devices.AbstractBaseDevice
+
numberOfDataObjects() - Method in class neureka.devices.AbstractBaseDevice
 
-
numberOfDataObjects() - Method in interface neureka.devices.Device
+
numberOfDataObjects() - Method in interface neureka.devices.Device
-
Note that this is not necessarily equal to Storage.numberOfStored(), because +
Note that this is not necessarily equal to Storage.numberOfStored(), because multiple tensors may share a single Data object.
-
numberOfOperationsWithin(List<String>) - Static method in class neureka.math.parsing.ParseUtil
+
numberOfOperationsWithin(List<String>) - Static method in class neureka.math.parsing.ParseUtil
 
-
numberOfStored() - Method in class neureka.devices.AbstractBaseDevice
+
numberOfStored() - Method in class neureka.devices.AbstractBaseDevice
 
-
numberOfStored() - Method in class neureka.devices.file.IDXHandle
+
numberOfStored() - Method in interface neureka.devices.Storage
 
-
numberOfStored() - Method in interface neureka.devices.Storage
-
 
-
NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType> - Interface in neureka.dtype
+
NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType> - Interface in neureka.dtype
This interface enables "Polymorphic" utility by defining common functionalities used for handling various numeric types.
-

O

-
-
objBooleansToPrimBooleans(Boolean[]) - Static method in class neureka.common.utility.DataConverter.Utility
+ + + +

O

+
+
objBooleansToPrimBooleans(Boolean[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objBytesToPrimBytes(Byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
objBytesToPrimBytes(Byte[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objCharsToPrimChars(Character[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
objCharsToPrimChars(Character[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objDoublesToPrimDoubles(Double[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
objDoublesToPrimDoubles(Double[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objectsToBytes(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
+
objectsToBytes(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objectsToDoubles(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
+
objectsToDoubles(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objectsToFloats(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
+
objectsToFloats(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objectsToInts(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
+
objectsToInts(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objectsToLongs(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
+
objectsToLongs(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objectsToShorts(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
+
objectsToShorts(Object[], int) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objFloatsToPrimFloats(Float[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
objFloatsToPrimFloats(Float[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objIntsToPrimInts(Integer[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
objIntsToPrimInts(Integer[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objLongsToPrimLongs(Long[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
objLongsToPrimLongs(Long[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
objShortsToPrimShorts(Short[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
objShortsToPrimShorts(Short[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
of(boolean...) - Static method in interface neureka.Data
+
of(ADAction) - Static method in interface neureka.autograd.ADAction
 
-
of(boolean...) - Static method in interface neureka.Nda
-
-
Constructs a vector of booleans based on the provided array.
-
-
of(boolean...) - Static method in interface neureka.Tensor
+
of(Tensor<?>, ADAction) - Static method in interface neureka.autograd.ADAction
+
 
+
of(Tensor<?>...) - Static method in class neureka.backend.api.ExecutionCall
-
Constructs a vector of booleans based on the provided array.
+
Use this factory method to build ExecutionCall instances in a readable fashion.
-
of(byte) - Static method in interface neureka.Tensor
+
of(ImplementationReceiver) - Static method in class neureka.backend.api.ini.BackendRegistry
 
-
of(byte...) - Static method in interface neureka.Data
+
of(Supplier<V>) - Static method in class neureka.backend.api.LazyRef
 
-
of(byte...) - Static method in interface neureka.Nda
-
-
Constructs a vector of bytes based on the provided array.
-
-
of(byte...) - Static method in interface neureka.Tensor
-
-
Constructs a vector of bytes based on the provided array.
-
-
of(char...) - Static method in interface neureka.Data
+
of(Tensor<?>) - Static method in class neureka.backend.api.Result
 
-
of(double) - Static method in interface neureka.Nda
+
of(Class<V>, V...) - Static method in interface neureka.Data
 
-
of(double) - Static method in interface neureka.Tensor
+
of(float...) - Static method in interface neureka.Data
 
-
of(double...) - Static method in interface neureka.Data
+
of(double...) - Static method in interface neureka.Data
 
-
of(double...) - Static method in interface neureka.Nda
-
-
Constructs a vector of doubles based on the provided array.
-
-
of(double...) - Static method in interface neureka.Tensor
-
-
Constructs a vector of doubles based on the provided array.
-
-
of(float) - Static method in interface neureka.Tensor
+
of(int...) - Static method in interface neureka.Data
 
-
of(float...) - Static method in interface neureka.Data
+
of(long...) - Static method in interface neureka.Data
 
-
of(float...) - Static method in interface neureka.Nda
-
-
Constructs a vector of floats based on the provided array.
-
-
of(float...) - Static method in interface neureka.Tensor
-
-
Constructs a vector of floats based on the provided array.
-
-
of(int) - Static method in class neureka.math.args.Arg.Axis
+
of(byte...) - Static method in interface neureka.Data
 
-
of(int) - Static method in class neureka.math.args.Arg.DerivIdx
+
of(short...) - Static method in interface neureka.Data
 
-
of(int) - Static method in class neureka.math.args.Arg.MinRank
+
of(boolean...) - Static method in interface neureka.Data
 
-
of(int) - Static method in class neureka.math.args.Arg.VarIdx
+
of(char...) - Static method in interface neureka.Data
 
-
of(int) - Static method in interface neureka.Tensor
+
of(String...) - Static method in interface neureka.Data
 
-
of(int...) - Static method in interface neureka.Data
+
of(Class<T>) - Static method in class neureka.dtype.DataType
 
-
of(int[]) - Static method in class neureka.math.args.Arg.Ends
+
of(int) - Static method in class neureka.math.args.Arg.Axis
 
-
of(int...) - Static method in class neureka.math.args.Arg.Indices
+
of(Tensor<V>) - Static method in class neureka.math.args.Arg.Derivative
 
-
of(int...) - Static method in class neureka.math.args.Arg.Offset
+
of(int) - Static method in class neureka.math.args.Arg.DerivIdx
 
-
of(int...) - Static method in class neureka.math.args.Arg.Shape
+
of(int[]) - Static method in class neureka.math.args.Arg.Ends
 
-
of(int...) - Static method in class neureka.math.args.Arg.Stride
+
of(int...) - Static method in class neureka.math.args.Arg.Indices
 
-
of(int...) - Static method in interface neureka.Nda
-
-
Constructs a vector of ints based on the provided array.
-
-
of(int...) - Static method in interface neureka.ndim.NDConstructor
+
of(NDConfiguration.Layout) - Static method in class neureka.math.args.Arg.Layout
 
-
of(int...) - Static method in interface neureka.Shape
-
-
This method is used to create a Shape instance from an array of integers.
-
-
of(int...) - Static method in interface neureka.Tensor
-
-
Constructs a vector of ints based on the provided array.
-
-
of(int[], int[], int[], int[], int[]) - Static method in interface neureka.ndim.config.NDConfiguration
+
of(int) - Static method in class neureka.math.args.Arg.MinRank
+
 
+
of(int...) - Static method in class neureka.math.args.Arg.Offset
+
 
+
of(String) - Static method in class neureka.math.args.Arg.Seed
+
 
+
of(long) - Static method in class neureka.math.args.Arg.Seed
+
 
+
of(int...) - Static method in class neureka.math.args.Arg.Shape
 
-
of(int[], int[], int[], int[], int[]) - Static method in interface neureka.ndim.NDConstructor
+
of(int...) - Static method in class neureka.math.args.Arg.Stride
 
-
of(long) - Static method in class neureka.math.args.Arg.Seed
+
of(Device<?>) - Static method in class neureka.math.args.Arg.TargetDevice
 
-
of(long) - Static method in interface neureka.Tensor
+
of(int) - Static method in class neureka.math.args.Arg.VarIdx
 
-
of(long...) - Static method in interface neureka.Data
+
of(Arg<?>...) - Static method in class neureka.math.args.Args
 
-
of(long...) - Static method in interface neureka.Nda
+
of(String) - Static method in interface neureka.math.Function
-
Constructs a vector of longs based on the provided array.
+
This static factory method will return Function instances + based on a provided mathematical String expression describing the function + using 'I[0]', 'I[1]', 'I[2]'...
-
of(long...) - Static method in interface neureka.Tensor
+
of(String, boolean) - Static method in interface neureka.math.Function
-
Constructs a vector of longs based on the provided array.
+
This static factory method will return Function instances + based on a provided mathematical String expression describing the function + using 'I[0]', 'I[1]', 'I[2]'...
-
of(short) - Static method in interface neureka.Tensor
+
of(String, boolean) - Static method in class neureka.math.implementations.FunctionInput
 
-
of(short...) - Static method in interface neureka.Data
+
of(Class<V>) - Static method in interface neureka.Nda
+
+
This is the entry point to the fluent nd-array builder API for building + Nda instances in a readable and type safe fashion.
+
+
of(double) - Static method in interface neureka.Nda
 
-
of(short...) - Static method in interface neureka.Nda
+
of(float...) - Static method in interface neureka.Nda
-
Constructs a vector of shorts based on the provided array.
+
Constructs a vector of floats based on the provided array.
-
of(short...) - Static method in interface neureka.Tensor
+
of(double...) - Static method in interface neureka.Nda
-
Constructs a vector of shorts based on the provided array.
+
Constructs a vector of doubles based on the provided array.
-
of(Class<T>) - Static method in class neureka.dtype.DataType
-
 
-
of(Class<T>, List<Object>) - Static method in interface neureka.Tensor
+
of(byte...) - Static method in interface neureka.Nda
-
This factory method will turn a list of values or nested lists of values into a Tensor - instance with the corresponding rank and shape and whose values - are of the provided type.
+
Constructs a vector of bytes based on the provided array.
-
of(Class<T>, Shape, Filler<T>) - Static method in interface neureka.Tensor
+
of(int...) - Static method in interface neureka.Nda
-
This factory method allows the creation of tensors with an additional initialization - lambda for filling the underlying data array with desired values.
+
Constructs a vector of ints based on the provided array.
-
of(Class<V>) - Static method in interface neureka.Nda
+
of(long...) - Static method in interface neureka.Nda
-
This is the entry point to the fluent nd-array builder API for building - Nda instances in a readable and type safe fashion.
+
Constructs a vector of longs based on the provided array.
-
of(Class<V>) - Static method in interface neureka.Tensor
+
of(short...) - Static method in interface neureka.Nda
-
This is the entry point to the fluent tensor builder API for building - Tensor instances in a readable and type safe fashion.
+
Constructs a vector of shorts based on the provided array.
-
of(Class<V>, List<Integer>, Object) - Static method in interface neureka.Tensor
+
of(boolean...) - Static method in interface neureka.Nda
-
Use this to construct and return a tensor of the specified type, shape and data object.
+
Constructs a vector of booleans based on the provided array.
-
of(Class<V>, List<Integer>, List<V>) - Static method in interface neureka.Tensor
+
of(T...) - Static method in interface neureka.Nda
-
Use this to construct and return a tensor of the specified type, shape and data object.
+
Constructs a vector of objects based on the provided array.
-
of(Class<V>, Shape, Number) - Static method in interface neureka.Tensor
+
of(Shape, double...) - Static method in interface neureka.Nda
-
Use this to construct and return a tensor of the specified type, shape and number.
+
Use this to construct and return a double based nd-array of the specified shape and initial values.
-
of(Class<V>, Shape, Object) - Static method in interface neureka.Tensor
+
of(Shape, float...) - Static method in interface neureka.Nda
-
Use this to construct and return a tensor of the specified type, shape and data object.
+
Use this to construct and return a float based nd-array of the specified shape and initial values.
-
of(Class<V>, Shape, List<V>) - Static method in interface neureka.Tensor
+
of(Shape, byte...) - Static method in interface neureka.Nda
-
Use this to construct and return a tensor of the specified type, shape and list of items.
+
Use this to construct and return a byte based nd-array of the specified shape and initial values.
-
of(Class<V>, Shape, Arg.Seed) - Static method in interface neureka.Tensor
+
of(Shape, int...) - Static method in interface neureka.Nda
-
Use this to construct and return a seeded tensor of the specified type.
+
Use this to construct and return a int based nd-array of the specified shape and initial values.
-
of(Class<V>, V...) - Static method in interface neureka.Data
-
 
-
of(Iterable<? extends Number>) - Static method in interface neureka.Shape
+
of(Shape, long...) - Static method in interface neureka.Nda
-
This method is used to create a Shape instance from an iterable of numbers - whose integer values are used to describe the shape of a nd-array.
+
Use this to construct and return a long based nd-array of the specified shape and initial values.
-
of(Iterable<T>) - Static method in interface neureka.Nda
+
of(Shape, short...) - Static method in interface neureka.Nda
-
Constructs a vector of objects based on the provided iterable.
+
Use this to construct and return a short based nd-array of the specified shape and initial values.
+
+
of(Shape, boolean...) - Static method in interface neureka.Nda
+
+
Use this to construct and return a boolean based nd-array of the specified shape and initial values.
-
of(Iterable<T>) - Static method in interface neureka.Tensor
+
of(Shape, T...) - Static method in interface neureka.Nda
+
+
Use this to construct and return an object based nd-array of the specified shape and initial values.
+
+
of(Iterable<T>) - Static method in interface neureka.Nda
Constructs a vector of objects based on the provided iterable.
-
of(Object...) - Static method in interface neureka.Tensor
+
of(List<T>) - Static method in interface neureka.Nda
-
This static Tensor factory method tries to interpret the provided - arguments to create the instance the use might wants.
+
Constructs a vector of objects based on the provided list.
-
of(String) - Static method in class neureka.math.args.Arg.Seed
+
of(int[], int[], int[], int[], int[]) - Static method in interface neureka.ndim.config.NDConfiguration
 
-
of(String) - Static method in interface neureka.math.Function
+
of(Tensor<?>) - Static method in interface neureka.ndim.iterator.NDIterator
-
This static factory method will return Function instances - based on a provided mathematical String expression describing the function - using 'I[0]', 'I[1]', 'I[2]'...
+
Use this to instantiate NDIterators optimized for the provided tensor.
-
of(String...) - Static method in interface neureka.Data
-
 
-
of(String, boolean) - Static method in interface neureka.math.Function
+
of(Tensor<?>, NDIterator.NonVirtual) - Static method in interface neureka.ndim.iterator.NDIterator
-
This static factory method will return Function instances - based on a provided mathematical String expression describing the function - using 'I[0]', 'I[1]', 'I[2]'...
+
Use this to instantiate NDIterators optimized for the provided tensor + which may not be allowed to be a VirtualNDIterator instance.
+
+
of(NDConfiguration, NDIterator.NonVirtual) - Static method in interface neureka.ndim.iterator.NDIterator
+
+
Use this to instantiate NDIterators optimized for the provided NDConfiguration + which may not be allowed to be a VirtualNDIterator instance.
-
of(String, boolean) - Static method in class neureka.math.implementations.FunctionInput
+
of(int[], int[], int[], int[], int[]) - Static method in interface neureka.ndim.NDConstructor
+
 
+
of(NDConfiguration) - Static method in interface neureka.ndim.NDConstructor
 
-
of(String, boolean, List<Tensor<V>>) - Static method in interface neureka.Tensor
+
of(Shape) - Static method in interface neureka.ndim.NDConstructor
+
 
+
of(int...) - Static method in interface neureka.ndim.NDConstructor
+
 
+
of(Optimization<T>) - Static method in interface neureka.optimization.Optimizer
+
 
+
of(List<? extends Number>) - Static method in interface neureka.Shape
-
This method takes a list of tensors and a String expression describing - operations which ought to be applied to the tensors in said list.
+
This method is used to create a Shape instance from a list of numbers + whose integer values are used to describe the shape of a nd-array.
-
of(String, boolean, Tensor<V>...) - Static method in interface neureka.Tensor
+
of(Stream<? extends Number>) - Static method in interface neureka.Shape
-
This method takes an array of tensors and a String expression describing - operations which ought to be applied to the tensors in said array.
+
This method is used to create a Shape instance from a stream of numbers + whose integer values are used to describe the shape of a nd-array.
-
of(String, List<Tensor<V>>) - Static method in interface neureka.Tensor
+
of(Iterable<? extends Number>) - Static method in interface neureka.Shape
-
This factory method allows for the creation and execution of Function instances - without actually instantiating them manually, - where the result will then be returned by this factory method.
+
This method is used to create a Shape instance from an iterable of numbers + whose integer values are used to describe the shape of a nd-array.
+
+
of(int...) - Static method in interface neureka.Shape
+
+
This method is used to create a Shape instance from an array of integers.
-
of(String, Tensor<T>, char, Tensor<T>, String) - Static method in interface neureka.Tensor
+
of(Tensor<T>, char, Tensor<T>) - Static method in interface neureka.Tensor
Use this to conveniently operate on 2 tensors.
-
of(String, Tensor<T>, String) - Static method in interface neureka.Tensor
+
of(Tensor<T>, char, Tensor<T>, char, Tensor<T>) - Static method in interface neureka.Tensor
+
+
Use this to conveniently operate on 3 tensors.
+
+
of(String, Tensor<T>, String) - Static method in interface neureka.Tensor
Use this to conveniently operate on a tensor.
-
of(String, Tensor<T>, String, Tensor<T>, String, Tensor<T>, String) - Static method in interface neureka.Tensor
+
of(String, Tensor<T>, char, Tensor<T>, String) - Static method in interface neureka.Tensor
+
+
Use this to conveniently operate on 2 tensors.
+
+
of(String, Tensor<T>, String, Tensor<T>, String, Tensor<T>, String) - Static method in interface neureka.Tensor
Use this to conveniently operate on 3 tensors.
-
of(String, Tensor<V>) - Static method in interface neureka.Tensor
+
of(Object...) - Static method in interface neureka.Tensor
-
This method takes a tensor and a String expression describing - operations which ought to be applied to said tensor.
+
This static Tensor factory method tries to interpret the provided + arguments to create the instance the use might wants.
-
of(String, Tensor<V>...) - Static method in interface neureka.Tensor
+
of(Iterable<T>) - Static method in interface neureka.Tensor
-
This method takes an array of tensors and a String expression describing - operations which ought to be applied to the tensors in said array.
+
Constructs a vector of objects based on the provided iterable.
-
of(String, V...) - Static method in interface neureka.Tensor
+
of(List<Integer>, T) - Static method in interface neureka.Tensor
-
This factory method allows for the creation and execution of Function instances - without actually instantiating them manually, - where the result will then be returned by this factory method.
+
This is a convenient factory method for creating Tensor instances for + values of type T based on a list of integers + defining a shape made up of axes sizes as well as a scalar value of type T + which will fill out the data array spanned by the provided shape information.
-
of(Supplier<V>) - Static method in class neureka.backend.api.LazyRef
-
 
-
of(List<? extends Number>) - Static method in interface neureka.Shape
+
of(Shape, T) - Static method in interface neureka.Tensor
-
This method is used to create a Shape instance from a list of numbers - whose integer values are used to describe the shape of a nd-array.
+
This is a convenient factory method for creating Tensor instances for + representing items of type T.
-
of(List<? extends Number>, String) - Static method in interface neureka.Tensor
+
of(List<? extends Number>, String) - Static method in interface neureka.Tensor
This factory method will create and return a Tensor instance - based on a list of Number instances whose rounded values will be interpreted as + based on a list of Number instances whose rounded values will be interpreted as the shape of this new Tensor instance and a seed which will serve as a source of pseudo randomness to generate the values for the new instance.
-
of(List<? extends Number>, List<V>) - Static method in interface neureka.Tensor
+
of(List<? extends Number>, List<V>) - Static method in interface neureka.Tensor
Creates a new Tensor instance based on a list of numbers representing the shape, and a list of values representing the value of the resulting tensor.
-
of(List<Integer>, T) - Static method in interface neureka.Tensor
+
of(Shape, List<V>) - Static method in interface neureka.Tensor
-
This is a convenient factory method for creating Tensor instances for - values of type Tensor based on a list of integers - defining a shape made up of axes sizes as well as a scalar value of type Tensor - which will fill out the data array spanned by the provided shape information.
+
Creates a new Tensor instance based on a shape tuple of numbers representing the nd-array shape, + and a list of items representing the value of the resulting tensor.
-
of(List<Object>) - Static method in interface neureka.Tensor
+
of(List<Object>) - Static method in interface neureka.Tensor
This factory method will turn a list of values or nested lists of values into a Tensor instance with the corresponding rank and shape.
-
of(List<T>) - Static method in interface neureka.Nda
+
of(Class<T>, List<Object>) - Static method in interface neureka.Tensor
-
Constructs a vector of objects based on the provided list.
+
This factory method will turn a list of values or nested lists of values into a Tensor + instance with the corresponding rank and shape and whose values + are of the provided type.
-
of(Stream<? extends Number>) - Static method in interface neureka.Shape
+
of(Class<V>) - Static method in interface neureka.Tensor
-
This method is used to create a Shape instance from a stream of numbers - whose integer values are used to describe the shape of a nd-array.
+
This is the entry point to the fluent tensor builder API for building + Tensor instances in a readable and type safe fashion.
+
+
of(double...) - Static method in interface neureka.Tensor
+
+
Constructs a vector of doubles based on the provided array.
-
of(ADAction) - Static method in interface neureka.autograd.ADAction
+
of(double) - Static method in interface neureka.Tensor
 
-
of(ImplementationReceiver) - Static method in class neureka.backend.api.ini.BackendRegistry
+
of(float...) - Static method in interface neureka.Tensor
+
+
Constructs a vector of floats based on the provided array.
+
+
of(float) - Static method in interface neureka.Tensor
 
-
of(Device<?>) - Static method in class neureka.math.args.Arg.TargetDevice
+
of(byte...) - Static method in interface neureka.Tensor
+
+
Constructs a vector of bytes based on the provided array.
+
+
of(byte) - Static method in interface neureka.Tensor
 
-
of(DataType<T>, List<Integer>, Filler<T>) - Static method in interface neureka.Tensor
+
of(int...) - Static method in interface neureka.Tensor
-
This factory method allows the creation of tensors with an additional initialization - lambda for filling the underlying data array with desired values.
+
Constructs a vector of ints based on the provided array.
-
of(DataType<T>, Shape, Filler<T>) - Static method in interface neureka.Tensor
+
of(int) - Static method in interface neureka.Tensor
+
 
+
of(long...) - Static method in interface neureka.Tensor
-
This factory method allows the creation of tensors with an additional initialization - lambda for filling the underlying data array with desired values.
+
Constructs a vector of longs based on the provided array.
-
of(DataType<V>, List<Integer>, List<V>) - Static method in interface neureka.Tensor
+
of(long) - Static method in interface neureka.Tensor
+
 
+
of(short...) - Static method in interface neureka.Tensor
-
Use this to construct and return a tensor of the specified type, shape and data object.
+
Constructs a vector of shorts based on the provided array.
-
of(DataType<V>, Device<N>, Shape, Object) - Static method in interface neureka.Tensor
+
of(short) - Static method in interface neureka.Tensor
+
 
+
of(boolean...) - Static method in interface neureka.Tensor
-
This factory method is among the most flexible and forgiving ways to create a Tensor instance.
+
Constructs a vector of booleans based on the provided array.
-
of(DataType<V>, NDConstructor, Data<V>) - Static method in interface neureka.Tensor
+
of(Class<V>, Shape, Arg.Seed) - Static method in interface neureka.Tensor
-
This factory method a raw tensor constructor which will not perform any type checking - or data conversion on the data provided to it.
+
Use this to construct and return a seeded tensor of the specified type.
-
of(DataType<V>, Shape) - Static method in interface neureka.Tensor
+
of(Shape, double) - Static method in interface neureka.Tensor
-
Use this to construct and return a tensor of the specified type and shape.
+
Use this to construct and return a homogeneously populated double tensor of the specified shape.
-
of(DataType<V>, Shape, Object) - Static method in interface neureka.Tensor
+
of(Shape, double[]) - Static method in interface neureka.Tensor
-
This factory method is among the most flexible and forgiving ways to create a Tensor instance.
+
Use this to construct and return a double tensor of the specified shape and initial values.
-
of(DataType<V>, Shape, List<V>) - Static method in interface neureka.Tensor
+
of(Shape, int[]) - Static method in interface neureka.Tensor
-
Use this to construct and return a tensor of the specified type, shape and a list of items.
+
Use this to construct and return an int tensor of the specified shape and initial values.
-
of(Arg<?>...) - Static method in class neureka.math.args.Args
-
 
-
of(NDConfiguration) - Static method in interface neureka.ndim.NDConstructor
-
 
-
of(NDConfiguration.Layout) - Static method in class neureka.math.args.Arg.Layout
-
 
-
of(NDConfiguration, NDIterator.NonVirtual) - Static method in interface neureka.ndim.iterator.NDIterator
+
of(Shape, byte[]) - Static method in interface neureka.Tensor
-
Use this to instantiate NDIterators optimized for the provided NDConfiguration - which may not be allowed to be a VirtualNDIterator instance.
+
Use this to construct and return a byte tensor of the specified shape and initial values.
-
of(Optimization<T>) - Static method in interface neureka.optimization.Optimizer
-
 
-
of(Shape) - Static method in interface neureka.ndim.NDConstructor
-
 
-
of(Shape, boolean...) - Static method in interface neureka.Nda
+
of(Shape, long[]) - Static method in interface neureka.Tensor
-
Use this to construct and return a boolean based nd-array of the specified shape and initial values.
+
Use this to construct and return a long tensor of the specified shape and initial values.
-
of(Shape, boolean[]) - Static method in interface neureka.Tensor
+
of(Shape, short[]) - Static method in interface neureka.Tensor
-
Use this to construct and return a boolean tensor of the specified shape and initial values.
+
Use this to construct and return a short tensor of the specified shape and initial values.
-
of(Shape, byte...) - Static method in interface neureka.Nda
+
of(Shape, float[]) - Static method in interface neureka.Tensor
-
Use this to construct and return a byte based nd-array of the specified shape and initial values.
+
Use this to construct and return a float tensor of the specified shape and initial values.
-
of(Shape, byte[]) - Static method in interface neureka.Tensor
+
of(Shape, float) - Static method in interface neureka.Tensor
-
Use this to construct and return a byte tensor of the specified shape and initial values.
+
Use this to construct and return a homogeneously populated float tensor of the specified shape.
-
of(Shape, double) - Static method in interface neureka.Tensor
+
of(Shape, boolean[]) - Static method in interface neureka.Tensor
-
Use this to construct and return a homogeneously populated double tensor of the specified shape.
+
Use this to construct and return a boolean tensor of the specified shape and initial values.
-
of(Shape, double...) - Static method in interface neureka.Nda
+
of(Shape, Data<V>) - Static method in interface neureka.Tensor
-
Use this to construct and return a double based nd-array of the specified shape and initial values.
+
Use this to construct and return a tensor of the specified shape and data object.
+ This method is typically used like this:
-
of(Shape, double[]) - Static method in interface neureka.Tensor
+
of(DataType<V>, Shape) - Static method in interface neureka.Tensor
-
Use this to construct and return a double tensor of the specified shape and initial values.
+
Use this to construct and return a tensor of the specified type and shape.
-
of(Shape, float) - Static method in interface neureka.Tensor
+
of(Class<V>, Shape, Object) - Static method in interface neureka.Tensor
-
Use this to construct and return a homogeneously populated float tensor of the specified shape.
+
Use this to construct and return a tensor of the specified type, shape and data object.
-
of(Shape, float...) - Static method in interface neureka.Nda
+
of(Class<V>, List<Integer>, Object) - Static method in interface neureka.Tensor
-
Use this to construct and return a float based nd-array of the specified shape and initial values.
+
Use this to construct and return a tensor of the specified type, shape and data object.
-
of(Shape, float[]) - Static method in interface neureka.Tensor
+
of(Class<V>, Shape, Number) - Static method in interface neureka.Tensor
-
Use this to construct and return a float tensor of the specified shape and initial values.
+
Use this to construct and return a tensor of the specified type, shape and number.
-
of(Shape, int...) - Static method in interface neureka.Nda
+
of(Class<V>, List<Integer>, List<V>) - Static method in interface neureka.Tensor
-
Use this to construct and return a int based nd-array of the specified shape and initial values.
+
Use this to construct and return a tensor of the specified type, shape and data object.
-
of(Shape, int[]) - Static method in interface neureka.Tensor
+
of(Class<V>, Shape, List<V>) - Static method in interface neureka.Tensor
-
Use this to construct and return an int tensor of the specified shape and initial values.
+
Use this to construct and return a tensor of the specified type, shape and list of items.
-
of(Shape, long...) - Static method in interface neureka.Nda
+
of(DataType<V>, List<Integer>, List<V>) - Static method in interface neureka.Tensor
-
Use this to construct and return a long based nd-array of the specified shape and initial values.
+
Use this to construct and return a tensor of the specified type, shape and data object.
-
of(Shape, long[]) - Static method in interface neureka.Tensor
+
of(DataType<V>, Shape, List<V>) - Static method in interface neureka.Tensor
-
Use this to construct and return a long tensor of the specified shape and initial values.
+
Use this to construct and return a tensor of the specified type, shape and a list of items.
-
of(Shape, short...) - Static method in interface neureka.Nda
+
of(DataType<V>, Shape, Object) - Static method in interface neureka.Tensor
-
Use this to construct and return a short based nd-array of the specified shape and initial values.
+
This factory method is among the most flexible and forgiving ways to create a Tensor instance.
-
of(Shape, short[]) - Static method in interface neureka.Tensor
+
of(DataType<V>, Device<N>, Shape, Object) - Static method in interface neureka.Tensor
-
Use this to construct and return a short tensor of the specified shape and initial values.
+
This factory method is among the most flexible and forgiving ways to create a Tensor instance.
-
of(Shape, List<V>) - Static method in interface neureka.Tensor
+
of(DataType<V>, NDConstructor, Data<V>) - Static method in interface neureka.Tensor
-
Creates a new Tensor instance based on a shape tuple of numbers representing the nd-array shape, - and a list of items representing the value of the resulting tensor.
+
This factory method a raw tensor constructor which will not perform any type checking + or data conversion on the data provided to it.
-
of(Shape, Data<V>) - Static method in interface neureka.Tensor
+
of(DataType<T>, List<Integer>, Filler<T>) - Static method in interface neureka.Tensor
-
Use this to construct and return a tensor of the specified shape and data object.
- This method is typically used like this:
+
This factory method allows the creation of tensors with an additional initialization + lambda for filling the underlying data array with desired values.
-
of(Shape, T) - Static method in interface neureka.Tensor
+
of(DataType<T>, Shape, Filler<T>) - Static method in interface neureka.Tensor
-
This is a convenient factory method for creating Tensor instances for - representing items of type Tensor.
+
This factory method allows the creation of tensors with an additional initialization + lambda for filling the underlying data array with desired values.
-
of(Shape, T...) - Static method in interface neureka.Nda
+
of(Class<T>, Shape, Filler<T>) - Static method in interface neureka.Tensor
-
Use this to construct and return an object based nd-array of the specified shape and initial values.
+
This factory method allows the creation of tensors with an additional initialization + lambda for filling the underlying data array with desired values.
-
of(Tensor<?>) - Static method in class neureka.backend.api.Result
-
 
-
of(Tensor<?>) - Static method in interface neureka.ndim.iterator.NDIterator
+
of(String, V...) - Static method in interface neureka.Tensor
-
Use this to instantiate NDIterators optimized for the provided tensor.
+
This factory method allows for the creation and execution of Function instances + without actually instantiating them manually, + where the result will then be returned by this factory method.
-
of(Tensor<?>...) - Static method in class neureka.backend.api.ExecutionCall
+
of(String, List<Tensor<V>>) - Static method in interface neureka.Tensor
-
Use this factory method to build ExecutionCall instances in a readable fashion.
+
This factory method allows for the creation and execution of Function instances + without actually instantiating them manually, + where the result will then be returned by this factory method.
-
of(Tensor<?>, ADAction) - Static method in interface neureka.autograd.ADAction
-
 
-
of(Tensor<?>, NDIterator.NonVirtual) - Static method in interface neureka.ndim.iterator.NDIterator
+
of(String, boolean, List<Tensor<V>>) - Static method in interface neureka.Tensor
-
Use this to instantiate NDIterators optimized for the provided tensor - which may not be allowed to be a VirtualNDIterator instance.
+
This method takes a list of tensors and a String expression describing + operations which ought to be applied to the tensors in said list.
-
of(Tensor<T>, char, Tensor<T>) - Static method in interface neureka.Tensor
+
of(String, Tensor<V>) - Static method in interface neureka.Tensor
-
Use this to conveniently operate on 2 tensors.
+
This method takes a tensor and a String expression describing + operations which ought to be applied to said tensor.
-
of(Tensor<T>, char, Tensor<T>, char, Tensor<T>) - Static method in interface neureka.Tensor
+
of(String, Tensor<V>...) - Static method in interface neureka.Tensor
-
Use this to conveniently operate on 3 tensors.
+
This method takes an array of tensors and a String expression describing + operations which ought to be applied to the tensors in said array.
-
of(Tensor<V>) - Static method in class neureka.math.args.Arg.Derivative
-
 
-
of(T...) - Static method in interface neureka.Nda
+
of(String, boolean, Tensor<V>...) - Static method in interface neureka.Tensor
-
Constructs a vector of objects based on the provided array.
+
This method takes an array of tensors and a String expression describing + operations which ought to be applied to the tensors in said array.
-
ofAny(Class<V>, Shape, Object) - Static method in interface neureka.Tensor
+
ofAny(Class<V>, Shape, Object) - Static method in interface neureka.Tensor
Use this to construct and return a tensor of the specified type, shape and data object.
-
ofBigDecimals() - Static method in interface neureka.Nda
+
ofBigDecimals() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(BigDecimal.class) - used to build Ndas storing BigDecimals.
+ used to build Ndas storing BigDecimals.
-
ofBooleans() - Static method in interface neureka.Nda
+
ofBooleans() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(Boolean.class) - used to build Ndas storing Booleans.
+ used to build Ndas storing Booleans.
-
ofBytes() - Static method in interface neureka.Nda
+
ofBytes() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(Byte.class) - used to build Ndas storing Bytes.
+ used to build Ndas storing Bytes.
-
ofBytes() - Static method in interface neureka.Tensor
+
ofBytes() - Static method in interface neureka.Tensor
-
This is a simple convenience method which is simply calling the Tensor.of(Class) +
This is a simple convenience method which is simply calling the Tensor.of(Class) method like so: of(Byte.class).
-
ofChars() - Static method in interface neureka.Nda
+
ofChars() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(Character.class) - used to build Ndas storing Characters.
+ used to build Ndas storing Characters.
-
ofDoubles() - Static method in interface neureka.Nda
+
ofDoubles() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(Double.class) - used to build Ndas storing Doubles.
+ used to build Ndas storing Doubles.
-
ofDoubles() - Static method in interface neureka.Tensor
+
ofDoubles() - Static method in interface neureka.Tensor
-
This is a simple convenience method which is simply calling the Tensor.of(Class) +
This is a simple convenience method which is simply calling the Tensor.of(Class) method like so: of(Double.class).
-
ofFloats() - Static method in interface neureka.Nda
+
ofFloats() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(Float.class) - used to build Ndas storing Floats.
+ used to build Ndas storing Floats.
-
ofFloats() - Static method in interface neureka.Tensor
+
ofFloats() - Static method in interface neureka.Tensor
-
This is a simple convenience method which is simply calling the Tensor.of(Class) +
This is a simple convenience method which is simply calling the Tensor.of(Class) method like so: of(Float.class).
-
offset() - Method in interface neureka.ndim.config.NDConfiguration
+
offset() - Method in interface neureka.ndim.config.NDConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
offset(int) - Method in interface neureka.ndim.config.NDConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
offset() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
offset() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
offset() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
offset() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
offset() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
offset() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
offset() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
offset(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
offset() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset() - Method in interface neureka.ndim.NDimensional
+
offset(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
The offset is the position of a slice within the n-dimensional - data array of its parent array.
+ data array of its parent tensor.
-
offset(int) - Method in interface neureka.ndim.config.NDConfiguration
+
offset() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
offset() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
offset() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
offset() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
offset() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
offset() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
offset() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
offset(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
offset() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
offset(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
The offset is the position of a slice within the n-dimensional data array of its parent tensor.
-
offset(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
offset() - Method in interface neureka.ndim.NDimensional
The offset is the position of a slice within the n-dimensional - data array of its parent tensor.
+ data array of its parent array.
-
OFFSET_MATRIX - Enum constant in enum class neureka.ndim.config.NDTrait
+
ofGradient(Optimization<T>) - Static method in interface neureka.optimization.Optimizer
 
-
ofGradient(Optimization<T>) - Static method in interface neureka.optimization.Optimizer
-
 
-
ofInts() - Static method in interface neureka.Nda
+
ofInts() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(Integer.class) - used to build Ndas storing Integers.
+ used to build Ndas storing Integers.
-
ofInts() - Static method in interface neureka.Tensor
+
ofInts() - Static method in interface neureka.Tensor
-
This is a simple convenience method which is simply calling the Tensor.of(Class) +
This is a simple convenience method which is simply calling the Tensor.of(Class) method like so: of(Integer.class).
-
ofLongs() - Static method in interface neureka.Nda
+
ofLongs() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(Long.class) - used to build Ndas storing Longs.
+ used to build Ndas storing Longs.
-
ofNumbers() - Static method in interface neureka.Nda
+
ofNumbers() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(Number.class) - used to build Ndas storing Numbers.
+ used to build Ndas storing Numbers.
-
ofObjects() - Static method in interface neureka.Nda
+
ofObjects() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(Object.class) - used to build Ndas storing Objects.
+ used to build Ndas storing Objects.
-
ofRandom(Class<V>, int...) - Static method in interface neureka.Tensor
+
ofRandom(Class<V>, int...) - Static method in interface neureka.Tensor
This factory method produces a randomly populated tensor of the provided type and shape using a hard coded default seed.
-
ofShorts() - Static method in interface neureka.Nda
+
ofShorts() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(Short.class) - used to build Ndas storing Shorts.
+ used to build Ndas storing Shorts.
-
ofShorts() - Static method in interface neureka.Tensor
+
ofShorts() - Static method in interface neureka.Tensor
-
This is a simple convenience method which is simply calling the Tensor.of(Class) +
This is a simple convenience method which is simply calling the Tensor.of(Class) method like so: of(Short.class).
-
ofStrings() - Static method in interface neureka.Nda
+
ofStrings() - Static method in interface neureka.Nda
This is a shortcut method for Nda.of(String.class) - used to build Ndas storing Strings.
+ used to build Ndas storing Strings.
-
OKAY - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
+
OKAY - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
 
-
on(D) - Method in class neureka.backend.api.ExecutionCall.Builder
+
on(D) - Method in class neureka.backend.api.ExecutionCall.Builder
 
-
on(Device<V>) - Method in class neureka.fluent.building.NdaBuilder
+
on(Device<? super V>) - Method in class neureka.fluent.building.NdaBuilder
 
-
on(Device<V>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorOnDevice
+
on(Device<? super V>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorOnDevice
Use this to specify the type onto which the tensor should be stored.
-
ONE - Enum constant in enum class neureka.devices.host.concurrent.Parallelism
-
-
1
-
-
OpenCLDevice - Class in neureka.devices.opencl
+
OpenCLDevice - Class in neureka.devices.opencl
This class models OpenCL supporting accelerator hardware like GPUs or FPGAs for storing tensors and executing operations on them.
-
OpenCLDevice.Query - Class in neureka.devices.opencl
+
OpenCLDevice.Query - Class in neureka.devices.opencl
 
-
OpenCLDevice.Type - Enum Class in neureka.devices.opencl
+
OpenCLDevice.Type - Enum in neureka.devices.opencl
 
-
OpenCLPlatform - Class in neureka.devices.opencl
+
OpenCLPlatform - Class in neureka.devices.opencl
This class models the OpenCL concept of platforms, which refer to device vendors / or vendor OpenCL runtime drivers.
-
OpenCLPlatform(cl_platform_id) - Constructor for class neureka.devices.opencl.OpenCLPlatform
+
OpenCLPlatform(cl_platform_id) - Constructor for class neureka.devices.opencl.OpenCLPlatform
 
-
Operation - Interface in neureka.backend.api
+
Operation - Interface in neureka.backend.api
This interface is part of the backend API, and it embodies the top layer of the 3 tier backend architecture.
-
OperationBuilder - Class in neureka.backend.api.template.operations
+
OperationBuilder - Class in neureka.backend.api.template.operations
This builder class builds instances of the Operation interface.
-
OperationBuilder() - Constructor for class neureka.backend.api.template.operations.OperationBuilder
+
OperationBuilder() - Constructor for class neureka.backend.api.template.operations.OperationBuilder
 
-
OperationBuilder.Derivation - Interface in neureka.backend.api.template.operations
+
OperationBuilder.Derivation - Interface in neureka.backend.api.template.operations
 
-
OperationBuilder.Stringifier - Interface in neureka.backend.api.template.operations
+
OperationBuilder.Stringifier - Interface in neureka.backend.api.template.operations
 
-
operationForF32(boolean, long, long) - Static method in class neureka.backend.main.operations.linear.internal.blas.GEMM
+
operationForF32(boolean, long, long) - Static method in class neureka.backend.main.operations.linear.internal.blas.GEMM
 
-
operationForF64(boolean, long, long) - Static method in class neureka.backend.main.operations.linear.internal.blas.GEMM
+
operationForF64(boolean, long, long) - Static method in class neureka.backend.main.operations.linear.internal.blas.GEMM
 
-
operationForI32(boolean, long, long) - Static method in class neureka.backend.main.operations.linear.internal.blas.IGEMM
+
operationForI32(boolean, long, long) - Static method in class neureka.backend.main.operations.linear.internal.blas.IGEMM
 
-
operationForI64(boolean, long, long) - Static method in class neureka.backend.main.operations.linear.internal.blas.IGEMM
+
operationForI64(boolean, long, long) - Static method in class neureka.backend.main.operations.linear.internal.blas.IGEMM
 
-
operationName() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
operationName() - Method in class neureka.backend.api.template.operations.AbstractOperation
Override this if you want your operation to have a string representation with a custom prefix which is something other than the simple class name!
-
operator(String) - Method in class neureka.backend.api.template.operations.OperationBuilder
+
operator(String) - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
Optimization<V> - Interface in neureka.optimization
+
Optimization<V> - Interface in neureka.optimization
 
-
optimize() - Method in class neureka.devices.opencl.utility.CLFunctionCompiler
+
optimize() - Method in class neureka.devices.opencl.utility.CLFunctionCompiler
 
-
optimize(Tensor<V>) - Method in class neureka.optimization.implementations.AdaGrad
+
optimize(Tensor<V>) - Method in class neureka.optimization.implementations.AdaGrad
 
-
optimize(Tensor<V>) - Method in class neureka.optimization.implementations.ADAM
+
optimize(Tensor<V>) - Method in class neureka.optimization.implementations.ADAM
 
-
optimize(Tensor<V>) - Method in class neureka.optimization.implementations.Momentum
+
optimize(Tensor<V>) - Method in class neureka.optimization.implementations.Momentum
 
-
optimize(Tensor<V>) - Method in class neureka.optimization.implementations.RMSProp
+
optimize(Tensor<V>) - Method in class neureka.optimization.implementations.RMSProp
 
-
optimize(Tensor<V>) - Method in class neureka.optimization.implementations.SGD
+
optimize(Tensor<V>) - Method in class neureka.optimization.implementations.SGD
 
-
optimize(Tensor<V>) - Method in interface neureka.optimization.Optimization
+
optimize(Tensor<V>) - Method in interface neureka.optimization.Optimization
 
-
optimizedFunctionOf(Function, String) - Method in interface neureka.devices.Device
+
optimizedFunctionOf(Function, String) - Method in interface neureka.devices.Device
This method tries to allow this device to produce an optimized Function based on the provided function.
-
optimizedOperationOf(Function, String) - Method in interface neureka.devices.Device
+
optimizedOperationOf(Function, String) - Method in interface neureka.devices.Device
This method tries to allow this device to produce an optimized Operation based on the provided function.
-
optimizedOperationOf(Function, String) - Method in class neureka.devices.file.FileDevice
+
optimizedOperationOf(Function, String) - Method in class neureka.devices.file.FileDevice
 
-
optimizedOperationOf(Function, String) - Method in class neureka.devices.host.CPU
+
optimizedOperationOf(Function, String) - Method in class neureka.devices.host.CPU
 
-
optimizedOperationOf(Function, String) - Method in class neureka.devices.opencl.OpenCLDevice
+
optimizedOperationOf(Function, String) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
Optimizer<V> - Interface in neureka.optimization
+
Optimizer<V> - Interface in neureka.optimization
Optimizers are tensor components which implement the Optimization (functional) interface applying various optimization algorithms to the gradients of tensors.
-
OptimizerFactory - Interface in neureka.optimization
+
OptimizerFactory - Interface in neureka.optimization
 
-
orElse(T) - Method in interface neureka.backend.api.Call.Else
+
orElse(T) - Method in interface neureka.backend.api.Call.Else
 
-
orElse(V) - Method in interface neureka.Nda.Item
+
orElse(V) - Method in interface neureka.Nda.Item
Get the value at the targeted position or return the provided default value if the item does not exist.
-
orElseNull() - Method in interface neureka.Nda.Item
+
orElseNull() - Method in interface neureka.Nda.Item
Get the value at the targeted position or return null if the item does not exist.
-
OS_MEMORY_PAGE_SIZE - Static variable in class neureka.devices.host.machine.Hardware
+
OS_MEMORY_PAGE_SIZE - Static variable in class neureka.devices.host.machine.Hardware
Practically all architectures/OS:s have a page size of 4k (one notable exception is Solaris/SPARC that have 8k)
-
owner() - Method in interface neureka.Data
+
owner() - Method in interface neureka.Data
 
-
owner() - Method in class neureka.devices.AbstractDeviceData
+
owner() - Method in class neureka.devices.AbstractDeviceData
 
-

P

-
-
pad(int, String) - Static method in class neureka.view.NdaAsString.Util
-
 
-
pad(String, int) - Static method in class neureka.view.NdaAsString.Util
+ + + +

P

+
+
pad(int, String) - Static method in class neureka.view.NdaAsString.Util
 
-
parallelism(IntSupplier) - Method in class neureka.devices.host.concurrent.WorkScheduler.Divider
+
pad(String, int) - Static method in class neureka.view.NdaAsString.Util
 
-
Parallelism - Enum Class in neureka.devices.host.concurrent
+
Parallelism - Enum in neureka.devices.host.concurrent
A set of standard levels of parallelism derived from the number of available cores and optionally capped by reserving a specified amount of memory per thread.
-
PARALLELIZATION_THRESHOLD - Static variable in class neureka.devices.host.CPU
+
parallelism(IntSupplier) - Method in class neureka.devices.host.concurrent.WorkScheduler.Divider
 
-
parse(String, boolean) - Method in class neureka.math.parsing.FunctionParser
+
PARALLELIZATION_THRESHOLD - Static variable in class neureka.devices.host.CPU
 
-
parse(Operation, int, boolean) - Method in class neureka.math.parsing.FunctionParser
+
parse(Operation, int, boolean) - Method in class neureka.math.parsing.FunctionParser
 
-
ParsedCLImplementation - Class in neureka.backend.main.implementations
+
parse(String, boolean) - Method in class neureka.math.parsing.FunctionParser
 
-
ParsedCLImplementation(ImplementationFor<OpenCLDevice>, int, String, String, String, String, Function<KernelCode, KernelCode[]>) - Constructor for class neureka.backend.main.implementations.ParsedCLImplementation
+
ParsedCLImplementation - Class in neureka.backend.main.implementations
 
-
parsedOperation(String, int) - Static method in class neureka.math.parsing.ParseUtil
+
ParsedCLImplementation(ImplementationFor<OpenCLDevice>, int, String, String, String, String, Function<KernelCode, KernelCode[]>) - Constructor for class neureka.backend.main.implementations.ParsedCLImplementation
 
-
ParseUtil - Class in neureka.math.parsing
+
parsedOperation(String, int) - Static method in class neureka.math.parsing.ParseUtil
+
 
+
ParseUtil - Class in neureka.math.parsing
Utility for parsing function expressions.
-
partialDerivative() - Method in interface neureka.autograd.ADAction
-
 
-
pass(byte) - Method in class neureka.devices.opencl.KernelCaller
-
 
-
pass(byte...) - Method in class neureka.devices.opencl.KernelCaller
-
 
-
pass(double) - Method in class neureka.devices.opencl.KernelCaller
+
partialDerivative() - Method in interface neureka.autograd.ADAction
 
-
pass(double...) - Method in class neureka.devices.opencl.KernelCaller
-
 
-
pass(float) - Method in class neureka.devices.opencl.KernelCaller
-
 
-
pass(float...) - Method in class neureka.devices.opencl.KernelCaller
+
pass(Tensor<T>) - Method in class neureka.devices.opencl.KernelCaller
-
Use this to pass an array of float values to the kernel.
+
This method passes 1 argument to the kernel.
-
pass(int) - Method in class neureka.devices.opencl.KernelCaller
+
pass(int) - Method in class neureka.devices.opencl.KernelCaller
 
-
pass(int...) - Method in class neureka.devices.opencl.KernelCaller
+
pass(int...) - Method in class neureka.devices.opencl.KernelCaller
Use this to pass an array of int values to the kernel.
-
pass(long) - Method in class neureka.devices.opencl.KernelCaller
+
pass(float...) - Method in class neureka.devices.opencl.KernelCaller
+
+
Use this to pass an array of float values to the kernel.
+
+
pass(double...) - Method in class neureka.devices.opencl.KernelCaller
 
-
pass(long...) - Method in class neureka.devices.opencl.KernelCaller
+
pass(short...) - Method in class neureka.devices.opencl.KernelCaller
 
-
pass(short) - Method in class neureka.devices.opencl.KernelCaller
+
pass(long...) - Method in class neureka.devices.opencl.KernelCaller
 
-
pass(short...) - Method in class neureka.devices.opencl.KernelCaller
+
pass(byte...) - Method in class neureka.devices.opencl.KernelCaller
 
-
pass(Number) - Method in class neureka.devices.opencl.KernelCaller
+
pass(float) - Method in class neureka.devices.opencl.KernelCaller
 
-
pass(Tensor<T>) - Method in class neureka.devices.opencl.KernelCaller
-
-
This method passes 1 argument to the kernel.
-
-
passAllOf(Tensor<Number>) - Method in class neureka.devices.opencl.KernelCaller
+
pass(double) - Method in class neureka.devices.opencl.KernelCaller
+
 
+
pass(short) - Method in class neureka.devices.opencl.KernelCaller
+
 
+
pass(long) - Method in class neureka.devices.opencl.KernelCaller
+
 
+
pass(byte) - Method in class neureka.devices.opencl.KernelCaller
+
 
+
pass(Number) - Method in class neureka.devices.opencl.KernelCaller
+
 
+
passAllOf(Tensor<Number>) - Method in class neureka.devices.opencl.KernelCaller
This method passes 2 arguments to the kernel.
-
passConfOf(Tensor<Number>) - Method in class neureka.devices.opencl.KernelCaller
+
passConfOf(Tensor<Number>) - Method in class neureka.devices.opencl.KernelCaller
This method passes the ND-Configuration in the form of a flattened int array to the kernel.
-
passLocalFloats(long) - Method in class neureka.devices.opencl.KernelCaller
+
passLocalFloats(long) - Method in class neureka.devices.opencl.KernelCaller
+
 
+
pendingCount() - Method in class neureka.autograd.JITProp
 
-
pendingCount() - Method in class neureka.autograd.JITProp
+
PERFECT - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
 
-
PERFECT - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
+
Permute - Class in neureka.backend.main.operations.other
 
-
permute() - Method in class neureka.math.Functions
+
Permute() - Constructor for class neureka.backend.main.operations.other.Permute
 
-
permute(int...) - Method in interface neureka.Nda
+
permute() - Method in class neureka.math.Functions
+
 
+
permute(int...) - Method in interface neureka.Nda
Returns a view of the original tensor input with its dimensions permuted.
Consider a 3-dimensional tensor x with shape (2×3×5), then calling x.permute(1, 0, 2) will return a 3-dimensional tensor of shape (3×2×5).
-
permute(int...) - Method in interface neureka.Tensor
+
permute(int...) - Method in interface neureka.Tensor
Returns a view of the original tensor input with its dimensions permuted.
Consider a 3-dimensional tensor x with shape (2×3×5), then calling x.permute(1, 0, 2) will return a 3-dimensional tensor of shape (3×2×5).
-
Permute - Class in neureka.backend.main.operations.other
-
 
-
Permute() - Constructor for class neureka.backend.main.operations.other.Permute
+
Permuted1DConfiguration - Class in neureka.ndim.config.types.permuted
 
-
Permuted1DConfiguration - Class in neureka.ndim.config.types.permuted
+
Permuted1DConfiguration(int, int, int) - Constructor for class neureka.ndim.config.types.permuted.Permuted1DConfiguration
 
-
Permuted1DConfiguration(int, int, int) - Constructor for class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
Permuted2DCIterator - Class in neureka.ndim.iterator.types.permuted
 
-
Permuted2DCIterator - Class in neureka.ndim.iterator.types.permuted
+
Permuted2DCIterator(Permuted2DConfiguration) - Constructor for class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
 
-
Permuted2DCIterator(Permuted2DConfiguration) - Constructor for class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
+
Permuted2DConfiguration - Class in neureka.ndim.config.types.permuted
 
-
Permuted2DConfiguration - Class in neureka.ndim.config.types.permuted
+
Permuted2DConfiguration(int[], int[], int[]) - Constructor for class neureka.ndim.config.types.permuted.Permuted2DConfiguration
 
-
Permuted2DConfiguration(int[], int[], int[]) - Constructor for class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
Permuted3DCIterator - Class in neureka.ndim.iterator.types.permuted
 
-
Permuted3DCIterator - Class in neureka.ndim.iterator.types.permuted
+
Permuted3DCIterator(Permuted3DConfiguration) - Constructor for class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
 
-
Permuted3DCIterator(Permuted3DConfiguration) - Constructor for class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
+
Permuted3DConfiguration - Class in neureka.ndim.config.types.permuted
 
-
Permuted3DConfiguration - Class in neureka.ndim.config.types.permuted
+
Permuted3DConfiguration(int[], int[], int[]) - Constructor for class neureka.ndim.config.types.permuted.Permuted3DConfiguration
 
-
Permuted3DConfiguration(int[], int[], int[]) - Constructor for class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
PermutedNDConfiguration - Class in neureka.ndim.config.types.permuted
 
-
PermutedNDConfiguration - Class in neureka.ndim.config.types.permuted
+
PermutedNDConfiguration(int[], int[], int[]) - Constructor for class neureka.ndim.config.types.permuted.PermutedNDConfiguration
 
-
PermutedNDConfiguration(int[], int[], int[]) - Constructor for class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
plus() - Method in class neureka.math.Functions
 
-
plus() - Method in class neureka.math.Functions
-
 
-
plus(Tensor<V>) - Method in interface neureka.Tensor
+
plus(Tensor<V>) - Method in interface neureka.Tensor
This method will produce the addition of two tensors with the same rank (or two ranks which can be made compatible with padding ones), where the left operand is this Tensor instance and the right operand is the tensor passed to the method.
-
plus(V) - Method in interface neureka.Tensor
+
plus(V) - Method in interface neureka.Tensor
This method will create a new Tensor with the provided double scalar added to all elements of this Tensor.
-
plusAssign() - Method in class neureka.math.Functions
+
plusAssign() - Method in class neureka.math.Functions
 
-
plusAssign(Tensor<T>) - Method in interface neureka.MutateTensor
+
plusAssign(Tensor<T>) - Method in interface neureka.MutateTensor
Performs an addition of the passed tensor to this tensor.
-
pow() - Method in class neureka.math.Functions
+
pow() - Method in class neureka.math.Functions
+
 
+
powAssign() - Method in class neureka.math.Functions
 
-
powAssign() - Method in class neureka.math.Functions
+
Power - Class in neureka.backend.main.operations.operator
 
-
power(Tensor<V>) - Method in interface neureka.Tensor
+
Power() - Constructor for class neureka.backend.main.operations.operator.Power
+
 
+
power(Tensor<V>) - Method in interface neureka.Tensor
This will produce the power of two tensors with the same rank (or two ranks which can be made compatible with padding ones), where the left operand is this Tensor instance and the right operand is the tensor passed to the method.
-
power(V) - Method in interface neureka.Tensor
+
power(V) - Method in interface neureka.Tensor
Raises all items of this tensor to the power of the provided value.
-
Power - Class in neureka.backend.main.operations.operator
-
 
-
Power() - Constructor for class neureka.backend.main.operations.operator.Power
-
 
-
PREDEFINED - Static variable in class neureka.devices.host.machine.Hardware
+
PREDEFINED - Static variable in class neureka.devices.host.machine.Hardware
Should contain all available hardware in ascending "power" order.
-
prefVecWidthChar() - Method in class neureka.devices.opencl.OpenCLDevice
+
prefVecWidthChar() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
prefVecWidthDouble() - Method in class neureka.devices.opencl.OpenCLDevice
+
prefVecWidthDouble() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
prefVecWidthFloat() - Method in class neureka.devices.opencl.OpenCLDevice
+
prefVecWidthFloat() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
prefVecWidthInt() - Method in class neureka.devices.opencl.OpenCLDevice
+
prefVecWidthInt() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
prefVecWidthLong() - Method in class neureka.devices.opencl.OpenCLDevice
+
prefVecWidthLong() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
prefVecWidthShort() - Method in class neureka.devices.opencl.OpenCLDevice
+
prefVecWidthShort() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
prepare(ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.api.fun.ExecutionPreparation
+
prepare(ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.api.fun.ExecutionPreparation
 
-
prepare(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
prepare(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
Preparing refers to instantiating output tensors for the provided ExecutionCall.
-
prepare(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
+
prepare(ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
 
-
prepareAndExecute(ExecutionCall<? extends Device<?>>, FinalExecutor) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
prepareAndExecute(ExecutionCall<? extends Device<?>>, FinalExecutor) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
process(T) - Method in class neureka.common.utility.Cache
+
process(T) - Method in class neureka.common.utility.Cache
 
-
produceNDC(boolean) - Method in interface neureka.ndim.NDConstructor
+
produceNDC(boolean) - Method in interface neureka.ndim.NDConstructor
 
-
Product - Class in neureka.backend.main.operations.indexer
+
Product - Class in neureka.backend.main.operations.indexer
This type of operation belongs to the same species as the Summation operation.
-
Product() - Constructor for class neureka.backend.main.operations.indexer.Product
+
Product() - Constructor for class neureka.backend.main.operations.indexer.Product
 
-
propertyOf(Tensor<?>) - Method in interface neureka.backend.api.Call.TensorProperty
+
propertyOf(Tensor<?>) - Method in interface neureka.backend.api.Call.TensorProperty
 
-
providesGradient() - Method in class neureka.math.implementations.FunctionInput
+
providesGradient() - Method in class neureka.math.implementations.FunctionInput
 
-
providesGradient() - Method in class neureka.math.implementations.FunctionVariable
+
providesGradient() - Method in class neureka.math.implementations.FunctionVariable
 
-
put(String, OpenCLDevice.cl_ad_hoc) - Method in class neureka.devices.opencl.KernelCache
+
put(String, OpenCLDevice.cl_ad_hoc) - Method in class neureka.devices.opencl.KernelCache
 
-
put(Function) - Method in class neureka.math.FunctionCache
+
put(Function) - Method in class neureka.math.FunctionCache
 
-
putAt(int[], T) - Method in interface neureka.MutateNda
+
putAt(Map<?, Integer>, Nda<T>) - Method in interface neureka.MutateNda
-
Use this to put a single item at a particular - position within this nd-array.
+
This method enables assigning a provided nd-array to be a subset/slice of this nd-array! + It takes a key which is used to configure a slice + sharing the same underlying data as the original nd-array.
-
putAt(int[], T) - Method in interface neureka.MutateTensor
+
putAt(int[], T) - Method in interface neureka.MutateNda
Use this to put a single item at a particular position within this nd-array.
-
putAt(int, T) - Method in interface neureka.MutateNda
-
-
Individual entries for value items in this nd-array can be set - via this method.
-
-
putAt(int, T) - Method in interface neureka.MutateTensor
+
putAt(int, T) - Method in interface neureka.MutateNda
Individual entries for value items in this nd-array can be set via this method.
-
putAt(List<?>, Nda<T>) - Method in interface neureka.MutateNda
+
putAt(List<?>, Nda<T>) - Method in interface neureka.MutateNda
This method enables injecting slices of nd-array to be assigned into this nd-array! It takes a key of various types which is used to configure a slice nd-array sharing the same underlying data as the original nd-array.
-
putAt(List<?>, Nda<T>) - Method in interface neureka.MutateTensor
-
-
This method enables injecting slices of nd-array to be assigned into this nd-array! - It takes a key of various types which is used to configure a slice - nd-array sharing the same underlying data as the original nd-array.
-
-
putAt(List<?>, T) - Method in interface neureka.MutateNda
-
-
Use this to place a single item at a particular position within this nd-array!
-
-
putAt(List<?>, T) - Method in interface neureka.MutateTensor
+
putAt(List<?>, T) - Method in interface neureka.MutateNda
Use this to place a single item at a particular position within this nd-array!
-
putAt(Map<?, Integer>, Nda<T>) - Method in interface neureka.MutateNda
+
putAt(Map<?, Integer>, Nda<T>) - Method in interface neureka.MutateTensor
This method enables assigning a provided nd-array to be a subset/slice of this nd-array! It takes a key which is used to configure a slice sharing the same underlying data as the original nd-array.
-
putAt(Map<?, Integer>, Nda<T>) - Method in interface neureka.MutateTensor
+
putAt(int[], T) - Method in interface neureka.MutateTensor
-
This method enables assigning a provided nd-array to be a subset/slice of this nd-array! - It takes a key which is used to configure a slice - sharing the same underlying data as the original nd-array.
+
Use this to put a single item at a particular + position within this nd-array.
+
+
putAt(int, T) - Method in interface neureka.MutateTensor
+
+
Individual entries for value items in this nd-array can be set + via this method.
+
+
putAt(List<?>, Nda<T>) - Method in interface neureka.MutateTensor
+
+
This method enables injecting slices of nd-array to be assigned into this nd-array! + It takes a key of various types which is used to configure a slice + nd-array sharing the same underlying data as the original nd-array.
+
+
putAt(List<?>, T) - Method in interface neureka.MutateTensor
+
+
Use this to place a single item at a particular position within this nd-array!
-

Q

-
-
quad() - Method in class neureka.math.Functions
+ + + +

Q

+
+
quad() - Method in class neureka.math.Functions
+
 
+
QUADRATIC - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
Quadratic - Class in neureka.backend.main.operations.functions
+
Quadratic - Class in neureka.backend.main.operations.functions
 
-
Quadratic() - Constructor for class neureka.backend.main.operations.functions.Quadratic
+
Quadratic() - Constructor for class neureka.backend.main.operations.functions.Quadratic
 
-
QUADRATIC - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
Query() - Constructor for class neureka.devices.opencl.OpenCLDevice.Query
 
-
query() - Static method in class neureka.devices.opencl.utility.DeviceQuery
+
query() - Static method in class neureka.devices.opencl.utility.DeviceQuery
The entry point of this program
-
Query() - Constructor for class neureka.devices.opencl.OpenCLDevice.Query
-
 
-

R

-
-
random() - Method in class neureka.math.Functions
+ + + +

R

+
+
random() - Method in class neureka.math.Functions
 
-
Randomization - Class in neureka.backend.main.operations.other
+
Randomization - Class in neureka.backend.main.operations.other
This Operation takes an optional user seed, the shape of its input tensor, and @@ -7128,868 +7096,855 @@

R

floats or doubles with a gaussian distribution where the mean is 0 and the standard deviation is 1.
-
Randomization() - Constructor for class neureka.backend.main.operations.other.Randomization
+
Randomization() - Constructor for class neureka.backend.main.operations.other.Randomization
 
-
rank() - Method in interface neureka.ndim.config.NDConfiguration
+
rank() - Method in interface neureka.ndim.config.NDConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
rank() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
rank() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
rank() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
rank() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
rank() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
rank() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
rank() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
rank() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
rank() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
rank() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
rank() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
rank() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
rank() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
rank() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
rank() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
rank() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
This method returns the number of axis of a nd-array / Tensor which is equal to the length of the shape of an nd-array / Tensor.
-
rank() - Method in interface neureka.ndim.iterator.NDIterator
+
rank() - Method in interface neureka.ndim.iterator.NDIterator
 
-
rank() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
rank() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
 
-
rank() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
rank() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
 
-
rank() - Method in interface neureka.ndim.NDimensional
+
rank() - Method in interface neureka.ndim.NDimensional
 
-
read(List<Object>, Function<Object, Object>) - Static method in class neureka.common.utility.ListReader
+
read(List<Object>, Function<Object, Object>) - Static method in class neureka.common.utility.ListReader
Reads the provided data and turns it into a ListReader.Result object, containing a flattened list of the data alongside its shape and data type.
-
readAll(boolean) - Method in interface neureka.devices.Device.Access
+
readAll(boolean) - Method in interface neureka.devices.Device.Access
Use this to read the full data array of the accessed tensor.
-
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.F32
+
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.F32
 
-
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.F64
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.F32
 
-
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I16
+
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.F64
 
-
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I32
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.F64
 
-
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I64
+
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I16
 
-
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I8
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I16
 
-
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI16
+
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I32
 
-
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI32
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I32
 
-
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI64
+
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I64
 
-
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI8
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I64
 
-
readAndConvertForeignDataFrom(DataInput, int) - Method in interface neureka.dtype.NumericType
-
-
This method expects the provided stream to spit out bytes which can be read as holder type elements.
-
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.F32
+
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I8
 
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.F64
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I8
 
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I16
+
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI16
 
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I32
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI16
 
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I64
+
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI32
 
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I8
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI32
 
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI16
+
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI64
 
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI32
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI64
 
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI64
+
readAndConvertForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI8
 
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI8
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI8
 
-
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in interface neureka.dtype.NumericType
+
readAndConvertForeignDataFrom(DataInput, int) - Method in interface neureka.dtype.NumericType
+
+
This method expects the provided stream to spit out bytes which can be read as holder type elements.
+
+
readAndConvertForeignDataFrom(Iterator<T>, int) - Method in interface neureka.dtype.NumericType
This method expects the provided iterator to return elements which can be read as holder type elements.
-
readArray(Class<A>, int, int) - Method in interface neureka.devices.Device.Access
+
readArray(Class<A>, int, int) - Method in interface neureka.devices.Device.Access
Use this to read an array of items from the accessed tensor by specifying a start position of the chunk of data that should be read.
-
readAt(int) - Method in interface neureka.devices.Device.Access
+
readAt(int) - Method in interface neureka.devices.Device.Access
Find a particular tensor item by providing its location.
-
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.F32
+
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.F32
 
-
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.F64
+
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.F32
 
-
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I16
+
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.F64
 
-
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I32
+
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.F64
 
-
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I64
+
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I16
 
-
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I8
+
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I16
 
-
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI16
+
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I32
 
-
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI32
+
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I32
 
-
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI64
+
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I64
 
-
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI8
+
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I64
 
-
readForeignDataFrom(DataInput, int) - Method in interface neureka.dtype.NumericType
-
-
This method expects the provided stream to spit out bytes which can be read as target type elements.
-
-
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.F32
+
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.I8
 
-
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.F64
+
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I8
 
-
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I16
+
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI16
 
-
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I32
+
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI16
 
-
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I64
+
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI32
 
-
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.I8
+
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI32
 
-
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI16
+
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI64
 
-
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI32
+
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI64
 
-
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI64
+
readForeignDataFrom(DataInput, int) - Method in class neureka.dtype.custom.UI8
 
-
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI8
+
readForeignDataFrom(Iterator<T>, int) - Method in class neureka.dtype.custom.UI8
 
-
readForeignDataFrom(Iterator<T>, int) - Method in interface neureka.dtype.NumericType
+
readForeignDataFrom(DataInput, int) - Method in interface neureka.dtype.NumericType
+
+
This method expects the provided stream to spit out bytes which can be read as target type elements.
+
+
readForeignDataFrom(Iterator<T>, int) - Method in interface neureka.dtype.NumericType
This method expects the provided iterator to return elements which can be read as holder type elements.
-
readResource(String) - Method in class neureka.Neureka.Utility
+
readResource(String) - Method in class neureka.Neureka.Utility
Helper method which reads the file with the given name and returns the contents of this file as a String.
-
rearrange(int[], int[]) - Static method in class neureka.ndim.config.NDConfiguration.Utility
+
rearrange(int[], int[], int[]) - Method in enum neureka.ndim.config.NDConfiguration.Layout
 
-
rearrange(int[], int[], int[]) - Method in enum class neureka.ndim.config.NDConfiguration.Layout
+
rearrange(int[], int[]) - Static method in class neureka.ndim.config.NDConfiguration.Utility
 
-
rearrangeInputs(int...) - Method in class neureka.backend.api.Call
+
rearrangeInputs(int...) - Method in class neureka.backend.api.Call
 
-
ReceiveForDevice<D extends Device<?>> - Interface in neureka.backend.api.ini
+
ReceiveForDevice<D extends Device<?>> - Interface in neureka.backend.api.ini
 
-
ReceiveForOperation<D extends Device<?>> - Interface in neureka.backend.api.ini
+
ReceiveForOperation<D extends Device<?>> - Interface in neureka.backend.api.ini
 
-
recompile() - Method in class neureka.devices.opencl.OpenCLPlatform
+
recompile() - Method in class neureka.devices.opencl.OpenCLPlatform
 
-
ReferenceCounter - Class in neureka.devices
+
ReferenceCounter - Class in neureka.devices
 
-
ReferenceCounter(Consumer<ReferenceCounter.ChangeEvent>) - Constructor for class neureka.devices.ReferenceCounter
+
ReferenceCounter(Consumer<ReferenceCounter.ChangeEvent>) - Constructor for class neureka.devices.ReferenceCounter
 
-
ReferenceCounter.ChangeEvent - Class in neureka.devices
+
ReferenceCounter.ChangeEvent - Class in neureka.devices
 
-
ReferenceCounter.ChangeType - Enum Class in neureka.devices
+
ReferenceCounter.ChangeType - Enum in neureka.devices
 
-
register(Object, Runnable) - Method in interface neureka.devices.DeviceCleaner
+
register(Object, Runnable) - Method in interface neureka.devices.DeviceCleaner
 
-
Relation<V> - Class in neureka.framing
+
Relation<V> - Class in neureka.framing
This class is an important tensor component responsible for managing the relationships between slices and the tensors from which they have been derived.
-
relayout() - Method in class neureka.math.Functions
+
ReLayout - Class in neureka.backend.main.operations.other
 
-
ReLayout - Class in neureka.backend.main.operations.other
+
ReLayout() - Constructor for class neureka.backend.main.operations.other.ReLayout
 
-
ReLayout() - Constructor for class neureka.backend.main.operations.other.ReLayout
+
relayout() - Method in class neureka.math.Functions
 
-
relu() - Method in class neureka.math.Functions
+
RELU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
relu() - Method in interface neureka.Tensor
-
-
This method is a functionally identical to the following alternatives:
-
-
ReLU - Class in neureka.backend.main.operations.functions
+
ReLU - Class in neureka.backend.main.operations.functions
 
-
ReLU() - Constructor for class neureka.backend.main.operations.functions.ReLU
+
ReLU() - Constructor for class neureka.backend.main.operations.functions.ReLU
 
-
RELU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
relu() - Method in class neureka.math.Functions
 
-
rem(int) - Method in interface neureka.Tensor
+
relu() - Method in interface neureka.Tensor
+
+
This method is a functionally identical to the following alternatives:
+
+
rem(int) - Method in interface neureka.Tensor
-
This method is synonymous to the Tensor.mod(int) method.
+
This method is synonymous to the Tensor.mod(int) method.
-
remove(Class<T>) - Method in class neureka.common.composition.AbstractComponentOwner
+
remove(Class<T>) - Method in class neureka.common.composition.AbstractComponentOwner
This method removes a component identified by the passed Class instance if found in the stored component collection.
-
remove(Class<T>) - Method in interface neureka.common.composition.ComponentOwner
+
remove(Class<T>) - Method in interface neureka.common.composition.ComponentOwner
Use this to remove a component of the specified component type class.
-
removeChild(Tensor<V>) - Method in class neureka.framing.Relation
-
 
-
REMOVED - Enum constant in enum class neureka.common.composition.Component.IsBeing
+
removeChild(Tensor<V>) - Method in class neureka.framing.Relation
 
-
replace(Object) - Method in class neureka.framing.fluent.AxisFrame
+
replace(Object) - Method in class neureka.framing.fluent.AxisFrame
 
-
replace(ValueType) - Method in interface neureka.framing.fluent.Replace
+
Replace<ValueType,ReplacementType,ReturnType> - Interface in neureka.framing.fluent
 
-
Replace<ValueType,ReplacementType,ReturnType> - Interface in neureka.framing.fluent
+
replace(ValueType) - Method in interface neureka.framing.fluent.Replace
 
-
REPLACED - Enum constant in enum class neureka.common.composition.Component.IsBeing
+
replacer(Replace<Object, Object, NDFrame<ValueType>>) - Method in class neureka.framing.fluent.AxisFrame.Builder
 
-
replacer(Replace<Object, Object, NDFrame<ValueType>>) - Method in class neureka.framing.fluent.AxisFrame.Builder
-
 
-
representing(Nda<?>) - Static method in class neureka.view.NdaAsString
+
representing(Nda<?>) - Static method in class neureka.view.NdaAsString
A builder providing multiple different configuration options for building a NdaAsString instance in a fluent way.
-
reset() - Method in class neureka.backend.api.BackendContext
+
reset() - Method in class neureka.backend.api.BackendContext
 
-
reset() - Method in interface neureka.backend.api.BackendExtension
+
reset() - Method in interface neureka.backend.api.BackendExtension
-
This will indirectly be called through the Neureka.reset() method, +
This will indirectly be called through the Neureka.reset() method, which is responsible for resetting the library settings.
-
reset() - Method in class neureka.backend.ocl.CLBackend
+
reset() - Method in class neureka.backend.ocl.CLBackend
 
-
reset() - Method in class neureka.backend.ocl.CLSettings
+
reset() - Method in class neureka.backend.ocl.CLSettings
 
-
reset() - Method in class neureka.Neureka
+
reset() - Method in class neureka.Neureka
This method will try to reload the "library_settings.groovy" script which will re-configure the library wide Neureka.Settings instance nested inside Neureka.
-
reshape() - Method in class neureka.math.Functions
+
Reshape - Class in neureka.backend.main.operations.other
+
 
+
Reshape() - Constructor for class neureka.backend.main.operations.other.Reshape
 
-
reshape(int...) - Method in interface neureka.Nda
+
reshape() - Method in class neureka.math.Functions
+
 
+
reshape(int...) - Method in interface neureka.Nda
Returns a nd-array with the same data and number of elements as this nd-array, but with the specified shape.
-
reshape(int...) - Method in interface neureka.Tensor
+
reshape(int...) - Method in interface neureka.Tensor
Returns a nd-array with the same data and number of elements as this nd-array, but with the specified shape.
-
Reshape - Class in neureka.backend.main.operations.other
-
 
-
Reshape() - Constructor for class neureka.backend.main.operations.other.Reshape
+
resolve() - Method in class neureka.fluent.slicing.AxisSliceBuilder
 
-
resolve() - Method in class neureka.fluent.slicing.AxisSliceBuilder
+
restore(Tensor<Object>) - Method in class neureka.devices.file.FileDevice
+
restore(Tensor<Object>) - Method in class neureka.devices.host.CPU
 
-
restore(Tensor<Number>) - Method in class neureka.devices.opencl.OpenCLDevice
+
restore(Tensor<Number>) - Method in class neureka.devices.opencl.OpenCLDevice
This method assumes that the passed tensor is stored on this device instance.
-
restore(Tensor<Object>) - Method in class neureka.devices.file.FileDevice
-
restore(Tensor<Object>) - Method in class neureka.devices.host.CPU
-
 
-
restore(Tensor<Number>) - Method in class neureka.devices.file.IDXHandle
-
 
-
restore(Tensor<V>) - Method in interface neureka.devices.Storage
+
restore(Tensor<V>) - Method in interface neureka.devices.Storage
 
-
Result - Class in neureka.backend.api
+
Result - Class in neureka.backend.api
An immutable wrapper for a tensor as a result of anb Execution as well as an ADActionSupplier for providing auto-differentiation support.
-
RGB_1INT - Enum constant in enum class neureka.Tensor.ImageType
-
 
-
RGB_555_USHORT - Enum constant in enum class neureka.Tensor.ImageType
-
 
-
RGB_565_USHORT - Enum constant in enum class neureka.Tensor.ImageType
-
 
-
RMSProp<V extends Number> - Class in neureka.optimization.implementations
+
RMSProp<V extends java.lang.Number> - Class in neureka.optimization.implementations
Root Mean Squared Propagation, or RMSProp, is an extension of gradient descent and the AdaGrad version of gradient descent that uses a decaying average of partial gradients in the adaptation of the step size for each parameter.
-
RMSProp - Static variable in interface neureka.optimization.Optimizer
-
 
-
RMSPropFactory - Class in neureka.optimization.implementations
+
RMSProp - Static variable in interface neureka.optimization.Optimizer
 
-
RMSPropFactory() - Constructor for class neureka.optimization.implementations.RMSPropFactory
+
RMSPropFactory - Class in neureka.optimization.implementations
 
-
ROW_MAJOR - Enum constant in enum class neureka.ndim.config.NDConfiguration.Layout
+
RMSPropFactory() - Constructor for class neureka.optimization.implementations.RMSPropFactory
 
-
ROW_MAJOR - Enum constant in enum class neureka.ndim.config.NDTrait
-
 
-
rqsGradient() - Method in interface neureka.Tensor
+
rqsGradient() - Method in interface neureka.Tensor
This flag will indirectly trigger the activation of the autograd / auto-differentiation system of this library! If the flag is set to 'true' and the tensor is used for computation then - it will also receive gradients when the Tensor.backward() method is being called + it will also receive gradients when the Tensor.backward() method is being called on any descendant tensor within the computation graph.
-
run(Runnable) - Method in class neureka.backend.api.BackendContext.Runner
+
run(Runnable) - Method in class neureka.backend.api.BackendContext.Runner
Use this method to supply a lambda which will be executed in the BackendContext which produced this very BackendContext.Runner instance.
-
run(ExecutionCall<D>) - Method in interface neureka.backend.api.ImplementationFor
+
run(ExecutionCall<D>) - Method in interface neureka.backend.api.ImplementationFor
This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented.
-
run(ExecutionCall<D>) - Method in class neureka.backend.api.template.implementations.AbstractImplementationFor
-
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcast
+
run(ExecutionCall<D>) - Method in class neureka.backend.api.template.implementations.AbstractImplementationFor
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastAddition
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastDivision
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastIdentity
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.convolution.AbstractCPUConvolution
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastModulo
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWise
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastMultiplication
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.elementwise.CPUElementwiseAssignFun
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastPower
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.elementwise.CPUElementwiseFunction
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastSubtraction
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.elementwise.CPURandomization
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcast
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.linear.CPUDot
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.matmul.CPUMatMul
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.scalar.CPUScalarBroadcastFunction
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.scalar.CPUScalarFunction
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.convolution.AbstractCPUConvolution
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.operations.other.internal.CPUReduce
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.elementwise.CLRandomization
 
-
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.operations.other.internal.CPUSum
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWise
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastAddition
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.elementwise.CPUElementwiseAssignFun
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastDivision
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.elementwise.CPUElementwiseFunction
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastIdentity
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.elementwise.CPURandomization
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastModulo
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.linear.CLDot
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastMultiplication
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.linear.CPUDot
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastPower
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.matmul.CPUMatMul
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.broadcast.CLScalarBroadcastSubtraction
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.scalar.CLScalarFunction
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.elementwise.CLRandomization
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.scalar.CPUScalarBroadcastFunction
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.linear.CLDot
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.scalar.CPUScalarFunction
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.implementations.scalar.CLScalarFunction
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.operations.linear.internal.opencl.CLGEMM
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.operations.linear.internal.opencl.CLGEMM
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.operations.linear.internal.opencl.CLReduce
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.operations.linear.internal.opencl.CLReduce
+
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.operations.linear.internal.opencl.CLSum
 
-
run(ExecutionCall<OpenCLDevice>) - Method in class neureka.backend.main.operations.linear.internal.opencl.CLSum
-
 
-
run(Tensor<Float>, OpenCLDevice) - Static method in class neureka.backend.main.operations.linear.internal.opencl.CLSum
+
run(Tensor<Float>, OpenCLDevice) - Static method in class neureka.backend.main.operations.linear.internal.opencl.CLSum
This method compiles and executes the kernel that will return the sum of the elements in the in tensor.
-
runAndGet(Supplier<T>) - Method in class neureka.backend.api.BackendContext.Runner
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.operations.other.internal.CPUReduce
+
 
+
run(ExecutionCall<CPU>) - Method in class neureka.backend.main.operations.other.internal.CPUSum
+
 
+
runAndGet(Supplier<T>) - Method in class neureka.backend.api.BackendContext.Runner
Use this method to supply a lambda which will be executed in the BackendContext which produced this very BackendContext.Runner instance.
-
runner() - Method in class neureka.backend.api.BackendContext
+
runner() - Method in class neureka.backend.api.BackendContext
A BackendContext.Runner wraps both the called context as well as the context of the caller in order to perform temporary context switching during the execution of lambdas passed to the BackendContext.Runner.
-
running(Operation) - Method in class neureka.backend.api.ExecutionCall.Builder
+
running(Operation) - Method in class neureka.backend.api.ExecutionCall.Builder
 
-

S

-
-
scalar(V) - Method in class neureka.fluent.building.NdaBuilder
+ + + +

S

+
+
scalar(V) - Method in class neureka.fluent.building.NdaBuilder
 
-
scalar(V) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
+
scalar(V) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
This method created and return a scalar Tensor instance which wraps the provided value.
-
scalar(V) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
+
scalar(V) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
This method created and return a scalar Tensor instance which wraps the provided value.
-
ScalarAbsolute - Class in neureka.backend.main.implementations.fun
+
ScalarAbsolute - Class in neureka.backend.main.implementations.fun
 
-
ScalarAbsolute() - Constructor for class neureka.backend.main.implementations.fun.ScalarAbsolute
+
ScalarAbsolute() - Constructor for class neureka.backend.main.implementations.fun.ScalarAbsolute
 
-
ScalarAlgorithm - Class in neureka.backend.main.algorithms
+
ScalarAlgorithm - Class in neureka.backend.main.algorithms
 
-
ScalarAlgorithm() - Constructor for class neureka.backend.main.algorithms.ScalarAlgorithm
+
ScalarAlgorithm() - Constructor for class neureka.backend.main.algorithms.ScalarAlgorithm
 
-
ScalarBroadcast - Class in neureka.backend.main.algorithms
+
ScalarBroadcast - Class in neureka.backend.main.algorithms
 
-
ScalarBroadcast(ScalarFun) - Constructor for class neureka.backend.main.algorithms.ScalarBroadcast
+
ScalarBroadcast(ScalarFun) - Constructor for class neureka.backend.main.algorithms.ScalarBroadcast
 
-
ScalarCbrt - Class in neureka.backend.main.implementations.fun
+
ScalarCbrt - Class in neureka.backend.main.implementations.fun
 
-
ScalarCbrt() - Constructor for class neureka.backend.main.implementations.fun.ScalarCbrt
+
ScalarCbrt() - Constructor for class neureka.backend.main.implementations.fun.ScalarCbrt
 
-
ScalarCosinus - Class in neureka.backend.main.implementations.fun
+
ScalarCosinus - Class in neureka.backend.main.implementations.fun
 
-
ScalarCosinus() - Constructor for class neureka.backend.main.implementations.fun.ScalarCosinus
+
ScalarCosinus() - Constructor for class neureka.backend.main.implementations.fun.ScalarCosinus
 
-
ScalarExp - Class in neureka.backend.main.implementations.fun
+
ScalarExp - Class in neureka.backend.main.implementations.fun
 
-
ScalarExp() - Constructor for class neureka.backend.main.implementations.fun.ScalarExp
+
ScalarExp() - Constructor for class neureka.backend.main.implementations.fun.ScalarExp
 
-
ScalarFun - Interface in neureka.backend.main.implementations.fun.api
+
ScalarFun - Interface in neureka.backend.main.implementations.fun.api
 
-
ScalarGaSU - Class in neureka.backend.main.implementations.fun
+
ScalarGaSU - Class in neureka.backend.main.implementations.fun
The Self Gated ScalarSoftsign Unit is based on the ScalarSoftsign function (a computationally cheap non-exponential quasi ScalarTanh) making it a polynomially based version of the ScalarGaTU function which is itself based on the ScalarTanh function.
-
ScalarGaSU() - Constructor for class neureka.backend.main.implementations.fun.ScalarGaSU
+
ScalarGaSU() - Constructor for class neureka.backend.main.implementations.fun.ScalarGaSU
 
-
ScalarGaTU - Class in neureka.backend.main.implementations.fun
+
ScalarGaTU - Class in neureka.backend.main.implementations.fun
The Self Gated ScalarTanh Unit is based on the ScalarTanh making it an exponentiation based version of the ScalarGaSU function which is itself based on the ScalarSoftsign function (a computationally cheap non-exponential quasi ScalarTanh).
-
ScalarGaTU() - Constructor for class neureka.backend.main.implementations.fun.ScalarGaTU
+
ScalarGaTU() - Constructor for class neureka.backend.main.implementations.fun.ScalarGaTU
 
-
ScalarGaussian - Class in neureka.backend.main.implementations.fun
+
ScalarGaussian - Class in neureka.backend.main.implementations.fun
 
-
ScalarGaussian() - Constructor for class neureka.backend.main.implementations.fun.ScalarGaussian
+
ScalarGaussian() - Constructor for class neureka.backend.main.implementations.fun.ScalarGaussian
 
-
ScalarGaussianFast - Class in neureka.backend.main.implementations.fun
+
ScalarGaussianFast - Class in neureka.backend.main.implementations.fun
 
-
ScalarGaussianFast() - Constructor for class neureka.backend.main.implementations.fun.ScalarGaussianFast
+
ScalarGaussianFast() - Constructor for class neureka.backend.main.implementations.fun.ScalarGaussianFast
 
-
ScalarGeLU - Class in neureka.backend.main.implementations.fun
+
ScalarGeLU - Class in neureka.backend.main.implementations.fun
The GELU activation function is based on the standard Gaussian cumulative distribution function and is defined as x Φ( x ) and implemented as x * sigmoid(x * 1.702).
-
ScalarGeLU() - Constructor for class neureka.backend.main.implementations.fun.ScalarGeLU
+
ScalarGeLU() - Constructor for class neureka.backend.main.implementations.fun.ScalarGeLU
 
-
ScalarIdentity - Class in neureka.backend.main.implementations.fun
+
ScalarIdentity - Class in neureka.backend.main.implementations.fun
 
-
ScalarIdentity() - Constructor for class neureka.backend.main.implementations.fun.ScalarIdentity
+
ScalarIdentity() - Constructor for class neureka.backend.main.implementations.fun.ScalarIdentity
 
-
ScalarLog10 - Class in neureka.backend.main.implementations.fun
+
ScalarLog10 - Class in neureka.backend.main.implementations.fun
 
-
ScalarLog10() - Constructor for class neureka.backend.main.implementations.fun.ScalarLog10
+
ScalarLog10() - Constructor for class neureka.backend.main.implementations.fun.ScalarLog10
 
-
ScalarLogarithm - Class in neureka.backend.main.implementations.fun
+
ScalarLogarithm - Class in neureka.backend.main.implementations.fun
 
-
ScalarLogarithm() - Constructor for class neureka.backend.main.implementations.fun.ScalarLogarithm
+
ScalarLogarithm() - Constructor for class neureka.backend.main.implementations.fun.ScalarLogarithm
 
-
ScalarQuadratic - Class in neureka.backend.main.implementations.fun
+
ScalarQuadratic - Class in neureka.backend.main.implementations.fun
 
-
ScalarQuadratic() - Constructor for class neureka.backend.main.implementations.fun.ScalarQuadratic
+
ScalarQuadratic() - Constructor for class neureka.backend.main.implementations.fun.ScalarQuadratic
 
-
ScalarReLU - Class in neureka.backend.main.implementations.fun
+
ScalarReLU - Class in neureka.backend.main.implementations.fun
 
-
ScalarReLU() - Constructor for class neureka.backend.main.implementations.fun.ScalarReLU
+
ScalarReLU() - Constructor for class neureka.backend.main.implementations.fun.ScalarReLU
 
-
ScalarSeLU - Class in neureka.backend.main.implementations.fun
+
ScalarSeLU - Class in neureka.backend.main.implementations.fun
The Scaled Exponential Linear Unit, or SELU, is an activation function that induces self-normalizing properties.
-
ScalarSeLU() - Constructor for class neureka.backend.main.implementations.fun.ScalarSeLU
+
ScalarSeLU() - Constructor for class neureka.backend.main.implementations.fun.ScalarSeLU
 
-
ScalarSigmoid - Class in neureka.backend.main.implementations.fun
+
ScalarSigmoid - Class in neureka.backend.main.implementations.fun
 
-
ScalarSigmoid() - Constructor for class neureka.backend.main.implementations.fun.ScalarSigmoid
+
ScalarSigmoid() - Constructor for class neureka.backend.main.implementations.fun.ScalarSigmoid
 
-
ScalarSiLU - Class in neureka.backend.main.implementations.fun
+
ScalarSiLU - Class in neureka.backend.main.implementations.fun
The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x).
-
ScalarSiLU() - Constructor for class neureka.backend.main.implementations.fun.ScalarSiLU
+
ScalarSiLU() - Constructor for class neureka.backend.main.implementations.fun.ScalarSiLU
 
-
ScalarSinus - Class in neureka.backend.main.implementations.fun
+
ScalarSinus - Class in neureka.backend.main.implementations.fun
 
-
ScalarSinus() - Constructor for class neureka.backend.main.implementations.fun.ScalarSinus
+
ScalarSinus() - Constructor for class neureka.backend.main.implementations.fun.ScalarSinus
 
-
ScalarSoftplus - Class in neureka.backend.main.implementations.fun
+
ScalarSoftplus - Class in neureka.backend.main.implementations.fun
SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.
-
ScalarSoftplus() - Constructor for class neureka.backend.main.implementations.fun.ScalarSoftplus
+
ScalarSoftplus() - Constructor for class neureka.backend.main.implementations.fun.ScalarSoftplus
 
-
ScalarSoftsign - Class in neureka.backend.main.implementations.fun
+
ScalarSoftsign - Class in neureka.backend.main.implementations.fun
The softsign function, defined as x / ( 1 + Math.abs( x ) ), is a computationally cheap 0 centered activation function which rescales the inputs between -1 and 1, very much like the ScalarTanh function.
-
ScalarSoftsign() - Constructor for class neureka.backend.main.implementations.fun.ScalarSoftsign
+
ScalarSoftsign() - Constructor for class neureka.backend.main.implementations.fun.ScalarSoftsign
+
 
+
ScalarSqrt - Class in neureka.backend.main.implementations.fun
 
-
ScalarSqrt - Class in neureka.backend.main.implementations.fun
+
ScalarSqrt() - Constructor for class neureka.backend.main.implementations.fun.ScalarSqrt
 
-
ScalarSqrt() - Constructor for class neureka.backend.main.implementations.fun.ScalarSqrt
+
ScalarSumAlgorithm - Class in neureka.backend.main.algorithms
 
-
ScalarSumAlgorithm - Class in neureka.backend.main.algorithms
+
ScalarSumAlgorithm() - Constructor for class neureka.backend.main.algorithms.ScalarSumAlgorithm
 
-
ScalarSumAlgorithm() - Constructor for class neureka.backend.main.algorithms.ScalarSumAlgorithm
+
ScalarTanh - Class in neureka.backend.main.implementations.fun
 
-
ScalarTanh - Class in neureka.backend.main.implementations.fun
+
ScalarTanh() - Constructor for class neureka.backend.main.implementations.fun.ScalarTanh
 
-
ScalarTanh() - Constructor for class neureka.backend.main.implementations.fun.ScalarTanh
+
ScalarTanhFast - Class in neureka.backend.main.implementations.fun
 
-
ScalarTanhFast - Class in neureka.backend.main.implementations.fun
+
ScalarTanhFast() - Constructor for class neureka.backend.main.implementations.fun.ScalarTanhFast
 
-
ScalarTanhFast() - Constructor for class neureka.backend.main.implementations.fun.ScalarTanhFast
+
SELU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
selu() - Method in class neureka.math.Functions
+
selu(double) - Static method in class neureka.backend.main.implementations.fun.ScalarSeLU
+
 
+
SeLU - Class in neureka.backend.main.operations.functions
The Scaled Exponential Linear Unit, or SELU, is an activation functions that induce self-normalizing properties.
-
selu(double) - Static method in class neureka.backend.main.implementations.fun.ScalarSeLU
+
SeLU() - Constructor for class neureka.backend.main.operations.functions.SeLU
 
-
SeLU - Class in neureka.backend.main.operations.functions
+
selu() - Method in class neureka.math.Functions
The Scaled Exponential Linear Unit, or SELU, is an activation functions that induce self-normalizing properties.
-
SeLU() - Constructor for class neureka.backend.main.operations.functions.SeLU
-
 
-
SELU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
-
 
-
sequential(int, CPU.RangeWorkload) - Method in class neureka.devices.host.CPU.JVMExecutor
+
sequential(int, CPU.RangeWorkload) - Method in class neureka.devices.host.CPU.JVMExecutor
This method will simply execute the provided CPU.RangeWorkload lambda sequentially with 0 as the start index and workloadSize as the exclusive range.
-
set(int) - Method in interface neureka.framing.fluent.Set
+
set(BackendExtension) - Method in class neureka.backend.api.BackendContext
+
+
Registers the provided BackendExtension instance + which can then be accessed via BackendContext.find(Class).
+
+
set(Class<? extends Operation>, Class<? extends A>, Function<LoadingContext, ImplementationFor<D>>) - Method in interface neureka.backend.api.ini.ReceiveForDevice
 
-
set(int[]) - Method in interface neureka.ndim.iterator.NDIterator
+
set(Class<? extends DeviceAlgorithm>, Function<LoadingContext, ImplementationFor<D>>) - Method in interface neureka.backend.api.ini.ReceiveForOperation
 
-
set(int[]) - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
-
set(int[]) - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
-
set(int[]) - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
-
set(int[]) - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
-
set(int[]) - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
-
set(int[]) - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
-
set(int[]) - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
-
set(int[]) - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
-
set(int[]) - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
set(T) - Method in class neureka.common.composition.AbstractComponentOwner
+
+
This methods stores the passed component inside the component + collection of this class...
+
+
set(T) - Method in interface neureka.common.composition.ComponentOwner
+
+
Use this to set a component.
+
+
Set<V> - Interface in neureka.framing.fluent
 
-
set(int[]) - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
set(int) - Method in interface neureka.framing.fluent.Set
 
-
set(int[], T) - Method in interface neureka.MutateNda
+
set(V) - Method in interface neureka.MutateNda.Item
-
Use this to place a single item at a particular position within this nd-array!
+
Set the value at the targeted position.
-
set(int[], T) - Method in interface neureka.MutateTensor
+
set(int[], T) - Method in interface neureka.MutateNda
Use this to place a single item at a particular position within this nd-array!
-
set(int, int) - Method in interface neureka.ndim.iterator.NDIterator
-
 
-
set(int, int) - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
-
set(int, int) - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
-
set(int, int) - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
-
set(int, int) - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
-
set(int, int) - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
-
set(int, int) - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
-
set(int, int) - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
-
set(int, int) - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
-
set(int, int) - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
-
 
-
set(int, int) - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
-
 
-
set(int, int, int, T) - Method in interface neureka.MutateNda
+
set(int, int, T) - Method in interface neureka.MutateNda
 
-
set(int, int, int, T) - Method in interface neureka.MutateTensor
-
set(int, int, T) - Method in interface neureka.MutateNda
+
set(int, int, int, T) - Method in interface neureka.MutateNda
 
-
set(int, int, T) - Method in interface neureka.MutateTensor
-
set(int, T) - Method in interface neureka.MutateNda
+
set(int, T) - Method in interface neureka.MutateNda
Individual entries for value items in this nd-array can be set via this method.
-
set(int, T) - Method in interface neureka.MutateTensor
+
set(int[], T) - Method in interface neureka.MutateTensor
+
+
Use this to place a single item at a particular position within this nd-array!
+
+
set(int, int, T) - Method in interface neureka.MutateTensor
+
set(int, int, int, T) - Method in interface neureka.MutateTensor
+
set(int, T) - Method in interface neureka.MutateTensor
Individual entries for value items in this nd-array can be set via this method.
-
set(Class<? extends DeviceAlgorithm>, Function<LoadingContext, ImplementationFor<D>>) - Method in interface neureka.backend.api.ini.ReceiveForOperation
+
set(int, int) - Method in interface neureka.ndim.iterator.NDIterator
 
-
set(Class<? extends Operation>, Class<? extends A>, Function<LoadingContext, ImplementationFor<D>>) - Method in interface neureka.backend.api.ini.ReceiveForDevice
+
set(int[]) - Method in interface neureka.ndim.iterator.NDIterator
 
-
set(BackendExtension) - Method in class neureka.backend.api.BackendContext
-
-
Registers the provided BackendExtension instance - which can then be accessed via BackendContext.find(Class).
-
-
set(Neureka) - Static method in class neureka.Neureka
+
set(int, int) - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
+
set(int[]) - Method in class neureka.ndim.iterator.types.permuted.Permuted2DCIterator
+
set(int, int) - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
+
set(int[]) - Method in class neureka.ndim.iterator.types.permuted.Permuted3DCIterator
+
set(int, int) - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
+
set(int[]) - Method in class neureka.ndim.iterator.types.simple.Simple1DCIterator
+
set(int, int) - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
+
set(int[]) - Method in class neureka.ndim.iterator.types.simple.Simple2DCIterator
+
set(int, int) - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
+
set(int[]) - Method in class neureka.ndim.iterator.types.simple.Simple3DCIterator
+
set(int, int) - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
+
set(int[]) - Method in class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
+
set(int, int) - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
+
set(int[]) - Method in class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
+
set(int, int) - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
+
set(int[]) - Method in class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
+
set(int, int) - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
 
+
set(int[]) - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
 
+
set(int, int) - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
 
+
set(int[]) - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
 
+
set(Neureka) - Static method in class neureka.Neureka
Neureka is a thread local singleton.
-
set(OptimizerFactory) - Method in interface neureka.Tensor
+
set(OptimizerFactory) - Method in interface neureka.Tensor
Configures an Optimizer for this tensor based on the given OptimizerFactory which will be used to create a new Optimizer instance specific to this tensor.
-
set(T) - Method in class neureka.common.composition.AbstractComponentOwner
-
-
This methods stores the passed component inside the component - collection of this class...
-
-
set(T) - Method in interface neureka.common.composition.ComponentOwner
-
-
Use this to set a component.
-
-
set(V) - Method in interface neureka.MutateNda.Item
-
-
Set the value at the targeted position.
-
-
Set<V> - Interface in neureka.framing.fluent
-
 
-
setAlgorithm(Class<T>, T) - Method in interface neureka.backend.api.Operation
+
setAlgorithm(Class<T>, T) - Method in interface neureka.backend.api.Operation
Operation implementations embody a component system hosting unique Algorithm instances.
-
setAlgorithm(Class<T>, T) - Method in class neureka.backend.api.template.operations.AbstractOperation
+
setAlgorithm(T) - Method in interface neureka.backend.api.Operation
+
 
+
setAlgorithm(Class<T>, T) - Method in class neureka.backend.api.template.operations.AbstractOperation
Operation implementations embody a component system hosting unique Algorithm instances.
-
setAlgorithm(T) - Method in interface neureka.backend.api.Operation
+
setAutoConvertToFloat(boolean) - Method in class neureka.backend.ocl.CLSettings
 
-
setAutoConvertToFloat(boolean) - Method in class neureka.backend.ocl.CLSettings
-
 
-
setAutogradModeFor(ADSupportPredicate) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
+
setAutogradModeFor(ADSupportPredicate) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
A ADSupportPredicate lambda checks what kind of auto differentiation mode an Algorithm supports for a given ExecutionCall.
-
setAutogradModeFor(ADSupportPredicate) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
setAutogradModeFor(ADSupportPredicate) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
A ADSupportPredicate lambda checks what kind of auto differentiation mode an Algorithm supports for a given ExecutionCall.
-
setBackend(BackendContext) - Method in class neureka.Neureka
+
setBackend(BackendContext) - Method in class neureka.Neureka
Use this method to attach a backend context (for operations) to this thread local library context.
-
setCallPreparation(ExecutionPreparation) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
setCallPreparation(ExecutionPreparation) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
An Algorithm will produce a Result when executing an ExecutionCall.
-
setCellSize(int) - Method in class neureka.view.NDPrintSettings
+
setCellSize(int) - Method in class neureka.view.NDPrintSettings
A cell size refers to the number of characters reserved to - the String representation of a single element.
+ the String representation of a single element.
-
setData(Data<T>) - Method in interface neureka.MutateTensor
+
setData(Data<T>) - Method in interface neureka.MutateTensor
At the heart of every tensor is the Data object, which holds the actual data array, a sequence of values of the same type.
-
setDataAt(int, T) - Method in interface neureka.MutateTensor
+
setDataAt(int, T) - Method in interface neureka.MutateTensor
A tensor ought to have some way to selectively modify its underlying data array.
-
setDefaultDataTypeClass(Class<?>) - Method in class neureka.Neureka.Settings.DType
+
setDefaultDataTypeClass(Class<?>) - Method in class neureka.Neureka.Settings.DType
The default data type is not relevant most of the time.
-
setDerivation(OperationBuilder.Derivation) - Method in class neureka.backend.api.template.operations.OperationBuilder
+
setDerivation(OperationBuilder.Derivation) - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
setExecution(Execution) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
+
setExecution(Execution) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
 
-
setExecution(Execution) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
setExecution(Execution) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
 
-
setGradientApplyRequested(boolean) - Method in interface neureka.Tensor
+
setGradientApplyRequested(boolean) - Method in interface neureka.Tensor
This flag works alongside two autograd features which can be enabled inside the library settings.
-
setHasDerivatives(boolean) - Method in class neureka.view.NDPrintSettings
-
 
-
setHasGradient(boolean) - Method in class neureka.view.NDPrintSettings
+
setHasDerivatives(boolean) - Method in class neureka.view.NDPrintSettings
 
-
setHasRecursiveGraph(boolean) - Method in class neureka.view.NDPrintSettings
+
setHasGradient(boolean) - Method in class neureka.view.NDPrintSettings
 
-
setHasShape(boolean) - Method in class neureka.view.NDPrintSettings
+
setHasRecursiveGraph(boolean) - Method in class neureka.view.NDPrintSettings
 
-
setHasSlimNumbers(boolean) - Method in class neureka.view.NDPrintSettings
+
setHasShape(boolean) - Method in class neureka.view.NDPrintSettings
 
-
setHasValue(boolean) - Method in class neureka.view.NDPrintSettings
+
setHasSlimNumbers(boolean) - Method in class neureka.view.NDPrintSettings
 
-
setImplementationFor(Class<D>, E) - Method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
setHasValue(boolean) - Method in class neureka.view.NDPrintSettings
 
-
setImplementationFor(Class<D>, I) - Method in interface neureka.backend.api.DeviceAlgorithm
+
setImplementationFor(Class<D>, I) - Method in interface neureka.backend.api.DeviceAlgorithm
Implementations of the DeviceAlgorithm interface ought to express a compositional design pattern.
-
setIndent(String) - Method in class neureka.view.NDPrintSettings
+
setImplementationFor(Class<D>, E) - Method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
setIndex(int) - Method in interface neureka.framing.fluent.AxisFrame.Set
+
setIndent(String) - Method in class neureka.view.NDPrintSettings
 
-
setIsApplyingGradientWhenRequested(boolean) - Method in class neureka.Neureka.Settings.AutoGrad
+
setIndex(int) - Method in interface neureka.framing.fluent.AxisFrame.Set
+
 
+
setIsApplyingGradientWhenRequested(boolean) - Method in class neureka.Neureka.Settings.AutoGrad
Gradients will only be applied if requested.
-
setIsApplyingGradientWhenTensorIsUsed(boolean) - Method in class neureka.Neureka.Settings.AutoGrad
+
setIsApplyingGradientWhenTensorIsUsed(boolean) - Method in class neureka.Neureka.Settings.AutoGrad
Gradients will automatically be applied (or JITed) to tensors as soon as they are being used for calculation (GraphNode instantiation).
-
setIsAutoConvertingExternalDataToJVMTypes(boolean) - Method in class neureka.Neureka.Settings.DType
+
setIsAutoConvertingExternalDataToJVMTypes(boolean) - Method in class neureka.Neureka.Settings.DType
This flag will determine if foreign data types will be converted into the next best fit (in terms of bits) or if it should be converted into something that does not mess with the representation of the data.
-
setIsCellBound(boolean) - Method in class neureka.view.NDPrintSettings
+
setIsCellBound(boolean) - Method in class neureka.view.NDPrintSettings
 
-
setIsDeletingIntermediateTensors(boolean) - Method in class neureka.Neureka.Settings.Debug
+
setIsDeletingIntermediateTensors(boolean) - Method in class neureka.Neureka.Settings.Debug
Function instances will produce hidden intermediate results when executing an array of inputs.
-
setIsIntermediate(boolean) - Method in interface neureka.MutateTensor
+
setIsIntermediate(boolean) - Method in interface neureka.MutateTensor
Intermediate tensors are internal non-user tensors which may be eligible for deletion when further consumed by a Function.
-
setIsKeepingDerivativeTargetPayloads(boolean) - Method in class neureka.Neureka.Settings.Debug
+
setIsKeepingDerivativeTargetPayloads(boolean) - Method in class neureka.Neureka.Settings.Debug
Every derivative is calculated with respect to some graph node.
-
setIsLegacy(boolean) - Method in class neureka.view.NDPrintSettings
+
setIsLegacy(boolean) - Method in class neureka.view.NDPrintSettings
This flag determines the usage of bracket types, where "[1x3]:(1, 2, 3)" would be the legacy version of "(1x3):[1, 2, 3]".
-
setIsLocked(boolean) - Method in class neureka.Neureka.Settings
+
setIsLocked(boolean) - Method in class neureka.Neureka.Settings
Can be used to lock or unlock the settings of the current thread-local Neureka instance.
-
setIsMultiline(boolean) - Method in class neureka.view.NDPrintSettings
+
setIsMultiline(boolean) - Method in class neureka.view.NDPrintSettings
 
-
setIsOnlyUsingDefaultNDConfiguration(boolean) - Method in class neureka.Neureka.Settings.NDim
+
setIsOnlyUsingDefaultNDConfiguration(boolean) - Method in class neureka.Neureka.Settings.NDim
Setting this flag determines which NDConfiguration implementations should be used for nd-arrays/tensors.
-
setIsPreventingInlineOperations(boolean) - Method in class neureka.Neureka.Settings.AutoGrad
+
setIsPreventingInlineOperations(boolean) - Method in class neureka.Neureka.Settings.AutoGrad
Inline operations are operations where the data of a tensor passed into an operation is being modified.
-
setIsRetainingPendingErrorForJITProp(boolean) - Method in class neureka.Neureka.Settings.AutoGrad
+
setIsRetainingPendingErrorForJITProp(boolean) - Method in class neureka.Neureka.Settings.AutoGrad
This flag enables an optimization technique which only propagates error values to gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them @@ -7997,2283 +7952,2257 @@

S

If the flag is set to true
then error values will accumulate at such junction nodes.
-
setIsScientific(boolean) - Method in class neureka.view.NDPrintSettings
+
setIsScientific(boolean) - Method in class neureka.view.NDPrintSettings
 
-
setIsSuitableFor(SuitabilityPredicate) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
+
setIsSuitableFor(SuitabilityPredicate) - Method in class neureka.backend.api.template.algorithms.AbstractFunAlgorithm
The SuitabilityPredicate received by this method checks if a given instance of an ExecutionCall is suitable to be executed in ImplementationFor instances residing in this Algorithm as components.
-
setIsSuitableFor(SuitabilityPredicate) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
setIsSuitableFor(SuitabilityPredicate) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
The SuitabilityPredicate received by this method checks if a given instance of an ExecutionCall is suitable to be executed in ImplementationFor instances residing in this Algorithm as components.
-
setIsVirtual(boolean) - Method in interface neureka.MutateTensor
+
setIsVirtual(boolean) - Method in interface neureka.MutateTensor
Virtualizing is the opposite to actualizing a tensor.
-
setItemAt(int, T) - Method in interface neureka.MutateNda
+
setItemAt(int, T) - Method in interface neureka.MutateNda
An NDArray implementation ought to have some way to selectively modify its underlying value.
-
setItemAt(int, T) - Method in interface neureka.MutateTensor
+
setItemAt(int, T) - Method in interface neureka.MutateTensor
An NDArray implementation ought to have some way to selectively modify its underlying value.
-
setItems(Object) - Method in interface neureka.MutateNda
+
setItems(Object) - Method in interface neureka.MutateNda
This method will receive an object an try to interpret it or its contents to be set as value for this nd-array.
-
setItems(Object) - Method in interface neureka.MutateTensor
+
setItems(Object) - Method in interface neureka.MutateTensor
This method will receive an object an try to interpret it or its contents to be set as value for this nd-array.
-
setNDConf(NDConfiguration) - Method in interface neureka.MutateTensor
+
setNDConf(NDConfiguration) - Method in interface neureka.MutateTensor
This method sets the NDConfiguration of this NDArray.
-
setPostfix(String) - Method in class neureka.view.NDPrintSettings
+
setPostfix(String) - Method in class neureka.view.NDPrintSettings
 
-
setPrefix(String) - Method in class neureka.view.NDPrintSettings
+
setPrefix(String) - Method in class neureka.view.NDPrintSettings
 
-
setRowLimit(int) - Method in class neureka.view.NDPrintSettings
+
setRowLimit(int) - Method in class neureka.view.NDPrintSettings
Very large tensors with a rank larger than 1 might take a lot - of vertical space when converted to a String.
+ of vertical space when converted to a String.
-
setRqsGradient(boolean) - Method in interface neureka.Tensor
+
setRqsGradient(boolean) - Method in interface neureka.Tensor
Setting this flag to true will tell the autograd system to accumulate gradients at this tensor.
-
setSupplyADActionFor(ADActionSupplier) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
+
setSupplyADActionFor(ADActionSupplier) - Method in class neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm
This method receives a ADActionSupplier which will supply ADAction instances which can perform backward and forward auto differentiation.
-
setter(At<Object, AxisFrame.Set<ValueType>>) - Method in class neureka.framing.fluent.AxisFrame.Builder
+
setter(At<Object, AxisFrame.Set<ValueType>>) - Method in class neureka.framing.fluent.AxisFrame.Builder
 
-
settings() - Method in class neureka.Neureka
+
settings() - Method in class neureka.Neureka
 
-
settings(Object) - Method in class neureka.Neureka
+
settings(Object) - Method in class neureka.Neureka
This allows you to configure Neureka using a Groovy DSL.
-
SettingsLoader - Class in neureka.common.utility
+
SettingsLoader - Class in neureka.common.utility
This class is a helper class for Neureka instances (Thread local singletons).
-
SGD<V> - Class in neureka.optimization.implementations
+
SGD<V> - Class in neureka.optimization.implementations
Stochastic Gradient Descent is an iterative optimization technique that uses the gradient of a weight variable to adjust said variable, in order to reduce the error used to calculate said gradient.
-
SGD - Static variable in interface neureka.optimization.Optimizer
+
SGD - Static variable in interface neureka.optimization.Optimizer
 
-
SGDFactory - Class in neureka.optimization.implementations
+
SGDFactory - Class in neureka.optimization.implementations
 
-
SGDFactory() - Constructor for class neureka.optimization.implementations.SGDFactory
+
SGDFactory() - Constructor for class neureka.optimization.implementations.SGDFactory
 
-
shallowClone() - Method in interface neureka.Tensor
+
shallowClone() - Method in interface neureka.Tensor
 
-
shallowCopy() - Method in interface neureka.Nda
+
shallowCopy() - Method in interface neureka.Nda
This creates a copy where the underlying data is still the same.
-
shallowCopy() - Method in interface neureka.Tensor
+
shallowCopy() - Method in interface neureka.Tensor
This creates a copy where the underlying data is still the same.
-
shape() - Method in interface neureka.ndim.config.NDConfiguration
+
shape() - Method in interface neureka.ndim.config.NDConfiguration
This method returns an array of axis sizes.
-
shape() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
shape(int) - Method in interface neureka.ndim.config.NDConfiguration
-
This method returns an array of axis sizes.
+
This method receives an axis index and return the + size of the axis.
-
shape() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
shape() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
This method returns an array of axis sizes.
-
shape() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
-
This method returns an array of axis sizes.
+
This method receives an axis index and return the + size of the axis.
-
shape() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
shape() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
This method returns an array of axis sizes.
-
shape() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
-
This method returns an array of axis sizes.
+
This method receives an axis index and return the + size of the axis.
-
shape() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
shape() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
This method returns an array of axis sizes.
-
shape() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
-
This method returns an array of axis sizes.
+
This method receives an axis index and return the + size of the axis.
-
shape() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
shape() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
This method returns an array of axis sizes.
-
shape() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
-
This method returns an array of axis sizes.
+
This method receives an axis index and return the + size of the axis.
-
shape() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
shape() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
This method returns an array of axis sizes.
-
shape() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
-
This method returns an array of axis sizes.
+
This method receives an axis index and return the + size of the axis.
-
shape() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
shape() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
This method returns an array of axis sizes.
-
shape() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
-
This method returns an array of axis sizes.
+
This method receives an axis index and return the + size of the axis.
-
shape() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
shape() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
This method returns an array of axis sizes.
-
shape() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
shape(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
-
This method returns an array of axis sizes.
+
This method receives an axis index and return the + size of the axis.
-
shape() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
shape() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
This method returns an array of axis sizes.
-
shape() - Method in interface neureka.ndim.iterator.NDIterator
-
 
-
shape() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
-
shape() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
-
 
-
shape() - Method in interface neureka.ndim.NDimensional
-
 
-
shape(int) - Method in interface neureka.ndim.config.NDConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
This method receives an axis index and return the size of the axis.
-
shape(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
shape() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
-
This method receives an axis index and return the - size of the axis.
+
This method returns an array of axis sizes.
-
shape(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
This method receives an axis index and return the size of the axis.
-
shape(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
shape() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
-
This method receives an axis index and return the - size of the axis.
+
This method returns an array of axis sizes.
-
shape(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
This method receives an axis index and return the size of the axis.
-
shape(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
shape() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
-
This method receives an axis index and return the - size of the axis.
+
This method returns an array of axis sizes.
-
shape(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
This method receives an axis index and return the size of the axis.
-
shape(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
shape() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
-
This method receives an axis index and return the - size of the axis.
+
This method returns an array of axis sizes.
-
shape(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
This method receives an axis index and return the size of the axis.
-
shape(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
shape() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
-
This method receives an axis index and return the - size of the axis.
+
This method returns an array of axis sizes.
-
shape(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
This method receives an axis index and return the size of the axis.
-
shape(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
shape() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
-
This method receives an axis index and return the - size of the axis.
+
This method returns an array of axis sizes.
-
shape(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
This method receives an axis index and return the size of the axis.
-
shape(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
shape() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
-
This method receives an axis index and return the - size of the axis.
+
This method returns an array of axis sizes.
-
shape(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
This method receives an axis index and return the size of the axis.
-
shape(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
shape() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
-
This method receives an axis index and return the - size of the axis.
+
This method returns an array of axis sizes.
-
shape(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
shape(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
This method receives an axis index and return the size of the axis.
-
shape(int) - Method in interface neureka.ndim.iterator.NDIterator
+
shape(int) - Method in interface neureka.ndim.iterator.NDIterator
 
-
shape(int) - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
-
shape(int) - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
shape() - Method in interface neureka.ndim.iterator.NDIterator
 
-
shape(int) - Method in interface neureka.ndim.NDimensional
+
shape(int) - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
shape() - Method in class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
shape(int) - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
 
+
shape() - Method in class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
 
+
shape() - Method in interface neureka.ndim.NDimensional
+
 
+
shape(int) - Method in interface neureka.ndim.NDimensional
This method receives an axis index and return the size of the targeted axis / dimension.
-
Shape - Interface in neureka
+
Shape - Interface in neureka
Basically a tuple of integers which is used to describe the shape of an array.
-
shaped(int...) - Static method in interface neureka.Nda
+
shaped(int...) - Static method in interface neureka.Nda
Returns a Collector that accumulates the input elements into a new Nda with the specified shape.
-
shaped(int...) - Static method in interface neureka.Tensor
+
shaped(int...) - Static method in interface neureka.Tensor
Returns a Collector that accumulates the input elements into a new Tensor with the specified shape.
-
shaped(Shape) - Static method in interface neureka.Tensor
+
shaped(Shape) - Static method in interface neureka.Tensor
Returns a Collector that accumulates the input elements into a new Tensor with the specified shape.
-
shapeOfCon(int[], int[]) - Static method in class neureka.backend.main.operations.ConvUtil
+
shapeOfCon(int[], int[]) - Static method in class neureka.backend.main.operations.ConvUtil
+
 
+
shapeString(int[]) - Static method in class neureka.ndim.NDUtil
 
-
shapeString(int[]) - Static method in class neureka.ndim.NDUtil
+
shortToBigInteger(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
shortToBigInteger(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
shortToByte(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
shortToByte(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
shortToDouble(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
shortToDouble(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
shortToFloat(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
shortToFloat(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
shortToInt(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
shortToInt(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
shortToLong(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
 
-
shortToLong(short[]) - Static method in class neureka.common.utility.DataConverter.Utility
+
sig(double) - Static method in class neureka.backend.main.implementations.fun.ScalarSigmoid
 
-
sig() - Method in interface neureka.Tensor
+
sig() - Method in interface neureka.Tensor
This method is a functionally identical to the following alternatives:
-
sig(double) - Static method in class neureka.backend.main.implementations.fun.ScalarSigmoid
+
SIGMOID - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
sigmoid() - Method in class neureka.math.Functions
+
Sigmoid - Class in neureka.backend.main.operations.functions
 
-
sigmoid() - Method in interface neureka.Tensor
+
Sigmoid() - Constructor for class neureka.backend.main.operations.functions.Sigmoid
 
-
Sigmoid - Class in neureka.backend.main.operations.functions
+
sigmoid() - Method in class neureka.math.Functions
 
-
Sigmoid() - Constructor for class neureka.backend.main.operations.functions.Sigmoid
+
sigmoid() - Method in interface neureka.Tensor
 
-
SIGMOID - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
signed() - Method in class neureka.dtype.custom.F32
 
-
signed() - Method in class neureka.dtype.custom.F32
+
signed() - Method in class neureka.dtype.custom.F64
 
-
signed() - Method in class neureka.dtype.custom.F64
+
signed() - Method in class neureka.dtype.custom.I16
 
-
signed() - Method in class neureka.dtype.custom.I16
+
signed() - Method in class neureka.dtype.custom.I32
 
-
signed() - Method in class neureka.dtype.custom.I32
+
signed() - Method in class neureka.dtype.custom.I64
 
-
signed() - Method in class neureka.dtype.custom.I64
+
signed() - Method in class neureka.dtype.custom.I8
 
-
signed() - Method in class neureka.dtype.custom.I8
+
signed() - Method in class neureka.dtype.custom.UI16
 
-
signed() - Method in class neureka.dtype.custom.UI16
+
signed() - Method in class neureka.dtype.custom.UI32
 
-
signed() - Method in class neureka.dtype.custom.UI32
+
signed() - Method in class neureka.dtype.custom.UI64
 
-
signed() - Method in class neureka.dtype.custom.UI64
+
signed() - Method in class neureka.dtype.custom.UI8
 
-
signed() - Method in class neureka.dtype.custom.UI8
-
 
-
signed() - Method in interface neureka.dtype.NumericType
+
signed() - Method in interface neureka.dtype.NumericType
This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
-
silu() - Method in class neureka.math.Functions
+
SILU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
 
+
silu(double) - Static method in class neureka.backend.main.implementations.fun.ScalarSiLU
+
 
+
SiLU - Class in neureka.backend.main.operations.functions
The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x).
-
silu(double) - Static method in class neureka.backend.main.implementations.fun.ScalarSiLU
+
SiLU() - Constructor for class neureka.backend.main.operations.functions.SiLU
 
-
SiLU - Class in neureka.backend.main.operations.functions
+
silu() - Method in class neureka.math.Functions
The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x).
-
SiLU() - Constructor for class neureka.backend.main.operations.functions.SiLU
-
 
-
SILU - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
-
 
-
similarity(String, String) - Static method in class neureka.math.parsing.ParseUtil
+
similarity(String, String) - Static method in class neureka.math.parsing.ParseUtil
-
This method estimates the similarity between 2 provided String instances.
+
This method estimates the similarity between 2 provided String instances.
-
SIMPLE - Enum constant in enum class neureka.autograd.GraphNode.Print
-
 
-
SIMPLE - Enum constant in enum class neureka.ndim.config.NDTrait
+
Simple0DConfiguration - Class in neureka.ndim.config.types.simple
 
-
Simple0DConfiguration - Class in neureka.ndim.config.types.simple
+
Simple1DCIterator - Class in neureka.ndim.iterator.types.simple
 
-
Simple1DCIterator - Class in neureka.ndim.iterator.types.simple
+
Simple1DCIterator(Simple1DConfiguration) - Constructor for class neureka.ndim.iterator.types.simple.Simple1DCIterator
 
-
Simple1DCIterator(Simple1DConfiguration) - Constructor for class neureka.ndim.iterator.types.simple.Simple1DCIterator
+
Simple1DConfiguration - Class in neureka.ndim.config.types.simple
 
-
Simple1DConfiguration - Class in neureka.ndim.config.types.simple
+
Simple1DConfiguration(int, int) - Constructor for class neureka.ndim.config.types.simple.Simple1DConfiguration
 
-
Simple1DConfiguration(int, int) - Constructor for class neureka.ndim.config.types.simple.Simple1DConfiguration
+
Simple2DCIterator - Class in neureka.ndim.iterator.types.simple
 
-
Simple2DCIterator - Class in neureka.ndim.iterator.types.simple
+
Simple2DCIterator(Simple2DConfiguration) - Constructor for class neureka.ndim.iterator.types.simple.Simple2DCIterator
 
-
Simple2DCIterator(Simple2DConfiguration) - Constructor for class neureka.ndim.iterator.types.simple.Simple2DCIterator
+
Simple2DConfiguration - Class in neureka.ndim.config.types.simple
 
-
Simple2DConfiguration - Class in neureka.ndim.config.types.simple
+
Simple2DConfiguration(int[], int[]) - Constructor for class neureka.ndim.config.types.simple.Simple2DConfiguration
 
-
Simple2DConfiguration(int[], int[]) - Constructor for class neureka.ndim.config.types.simple.Simple2DConfiguration
+
Simple3DCIterator - Class in neureka.ndim.iterator.types.simple
 
-
Simple3DCIterator - Class in neureka.ndim.iterator.types.simple
+
Simple3DCIterator(Simple3DConfiguration) - Constructor for class neureka.ndim.iterator.types.simple.Simple3DCIterator
 
-
Simple3DCIterator(Simple3DConfiguration) - Constructor for class neureka.ndim.iterator.types.simple.Simple3DCIterator
+
Simple3DConfiguration - Class in neureka.ndim.config.types.simple
 
-
Simple3DConfiguration - Class in neureka.ndim.config.types.simple
+
Simple3DConfiguration(int[], int[]) - Constructor for class neureka.ndim.config.types.simple.Simple3DConfiguration
 
-
Simple3DConfiguration(int[], int[]) - Constructor for class neureka.ndim.config.types.simple.Simple3DConfiguration
+
SimpleCLImplementation - Class in neureka.backend.main.implementations
 
-
SimpleCLImplementation - Class in neureka.backend.main.implementations
+
SimpleCLImplementation(ImplementationFor<OpenCLDevice>, int, String, String) - Constructor for class neureka.backend.main.implementations.SimpleCLImplementation
 
-
SimpleCLImplementation(ImplementationFor<OpenCLDevice>, int, String, String) - Constructor for class neureka.backend.main.implementations.SimpleCLImplementation
+
SimpleNDConfiguration - Class in neureka.ndim.config.types.simple
 
-
SimpleNDConfiguration - Class in neureka.ndim.config.types.simple
+
SimpleNDConfiguration(int[], int[]) - Constructor for class neureka.ndim.config.types.simple.SimpleNDConfiguration
 
-
SimpleNDConfiguration(int[], int[]) - Constructor for class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
SimpleReshapeView - Class in neureka.ndim.config.types.views
 
-
SimpleReshapeView - Class in neureka.ndim.config.types.views
+
SimpleReshapeView(int[], NDConfiguration) - Constructor for class neureka.ndim.config.types.views.SimpleReshapeView
 
-
SimpleReshapeView(int[], NDConfiguration) - Constructor for class neureka.ndim.config.types.views.SimpleReshapeView
+
sin() - Method in class neureka.math.Functions
 
-
sin() - Method in class neureka.math.Functions
-
 
-
sin() - Method in interface neureka.Tensor
+
sin() - Method in interface neureka.Tensor
This method is a functionally identical to the following alternatives:
-
singleFPConfig() - Method in class neureka.devices.opencl.OpenCLDevice
+
singleFPConfig() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
Sinus - Class in neureka.backend.main.operations.functions
+
SINUS - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
Sinus() - Constructor for class neureka.backend.main.operations.functions.Sinus
+
Sinus - Class in neureka.backend.main.operations.functions
 
-
SINUS - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
Sinus() - Constructor for class neureka.backend.main.operations.functions.Sinus
 
-
size() - Method in class neureka.autograd.GraphNode
+
size() - Method in class neureka.autograd.GraphNode
This is the number of AD-actions stored inside this node.
-
size() - Method in class neureka.backend.api.BackendContext
+
size() - Method in class neureka.backend.api.BackendContext
+
 
+
size() - Method in class neureka.common.utility.Cache
 
-
size() - Method in class neureka.common.utility.Cache
+
size() - Method in interface neureka.ndim.config.NDConfiguration
 
-
size() - Method in interface neureka.ndim.config.NDConfiguration
+
size() - Method in interface neureka.ndim.NDimensional
 
-
size() - Method in interface neureka.ndim.NDimensional
+
size() - Method in interface neureka.Shape
 
-
size() - Method in interface neureka.Shape
+
sizeOfShape(int[]) - Static method in class neureka.ndim.config.NDConfiguration.Utility
 
-
sizeOfShape(int[]) - Static method in class neureka.ndim.config.NDConfiguration.Utility
+
Slice - Class in neureka.backend.main.operations.other
 
-
slice() - Method in interface neureka.Nda
+
Slice() - Constructor for class neureka.backend.main.operations.other.Slice
+
 
+
slice(Object[], Tensor<ValType>) - Static method in class neureka.fluent.slicing.SmartSlicer
+
 
+
slice() - Method in interface neureka.Nda
This method returns a SliceBuilder instance exposing a simple builder API which enables the configuration of a slice of the current nd-array via method chaining.
-
slice() - Method in interface neureka.Tensor
+
slice(int, int) - Method in interface neureka.Shape
+
 
+
slice(int) - Method in interface neureka.Shape
+
 
+
slice() - Method in interface neureka.Tensor
This method returns a SliceBuilder instance exposing a simple builder API which enables the configuration of a slice of the current nd-array via method chaining.
-
slice(int) - Method in interface neureka.Shape
-
 
-
slice(int, int) - Method in interface neureka.Shape
-
 
-
slice(Object[], Tensor<ValType>) - Static method in class neureka.fluent.slicing.SmartSlicer
-
 
-
Slice - Class in neureka.backend.main.operations.other
-
 
-
Slice() - Constructor for class neureka.backend.main.operations.other.Slice
-
 
-
SliceBuilder<V> - Class in neureka.fluent.slicing
+
SliceBuilder<V> - Class in neureka.fluent.slicing
This class is the heart of the slice builder API, collecting range configurations by exposing an API consisting of multiple interfaces which form a call state transition graph.
-
SliceBuilder(Tensor<V>) - Constructor for class neureka.fluent.slicing.SliceBuilder
+
SliceBuilder(Tensor<V>) - Constructor for class neureka.fluent.slicing.SliceBuilder
An instance of a slice builder does not perform the actual slicing itself! Instead, it merely serves as a collector of slice configuration data.
-
sliceCount() - Method in interface neureka.Nda
+
sliceCount() - Method in interface neureka.Nda
This method returns the number of slices which have been created from this nd-array.
-
sliceCount() - Method in interface neureka.Tensor
+
sliceCount() - Method in interface neureka.Tensor
This method returns the number of slices which have been created from this nd-array.
-
Sliced0DConfiguration - Class in neureka.ndim.config.types.sliced
+
Sliced0DConfiguration - Class in neureka.ndim.config.types.sliced
 
-
Sliced0DConfiguration(int, int) - Constructor for class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
Sliced0DConfiguration(int, int) - Constructor for class neureka.ndim.config.types.sliced.Sliced0DConfiguration
 
-
Sliced1DCIterator - Class in neureka.ndim.iterator.types.sliced
+
Sliced1DCIterator - Class in neureka.ndim.iterator.types.sliced
 
-
Sliced1DCIterator(Sliced1DConfiguration) - Constructor for class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
+
Sliced1DCIterator(Sliced1DConfiguration) - Constructor for class neureka.ndim.iterator.types.sliced.Sliced1DCIterator
 
-
Sliced1DConfiguration - Class in neureka.ndim.config.types.sliced
+
Sliced1DConfiguration - Class in neureka.ndim.config.types.sliced
 
-
Sliced1DConfiguration(int, int, int, int, int) - Constructor for class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
Sliced1DConfiguration(int, int, int, int, int) - Constructor for class neureka.ndim.config.types.sliced.Sliced1DConfiguration
 
-
Sliced2DCIterator - Class in neureka.ndim.iterator.types.sliced
+
Sliced2DCIterator - Class in neureka.ndim.iterator.types.sliced
 
-
Sliced2DCIterator(Sliced2DConfiguration) - Constructor for class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
+
Sliced2DCIterator(Sliced2DConfiguration) - Constructor for class neureka.ndim.iterator.types.sliced.Sliced2DCIterator
 
-
Sliced2DConfiguration - Class in neureka.ndim.config.types.sliced
+
Sliced2DConfiguration - Class in neureka.ndim.config.types.sliced
 
-
Sliced2DConfiguration(int[], int[], int[], int[], int[]) - Constructor for class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
Sliced2DConfiguration(int[], int[], int[], int[], int[]) - Constructor for class neureka.ndim.config.types.sliced.Sliced2DConfiguration
 
-
Sliced3DCIterator - Class in neureka.ndim.iterator.types.sliced
+
Sliced3DCIterator - Class in neureka.ndim.iterator.types.sliced
 
-
Sliced3DCIterator(Sliced3DConfiguration) - Constructor for class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
+
Sliced3DCIterator(Sliced3DConfiguration) - Constructor for class neureka.ndim.iterator.types.sliced.Sliced3DCIterator
 
-
Sliced3DConfiguration - Class in neureka.ndim.config.types.sliced
+
Sliced3DConfiguration - Class in neureka.ndim.config.types.sliced
 
-
Sliced3DConfiguration(int[], int[], int[], int[], int[]) - Constructor for class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
Sliced3DConfiguration(int[], int[], int[], int[], int[]) - Constructor for class neureka.ndim.config.types.sliced.Sliced3DConfiguration
 
-
SlicedNDConfiguration - Class in neureka.ndim.config.types.sliced
+
SlicedNDConfiguration - Class in neureka.ndim.config.types.sliced
 
-
SlicedNDConfiguration(int[], int[], int[], int[], int[]) - Constructor for class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
SlicedNDConfiguration(int[], int[], int[], int[], int[]) - Constructor for class neureka.ndim.config.types.sliced.SlicedNDConfiguration
 
-
SlicedNDIterator - Class in neureka.ndim.iterator.types.sliced
+
SlicedNDIterator - Class in neureka.ndim.iterator.types.sliced
 
-
SlicedNDIterator(NDConfiguration) - Constructor for class neureka.ndim.iterator.types.sliced.SlicedNDIterator
+
SlicedNDIterator(NDConfiguration) - Constructor for class neureka.ndim.iterator.types.sliced.SlicedNDIterator
 
-
SmartSlicer - Class in neureka.fluent.slicing
+
SmartSlicer - Class in neureka.fluent.slicing
This class is responsible for receiving any input and trying to interpret it so that a slice can be formed.
-
SmartSlicer() - Constructor for class neureka.fluent.slicing.SmartSlicer
+
SmartSlicer() - Constructor for class neureka.fluent.slicing.SmartSlicer
 
-
softmax() - Method in interface neureka.Tensor
+
softmax() - Method in interface neureka.Tensor
 
-
softmax(int) - Method in interface neureka.Tensor
+
softmax(int) - Method in interface neureka.Tensor
 
-
softmax(int...) - Method in interface neureka.Tensor
+
softmax(int...) - Method in interface neureka.Tensor
Calculates the softmax function along the specified axes.
-
softplus() - Method in class neureka.math.Functions
+
SOFTPLUS - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
 
+
Softplus - Class in neureka.backend.main.operations.functions
+
+
SoftPlus is a smooth approximation to the ReLU function and can be used + to constrain the output of a machine to always be positive.
+
+
Softplus() - Constructor for class neureka.backend.main.operations.functions.Softplus
+
 
+
softplus() - Method in class neureka.math.Functions
SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.
-
softplus() - Method in interface neureka.Tensor
+
softplus() - Method in interface neureka.Tensor
This method is a functionally identical to the following alternatives:
-
Softplus - Class in neureka.backend.main.operations.functions
-
-
SoftPlus is a smooth approximation to the ReLU function and can be used - to constrain the output of a machine to always be positive.
-
-
Softplus() - Constructor for class neureka.backend.main.operations.functions.Softplus
+
SOFTSIGN - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
 
+
softsign(double) - Static method in class neureka.backend.main.implementations.fun.ScalarSoftsign
 
-
SOFTPLUS - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
softsign(float) - Static method in class neureka.backend.main.implementations.fun.ScalarSoftsign
 
-
softsign() - Method in class neureka.math.Functions
+
Softsign - Class in neureka.backend.main.operations.functions
The softsign function, defined as x / ( 1 + Math.abs( x ) ), is a computationally cheap 0 centered activation function which rescales the inputs between -1 and 1, very much like the Tanh function.
-
softsign(double) - Static method in class neureka.backend.main.implementations.fun.ScalarSoftsign
+
Softsign() - Constructor for class neureka.backend.main.operations.functions.Softsign
 
-
softsign(float) - Static method in class neureka.backend.main.implementations.fun.ScalarSoftsign
-
 
-
Softsign - Class in neureka.backend.main.operations.functions
+
softsign() - Method in class neureka.math.Functions
The softsign function, defined as x / ( 1 + Math.abs( x ) ), is a computationally cheap 0 centered activation function which rescales the inputs between -1 and 1, very much like the Tanh function.
-
Softsign() - Constructor for class neureka.backend.main.operations.functions.Softsign
-
 
-
SOFTSIGN - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
spaces(int) - Static method in class neureka.view.NdaAsString.Util
 
-
spaces(int) - Static method in class neureka.view.NdaAsString.Util
-
 
-
spread() - Method in interface neureka.ndim.config.NDConfiguration
+
spread() - Method in interface neureka.ndim.config.NDConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
spread(int) - Method in interface neureka.ndim.config.NDConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
spread() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
spread() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
spread() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
spread() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
spread() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
spread() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
spread() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
spread(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
spread() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread() - Method in interface neureka.ndim.NDimensional
-
 
-
spread(int) - Method in interface neureka.ndim.config.NDConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
spread() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
spread() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
spread() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
spread() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
spread() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
spread() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
spread() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
spread() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
spread(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
spread(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
-
sqrt() - Method in class neureka.math.Functions
+
spread() - Method in interface neureka.ndim.NDimensional
 
-
sqrt() - Method in interface neureka.Tensor
-
-
This method is a functionally identical to the following alternatives:
-
-
Sqrt - Class in neureka.backend.main.operations.functions
+
SQRT - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
 
+
Sqrt - Class in neureka.backend.main.operations.functions
 
-
Sqrt() - Constructor for class neureka.backend.main.operations.functions.Sqrt
+
Sqrt() - Constructor for class neureka.backend.main.operations.functions.Sqrt
 
-
SQRT - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
sqrt() - Method in class neureka.math.Functions
 
-
StaticKernelSource - Interface in neureka.devices.opencl
+
sqrt() - Method in interface neureka.Tensor
+
+
This method is a functionally identical to the following alternatives:
+
+
StaticKernelSource - Interface in neureka.devices.opencl
 
-
step(double) - Method in class neureka.fluent.building.NdaBuilder
+
step(double) - Method in class neureka.fluent.building.NdaBuilder
 
-
step(double) - Method in interface neureka.fluent.building.states.Step
+
Step<V> - Interface in neureka.fluent.building.states
+
+
This interface defines the last step in the call transition graph of the fluent builder API when + building a Tensor instance populated based on the values within a defined range.
+
+
step(double) - Method in interface neureka.fluent.building.states.Step
This is the last step in the call transition graph of the fluent builder API when building a Tensor instance populated based on the values within a defined range.
-
step(double) - Method in interface neureka.fluent.building.states.StepForTensor
+
step(double) - Method in interface neureka.fluent.building.states.StepForTensor
This is the last step in the call transition graph of the fluent builder API when building a Tensor instance populated based on the values within a defined range.
-
step(int) - Method in class neureka.fluent.slicing.AxisSliceBuilder
+
step(int) - Method in class neureka.fluent.slicing.AxisSliceBuilder
This method returns an instance of this very AxisSliceBuilder instance disguised by the AxisOrGet interface.
-
step(int) - Method in interface neureka.fluent.slicing.states.StepsOrAxisOrGet
+
step(int) - Method in interface neureka.fluent.slicing.states.StepsOrAxisOrGet
This method allows one to specify a step size within the slice range previously specified for the currently sliced axis.
-
step(int) - Method in interface neureka.fluent.slicing.states.StepsOrAxisOrGetTensor
+
step(int) - Method in interface neureka.fluent.slicing.states.StepsOrAxisOrGetTensor
This method allows one to specify a step size within the slice range previously specified for the currently sliced axis.
-
Step<V> - Interface in neureka.fluent.building.states
-
-
This interface defines the last step in the call transition graph of the fluent builder API when - building a Tensor instance populated based on the values within a defined range.
-
-
StepForTensor<V> - Interface in neureka.fluent.building.states
+
StepForTensor<V> - Interface in neureka.fluent.building.states
 
-
StepsOrAxisOrGet<V> - Interface in neureka.fluent.slicing.states
+
StepsOrAxisOrGet<V> - Interface in neureka.fluent.slicing.states
This interface extends the AxisOrGet interface which provides the option to either continue slicing another axis or simply trigger the creation and return of a slice instance based on the already provided slice configuration.
-
StepsOrAxisOrGetTensor<V> - Interface in neureka.fluent.slicing.states
+
StepsOrAxisOrGetTensor<V> - Interface in neureka.fluent.slicing.states
 
-
Storage<V> - Interface in neureka.devices
+
Storage<V> - Interface in neureka.devices
This is an abstract interface which simply describes "a thing that stores tensors".
-
store(Tensor<T>) - Method in class neureka.devices.AbstractDevice
+
store(Tensor<T>) - Method in class neureka.devices.AbstractDevice
Implementations of this method ought to store the data of the tensor in whatever formant suites the underlying implementation and or final type.
-
store(Tensor<T>) - Method in class neureka.devices.file.CSVHandle
+
store(Tensor<T>) - Method in class neureka.devices.file.CSVHandle
 
-
store(Tensor<T>) - Method in class neureka.devices.file.FileDevice
+
store(Tensor<T>) - Method in class neureka.devices.file.FileDevice
Implementations of this method ought to store the data of the tensor in whatever formant suites the underlying implementation and or final type.
-
store(Tensor<T>) - Method in class neureka.devices.file.IDXHandle
-
 
-
store(Tensor<T>) - Method in class neureka.devices.host.CPU
-
 
-
store(Tensor<T>) - Method in interface neureka.devices.Storage
+
store(Tensor<T>, String) - Method in class neureka.devices.file.FileDevice
-
Implementations of this method ought to store the data - of the tensor in whatever formant suites the underlying - implementation and or final type.
+
Stores the given tensor in the file system with the given filename.
-
store(Tensor<T>, String) - Method in class neureka.devices.file.FileDevice
+
store(Tensor<T>, String, Map<String, Object>) - Method in class neureka.devices.file.FileDevice
Stores the given tensor in the file system with the given filename.
-
store(Tensor<T>, String, Map<String, Object>) - Method in class neureka.devices.file.FileDevice
+
store(Tensor<T>) - Method in class neureka.devices.file.IDXHandle
+
 
+
store(Tensor<T>) - Method in class neureka.devices.host.CPU
+
 
+
store(Tensor<T>) - Method in interface neureka.devices.Storage
-
Stores the given tensor in the file system with the given filename.
+
Implementations of this method ought to store the data + of the tensor in whatever formant suites the underlying + implementation and or final type.
-
stream() - Method in interface neureka.Nda
+
stream() - Method in interface neureka.Nda
 
-
stream() - Method in interface neureka.Shape
+
stream() - Method in interface neureka.Shape
 
-
strides() - Method in interface neureka.ndim.config.NDConfiguration
+
strides() - Method in interface neureka.ndim.config.NDConfiguration
The array returned by this method is used to translate an array of axis indices to a single ata array index.
-
strides() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
strides(int) - Method in interface neureka.ndim.config.NDConfiguration
-
The array returned by this method is used to translate an array - of axis indices to a single ata array index.
+
This method receives an axis index and returns the + translation value for the targeted axis.
-
strides() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
strides() - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
The array returned by this method is used to translate an array of axis indices to a single ata array index.
-
strides() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
-
The array returned by this method is used to translate an array - of axis indices to a single ata array index.
+
This method receives an axis index and returns the + translation value for the targeted axis.
-
strides() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
strides() - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
The array returned by this method is used to translate an array of axis indices to a single ata array index.
-
strides() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
-
The array returned by this method is used to translate an array - of axis indices to a single ata array index.
+
This method receives an axis index and returns the + translation value for the targeted axis.
-
strides() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
strides() - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
The array returned by this method is used to translate an array of axis indices to a single ata array index.
-
strides() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
-
The array returned by this method is used to translate an array - of axis indices to a single ata array index.
+
This method receives an axis index and returns the + translation value for the targeted axis.
-
strides() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
strides() - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
The array returned by this method is used to translate an array of axis indices to a single ata array index.
-
strides() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
-
The array returned by this method is used to translate an array - of axis indices to a single ata array index.
+
This method receives an axis index and returns the + translation value for the targeted axis.
-
strides() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
strides() - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
The array returned by this method is used to translate an array of axis indices to a single ata array index.
-
strides() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
-
The array returned by this method is used to translate an array - of axis indices to a single ata array index.
+
This method receives an axis index and returns the + translation value for the targeted axis.
-
strides() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
strides() - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
The array returned by this method is used to translate an array of axis indices to a single ata array index.
-
strides() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
-
The array returned by this method is used to translate an array - of axis indices to a single ata array index.
+
This method receives an axis index and returns the + translation value for the targeted axis.
-
strides() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
strides() - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
The array returned by this method is used to translate an array of axis indices to a single ata array index.
-
strides() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
strides(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
-
The array returned by this method is used to translate an array - of axis indices to a single ata array index.
+
This method receives an axis index and returns the + translation value for the targeted axis.
-
strides() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
strides() - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
The array returned by this method is used to translate an array of axis indices to a single ata array index.
-
strides() - Method in interface neureka.ndim.NDimensional
-
 
-
strides(int) - Method in interface neureka.ndim.config.NDConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
This method receives an axis index and returns the translation value for the targeted axis.
-
strides(int) - Method in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
strides() - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
-
This method receives an axis index and returns the - translation value for the targeted axis.
+
The array returned by this method is used to translate an array + of axis indices to a single ata array index.
-
strides(int) - Method in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
This method receives an axis index and returns the translation value for the targeted axis.
-
strides(int) - Method in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
strides() - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
-
This method receives an axis index and returns the - translation value for the targeted axis.
+
The array returned by this method is used to translate an array + of axis indices to a single ata array index.
-
strides(int) - Method in class neureka.ndim.config.types.permuted.PermutedNDConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
This method receives an axis index and returns the translation value for the targeted axis.
-
strides(int) - Method in class neureka.ndim.config.types.simple.Simple0DConfiguration
+
strides() - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
-
This method receives an axis index and returns the - translation value for the targeted axis.
+
The array returned by this method is used to translate an array + of axis indices to a single ata array index.
-
strides(int) - Method in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
This method receives an axis index and returns the translation value for the targeted axis.
-
strides(int) - Method in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
strides() - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
-
This method receives an axis index and returns the - translation value for the targeted axis.
+
The array returned by this method is used to translate an array + of axis indices to a single ata array index.
-
strides(int) - Method in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
This method receives an axis index and returns the translation value for the targeted axis.
-
strides(int) - Method in class neureka.ndim.config.types.simple.SimpleNDConfiguration
+
strides() - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
-
This method receives an axis index and returns the - translation value for the targeted axis.
+
The array returned by this method is used to translate an array + of axis indices to a single ata array index.
-
strides(int) - Method in class neureka.ndim.config.types.sliced.Sliced0DConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
This method receives an axis index and returns the translation value for the targeted axis.
-
strides(int) - Method in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
strides() - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
-
This method receives an axis index and returns the - translation value for the targeted axis.
+
The array returned by this method is used to translate an array + of axis indices to a single ata array index.
-
strides(int) - Method in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
This method receives an axis index and returns the translation value for the targeted axis.
-
strides(int) - Method in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
strides() - Method in class neureka.ndim.config.types.views.SimpleReshapeView
-
This method receives an axis index and returns the - translation value for the targeted axis.
+
The array returned by this method is used to translate an array + of axis indices to a single ata array index.
-
strides(int) - Method in class neureka.ndim.config.types.sliced.SlicedNDConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
This method receives an axis index and returns the translation value for the targeted axis.
-
strides(int) - Method in class neureka.ndim.config.types.views.SimpleReshapeView
+
strides() - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
-
This method receives an axis index and returns the - translation value for the targeted axis.
+
The array returned by this method is used to translate an array + of axis indices to a single ata array index.
-
strides(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
+
strides(int) - Method in class neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
This method receives an axis index and returns the translation value for the targeted axis.
-
stringifier(OperationBuilder.Stringifier) - Method in class neureka.backend.api.template.operations.OperationBuilder
+
strides() - Method in interface neureka.ndim.NDimensional
 
-
stringify(String[]) - Method in interface neureka.backend.api.Operation
+
stringifier(OperationBuilder.Stringifier) - Method in class neureka.backend.api.template.operations.OperationBuilder
 
-
stringify(String[]) - Method in class neureka.backend.api.template.operations.AbstractOperation
-
stringify(String[]) - Method in interface neureka.backend.api.template.operations.OperationBuilder.Stringifier
+
stringify(String[]) - Method in interface neureka.backend.api.Operation
 
-
stringify(String[]) - Method in class neureka.backend.main.operations.functions.SiLU
+
stringify(String[]) - Method in class neureka.backend.api.template.operations.AbstractOperation
+
stringify(String[]) - Method in interface neureka.backend.api.template.operations.OperationBuilder.Stringifier
 
-
stringify(String[]) - Method in class neureka.backend.main.operations.linear.XConvLeft
+
stringify(String[]) - Method in class neureka.backend.main.operations.linear.XConvLeft
 
-
stringify(String[]) - Method in class neureka.backend.main.operations.linear.XConvRight
+
stringify(String[]) - Method in class neureka.backend.main.operations.linear.XConvRight
 
-
stringify(String[]) - Method in class neureka.backend.main.operations.other.AssignLeft
+
stringify(String[]) - Method in class neureka.backend.main.operations.other.AssignLeft
 
-
stringify(String[]) - Method in class neureka.backend.main.operations.other.Permute
+
stringify(String[]) - Method in class neureka.backend.main.operations.other.Permute
 
-
submit(int, CPU.IndexedWorkload) - Method in class neureka.devices.host.concurrent.WorkScheduler.Divider
+
submit(int, CPU.IndexedWorkload) - Method in class neureka.devices.host.concurrent.WorkScheduler.Divider
 
-
Subtraction - Class in neureka.backend.main.operations.operator
+
Subtraction - Class in neureka.backend.main.operations.operator
 
-
Subtraction() - Constructor for class neureka.backend.main.operations.operator.Subtraction
+
Subtraction() - Constructor for class neureka.backend.main.operations.operator.Subtraction
 
-
suitabilityIfValid(float) - Method in class neureka.backend.api.Call.Validator
+
suitabilityIfValid(float) - Method in class neureka.backend.api.Call.Validator
 
-
SuitabilityPredicate - Interface in neureka.backend.api.fun
+
SuitabilityPredicate - Interface in neureka.backend.api.fun
The SuitabilityPredicate checks if a given instance of an ExecutionCall is suitable to be executed in ImplementationFor residing in this Algorithm as components.
-
sum() - Method in class neureka.math.Functions
+
Sum - Class in neureka.backend.main.operations.other
 
-
sum() - Method in interface neureka.Tensor
+
Sum() - Constructor for class neureka.backend.main.operations.other.Sum
+
 
+
sum() - Method in class neureka.math.Functions
+
 
+
sum() - Method in interface neureka.Tensor
Calculate the sum value of all values within this tensor and returns it in the form of a scalar tensor.
-
sum(int) - Method in interface neureka.Tensor
+
sum(int) - Method in interface neureka.Tensor
Calculate the sum value of all values within this tensor along the specified axis and returns it in the form of a tensor.
-
sum(int...) - Method in interface neureka.Tensor
+
sum(int...) - Method in interface neureka.Tensor
Calculate the sum value of all values within this tensor along the specified axes and returns it in the form of a tensor.
-
Sum - Class in neureka.backend.main.operations.other
-
 
-
Sum() - Constructor for class neureka.backend.main.operations.other.Sum
-
 
-
SumAlgorithm - Class in neureka.backend.main.algorithms
+
SumAlgorithm - Class in neureka.backend.main.algorithms
 
-
SumAlgorithm() - Constructor for class neureka.backend.main.algorithms.SumAlgorithm
+
SumAlgorithm() - Constructor for class neureka.backend.main.algorithms.SumAlgorithm
 
-
Summation - Class in neureka.backend.main.operations.indexer
+
Summation - Class in neureka.backend.main.operations.indexer
This type of operation belongs to the same species as the Product operation.
-
Summation() - Constructor for class neureka.backend.main.operations.indexer.Summation
+
Summation() - Constructor for class neureka.backend.main.operations.indexer.Summation
 
-
supplyADActionFor(Function, ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.api.fun.ADActionSupplier
+
supplyADActionFor(Function, ExecutionCall<? extends Device<?>>) - Method in interface neureka.backend.api.fun.ADActionSupplier
This method ought to return a new instance if the ADAction class responsible for performing automatic differentiation both for forward and backward mode differentiation.
-
supplyADActionFor(Function, ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
+
supplyADActionFor(Function, ExecutionCall<? extends Device<?>>) - Method in class neureka.backend.api.template.algorithms.FallbackAlgorithm
 
-
supports(Class<T>) - Method in interface neureka.backend.api.Operation
+
supports(Class<T>) - Method in interface neureka.backend.api.Operation
 
-
supports(Class<T>) - Method in class neureka.backend.api.template.operations.AbstractOperation
+
supports(Class<T>) - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
supportsAlgorithm(Class<T>) - Method in interface neureka.backend.api.Operation
+
supportsAlgorithm(Class<T>) - Method in interface neureka.backend.api.Operation
This method checks if this Operation contains an instance of the Algorithm implementation specified via its type class.
-
supportsAlgorithm(Class<T>) - Method in class neureka.backend.api.template.operations.AbstractOperation
+
supportsAlgorithm(Class<T>) - Method in class neureka.backend.api.template.operations.AbstractOperation
This method checks if this Operation contains an instance of the Algorithm implementation specified via its type class.
-
SYMMETRIC - Enum constant in enum class neureka.ndim.config.NDConfiguration.Layout
-
 
-

T

-
-
T() - Method in interface neureka.Tensor
+ + + +

T

+
+
T() - Method in interface neureka.Tensor
Creates and returns a new Tensor instance which is a transposed twin of this instance.
- This is a shorter alternative to the functionally identical Tensor.getT() method.
+ This is a shorter alternative to the functionally identical Tensor.getT() method.
-
tanh() - Method in class neureka.math.Functions
+
TANH - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
tanh() - Method in interface neureka.Tensor
-
-
This method is a functionally identical to the following alternatives:
-
-
tanh(double) - Static method in class neureka.backend.main.implementations.fun.ScalarTanh
+
tanh(double) - Static method in class neureka.backend.main.implementations.fun.ScalarTanh
 
-
tanh(float) - Static method in class neureka.backend.main.implementations.fun.ScalarTanh
+
tanh(float) - Static method in class neureka.backend.main.implementations.fun.ScalarTanh
 
-
Tanh - Class in neureka.backend.main.operations.functions
+
Tanh - Class in neureka.backend.main.operations.functions
 
-
Tanh() - Constructor for class neureka.backend.main.operations.functions.Tanh
+
Tanh() - Constructor for class neureka.backend.main.operations.functions.Tanh
 
-
TANH - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
tanh() - Method in class neureka.math.Functions
 
-
TANH_FAST - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
+
tanh() - Method in interface neureka.Tensor
+
+
This method is a functionally identical to the following alternatives:
+
+
TANH_FAST - Static variable in interface neureka.backend.main.implementations.fun.api.ScalarFun
 
-
TanhFast - Class in neureka.backend.main.operations.functions
+
TanhFast - Class in neureka.backend.main.operations.functions
 
-
TanhFast() - Constructor for class neureka.backend.main.operations.functions.TanhFast
+
TanhFast() - Constructor for class neureka.backend.main.operations.functions.TanhFast
 
-
targetArrayType() - Method in class neureka.dtype.custom.F32
+
targetArrayType() - Method in class neureka.dtype.custom.F32
 
-
targetArrayType() - Method in class neureka.dtype.custom.F64
+
targetArrayType() - Method in class neureka.dtype.custom.F64
 
-
targetArrayType() - Method in class neureka.dtype.custom.I16
+
targetArrayType() - Method in class neureka.dtype.custom.I16
 
-
targetArrayType() - Method in class neureka.dtype.custom.I32
+
targetArrayType() - Method in class neureka.dtype.custom.I32
 
-
targetArrayType() - Method in class neureka.dtype.custom.I64
+
targetArrayType() - Method in class neureka.dtype.custom.I64
 
-
targetArrayType() - Method in class neureka.dtype.custom.I8
+
targetArrayType() - Method in class neureka.dtype.custom.I8
 
-
targetArrayType() - Method in class neureka.dtype.custom.UI16
+
targetArrayType() - Method in class neureka.dtype.custom.UI16
 
-
targetArrayType() - Method in class neureka.dtype.custom.UI32
+
targetArrayType() - Method in class neureka.dtype.custom.UI32
 
-
targetArrayType() - Method in class neureka.dtype.custom.UI64
+
targetArrayType() - Method in class neureka.dtype.custom.UI64
 
-
targetArrayType() - Method in class neureka.dtype.custom.UI8
+
targetArrayType() - Method in class neureka.dtype.custom.UI8
 
-
targetArrayType() - Method in interface neureka.dtype.NumericType
+
targetArrayType() - Method in interface neureka.dtype.NumericType
The target type is the targeted JVM data-type which can represent the holder type.
-
targetToForeignHolderBytes(Byte) - Method in class neureka.dtype.custom.I8
+
targetToForeignHolderBytes(Float) - Method in class neureka.dtype.custom.F32
 
-
targetToForeignHolderBytes(Double) - Method in class neureka.dtype.custom.F64
+
targetToForeignHolderBytes(Double) - Method in class neureka.dtype.custom.F64
 
-
targetToForeignHolderBytes(Float) - Method in class neureka.dtype.custom.F32
+
targetToForeignHolderBytes(Short) - Method in class neureka.dtype.custom.I16
 
-
targetToForeignHolderBytes(Integer) - Method in class neureka.dtype.custom.I32
+
targetToForeignHolderBytes(Integer) - Method in class neureka.dtype.custom.I32
 
-
targetToForeignHolderBytes(Integer) - Method in class neureka.dtype.custom.UI16
+
targetToForeignHolderBytes(Long) - Method in class neureka.dtype.custom.I64
 
-
targetToForeignHolderBytes(Long) - Method in class neureka.dtype.custom.I64
+
targetToForeignHolderBytes(Byte) - Method in class neureka.dtype.custom.I8
 
-
targetToForeignHolderBytes(Long) - Method in class neureka.dtype.custom.UI32
+
targetToForeignHolderBytes(Integer) - Method in class neureka.dtype.custom.UI16
 
-
targetToForeignHolderBytes(Short) - Method in class neureka.dtype.custom.I16
+
targetToForeignHolderBytes(Long) - Method in class neureka.dtype.custom.UI32
 
-
targetToForeignHolderBytes(Short) - Method in class neureka.dtype.custom.UI8
+
targetToForeignHolderBytes(BigInteger) - Method in class neureka.dtype.custom.UI64
 
-
targetToForeignHolderBytes(BigInteger) - Method in class neureka.dtype.custom.UI64
+
targetToForeignHolderBytes(Short) - Method in class neureka.dtype.custom.UI8
 
-
targetToForeignHolderBytes(TargetType) - Method in interface neureka.dtype.NumericType
+
targetToForeignHolderBytes(TargetType) - Method in interface neureka.dtype.NumericType
 
-
targetType() - Method in class neureka.dtype.custom.F32
+
targetType() - Method in class neureka.dtype.custom.F32
 
-
targetType() - Method in class neureka.dtype.custom.F64
+
targetType() - Method in class neureka.dtype.custom.F64
 
-
targetType() - Method in class neureka.dtype.custom.I16
+
targetType() - Method in class neureka.dtype.custom.I16
 
-
targetType() - Method in class neureka.dtype.custom.I32
+
targetType() - Method in class neureka.dtype.custom.I32
 
-
targetType() - Method in class neureka.dtype.custom.I64
+
targetType() - Method in class neureka.dtype.custom.I64
 
-
targetType() - Method in class neureka.dtype.custom.I8
+
targetType() - Method in class neureka.dtype.custom.I8
 
-
targetType() - Method in class neureka.dtype.custom.UI16
+
targetType() - Method in class neureka.dtype.custom.UI16
 
-
targetType() - Method in class neureka.dtype.custom.UI32
+
targetType() - Method in class neureka.dtype.custom.UI32
 
-
targetType() - Method in class neureka.dtype.custom.UI64
+
targetType() - Method in class neureka.dtype.custom.UI64
 
-
targetType() - Method in class neureka.dtype.custom.UI8
+
targetType() - Method in class neureka.dtype.custom.UI8
 
-
targetType() - Method in interface neureka.dtype.NumericType
+
targetType() - Method in interface neureka.dtype.NumericType
The target type is the targeted JVM data-type which can represent the holder type.
-
Tensor<V> - Interface in neureka
+
Tensor<V> - Interface in neureka
A Tensor is a mathematical concept and type of multidimensional data-structure with certain transformation properties.
-
Tensor.ImageType - Enum Class in neureka
+
Tensor.ImageType - Enum in neureka
-
Use this enum as argument for the Tensor.asImage(Tensor.ImageType) method to +
Use this enum as argument for the Tensor.asImage(Tensor.ImageType) method to specify the type of image that should be returned.
-
tensors(Call.TensorsCondition) - Method in class neureka.backend.api.Call.Validator
+
tensors(Call.TensorsCondition) - Method in class neureka.backend.api.Call.Validator
 
-
TERRIBLE - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
+
TERRIBLE - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
 
-
THREAD_PREFIX - Static variable in class neureka.devices.host.CPU
+
THREAD_PREFIX - Static variable in class neureka.devices.host.CPU
 
-
threaded(int, int, CPU.RangeWorkload) - Method in class neureka.devices.host.CPU.JVMExecutor
+
threaded(int, CPU.RangeWorkload) - Method in class neureka.devices.host.CPU.JVMExecutor
-
Takes the provided range and divides it into multithreaded workloads.
+
This method slices the provided workload size into multiple ranges which can be executed in parallel.
-
threaded(int, CPU.IndexedWorkload) - Method in class neureka.devices.host.CPU.JVMExecutor
+
threaded(int, CPU.IndexedWorkload) - Method in class neureka.devices.host.CPU.JVMExecutor
Executes the provided workload lambda across multiple threads where the provided worker lambda will receive the index/id of the current worker.
-
threaded(int, CPU.RangeWorkload) - Method in class neureka.devices.host.CPU.JVMExecutor
+
threaded(int, int, CPU.RangeWorkload) - Method in class neureka.devices.host.CPU.JVMExecutor
-
This method slices the provided workload size into multiple ranges which can be executed in parallel.
+
Takes the provided range and divides it into multithreaded workloads.
-
threads - Variable in class neureka.devices.host.machine.BasicMachine
+
threads - Variable in class neureka.devices.host.machine.BasicMachine
 
-
THREADS - Enum constant in enum class neureka.devices.host.concurrent.Parallelism
-
-
The total number of threads (incl.
-
-
threshold(int) - Method in class neureka.devices.host.concurrent.WorkScheduler.Divider
+
threshold(int) - Method in class neureka.devices.host.concurrent.WorkScheduler.Divider
 
-
times(Tensor<V>) - Method in interface neureka.Tensor
+
times(Tensor<V>) - Method in interface neureka.Tensor
-
This is a functionally identical synonym to the Tensor.multiply(Tensor) method.
+
This is a functionally identical synonym to the Tensor.multiply(Tensor) method.
-
times(V) - Method in interface neureka.Tensor
-
 
-
timesAssign(Tensor<T>) - Method in interface neureka.MutateTensor
+
times(V) - Method in interface neureka.Tensor
 
-
timesAssign(T) - Method in interface neureka.MutateTensor
+
timesAssign(Tensor<T>) - Method in interface neureka.MutateTensor
 
-
to(int) - Method in class neureka.fluent.slicing.AxisSliceBuilder
-
-
This method returns an instance of this very AxisSliceBuilder instance - disguised by the StepsOrAxisOrGet interface.
-
-
to(int) - Method in interface neureka.fluent.slicing.states.To
-
-
This is the second part for defining the slice range of a specified axis within - the call transition graph exposed by the slice fluent builder API.
-
-
to(int) - Method in interface neureka.fluent.slicing.states.ToForTensor
-
-
This is the second part for defining the slice range of a specified axis within - the call transition graph exposed by the slice fluent builder API.
-
-
to(String) - Method in interface neureka.Tensor
+
timesAssign(T) - Method in interface neureka.MutateTensor
 
-
to(Device<?>) - Method in interface neureka.Tensor
-
-
This method takes a Device and tries to migrate the contents of this Tensor - instance to that Device!
-
-
to(T) - Static method in class neureka.backend.api.Call
+
to(T) - Static method in class neureka.backend.api.Call
 
-
to(V) - Method in class neureka.fluent.building.NdaBuilder
+
to(V) - Method in class neureka.fluent.building.NdaBuilder
 
-
to(V) - Method in interface neureka.fluent.building.states.To
+
To<V> - Interface in neureka.fluent.building.states
This step in the call transition graph of the fluent builder API is a followup call - from the IterByOrIterFromOrAll.andFillFrom(Object) method which + from the IterByOrIterFromOrAll.andFillFrom(Object) method which expects a range to be specified whose values will be used to populate the Tensor instance.
-
to(V) - Method in interface neureka.fluent.building.states.ToForTensor
+
to(V) - Method in interface neureka.fluent.building.states.To
This step in the call transition graph of the fluent builder API is a followup call - from the IterByOrIterFromOrAll.andFillFrom(Object) method which + from the IterByOrIterFromOrAll.andFillFrom(Object) method which expects a range to be specified whose values will be used to populate the Tensor instance.
-
To<V> - Interface in neureka.fluent.building.states
+
to(V) - Method in interface neureka.fluent.building.states.ToForTensor
This step in the call transition graph of the fluent builder API is a followup call - from the IterByOrIterFromOrAll.andFillFrom(Object) method which + from the IterByOrIterFromOrAll.andFillFrom(Object) method which expects a range to be specified whose values will be used to populate the Tensor instance.
-
To<V> - Interface in neureka.fluent.slicing.states
+
to(int) - Method in class neureka.fluent.slicing.AxisSliceBuilder
+
+
This method returns an instance of this very AxisSliceBuilder instance + disguised by the StepsOrAxisOrGet interface.
+
+
To<V> - Interface in neureka.fluent.slicing.states
This is the second part for defining the slice range of a specified axis within the call transition graph exposed by the slice builder API.
-
toByteArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
+
to(int) - Method in interface neureka.fluent.slicing.states.To
+
+
This is the second part for defining the slice range of a specified axis within + the call transition graph exposed by the slice fluent builder API.
+
+
to(int) - Method in interface neureka.fluent.slicing.states.ToForTensor
+
+
This is the second part for defining the slice range of a specified axis within + the call transition graph exposed by the slice fluent builder API.
+
+
to(Device<?>) - Method in interface neureka.Tensor
+
+
This method takes a Device and tries to migrate the contents of this Tensor + instance to that Device!
+
+
to(String) - Method in interface neureka.Tensor
+
 
+
toByteArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
+
 
+
toDoubleArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
 
-
toDoubleArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
+
toFloatArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
 
-
toFloatArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
+
ToForTensor<V> - Interface in neureka.fluent.building.states
 
-
ToForTensor<V> - Interface in neureka.fluent.building.states
+
ToForTensor<V> - Interface in neureka.fluent.slicing.states
 
-
ToForTensor<V> - Interface in neureka.fluent.slicing.states
+
toIntArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
 
-
toIntArray() - Method in interface neureka.Shape
+
toIntArray() - Method in interface neureka.Shape
 
-
toIntArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
+
toLayout(Tensor<?>, NDConfiguration.Layout) - Static method in class neureka.backend.main.operations.other.ReLayout
 
-
toLayout(NDConfiguration.Layout) - Method in interface neureka.MutateTensor
+
toLayout(NDConfiguration.Layout) - Method in interface neureka.MutateTensor
This method allows you to modify the data-layout of this AbstractNda.
-
toLayout(Tensor<?>, NDConfiguration.Layout) - Static method in class neureka.backend.main.operations.other.ReLayout
+
toLongArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
 
-
toLongArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
+
toObjectArray(Function<Integer, Object>) - Method in class neureka.common.utility.DataConverter.ForTensor
 
-
toObjectArray(Function<Integer, Object>) - Method in class neureka.common.utility.DataConverter.ForTensor
-
 
-
toOptional() - Method in interface neureka.Nda.Item
+
toOptional() - Method in interface neureka.Nda.Item
Converts this item into an optional value.
-
toShortArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
+
toShortArray(Function<Integer, Number>) - Method in class neureka.common.utility.DataConverter.ForTensor
+
 
+
toString() - Method in class neureka.autograd.GraphNode
 
-
toString() - Method in class neureka.autograd.GraphNode
+
toString(GraphNode.Print) - Method in class neureka.autograd.GraphNode
 
-
toString() - Method in class neureka.autograd.JITProp
+
toString() - Method in class neureka.autograd.JITProp
 
-
toString() - Method in class neureka.backend.api.BackendContext
+
toString() - Method in class neureka.backend.api.BackendContext
 
-
toString() - Method in class neureka.backend.api.ExecutionCall
+
toString() - Method in class neureka.backend.api.ExecutionCall
 
-
toString() - Method in class neureka.backend.api.LazyRef
+
toString() - Method in class neureka.backend.api.LazyRef
 
-
toString() - Method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
toString() - Method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
toString() - Method in class neureka.backend.api.template.operations.AbstractOperation
+
toString() - Method in class neureka.backend.api.template.operations.AbstractOperation
 
-
toString() - Method in class neureka.backend.ocl.CLBackend
+
toString() - Method in class neureka.backend.ocl.CLBackend
 
-
toString() - Method in class neureka.devices.file.FileDevice
+
toString() - Method in class neureka.devices.file.FileDevice
 
-
toString() - Method in class neureka.devices.host.CPU
+
toString() - Method in class neureka.devices.host.CPU
 
-
toString() - Method in class neureka.devices.host.machine.BasicMachine
+
toString() - Method in class neureka.devices.host.machine.BasicMachine
 
-
toString() - Method in class neureka.devices.host.machine.ConcreteMachine
+
toString() - Method in class neureka.devices.host.machine.ConcreteMachine
 
-
toString() - Method in class neureka.devices.host.machine.Hardware
+
toString() - Method in class neureka.devices.host.machine.Hardware
 
-
toString() - Method in class neureka.devices.opencl.OpenCLDevice
+
toString() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
toString() - Method in class neureka.devices.opencl.OpenCLPlatform
+
toString() - Method in class neureka.devices.opencl.OpenCLPlatform
 
-
toString() - Method in class neureka.dtype.DataType
+
toString() - Method in class neureka.dtype.DataType
 
-
toString() - Method in class neureka.framing.NDFrame
+
toString() - Method in class neureka.framing.NDFrame
 
-
toString() - Method in class neureka.framing.Relation
+
toString() - Method in class neureka.framing.Relation
 
-
toString() - Method in class neureka.math.args.Arg
+
toString() - Method in class neureka.math.args.Arg
 
-
toString() - Method in interface neureka.math.Function
+
toString() - Method in interface neureka.math.Function
Turns this function into a string representation which can be used to reconstruct this function or combine it with other function strings to parse entirely new functions...
-
toString() - Method in class neureka.math.FunctionCache
+
toString() - Method in class neureka.math.FunctionCache
 
-
toString() - Method in class neureka.math.Functions
+
toString() - Method in class neureka.math.Functions
 
-
toString() - Method in class neureka.math.implementations.FunctionConstant
+
toString() - Method in class neureka.math.implementations.FunctionConstant
 
-
toString() - Method in class neureka.math.implementations.FunctionInput
+
toString() - Method in class neureka.math.implementations.FunctionInput
 
-
toString() - Method in class neureka.math.implementations.FunctionNode
+
toString() - Method in class neureka.math.implementations.FunctionNode
 
-
toString() - Method in class neureka.math.implementations.FunctionVariable
+
toString() - Method in class neureka.math.implementations.FunctionVariable
 
-
toString() - Method in interface neureka.Nda
+
toString(NDPrintSettings) - Method in interface neureka.Nda
-
This method returns a String representation of this nd-array.
+
Use this to turn this nd-array into a String instance based on the provided + NDPrintSettings instance, which allows you to configure things + like the number of chars per entry, delimiters, the number of items per line, etc.
-
toString() - Method in class neureka.ndim.config.AbstractNDC
-
 
-
toString() - Method in class neureka.Neureka.Settings.AutoGrad
-
 
-
toString() - Method in class neureka.Neureka.Settings.Debug
+
toString(Consumer<NDPrintSettings>) - Method in interface neureka.Nda
+
+
This allows you to provide a lambda which configures how this nd-array should be + converted to String instances.
+
+
toString() - Method in interface neureka.Nda
+
+
This method returns a String representation of this nd-array.
+
+
toString() - Method in class neureka.ndim.config.AbstractNDC
 
-
toString() - Method in class neureka.Neureka.Settings.DType
+
toString() - Method in class neureka.Neureka.Settings.AutoGrad
 
-
toString() - Method in class neureka.Neureka.Settings.NDim
+
toString() - Method in class neureka.Neureka.Settings.Debug
 
-
toString() - Method in class neureka.Neureka.Settings
+
toString() - Method in class neureka.Neureka.Settings.DType
 
-
toString() - Method in class neureka.Neureka.Settings.View
+
toString() - Method in class neureka.Neureka.Settings.NDim
 
-
toString() - Method in class neureka.Neureka
+
toString() - Method in class neureka.Neureka.Settings
 
-
toString() - Method in class neureka.view.NdaAsString
+
toString() - Method in class neureka.Neureka.Settings.View
 
-
toString(String) - Method in interface neureka.Tensor
+
toString() - Method in class neureka.Neureka
 
-
toString(Consumer<NDPrintSettings>) - Method in interface neureka.Nda
-
-
This allows you to provide a lambda which configures how this nd-array should be - converted to String instances.
-
-
toString(Consumer<NDPrintSettings>) - Method in interface neureka.Tensor
-
-
This allows you to provide a lambda which configures how this nd-array should be - converted to String instances.
-
-
toString(GraphNode.Print) - Method in class neureka.autograd.GraphNode
+
toString(String) - Method in interface neureka.Tensor
 
-
toString(NDPrintSettings) - Method in interface neureka.Nda
+
toString(NDPrintSettings) - Method in interface neureka.Tensor
-
Use this to turn this nd-array into a String instance based on the provided +
Use this to turn this nd-array into a String instance based on the provided NDPrintSettings instance, which allows you to configure things like the number of chars per entry, delimiters, the number of items per line, etc.
-
toString(NDPrintSettings) - Method in interface neureka.Tensor
+
toString(Consumer<NDPrintSettings>) - Method in interface neureka.Tensor
-
Use this to turn this nd-array into a String instance based on the provided - NDPrintSettings instance, which allows you to configure things - like the number of chars per entry, delimiters, the number of items per line, etc.
+
This allows you to provide a lambda which configures how this nd-array should be + converted to String instances.
-
toTarget(HolderType) - Method in interface neureka.dtype.NumericType
+
toString() - Method in class neureka.view.NdaAsString
 
-
toTarget(Byte) - Method in class neureka.dtype.custom.I8
+
toTarget(Float) - Method in class neureka.dtype.custom.F32
 
-
toTarget(Byte) - Method in class neureka.dtype.custom.UI8
+
toTarget(Double) - Method in class neureka.dtype.custom.F64
 
-
toTarget(Double) - Method in class neureka.dtype.custom.F64
+
toTarget(Short) - Method in class neureka.dtype.custom.I16
 
-
toTarget(Float) - Method in class neureka.dtype.custom.F32
+
toTarget(Integer) - Method in class neureka.dtype.custom.I32
 
-
toTarget(Integer) - Method in class neureka.dtype.custom.I32
+
toTarget(Long) - Method in class neureka.dtype.custom.I64
 
-
toTarget(Integer) - Method in class neureka.dtype.custom.UI32
+
toTarget(Byte) - Method in class neureka.dtype.custom.I8
 
-
toTarget(Long) - Method in class neureka.dtype.custom.I64
+
toTarget(Short) - Method in class neureka.dtype.custom.UI16
 
-
toTarget(Long) - Method in class neureka.dtype.custom.UI64
+
toTarget(Integer) - Method in class neureka.dtype.custom.UI32
 
-
toTarget(Short) - Method in class neureka.dtype.custom.I16
+
toTarget(Long) - Method in class neureka.dtype.custom.UI64
 
-
toTarget(Short) - Method in class neureka.dtype.custom.UI16
+
toTarget(Byte) - Method in class neureka.dtype.custom.UI8
 
-
toType(Class<V>) - Method in interface neureka.MutateNda
+
toTarget(HolderType) - Method in interface neureka.dtype.NumericType
+
 
+
toType(Class<V>) - Method in interface neureka.MutateNda
This method is an inline operation which changes the underlying data of this tensor.
-
toType(Class<V>) - Method in interface neureka.MutateTensor
+
toType(Class<V>) - Method in interface neureka.MutateTensor
This method is an inline operation which changes the underlying data of this tensor.
-
transpose(int, int) - Method in interface neureka.Nda
+
transpose(Tensor<T>) - Static method in class neureka.backend.main.algorithms.Util
+
 
+
transpose(int, int) - Method in interface neureka.Nda
Returns a view of the original tensor input the targeted axes are swapped / transposed.
-
transpose(int, int) - Method in interface neureka.Tensor
+
transpose(int, int) - Method in interface neureka.Tensor
Returns a view of the original tensor input the targeted axes are swapped / transposed.
-
transpose(Tensor<T>) - Static method in class neureka.backend.main.algorithms.Util
-
 
-
transpose2D() - Method in class neureka.math.Functions
-
 
-
TRUE - Enum constant in enum class neureka.ndim.iterator.NDIterator.NonVirtual
+
transpose2D() - Method in class neureka.math.Functions
 
-
tryGroovyClosureOn(Object, Object) - Static method in class neureka.common.utility.SettingsLoader
+
tryGroovyClosureOn(Object, Object) - Static method in class neureka.common.utility.SettingsLoader
This method makes it possible to configure the library via a Groovy DSL!
-
tryGroovyScriptsOn(Neureka, Consumer<String>) - Static method in class neureka.common.utility.SettingsLoader
+
tryGroovyScriptsOn(Neureka, Consumer<String>) - Static method in class neureka.common.utility.SettingsLoader
 
-
TWO - Enum constant in enum class neureka.devices.host.concurrent.Parallelism
-
-
2
-
-
type() - Method in class neureka.autograd.GraphNode
+
type() - Method in class neureka.autograd.GraphNode
 
-
type() - Method in interface neureka.common.composition.Component.OwnerChangeRequest
+
TYPE - Static variable in class neureka.backend.main.implementations.broadcast.CLScalarBroadcast
+
 
+
type() - Method in interface neureka.common.composition.Component.OwnerChangeRequest
This method will return one of the following states: Component.IsBeing.ADDED, Component.IsBeing.REMOVED, Component.IsBeing.REPLACED, Component.IsBeing.UPDATED
-
type() - Method in class neureka.devices.opencl.OpenCLDevice
-
 
-
type() - Method in class neureka.devices.ReferenceCounter.ChangeEvent
+
type() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
TYPE - Static variable in class neureka.backend.main.implementations.broadcast.CLScalarBroadcast
+
type() - Method in class neureka.devices.ReferenceCounter.ChangeEvent
 
-
typeClassImplements(Class<?>) - Method in class neureka.dtype.DataType
+
typeClassImplements(Class<?>) - Method in class neureka.dtype.DataType
 
-

U

-
-
UBUNTU - Enum constant in enum class neureka.devices.opencl.utility.Messages.Tips
+ + + +

U

+
+
UI16 - Class in neureka.dtype.custom
 
-
UI16 - Class in neureka.dtype.custom
+
UI16() - Constructor for class neureka.dtype.custom.UI16
 
-
UI16() - Constructor for class neureka.dtype.custom.UI16
+
UI32 - Class in neureka.dtype.custom
 
-
UI32 - Class in neureka.dtype.custom
+
UI32() - Constructor for class neureka.dtype.custom.UI32
 
-
UI32() - Constructor for class neureka.dtype.custom.UI32
+
UI64 - Class in neureka.dtype.custom
 
-
UI64 - Class in neureka.dtype.custom
+
UI64() - Constructor for class neureka.dtype.custom.UI64
 
-
UI64() - Constructor for class neureka.dtype.custom.UI64
+
UI8 - Class in neureka.dtype.custom
 
-
UI8 - Class in neureka.dtype.custom
+
UI8() - Constructor for class neureka.dtype.custom.UI8
 
-
UI8() - Constructor for class neureka.dtype.custom.UI8
-
 
-
units - Variable in class neureka.devices.host.machine.CommonMachine
+
units - Variable in class neureka.devices.host.machine.CommonMachine
The number of top level (L3 or L2) cache units.
-
UNITS - Enum constant in enum class neureka.devices.host.concurrent.Parallelism
-
-
The number of top level (L2 or L3) cache units
-
-
UNKNOWN - Enum constant in enum class neureka.devices.opencl.OpenCLDevice.Type
-
 
-
UNKNOWN - Enum constant in enum class neureka.devices.opencl.utility.Messages.Tips
+
unpackAndCorrect(String) - Static method in class neureka.math.parsing.ParseUtil
 
-
unpackAndCorrect(String) - Static method in class neureka.math.parsing.ParseUtil
+
UNSUITABLE - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
 
-
UNSPECIFIC - Enum constant in enum class neureka.ndim.config.NDConfiguration.Layout
-
 
-
UNSUITABLE - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
-
 
-
upcast(Class<U>) - Method in interface neureka.MutateTensor
+
upcast(Class<U>) - Method in interface neureka.MutateTensor
Use this to do a runtime checked upcast of the type parameter of the tensor.
-
update(Component.OwnerChangeRequest<Extensions>) - Method in class neureka.backend.ocl.CLBackend
+
update(Component.OwnerChangeRequest<Tensor<V>>) - Method in class neureka.autograd.GraphNode
+
 
+
update(Component.OwnerChangeRequest<Extensions>) - Method in class neureka.backend.ocl.CLBackend
Updating the CLContext will cause the list of existing OpenCLPlatform instances to be cleared and refilled with completely new OpenCLPlatform instances.
-
update(Component.OwnerChangeRequest<Args>) - Method in class neureka.math.args.Arg
-
 
-
update(Component.OwnerChangeRequest<Tensor<Number>>) - Method in class neureka.devices.opencl.OpenCLDevice
-
 
-
update(Component.OwnerChangeRequest<Tensor<Object>>) - Method in class neureka.devices.file.FileDevice
-
 
-
update(Component.OwnerChangeRequest<Tensor<Object>>) - Method in class neureka.devices.host.CPU
+
update(Component.OwnerChangeRequest<O>) - Method in interface neureka.common.composition.Component
-
This method is part of the component system built into the Tensor class.
+
Components are not the slaves of their owners.
-
update(Component.OwnerChangeRequest<Tensor<V>>) - Method in class neureka.autograd.GraphNode
-
 
-
update(Component.OwnerChangeRequest<Tensor<V>>) - Method in class neureka.devices.AbstractDevice
+
update(Component.OwnerChangeRequest<Tensor<V>>) - Method in class neureka.devices.AbstractDevice
A Device is a component of a tensor.
-
update(Component.OwnerChangeRequest<Tensor<V>>) - Method in class neureka.framing.NDFrame
+
update(Component.OwnerChangeRequest<Tensor<Object>>) - Method in class neureka.devices.file.FileDevice
+
 
+
update(Component.OwnerChangeRequest<Tensor<Object>>) - Method in class neureka.devices.host.CPU
+
+
This method is part of the component system built into the Tensor class.
+
+
update(Component.OwnerChangeRequest<Tensor<Number>>) - Method in class neureka.devices.opencl.OpenCLDevice
+
 
+
update(Component.OwnerChangeRequest<Tensor<V>>) - Method in class neureka.framing.NDFrame
 
-
update(Component.OwnerChangeRequest<Tensor<V>>) - Method in class neureka.framing.Relation
+
update(Component.OwnerChangeRequest<Tensor<V>>) - Method in class neureka.framing.Relation
 
-
update(Component.OwnerChangeRequest<Tensor<V>>) - Method in interface neureka.Tensor
+
update(Component.OwnerChangeRequest<Args>) - Method in class neureka.math.args.Arg
+
 
+
update(Component.OwnerChangeRequest<Tensor<V>>) - Method in interface neureka.Tensor
Important : Components of type Tensor are simply gradients! Currently, this method is used only to catch illegal arguments which is for example the case when trying to attach a gradient with a different shape...
-
update(Component.OwnerChangeRequest<O>) - Method in interface neureka.common.composition.Component
-
-
Components are not the slaves of their owners.
-
-
UPDATED - Enum constant in enum class neureka.common.composition.Component.IsBeing
-
 
-
usages() - Method in interface neureka.Data
+
usages() - Method in interface neureka.Data
This method returns the number of times this data object is currently in use by a nd-array, meaning that the number of usages is also the number of nd-arrays which are currently referencing this data object.
-
usages() - Method in class neureka.devices.AbstractDeviceData
+
usages() - Method in class neureka.devices.AbstractDeviceData
 
-
usesAD() - Method in class neureka.autograd.GraphNode
+
usesAD() - Method in class neureka.autograd.GraphNode
This gradient node is involved in auto-differentiation.
-
usesForwardAD() - Method in class neureka.autograd.GraphNode
+
usesForwardAD() - Method in class neureka.autograd.GraphNode
This node propagates forward.
-
usesReverseAD() - Method in class neureka.autograd.GraphNode
+
usesReverseAD() - Method in class neureka.autograd.GraphNode
This node propagates _backward.
-
Util - Class in neureka.backend.main.algorithms
+
Util - Class in neureka.backend.main.algorithms
 
-
Util() - Constructor for class neureka.backend.main.algorithms.Util
+
Util() - Constructor for class neureka.backend.main.algorithms.Util
 
-
Util() - Constructor for class neureka.view.NdaAsString.Util
+
Util() - Constructor for class neureka.view.NdaAsString.Util
 
-
utility() - Method in class neureka.Neureka
+
Utility() - Constructor for class neureka.common.utility.DataConverter.Utility
 
-
Utility() - Constructor for class neureka.common.utility.DataConverter.Utility
+
Utility() - Constructor for class neureka.ndim.config.NDConfiguration.Utility
 
-
Utility() - Constructor for class neureka.ndim.config.NDConfiguration.Utility
+
utility() - Method in class neureka.Neureka
 
-
Utility() - Constructor for class neureka.Neureka.Utility
+
Utility() - Constructor for class neureka.Neureka.Utility
 
-

V

-
-
validate() - Method in class neureka.backend.api.Call
+ + + +

V

+
+
validate() - Method in class neureka.backend.api.Call
 
-
Validator() - Constructor for class neureka.backend.api.Call.Validator
+
Validator() - Constructor for class neureka.backend.api.Call.Validator
 
-
valOf(Class<T>) - Method in class neureka.math.args.Args
+
valOf(Class<T>) - Method in class neureka.math.args.Args
 
-
valOfOr(Class<T>, V) - Method in class neureka.math.args.Args
+
valOfOr(Class<T>, V) - Method in class neureka.math.args.Args
 
-
value() - Method in class neureka.math.implementations.FunctionConstant
+
value() - Method in class neureka.math.implementations.FunctionConstant
 
-
valueOf(String) - Static method in enum class neureka.autograd.GraphNode.Print
+
valueOf(String) - Static method in enum neureka.autograd.GraphNode.Print
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.backend.api.AutoDiffMode
+
valueOf(String) - Static method in enum neureka.backend.api.AutoDiffMode
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.backend.main.operations.linear.internal.opencl.CLReduce.Type
+
valueOf(String) - Static method in enum neureka.backend.main.operations.linear.internal.opencl.CLReduce.Type
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.backend.main.operations.other.internal.CPUReduce.Type
+
valueOf(String) - Static method in enum neureka.backend.main.operations.other.internal.CPUReduce.Type
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.common.composition.Component.IsBeing
+
valueOf(String) - Static method in enum neureka.common.composition.Component.IsBeing
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.devices.host.concurrent.Parallelism
+
valueOf(String) - Static method in enum neureka.devices.host.concurrent.Parallelism
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.devices.opencl.OpenCLDevice.Type
+
valueOf(String) - Static method in enum neureka.devices.opencl.OpenCLDevice.Type
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.devices.opencl.utility.Messages.Tips
+
valueOf(String) - Static method in enum neureka.devices.opencl.utility.Messages.Tips
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.devices.ReferenceCounter.ChangeType
+
valueOf(String) - Static method in enum neureka.devices.ReferenceCounter.ChangeType
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.ndim.config.NDConfiguration.Layout
+
valueOf(String) - Static method in enum neureka.ndim.config.NDConfiguration.Layout
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.ndim.config.NDTrait
+
valueOf(String) - Static method in enum neureka.ndim.config.NDTrait
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.ndim.iterator.NDIterator.NonVirtual
+
valueOf(String) - Static method in enum neureka.ndim.iterator.NDIterator.NonVirtual
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
valueOf(String) - Static method in enum class neureka.Tensor.ImageType
+
valueOf(String) - Static method in enum neureka.Tensor.ImageType
-
Returns the enum constant of this class with the specified name.
+
Returns the enum constant of this type with the specified name.
-
values() - Static method in enum class neureka.autograd.GraphNode.Print
+
values() - Static method in enum neureka.autograd.GraphNode.Print
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.backend.api.AutoDiffMode
+
values() - Static method in enum neureka.backend.api.AutoDiffMode
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.backend.main.operations.linear.internal.opencl.CLReduce.Type
+
values() - Static method in enum neureka.backend.main.operations.linear.internal.opencl.CLReduce.Type
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.backend.main.operations.other.internal.CPUReduce.Type
+
values() - Static method in enum neureka.backend.main.operations.other.internal.CPUReduce.Type
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.common.composition.Component.IsBeing
+
values() - Static method in enum neureka.common.composition.Component.IsBeing
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.devices.host.concurrent.Parallelism
+
values() - Static method in enum neureka.devices.host.concurrent.Parallelism
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.devices.opencl.OpenCLDevice.Type
+
values() - Static method in enum neureka.devices.opencl.OpenCLDevice.Type
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.devices.opencl.utility.Messages.Tips
+
values() - Static method in enum neureka.devices.opencl.utility.Messages.Tips
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.devices.ReferenceCounter.ChangeType
+
values() - Static method in enum neureka.devices.ReferenceCounter.ChangeType
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.ndim.config.NDConfiguration.Layout
+
values() - Static method in enum neureka.ndim.config.NDConfiguration.Layout
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.ndim.config.NDTrait
+
values() - Static method in enum neureka.ndim.config.NDTrait
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.ndim.iterator.NDIterator.NonVirtual
+
values() - Static method in enum neureka.ndim.iterator.NDIterator.NonVirtual
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
values() - Static method in enum class neureka.Tensor.ImageType
+
values() - Static method in enum neureka.Tensor.ImageType
-
Returns an array containing the constants of this enum class, in +
Returns an array containing the constants of this enum type, in the order they are declared.
-
vector(Iterable<V>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
+
vector(Object[]) - Method in class neureka.fluent.building.NdaBuilder
+
 
+
vector(V...) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
This method creates and returns a vector Tensor instance which wraps the provided values.
-
vector(Iterable<V>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
+
vector(List<V>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
This method creates and returns a vector Tensor instance which wraps the provided values.
-
vector(Object[]) - Method in class neureka.fluent.building.NdaBuilder
-
 
-
vector(List<V>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
+
vector(Iterable<V>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
This method creates and returns a vector Tensor instance which wraps the provided values.
-
vector(List<V>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
+
vector(V...) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
This method creates and returns a vector Tensor instance which wraps the provided values.
-
vector(V...) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
+
vector(List<V>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
This method creates and returns a vector Tensor instance which wraps the provided values.
-
vector(V...) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
+
vector(Iterable<V>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
This method creates and returns a vector Tensor instance which wraps the provided values.
-
vendor() - Method in class neureka.devices.opencl.OpenCLDevice
+
vendor() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
version() - Method in class neureka.devices.opencl.OpenCLDevice
+
version() - Method in class neureka.devices.opencl.OpenCLDevice
 
-
version() - Static method in class neureka.Neureka
+
version() - Static method in class neureka.Neureka
 
-
VERY_GOOD - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
+
VERY_GOOD - Static variable in interface neureka.backend.api.fun.SuitabilityPredicate
 
-
view() - Method in class neureka.Neureka.Settings
+
view() - Method in class neureka.Neureka.Settings
 
-
view(Object) - Method in class neureka.Neureka.Settings
+
view(Object) - Method in class neureka.Neureka.Settings
 
-
virtualize() - Method in interface neureka.devices.Device.Access
+
virtualize() - Method in interface neureka.devices.Device.Access
 
-
virtualize() - Method in class neureka.devices.host.machine.Hardware
+
virtualize() - Method in class neureka.devices.host.machine.Hardware
 
-
VirtualNDConfiguration - Class in neureka.ndim.config.types.views.virtual
+
VirtualNDConfiguration - Class in neureka.ndim.config.types.views.virtual
VirtualNDConfigurations represent tensors which are filled homogeneously with a single value exclusively, like for example a tensor filled with only zeros.
-
VirtualNDIterator - Class in neureka.ndim.iterator.types.virtual
+
VirtualNDIterator - Class in neureka.ndim.iterator.types.virtual
 
-
VirtualNDIterator(VirtualNDConfiguration) - Constructor for class neureka.ndim.iterator.types.virtual.VirtualNDIterator
+
VirtualNDIterator(VirtualNDConfiguration) - Constructor for class neureka.ndim.iterator.types.virtual.VirtualNDIterator
 
-

W

-
-
WINDOWS - Enum constant in enum class neureka.devices.opencl.utility.Messages.Tips
+ + + +

W

+
+
with(Tensor<N>...) - Method in class neureka.backend.api.Call.Builder
+
 
+
with(F) - Method in interface neureka.backend.main.algorithms.internal.WithForward
 
-
with(F) - Method in interface neureka.backend.main.algorithms.internal.WithForward
+
With<ValueType,TargetType> - Interface in neureka.framing.fluent
 
-
with(String) - Method in class neureka.view.NDPrintSettings
+
with(ValueType) - Method in interface neureka.framing.fluent.With
 
-
with(Arg<?>...) - Method in interface neureka.math.Function
+
with(Arg<?>...) - Method in interface neureka.math.Function
Use this to call this Function alongside with some additional meta-arguments which will be passed to the underlying Operation(s).
-
with(Args) - Method in interface neureka.math.Function
+
with(Args) - Method in interface neureka.math.Function
Use this to call this Function alongside with some additional meta-arguments which will be passed to the underlying Operation(s).
-
with(Tensor<N>...) - Method in class neureka.backend.api.Call.Builder
-
 
-
with(NDPrintSettings) - Method in class neureka.view.NDPrintSettings
-
 
-
with(ValueType) - Method in interface neureka.framing.fluent.With
+
with(NDPrintSettings) - Method in class neureka.view.NDPrintSettings
 
-
With<ValueType,TargetType> - Interface in neureka.framing.fluent
+
with(String) - Method in class neureka.view.NDPrintSettings
 
-
withADAction(ADAction) - Method in class neureka.backend.api.Result
+
withADAction(ADAction) - Method in class neureka.backend.api.Result
 
-
withAddedInputAt(int, Tensor<?>) - Method in class neureka.backend.api.ExecutionCall
+
withAddedInputAt(int, Tensor<?>) - Method in class neureka.backend.api.ExecutionCall
 
-
withArgs(Arg<?>...) - Method in class neureka.backend.api.ExecutionCall
+
withArgs(Arg<?>...) - Method in class neureka.backend.api.ExecutionCall
Use this to produce a clone with a new set of meta arguments.
-
withArity(int) - Static method in class neureka.backend.main.implementations.CPUImplementation
+
withArity(int) - Static method in class neureka.backend.main.implementations.CPUImplementation
 
-
withAutoDiff(ADActionSupplier) - Method in class neureka.backend.api.Result
+
withAutoDiff(ADActionSupplier) - Method in class neureka.backend.api.Result
 
-
withAxesLabels(List<List<Object>>) - Method in class neureka.framing.NDFrame
+
withAxesLabels(List<List<Object>>) - Method in class neureka.framing.NDFrame
 
-
withConfig(String) - Method in interface neureka.view.NdaAsString.Builder
+
withConfig(NDPrintSettings) - Method in interface neureka.view.NdaAsString.Builder
 
-
withConfig(NDPrintSettings) - Method in interface neureka.view.NdaAsString.Builder
+
withConfig(String) - Method in interface neureka.view.NdaAsString.Builder
 
-
withDecayRate(double) - Method in class neureka.optimization.implementations.MomentumFactory
+
withDecayRate(double) - Method in class neureka.optimization.implementations.MomentumFactory
 
-
withDecayRate(double) - Method in class neureka.optimization.implementations.RMSPropFactory
+
withDecayRate(double) - Method in class neureka.optimization.implementations.RMSPropFactory
 
-
WithForward<F> - Interface in neureka.backend.main.algorithms.internal
+
WithForward<F> - Interface in neureka.backend.main.algorithms.internal
 
-
withInputAt(int, Tensor<?>) - Method in class neureka.backend.api.ExecutionCall
+
withInputAt(int, Tensor<?>) - Method in class neureka.backend.api.ExecutionCall
 
-
withInputs(Tensor<?>...) - Method in class neureka.backend.api.ExecutionCall
+
withInputs(Tensor<?>...) - Method in class neureka.backend.api.ExecutionCall
Use this to produce a clone with a new array of input tensors.
-
withLabel(String) - Method in class neureka.framing.NDFrame
+
withLabel(String) - Method in class neureka.framing.NDFrame
 
-
withLabel(String) - Method in interface neureka.Nda
+
withLabel(String) - Method in interface neureka.Nda
 
-
withLabel(String) - Method in interface neureka.Tensor
-
withLabels(String[]...) - Method in interface neureka.Nda
+
withLabel(String) - Method in interface neureka.Tensor
+
withLabels(String[]...) - Method in interface neureka.Nda
-
This method receives a nested String array which +
This method receives a nested String array which ought to contain a label for the index of this nd-array.
-
withLabels(String[]...) - Method in interface neureka.Tensor
+
withLabels(List<List<Object>>) - Method in interface neureka.Nda
-
This method receives a nested String array which +
This method receives a nested String list which ought to contain a label for the index of this nd-array.
-
withLabels(List<List<Object>>) - Method in interface neureka.Nda
+
withLabels(Map<Object, List<Object>>) - Method in interface neureka.Nda
-
This method receives a nested String list which - ought to contain a label for the index of this nd-array.
+
This method provides the ability to + label not only the indices of the shape of this nd-array, but also + the dimension of the shape.
-
withLabels(List<List<Object>>) - Method in interface neureka.Tensor
+
withLabels(String[]...) - Method in interface neureka.Tensor
-
This method receives a nested String list which +
This method receives a nested String array which ought to contain a label for the index of this nd-array.
-
withLabels(Map<Object, List<Object>>) - Method in interface neureka.Nda
+
withLabels(List<List<Object>>) - Method in interface neureka.Tensor
-
This method provides the ability to - label not only the indices of the shape of this nd-array, but also - the dimension of the shape.
+
This method receives a nested String list which + ought to contain a label for the index of this nd-array.
-
withLabels(Map<Object, List<Object>>) - Method in interface neureka.Tensor
+
withLabels(Map<Object, List<Object>>) - Method in interface neureka.Tensor
This method provides the ability to label not only the indices of the shape of this nd-array, but also the dimension of the shape.
-
withLearningRate(double) - Method in class neureka.optimization.implementations.AdaGradFactory
+
withLearningRate(double) - Method in class neureka.optimization.implementations.AdaGradFactory
 
-
withLearningRate(double) - Method in class neureka.optimization.implementations.ADAMFactory
+
withLearningRate(double) - Method in class neureka.optimization.implementations.ADAMFactory
 
-
withLearningRate(double) - Method in class neureka.optimization.implementations.MomentumFactory
+
withLearningRate(double) - Method in class neureka.optimization.implementations.MomentumFactory
 
-
withLearningRate(double) - Method in class neureka.optimization.implementations.RMSPropFactory
+
withLearningRate(double) - Method in class neureka.optimization.implementations.RMSPropFactory
 
-
withLearningRate(double) - Method in class neureka.optimization.implementations.SGDFactory
+
withLearningRate(double) - Method in class neureka.optimization.implementations.SGDFactory
 
-
withName(String) - Static method in interface neureka.backend.api.Algorithm
+
withName(String) - Static method in interface neureka.backend.api.Algorithm
This is a factory method for creating a new instance of this FunAlgorithm class.
-
withName(String) - Static method in interface neureka.backend.api.DeviceAlgorithm
+
withName(String) - Static method in interface neureka.backend.api.DeviceAlgorithm
This is a factory method for creating a new instance of this FunDeviceAlgorithm class.
-
withOperation(Operation) - Method in class neureka.backend.api.ExecutionCall
+
withOperation(Operation) - Method in class neureka.backend.api.ExecutionCall
 
-
withRemovedInputAt(int) - Method in class neureka.backend.api.ExecutionCall
+
withRemovedInputAt(int) - Method in class neureka.backend.api.ExecutionCall
 
-
withShape(int...) - Method in class neureka.fluent.building.NdaBuilder
+
withShape(int...) - Method in class neureka.fluent.building.NdaBuilder
 
-
withShape(int...) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
+
withShape(int...) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
Define a tensor shape by passing an array of int values to this method, which represent the shape of the Tensor that should be built.
-
withShape(int...) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
+
withShape(List<N>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
-
Define a tensor shape by passing an array of int values to this method, +
Define a tensor shape by passing a list of numbers to this method, which represent the shape of the Tensor that should be built.
-
withShape(List<N>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVector
+
withShape(int...) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
-
Define a tensor shape by passing a list of numbers to this method, +
Define a tensor shape by passing an array of int values to this method, which represent the shape of the Tensor that should be built.
-
withShape(List<N>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
+
withShape(List<N>) - Method in interface neureka.fluent.building.states.WithShapeOrScalarOrVectorTensor
Define a tensor shape by passing a list of numbers to this method, which represent the shape of the Tensor that should be built.
-
WithShapeOrScalarOrVector<V> - Interface in neureka.fluent.building.states
+
WithShapeOrScalarOrVector<V> - Interface in neureka.fluent.building.states
 
-
WithShapeOrScalarOrVectorOnDevice<V> - Interface in neureka.fluent.building.states
+
WithShapeOrScalarOrVectorOnDevice<V> - Interface in neureka.fluent.building.states
 
-
WithShapeOrScalarOrVectorTensor<V> - Interface in neureka.fluent.building.states
+
WithShapeOrScalarOrVectorTensor<V> - Interface in neureka.fluent.building.states
 
-
withTime(long) - Method in class neureka.optimization.implementations.ADAMFactory
+
withTime(long) - Method in class neureka.optimization.implementations.ADAMFactory
 
-
WorkScheduler - Class in neureka.devices.host.concurrent
+
WorkScheduler - Class in neureka.devices.host.concurrent
An API for registering workloads which will be divided into smaller workloads so that they can be executed efficiently by a thread pool...
-
WorkScheduler() - Constructor for class neureka.devices.host.concurrent.WorkScheduler
+
WorkScheduler() - Constructor for class neureka.devices.host.concurrent.WorkScheduler
 
-
WorkScheduler.Divider - Class in neureka.devices.host.concurrent
+
WorkScheduler.Divider - Class in neureka.devices.host.concurrent
Divides workloads until they can be processed efficiently and then submits them to a thread pool for execution...
-
write(V) - Method in interface neureka.devices.Device.Access
+
write(V) - Method in interface neureka.devices.Device.Access
Use this to write a single scalar item into the accessed tensor at one or more positions within the tensor.
-
writeDataTo(DataOutput, Iterator<Byte>) - Method in class neureka.dtype.custom.I8
-
 
-
writeDataTo(DataOutput, Iterator<TargetType>) - Method in interface neureka.dtype.NumericType
+
writeDataTo(DataOutput, Iterator<TargetType>) - Method in interface neureka.dtype.NumericType
This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
-
writeFrom(Object) - Method in interface neureka.devices.Device.Access
+
writeFrom(Object, int) - Method in interface neureka.devices.Device.Access
-
Use this method to write data to the provided tensor, given that - the tensor is already stored on this device!

+
Use this to write data from an array into the accessed tensor.
-
writeFrom(Object, int) - Method in interface neureka.devices.Device.Access
+
writeFrom(Object) - Method in interface neureka.devices.Device.Access
-
Use this to write data from an array into the accessed tensor.
+
Use this method to write data to the provided tensor, given that + the tensor is already stored on this device!

-

X

-
-
XConvLeft - Class in neureka.backend.main.operations.linear
+ + + +

X

+
+
XConvLeft - Class in neureka.backend.main.operations.linear
 
-
XConvLeft() - Constructor for class neureka.backend.main.operations.linear.XConvLeft
+
XConvLeft() - Constructor for class neureka.backend.main.operations.linear.XConvLeft
 
-
XConvRight - Class in neureka.backend.main.operations.linear
+
XConvRight - Class in neureka.backend.main.operations.linear
 
-
XConvRight() - Constructor for class neureka.backend.main.operations.linear.XConvRight
+
XConvRight() - Constructor for class neureka.backend.main.operations.linear.XConvRight
 
-
xor(double) - Method in interface neureka.Tensor
+
xor(Tensor<V>) - Method in interface neureka.Tensor
-
This method is a functionally identical synonym to the Tensor.power(Tensor) method.
+
This method is a functionally identical synonym to the Tensor.power(Tensor) method.
-
xor(Tensor<V>) - Method in interface neureka.Tensor
+
xor(double) - Method in interface neureka.Tensor
-
This method is a functionally identical synonym to the Tensor.power(Tensor) method.
+
This method is a functionally identical synonym to the Tensor.power(Tensor) method.
-

_

-
-
_actualize(Tensor<?>) - Method in class neureka.devices.AbstractDevice
+ + + +

_

+
+
_actualize(Tensor<?>) - Method in class neureka.devices.AbstractDevice
 
-
_actualize(Tensor<?>) - Method in class neureka.devices.host.CPU
+
_actualize(Tensor<?>) - Method in class neureka.devices.host.CPU
 
-
_actualize(Tensor<?>) - Method in class neureka.devices.opencl.OpenCLDevice
+
_actualize(Tensor<?>) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
_approveExecutionOf(Tensor<?>[], int, Operation) - Method in class neureka.devices.AbstractDevice
+
_approveExecutionOf(Tensor<?>[], int, Operation) - Method in class neureka.devices.AbstractDevice
This method is the internal approval routine called by its public counterpart and implemented by classes extending this very abstract class.
-
_approveExecutionOf(Tensor<?>[], int, Operation) - Method in class neureka.devices.host.CPU
+
_approveExecutionOf(Tensor<?>[], int, Operation) - Method in class neureka.devices.host.CPU
 
-
_approveExecutionOf(Tensor<?>[], int, Operation) - Method in class neureka.devices.opencl.OpenCLDevice
+
_approveExecutionOf(Tensor<?>[], int, Operation) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
_arguments - Variable in class neureka.backend.api.Call
+
_arguments - Variable in class neureka.backend.api.Call
Meta arguments which are usually specific to certain operations.
-
_arity - Variable in class neureka.backend.api.template.operations.AbstractOperation
+
_arity - Variable in class neureka.backend.api.template.operations.AbstractOperation
Arity is the number of arguments or operands that this function or operation takes.
-
_cacheArray(int[]) - Static method in class neureka.ndim.config.AbstractNDC
+
_cacheArray(int[]) - Static method in class neureka.ndim.config.AbstractNDC
This method receives an int array and returns an int array which can either be the one provided or an array found in the global int array cache residing inside this class.
-
_cached(T) - Static method in class neureka.ndim.config.AbstractNDC
+
_cached(T) - Static method in class neureka.ndim.config.AbstractNDC
 
-
_cleaning(Object, Runnable) - Method in class neureka.devices.AbstractDevice
+
_cleaning(Object, Runnable) - Method in class neureka.devices.AbstractDevice
 
-
_dataRef - Variable in class neureka.devices.AbstractDeviceData
+
_dataRef - Variable in class neureka.devices.AbstractDeviceData
 
-
_dataType - Variable in class neureka.devices.AbstractDeviceData
+
_dataType - Variable in class neureka.devices.AbstractDeviceData
 
-
_dataTypeOf(Object) - Method in class neureka.devices.AbstractDevice
+
_dataTypeOf(Object) - Method in class neureka.devices.AbstractDevice
 
-
_dataTypeOf(Object) - Method in class neureka.devices.host.CPU
+
_dataTypeOf(Object) - Method in class neureka.devices.host.CPU
 
-
_dataTypeOf(Object) - Method in class neureka.devices.opencl.OpenCLDevice
+
_dataTypeOf(Object) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
_deleteComponents() - Method in class neureka.common.composition.AbstractComponentOwner
+
_deleteComponents() - Method in class neureka.common.composition.AbstractComponentOwner
This method deletes the array of components of this component owner by nulling the array variable field.
-
_device - Variable in class neureka.backend.api.Call
+
_device - Variable in class neureka.backend.api.Call
This field references the device on which this ExecutionCall should be executed.
-
_fileName - Variable in class neureka.devices.file.IDXHandle
-
 
-
_function - Variable in class neureka.backend.api.template.operations.AbstractOperation
+
_function - Variable in class neureka.backend.api.template.operations.AbstractOperation
An operation may have two ways in which it can describe itself as String within a Function AST.
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcast
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcast
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastAddition
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastAddition
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastDivision
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastDivision
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastModulo
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastModulo
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastMultiplication
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastMultiplication
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastPower
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastPower
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSubtraction
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSubtraction
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSummation
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSummation
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalaBroadcastPower
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalaBroadcastPower
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastDivision
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastDivision
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastIdentity
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastIdentity
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastModulo
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastModulo
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastSubtraction
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastSubtraction
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWise
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWise
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseAddition
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseAddition
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseDivision
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseDivision
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseModulo
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseModulo
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseMultiplication
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseMultiplication
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWisePower
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWisePower
 
-
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseSubtraction
+
_getDeriveAt0() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseSubtraction
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcast
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcast
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastAddition
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastAddition
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastDivision
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastDivision
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastModulo
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastModulo
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastMultiplication
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastMultiplication
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastPower
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastPower
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSubtraction
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSubtraction
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSummation
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSummation
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalaBroadcastPower
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalaBroadcastPower
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastDivision
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastDivision
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastIdentity
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastIdentity
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastModulo
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastModulo
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastSubtraction
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastSubtraction
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWise
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWise
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseAddition
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseAddition
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseDivision
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseDivision
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseModulo
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseModulo
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseMultiplication
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseMultiplication
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWisePower
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWisePower
 
-
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseSubtraction
+
_getDeriveAt1() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseSubtraction
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcast
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcast
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastAddition
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastAddition
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastDivision
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastDivision
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastModulo
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastModulo
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastMultiplication
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastMultiplication
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastPower
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastPower
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSubtraction
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSubtraction
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSummation
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUBroadcastSummation
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalaBroadcastPower
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalaBroadcastPower
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastDivision
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastDivision
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastIdentity
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastIdentity
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastModulo
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastModulo
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
 
-
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastSubtraction
+
_getFun() - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcastSubtraction
 
-
_getFun() - Method in class neureka.backend.main.implementations.convolution.AbstractCPUConvolution
+
_getFun() - Method in class neureka.backend.main.implementations.convolution.AbstractCPUConvolution
 
-
_getFun() - Method in class neureka.backend.main.implementations.convolution.CPUConvolution
+
_getFun() - Method in class neureka.backend.main.implementations.convolution.CPUConvolution
 
-
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWise
+
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWise
 
-
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseAddition
+
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseAddition
 
-
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseDivision
+
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseDivision
 
-
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseModulo
+
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseModulo
 
-
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseMultiplication
+
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseMultiplication
 
-
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWisePower
+
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWisePower
 
-
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseSubtraction
+
_getFun() - Method in class neureka.backend.main.implementations.elementwise.CPUBiElementWiseSubtraction
 
-
_implementations - Variable in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
_implementations - Variable in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
_inputs - Variable in class neureka.backend.api.Call
+
_inputs - Variable in class neureka.backend.api.Call
The tensor arguments from which an operation will either read or to which it will write.
-
_isDifferentiable - Variable in class neureka.backend.api.template.operations.AbstractOperation
+
_isDifferentiable - Variable in class neureka.backend.api.template.operations.AbstractOperation
Certain operations are not differentiable, meaning they cannot participate in neither forward nor reverse mode differentiation.
-
_isIndexer - Variable in class neureka.backend.api.template.operations.AbstractOperation
+
_isIndexer - Variable in class neureka.backend.api.template.operations.AbstractOperation
This flag determines if this operation is auto-indexing passed input arguments.
-
_isInline - Variable in class neureka.backend.api.template.operations.AbstractOperation
+
_isInline - Variable in class neureka.backend.api.template.operations.AbstractOperation
Inline operations are operations which change the state of the arguments passed to them.
-
_isOperator - Variable in class neureka.backend.api.template.operations.AbstractOperation
+
_isOperator - Variable in class neureka.backend.api.template.operations.AbstractOperation
 
-
_loadData() - Method in class neureka.devices.file.CSVHandle
+
_loadData() - Method in class neureka.devices.file.CSVHandle
 
-
_loadData() - Method in class neureka.devices.file.IDXHandle
+
_loadData() - Method in class neureka.devices.file.IDXHandle
 
-
_loadFile() - Method in class neureka.devices.file.IDXHandle
+
_log - Variable in class neureka.devices.AbstractDevice
 
-
_loadFileInputStream() - Method in class neureka.devices.file.IDXHandle
+
_numberOfDataObjects - Variable in class neureka.devices.AbstractBaseDevice
 
-
_log - Variable in class neureka.devices.AbstractDevice
+
_numberOfTensors - Variable in class neureka.devices.AbstractBaseDevice
 
-
_LOG - Static variable in class neureka.devices.file.IDXHandle
-
 
-
_numberOfDataObjects - Variable in class neureka.devices.AbstractBaseDevice
-
 
-
_numberOfTensors - Variable in class neureka.devices.AbstractBaseDevice
-
 
-
_operator - Variable in class neureka.backend.api.template.operations.AbstractOperation
+
_operator - Variable in class neureka.backend.api.template.operations.AbstractOperation
An operation may have two ways in which it can describe itself as String within a Function AST.
-
_owner - Variable in class neureka.devices.AbstractDeviceData
+
_owner - Variable in class neureka.devices.AbstractDeviceData
 
-
_prepareForExecution(ExecutionCall<? extends Device<?>>) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
+
_prepareForExecution(ExecutionCall<? extends Device<?>>) - Static method in class neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm
 
-
_readAll(Tensor<T>, boolean) - Method in class neureka.devices.AbstractDevice
+
_readAll(Tensor<T>, boolean) - Method in class neureka.devices.AbstractDevice
 
-
_readAll(Tensor<T>, boolean) - Method in class neureka.devices.host.CPU
+
_readAll(Tensor<T>, boolean) - Method in class neureka.devices.host.CPU
 
-
_readAll(Tensor<T>, boolean) - Method in class neureka.devices.opencl.OpenCLDevice
+
_readAll(Tensor<T>, boolean) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
_readArray(Tensor<T>, Class<A>, int, int) - Method in class neureka.devices.AbstractDevice
+
_readArray(Tensor<T>, Class<A>, int, int) - Method in class neureka.devices.AbstractDevice
 
-
_readArray(Tensor<T>, Class<A>, int, int) - Method in class neureka.devices.host.CPU
+
_readArray(Tensor<T>, Class<A>, int, int) - Method in class neureka.devices.host.CPU
 
-
_readArray(Tensor<T>, Class<A>, int, int) - Method in class neureka.devices.opencl.OpenCLDevice
+
_readArray(Tensor<T>, Class<A>, int, int) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
_readItem(Tensor<T>, int) - Method in class neureka.devices.AbstractDevice
+
_readItem(Tensor<T>, int) - Method in class neureka.devices.AbstractDevice
 
-
_readItem(Tensor<T>, int) - Method in class neureka.devices.host.CPU
+
_readItem(Tensor<T>, int) - Method in class neureka.devices.host.CPU
 
-
_readItem(Tensor<T>, int) - Method in class neureka.devices.opencl.OpenCLDevice
+
_readItem(Tensor<T>, int) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
_refCounter - Variable in class neureka.devices.AbstractDeviceData
+
_refCounter - Variable in class neureka.devices.AbstractDeviceData
 
-
_removeOrReject(T) - Method in class neureka.backend.api.Extensions
+
_removeOrReject(T) - Method in class neureka.backend.api.Extensions
 
-
_removeOrReject(T) - Method in class neureka.common.composition.AbstractComponentOwner
+
_removeOrReject(T) - Method in class neureka.common.composition.AbstractComponentOwner
An implementation of this method checks if the passed component should be removed from the component collection of this class or its removal should be "rejected".
-
_removeOrReject(T) - Method in class neureka.math.args.Args
+
_removeOrReject(T) - Method in class neureka.math.args.Args
 
-
_set(Component<T>) - Method in class neureka.common.composition.AbstractComponentOwner
+
_set(Component<T>) - Method in class neureka.common.composition.AbstractComponentOwner
 
-
_setOrReject(T) - Method in class neureka.backend.api.Extensions
+
_setOrReject(T) - Method in class neureka.backend.api.Extensions
 
-
_setOrReject(T) - Method in class neureka.common.composition.AbstractComponentOwner
+
_setOrReject(T) - Method in class neureka.common.composition.AbstractComponentOwner
This abstract method ought to be implemented further down the inheritance hierarchy where it's responsibility @@ -10282,111 +10211,153 @@

_

should be added or "rejected" to the component collection of this class.
-
_setOrReject(T) - Method in class neureka.math.args.Args
+
_setOrReject(T) - Method in class neureka.math.args.Args
 
-
_shape - Variable in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
+
_shape - Variable in class neureka.ndim.config.types.permuted.Permuted1DConfiguration
The shape of the NDArray.
-
_shape - Variable in class neureka.ndim.config.types.simple.Simple1DConfiguration
+
_shape - Variable in class neureka.ndim.config.types.simple.Simple1DConfiguration
The shape of the NDArray.
-
_shape - Variable in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
+
_shape - Variable in class neureka.ndim.config.types.sliced.Sliced1DConfiguration
The shape of the NDArray.
-
_shape1 - Variable in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
+
_shape1 - Variable in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
The shape of the NDArray.
-
_shape1 - Variable in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
_shape1 - Variable in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
The shape of the NDArray.
-
_shape1 - Variable in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
_shape1 - Variable in class neureka.ndim.config.types.simple.Simple2DConfiguration
The shape of the NDArray.
-
_shape1 - Variable in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
_shape1 - Variable in class neureka.ndim.config.types.simple.Simple3DConfiguration
The shape of the NDArray.
-
_shape1 - Variable in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
_shape1 - Variable in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
The shape of the NDArray.
-
_shape1 - Variable in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
_shape1 - Variable in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
The shape of the NDArray.
-
_shape2 - Variable in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
-
 
-
_shape2 - Variable in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
_shape2 - Variable in class neureka.ndim.config.types.permuted.Permuted2DConfiguration
 
-
_shape2 - Variable in class neureka.ndim.config.types.simple.Simple2DConfiguration
+
_shape2 - Variable in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
 
-
_shape2 - Variable in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
_shape2 - Variable in class neureka.ndim.config.types.simple.Simple2DConfiguration
 
-
_shape2 - Variable in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
+
_shape2 - Variable in class neureka.ndim.config.types.simple.Simple3DConfiguration
 
-
_shape2 - Variable in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
_shape2 - Variable in class neureka.ndim.config.types.sliced.Sliced2DConfiguration
 
-
_shape3 - Variable in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
+
_shape2 - Variable in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
 
-
_shape3 - Variable in class neureka.ndim.config.types.simple.Simple3DConfiguration
+
_shape3 - Variable in class neureka.ndim.config.types.permuted.Permuted3DConfiguration
 
-
_shape3 - Variable in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
+
_shape3 - Variable in class neureka.ndim.config.types.simple.Simple3DConfiguration
 
-
_simpleReshape(int[], NDConfiguration) - Static method in class neureka.ndim.config.AbstractNDC
+
_shape3 - Variable in class neureka.ndim.config.types.sliced.Sliced3DConfiguration
 
-
_size - Variable in class neureka.devices.file.IDXHandle
+
_simpleReshape(int[], NDConfiguration) - Static method in class neureka.ndim.config.AbstractNDC
 
-
_sizeOccupiedBy(Tensor<T>) - Method in class neureka.devices.AbstractDevice
+
_sizeOccupiedBy(Tensor<T>) - Method in class neureka.devices.AbstractDevice
 
-
_sizeOccupiedBy(Tensor<T>) - Method in class neureka.devices.host.CPU
+
_sizeOccupiedBy(Tensor<T>) - Method in class neureka.devices.host.CPU
 
-
_sizeOccupiedBy(Tensor<T>) - Method in class neureka.devices.opencl.OpenCLDevice
+
_sizeOccupiedBy(Tensor<T>) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
_swap(Tensor<T>, Tensor<T>) - Method in class neureka.devices.AbstractDevice
+
_swap(Tensor<T>, Tensor<T>) - Method in class neureka.devices.AbstractDevice
This method is used internally mostly and should not be used in most cases.
-
_swap(Tensor<T>, Tensor<T>) - Method in class neureka.devices.host.CPU
+
_swap(Tensor<T>, Tensor<T>) - Method in class neureka.devices.host.CPU
 
-
_swap(Tensor<T>, Tensor<T>) - Method in class neureka.devices.opencl.OpenCLDevice
+
_swap(Tensor<T>, Tensor<T>) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
_this() - Method in class neureka.common.composition.AbstractComponentOwner
+
_this() - Method in class neureka.common.composition.AbstractComponentOwner
 
-
_transferFrom(AbstractComponentOwner<C>) - Method in class neureka.common.composition.AbstractComponentOwner
+
_transferFrom(AbstractComponentOwner<C>) - Method in class neureka.common.composition.AbstractComponentOwner
A component owner might need to exchange components.
-
_virtualize(Tensor<?>) - Method in class neureka.devices.AbstractDevice
+
_virtualize(Tensor<?>) - Method in class neureka.devices.AbstractDevice
 
-
_virtualize(Tensor<?>) - Method in class neureka.devices.host.CPU
+
_virtualize(Tensor<?>) - Method in class neureka.devices.host.CPU
 
-
_virtualize(Tensor<?>) - Method in class neureka.devices.opencl.OpenCLDevice
+
_virtualize(Tensor<?>) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
_work(int, int) - Method in class neureka.devices.host.concurrent.WorkScheduler
+
_work(int, int) - Method in class neureka.devices.host.concurrent.WorkScheduler
 
-
_workloadFor(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
+
_workloadFor(ExecutionCall<CPU>) - Method in class neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
 
-
_writeArray(Tensor<T>, Object, int, int, int) - Method in class neureka.devices.AbstractDevice
+
_writeArray(Tensor<T>, Object, int, int, int) - Method in class neureka.devices.AbstractDevice
 
-
_writeArray(Tensor<T>, Object, int, int, int) - Method in class neureka.devices.host.CPU
+
_writeArray(Tensor<T>, Object, int, int, int) - Method in class neureka.devices.host.CPU
 
-
_writeArray(Tensor<T>, Object, int, int, int) - Method in class neureka.devices.opencl.OpenCLDevice
+
_writeArray(Tensor<T>, Object, int, int, int) - Method in class neureka.devices.opencl.OpenCLDevice
 
-
_writeItem(Tensor<T>, T, int, int) - Method in class neureka.devices.AbstractDevice
+
_writeItem(Tensor<T>, T, int, int) - Method in class neureka.devices.AbstractDevice
 
-
_writeItem(Tensor<T>, T, int, int) - Method in class neureka.devices.host.CPU
+
_writeItem(Tensor<T>, T, int, int) - Method in class neureka.devices.host.CPU
 
-
_writeItem(Tensor<T>, T, int, int) - Method in class neureka.devices.opencl.OpenCLDevice
+
_writeItem(Tensor<T>, T, int, int) - Method in class neureka.devices.opencl.OpenCLDevice
 
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+ diff --git a/docs/jdocs/index.html b/docs/jdocs/index.html index 263403389..394815a3f 100644 --- a/docs/jdocs/index.html +++ b/docs/jdocs/index.html @@ -1,263 +1,75 @@ - + + - -Overview (neureka 1.0.0 API) - - - - - - - - - - + +neureka 1.0.1 API + - - + + + + + + + <noscript> <div>JavaScript is disabled on your browser.</div> </noscript> -<div class="flex-box"> -<header role="banner" class="flex-header"> -<nav role="navigation"> -<!-- ========= START OF TOP NAVBAR ======= --> -<div class="top-nav" id="navbar-top"> -<div class="skip-nav"><a href="#skip-navbar-top" title="Skip navigation links">Skip navigation links</a></div> -<ul id="navbar-top-firstrow" class="nav-list" title="Navigation"> -<li class="nav-bar-cell1-rev">Overview</li> -<li>Package</li> -<li>Class</li> -<li><a href="overview-tree.html">Tree</a></li> -<li><a href="deprecated-list.html">Deprecated</a></li> -<li><a href="index-all.html">Index</a></li> -<li><a href="help-doc.html#overview">Help</a></li> -</ul> -</div> -<div class="sub-nav"> -<div class="nav-list-search"><label for="search-input">SEARCH:</label> -<input type="text" id="search-input" value="search" disabled="disabled"> -<input type="reset" id="reset-button" value="reset" disabled="disabled"> -</div> -</div> -<!-- ========= END OF TOP NAVBAR ========= --> -<span class="skip-nav" id="skip-navbar-top"></span></nav> -</header> -<div class="flex-content"> -<main role="main"> -<div class="header"> -<h1 class="title">neureka 1.0.0 API</h1> -</div> -<div id="all-packages-table"> -<div class="caption"><span>Packages</span></div> -<div class="summary-table two-column-summary"> -<div class="table-header col-first">Package</div> -<div class="table-header col-last">Description</div> -<div class="col-first even-row-color all-packages-table all-packages-table-tab1"><a href="neureka/package-summary.html">neureka</a></div> -<div class="col-last even-row-color all-packages-table all-packages-table-tab1">&nbsp;</div> -<div class="col-first odd-row-color 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-<div class="col-first odd-row-color all-packages-table all-packages-table-tab1"><a href="neureka/backend/api/template/algorithms/package-summary.html">neureka.backend.api.template.algorithms</a></div> -<div class="col-last odd-row-color all-packages-table all-packages-table-tab1">&nbsp;</div> -<div class="col-first even-row-color all-packages-table all-packages-table-tab1"><a href="neureka/backend/api/template/implementations/package-summary.html">neureka.backend.api.template.implementations</a></div> -<div class="col-last even-row-color all-packages-table all-packages-table-tab1">&nbsp;</div> -<div class="col-first odd-row-color all-packages-table all-packages-table-tab1"><a href="neureka/backend/api/template/operations/package-summary.html">neureka.backend.api.template.operations</a></div> -<div class="col-last odd-row-color all-packages-table all-packages-table-tab1">&nbsp;</div> -<div class="col-first even-row-color all-packages-table all-packages-table-tab1"><a href="neureka/backend/cpu/package-summary.html">neureka.backend.cpu</a></div> -<div class="col-last even-row-color all-packages-table all-packages-table-tab1">&nbsp;</div> -<div class="col-first odd-row-color all-packages-table all-packages-table-tab1"><a href="neureka/backend/main/algorithms/package-summary.html">neureka.backend.main.algorithms</a></div> -<div class="col-last odd-row-color all-packages-table all-packages-table-tab1"> -<div class="block">Everything in this package should be considered library-private! - <b>DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE!</b> - Code inside this package or any sub-packages might change frequently...</div> -</div> -<div class="col-first even-row-color all-packages-table all-packages-table-tab1"><a href="neureka/backend/main/algorithms/internal/package-summary.html">neureka.backend.main.algorithms.internal</a></div> -<div class="col-last even-row-color all-packages-table all-packages-table-tab1">&nbsp;</div> -<div class="col-first odd-row-color all-packages-table all-packages-table-tab1"><a href="neureka/backend/main/implementations/package-summary.html">neureka.backend.main.implementations</a></div> -<div class="col-last odd-row-color all-packages-table all-packages-table-tab1"> -<div class="block">Everything in this package should be considered library-private! - <b>DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE!</b> - Code inside this package or any sub-packages might change frequently...</div> -</div> -<div class="col-first even-row-color all-packages-table all-packages-table-tab1"><a href="neureka/backend/main/implementations/broadcast/package-summary.html">neureka.backend.main.implementations.broadcast</a></div> -<div class="col-last even-row-color all-packages-table all-packages-table-tab1">&nbsp;</div> -<div class="col-first odd-row-color all-packages-table all-packages-table-tab1"><a 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Oracle designates this - * particular file as subject to the "Classpath" exception as provided - * by Oracle in the LICENSE file that accompanied this code. - * - * This code is distributed in the hope that it will be useful, but WITHOUT - * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or - * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License - * version 2 for more details (a copy is included in the LICENSE file that - * accompanied this code). - * - * You should have received a copy of the GNU General Public License version - * 2 along with this work; if not, write to the Free Software Foundation, - * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. - * - * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA - * or visit www.oracle.com if you need additional information or have any - * questions. - */ - -.ui-state-active, -.ui-widget-content .ui-state-active, -.ui-widget-header .ui-state-active, -a.ui-button:active, -.ui-button:active, -.ui-button.ui-state-active:hover { - /* Overrides the color of selection used in jQuery UI */ - background: #F8981D; -} diff --git a/docs/jdocs/jquery/external/jquery/jquery.js b/docs/jdocs/jquery/external/jquery/jquery.js deleted file mode 100644 index 50937333b..000000000 --- a/docs/jdocs/jquery/external/jquery/jquery.js +++ /dev/null @@ -1,10872 +0,0 @@ -/*! - * jQuery JavaScript Library v3.5.1 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2020-05-04T22:49Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - return typeof obj === "function" && typeof obj.nodeType !== "number"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.5.1", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; - - // Prevent Object.prototype pollution - // Prevent never-ending loop - if ( name === "__proto__" || target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - src = target[ name ]; - - // Ensure proper type for the source value - if ( copyIsArray && !Array.isArray( src ) ) { - clone = []; - } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { - clone = {}; - } else { - clone = src; - } - copyIsArray = false; - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.5 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2020-03-14 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem.namespaceURI, - docElem = ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -}; -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - return result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px"; - tr.style.height = "1px"; - trChild.style.height = "9px"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( - dataPriv.get( cur, "events" ) || Object.create( null ) - )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script - if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); - } ); - }, - - unwrap: function( selector ) { - this.parent( selector ).not( "body" ).each( function() { - jQuery( this ).replaceWith( this.childNodes ); - } ); - return this; - } -} ); - - -jQuery.expr.pseudos.hidden = function( elem ) { - return !jQuery.expr.pseudos.visible( elem ); -}; -jQuery.expr.pseudos.visible = function( elem ) { - return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); -}; - - - - -jQuery.ajaxSettings.xhr = function() { - try { - return new window.XMLHttpRequest(); - } catch ( e ) {} -}; - -var xhrSuccessStatus = { - - // File protocol always yields status code 0, assume 200 - 0: 200, - - // Support: IE <=9 only - // #1450: sometimes IE returns 1223 when it should be 204 - 1223: 204 - }, - xhrSupported = jQuery.ajaxSettings.xhr(); - -support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); -support.ajax = xhrSupported = !!xhrSupported; - -jQuery.ajaxTransport( function( options ) { - var callback, errorCallback; - - // Cross domain only allowed if supported through XMLHttpRequest - if ( support.cors || xhrSupported && !options.crossDomain ) { - return { - send: function( headers, complete ) { - var i, - xhr = options.xhr(); - - xhr.open( - options.type, - options.url, - options.async, - options.username, - options.password - ); - - // Apply custom fields if provided - if ( options.xhrFields ) { - for ( i in options.xhrFields ) { - xhr[ i ] = options.xhrFields[ i ]; - } - } - - // Override mime type if needed - if ( options.mimeType && xhr.overrideMimeType ) { - xhr.overrideMimeType( options.mimeType ); - } - - // X-Requested-With header - // For cross-domain requests, seeing as conditions for a preflight are - // akin to a jigsaw puzzle, we simply never set it to be sure. - // (it can always be set on a per-request basis or even using ajaxSetup) - // For same-domain requests, won't change header if already provided. - if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { - headers[ "X-Requested-With" ] = "XMLHttpRequest"; - } - - // Set headers - for ( i in headers ) { - xhr.setRequestHeader( i, headers[ i ] ); - } - - // Callback - callback = function( type ) { - return function() { - if ( callback ) { - callback = errorCallback = xhr.onload = - xhr.onerror = xhr.onabort = xhr.ontimeout = - xhr.onreadystatechange = null; - - if ( type === "abort" ) { - xhr.abort(); - } else if ( type === "error" ) { - - // Support: IE <=9 only - // On a manual native abort, IE9 throws - // errors on any property access that is not readyState - if ( typeof xhr.status !== "number" ) { - complete( 0, "error" ); - } else { - complete( - - // File: protocol always yields status 0; see #8605, #14207 - xhr.status, - xhr.statusText - ); - } - } else { - complete( - xhrSuccessStatus[ xhr.status ] || xhr.status, - xhr.statusText, - - // Support: IE <=9 only - // IE9 has no XHR2 but throws on binary (trac-11426) - // For XHR2 non-text, let the caller handle it (gh-2498) - ( xhr.responseType || "text" ) !== "text" || - typeof xhr.responseText !== "string" ? - { binary: xhr.response } : - { text: xhr.responseText }, - xhr.getAllResponseHeaders() - ); - } - } - }; - }; - - // Listen to events - xhr.onload = callback(); - errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); - - // Support: IE 9 only - // Use onreadystatechange to replace onabort - // to handle uncaught aborts - if ( xhr.onabort !== undefined ) { - xhr.onabort = errorCallback; - } else { - xhr.onreadystatechange = function() { - - // Check readyState before timeout as it changes - if ( xhr.readyState === 4 ) { - - // Allow onerror to be called first, - // but that will not handle a native abort - // Also, save errorCallback to a variable - // as xhr.onerror cannot be accessed - window.setTimeout( function() { - if ( callback ) { - errorCallback(); - } - } ); - } - }; - } - - // Create the abort callback - callback = callback( "abort" ); - - try { - - // Do send the request (this may raise an exception) - xhr.send( options.hasContent && options.data || null ); - } catch ( e ) { - - // #14683: Only rethrow if this hasn't been notified as an error yet - if ( callback ) { - throw e; - } - } - }, - - abort: function() { - if ( callback ) { - callback(); - } - } - }; - } -} ); - - - - -// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) -jQuery.ajaxPrefilter( function( s ) { - if ( s.crossDomain ) { - s.contents.script = false; - } -} ); - -// Install script dataType -jQuery.ajaxSetup( { - accepts: { - script: "text/javascript, application/javascript, " + - "application/ecmascript, application/x-ecmascript" - }, - contents: { - script: /\b(?:java|ecma)script\b/ - }, - converters: { - "text script": function( text ) { - jQuery.globalEval( text ); - return text; - } - } -} ); - -// Handle cache's special case and crossDomain -jQuery.ajaxPrefilter( "script", function( s ) { - if ( s.cache === undefined ) { - s.cache = false; - } - if ( s.crossDomain ) { - s.type = "GET"; - } -} ); - -// Bind script tag hack transport -jQuery.ajaxTransport( "script", function( s ) { - - // This transport only deals with cross domain or forced-by-attrs requests - if ( s.crossDomain || s.scriptAttrs ) { - var script, callback; - return { - send: function( _, complete ) { - script = jQuery( "\r\n"; - -// inject VBScript -document.write(IEBinaryToArray_ByteStr_Script); - -global.JSZipUtils._getBinaryFromXHR = function (xhr) { - var binary = xhr.responseBody; - var byteMapping = {}; - for ( var i = 0; i < 256; i++ ) { - for ( var j = 0; j < 256; j++ ) { - byteMapping[ String.fromCharCode( i + (j << 8) ) ] = - String.fromCharCode(i) + String.fromCharCode(j); - } - } - var rawBytes = IEBinaryToArray_ByteStr(binary); - var lastChr = IEBinaryToArray_ByteStr_Last(binary); - return rawBytes.replace(/[\s\S]/g, function( match ) { - return byteMapping[match]; - }) + lastChr; -}; - -// enforcing Stuk's coding style -// vim: set shiftwidth=4 softtabstop=4: - -},{}]},{},[1]) -; diff --git a/docs/jdocs/jquery/jszip-utils/dist/jszip-utils-ie.min.js b/docs/jdocs/jquery/jszip-utils/dist/jszip-utils-ie.min.js deleted file mode 100644 index 93d8bc8ef..000000000 --- a/docs/jdocs/jquery/jszip-utils/dist/jszip-utils-ie.min.js +++ /dev/null @@ -1,10 +0,0 @@ -/*! - -JSZipUtils - A collection of cross-browser utilities to go along with JSZip. - - -(c) 2014 Stuart Knightley, David Duponchel -Dual licenced under the MIT license or GPLv3. See https://raw.github.com/Stuk/jszip-utils/master/LICENSE.markdown. - -*/ -!function a(b,c,d){function e(g,h){if(!c[g]){if(!b[g]){var i="function"==typeof require&&require;if(!h&&i)return i(g,!0);if(f)return f(g,!0);throw new Error("Cannot find module '"+g+"'")}var j=c[g]={exports:{}};b[g][0].call(j.exports,function(a){var c=b[g][1][a];return e(c?c:a)},j,j.exports,a,b,c,d)}return c[g].exports}for(var f="function"==typeof require&&require,g=0;g\r\n";document.write(b),a.JSZipUtils._getBinaryFromXHR=function(a){for(var b=a.responseBody,c={},d=0;256>d;d++)for(var e=0;256>e;e++)c[String.fromCharCode(d+(e<<8))]=String.fromCharCode(d)+String.fromCharCode(e);var f=IEBinaryToArray_ByteStr(b),g=IEBinaryToArray_ByteStr_Last(b);return f.replace(/[\s\S]/g,function(a){return c[a]})+g}},{}]},{},[1]); diff --git a/docs/jdocs/jquery/jszip-utils/dist/jszip-utils.js b/docs/jdocs/jquery/jszip-utils/dist/jszip-utils.js deleted file mode 100644 index 775895ec9..000000000 --- a/docs/jdocs/jquery/jszip-utils/dist/jszip-utils.js +++ /dev/null @@ -1,118 +0,0 @@ -/*! - -JSZipUtils - A collection of cross-browser utilities to go along with JSZip. - - -(c) 2014 Stuart Knightley, David Duponchel -Dual licenced under the MIT license or GPLv3. See https://raw.github.com/Stuk/jszip-utils/master/LICENSE.markdown. - -*/ -!function(e){"object"==typeof exports?module.exports=e():"function"==typeof define&&define.amd?define(e):"undefined"!=typeof window?window.JSZipUtils=e():"undefined"!=typeof global?global.JSZipUtils=e():"undefined"!=typeof self&&(self.JSZipUtils=e())}(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);throw new Error("Cannot find module '"+o+"'")}var f=n[o]={exports:{}};t[o][0].call(f.exports,function(e){var n=t[o][1][e];return s(n?n:e)},f,f.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o - -(c) 2014 Stuart Knightley, David Duponchel -Dual licenced under the MIT license or GPLv3. See https://raw.github.com/Stuk/jszip-utils/master/LICENSE.markdown. - -*/ -!function(a){"object"==typeof exports?module.exports=a():"function"==typeof define&&define.amd?define(a):"undefined"!=typeof window?window.JSZipUtils=a():"undefined"!=typeof global?global.JSZipUtils=a():"undefined"!=typeof self&&(self.JSZipUtils=a())}(function(){return function a(b,c,d){function e(g,h){if(!c[g]){if(!b[g]){var i="function"==typeof require&&require;if(!h&&i)return i(g,!0);if(f)return f(g,!0);throw new Error("Cannot find module '"+g+"'")}var j=c[g]={exports:{}};b[g][0].call(j.exports,function(a){var c=b[g][1][a];return e(c?c:a)},j,j.exports,a,b,c,d)}return c[g].exports}for(var f="function"==typeof require&&require,g=0;g - -(c) 2009-2016 Stuart Knightley -Dual licenced under the MIT license or GPLv3. See https://raw.github.com/Stuk/jszip/master/LICENSE.markdown. - -JSZip uses the library pako released under the MIT license : -https://github.com/nodeca/pako/blob/master/LICENSE -*/ - -(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.JSZip = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o> 2; - enc2 = ((chr1 & 3) << 4) | (chr2 >> 4); - enc3 = remainingBytes > 1 ? (((chr2 & 15) << 2) | (chr3 >> 6)) : 64; - enc4 = remainingBytes > 2 ? (chr3 & 63) : 64; - - output.push(_keyStr.charAt(enc1) + _keyStr.charAt(enc2) + _keyStr.charAt(enc3) + _keyStr.charAt(enc4)); - - } - - return output.join(""); -}; - -// public method for decoding -exports.decode = function(input) { - var chr1, chr2, chr3; - var enc1, enc2, enc3, enc4; - var i = 0, resultIndex = 0; - - var dataUrlPrefix = "data:"; - - if (input.substr(0, dataUrlPrefix.length) === dataUrlPrefix) { - // This is a common error: people give a data url - // (data:image/png;base64,iVBOR...) with a {base64: true} and - // wonders why things don't work. - // We can detect that the string input looks like a data url but we - // *can't* be sure it is one: removing everything up to the comma would - // be too dangerous. - throw new Error("Invalid base64 input, it looks like a data url."); - } - - input = input.replace(/[^A-Za-z0-9\+\/\=]/g, ""); - - var totalLength = input.length * 3 / 4; - if(input.charAt(input.length - 1) === _keyStr.charAt(64)) { - totalLength--; - } - if(input.charAt(input.length - 2) === _keyStr.charAt(64)) { - totalLength--; - } - if (totalLength % 1 !== 0) { - // totalLength is not an integer, the length does not match a valid - // base64 content. That can happen if: - // - the input is not a base64 content - // - the input is *almost* a base64 content, with a extra chars at the - // beginning or at the end - // - the input uses a base64 variant (base64url for example) - throw new Error("Invalid base64 input, bad content length."); - } - var output; - if (support.uint8array) { - output = new Uint8Array(totalLength|0); - } else { - output = new Array(totalLength|0); - } - - while (i < input.length) { - - enc1 = _keyStr.indexOf(input.charAt(i++)); - enc2 = _keyStr.indexOf(input.charAt(i++)); - enc3 = _keyStr.indexOf(input.charAt(i++)); - enc4 = _keyStr.indexOf(input.charAt(i++)); - - chr1 = (enc1 << 2) | (enc2 >> 4); - chr2 = ((enc2 & 15) << 4) | (enc3 >> 2); - chr3 = ((enc3 & 3) << 6) | enc4; - - output[resultIndex++] = chr1; - - if (enc3 !== 64) { - output[resultIndex++] = chr2; - } - if (enc4 !== 64) { - output[resultIndex++] = chr3; - } - - } - - return output; -}; - -},{"./support":30,"./utils":32}],2:[function(require,module,exports){ -'use strict'; - -var external = require("./external"); -var DataWorker = require('./stream/DataWorker'); -var DataLengthProbe = require('./stream/DataLengthProbe'); -var Crc32Probe = require('./stream/Crc32Probe'); -var DataLengthProbe = require('./stream/DataLengthProbe'); - -/** - * Represent a compressed object, with everything needed to decompress it. - * @constructor - * @param {number} compressedSize the size of the data compressed. - * @param {number} uncompressedSize the size of the data after decompression. - * @param {number} crc32 the crc32 of the decompressed file. - * @param {object} compression the type of compression, see lib/compressions.js. - * @param {String|ArrayBuffer|Uint8Array|Buffer} data the compressed data. - */ -function CompressedObject(compressedSize, uncompressedSize, crc32, compression, data) { - this.compressedSize = compressedSize; - this.uncompressedSize = uncompressedSize; - this.crc32 = crc32; - this.compression = compression; - this.compressedContent = data; -} - -CompressedObject.prototype = { - /** - * Create a worker to get the uncompressed content. - * @return {GenericWorker} the worker. - */ - getContentWorker : function () { - var worker = new DataWorker(external.Promise.resolve(this.compressedContent)) - .pipe(this.compression.uncompressWorker()) - .pipe(new DataLengthProbe("data_length")); - - var that = this; - worker.on("end", function () { - if(this.streamInfo['data_length'] !== that.uncompressedSize) { - throw new Error("Bug : uncompressed data size mismatch"); - } - }); - return worker; - }, - /** - * Create a worker to get the compressed content. - * @return {GenericWorker} the worker. - */ - getCompressedWorker : function () { - return new DataWorker(external.Promise.resolve(this.compressedContent)) - .withStreamInfo("compressedSize", this.compressedSize) - .withStreamInfo("uncompressedSize", this.uncompressedSize) - .withStreamInfo("crc32", this.crc32) - .withStreamInfo("compression", this.compression) - ; - } -}; - -/** - * Chain the given worker with other workers to compress the content with the - * given compresion. - * @param {GenericWorker} uncompressedWorker the worker to pipe. - * @param {Object} compression the compression object. - * @param {Object} compressionOptions the options to use when compressing. - * @return {GenericWorker} the new worker compressing the content. - */ -CompressedObject.createWorkerFrom = function (uncompressedWorker, compression, compressionOptions) { - return uncompressedWorker - .pipe(new Crc32Probe()) - .pipe(new DataLengthProbe("uncompressedSize")) - .pipe(compression.compressWorker(compressionOptions)) - .pipe(new DataLengthProbe("compressedSize")) - .withStreamInfo("compression", compression); -}; - -module.exports = CompressedObject; - -},{"./external":6,"./stream/Crc32Probe":25,"./stream/DataLengthProbe":26,"./stream/DataWorker":27}],3:[function(require,module,exports){ -'use strict'; - -var GenericWorker = require("./stream/GenericWorker"); - -exports.STORE = { - magic: "\x00\x00", - compressWorker : function (compressionOptions) { - return new GenericWorker("STORE compression"); - }, - uncompressWorker : function () { - return new GenericWorker("STORE decompression"); - } -}; -exports.DEFLATE = require('./flate'); - -},{"./flate":7,"./stream/GenericWorker":28}],4:[function(require,module,exports){ -'use strict'; - -var utils = require('./utils'); - -/** - * The following functions come from pako, from pako/lib/zlib/crc32.js - * released under the MIT license, see pako https://github.com/nodeca/pako/ - */ - -// Use ordinary array, since untyped makes no boost here -function makeTable() { - var c, table = []; - - for(var n =0; n < 256; n++){ - c = n; - for(var k =0; k < 8; k++){ - c = ((c&1) ? (0xEDB88320 ^ (c >>> 1)) : (c >>> 1)); - } - table[n] = c; - } - - return table; -} - -// Create table on load. Just 255 signed longs. Not a problem. -var crcTable = makeTable(); - - -function crc32(crc, buf, len, pos) { - var t = crcTable, end = pos + len; - - crc = crc ^ (-1); - - for (var i = pos; i < end; i++ ) { - crc = (crc >>> 8) ^ t[(crc ^ buf[i]) & 0xFF]; - } - - return (crc ^ (-1)); // >>> 0; -} - -// That's all for the pako functions. - -/** - * Compute the crc32 of a string. - * This is almost the same as the function crc32, but for strings. Using the - * same function for the two use cases leads to horrible performances. - * @param {Number} crc the starting value of the crc. - * @param {String} str the string to use. - * @param {Number} len the length of the string. - * @param {Number} pos the starting position for the crc32 computation. - * @return {Number} the computed crc32. - */ -function crc32str(crc, str, len, pos) { - var t = crcTable, end = pos + len; - - crc = crc ^ (-1); - - for (var i = pos; i < end; i++ ) { - crc = (crc >>> 8) ^ t[(crc ^ str.charCodeAt(i)) & 0xFF]; - } - - return (crc ^ (-1)); // >>> 0; -} - -module.exports = function crc32wrapper(input, crc) { - if (typeof input === "undefined" || !input.length) { - return 0; - } - - var isArray = utils.getTypeOf(input) !== "string"; - - if(isArray) { - return crc32(crc|0, input, input.length, 0); - } else { - return crc32str(crc|0, input, input.length, 0); - } -}; - -},{"./utils":32}],5:[function(require,module,exports){ -'use strict'; -exports.base64 = false; -exports.binary = false; -exports.dir = false; -exports.createFolders = true; -exports.date = null; -exports.compression = null; -exports.compressionOptions = null; -exports.comment = null; -exports.unixPermissions = null; -exports.dosPermissions = null; - -},{}],6:[function(require,module,exports){ -/* global Promise */ -'use strict'; - -// load the global object first: -// - it should be better integrated in the system (unhandledRejection in node) -// - the environment may have a custom Promise implementation (see zone.js) -var ES6Promise = null; -if (typeof Promise !== "undefined") { - ES6Promise = Promise; -} else { - ES6Promise = require("lie"); -} - -/** - * Let the user use/change some implementations. - */ -module.exports = { - Promise: ES6Promise -}; - -},{"lie":37}],7:[function(require,module,exports){ -'use strict'; -var USE_TYPEDARRAY = (typeof Uint8Array !== 'undefined') && (typeof Uint16Array !== 'undefined') && (typeof Uint32Array !== 'undefined'); - -var pako = require("pako"); -var utils = require("./utils"); -var GenericWorker = require("./stream/GenericWorker"); - -var ARRAY_TYPE = USE_TYPEDARRAY ? "uint8array" : "array"; - -exports.magic = "\x08\x00"; - -/** - * Create a worker that uses pako to inflate/deflate. - * @constructor - * @param {String} action the name of the pako function to call : either "Deflate" or "Inflate". - * @param {Object} options the options to use when (de)compressing. - */ -function FlateWorker(action, options) { - GenericWorker.call(this, "FlateWorker/" + action); - - this._pako = null; - this._pakoAction = action; - this._pakoOptions = options; - // the `meta` object from the last chunk received - // this allow this worker to pass around metadata - this.meta = {}; -} - -utils.inherits(FlateWorker, GenericWorker); - -/** - * @see GenericWorker.processChunk - */ -FlateWorker.prototype.processChunk = function (chunk) { - this.meta = chunk.meta; - if (this._pako === null) { - this._createPako(); - } - this._pako.push(utils.transformTo(ARRAY_TYPE, chunk.data), false); -}; - -/** - * @see GenericWorker.flush - */ -FlateWorker.prototype.flush = function () { - GenericWorker.prototype.flush.call(this); - if (this._pako === null) { - this._createPako(); - } - this._pako.push([], true); -}; -/** - * @see GenericWorker.cleanUp - */ -FlateWorker.prototype.cleanUp = function () { - GenericWorker.prototype.cleanUp.call(this); - this._pako = null; -}; - -/** - * Create the _pako object. - * TODO: lazy-loading this object isn't the best solution but it's the - * quickest. The best solution is to lazy-load the worker list. See also the - * issue #446. - */ -FlateWorker.prototype._createPako = function () { - this._pako = new pako[this._pakoAction]({ - raw: true, - level: this._pakoOptions.level || -1 // default compression - }); - var self = this; - this._pako.onData = function(data) { - self.push({ - data : data, - meta : self.meta - }); - }; -}; - -exports.compressWorker = function (compressionOptions) { - return new FlateWorker("Deflate", compressionOptions); -}; -exports.uncompressWorker = function () { - return new FlateWorker("Inflate", {}); -}; - -},{"./stream/GenericWorker":28,"./utils":32,"pako":38}],8:[function(require,module,exports){ -'use strict'; - -var utils = require('../utils'); -var GenericWorker = require('../stream/GenericWorker'); -var utf8 = require('../utf8'); -var crc32 = require('../crc32'); -var signature = require('../signature'); - -/** - * Transform an integer into a string in hexadecimal. - * @private - * @param {number} dec the number to convert. - * @param {number} bytes the number of bytes to generate. - * @returns {string} the result. - */ -var decToHex = function(dec, bytes) { - var hex = "", i; - for (i = 0; i < bytes; i++) { - hex += String.fromCharCode(dec & 0xff); - dec = dec >>> 8; - } - return hex; -}; - -/** - * Generate the UNIX part of the external file attributes. - * @param {Object} unixPermissions the unix permissions or null. - * @param {Boolean} isDir true if the entry is a directory, false otherwise. - * @return {Number} a 32 bit integer. - * - * adapted from http://unix.stackexchange.com/questions/14705/the-zip-formats-external-file-attribute : - * - * TTTTsstrwxrwxrwx0000000000ADVSHR - * ^^^^____________________________ file type, see zipinfo.c (UNX_*) - * ^^^_________________________ setuid, setgid, sticky - * ^^^^^^^^^________________ permissions - * ^^^^^^^^^^______ not used ? - * ^^^^^^ DOS attribute bits : Archive, Directory, Volume label, System file, Hidden, Read only - */ -var generateUnixExternalFileAttr = function (unixPermissions, isDir) { - - var result = unixPermissions; - if (!unixPermissions) { - // I can't use octal values in strict mode, hence the hexa. - // 040775 => 0x41fd - // 0100664 => 0x81b4 - result = isDir ? 0x41fd : 0x81b4; - } - return (result & 0xFFFF) << 16; -}; - -/** - * Generate the DOS part of the external file attributes. - * @param {Object} dosPermissions the dos permissions or null. - * @param {Boolean} isDir true if the entry is a directory, false otherwise. - * @return {Number} a 32 bit integer. - * - * Bit 0 Read-Only - * Bit 1 Hidden - * Bit 2 System - * Bit 3 Volume Label - * Bit 4 Directory - * Bit 5 Archive - */ -var generateDosExternalFileAttr = function (dosPermissions, isDir) { - - // the dir flag is already set for compatibility - return (dosPermissions || 0) & 0x3F; -}; - -/** - * Generate the various parts used in the construction of the final zip file. - * @param {Object} streamInfo the hash with informations about the compressed file. - * @param {Boolean} streamedContent is the content streamed ? - * @param {Boolean} streamingEnded is the stream finished ? - * @param {number} offset the current offset from the start of the zip file. - * @param {String} platform let's pretend we are this platform (change platform dependents fields) - * @param {Function} encodeFileName the function to encode the file name / comment. - * @return {Object} the zip parts. - */ -var generateZipParts = function(streamInfo, streamedContent, streamingEnded, offset, platform, encodeFileName) { - var file = streamInfo['file'], - compression = streamInfo['compression'], - useCustomEncoding = encodeFileName !== utf8.utf8encode, - encodedFileName = utils.transformTo("string", encodeFileName(file.name)), - utfEncodedFileName = utils.transformTo("string", utf8.utf8encode(file.name)), - comment = file.comment, - encodedComment = utils.transformTo("string", encodeFileName(comment)), - utfEncodedComment = utils.transformTo("string", utf8.utf8encode(comment)), - useUTF8ForFileName = utfEncodedFileName.length !== file.name.length, - useUTF8ForComment = utfEncodedComment.length !== comment.length, - dosTime, - dosDate, - extraFields = "", - unicodePathExtraField = "", - unicodeCommentExtraField = "", - dir = file.dir, - date = file.date; - - - var dataInfo = { - crc32 : 0, - compressedSize : 0, - uncompressedSize : 0 - }; - - // if the content is streamed, the sizes/crc32 are only available AFTER - // the end of the stream. - if (!streamedContent || streamingEnded) { - dataInfo.crc32 = streamInfo['crc32']; - dataInfo.compressedSize = streamInfo['compressedSize']; - dataInfo.uncompressedSize = streamInfo['uncompressedSize']; - } - - var bitflag = 0; - if (streamedContent) { - // Bit 3: the sizes/crc32 are set to zero in the local header. - // The correct values are put in the data descriptor immediately - // following the compressed data. - bitflag |= 0x0008; - } - if (!useCustomEncoding && (useUTF8ForFileName || useUTF8ForComment)) { - // Bit 11: Language encoding flag (EFS). - bitflag |= 0x0800; - } - - - var extFileAttr = 0; - var versionMadeBy = 0; - if (dir) { - // dos or unix, we set the dos dir flag - extFileAttr |= 0x00010; - } - if(platform === "UNIX") { - versionMadeBy = 0x031E; // UNIX, version 3.0 - extFileAttr |= generateUnixExternalFileAttr(file.unixPermissions, dir); - } else { // DOS or other, fallback to DOS - versionMadeBy = 0x0014; // DOS, version 2.0 - extFileAttr |= generateDosExternalFileAttr(file.dosPermissions, dir); - } - - // date - // @see http://www.delorie.com/djgpp/doc/rbinter/it/52/13.html - // @see http://www.delorie.com/djgpp/doc/rbinter/it/65/16.html - // @see http://www.delorie.com/djgpp/doc/rbinter/it/66/16.html - - dosTime = date.getUTCHours(); - dosTime = dosTime << 6; - dosTime = dosTime | date.getUTCMinutes(); - dosTime = dosTime << 5; - dosTime = dosTime | date.getUTCSeconds() / 2; - - dosDate = date.getUTCFullYear() - 1980; - dosDate = dosDate << 4; - dosDate = dosDate | (date.getUTCMonth() + 1); - dosDate = dosDate << 5; - dosDate = dosDate | date.getUTCDate(); - - if (useUTF8ForFileName) { - // set the unicode path extra field. unzip needs at least one extra - // field to correctly handle unicode path, so using the path is as good - // as any other information. This could improve the situation with - // other archive managers too. - // This field is usually used without the utf8 flag, with a non - // unicode path in the header (winrar, winzip). This helps (a bit) - // with the messy Windows' default compressed folders feature but - // breaks on p7zip which doesn't seek the unicode path extra field. - // So for now, UTF-8 everywhere ! - unicodePathExtraField = - // Version - decToHex(1, 1) + - // NameCRC32 - decToHex(crc32(encodedFileName), 4) + - // UnicodeName - utfEncodedFileName; - - extraFields += - // Info-ZIP Unicode Path Extra Field - "\x75\x70" + - // size - decToHex(unicodePathExtraField.length, 2) + - // content - unicodePathExtraField; - } - - if(useUTF8ForComment) { - - unicodeCommentExtraField = - // Version - decToHex(1, 1) + - // CommentCRC32 - decToHex(crc32(encodedComment), 4) + - // UnicodeName - utfEncodedComment; - - extraFields += - // Info-ZIP Unicode Path Extra Field - "\x75\x63" + - // size - decToHex(unicodeCommentExtraField.length, 2) + - // content - unicodeCommentExtraField; - } - - var header = ""; - - // version needed to extract - header += "\x0A\x00"; - // general purpose bit flag - header += decToHex(bitflag, 2); - // compression method - header += compression.magic; - // last mod file time - header += decToHex(dosTime, 2); - // last mod file date - header += decToHex(dosDate, 2); - // crc-32 - header += decToHex(dataInfo.crc32, 4); - // compressed size - header += decToHex(dataInfo.compressedSize, 4); - // uncompressed size - header += decToHex(dataInfo.uncompressedSize, 4); - // file name length - header += decToHex(encodedFileName.length, 2); - // extra field length - header += decToHex(extraFields.length, 2); - - - var fileRecord = signature.LOCAL_FILE_HEADER + header + encodedFileName + extraFields; - - var dirRecord = signature.CENTRAL_FILE_HEADER + - // version made by (00: DOS) - decToHex(versionMadeBy, 2) + - // file header (common to file and central directory) - header + - // file comment length - decToHex(encodedComment.length, 2) + - // disk number start - "\x00\x00" + - // internal file attributes TODO - "\x00\x00" + - // external file attributes - decToHex(extFileAttr, 4) + - // relative offset of local header - decToHex(offset, 4) + - // file name - encodedFileName + - // extra field - extraFields + - // file comment - encodedComment; - - return { - fileRecord: fileRecord, - dirRecord: dirRecord - }; -}; - -/** - * Generate the EOCD record. - * @param {Number} entriesCount the number of entries in the zip file. - * @param {Number} centralDirLength the length (in bytes) of the central dir. - * @param {Number} localDirLength the length (in bytes) of the local dir. - * @param {String} comment the zip file comment as a binary string. - * @param {Function} encodeFileName the function to encode the comment. - * @return {String} the EOCD record. - */ -var generateCentralDirectoryEnd = function (entriesCount, centralDirLength, localDirLength, comment, encodeFileName) { - var dirEnd = ""; - var encodedComment = utils.transformTo("string", encodeFileName(comment)); - - // end of central dir signature - dirEnd = signature.CENTRAL_DIRECTORY_END + - // number of this disk - "\x00\x00" + - // number of the disk with the start of the central directory - "\x00\x00" + - // total number of entries in the central directory on this disk - decToHex(entriesCount, 2) + - // total number of entries in the central directory - decToHex(entriesCount, 2) + - // size of the central directory 4 bytes - decToHex(centralDirLength, 4) + - // offset of start of central directory with respect to the starting disk number - decToHex(localDirLength, 4) + - // .ZIP file comment length - decToHex(encodedComment.length, 2) + - // .ZIP file comment - encodedComment; - - return dirEnd; -}; - -/** - * Generate data descriptors for a file entry. - * @param {Object} streamInfo the hash generated by a worker, containing informations - * on the file entry. - * @return {String} the data descriptors. - */ -var generateDataDescriptors = function (streamInfo) { - var descriptor = ""; - descriptor = signature.DATA_DESCRIPTOR + - // crc-32 4 bytes - decToHex(streamInfo['crc32'], 4) + - // compressed size 4 bytes - decToHex(streamInfo['compressedSize'], 4) + - // uncompressed size 4 bytes - decToHex(streamInfo['uncompressedSize'], 4); - - return descriptor; -}; - - -/** - * A worker to concatenate other workers to create a zip file. - * @param {Boolean} streamFiles `true` to stream the content of the files, - * `false` to accumulate it. - * @param {String} comment the comment to use. - * @param {String} platform the platform to use, "UNIX" or "DOS". - * @param {Function} encodeFileName the function to encode file names and comments. - */ -function ZipFileWorker(streamFiles, comment, platform, encodeFileName) { - GenericWorker.call(this, "ZipFileWorker"); - // The number of bytes written so far. This doesn't count accumulated chunks. - this.bytesWritten = 0; - // The comment of the zip file - this.zipComment = comment; - // The platform "generating" the zip file. - this.zipPlatform = platform; - // the function to encode file names and comments. - this.encodeFileName = encodeFileName; - // Should we stream the content of the files ? - this.streamFiles = streamFiles; - // If `streamFiles` is false, we will need to accumulate the content of the - // files to calculate sizes / crc32 (and write them *before* the content). - // This boolean indicates if we are accumulating chunks (it will change a lot - // during the lifetime of this worker). - this.accumulate = false; - // The buffer receiving chunks when accumulating content. - this.contentBuffer = []; - // The list of generated directory records. - this.dirRecords = []; - // The offset (in bytes) from the beginning of the zip file for the current source. - this.currentSourceOffset = 0; - // The total number of entries in this zip file. - this.entriesCount = 0; - // the name of the file currently being added, null when handling the end of the zip file. - // Used for the emited metadata. - this.currentFile = null; - - - - this._sources = []; -} -utils.inherits(ZipFileWorker, GenericWorker); - -/** - * @see GenericWorker.push - */ -ZipFileWorker.prototype.push = function (chunk) { - - var currentFilePercent = chunk.meta.percent || 0; - var entriesCount = this.entriesCount; - var remainingFiles = this._sources.length; - - if(this.accumulate) { - this.contentBuffer.push(chunk); - } else { - this.bytesWritten += chunk.data.length; - - GenericWorker.prototype.push.call(this, { - data : chunk.data, - meta : { - currentFile : this.currentFile, - percent : entriesCount ? (currentFilePercent + 100 * (entriesCount - remainingFiles - 1)) / entriesCount : 100 - } - }); - } -}; - -/** - * The worker started a new source (an other worker). - * @param {Object} streamInfo the streamInfo object from the new source. - */ -ZipFileWorker.prototype.openedSource = function (streamInfo) { - this.currentSourceOffset = this.bytesWritten; - this.currentFile = streamInfo['file'].name; - - var streamedContent = this.streamFiles && !streamInfo['file'].dir; - - // don't stream folders (because they don't have any content) - if(streamedContent) { - var record = generateZipParts(streamInfo, streamedContent, false, this.currentSourceOffset, this.zipPlatform, this.encodeFileName); - this.push({ - data : record.fileRecord, - meta : {percent:0} - }); - } else { - // we need to wait for the whole file before pushing anything - this.accumulate = true; - } -}; - -/** - * The worker finished a source (an other worker). - * @param {Object} streamInfo the streamInfo object from the finished source. - */ -ZipFileWorker.prototype.closedSource = function (streamInfo) { - this.accumulate = false; - var streamedContent = this.streamFiles && !streamInfo['file'].dir; - var record = generateZipParts(streamInfo, streamedContent, true, this.currentSourceOffset, this.zipPlatform, this.encodeFileName); - - this.dirRecords.push(record.dirRecord); - if(streamedContent) { - // after the streamed file, we put data descriptors - this.push({ - data : generateDataDescriptors(streamInfo), - meta : {percent:100} - }); - } else { - // the content wasn't streamed, we need to push everything now - // first the file record, then the content - this.push({ - data : record.fileRecord, - meta : {percent:0} - }); - while(this.contentBuffer.length) { - this.push(this.contentBuffer.shift()); - } - } - this.currentFile = null; -}; - -/** - * @see GenericWorker.flush - */ -ZipFileWorker.prototype.flush = function () { - - var localDirLength = this.bytesWritten; - for(var i = 0; i < this.dirRecords.length; i++) { - this.push({ - data : this.dirRecords[i], - meta : {percent:100} - }); - } - var centralDirLength = this.bytesWritten - localDirLength; - - var dirEnd = generateCentralDirectoryEnd(this.dirRecords.length, centralDirLength, localDirLength, this.zipComment, this.encodeFileName); - - this.push({ - data : dirEnd, - meta : {percent:100} - }); -}; - -/** - * Prepare the next source to be read. - */ -ZipFileWorker.prototype.prepareNextSource = function () { - this.previous = this._sources.shift(); - this.openedSource(this.previous.streamInfo); - if (this.isPaused) { - this.previous.pause(); - } else { - this.previous.resume(); - } -}; - -/** - * @see GenericWorker.registerPrevious - */ -ZipFileWorker.prototype.registerPrevious = function (previous) { - this._sources.push(previous); - var self = this; - - previous.on('data', function (chunk) { - self.processChunk(chunk); - }); - previous.on('end', function () { - self.closedSource(self.previous.streamInfo); - if(self._sources.length) { - self.prepareNextSource(); - } else { - self.end(); - } - }); - previous.on('error', function (e) { - self.error(e); - }); - return this; -}; - -/** - * @see GenericWorker.resume - */ -ZipFileWorker.prototype.resume = function () { - if(!GenericWorker.prototype.resume.call(this)) { - return false; - } - - if (!this.previous && this._sources.length) { - this.prepareNextSource(); - return true; - } - if (!this.previous && !this._sources.length && !this.generatedError) { - this.end(); - return true; - } -}; - -/** - * @see GenericWorker.error - */ -ZipFileWorker.prototype.error = function (e) { - var sources = this._sources; - if(!GenericWorker.prototype.error.call(this, e)) { - return false; - } - for(var i = 0; i < sources.length; i++) { - try { - sources[i].error(e); - } catch(e) { - // the `error` exploded, nothing to do - } - } - return true; -}; - -/** - * @see GenericWorker.lock - */ -ZipFileWorker.prototype.lock = function () { - GenericWorker.prototype.lock.call(this); - var sources = this._sources; - for(var i = 0; i < sources.length; i++) { - sources[i].lock(); - } -}; - -module.exports = ZipFileWorker; - -},{"../crc32":4,"../signature":23,"../stream/GenericWorker":28,"../utf8":31,"../utils":32}],9:[function(require,module,exports){ -'use strict'; - -var compressions = require('../compressions'); -var ZipFileWorker = require('./ZipFileWorker'); - -/** - * Find the compression to use. - * @param {String} fileCompression the compression defined at the file level, if any. - * @param {String} zipCompression the compression defined at the load() level. - * @return {Object} the compression object to use. - */ -var getCompression = function (fileCompression, zipCompression) { - - var compressionName = fileCompression || zipCompression; - var compression = compressions[compressionName]; - if (!compression) { - throw new Error(compressionName + " is not a valid compression method !"); - } - return compression; -}; - -/** - * Create a worker to generate a zip file. - * @param {JSZip} zip the JSZip instance at the right root level. - * @param {Object} options to generate the zip file. - * @param {String} comment the comment to use. - */ -exports.generateWorker = function (zip, options, comment) { - - var zipFileWorker = new ZipFileWorker(options.streamFiles, comment, options.platform, options.encodeFileName); - var entriesCount = 0; - try { - - zip.forEach(function (relativePath, file) { - entriesCount++; - var compression = getCompression(file.options.compression, options.compression); - var compressionOptions = file.options.compressionOptions || options.compressionOptions || {}; - var dir = file.dir, date = file.date; - - file._compressWorker(compression, compressionOptions) - .withStreamInfo("file", { - name : relativePath, - dir : dir, - date : date, - comment : file.comment || "", - unixPermissions : file.unixPermissions, - dosPermissions : file.dosPermissions - }) - .pipe(zipFileWorker); - }); - zipFileWorker.entriesCount = entriesCount; - } catch (e) { - zipFileWorker.error(e); - } - - return zipFileWorker; -}; - -},{"../compressions":3,"./ZipFileWorker":8}],10:[function(require,module,exports){ -'use strict'; - -/** - * Representation a of zip file in js - * @constructor - */ -function JSZip() { - // if this constructor is used without `new`, it adds `new` before itself: - if(!(this instanceof JSZip)) { - return new JSZip(); - } - - if(arguments.length) { - throw new Error("The constructor with parameters has been removed in JSZip 3.0, please check the upgrade guide."); - } - - // object containing the files : - // { - // "folder/" : {...}, - // "folder/data.txt" : {...} - // } - this.files = {}; - - this.comment = null; - - // Where we are in the hierarchy - this.root = ""; - this.clone = function() { - var newObj = new JSZip(); - for (var i in this) { - if (typeof this[i] !== "function") { - newObj[i] = this[i]; - } - } - return newObj; - }; -} -JSZip.prototype = require('./object'); -JSZip.prototype.loadAsync = require('./load'); -JSZip.support = require('./support'); -JSZip.defaults = require('./defaults'); - -// TODO find a better way to handle this version, -// a require('package.json').version doesn't work with webpack, see #327 -JSZip.version = "3.2.0"; - -JSZip.loadAsync = function (content, options) { - return new JSZip().loadAsync(content, options); -}; - -JSZip.external = require("./external"); -module.exports = JSZip; - -},{"./defaults":5,"./external":6,"./load":11,"./object":15,"./support":30}],11:[function(require,module,exports){ -'use strict'; -var utils = require('./utils'); -var external = require("./external"); -var utf8 = require('./utf8'); -var utils = require('./utils'); -var ZipEntries = require('./zipEntries'); -var Crc32Probe = require('./stream/Crc32Probe'); -var nodejsUtils = require("./nodejsUtils"); - -/** - * Check the CRC32 of an entry. - * @param {ZipEntry} zipEntry the zip entry to check. - * @return {Promise} the result. - */ -function checkEntryCRC32(zipEntry) { - return new external.Promise(function (resolve, reject) { - var worker = zipEntry.decompressed.getContentWorker().pipe(new Crc32Probe()); - worker.on("error", function (e) { - reject(e); - }) - .on("end", function () { - if (worker.streamInfo.crc32 !== zipEntry.decompressed.crc32) { - reject(new Error("Corrupted zip : CRC32 mismatch")); - } else { - resolve(); - } - }) - .resume(); - }); -} - -module.exports = function(data, options) { - var zip = this; - options = utils.extend(options || {}, { - base64: false, - checkCRC32: false, - optimizedBinaryString: false, - createFolders: false, - decodeFileName: utf8.utf8decode - }); - - if (nodejsUtils.isNode && nodejsUtils.isStream(data)) { - return external.Promise.reject(new Error("JSZip can't accept a stream when loading a zip file.")); - } - - return utils.prepareContent("the loaded zip file", data, true, options.optimizedBinaryString, options.base64) - .then(function(data) { - var zipEntries = new ZipEntries(options); - zipEntries.load(data); - return zipEntries; - }).then(function checkCRC32(zipEntries) { - var promises = [external.Promise.resolve(zipEntries)]; - var files = zipEntries.files; - if (options.checkCRC32) { - for (var i = 0; i < files.length; i++) { - promises.push(checkEntryCRC32(files[i])); - } - } - return external.Promise.all(promises); - }).then(function addFiles(results) { - var zipEntries = results.shift(); - var files = zipEntries.files; - for (var i = 0; i < files.length; i++) { - var input = files[i]; - zip.file(input.fileNameStr, input.decompressed, { - binary: true, - optimizedBinaryString: true, - date: input.date, - dir: input.dir, - comment : input.fileCommentStr.length ? input.fileCommentStr : null, - unixPermissions : input.unixPermissions, - dosPermissions : input.dosPermissions, - createFolders: options.createFolders - }); - } - if (zipEntries.zipComment.length) { - zip.comment = zipEntries.zipComment; - } - - return zip; - }); -}; - -},{"./external":6,"./nodejsUtils":14,"./stream/Crc32Probe":25,"./utf8":31,"./utils":32,"./zipEntries":33}],12:[function(require,module,exports){ -"use strict"; - -var utils = require('../utils'); -var GenericWorker = require('../stream/GenericWorker'); - -/** - * A worker that use a nodejs stream as source. - * @constructor - * @param {String} filename the name of the file entry for this stream. - * @param {Readable} stream the nodejs stream. - */ -function NodejsStreamInputAdapter(filename, stream) { - GenericWorker.call(this, "Nodejs stream input adapter for " + filename); - this._upstreamEnded = false; - this._bindStream(stream); -} - -utils.inherits(NodejsStreamInputAdapter, GenericWorker); - -/** - * Prepare the stream and bind the callbacks on it. - * Do this ASAP on node 0.10 ! A lazy binding doesn't always work. - * @param {Stream} stream the nodejs stream to use. - */ -NodejsStreamInputAdapter.prototype._bindStream = function (stream) { - var self = this; - this._stream = stream; - stream.pause(); - stream - .on("data", function (chunk) { - self.push({ - data: chunk, - meta : { - percent : 0 - } - }); - }) - .on("error", function (e) { - if(self.isPaused) { - this.generatedError = e; - } else { - self.error(e); - } - }) - .on("end", function () { - if(self.isPaused) { - self._upstreamEnded = true; - } else { - self.end(); - } - }); -}; -NodejsStreamInputAdapter.prototype.pause = function () { - if(!GenericWorker.prototype.pause.call(this)) { - return false; - } - this._stream.pause(); - return true; -}; -NodejsStreamInputAdapter.prototype.resume = function () { - if(!GenericWorker.prototype.resume.call(this)) { - return false; - } - - if(this._upstreamEnded) { - this.end(); - } else { - this._stream.resume(); - } - - return true; -}; - -module.exports = NodejsStreamInputAdapter; - -},{"../stream/GenericWorker":28,"../utils":32}],13:[function(require,module,exports){ -'use strict'; - -var Readable = require('readable-stream').Readable; - -var utils = require('../utils'); -utils.inherits(NodejsStreamOutputAdapter, Readable); - -/** -* A nodejs stream using a worker as source. -* @see the SourceWrapper in http://nodejs.org/api/stream.html -* @constructor -* @param {StreamHelper} helper the helper wrapping the worker -* @param {Object} options the nodejs stream options -* @param {Function} updateCb the update callback. -*/ -function NodejsStreamOutputAdapter(helper, options, updateCb) { - Readable.call(this, options); - this._helper = helper; - - var self = this; - helper.on("data", function (data, meta) { - if (!self.push(data)) { - self._helper.pause(); - } - if(updateCb) { - updateCb(meta); - } - }) - .on("error", function(e) { - self.emit('error', e); - }) - .on("end", function () { - self.push(null); - }); -} - - -NodejsStreamOutputAdapter.prototype._read = function() { - this._helper.resume(); -}; - -module.exports = NodejsStreamOutputAdapter; - -},{"../utils":32,"readable-stream":16}],14:[function(require,module,exports){ -'use strict'; - -module.exports = { - /** - * True if this is running in Nodejs, will be undefined in a browser. - * In a browser, browserify won't include this file and the whole module - * will be resolved an empty object. - */ - isNode : typeof Buffer !== "undefined", - /** - * Create a new nodejs Buffer from an existing content. - * @param {Object} data the data to pass to the constructor. - * @param {String} encoding the encoding to use. - * @return {Buffer} a new Buffer. - */ - newBufferFrom: function(data, encoding) { - if (Buffer.from && Buffer.from !== Uint8Array.from) { - return Buffer.from(data, encoding); - } else { - if (typeof data === "number") { - // Safeguard for old Node.js versions. On newer versions, - // Buffer.from(number) / Buffer(number, encoding) already throw. - throw new Error("The \"data\" argument must not be a number"); - } - return new Buffer(data, encoding); - } - }, - /** - * Create a new nodejs Buffer with the specified size. - * @param {Integer} size the size of the buffer. - * @return {Buffer} a new Buffer. - */ - allocBuffer: function (size) { - if (Buffer.alloc) { - return Buffer.alloc(size); - } else { - var buf = new Buffer(size); - buf.fill(0); - return buf; - } - }, - /** - * Find out if an object is a Buffer. - * @param {Object} b the object to test. - * @return {Boolean} true if the object is a Buffer, false otherwise. - */ - isBuffer : function(b){ - return Buffer.isBuffer(b); - }, - - isStream : function (obj) { - return obj && - typeof obj.on === "function" && - typeof obj.pause === "function" && - typeof obj.resume === "function"; - } -}; - -},{}],15:[function(require,module,exports){ -'use strict'; -var utf8 = require('./utf8'); -var utils = require('./utils'); -var GenericWorker = require('./stream/GenericWorker'); -var StreamHelper = require('./stream/StreamHelper'); -var defaults = require('./defaults'); -var CompressedObject = require('./compressedObject'); -var ZipObject = require('./zipObject'); -var generate = require("./generate"); -var nodejsUtils = require("./nodejsUtils"); -var NodejsStreamInputAdapter = require("./nodejs/NodejsStreamInputAdapter"); - - -/** - * Add a file in the current folder. - * @private - * @param {string} name the name of the file - * @param {String|ArrayBuffer|Uint8Array|Buffer} data the data of the file - * @param {Object} originalOptions the options of the file - * @return {Object} the new file. - */ -var fileAdd = function(name, data, originalOptions) { - // be sure sub folders exist - var dataType = utils.getTypeOf(data), - parent; - - - /* - * Correct options. - */ - - var o = utils.extend(originalOptions || {}, defaults); - o.date = o.date || new Date(); - if (o.compression !== null) { - o.compression = o.compression.toUpperCase(); - } - - if (typeof o.unixPermissions === "string") { - o.unixPermissions = parseInt(o.unixPermissions, 8); - } - - // UNX_IFDIR 0040000 see zipinfo.c - if (o.unixPermissions && (o.unixPermissions & 0x4000)) { - o.dir = true; - } - // Bit 4 Directory - if (o.dosPermissions && (o.dosPermissions & 0x0010)) { - o.dir = true; - } - - if (o.dir) { - name = forceTrailingSlash(name); - } - if (o.createFolders && (parent = parentFolder(name))) { - folderAdd.call(this, parent, true); - } - - var isUnicodeString = dataType === "string" && o.binary === false && o.base64 === false; - if (!originalOptions || typeof originalOptions.binary === "undefined") { - o.binary = !isUnicodeString; - } - - - var isCompressedEmpty = (data instanceof CompressedObject) && data.uncompressedSize === 0; - - if (isCompressedEmpty || o.dir || !data || data.length === 0) { - o.base64 = false; - o.binary = true; - data = ""; - o.compression = "STORE"; - dataType = "string"; - } - - /* - * Convert content to fit. - */ - - var zipObjectContent = null; - if (data instanceof CompressedObject || data instanceof GenericWorker) { - zipObjectContent = data; - } else if (nodejsUtils.isNode && nodejsUtils.isStream(data)) { - zipObjectContent = new NodejsStreamInputAdapter(name, data); - } else { - zipObjectContent = utils.prepareContent(name, data, o.binary, o.optimizedBinaryString, o.base64); - } - - var object = new ZipObject(name, zipObjectContent, o); - this.files[name] = object; - /* - TODO: we can't throw an exception because we have async promises - (we can have a promise of a Date() for example) but returning a - promise is useless because file(name, data) returns the JSZip - object for chaining. Should we break that to allow the user - to catch the error ? - - return external.Promise.resolve(zipObjectContent) - .then(function () { - return object; - }); - */ -}; - -/** - * Find the parent folder of the path. - * @private - * @param {string} path the path to use - * @return {string} the parent folder, or "" - */ -var parentFolder = function (path) { - if (path.slice(-1) === '/') { - path = path.substring(0, path.length - 1); - } - var lastSlash = path.lastIndexOf('/'); - return (lastSlash > 0) ? path.substring(0, lastSlash) : ""; -}; - -/** - * Returns the path with a slash at the end. - * @private - * @param {String} path the path to check. - * @return {String} the path with a trailing slash. - */ -var forceTrailingSlash = function(path) { - // Check the name ends with a / - if (path.slice(-1) !== "/") { - path += "/"; // IE doesn't like substr(-1) - } - return path; -}; - -/** - * Add a (sub) folder in the current folder. - * @private - * @param {string} name the folder's name - * @param {boolean=} [createFolders] If true, automatically create sub - * folders. Defaults to false. - * @return {Object} the new folder. - */ -var folderAdd = function(name, createFolders) { - createFolders = (typeof createFolders !== 'undefined') ? createFolders : defaults.createFolders; - - name = forceTrailingSlash(name); - - // Does this folder already exist? - if (!this.files[name]) { - fileAdd.call(this, name, null, { - dir: true, - createFolders: createFolders - }); - } - return this.files[name]; -}; - -/** -* Cross-window, cross-Node-context regular expression detection -* @param {Object} object Anything -* @return {Boolean} true if the object is a regular expression, -* false otherwise -*/ -function isRegExp(object) { - return Object.prototype.toString.call(object) === "[object RegExp]"; -} - -// return the actual prototype of JSZip -var out = { - /** - * @see loadAsync - */ - load: function() { - throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide."); - }, - - - /** - * Call a callback function for each entry at this folder level. - * @param {Function} cb the callback function: - * function (relativePath, file) {...} - * It takes 2 arguments : the relative path and the file. - */ - forEach: function(cb) { - var filename, relativePath, file; - for (filename in this.files) { - if (!this.files.hasOwnProperty(filename)) { - continue; - } - file = this.files[filename]; - relativePath = filename.slice(this.root.length, filename.length); - if (relativePath && filename.slice(0, this.root.length) === this.root) { // the file is in the current root - cb(relativePath, file); // TODO reverse the parameters ? need to be clean AND consistent with the filter search fn... - } - } - }, - - /** - * Filter nested files/folders with the specified function. - * @param {Function} search the predicate to use : - * function (relativePath, file) {...} - * It takes 2 arguments : the relative path and the file. - * @return {Array} An array of matching elements. - */ - filter: function(search) { - var result = []; - this.forEach(function (relativePath, entry) { - if (search(relativePath, entry)) { // the file matches the function - result.push(entry); - } - - }); - return result; - }, - - /** - * Add a file to the zip file, or search a file. - * @param {string|RegExp} name The name of the file to add (if data is defined), - * the name of the file to find (if no data) or a regex to match files. - * @param {String|ArrayBuffer|Uint8Array|Buffer} data The file data, either raw or base64 encoded - * @param {Object} o File options - * @return {JSZip|Object|Array} this JSZip object (when adding a file), - * a file (when searching by string) or an array of files (when searching by regex). - */ - file: function(name, data, o) { - if (arguments.length === 1) { - if (isRegExp(name)) { - var regexp = name; - return this.filter(function(relativePath, file) { - return !file.dir && regexp.test(relativePath); - }); - } - else { // text - var obj = this.files[this.root + name]; - if (obj && !obj.dir) { - return obj; - } else { - return null; - } - } - } - else { // more than one argument : we have data ! - name = this.root + name; - fileAdd.call(this, name, data, o); - } - return this; - }, - - /** - * Add a directory to the zip file, or search. - * @param {String|RegExp} arg The name of the directory to add, or a regex to search folders. - * @return {JSZip} an object with the new directory as the root, or an array containing matching folders. - */ - folder: function(arg) { - if (!arg) { - return this; - } - - if (isRegExp(arg)) { - return this.filter(function(relativePath, file) { - return file.dir && arg.test(relativePath); - }); - } - - // else, name is a new folder - var name = this.root + arg; - var newFolder = folderAdd.call(this, name); - - // Allow chaining by returning a new object with this folder as the root - var ret = this.clone(); - ret.root = newFolder.name; - return ret; - }, - - /** - * Delete a file, or a directory and all sub-files, from the zip - * @param {string} name the name of the file to delete - * @return {JSZip} this JSZip object - */ - remove: function(name) { - name = this.root + name; - var file = this.files[name]; - if (!file) { - // Look for any folders - if (name.slice(-1) !== "/") { - name += "/"; - } - file = this.files[name]; - } - - if (file && !file.dir) { - // file - delete this.files[name]; - } else { - // maybe a folder, delete recursively - var kids = this.filter(function(relativePath, file) { - return file.name.slice(0, name.length) === name; - }); - for (var i = 0; i < kids.length; i++) { - delete this.files[kids[i].name]; - } - } - - return this; - }, - - /** - * Generate the complete zip file - * @param {Object} options the options to generate the zip file : - * - compression, "STORE" by default. - * - type, "base64" by default. Values are : string, base64, uint8array, arraybuffer, blob. - * @return {String|Uint8Array|ArrayBuffer|Buffer|Blob} the zip file - */ - generate: function(options) { - throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide."); - }, - - /** - * Generate the complete zip file as an internal stream. - * @param {Object} options the options to generate the zip file : - * - compression, "STORE" by default. - * - type, "base64" by default. Values are : string, base64, uint8array, arraybuffer, blob. - * @return {StreamHelper} the streamed zip file. - */ - generateInternalStream: function(options) { - var worker, opts = {}; - try { - opts = utils.extend(options || {}, { - streamFiles: false, - compression: "STORE", - compressionOptions : null, - type: "", - platform: "DOS", - comment: null, - mimeType: 'application/zip', - encodeFileName: utf8.utf8encode - }); - - opts.type = opts.type.toLowerCase(); - opts.compression = opts.compression.toUpperCase(); - - // "binarystring" is prefered but the internals use "string". - if(opts.type === "binarystring") { - opts.type = "string"; - } - - if (!opts.type) { - throw new Error("No output type specified."); - } - - utils.checkSupport(opts.type); - - // accept nodejs `process.platform` - if( - opts.platform === 'darwin' || - opts.platform === 'freebsd' || - opts.platform === 'linux' || - opts.platform === 'sunos' - ) { - opts.platform = "UNIX"; - } - if (opts.platform === 'win32') { - opts.platform = "DOS"; - } - - var comment = opts.comment || this.comment || ""; - worker = generate.generateWorker(this, opts, comment); - } catch (e) { - worker = new GenericWorker("error"); - worker.error(e); - } - return new StreamHelper(worker, opts.type || "string", opts.mimeType); - }, - /** - * Generate the complete zip file asynchronously. - * @see generateInternalStream - */ - generateAsync: function(options, onUpdate) { - return this.generateInternalStream(options).accumulate(onUpdate); - }, - /** - * Generate the complete zip file asynchronously. - * @see generateInternalStream - */ - generateNodeStream: function(options, onUpdate) { - options = options || {}; - if (!options.type) { - options.type = "nodebuffer"; - } - return this.generateInternalStream(options).toNodejsStream(onUpdate); - } -}; -module.exports = out; - -},{"./compressedObject":2,"./defaults":5,"./generate":9,"./nodejs/NodejsStreamInputAdapter":12,"./nodejsUtils":14,"./stream/GenericWorker":28,"./stream/StreamHelper":29,"./utf8":31,"./utils":32,"./zipObject":35}],16:[function(require,module,exports){ -/* - * This file is used by module bundlers (browserify/webpack/etc) when - * including a stream implementation. We use "readable-stream" to get a - * consistent behavior between nodejs versions but bundlers often have a shim - * for "stream". Using this shim greatly improve the compatibility and greatly - * reduce the final size of the bundle (only one stream implementation, not - * two). - */ -module.exports = require("stream"); - -},{"stream":undefined}],17:[function(require,module,exports){ -'use strict'; -var DataReader = require('./DataReader'); -var utils = require('../utils'); - -function ArrayReader(data) { - DataReader.call(this, data); - for(var i = 0; i < this.data.length; i++) { - data[i] = data[i] & 0xFF; - } -} -utils.inherits(ArrayReader, DataReader); -/** - * @see DataReader.byteAt - */ -ArrayReader.prototype.byteAt = function(i) { - return this.data[this.zero + i]; -}; -/** - * @see DataReader.lastIndexOfSignature - */ -ArrayReader.prototype.lastIndexOfSignature = function(sig) { - var sig0 = sig.charCodeAt(0), - sig1 = sig.charCodeAt(1), - sig2 = sig.charCodeAt(2), - sig3 = sig.charCodeAt(3); - for (var i = this.length - 4; i >= 0; --i) { - if (this.data[i] === sig0 && this.data[i + 1] === sig1 && this.data[i + 2] === sig2 && this.data[i + 3] === sig3) { - return i - this.zero; - } - } - - return -1; -}; -/** - * @see DataReader.readAndCheckSignature - */ -ArrayReader.prototype.readAndCheckSignature = function (sig) { - var sig0 = sig.charCodeAt(0), - sig1 = sig.charCodeAt(1), - sig2 = sig.charCodeAt(2), - sig3 = sig.charCodeAt(3), - data = this.readData(4); - return sig0 === data[0] && sig1 === data[1] && sig2 === data[2] && sig3 === data[3]; -}; -/** - * @see DataReader.readData - */ -ArrayReader.prototype.readData = function(size) { - this.checkOffset(size); - if(size === 0) { - return []; - } - var result = this.data.slice(this.zero + this.index, this.zero + this.index + size); - this.index += size; - return result; -}; -module.exports = ArrayReader; - -},{"../utils":32,"./DataReader":18}],18:[function(require,module,exports){ -'use strict'; -var utils = require('../utils'); - -function DataReader(data) { - this.data = data; // type : see implementation - this.length = data.length; - this.index = 0; - this.zero = 0; -} -DataReader.prototype = { - /** - * Check that the offset will not go too far. - * @param {string} offset the additional offset to check. - * @throws {Error} an Error if the offset is out of bounds. - */ - checkOffset: function(offset) { - this.checkIndex(this.index + offset); - }, - /** - * Check that the specified index will not be too far. - * @param {string} newIndex the index to check. - * @throws {Error} an Error if the index is out of bounds. - */ - checkIndex: function(newIndex) { - if (this.length < this.zero + newIndex || newIndex < 0) { - throw new Error("End of data reached (data length = " + this.length + ", asked index = " + (newIndex) + "). Corrupted zip ?"); - } - }, - /** - * Change the index. - * @param {number} newIndex The new index. - * @throws {Error} if the new index is out of the data. - */ - setIndex: function(newIndex) { - this.checkIndex(newIndex); - this.index = newIndex; - }, - /** - * Skip the next n bytes. - * @param {number} n the number of bytes to skip. - * @throws {Error} if the new index is out of the data. - */ - skip: function(n) { - this.setIndex(this.index + n); - }, - /** - * Get the byte at the specified index. - * @param {number} i the index to use. - * @return {number} a byte. - */ - byteAt: function(i) { - // see implementations - }, - /** - * Get the next number with a given byte size. - * @param {number} size the number of bytes to read. - * @return {number} the corresponding number. - */ - readInt: function(size) { - var result = 0, - i; - this.checkOffset(size); - for (i = this.index + size - 1; i >= this.index; i--) { - result = (result << 8) + this.byteAt(i); - } - this.index += size; - return result; - }, - /** - * Get the next string with a given byte size. - * @param {number} size the number of bytes to read. - * @return {string} the corresponding string. - */ - readString: function(size) { - return utils.transformTo("string", this.readData(size)); - }, - /** - * Get raw data without conversion, bytes. - * @param {number} size the number of bytes to read. - * @return {Object} the raw data, implementation specific. - */ - readData: function(size) { - // see implementations - }, - /** - * Find the last occurence of a zip signature (4 bytes). - * @param {string} sig the signature to find. - * @return {number} the index of the last occurence, -1 if not found. - */ - lastIndexOfSignature: function(sig) { - // see implementations - }, - /** - * Read the signature (4 bytes) at the current position and compare it with sig. - * @param {string} sig the expected signature - * @return {boolean} true if the signature matches, false otherwise. - */ - readAndCheckSignature: function(sig) { - // see implementations - }, - /** - * Get the next date. - * @return {Date} the date. - */ - readDate: function() { - var dostime = this.readInt(4); - return new Date(Date.UTC( - ((dostime >> 25) & 0x7f) + 1980, // year - ((dostime >> 21) & 0x0f) - 1, // month - (dostime >> 16) & 0x1f, // day - (dostime >> 11) & 0x1f, // hour - (dostime >> 5) & 0x3f, // minute - (dostime & 0x1f) << 1)); // second - } -}; -module.exports = DataReader; - -},{"../utils":32}],19:[function(require,module,exports){ -'use strict'; -var Uint8ArrayReader = require('./Uint8ArrayReader'); -var utils = require('../utils'); - -function NodeBufferReader(data) { - Uint8ArrayReader.call(this, data); -} -utils.inherits(NodeBufferReader, Uint8ArrayReader); - -/** - * @see DataReader.readData - */ -NodeBufferReader.prototype.readData = function(size) { - this.checkOffset(size); - var result = this.data.slice(this.zero + this.index, this.zero + this.index + size); - this.index += size; - return result; -}; -module.exports = NodeBufferReader; - -},{"../utils":32,"./Uint8ArrayReader":21}],20:[function(require,module,exports){ -'use strict'; -var DataReader = require('./DataReader'); -var utils = require('../utils'); - -function StringReader(data) { - DataReader.call(this, data); -} -utils.inherits(StringReader, DataReader); -/** - * @see DataReader.byteAt - */ -StringReader.prototype.byteAt = function(i) { - return this.data.charCodeAt(this.zero + i); -}; -/** - * @see DataReader.lastIndexOfSignature - */ -StringReader.prototype.lastIndexOfSignature = function(sig) { - return this.data.lastIndexOf(sig) - this.zero; -}; -/** - * @see DataReader.readAndCheckSignature - */ -StringReader.prototype.readAndCheckSignature = function (sig) { - var data = this.readData(4); - return sig === data; -}; -/** - * @see DataReader.readData - */ -StringReader.prototype.readData = function(size) { - this.checkOffset(size); - // this will work because the constructor applied the "& 0xff" mask. - var result = this.data.slice(this.zero + this.index, this.zero + this.index + size); - this.index += size; - return result; -}; -module.exports = StringReader; - -},{"../utils":32,"./DataReader":18}],21:[function(require,module,exports){ -'use strict'; -var ArrayReader = require('./ArrayReader'); -var utils = require('../utils'); - -function Uint8ArrayReader(data) { - ArrayReader.call(this, data); -} -utils.inherits(Uint8ArrayReader, ArrayReader); -/** - * @see DataReader.readData - */ -Uint8ArrayReader.prototype.readData = function(size) { - this.checkOffset(size); - if(size === 0) { - // in IE10, when using subarray(idx, idx), we get the array [0x00] instead of []. - return new Uint8Array(0); - } - var result = this.data.subarray(this.zero + this.index, this.zero + this.index + size); - this.index += size; - return result; -}; -module.exports = Uint8ArrayReader; - -},{"../utils":32,"./ArrayReader":17}],22:[function(require,module,exports){ -'use strict'; - -var utils = require('../utils'); -var support = require('../support'); -var ArrayReader = require('./ArrayReader'); -var StringReader = require('./StringReader'); -var NodeBufferReader = require('./NodeBufferReader'); -var Uint8ArrayReader = require('./Uint8ArrayReader'); - -/** - * Create a reader adapted to the data. - * @param {String|ArrayBuffer|Uint8Array|Buffer} data the data to read. - * @return {DataReader} the data reader. - */ -module.exports = function (data) { - var type = utils.getTypeOf(data); - utils.checkSupport(type); - if (type === "string" && !support.uint8array) { - return new StringReader(data); - } - if (type === "nodebuffer") { - return new NodeBufferReader(data); - } - if (support.uint8array) { - return new Uint8ArrayReader(utils.transformTo("uint8array", data)); - } - return new ArrayReader(utils.transformTo("array", data)); -}; - -},{"../support":30,"../utils":32,"./ArrayReader":17,"./NodeBufferReader":19,"./StringReader":20,"./Uint8ArrayReader":21}],23:[function(require,module,exports){ -'use strict'; -exports.LOCAL_FILE_HEADER = "PK\x03\x04"; -exports.CENTRAL_FILE_HEADER = "PK\x01\x02"; -exports.CENTRAL_DIRECTORY_END = "PK\x05\x06"; -exports.ZIP64_CENTRAL_DIRECTORY_LOCATOR = "PK\x06\x07"; -exports.ZIP64_CENTRAL_DIRECTORY_END = "PK\x06\x06"; -exports.DATA_DESCRIPTOR = "PK\x07\x08"; - -},{}],24:[function(require,module,exports){ -'use strict'; - -var GenericWorker = require('./GenericWorker'); -var utils = require('../utils'); - -/** - * A worker which convert chunks to a specified type. - * @constructor - * @param {String} destType the destination type. - */ -function ConvertWorker(destType) { - GenericWorker.call(this, "ConvertWorker to " + destType); - this.destType = destType; -} -utils.inherits(ConvertWorker, GenericWorker); - -/** - * @see GenericWorker.processChunk - */ -ConvertWorker.prototype.processChunk = function (chunk) { - this.push({ - data : utils.transformTo(this.destType, chunk.data), - meta : chunk.meta - }); -}; -module.exports = ConvertWorker; - -},{"../utils":32,"./GenericWorker":28}],25:[function(require,module,exports){ -'use strict'; - -var GenericWorker = require('./GenericWorker'); -var crc32 = require('../crc32'); -var utils = require('../utils'); - -/** - * A worker which calculate the crc32 of the data flowing through. - * @constructor - */ -function Crc32Probe() { - GenericWorker.call(this, "Crc32Probe"); - this.withStreamInfo("crc32", 0); -} -utils.inherits(Crc32Probe, GenericWorker); - -/** - * @see GenericWorker.processChunk - */ -Crc32Probe.prototype.processChunk = function (chunk) { - this.streamInfo.crc32 = crc32(chunk.data, this.streamInfo.crc32 || 0); - this.push(chunk); -}; -module.exports = Crc32Probe; - -},{"../crc32":4,"../utils":32,"./GenericWorker":28}],26:[function(require,module,exports){ -'use strict'; - -var utils = require('../utils'); -var GenericWorker = require('./GenericWorker'); - -/** - * A worker which calculate the total length of the data flowing through. - * @constructor - * @param {String} propName the name used to expose the length - */ -function DataLengthProbe(propName) { - GenericWorker.call(this, "DataLengthProbe for " + propName); - this.propName = propName; - this.withStreamInfo(propName, 0); -} -utils.inherits(DataLengthProbe, GenericWorker); - -/** - * @see GenericWorker.processChunk - */ -DataLengthProbe.prototype.processChunk = function (chunk) { - if(chunk) { - var length = this.streamInfo[this.propName] || 0; - this.streamInfo[this.propName] = length + chunk.data.length; - } - GenericWorker.prototype.processChunk.call(this, chunk); -}; -module.exports = DataLengthProbe; - - -},{"../utils":32,"./GenericWorker":28}],27:[function(require,module,exports){ -'use strict'; - -var utils = require('../utils'); -var GenericWorker = require('./GenericWorker'); - -// the size of the generated chunks -// TODO expose this as a public variable -var DEFAULT_BLOCK_SIZE = 16 * 1024; - -/** - * A worker that reads a content and emits chunks. - * @constructor - * @param {Promise} dataP the promise of the data to split - */ -function DataWorker(dataP) { - GenericWorker.call(this, "DataWorker"); - var self = this; - this.dataIsReady = false; - this.index = 0; - this.max = 0; - this.data = null; - this.type = ""; - - this._tickScheduled = false; - - dataP.then(function (data) { - self.dataIsReady = true; - self.data = data; - self.max = data && data.length || 0; - self.type = utils.getTypeOf(data); - if(!self.isPaused) { - self._tickAndRepeat(); - } - }, function (e) { - self.error(e); - }); -} - -utils.inherits(DataWorker, GenericWorker); - -/** - * @see GenericWorker.cleanUp - */ -DataWorker.prototype.cleanUp = function () { - GenericWorker.prototype.cleanUp.call(this); - this.data = null; -}; - -/** - * @see GenericWorker.resume - */ -DataWorker.prototype.resume = function () { - if(!GenericWorker.prototype.resume.call(this)) { - return false; - } - - if (!this._tickScheduled && this.dataIsReady) { - this._tickScheduled = true; - utils.delay(this._tickAndRepeat, [], this); - } - return true; -}; - -/** - * Trigger a tick a schedule an other call to this function. - */ -DataWorker.prototype._tickAndRepeat = function() { - this._tickScheduled = false; - if(this.isPaused || this.isFinished) { - return; - } - this._tick(); - if(!this.isFinished) { - utils.delay(this._tickAndRepeat, [], this); - this._tickScheduled = true; - } -}; - -/** - * Read and push a chunk. - */ -DataWorker.prototype._tick = function() { - - if(this.isPaused || this.isFinished) { - return false; - } - - var size = DEFAULT_BLOCK_SIZE; - var data = null, nextIndex = Math.min(this.max, this.index + size); - if (this.index >= this.max) { - // EOF - return this.end(); - } else { - switch(this.type) { - case "string": - data = this.data.substring(this.index, nextIndex); - break; - case "uint8array": - data = this.data.subarray(this.index, nextIndex); - break; - case "array": - case "nodebuffer": - data = this.data.slice(this.index, nextIndex); - break; - } - this.index = nextIndex; - return this.push({ - data : data, - meta : { - percent : this.max ? this.index / this.max * 100 : 0 - } - }); - } -}; - -module.exports = DataWorker; - -},{"../utils":32,"./GenericWorker":28}],28:[function(require,module,exports){ -'use strict'; - -/** - * A worker that does nothing but passing chunks to the next one. This is like - * a nodejs stream but with some differences. On the good side : - * - it works on IE 6-9 without any issue / polyfill - * - it weights less than the full dependencies bundled with browserify - * - it forwards errors (no need to declare an error handler EVERYWHERE) - * - * A chunk is an object with 2 attributes : `meta` and `data`. The former is an - * object containing anything (`percent` for example), see each worker for more - * details. The latter is the real data (String, Uint8Array, etc). - * - * @constructor - * @param {String} name the name of the stream (mainly used for debugging purposes) - */ -function GenericWorker(name) { - // the name of the worker - this.name = name || "default"; - // an object containing metadata about the workers chain - this.streamInfo = {}; - // an error which happened when the worker was paused - this.generatedError = null; - // an object containing metadata to be merged by this worker into the general metadata - this.extraStreamInfo = {}; - // true if the stream is paused (and should not do anything), false otherwise - this.isPaused = true; - // true if the stream is finished (and should not do anything), false otherwise - this.isFinished = false; - // true if the stream is locked to prevent further structure updates (pipe), false otherwise - this.isLocked = false; - // the event listeners - this._listeners = { - 'data':[], - 'end':[], - 'error':[] - }; - // the previous worker, if any - this.previous = null; -} - -GenericWorker.prototype = { - /** - * Push a chunk to the next workers. - * @param {Object} chunk the chunk to push - */ - push : function (chunk) { - this.emit("data", chunk); - }, - /** - * End the stream. - * @return {Boolean} true if this call ended the worker, false otherwise. - */ - end : function () { - if (this.isFinished) { - return false; - } - - this.flush(); - try { - this.emit("end"); - this.cleanUp(); - this.isFinished = true; - } catch (e) { - this.emit("error", e); - } - return true; - }, - /** - * End the stream with an error. - * @param {Error} e the error which caused the premature end. - * @return {Boolean} true if this call ended the worker with an error, false otherwise. - */ - error : function (e) { - if (this.isFinished) { - return false; - } - - if(this.isPaused) { - this.generatedError = e; - } else { - this.isFinished = true; - - this.emit("error", e); - - // in the workers chain exploded in the middle of the chain, - // the error event will go downward but we also need to notify - // workers upward that there has been an error. - if(this.previous) { - this.previous.error(e); - } - - this.cleanUp(); - } - return true; - }, - /** - * Add a callback on an event. - * @param {String} name the name of the event (data, end, error) - * @param {Function} listener the function to call when the event is triggered - * @return {GenericWorker} the current object for chainability - */ - on : function (name, listener) { - this._listeners[name].push(listener); - return this; - }, - /** - * Clean any references when a worker is ending. - */ - cleanUp : function () { - this.streamInfo = this.generatedError = this.extraStreamInfo = null; - this._listeners = []; - }, - /** - * Trigger an event. This will call registered callback with the provided arg. - * @param {String} name the name of the event (data, end, error) - * @param {Object} arg the argument to call the callback with. - */ - emit : function (name, arg) { - if (this._listeners[name]) { - for(var i = 0; i < this._listeners[name].length; i++) { - this._listeners[name][i].call(this, arg); - } - } - }, - /** - * Chain a worker with an other. - * @param {Worker} next the worker receiving events from the current one. - * @return {worker} the next worker for chainability - */ - pipe : function (next) { - return next.registerPrevious(this); - }, - /** - * Same as `pipe` in the other direction. - * Using an API with `pipe(next)` is very easy. - * Implementing the API with the point of view of the next one registering - * a source is easier, see the ZipFileWorker. - * @param {Worker} previous the previous worker, sending events to this one - * @return {Worker} the current worker for chainability - */ - registerPrevious : function (previous) { - if (this.isLocked) { - throw new Error("The stream '" + this + "' has already been used."); - } - - // sharing the streamInfo... - this.streamInfo = previous.streamInfo; - // ... and adding our own bits - this.mergeStreamInfo(); - this.previous = previous; - var self = this; - previous.on('data', function (chunk) { - self.processChunk(chunk); - }); - previous.on('end', function () { - self.end(); - }); - previous.on('error', function (e) { - self.error(e); - }); - return this; - }, - /** - * Pause the stream so it doesn't send events anymore. - * @return {Boolean} true if this call paused the worker, false otherwise. - */ - pause : function () { - if(this.isPaused || this.isFinished) { - return false; - } - this.isPaused = true; - - if(this.previous) { - this.previous.pause(); - } - return true; - }, - /** - * Resume a paused stream. - * @return {Boolean} true if this call resumed the worker, false otherwise. - */ - resume : function () { - if(!this.isPaused || this.isFinished) { - return false; - } - this.isPaused = false; - - // if true, the worker tried to resume but failed - var withError = false; - if(this.generatedError) { - this.error(this.generatedError); - withError = true; - } - if(this.previous) { - this.previous.resume(); - } - - return !withError; - }, - /** - * Flush any remaining bytes as the stream is ending. - */ - flush : function () {}, - /** - * Process a chunk. This is usually the method overridden. - * @param {Object} chunk the chunk to process. - */ - processChunk : function(chunk) { - this.push(chunk); - }, - /** - * Add a key/value to be added in the workers chain streamInfo once activated. - * @param {String} key the key to use - * @param {Object} value the associated value - * @return {Worker} the current worker for chainability - */ - withStreamInfo : function (key, value) { - this.extraStreamInfo[key] = value; - this.mergeStreamInfo(); - return this; - }, - /** - * Merge this worker's streamInfo into the chain's streamInfo. - */ - mergeStreamInfo : function () { - for(var key in this.extraStreamInfo) { - if (!this.extraStreamInfo.hasOwnProperty(key)) { - continue; - } - this.streamInfo[key] = this.extraStreamInfo[key]; - } - }, - - /** - * Lock the stream to prevent further updates on the workers chain. - * After calling this method, all calls to pipe will fail. - */ - lock: function () { - if (this.isLocked) { - throw new Error("The stream '" + this + "' has already been used."); - } - this.isLocked = true; - if (this.previous) { - this.previous.lock(); - } - }, - - /** - * - * Pretty print the workers chain. - */ - toString : function () { - var me = "Worker " + this.name; - if (this.previous) { - return this.previous + " -> " + me; - } else { - return me; - } - } -}; - -module.exports = GenericWorker; - -},{}],29:[function(require,module,exports){ -'use strict'; - -var utils = require('../utils'); -var ConvertWorker = require('./ConvertWorker'); -var GenericWorker = require('./GenericWorker'); -var base64 = require('../base64'); -var support = require("../support"); -var external = require("../external"); - -var NodejsStreamOutputAdapter = null; -if (support.nodestream) { - try { - NodejsStreamOutputAdapter = require('../nodejs/NodejsStreamOutputAdapter'); - } catch(e) {} -} - -/** - * Apply the final transformation of the data. If the user wants a Blob for - * example, it's easier to work with an U8intArray and finally do the - * ArrayBuffer/Blob conversion. - * @param {String} type the name of the final type - * @param {String|Uint8Array|Buffer} content the content to transform - * @param {String} mimeType the mime type of the content, if applicable. - * @return {String|Uint8Array|ArrayBuffer|Buffer|Blob} the content in the right format. - */ -function transformZipOutput(type, content, mimeType) { - switch(type) { - case "blob" : - return utils.newBlob(utils.transformTo("arraybuffer", content), mimeType); - case "base64" : - return base64.encode(content); - default : - return utils.transformTo(type, content); - } -} - -/** - * Concatenate an array of data of the given type. - * @param {String} type the type of the data in the given array. - * @param {Array} dataArray the array containing the data chunks to concatenate - * @return {String|Uint8Array|Buffer} the concatenated data - * @throws Error if the asked type is unsupported - */ -function concat (type, dataArray) { - var i, index = 0, res = null, totalLength = 0; - for(i = 0; i < dataArray.length; i++) { - totalLength += dataArray[i].length; - } - switch(type) { - case "string": - return dataArray.join(""); - case "array": - return Array.prototype.concat.apply([], dataArray); - case "uint8array": - res = new Uint8Array(totalLength); - for(i = 0; i < dataArray.length; i++) { - res.set(dataArray[i], index); - index += dataArray[i].length; - } - return res; - case "nodebuffer": - return Buffer.concat(dataArray); - default: - throw new Error("concat : unsupported type '" + type + "'"); - } -} - -/** - * Listen a StreamHelper, accumulate its content and concatenate it into a - * complete block. - * @param {StreamHelper} helper the helper to use. - * @param {Function} updateCallback a callback called on each update. Called - * with one arg : - * - the metadata linked to the update received. - * @return Promise the promise for the accumulation. - */ -function accumulate(helper, updateCallback) { - return new external.Promise(function (resolve, reject){ - var dataArray = []; - var chunkType = helper._internalType, - resultType = helper._outputType, - mimeType = helper._mimeType; - helper - .on('data', function (data, meta) { - dataArray.push(data); - if(updateCallback) { - updateCallback(meta); - } - }) - .on('error', function(err) { - dataArray = []; - reject(err); - }) - .on('end', function (){ - try { - var result = transformZipOutput(resultType, concat(chunkType, dataArray), mimeType); - resolve(result); - } catch (e) { - reject(e); - } - dataArray = []; - }) - .resume(); - }); -} - -/** - * An helper to easily use workers outside of JSZip. - * @constructor - * @param {Worker} worker the worker to wrap - * @param {String} outputType the type of data expected by the use - * @param {String} mimeType the mime type of the content, if applicable. - */ -function StreamHelper(worker, outputType, mimeType) { - var internalType = outputType; - switch(outputType) { - case "blob": - case "arraybuffer": - internalType = "uint8array"; - break; - case "base64": - internalType = "string"; - break; - } - - try { - // the type used internally - this._internalType = internalType; - // the type used to output results - this._outputType = outputType; - // the mime type - this._mimeType = mimeType; - utils.checkSupport(internalType); - this._worker = worker.pipe(new ConvertWorker(internalType)); - // the last workers can be rewired without issues but we need to - // prevent any updates on previous workers. - worker.lock(); - } catch(e) { - this._worker = new GenericWorker("error"); - this._worker.error(e); - } -} - -StreamHelper.prototype = { - /** - * Listen a StreamHelper, accumulate its content and concatenate it into a - * complete block. - * @param {Function} updateCb the update callback. - * @return Promise the promise for the accumulation. - */ - accumulate : function (updateCb) { - return accumulate(this, updateCb); - }, - /** - * Add a listener on an event triggered on a stream. - * @param {String} evt the name of the event - * @param {Function} fn the listener - * @return {StreamHelper} the current helper. - */ - on : function (evt, fn) { - var self = this; - - if(evt === "data") { - this._worker.on(evt, function (chunk) { - fn.call(self, chunk.data, chunk.meta); - }); - } else { - this._worker.on(evt, function () { - utils.delay(fn, arguments, self); - }); - } - return this; - }, - /** - * Resume the flow of chunks. - * @return {StreamHelper} the current helper. - */ - resume : function () { - utils.delay(this._worker.resume, [], this._worker); - return this; - }, - /** - * Pause the flow of chunks. - * @return {StreamHelper} the current helper. - */ - pause : function () { - this._worker.pause(); - return this; - }, - /** - * Return a nodejs stream for this helper. - * @param {Function} updateCb the update callback. - * @return {NodejsStreamOutputAdapter} the nodejs stream. - */ - toNodejsStream : function (updateCb) { - utils.checkSupport("nodestream"); - if (this._outputType !== "nodebuffer") { - // an object stream containing blob/arraybuffer/uint8array/string - // is strange and I don't know if it would be useful. - // I you find this comment and have a good usecase, please open a - // bug report ! - throw new Error(this._outputType + " is not supported by this method"); - } - - return new NodejsStreamOutputAdapter(this, { - objectMode : this._outputType !== "nodebuffer" - }, updateCb); - } -}; - - -module.exports = StreamHelper; - -},{"../base64":1,"../external":6,"../nodejs/NodejsStreamOutputAdapter":13,"../support":30,"../utils":32,"./ConvertWorker":24,"./GenericWorker":28}],30:[function(require,module,exports){ -'use strict'; - -exports.base64 = true; -exports.array = true; -exports.string = true; -exports.arraybuffer = typeof ArrayBuffer !== "undefined" && typeof Uint8Array !== "undefined"; -exports.nodebuffer = typeof Buffer !== "undefined"; -// contains true if JSZip can read/generate Uint8Array, false otherwise. -exports.uint8array = typeof Uint8Array !== "undefined"; - -if (typeof ArrayBuffer === "undefined") { - exports.blob = false; -} -else { - var buffer = new ArrayBuffer(0); - try { - exports.blob = new Blob([buffer], { - type: "application/zip" - }).size === 0; - } - catch (e) { - try { - var Builder = self.BlobBuilder || self.WebKitBlobBuilder || self.MozBlobBuilder || self.MSBlobBuilder; - var builder = new Builder(); - builder.append(buffer); - exports.blob = builder.getBlob('application/zip').size === 0; - } - catch (e) { - exports.blob = false; - } - } -} - -try { - exports.nodestream = !!require('readable-stream').Readable; -} catch(e) { - exports.nodestream = false; -} - -},{"readable-stream":16}],31:[function(require,module,exports){ -'use strict'; - -var utils = require('./utils'); -var support = require('./support'); -var nodejsUtils = require('./nodejsUtils'); -var GenericWorker = require('./stream/GenericWorker'); - -/** - * The following functions come from pako, from pako/lib/utils/strings - * released under the MIT license, see pako https://github.com/nodeca/pako/ - */ - -// Table with utf8 lengths (calculated by first byte of sequence) -// Note, that 5 & 6-byte values and some 4-byte values can not be represented in JS, -// because max possible codepoint is 0x10ffff -var _utf8len = new Array(256); -for (var i=0; i<256; i++) { - _utf8len[i] = (i >= 252 ? 6 : i >= 248 ? 5 : i >= 240 ? 4 : i >= 224 ? 3 : i >= 192 ? 2 : 1); -} -_utf8len[254]=_utf8len[254]=1; // Invalid sequence start - -// convert string to array (typed, when possible) -var string2buf = function (str) { - var buf, c, c2, m_pos, i, str_len = str.length, buf_len = 0; - - // count binary size - for (m_pos = 0; m_pos < str_len; m_pos++) { - c = str.charCodeAt(m_pos); - if ((c & 0xfc00) === 0xd800 && (m_pos+1 < str_len)) { - c2 = str.charCodeAt(m_pos+1); - if ((c2 & 0xfc00) === 0xdc00) { - c = 0x10000 + ((c - 0xd800) << 10) + (c2 - 0xdc00); - m_pos++; - } - } - buf_len += c < 0x80 ? 1 : c < 0x800 ? 2 : c < 0x10000 ? 3 : 4; - } - - // allocate buffer - if (support.uint8array) { - buf = new Uint8Array(buf_len); - } else { - buf = new Array(buf_len); - } - - // convert - for (i=0, m_pos = 0; i < buf_len; m_pos++) { - c = str.charCodeAt(m_pos); - if ((c & 0xfc00) === 0xd800 && (m_pos+1 < str_len)) { - c2 = str.charCodeAt(m_pos+1); - if ((c2 & 0xfc00) === 0xdc00) { - c = 0x10000 + ((c - 0xd800) << 10) + (c2 - 0xdc00); - m_pos++; - } - } - if (c < 0x80) { - /* one byte */ - buf[i++] = c; - } else if (c < 0x800) { - /* two bytes */ - buf[i++] = 0xC0 | (c >>> 6); - buf[i++] = 0x80 | (c & 0x3f); - } else if (c < 0x10000) { - /* three bytes */ - buf[i++] = 0xE0 | (c >>> 12); - buf[i++] = 0x80 | (c >>> 6 & 0x3f); - buf[i++] = 0x80 | (c & 0x3f); - } else { - /* four bytes */ - buf[i++] = 0xf0 | (c >>> 18); - buf[i++] = 0x80 | (c >>> 12 & 0x3f); - buf[i++] = 0x80 | (c >>> 6 & 0x3f); - buf[i++] = 0x80 | (c & 0x3f); - } - } - - return buf; -}; - -// Calculate max possible position in utf8 buffer, -// that will not break sequence. If that's not possible -// - (very small limits) return max size as is. -// -// buf[] - utf8 bytes array -// max - length limit (mandatory); -var utf8border = function(buf, max) { - var pos; - - max = max || buf.length; - if (max > buf.length) { max = buf.length; } - - // go back from last position, until start of sequence found - pos = max-1; - while (pos >= 0 && (buf[pos] & 0xC0) === 0x80) { pos--; } - - // Fuckup - very small and broken sequence, - // return max, because we should return something anyway. - if (pos < 0) { return max; } - - // If we came to start of buffer - that means vuffer is too small, - // return max too. - if (pos === 0) { return max; } - - return (pos + _utf8len[buf[pos]] > max) ? pos : max; -}; - -// convert array to string -var buf2string = function (buf) { - var str, i, out, c, c_len; - var len = buf.length; - - // Reserve max possible length (2 words per char) - // NB: by unknown reasons, Array is significantly faster for - // String.fromCharCode.apply than Uint16Array. - var utf16buf = new Array(len*2); - - for (out=0, i=0; i 4) { utf16buf[out++] = 0xfffd; i += c_len-1; continue; } - - // apply mask on first byte - c &= c_len === 2 ? 0x1f : c_len === 3 ? 0x0f : 0x07; - // join the rest - while (c_len > 1 && i < len) { - c = (c << 6) | (buf[i++] & 0x3f); - c_len--; - } - - // terminated by end of string? - if (c_len > 1) { utf16buf[out++] = 0xfffd; continue; } - - if (c < 0x10000) { - utf16buf[out++] = c; - } else { - c -= 0x10000; - utf16buf[out++] = 0xd800 | ((c >> 10) & 0x3ff); - utf16buf[out++] = 0xdc00 | (c & 0x3ff); - } - } - - // shrinkBuf(utf16buf, out) - if (utf16buf.length !== out) { - if(utf16buf.subarray) { - utf16buf = utf16buf.subarray(0, out); - } else { - utf16buf.length = out; - } - } - - // return String.fromCharCode.apply(null, utf16buf); - return utils.applyFromCharCode(utf16buf); -}; - - -// That's all for the pako functions. - - -/** - * Transform a javascript string into an array (typed if possible) of bytes, - * UTF-8 encoded. - * @param {String} str the string to encode - * @return {Array|Uint8Array|Buffer} the UTF-8 encoded string. - */ -exports.utf8encode = function utf8encode(str) { - if (support.nodebuffer) { - return nodejsUtils.newBufferFrom(str, "utf-8"); - } - - return string2buf(str); -}; - - -/** - * Transform a bytes array (or a representation) representing an UTF-8 encoded - * string into a javascript string. - * @param {Array|Uint8Array|Buffer} buf the data de decode - * @return {String} the decoded string. - */ -exports.utf8decode = function utf8decode(buf) { - if (support.nodebuffer) { - return utils.transformTo("nodebuffer", buf).toString("utf-8"); - } - - buf = utils.transformTo(support.uint8array ? "uint8array" : "array", buf); - - return buf2string(buf); -}; - -/** - * A worker to decode utf8 encoded binary chunks into string chunks. - * @constructor - */ -function Utf8DecodeWorker() { - GenericWorker.call(this, "utf-8 decode"); - // the last bytes if a chunk didn't end with a complete codepoint. - this.leftOver = null; -} -utils.inherits(Utf8DecodeWorker, GenericWorker); - -/** - * @see GenericWorker.processChunk - */ -Utf8DecodeWorker.prototype.processChunk = function (chunk) { - - var data = utils.transformTo(support.uint8array ? "uint8array" : "array", chunk.data); - - // 1st step, re-use what's left of the previous chunk - if (this.leftOver && this.leftOver.length) { - if(support.uint8array) { - var previousData = data; - data = new Uint8Array(previousData.length + this.leftOver.length); - data.set(this.leftOver, 0); - data.set(previousData, this.leftOver.length); - } else { - data = this.leftOver.concat(data); - } - this.leftOver = null; - } - - var nextBoundary = utf8border(data); - var usableData = data; - if (nextBoundary !== data.length) { - if (support.uint8array) { - usableData = data.subarray(0, nextBoundary); - this.leftOver = data.subarray(nextBoundary, data.length); - } else { - usableData = data.slice(0, nextBoundary); - this.leftOver = data.slice(nextBoundary, data.length); - } - } - - this.push({ - data : exports.utf8decode(usableData), - meta : chunk.meta - }); -}; - -/** - * @see GenericWorker.flush - */ -Utf8DecodeWorker.prototype.flush = function () { - if(this.leftOver && this.leftOver.length) { - this.push({ - data : exports.utf8decode(this.leftOver), - meta : {} - }); - this.leftOver = null; - } -}; -exports.Utf8DecodeWorker = Utf8DecodeWorker; - -/** - * A worker to endcode string chunks into utf8 encoded binary chunks. - * @constructor - */ -function Utf8EncodeWorker() { - GenericWorker.call(this, "utf-8 encode"); -} -utils.inherits(Utf8EncodeWorker, GenericWorker); - -/** - * @see GenericWorker.processChunk - */ -Utf8EncodeWorker.prototype.processChunk = function (chunk) { - this.push({ - data : exports.utf8encode(chunk.data), - meta : chunk.meta - }); -}; -exports.Utf8EncodeWorker = Utf8EncodeWorker; - -},{"./nodejsUtils":14,"./stream/GenericWorker":28,"./support":30,"./utils":32}],32:[function(require,module,exports){ -'use strict'; - -var support = require('./support'); -var base64 = require('./base64'); -var nodejsUtils = require('./nodejsUtils'); -var setImmediate = require('set-immediate-shim'); -var external = require("./external"); - - -/** - * Convert a string that pass as a "binary string": it should represent a byte - * array but may have > 255 char codes. Be sure to take only the first byte - * and returns the byte array. - * @param {String} str the string to transform. - * @return {Array|Uint8Array} the string in a binary format. - */ -function string2binary(str) { - var result = null; - if (support.uint8array) { - result = new Uint8Array(str.length); - } else { - result = new Array(str.length); - } - return stringToArrayLike(str, result); -} - -/** - * Create a new blob with the given content and the given type. - * @param {String|ArrayBuffer} part the content to put in the blob. DO NOT use - * an Uint8Array because the stock browser of android 4 won't accept it (it - * will be silently converted to a string, "[object Uint8Array]"). - * - * Use only ONE part to build the blob to avoid a memory leak in IE11 / Edge: - * when a large amount of Array is used to create the Blob, the amount of - * memory consumed is nearly 100 times the original data amount. - * - * @param {String} type the mime type of the blob. - * @return {Blob} the created blob. - */ -exports.newBlob = function(part, type) { - exports.checkSupport("blob"); - - try { - // Blob constructor - return new Blob([part], { - type: type - }); - } - catch (e) { - - try { - // deprecated, browser only, old way - var Builder = self.BlobBuilder || self.WebKitBlobBuilder || self.MozBlobBuilder || self.MSBlobBuilder; - var builder = new Builder(); - builder.append(part); - return builder.getBlob(type); - } - catch (e) { - - // well, fuck ?! - throw new Error("Bug : can't construct the Blob."); - } - } - - -}; -/** - * The identity function. - * @param {Object} input the input. - * @return {Object} the same input. - */ -function identity(input) { - return input; -} - -/** - * Fill in an array with a string. - * @param {String} str the string to use. - * @param {Array|ArrayBuffer|Uint8Array|Buffer} array the array to fill in (will be mutated). - * @return {Array|ArrayBuffer|Uint8Array|Buffer} the updated array. - */ -function stringToArrayLike(str, array) { - for (var i = 0; i < str.length; ++i) { - array[i] = str.charCodeAt(i) & 0xFF; - } - return array; -} - -/** - * An helper for the function arrayLikeToString. - * This contains static informations and functions that - * can be optimized by the browser JIT compiler. - */ -var arrayToStringHelper = { - /** - * Transform an array of int into a string, chunk by chunk. - * See the performances notes on arrayLikeToString. - * @param {Array|ArrayBuffer|Uint8Array|Buffer} array the array to transform. - * @param {String} type the type of the array. - * @param {Integer} chunk the chunk size. - * @return {String} the resulting string. - * @throws Error if the chunk is too big for the stack. - */ - stringifyByChunk: function(array, type, chunk) { - var result = [], k = 0, len = array.length; - // shortcut - if (len <= chunk) { - return String.fromCharCode.apply(null, array); - } - while (k < len) { - if (type === "array" || type === "nodebuffer") { - result.push(String.fromCharCode.apply(null, array.slice(k, Math.min(k + chunk, len)))); - } - else { - result.push(String.fromCharCode.apply(null, array.subarray(k, Math.min(k + chunk, len)))); - } - k += chunk; - } - return result.join(""); - }, - /** - * Call String.fromCharCode on every item in the array. - * This is the naive implementation, which generate A LOT of intermediate string. - * This should be used when everything else fail. - * @param {Array|ArrayBuffer|Uint8Array|Buffer} array the array to transform. - * @return {String} the result. - */ - stringifyByChar: function(array){ - var resultStr = ""; - for(var i = 0; i < array.length; i++) { - resultStr += String.fromCharCode(array[i]); - } - return resultStr; - }, - applyCanBeUsed : { - /** - * true if the browser accepts to use String.fromCharCode on Uint8Array - */ - uint8array : (function () { - try { - return support.uint8array && String.fromCharCode.apply(null, new Uint8Array(1)).length === 1; - } catch (e) { - return false; - } - })(), - /** - * true if the browser accepts to use String.fromCharCode on nodejs Buffer. - */ - nodebuffer : (function () { - try { - return support.nodebuffer && String.fromCharCode.apply(null, nodejsUtils.allocBuffer(1)).length === 1; - } catch (e) { - return false; - } - })() - } -}; - -/** - * Transform an array-like object to a string. - * @param {Array|ArrayBuffer|Uint8Array|Buffer} array the array to transform. - * @return {String} the result. - */ -function arrayLikeToString(array) { - // Performances notes : - // -------------------- - // String.fromCharCode.apply(null, array) is the fastest, see - // see http://jsperf.com/converting-a-uint8array-to-a-string/2 - // but the stack is limited (and we can get huge arrays !). - // - // result += String.fromCharCode(array[i]); generate too many strings ! - // - // This code is inspired by http://jsperf.com/arraybuffer-to-string-apply-performance/2 - // TODO : we now have workers that split the work. Do we still need that ? - var chunk = 65536, - type = exports.getTypeOf(array), - canUseApply = true; - if (type === "uint8array") { - canUseApply = arrayToStringHelper.applyCanBeUsed.uint8array; - } else if (type === "nodebuffer") { - canUseApply = arrayToStringHelper.applyCanBeUsed.nodebuffer; - } - - if (canUseApply) { - while (chunk > 1) { - try { - return arrayToStringHelper.stringifyByChunk(array, type, chunk); - } catch (e) { - chunk = Math.floor(chunk / 2); - } - } - } - - // no apply or chunk error : slow and painful algorithm - // default browser on android 4.* - return arrayToStringHelper.stringifyByChar(array); -} - -exports.applyFromCharCode = arrayLikeToString; - - -/** - * Copy the data from an array-like to an other array-like. - * @param {Array|ArrayBuffer|Uint8Array|Buffer} arrayFrom the origin array. - * @param {Array|ArrayBuffer|Uint8Array|Buffer} arrayTo the destination array which will be mutated. - * @return {Array|ArrayBuffer|Uint8Array|Buffer} the updated destination array. - */ -function arrayLikeToArrayLike(arrayFrom, arrayTo) { - for (var i = 0; i < arrayFrom.length; i++) { - arrayTo[i] = arrayFrom[i]; - } - return arrayTo; -} - -// a matrix containing functions to transform everything into everything. -var transform = {}; - -// string to ? -transform["string"] = { - "string": identity, - "array": function(input) { - return stringToArrayLike(input, new Array(input.length)); - }, - "arraybuffer": function(input) { - return transform["string"]["uint8array"](input).buffer; - }, - "uint8array": function(input) { - return stringToArrayLike(input, new Uint8Array(input.length)); - }, - "nodebuffer": function(input) { - return stringToArrayLike(input, nodejsUtils.allocBuffer(input.length)); - } -}; - -// array to ? -transform["array"] = { - "string": arrayLikeToString, - "array": identity, - "arraybuffer": function(input) { - return (new Uint8Array(input)).buffer; - }, - "uint8array": function(input) { - return new Uint8Array(input); - }, - "nodebuffer": function(input) { - return nodejsUtils.newBufferFrom(input); - } -}; - -// arraybuffer to ? -transform["arraybuffer"] = { - "string": function(input) { - return arrayLikeToString(new Uint8Array(input)); - }, - "array": function(input) { - return arrayLikeToArrayLike(new Uint8Array(input), new Array(input.byteLength)); - }, - "arraybuffer": identity, - "uint8array": function(input) { - return new Uint8Array(input); - }, - "nodebuffer": function(input) { - return nodejsUtils.newBufferFrom(new Uint8Array(input)); - } -}; - -// uint8array to ? -transform["uint8array"] = { - "string": arrayLikeToString, - "array": function(input) { - return arrayLikeToArrayLike(input, new Array(input.length)); - }, - "arraybuffer": function(input) { - return input.buffer; - }, - "uint8array": identity, - "nodebuffer": function(input) { - return nodejsUtils.newBufferFrom(input); - } -}; - -// nodebuffer to ? -transform["nodebuffer"] = { - "string": arrayLikeToString, - "array": function(input) { - return arrayLikeToArrayLike(input, new Array(input.length)); - }, - "arraybuffer": function(input) { - return transform["nodebuffer"]["uint8array"](input).buffer; - }, - "uint8array": function(input) { - return arrayLikeToArrayLike(input, new Uint8Array(input.length)); - }, - "nodebuffer": identity -}; - -/** - * Transform an input into any type. - * The supported output type are : string, array, uint8array, arraybuffer, nodebuffer. - * If no output type is specified, the unmodified input will be returned. - * @param {String} outputType the output type. - * @param {String|Array|ArrayBuffer|Uint8Array|Buffer} input the input to convert. - * @throws {Error} an Error if the browser doesn't support the requested output type. - */ -exports.transformTo = function(outputType, input) { - if (!input) { - // undefined, null, etc - // an empty string won't harm. - input = ""; - } - if (!outputType) { - return input; - } - exports.checkSupport(outputType); - var inputType = exports.getTypeOf(input); - var result = transform[inputType][outputType](input); - return result; -}; - -/** - * Return the type of the input. - * The type will be in a format valid for JSZip.utils.transformTo : string, array, uint8array, arraybuffer. - * @param {Object} input the input to identify. - * @return {String} the (lowercase) type of the input. - */ -exports.getTypeOf = function(input) { - if (typeof input === "string") { - return "string"; - } - if (Object.prototype.toString.call(input) === "[object Array]") { - return "array"; - } - if (support.nodebuffer && nodejsUtils.isBuffer(input)) { - return "nodebuffer"; - } - if (support.uint8array && input instanceof Uint8Array) { - return "uint8array"; - } - if (support.arraybuffer && input instanceof ArrayBuffer) { - return "arraybuffer"; - } -}; - -/** - * Throw an exception if the type is not supported. - * @param {String} type the type to check. - * @throws {Error} an Error if the browser doesn't support the requested type. - */ -exports.checkSupport = function(type) { - var supported = support[type.toLowerCase()]; - if (!supported) { - throw new Error(type + " is not supported by this platform"); - } -}; - -exports.MAX_VALUE_16BITS = 65535; -exports.MAX_VALUE_32BITS = -1; // well, "\xFF\xFF\xFF\xFF\xFF\xFF\xFF\xFF" is parsed as -1 - -/** - * Prettify a string read as binary. - * @param {string} str the string to prettify. - * @return {string} a pretty string. - */ -exports.pretty = function(str) { - var res = '', - code, i; - for (i = 0; i < (str || "").length; i++) { - code = str.charCodeAt(i); - res += '\\x' + (code < 16 ? "0" : "") + code.toString(16).toUpperCase(); - } - return res; -}; - -/** - * Defer the call of a function. - * @param {Function} callback the function to call asynchronously. - * @param {Array} args the arguments to give to the callback. - */ -exports.delay = function(callback, args, self) { - setImmediate(function () { - callback.apply(self || null, args || []); - }); -}; - -/** - * Extends a prototype with an other, without calling a constructor with - * side effects. Inspired by nodejs' `utils.inherits` - * @param {Function} ctor the constructor to augment - * @param {Function} superCtor the parent constructor to use - */ -exports.inherits = function (ctor, superCtor) { - var Obj = function() {}; - Obj.prototype = superCtor.prototype; - ctor.prototype = new Obj(); -}; - -/** - * Merge the objects passed as parameters into a new one. - * @private - * @param {...Object} var_args All objects to merge. - * @return {Object} a new object with the data of the others. - */ -exports.extend = function() { - var result = {}, i, attr; - for (i = 0; i < arguments.length; i++) { // arguments is not enumerable in some browsers - for (attr in arguments[i]) { - if (arguments[i].hasOwnProperty(attr) && typeof result[attr] === "undefined") { - result[attr] = arguments[i][attr]; - } - } - } - return result; -}; - -/** - * Transform arbitrary content into a Promise. - * @param {String} name a name for the content being processed. - * @param {Object} inputData the content to process. - * @param {Boolean} isBinary true if the content is not an unicode string - * @param {Boolean} isOptimizedBinaryString true if the string content only has one byte per character. - * @param {Boolean} isBase64 true if the string content is encoded with base64. - * @return {Promise} a promise in a format usable by JSZip. - */ -exports.prepareContent = function(name, inputData, isBinary, isOptimizedBinaryString, isBase64) { - - // if inputData is already a promise, this flatten it. - var promise = external.Promise.resolve(inputData).then(function(data) { - - - var isBlob = support.blob && (data instanceof Blob || ['[object File]', '[object Blob]'].indexOf(Object.prototype.toString.call(data)) !== -1); - - if (isBlob && typeof FileReader !== "undefined") { - return new external.Promise(function (resolve, reject) { - var reader = new FileReader(); - - reader.onload = function(e) { - resolve(e.target.result); - }; - reader.onerror = function(e) { - reject(e.target.error); - }; - reader.readAsArrayBuffer(data); - }); - } else { - return data; - } - }); - - return promise.then(function(data) { - var dataType = exports.getTypeOf(data); - - if (!dataType) { - return external.Promise.reject( - new Error("Can't read the data of '" + name + "'. Is it " + - "in a supported JavaScript type (String, Blob, ArrayBuffer, etc) ?") - ); - } - // special case : it's way easier to work with Uint8Array than with ArrayBuffer - if (dataType === "arraybuffer") { - data = exports.transformTo("uint8array", data); - } else if (dataType === "string") { - if (isBase64) { - data = base64.decode(data); - } - else if (isBinary) { - // optimizedBinaryString === true means that the file has already been filtered with a 0xFF mask - if (isOptimizedBinaryString !== true) { - // this is a string, not in a base64 format. - // Be sure that this is a correct "binary string" - data = string2binary(data); - } - } - } - return data; - }); -}; - -},{"./base64":1,"./external":6,"./nodejsUtils":14,"./support":30,"set-immediate-shim":54}],33:[function(require,module,exports){ -'use strict'; -var readerFor = require('./reader/readerFor'); -var utils = require('./utils'); -var sig = require('./signature'); -var ZipEntry = require('./zipEntry'); -var utf8 = require('./utf8'); -var support = require('./support'); -// class ZipEntries {{{ -/** - * All the entries in the zip file. - * @constructor - * @param {Object} loadOptions Options for loading the stream. - */ -function ZipEntries(loadOptions) { - this.files = []; - this.loadOptions = loadOptions; -} -ZipEntries.prototype = { - /** - * Check that the reader is on the specified signature. - * @param {string} expectedSignature the expected signature. - * @throws {Error} if it is an other signature. - */ - checkSignature: function(expectedSignature) { - if (!this.reader.readAndCheckSignature(expectedSignature)) { - this.reader.index -= 4; - var signature = this.reader.readString(4); - throw new Error("Corrupted zip or bug: unexpected signature " + "(" + utils.pretty(signature) + ", expected " + utils.pretty(expectedSignature) + ")"); - } - }, - /** - * Check if the given signature is at the given index. - * @param {number} askedIndex the index to check. - * @param {string} expectedSignature the signature to expect. - * @return {boolean} true if the signature is here, false otherwise. - */ - isSignature: function(askedIndex, expectedSignature) { - var currentIndex = this.reader.index; - this.reader.setIndex(askedIndex); - var signature = this.reader.readString(4); - var result = signature === expectedSignature; - this.reader.setIndex(currentIndex); - return result; - }, - /** - * Read the end of the central directory. - */ - readBlockEndOfCentral: function() { - this.diskNumber = this.reader.readInt(2); - this.diskWithCentralDirStart = this.reader.readInt(2); - this.centralDirRecordsOnThisDisk = this.reader.readInt(2); - this.centralDirRecords = this.reader.readInt(2); - this.centralDirSize = this.reader.readInt(4); - this.centralDirOffset = this.reader.readInt(4); - - this.zipCommentLength = this.reader.readInt(2); - // warning : the encoding depends of the system locale - // On a linux machine with LANG=en_US.utf8, this field is utf8 encoded. - // On a windows machine, this field is encoded with the localized windows code page. - var zipComment = this.reader.readData(this.zipCommentLength); - var decodeParamType = support.uint8array ? "uint8array" : "array"; - // To get consistent behavior with the generation part, we will assume that - // this is utf8 encoded unless specified otherwise. - var decodeContent = utils.transformTo(decodeParamType, zipComment); - this.zipComment = this.loadOptions.decodeFileName(decodeContent); - }, - /** - * Read the end of the Zip 64 central directory. - * Not merged with the method readEndOfCentral : - * The end of central can coexist with its Zip64 brother, - * I don't want to read the wrong number of bytes ! - */ - readBlockZip64EndOfCentral: function() { - this.zip64EndOfCentralSize = this.reader.readInt(8); - this.reader.skip(4); - // this.versionMadeBy = this.reader.readString(2); - // this.versionNeeded = this.reader.readInt(2); - this.diskNumber = this.reader.readInt(4); - this.diskWithCentralDirStart = this.reader.readInt(4); - this.centralDirRecordsOnThisDisk = this.reader.readInt(8); - this.centralDirRecords = this.reader.readInt(8); - this.centralDirSize = this.reader.readInt(8); - this.centralDirOffset = this.reader.readInt(8); - - this.zip64ExtensibleData = {}; - var extraDataSize = this.zip64EndOfCentralSize - 44, - index = 0, - extraFieldId, - extraFieldLength, - extraFieldValue; - while (index < extraDataSize) { - extraFieldId = this.reader.readInt(2); - extraFieldLength = this.reader.readInt(4); - extraFieldValue = this.reader.readData(extraFieldLength); - this.zip64ExtensibleData[extraFieldId] = { - id: extraFieldId, - length: extraFieldLength, - value: extraFieldValue - }; - } - }, - /** - * Read the end of the Zip 64 central directory locator. - */ - readBlockZip64EndOfCentralLocator: function() { - this.diskWithZip64CentralDirStart = this.reader.readInt(4); - this.relativeOffsetEndOfZip64CentralDir = this.reader.readInt(8); - this.disksCount = this.reader.readInt(4); - if (this.disksCount > 1) { - throw new Error("Multi-volumes zip are not supported"); - } - }, - /** - * Read the local files, based on the offset read in the central part. - */ - readLocalFiles: function() { - var i, file; - for (i = 0; i < this.files.length; i++) { - file = this.files[i]; - this.reader.setIndex(file.localHeaderOffset); - this.checkSignature(sig.LOCAL_FILE_HEADER); - file.readLocalPart(this.reader); - file.handleUTF8(); - file.processAttributes(); - } - }, - /** - * Read the central directory. - */ - readCentralDir: function() { - var file; - - this.reader.setIndex(this.centralDirOffset); - while (this.reader.readAndCheckSignature(sig.CENTRAL_FILE_HEADER)) { - file = new ZipEntry({ - zip64: this.zip64 - }, this.loadOptions); - file.readCentralPart(this.reader); - this.files.push(file); - } - - if (this.centralDirRecords !== this.files.length) { - if (this.centralDirRecords !== 0 && this.files.length === 0) { - // We expected some records but couldn't find ANY. - // This is really suspicious, as if something went wrong. - throw new Error("Corrupted zip or bug: expected " + this.centralDirRecords + " records in central dir, got " + this.files.length); - } else { - // We found some records but not all. - // Something is wrong but we got something for the user: no error here. - // console.warn("expected", this.centralDirRecords, "records in central dir, got", this.files.length); - } - } - }, - /** - * Read the end of central directory. - */ - readEndOfCentral: function() { - var offset = this.reader.lastIndexOfSignature(sig.CENTRAL_DIRECTORY_END); - if (offset < 0) { - // Check if the content is a truncated zip or complete garbage. - // A "LOCAL_FILE_HEADER" is not required at the beginning (auto - // extractible zip for example) but it can give a good hint. - // If an ajax request was used without responseType, we will also - // get unreadable data. - var isGarbage = !this.isSignature(0, sig.LOCAL_FILE_HEADER); - - if (isGarbage) { - throw new Error("Can't find end of central directory : is this a zip file ? " + - "If it is, see https://stuk.github.io/jszip/documentation/howto/read_zip.html"); - } else { - throw new Error("Corrupted zip: can't find end of central directory"); - } - - } - this.reader.setIndex(offset); - var endOfCentralDirOffset = offset; - this.checkSignature(sig.CENTRAL_DIRECTORY_END); - this.readBlockEndOfCentral(); - - - /* extract from the zip spec : - 4) If one of the fields in the end of central directory - record is too small to hold required data, the field - should be set to -1 (0xFFFF or 0xFFFFFFFF) and the - ZIP64 format record should be created. - 5) The end of central directory record and the - Zip64 end of central directory locator record must - reside on the same disk when splitting or spanning - an archive. - */ - if (this.diskNumber === utils.MAX_VALUE_16BITS || this.diskWithCentralDirStart === utils.MAX_VALUE_16BITS || this.centralDirRecordsOnThisDisk === utils.MAX_VALUE_16BITS || this.centralDirRecords === utils.MAX_VALUE_16BITS || this.centralDirSize === utils.MAX_VALUE_32BITS || this.centralDirOffset === utils.MAX_VALUE_32BITS) { - this.zip64 = true; - - /* - Warning : the zip64 extension is supported, but ONLY if the 64bits integer read from - the zip file can fit into a 32bits integer. This cannot be solved : JavaScript represents - all numbers as 64-bit double precision IEEE 754 floating point numbers. - So, we have 53bits for integers and bitwise operations treat everything as 32bits. - see https://developer.mozilla.org/en-US/docs/JavaScript/Reference/Operators/Bitwise_Operators - and http://www.ecma-international.org/publications/files/ECMA-ST/ECMA-262.pdf section 8.5 - */ - - // should look for a zip64 EOCD locator - offset = this.reader.lastIndexOfSignature(sig.ZIP64_CENTRAL_DIRECTORY_LOCATOR); - if (offset < 0) { - throw new Error("Corrupted zip: can't find the ZIP64 end of central directory locator"); - } - this.reader.setIndex(offset); - this.checkSignature(sig.ZIP64_CENTRAL_DIRECTORY_LOCATOR); - this.readBlockZip64EndOfCentralLocator(); - - // now the zip64 EOCD record - if (!this.isSignature(this.relativeOffsetEndOfZip64CentralDir, sig.ZIP64_CENTRAL_DIRECTORY_END)) { - // console.warn("ZIP64 end of central directory not where expected."); - this.relativeOffsetEndOfZip64CentralDir = this.reader.lastIndexOfSignature(sig.ZIP64_CENTRAL_DIRECTORY_END); - if (this.relativeOffsetEndOfZip64CentralDir < 0) { - throw new Error("Corrupted zip: can't find the ZIP64 end of central directory"); - } - } - this.reader.setIndex(this.relativeOffsetEndOfZip64CentralDir); - this.checkSignature(sig.ZIP64_CENTRAL_DIRECTORY_END); - this.readBlockZip64EndOfCentral(); - } - - var expectedEndOfCentralDirOffset = this.centralDirOffset + this.centralDirSize; - if (this.zip64) { - expectedEndOfCentralDirOffset += 20; // end of central dir 64 locator - expectedEndOfCentralDirOffset += 12 /* should not include the leading 12 bytes */ + this.zip64EndOfCentralSize; - } - - var extraBytes = endOfCentralDirOffset - expectedEndOfCentralDirOffset; - - if (extraBytes > 0) { - // console.warn(extraBytes, "extra bytes at beginning or within zipfile"); - if (this.isSignature(endOfCentralDirOffset, sig.CENTRAL_FILE_HEADER)) { - // The offsets seem wrong, but we have something at the specified offset. - // So… we keep it. - } else { - // the offset is wrong, update the "zero" of the reader - // this happens if data has been prepended (crx files for example) - this.reader.zero = extraBytes; - } - } else if (extraBytes < 0) { - throw new Error("Corrupted zip: missing " + Math.abs(extraBytes) + " bytes."); - } - }, - prepareReader: function(data) { - this.reader = readerFor(data); - }, - /** - * Read a zip file and create ZipEntries. - * @param {String|ArrayBuffer|Uint8Array|Buffer} data the binary string representing a zip file. - */ - load: function(data) { - this.prepareReader(data); - this.readEndOfCentral(); - this.readCentralDir(); - this.readLocalFiles(); - } -}; -// }}} end of ZipEntries -module.exports = ZipEntries; - -},{"./reader/readerFor":22,"./signature":23,"./support":30,"./utf8":31,"./utils":32,"./zipEntry":34}],34:[function(require,module,exports){ -'use strict'; -var readerFor = require('./reader/readerFor'); -var utils = require('./utils'); -var CompressedObject = require('./compressedObject'); -var crc32fn = require('./crc32'); -var utf8 = require('./utf8'); -var compressions = require('./compressions'); -var support = require('./support'); - -var MADE_BY_DOS = 0x00; -var MADE_BY_UNIX = 0x03; - -/** - * Find a compression registered in JSZip. - * @param {string} compressionMethod the method magic to find. - * @return {Object|null} the JSZip compression object, null if none found. - */ -var findCompression = function(compressionMethod) { - for (var method in compressions) { - if (!compressions.hasOwnProperty(method)) { - continue; - } - if (compressions[method].magic === compressionMethod) { - return compressions[method]; - } - } - return null; -}; - -// class ZipEntry {{{ -/** - * An entry in the zip file. - * @constructor - * @param {Object} options Options of the current file. - * @param {Object} loadOptions Options for loading the stream. - */ -function ZipEntry(options, loadOptions) { - this.options = options; - this.loadOptions = loadOptions; -} -ZipEntry.prototype = { - /** - * say if the file is encrypted. - * @return {boolean} true if the file is encrypted, false otherwise. - */ - isEncrypted: function() { - // bit 1 is set - return (this.bitFlag & 0x0001) === 0x0001; - }, - /** - * say if the file has utf-8 filename/comment. - * @return {boolean} true if the filename/comment is in utf-8, false otherwise. - */ - useUTF8: function() { - // bit 11 is set - return (this.bitFlag & 0x0800) === 0x0800; - }, - /** - * Read the local part of a zip file and add the info in this object. - * @param {DataReader} reader the reader to use. - */ - readLocalPart: function(reader) { - var compression, localExtraFieldsLength; - - // we already know everything from the central dir ! - // If the central dir data are false, we are doomed. - // On the bright side, the local part is scary : zip64, data descriptors, both, etc. - // The less data we get here, the more reliable this should be. - // Let's skip the whole header and dash to the data ! - reader.skip(22); - // in some zip created on windows, the filename stored in the central dir contains \ instead of /. - // Strangely, the filename here is OK. - // I would love to treat these zip files as corrupted (see http://www.info-zip.org/FAQ.html#backslashes - // or APPNOTE#4.4.17.1, "All slashes MUST be forward slashes '/'") but there are a lot of bad zip generators... - // Search "unzip mismatching "local" filename continuing with "central" filename version" on - // the internet. - // - // I think I see the logic here : the central directory is used to display - // content and the local directory is used to extract the files. Mixing / and \ - // may be used to display \ to windows users and use / when extracting the files. - // Unfortunately, this lead also to some issues : http://seclists.org/fulldisclosure/2009/Sep/394 - this.fileNameLength = reader.readInt(2); - localExtraFieldsLength = reader.readInt(2); // can't be sure this will be the same as the central dir - // the fileName is stored as binary data, the handleUTF8 method will take care of the encoding. - this.fileName = reader.readData(this.fileNameLength); - reader.skip(localExtraFieldsLength); - - if (this.compressedSize === -1 || this.uncompressedSize === -1) { - throw new Error("Bug or corrupted zip : didn't get enough informations from the central directory " + "(compressedSize === -1 || uncompressedSize === -1)"); - } - - compression = findCompression(this.compressionMethod); - if (compression === null) { // no compression found - throw new Error("Corrupted zip : compression " + utils.pretty(this.compressionMethod) + " unknown (inner file : " + utils.transformTo("string", this.fileName) + ")"); - } - this.decompressed = new CompressedObject(this.compressedSize, this.uncompressedSize, this.crc32, compression, reader.readData(this.compressedSize)); - }, - - /** - * Read the central part of a zip file and add the info in this object. - * @param {DataReader} reader the reader to use. - */ - readCentralPart: function(reader) { - this.versionMadeBy = reader.readInt(2); - reader.skip(2); - // this.versionNeeded = reader.readInt(2); - this.bitFlag = reader.readInt(2); - this.compressionMethod = reader.readString(2); - this.date = reader.readDate(); - this.crc32 = reader.readInt(4); - this.compressedSize = reader.readInt(4); - this.uncompressedSize = reader.readInt(4); - var fileNameLength = reader.readInt(2); - this.extraFieldsLength = reader.readInt(2); - this.fileCommentLength = reader.readInt(2); - this.diskNumberStart = reader.readInt(2); - this.internalFileAttributes = reader.readInt(2); - this.externalFileAttributes = reader.readInt(4); - this.localHeaderOffset = reader.readInt(4); - - if (this.isEncrypted()) { - throw new Error("Encrypted zip are not supported"); - } - - // will be read in the local part, see the comments there - reader.skip(fileNameLength); - this.readExtraFields(reader); - this.parseZIP64ExtraField(reader); - this.fileComment = reader.readData(this.fileCommentLength); - }, - - /** - * Parse the external file attributes and get the unix/dos permissions. - */ - processAttributes: function () { - this.unixPermissions = null; - this.dosPermissions = null; - var madeBy = this.versionMadeBy >> 8; - - // Check if we have the DOS directory flag set. - // We look for it in the DOS and UNIX permissions - // but some unknown platform could set it as a compatibility flag. - this.dir = this.externalFileAttributes & 0x0010 ? true : false; - - if(madeBy === MADE_BY_DOS) { - // first 6 bits (0 to 5) - this.dosPermissions = this.externalFileAttributes & 0x3F; - } - - if(madeBy === MADE_BY_UNIX) { - this.unixPermissions = (this.externalFileAttributes >> 16) & 0xFFFF; - // the octal permissions are in (this.unixPermissions & 0x01FF).toString(8); - } - - // fail safe : if the name ends with a / it probably means a folder - if (!this.dir && this.fileNameStr.slice(-1) === '/') { - this.dir = true; - } - }, - - /** - * Parse the ZIP64 extra field and merge the info in the current ZipEntry. - * @param {DataReader} reader the reader to use. - */ - parseZIP64ExtraField: function(reader) { - - if (!this.extraFields[0x0001]) { - return; - } - - // should be something, preparing the extra reader - var extraReader = readerFor(this.extraFields[0x0001].value); - - // I really hope that these 64bits integer can fit in 32 bits integer, because js - // won't let us have more. - if (this.uncompressedSize === utils.MAX_VALUE_32BITS) { - this.uncompressedSize = extraReader.readInt(8); - } - if (this.compressedSize === utils.MAX_VALUE_32BITS) { - this.compressedSize = extraReader.readInt(8); - } - if (this.localHeaderOffset === utils.MAX_VALUE_32BITS) { - this.localHeaderOffset = extraReader.readInt(8); - } - if (this.diskNumberStart === utils.MAX_VALUE_32BITS) { - this.diskNumberStart = extraReader.readInt(4); - } - }, - /** - * Read the central part of a zip file and add the info in this object. - * @param {DataReader} reader the reader to use. - */ - readExtraFields: function(reader) { - var end = reader.index + this.extraFieldsLength, - extraFieldId, - extraFieldLength, - extraFieldValue; - - if (!this.extraFields) { - this.extraFields = {}; - } - - while (reader.index < end) { - extraFieldId = reader.readInt(2); - extraFieldLength = reader.readInt(2); - extraFieldValue = reader.readData(extraFieldLength); - - this.extraFields[extraFieldId] = { - id: extraFieldId, - length: extraFieldLength, - value: extraFieldValue - }; - } - }, - /** - * Apply an UTF8 transformation if needed. - */ - handleUTF8: function() { - var decodeParamType = support.uint8array ? "uint8array" : "array"; - if (this.useUTF8()) { - this.fileNameStr = utf8.utf8decode(this.fileName); - this.fileCommentStr = utf8.utf8decode(this.fileComment); - } else { - var upath = this.findExtraFieldUnicodePath(); - if (upath !== null) { - this.fileNameStr = upath; - } else { - // ASCII text or unsupported code page - var fileNameByteArray = utils.transformTo(decodeParamType, this.fileName); - this.fileNameStr = this.loadOptions.decodeFileName(fileNameByteArray); - } - - var ucomment = this.findExtraFieldUnicodeComment(); - if (ucomment !== null) { - this.fileCommentStr = ucomment; - } else { - // ASCII text or unsupported code page - var commentByteArray = utils.transformTo(decodeParamType, this.fileComment); - this.fileCommentStr = this.loadOptions.decodeFileName(commentByteArray); - } - } - }, - - /** - * Find the unicode path declared in the extra field, if any. - * @return {String} the unicode path, null otherwise. - */ - findExtraFieldUnicodePath: function() { - var upathField = this.extraFields[0x7075]; - if (upathField) { - var extraReader = readerFor(upathField.value); - - // wrong version - if (extraReader.readInt(1) !== 1) { - return null; - } - - // the crc of the filename changed, this field is out of date. - if (crc32fn(this.fileName) !== extraReader.readInt(4)) { - return null; - } - - return utf8.utf8decode(extraReader.readData(upathField.length - 5)); - } - return null; - }, - - /** - * Find the unicode comment declared in the extra field, if any. - * @return {String} the unicode comment, null otherwise. - */ - findExtraFieldUnicodeComment: function() { - var ucommentField = this.extraFields[0x6375]; - if (ucommentField) { - var extraReader = readerFor(ucommentField.value); - - // wrong version - if (extraReader.readInt(1) !== 1) { - return null; - } - - // the crc of the comment changed, this field is out of date. - if (crc32fn(this.fileComment) !== extraReader.readInt(4)) { - return null; - } - - return utf8.utf8decode(extraReader.readData(ucommentField.length - 5)); - } - return null; - } -}; -module.exports = ZipEntry; - -},{"./compressedObject":2,"./compressions":3,"./crc32":4,"./reader/readerFor":22,"./support":30,"./utf8":31,"./utils":32}],35:[function(require,module,exports){ -'use strict'; - -var StreamHelper = require('./stream/StreamHelper'); -var DataWorker = require('./stream/DataWorker'); -var utf8 = require('./utf8'); -var CompressedObject = require('./compressedObject'); -var GenericWorker = require('./stream/GenericWorker'); - -/** - * A simple object representing a file in the zip file. - * @constructor - * @param {string} name the name of the file - * @param {String|ArrayBuffer|Uint8Array|Buffer} data the data - * @param {Object} options the options of the file - */ -var ZipObject = function(name, data, options) { - this.name = name; - this.dir = options.dir; - this.date = options.date; - this.comment = options.comment; - this.unixPermissions = options.unixPermissions; - this.dosPermissions = options.dosPermissions; - - this._data = data; - this._dataBinary = options.binary; - // keep only the compression - this.options = { - compression : options.compression, - compressionOptions : options.compressionOptions - }; -}; - -ZipObject.prototype = { - /** - * Create an internal stream for the content of this object. - * @param {String} type the type of each chunk. - * @return StreamHelper the stream. - */ - internalStream: function (type) { - var result = null, outputType = "string"; - try { - if (!type) { - throw new Error("No output type specified."); - } - outputType = type.toLowerCase(); - var askUnicodeString = outputType === "string" || outputType === "text"; - if (outputType === "binarystring" || outputType === "text") { - outputType = "string"; - } - result = this._decompressWorker(); - - var isUnicodeString = !this._dataBinary; - - if (isUnicodeString && !askUnicodeString) { - result = result.pipe(new utf8.Utf8EncodeWorker()); - } - if (!isUnicodeString && askUnicodeString) { - result = result.pipe(new utf8.Utf8DecodeWorker()); - } - } catch (e) { - result = new GenericWorker("error"); - result.error(e); - } - - return new StreamHelper(result, outputType, ""); - }, - - /** - * Prepare the content in the asked type. - * @param {String} type the type of the result. - * @param {Function} onUpdate a function to call on each internal update. - * @return Promise the promise of the result. - */ - async: function (type, onUpdate) { - return this.internalStream(type).accumulate(onUpdate); - }, - - /** - * Prepare the content as a nodejs stream. - * @param {String} type the type of each chunk. - * @param {Function} onUpdate a function to call on each internal update. - * @return Stream the stream. - */ - nodeStream: function (type, onUpdate) { - return this.internalStream(type || "nodebuffer").toNodejsStream(onUpdate); - }, - - /** - * Return a worker for the compressed content. - * @private - * @param {Object} compression the compression object to use. - * @param {Object} compressionOptions the options to use when compressing. - * @return Worker the worker. - */ - _compressWorker: function (compression, compressionOptions) { - if ( - this._data instanceof CompressedObject && - this._data.compression.magic === compression.magic - ) { - return this._data.getCompressedWorker(); - } else { - var result = this._decompressWorker(); - if(!this._dataBinary) { - result = result.pipe(new utf8.Utf8EncodeWorker()); - } - return CompressedObject.createWorkerFrom(result, compression, compressionOptions); - } - }, - /** - * Return a worker for the decompressed content. - * @private - * @return Worker the worker. - */ - _decompressWorker : function () { - if (this._data instanceof CompressedObject) { - return this._data.getContentWorker(); - } else if (this._data instanceof GenericWorker) { - return this._data; - } else { - return new DataWorker(this._data); - } - } -}; - -var removedMethods = ["asText", "asBinary", "asNodeBuffer", "asUint8Array", "asArrayBuffer"]; -var removedFn = function () { - throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide."); -}; - -for(var i = 0; i < removedMethods.length; i++) { - ZipObject.prototype[removedMethods[i]] = removedFn; -} -module.exports = ZipObject; - -},{"./compressedObject":2,"./stream/DataWorker":27,"./stream/GenericWorker":28,"./stream/StreamHelper":29,"./utf8":31}],36:[function(require,module,exports){ -(function (global){ -'use strict'; -var Mutation = global.MutationObserver || global.WebKitMutationObserver; - -var scheduleDrain; - -{ - if (Mutation) { - var called = 0; - var observer = new Mutation(nextTick); - var element = global.document.createTextNode(''); - observer.observe(element, { - characterData: true - }); - scheduleDrain = function () { - element.data = (called = ++called % 2); - }; - } else if (!global.setImmediate && typeof global.MessageChannel !== 'undefined') { - var channel = new global.MessageChannel(); - channel.port1.onmessage = nextTick; - scheduleDrain = function () { - channel.port2.postMessage(0); - }; - } else if ('document' in global && 'onreadystatechange' in global.document.createElement('script')) { - scheduleDrain = function () { - - // Create a - - - - + + -
- -
-
-
Package neureka
-

Interface Data<V>

+
neureka
+

Interface Data<V>

-
-
-
Type Parameters:
+
+
+
    +
  • +
    +
    Type Parameters:
    V - The type of the data array.
    -
    +
    All Known Subinterfaces:
    -
    DeviceData<V>
    +
    DeviceData<V>
    -
    +
    All Known Implementing Classes:
    -
    AbstractDeviceData
    +
    AbstractDeviceData
    -
    public interface Data<V>
    +
    +
    public interface Data<V>
    A wrapper type for the raw data array of a tensor/nd-array, - which is typically provided by implementations of the Device interface. - Every tensor/nd-array has a Data object which it uses to access its raw data. + which is typically provided by implementations of the Device interface. + Every tensor/nd-array has a Data object which it uses to access its raw data. Use this to access the raw data of a nd-array and to check where it currently resides. But be careful as this exposes mutable state as well as backend specific implementations and types (e.g. OpenCL / JVM arrays).
    -
-
-
    + +
+
+
+
    +
  • -
  • -
    -

    Method Summary

    -
    -
    -
    -
    -
    Modifier and Type
    -
    Method
    -
    Description
    -
    default <D> D
    -
    as(Class<D> dataType)
    -
    +
      +
    • + + +

      Method Summary

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      All Methods Static Methods Instance Methods Abstract Methods Default Methods 
      Modifier and TypeMethod and Description
      default <D> Das(java.lang.Class<D> dataType)
      This returns the underlying raw data object of a nd-array or tensor.
      - -
      DataType<V>
      -
      dataType()
      -
       
      -
      default Object
      -
      get()
      -
      +
      DataType<V>dataType() 
      default java.lang.Objectget()
      This returns the underlying raw data object of a nd-array or tensor of a backend specific type (e.g.
      - -
      Object
      -
      getOrNull()
      -
      +
      java.lang.ObjectgetOrNull()
      This returns the underlying raw data object of a nd-array or tensor of a backend specific type (e.g.
      - -
      static Data<Boolean>
      -
      of(boolean... items)
      -
       
      -
      static Data<Byte>
      -
      of(byte... items)
      -
       
      -
      static Data<Character>
      -
      of(char... items)
      -
       
      -
      static Data<Double>
      -
      of(double... items)
      -
       
      -
      static Data<Float>
      -
      of(float... items)
      -
       
      -
      static Data<Integer>
      -
      of(int... items)
      -
       
      -
      static Data<Long>
      -
      of(long... items)
      -
       
      -
      static Data<Short>
      -
      of(short... items)
      -
       
      -
      static <V> Data<V>
      -
      of(Class<V> type, - V... data)
      -
       
      -
      static Data<String>
      -
      of(String... items)
      -
       
      -
      Device<V>
      -
      owner()
      -
       
      -
      int
      -
      usages()
      -
      +
      static Data<java.lang.Void>none() +
      This is a static factory method which returns a Data object + which does not contain any data.
      +
      static Data<java.lang.Boolean>of(boolean... items) 
      static Data<java.lang.Byte>of(byte... items) 
      static Data<java.lang.Character>of(char... items) 
      static <V> Data<V>of(java.lang.Class<V> type, + V... data) 
      static Data<java.lang.Double>of(double... items) 
      static Data<java.lang.Float>of(float... items) 
      static Data<java.lang.Integer>of(int... items) 
      static Data<java.lang.Long>of(long... items) 
      static Data<java.lang.Short>of(short... items) 
      static Data<java.lang.String>of(java.lang.String... items) 
      Device<V>owner() 
      intusages()
      This method returns the number of times this data object is currently in use by a nd-array, meaning that the number of usages is also the number of nd-arrays which are currently referencing this data object.
      - - - - - +
      +
      • +
- -
-
    +
+
+
    +
  • -
  • -
    -

    Method Details

    -
      -
    • -
      -

      of

      -
      static <V> Data<V> of(Class<V> type, - V... data)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          none

          +
          static Data<java.lang.Void> none()
          +
          This is a static factory method which returns a Data object + which does not contain any data. It is a sort of no-operation null object + which can be used to represent the absence of data. + A deleted tensor will typically have a Data object which does not contain any data.
          +
          +
          Returns:
          +
          A Data object which does not contain any data.
          +
          • -
        • -
          -

          of

          -
          static Data<Float> of(float... items)
          -
          +
        + + + + + +
          +
        • +

          of

          +
          static <V> Data<V> of(java.lang.Class<V> type,
+                      V... data)
          • -
        • -
          -

          of

          -
          static Data<Double> of(double... items)
          -
          +
        + + + +
          +
        • +

          of

          +
          static Data<java.lang.Float> of(float... items)
          • -
        • -
          -

          of

          -
          static Data<Integer> of(int... items)
          -
          +
        + + + +
          +
        • +

          of

          +
          static Data<java.lang.Double> of(double... items)
          • -
        • -
          -

          of

          -
          static Data<Long> of(long... items)
          -
          +
        + + + +
          +
        • +

          of

          +
          static Data<java.lang.Integer> of(int... items)
          • -
        • -
          -

          of

          -
          static Data<Byte> of(byte... items)
          -
          +
        + + + +
          +
        • +

          of

          +
          static Data<java.lang.Long> of(long... items)
          • -
        • -
          -

          of

          -
          static Data<Short> of(short... items)
          -
          +
        + + + +
          +
        • +

          of

          +
          static Data<java.lang.Byte> of(byte... items)
          • -
        • -
          -

          of

          -
          static Data<Boolean> of(boolean... items)
          -
          +
        + + + +
          +
        • +

          of

          +
          static Data<java.lang.Short> of(short... items)
          • -
        • -
          -

          of

          -
          static Data<Character> of(char... items)
          -
          +
        + + + +
          +
        • +

          of

          +
          static Data<java.lang.Boolean> of(boolean... items)
          +
          • +
        + + + +
          +
        • +

          of

          +
          static Data<java.lang.Character> of(char... items)
          • -
        • -
          -

          of

          -
          static Data<String> of(String... items)
          -
          +
        + + + +
          +
        • +

          of

          +
          static Data<java.lang.String> of(java.lang.String... items)
          • -
        • -
          -

          owner

          -
          Device<V> owner()
          -
          -
          Returns:
          +
        + + + +
          +
        • +

          owner

          +
          Device<V> owner()
          +
          +
          Returns:
          The owner of this data array wrapper (the device which allocated the memory).
          -
    • -
  • -
    -

    getOrNull

    -
    Object getOrNull()
    +
+ + + +
    +
  • +

    getOrNull

    +
    java.lang.Object getOrNull()
    This returns the underlying raw data object of a nd-array or tensor of a backend specific type (e.g. OpenCL memory object or JVM array). - Contrary to the Nda.getItems() ()} method, this will + Contrary to the Nda.getItems() ()} method, this will return an unbiased view on the raw data of this tensor. Be careful using this, as it exposes mutable state!
    -
    -
    Returns:
    +
    +
    Returns:
    The raw data object underlying a nd-array/tensor, or null if the data is not present.
    -
    • -
  • -
    -

    get

    -
    default Object get()
    +
+ + + +
    +
  • +

    get

    +
    default java.lang.Object get()
    This returns the underlying raw data object of a nd-array or tensor of a backend specific type (e.g. OpenCL memory object or JVM array). - Contrary to the Nda.getItems() ()} method, this will + Contrary to the Nda.getItems() ()} method, this will return an unbiased view on the raw data of this tensor. Be careful using this, as it exposes mutable state!
    -
    -
    Returns:
    +
    +
    Returns:
    The raw data object underlying a nd-array/tensor.
    -
    Throws:
    -
    NullPointerException - if the data reference is null.
    +
    Throws:
    +
    java.lang.NullPointerException - if the data reference is null.
    -
    • -
  • -
    -

    as

    -
    default <D> D as(Class<D> dataType)
    +
+ + + +
    +
  • +

    as

    +
    default <D> D as(java.lang.Class<D> dataType)
    This returns the underlying raw data object of a nd-array or tensor. - Contrary to the Nda.getItems() ()} method, this will + Contrary to the Nda.getItems() ()} method, this will return an unbiased view on the raw data of this tensor. Be careful using this, as it exposes mutable state!
    -
    -
    Parameters:
    +
    +
    Parameters:
    dataType - The type the underlying reference object is expected to have (this may be a JVM array or something device specific).
    -
    Returns:
    +
    Returns:
    The raw data object underlying a nd-array/tensor.
    -
    • -
  • -
    -

    dataType

    -
    DataType<V> dataType()
    -
    -
    Returns:
    +
+ + + +
    +
  • +

    dataType

    +
    DataType<V> dataType()
    +
    +
    Returns:
    The data type of the raw data array.
    -
    • -
  • -
    -

    usages

    -
    int usages()
    +
+ + + +
    +
  • +

    usages

    +
    int usages()
    This method returns the number of times this data object is currently in use by a nd-array, meaning that the number of usages is also the number of nd-arrays which are currently referencing this data object.
    The reason why this can be greater than one is because of the existence of sliced, transposed and reshaped nd-arrays which all share the same data object as their parent nd-array.
    -
    -
    Returns:
    +
    +
    Returns:
    The number of times this data object is currently in use by a nd-array.
    -
- - + + +
+
- + +
+ + +
Skip navigation links
+ + + +
+
+ + + +
+ +
+
+
    +
  • Summary: 
    • +
  • Nested | 
    • +
  • Field | 
    • +
  • Constr | 
    • +
  • Method
    • +
+
    +
  • Detail: 
    • +
  • Field | 
    • +
  • Constr | 
    • +
  • Method
    • +
+ + +
+ diff --git a/docs/jdocs/neureka/MutateNda.Item.html b/docs/jdocs/neureka/MutateNda.Item.html index bb3e6dc31..cc4545966 100644 --- a/docs/jdocs/neureka/MutateNda.Item.html +++ b/docs/jdocs/neureka/MutateNda.Item.html @@ -1,149 +1,249 @@ - + + - -MutateNda.Item (neureka 1.0.0 API) - - - - + +MutateNda.Item (neureka 1.0.1 API) - - - - - - + + -
- -
-
-
Package neureka
-

Interface MutateNda.Item<V>

+
neureka
+

Interface MutateNda.Item<V>

-
-
-
Type Parameters:
+
+
+
    +
  • +
    +
    Type Parameters:
    V - The type of the items of this nd-array.
    -
    +
    All Superinterfaces:
    -
    Nda.Item<V>
    +
    Nda.Item<V>
    -
    +
    Enclosing interface:
    -
    MutateNda<T>
    +
    MutateNda<T>
    -
    public static interface MutateNda.Item<V> -extends Nda.Item<V>
    -
    Instances of this are being returned by the Nda.at(int...) method, +
    +
    public static interface MutateNda.Item<V>
+extends Nda.Item<V>
    +
    Instances of this are being returned by the Nda.at(int...) method, and they allow you to get or set individual nd-array items
    -
-
-
    + +
+
+
+ - -
-
    +
+
+
    +
  • -
  • -
    -

    Method Details

    -
      -
    • -
      -

      set

      -
      void set(V value)
      +
        +
      • + + +

        Method Detail

        + + + + + +
          +
        • +

          set

          +
          void set(V value)
          Set the value at the targeted position.
          -
          -
          Parameters:
          +
          +
          Parameters:
          value - The value to be set at the targeted position.
          -
    -
- + + +
+
- + +
+ + +
Skip navigation links
+ + + + +
+
+ + + +
+
+
+
    +
  • Summary: 
    • +
  • Nested | 
    • +
  • Field | 
    • +
  • Constr | 
    • +
  • Method
    • +
+
    +
  • Detail: 
    • +
  • Field | 
    • +
  • Constr | 
    • +
  • Method
    • +
+ + +
+ diff --git a/docs/jdocs/neureka/MutateNda.html b/docs/jdocs/neureka/MutateNda.html index 4dce1ab70..d6d8777d9 100644 --- a/docs/jdocs/neureka/MutateNda.html +++ b/docs/jdocs/neureka/MutateNda.html @@ -1,269 +1,328 @@ - + + - -MutateNda (neureka 1.0.0 API) - - - - + +MutateNda (neureka 1.0.1 API) - - - - - - + + -
- -
-
-
Package neureka
-

Interface MutateNda<T>

+
neureka
+

Interface MutateNda<T>

-
-
+
+
+
    +
  • +
    All Known Subinterfaces:
    -
    MutateTensor<T>
    +
    MutateTensor<T>
    -
    public interface MutateNda<T>
    +
    +
    public interface MutateNda<T>
    Nd-arrays should be used as immutable data structures mostly, however sometimes it is important to mutate their state for performance reasons. This interface exposes several methods for mutating the state of this nd-array. The usage of methods exposed by this API is generally discouraged because the exposed state can easily lead to broken nd-arrays and exceptions...

    -
-
-
    + +
+
+
+
    +
  • -
  • -
    -

    Nested Class Summary

    -
    Nested Classes
    -
    -
    Modifier and Type
    -
    Interface
    -
    Description
    -
    static interface 
    -
    MutateNda.Item<V>
    -
    -
    Instances of this are being returned by the Nda.at(int...) method, +
      +
    • + + +

      Nested Class Summary

      + + + + + + + + + + +
      Nested Classes 
      Modifier and TypeInterface and Description
      static interface MutateNda.Item<V> +
      Instances of this are being returned by the Nda.at(int...) method, and they allow you to get or set individual nd-array items
      - - - +
      • +
    -
  • -
    -

    Method Summary

    -
    -
    -
    -
    -
    Modifier and Type
    -
    Method
    -
    Description
    -
    Nda<T>
    -
    assign(Nda<T> other)
    -
    +
      +
    • + + +

      Method Summary

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      All Methods Instance Methods Abstract Methods Default Methods 
      Modifier and TypeMethod and Description
      Nda<T>assign(Nda<T> other)
      Use this to assign the provided nd-array to this nd-array! This method is an inline operation which changes the underlying data of the nd-array.
      - -
      Nda<T>
      -
      assign(T other)
      -
      +
      Nda<T>assign(T other)
      Use this to assign the provided item to all elements of this nd-array! This method is an inline operation which changes the underlying data of the nd-array.
      - -
      MutateNda.Item<T>
      -
      at(int... indices)
      -
      -
      Exposes the MutateNda.Item interface which allows you to get or set individual nd-array items.
      -
      -
      Data<T>
      -
      getData()
      -
      -
      At the heart of every tensor is the Data object, which holds the actual data array, +
      MutateNda.Item<T>at(int... indices) +
      Exposes the MutateNda.Item interface which allows you to get or set individual nd-array items.
      +
      Data<T>getData() +
      At the heart of every tensor is the Data object, which holds the actual data array, a sequence of values of the same type.
      - -
      <A> A
      -
      getDataAs(Class<A> arrayTypeClass)
      -
      +
      <A> AgetDataAs(java.lang.Class<A> arrayTypeClass)
      This method returns the data of this nd-array as a Java array of the specified type.
      - -
      Nda<T>
      -
      label(String label)
      -
      +
      Nda<T>label(java.lang.String label)
      Sets the label of this nd-array.
      - -
      Nda<T>
      -
      labelAxes(String[]... labels)
      -
      -
      This method receives a nested String array which - ought to contain a label for the index of this tensor.
      -
      -
      Nda<T>
      -
      labelAxes(List<List<Object>> labels)
      -
      -
      This method receives a nested String list which +
      Nda<T>labelAxes(java.util.List<java.util.List<java.lang.Object>> labels) +
      This method receives a nested String list which ought to contain a label for the index of this tensor.
      - -
      Nda<T>
      -
      labelAxes(Map<Object,List<Object>> labels)
      -
      +
      Nda<T>labelAxes(java.util.Map<java.lang.Object,java.util.List<java.lang.Object>> labels)
      This method provides the ability to label not only the indices of the shape of this tensor, but also the dimension of the shape.
      - -
      Nda<T>
      -
      putAt(int[] indices, - T value)
      -
      +
      Nda<T>labelAxes(java.lang.String[]... labels) +
      This method receives a nested String array which + ought to contain a label for the index of this tensor.
      +
      Nda<T>putAt(int[] indices, + T value)
      Use this to put a single item at a particular position within this nd-array.
      - -
      Nda<T>
      -
      putAt(int index, - T value)
      -
      +
      Nda<T>putAt(int index, + T value)
      Individual entries for value items in this nd-array can be set via this method.
      - -
      Nda<T>
      -
      putAt(List<?> key, - Nda<T> value)
      -
      +
      Nda<T>putAt(java.util.List<?> key, + Nda<T> value)
      This method enables injecting slices of nd-array to be assigned into this nd-array! It takes a key of various types which is used to configure a slice nd-array sharing the same underlying data as the original nd-array.
      - -
      Nda<T>
      -
      putAt(List<?> indices, - T value)
      -
      +
      Nda<T>putAt(java.util.List<?> indices, + T value)
      Use this to place a single item at a particular position within this nd-array!
      - -
      Nda<T>
      -
      putAt(Map<?,Integer> key, - Nda<T> value)
      -
      +
      Nda<T>putAt(java.util.Map<?,java.lang.Integer> key, + Nda<T> value)
      This method enables assigning a provided nd-array to be a subset/slice of this nd-array! It takes a key which is used to configure a slice sharing the same underlying data as the original nd-array.
      - -
      Nda<T>
      -
      set(int[] indices, - T value)
      -
      +
      Nda<T>set(int[] indices, + T value)
      Use this to place a single item at a particular position within this nd-array!
      - -
      default Nda<T>
      -
      set(int i0, - int i1, - int i2, - T value)
      -
       
      -
      default Nda<T>
      -
      set(int i0, - int i1, - T value)
      -
       
      -
      Nda<T>
      -
      set(int index, - T value)
      -
      +
      default Nda<T>set(int i0, + int i1, + int i2, + T value) 
      default Nda<T>set(int i0, + int i1, + T value) 
      Nda<T>set(int index, + T value)
      Individual entries for value items in this nd-array can be set via this method.
      - -
      Nda<T>
      -
      setItemAt(int i, - T o)
      -
      +
      Nda<T>setItemAt(int i, + T o)
      An NDArray implementation ought to have some way to selectively modify its underlying value.
      - -
      Nda<T>
      -
      setItems(Object value)
      -
      +
      Nda<T>setItems(java.lang.Object value)
      This method will receive an object an try to interpret it or its contents to be set as value for this nd-array.
      - -
      <V> Nda<V>
      -
      toType(Class<V> typeClass)
      -
      +
      <V> Nda<V>toType(java.lang.Class<V> typeClass)
      This method is an inline operation which changes the underlying data of this tensor.
      - - - - - +
    -
    -
    -
      + +
    +
    • +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        putAt

        -
        Nda<T> putAt(Map<?,Integer> key, - Nda<T> value)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            putAt

            +
            Nda<T> putAt(java.util.Map<?,java.lang.Integer> key,
+             Nda<T> value)
            This method enables assigning a provided nd-array to be a subset/slice of this nd-array! It takes a key which is used to configure a slice sharing the same underlying data as the original nd-array. @@ -278,170 +337,219 @@

            putAt

            i... start indexAlias.
            j... end indexAlias. (inclusive!)
            k... step size.
            -
            -
            Parameters:
            +
            +
            Parameters:
            key - This object is a map defining a step and a targeted index or range of indices...
            value - The nd-array which ought to be assigned into a slice of this nd-array.
            -
            Returns:
            +
            Returns:
            A slice nd-array or scalar value.
            -
        • -
      • -
        -

        putAt

        -
        Nda<T> putAt(int[] indices, - T value)
        +
      + + + + + +
        +
      • +

        putAt

        +
        Nda<T> putAt(int[] indices,
+             T value)
        Use this to put a single item at a particular position within this nd-array.
        -
        -
        Parameters:
        +
        +
        Parameters:
        indices - The indices of the nd-position where the provided item should be placed.
        value - The item which should be placed at the position defined by the provided indices.
        -
        Returns:
        +
        Returns:
        This nd-array itself.
        -
      • -
    • -
      -

      set

      -
      Nda<T> set(int[] indices, - T value)
      +
    + + + + + +
      +
    • +

      set

      +
      Nda<T> set(int[] indices,
+           T value)
      Use this to place a single item at a particular position within this nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - An array of indices targeting a particular position in this nd-array...
      value - the value which ought to be placed at the targeted position.
      -
      Returns:
      +
      Returns:
      This very nd-array in order to enable method chaining...
      -
    • -
  • -
    -

    set

    -
    default Nda<T> set(int i0, - int i1, - T value)
    -
    +
+ + + + + +
    +
  • +

    set

    +
    default Nda<T> set(int i0,
+                   int i1,
+                   T value)
    • -
  • -
    -

    set

    -
    default Nda<T> set(int i0, - int i1, - int i2, - T value)
    -
    +
+ + + + + +
    +
  • +

    set

    +
    default Nda<T> set(int i0,
+                   int i1,
+                   int i2,
+                   T value)
    • -
  • -
    -

    putAt

    -
    Nda<T> putAt(int index, - T value)
    +
+ + + + + +
    +
  • +

    putAt

    +
    Nda<T> putAt(int index,
+             T value)
    Individual entries for value items in this nd-array can be set via this method.
    -
    -
    Parameters:
    +
    +
    Parameters:
    index - The scalar index targeting a specific value position within this nd-array which ought to be replaced by the one provided by the second parameter of this method.
    value - The item which ought to be placed at the targeted position.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining...
    -
    • -
  • -
    -

    set

    -
    Nda<T> set(int index, - T value)
    +
+ + + + + +
    +
  • +

    set

    +
    Nda<T> set(int index,
+           T value)
    Individual entries for value items in this nd-array can be set via this method.
    -
    -
    Parameters:
    +
    +
    Parameters:
    index - The scalar index targeting a specific value position within this nd-array which ought to be replaced by the one provided by the second parameter of this method.
    value - The item which ought to be placed at the targeted position.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining...
    -
    • -
  • -
    -

    putAt

    -
    Nda<T> putAt(List<?> key, - Nda<T> value)
    +
+ + + +
    +
  • +

    putAt

    +
    Nda<T> putAt(java.util.List<?> key,
+             Nda<T> value)
    This method enables injecting slices of nd-array to be assigned into this nd-array! It takes a key of various types which is used to configure a slice nd-array sharing the same underlying data as the original nd-array. This slice is then used to assign the second argument to it, namely the "value" argument.
    -
    -
    Parameters:
    +
    +
    Parameters:
    key - This object is a list defining a targeted index or range of indices...
    value - the nd-array which ought to be assigned to a slice of this nd-array.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining...
    -
    • -
  • -
    -

    putAt

    -
    Nda<T> putAt(List<?> indices, - T value)
    +
+ + + + + +
    +
  • +

    putAt

    +
    Nda<T> putAt(java.util.List<?> indices,
+             T value)
    Use this to place a single item at a particular position within this nd-array!
    -
    -
    Parameters:
    +
    +
    Parameters:
    indices - A list of indices targeting a particular position in this nd-array...
    value - the value which ought to be placed at the targeted position.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining...
    -
    • -
  • -
    -

    setItemAt

    -
    Nda<T> setItemAt(int i, - T o)
    +
+ + + + + +
    +
  • +

    setItemAt

    +
    Nda<T> setItemAt(int i,
+                 T o)
    An NDArray implementation ought to have some way to selectively modify its underlying value. This method simply overrides an element within this data array sitting at position "i".
    -
    -
    Parameters:
    +
    +
    Parameters:
    i - The index of the value array entry which ought to be addressed.
    o - The object which ought to be placed at the requested position.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining.
    -
    • -
  • -
    -

    setItems

    -
    Nda<T> setItems(Object value)
    +
+ + + +
    +
  • +

    setItems

    +
    Nda<T> setItems(java.lang.Object value)
    This method will receive an object an try to interpret it or its contents to be set as value for this nd-array. It will not necessarily replace the underlying data array object of this nd-array itself, but also try to convert and copy the provided value into the data array of this nd-array.
    -
    -
    Parameters:
    +
    +
    Parameters:
    value - The value which may be a scalar or array and will be used to populate this nd-array.
    -
    Returns:
    +
    Returns:
    This very nd-array to enable method chaining.
    -
    • -
  • -
    -

    toType

    -
    <V> Nda<V> toType(Class<V> typeClass)
    +
+ + + +
    +
  • +

    toType

    +
    <V> Nda<V> toType(java.lang.Class<V> typeClass)
    This method is an inline operation which changes the underlying data of this tensor. It converts the data types of the elements of this tensor to the specified type!

    @@ -457,21 +565,24 @@

    toType

    Therefore, there might be unexpected performance penalties or side effects associated with this method.

    -
    -
    Type Parameters:
    +
    +
    Type Parameters:
    V - The type parameter for the returned tensor.
    -
    Parameters:
    +
    Parameters:
    typeClass - The target type class for elements of this tensor.
    -
    Returns:
    +
    Returns:
    The same tensor instance whose data has been converted to hold a different type.
    -
    • -
  • -
    -

    getData

    -
    Data<T> getData()
    -
    At the heart of every tensor is the Data object, which holds the actual data array, +
+ + + +
    +
  • +

    getData

    +
    Data<T> getData()
    +
    At the heart of every tensor is the Data object, which holds the actual data array, a sequence of values of the same type. This method returns the data object of this nd-array.

    @@ -479,74 +590,91 @@

    getData

    It should be used for reading the data array of this nd-array. Modifying the data array of this nd-array can have side effects on other tensors because they might share the same data array (the might be slices).
    -
    -
    Returns:
    +
    +
    Returns:
    The data object of this nd-array.
    -
    • -
  • -
    -

    getDataAs

    -
    <A> A getDataAs(Class<A> arrayTypeClass)
    +
+ + + +
    +
  • +

    getDataAs

    +
    <A> A getDataAs(java.lang.Class<A> arrayTypeClass)
    This method returns the data of this nd-array as a Java array of the specified type.

    Warning: This method is not intended to be used for modifying the data array of this nd-array!

    -
    -
    Returns:
    +
    +
    Returns:
    The data of this nd-array as a Java array of the specified type.
    -
    • -
  • -
    -

    assign

    -
    Nda<T> assign(T other)
    +
+ + + + + +
    +
  • +

    assign

    +
    Nda<T> assign(T other)
    Use this to assign the provided item to all elements of this nd-array! This method is an inline operation which changes the underlying data of the nd-array.
    -
    -
    Parameters:
    +
    +
    Parameters:
    other - The item which ought to be assigned to all elements of this nd-array.
    -
    Returns:
    +
    Returns:
    This very nd-array to enable method chaining.
    -
    • -
  • -
    -

    assign

    -
    Nda<T> assign(Nda<T> other)
    +
+ + + +
    +
  • +

    assign

    +
    Nda<T> assign(Nda<T> other)
    Use this to assign the provided nd-array to this nd-array! This method is an inline operation which changes the underlying data of the nd-array.
    -
    -
    Parameters:
    +
    +
    Parameters:
    other - The nd-array which ought to be assigned to this nd-array.
    -
    Returns:
    +
    Returns:
    This very nd-array to enable method chaining.
    -
    • -
  • -
    -

    label

    -
    Nda<T> label(String label)
    +
+ + + +
    +
  • +

    label

    +
    Nda<T> label(java.lang.String label)
    Sets the label of this nd-array. The label is a human-readable string which can be used to identify this nd-array for example in a pretty-printed output.
    -
    -
    Parameters:
    +
    +
    Parameters:
    label - The label of this nd-array.
    -
    Returns:
    +
    Returns:
    This very nd-array to enable method chaining.
    -
    • -
  • -
    -

    labelAxes

    -
    Nda<T> labelAxes(String[]... labels)
    -
    This method receives a nested String array which +
+ + + +
    +
  • +

    labelAxes

    +
    Nda<T> labelAxes(java.lang.String[]... labels)
    +
    This method receives a nested String array which ought to contain a label for the index of this tensor. The index for a single element of this tensor would be an array of numbers as long as the rank where every number is @@ -558,19 +686,22 @@

    labelAxes

    dim 0 : ["A", "B"]
    dim 1 : ["1", "2", "3"]

    -
    -
    Parameters:
    +
    +
    Parameters:
    labels - A nested String array containing labels for indexes of the tensor dimensions.
    -
    Returns:
    +
    Returns:
    This tensor (method chaining).
    -
    • -
  • -
    -

    labelAxes

    -
    Nda<T> labelAxes(List<List<Object>> labels)
    -
    This method receives a nested String list which +
+ + + +
    +
  • +

    labelAxes

    +
    Nda<T> labelAxes(java.util.List<java.util.List<java.lang.Object>> labels)
    +
    This method receives a nested String list which ought to contain a label for the index of this tensor. The index for a single element of this tensor would be an array of numbers as long as the rank where every number is @@ -582,18 +713,21 @@

    labelAxes

    dim 0 : ["A", "B"]
    dim 1 : ["1", "2", "3"]

    -
    -
    Parameters:
    +
    +
    Parameters:
    labels - A nested String list containing labels for indexes of the tensor dimensions.
    -
    Returns:
    +
    Returns:
    This tensor (method chaining).
    -
    • -
  • -
    -

    labelAxes

    -
    Nda<T> labelAxes(Map<Object,List<Object>> labels)
    +
+ + + +
    +
  • +

    labelAxes

    +
    Nda<T> labelAxes(java.util.Map<java.lang.Object,java.util.List<java.lang.Object>> labels)
    This method provides the ability to label not only the indices of the shape of this tensor, but also the dimension of the shape. @@ -606,35 +740,97 @@

    labelAxes

    "dim 1" : ["1", "2", "3"]
    ]

    -
    -
    Parameters:
    +
    +
    Parameters:
    labels - A map in which the keys are dimension labels and the values are lists of index labels for the dimension.
    -
    Returns:
    +
    Returns:
    This tensor (method chaining).
    -
    • -
  • -
    -

    at

    -
    MutateNda.Item<T> at(int... indices)
    -
    Exposes the MutateNda.Item interface which allows you to get or set individual nd-array items.
    -
    -
    Parameters:
    +
+ + + +
    +
  • +

    at

    +
    MutateNda.Item<T> at(int... indices)
    +
    Exposes the MutateNda.Item interface which allows you to get or set individual nd-array items.
    +
    +
    Parameters:
    indices - The indices of the item to be returned.
    -
    Returns:
    +
    Returns:
    The item at the specified indices.
    -
- - + + +
+
- + +
+ + +
Skip navigation links
+ + + + +
+
+ + + +
+
+
+
    +
  • Summary: 
    • +
  • Nested | 
    • +
  • Field | 
    • +
  • Constr | 
    • +
  • Method
    • +
+
    +
  • Detail: 
    • +
  • Field | 
    • +
  • Constr | 
    • +
  • Method
    • +
+ + +
+ diff --git a/docs/jdocs/neureka/MutateTensor.html b/docs/jdocs/neureka/MutateTensor.html index 5b4507256..adfbe64d2 100644 --- a/docs/jdocs/neureka/MutateTensor.html +++ b/docs/jdocs/neureka/MutateTensor.html @@ -1,342 +1,423 @@ - + + - -MutateTensor (neureka 1.0.0 API) - - - - + +MutateTensor (neureka 1.0.1 API) - - - - - - + + -
- -
-
-
Package neureka
-

Interface MutateTensor<T>

+
neureka
+

Interface MutateTensor<T>

-
-
+
+
+
    +
  • +
    All Superinterfaces:
    -
    MutateNda<T>
    +
    MutateNda<T>
    -
    public interface MutateTensor<T> -extends MutateNda<T>
    +
    +
    public interface MutateTensor<T>
+extends MutateNda<T>
    Tensors should be considered immutable, however sometimes it is important to mutate their state for performance reasons. This interface exposes several methods for mutating the state of a tensor. The usage of methods exposed by this API is generally discouraged because the exposed state can easily lead to broken tensors and exceptions...

    -
-
-
    + +
+
+
+ - -
-
    +
+
+
    +
  • -
  • -
    -

    Method Details

    -
      -
    • -
      -

      putAt

      -
      Tensor<T> putAt(Map<?,Integer> key, - Nda<T> value)
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          putAt

          +
          Tensor<T> putAt(java.util.Map<?,java.lang.Integer> key,
+                Nda<T> value)
          This method enables assigning a provided nd-array to be a subset/slice of this nd-array! It takes a key which is used to configure a slice sharing the same underlying data as the original nd-array. @@ -351,224 +432,279 @@

          putAt

          i... start indexAlias.
          j... end indexAlias. (inclusive!)
          k... step size.
          -
          -
          Specified by:
          -
          putAt in interface MutateNda<T>
          -
          Parameters:
          +
          +
          Specified by:
          +
          putAt in interface MutateNda<T>
          +
          Parameters:
          key - This object is a map defining a step and a targeted index or range of indices...
          value - The nd-array which ought to be assigned into a slice of this nd-array.
          -
          Returns:
          +
          Returns:
          A slice nd-array or scalar value.
          -
      • -
    • -
      -

      putAt

      -
      Tensor<T> putAt(int[] indices, - T value)
      +
    + + + + + +
      +
    • +

      putAt

      +
      Tensor<T> putAt(int[] indices,
+                T value)
      Use this to put a single item at a particular position within this nd-array.
      -
      -
      Specified by:
      -
      putAt in interface MutateNda<T>
      -
      Parameters:
      +
      +
      Specified by:
      +
      putAt in interface MutateNda<T>
      +
      Parameters:
      indices - The indices of the nd-position where the provided item should be placed.
      value - The item which should be placed at the position defined by the provided indices.
      -
      Returns:
      +
      Returns:
      This nd-array itself.
      -
    • -
  • -
    -

    set

    -
    default Tensor<T> set(int[] indices, - T value)
    +
+ + + + + +
    +
  • +

    set

    +
    default Tensor<T> set(int[] indices,
+                      T value)
    Use this to place a single item at a particular position within this nd-array!
    -
    -
    Specified by:
    -
    set in interface MutateNda<T>
    -
    Parameters:
    +
    +
    Specified by:
    +
    set in interface MutateNda<T>
    +
    Parameters:
    indices - An array of indices targeting a particular position in this nd-array...
    value - the value which ought to be placed at the targeted position.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining...
    - -
    • -
  • -
    -

    set

    -
    default Tensor<T> set(int i0, - int i1, - T value)
    -
    -
    Specified by:
    -
    set in interface MutateNda<T>
    -
    -
    -
    • -
  • -
    -

    set

    -
    default Tensor<T> set(int i0, - int i1, - int i2, - T value)
    -
    -
    Specified by:
    -
    set in interface MutateNda<T>
    -
    -
    -
    • -
  • -
    -

    putAt

    -
    Tensor<T> putAt(int index, - T value)
    +
    • +
+ + + + + +
    +
  • +

    set

    +
    default Tensor<T> set(int i0,
+                      int i1,
+                      T value)
    +
    +
    Specified by:
    +
    set in interface MutateNda<T>
    +
    +
    • +
+ + + + + +
    +
  • +

    set

    +
    default Tensor<T> set(int i0,
+                      int i1,
+                      int i2,
+                      T value)
    +
    +
    Specified by:
    +
    set in interface MutateNda<T>
    +
    +
    • +
+ + + + + +
    +
  • +

    putAt

    +
    Tensor<T> putAt(int index,
+                T value)
    Individual entries for value items in this nd-array can be set via this method.
    -
    -
    Specified by:
    -
    putAt in interface MutateNda<T>
    -
    Parameters:
    +
    +
    Specified by:
    +
    putAt in interface MutateNda<T>
    +
    Parameters:
    index - The scalar index targeting a specific value position within this nd-array which ought to be replaced by the one provided by the second parameter of this method.
    value - The item which ought to be placed at the targeted position.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining...
    -
    • -
  • -
    -

    set

    -
    default Tensor<T> set(int index, - T value)
    +
+ + + + + +
    +
  • +

    set

    +
    default Tensor<T> set(int index,
+                      T value)
    Individual entries for value items in this nd-array can be set via this method.
    -
    -
    Specified by:
    -
    set in interface MutateNda<T>
    -
    Parameters:
    +
    +
    Specified by:
    +
    set in interface MutateNda<T>
    +
    Parameters:
    index - The scalar index targeting a specific value position within this nd-array which ought to be replaced by the one provided by the second parameter of this method.
    value - The item which ought to be placed at the targeted position.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining...
    -
    • -
  • -
    -

    putAt

    -
    Tensor<T> putAt(List<?> key, - Nda<T> value)
    +
+ + + +
    +
  • +

    putAt

    +
    Tensor<T> putAt(java.util.List<?> key,
+                Nda<T> value)
    This method enables injecting slices of nd-array to be assigned into this nd-array! It takes a key of various types which is used to configure a slice nd-array sharing the same underlying data as the original nd-array. This slice is then used to assign the second argument to it, namely the "value" argument.
    -
    -
    Specified by:
    -
    putAt in interface MutateNda<T>
    -
    Parameters:
    +
    +
    Specified by:
    +
    putAt in interface MutateNda<T>
    +
    Parameters:
    key - This object is a list defining a targeted index or range of indices...
    value - the nd-array which ought to be assigned to a slice of this nd-array.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining...
    -
    • -
  • -
    -

    putAt

    -
    Tensor<T> putAt(List<?> indices, - T value)
    +
+ + + + + +
    +
  • +

    putAt

    +
    Tensor<T> putAt(java.util.List<?> indices,
+                T value)
    Use this to place a single item at a particular position within this nd-array!
    -
    -
    Specified by:
    -
    putAt in interface MutateNda<T>
    -
    Parameters:
    +
    +
    Specified by:
    +
    putAt in interface MutateNda<T>
    +
    Parameters:
    indices - A list of indices targeting a particular position in this nd-array...
    value - the value which ought to be placed at the targeted position.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining...
    -
    • -
  • -
    -

    setItemAt

    -
    Tensor<T> setItemAt(int i, - T o)
    +
+ + + + + +
    +
  • +

    setItemAt

    +
    Tensor<T> setItemAt(int i,
+                    T o)
    An NDArray implementation ought to have some way to selectively modify its underlying value. This method simply overrides an element within this data array sitting at position "i".
    -
    -
    Specified by:
    -
    setItemAt in interface MutateNda<T>
    -
    Parameters:
    +
    +
    Specified by:
    +
    setItemAt in interface MutateNda<T>
    +
    Parameters:
    i - The index of the value array entry which ought to be addressed.
    o - The object which ought to be placed at the requested position.
    -
    Returns:
    +
    Returns:
    This very nd-array in order to enable method chaining.
    -
    • -
  • -
    -

    setItems

    -
    Tensor<T> setItems(Object value)
    +
+ + + +
    +
  • +

    setItems

    +
    Tensor<T> setItems(java.lang.Object value)
    This method will receive an object an try to interpret it or its contents to be set as value for this nd-array. It will not necessarily replace the underlying data array object of this nd-array itself, but also try to convert and copy the provided value into the data array of this nd-array.
    -
    -
    Specified by:
    -
    setItems in interface MutateNda<T>
    -
    Parameters:
    +
    +
    Specified by:
    +
    setItems in interface MutateNda<T>
    +
    Parameters:
    value - The value which may be a scalar or array and will be used to populate this nd-array.
    -
    Returns:
    +
    Returns:
    This very nd-array to enable method chaining.
    -
    • -
  • -
    -

    addToGradient

    -
    Tensor<T> addToGradient(Tensor<T> error)
    -
    This method takes the provided Tensor instance and adds its - contents to the contents of the Tensor which is set as gradient of this very Tensor.
    -
    -
    Parameters:
    +
+ + + +
    +
  • +

    addToGradient

    +
    Tensor<T> addToGradient(Tensor<T> error)
    +
    This method takes the provided Tensor instance and adds its + contents to the contents of the Tensor which is set as gradient of this very Tensor.
    +
    +
    Parameters:
    error - The error gradient which ought to be added to the gradient of this tensor.
    -
    Returns:
    +
    Returns:
    This very tensor instance to enable method chaining.
    -
    • -
  • -
    -

    setNDConf

    -
    Tensor<T> setNDConf(NDConfiguration configuration)
    +
+ + + +
    +
  • +

    setNDConf

    +
    Tensor<T> setNDConf(NDConfiguration configuration)
    This method sets the NDConfiguration of this NDArray. Therefore, it should not be used lightly as it can cause major internal inconsistencies.
    -
    -
    Parameters:
    +
    +
    Parameters:
    configuration - The new NDConfiguration instance which ought to be set.
    -
    Returns:
    +
    Returns:
    The final instance type of this class which enables method chaining.
    -
    • -
  • -
    -

    toType

    -
    <V> Tensor<V> toType(Class<V> typeClass)
    +
+ + + +
    +
  • +

    toType

    +
    <V> Tensor<V> toType(java.lang.Class<V> typeClass)
    This method is an inline operation which changes the underlying data of this tensor. It converts the data types of the elements of this tensor to the specified type!

    @@ -584,123 +720,146 @@

    toType

    Therefore, there might be unexpected performance penalties or side effects associated with this method.

    -
    -
    Specified by:
    -
    toType in interface MutateNda<T>
    -
    Type Parameters:
    +
    +
    Specified by:
    +
    toType in interface MutateNda<T>
    +
    Type Parameters:
    V - The type parameter for the returned tensor.
    -
    Parameters:
    +
    Parameters:
    typeClass - The target type class for elements of this tensor.
    -
    Returns:
    +
    Returns:
    The same tensor instance whose data has been converted to hold a different type.
    -
    • -
  • -
    -

    upcast

    -
    <U> Tensor<U> upcast(Class<U> superType)
    +
+ + + +
    +
  • +

    upcast

    +
    <U> Tensor<U> upcast(java.lang.Class<U> superType)
    Use this to do a runtime checked upcast of the type parameter of the tensor. - This is unsafe because it is in conflict with the Nda.itemType() + This is unsafe because it is in conflict with the Nda.itemType() method.
    -
    -
    Type Parameters:
    +
    +
    Type Parameters:
    U - The super type parameter of the value type of the tensor.
    -
    Parameters:
    +
    Parameters:
    superType - The class of the super type of the tensor's value type.
    -
    Returns:
    +
    Returns:
    A tensor whose type parameter is upcast.
    -
    • -
  • -
    -

    toLayout

    -
    Tensor<T> toLayout(NDConfiguration.Layout layout)
    +
+ + + +
    +
  • +

    toLayout

    +
    Tensor<T> toLayout(NDConfiguration.Layout layout)
    This method allows you to modify the data-layout of this AbstractNda. Warning! The method should not be used unless absolutely necessary. This is because it can cause unpredictable side effects especially for certain operations expecting a particular data layout (like for example matrix multiplication).
    -
    -
    Parameters:
    +
    +
    Parameters:
    layout - The layout of the data array (row or column major).
    -
    Returns:
    +
    Returns:
    The final instance type of this class which enables method chaining.
    -
    • -
  • -
    -

    incrementVersion

    -
    Tensor<T> incrementVersion(ExecutionCall<?> call)
    +
+ + + +
    +
  • +

    incrementVersion

    +
    Tensor<T> incrementVersion(ExecutionCall<?> call)
    This method is responsible for incrementing the "_version" field variable which represents the version of the data of this tensor. Meaning : Every time the underlying data (_value) changes this version ought to increment alongside. The method is called during the execution procedure.
    -
    -
    Parameters:
    +
    +
    Parameters:
    call - The context object containing all relevant information that defines a call for tensor execution.
    -
    Returns:
    +
    Returns:
    This very tensor instance. (factory pattern)
    -
    • -
  • -
    -

    setIsIntermediate

    -
    Tensor<T> setIsIntermediate(boolean isIntermediate)
    +
+ + + +
    +
  • +

    setIsIntermediate

    +
    Tensor<T> setIsIntermediate(boolean isIntermediate)
    Intermediate tensors are internal non-user tensors which may be eligible - for deletion when further consumed by a Function. + for deletion when further consumed by a Function. For the casual user of Neureka, this flag should always be false!
    -
    -
    Parameters:
    +
    +
    Parameters:
    isIntermediate - The truth value determining if this tensor is not a user tensor but an internal - tensor which may be eligible for deletion by Functions consuming it.
    -
    Returns:
    + tensor which may be eligible for deletion by Functions consuming it. +
    Returns:
    The tensor to which this unsafe API belongs.
    -
    • -
  • -
    -

    delete

    -
    Tensor<T> delete()
    +
+ + + +
    +
  • +

    delete

    +
    Tensor<T> delete()
    Although tensors will be garbage collected when they are not strongly referenced, there is also the option to manually free up the tensor and its associated data in a native environment. - This is especially useful when tensors are stored on a device like the OpenCLDevice. + This is especially useful when tensors are stored on a device like the OpenCLDevice. In that case calling this method will free the memory reserved for this tensor on the device. This manual memory freeing through this method can be faster than waiting for the garbage collector to kick in at a latr point in time...

    -
    -
    Returns:
    +
    +
    Returns:
    The tensor wo which this unsafe API belongs to allow for method chaining.
    -
    • -
  • -
    -

    setDataAt

    -
    Tensor<T> setDataAt(int i, - T o)
    +
+ + + + + +
    +
  • +

    setDataAt

    +
    Tensor<T> setDataAt(int i,
+                    T o)
    A tensor ought to have some way to selectively modify its underlying data array. This method simply overrides an element within this data array sitting at position "i".
    -
    -
    Parameters:
    +
    +
    Parameters:
    i - The index of the data array entry which ought to be addressed.
    o - The object which ought to be placed at the requested position.
    -
    Returns:
    +
    Returns:
    This very tensor in order to enable method chaining.
    -
    • -
  • -
    -

    setData

    -
    Tensor<T> setData(Data<T> data)
    -
    At the heart of every tensor is the Data object, which holds the actual data array, +
+ + + +
    +
  • +

    setData

    +
    Tensor<T> setData(Data<T> data)
    +
    At the heart of every tensor is the Data object, which holds the actual data array, a sequence of values of the same type. This method allows you to set the data of this tensor to a new data object. Changing the data object of a tensor will not change the shape of the tensor and how @@ -709,173 +868,218 @@

    setData

    Warning! This method should not be used unless absolutely necessary. This is because it can cause unpredictable side effects especially for certain operations expecting a particular data layout (like for example matrix multiplication).
    -
    -
    Parameters:
    +
    +
    Parameters:
    data - The new data object which ought to be set.
    -
    Returns:
    +
    Returns:
    The tensor in question, to allow for method chaining.
    -
    • -
  • -
    -

    getDataForWriting

    -
    default <A> A getDataForWriting(Class<A> arrayTypeClass)
    +
+ + + +
    +
  • +

    getDataForWriting

    +
    default <A> A getDataForWriting(java.lang.Class<A> arrayTypeClass)
    Use this to access the underlying writable data of this tensor if you want to modify it. This method will ensure that you receive an instance of whatever array type you provide or throw descriptive exceptions to make sure that any unwanted behaviour does not spread further in the backend.
    -
    -
    Type Parameters:
    +
    +
    Type Parameters:
    A - The type parameter of the provided type class.
    -
    Parameters:
    +
    Parameters:
    arrayTypeClass - The expected array type underlying the tensor.
    -
    Returns:
    +
    Returns:
    The underlying data array of this tensor.
    -
    • -
  • -
    -

    detach

    -
    Tensor<T> detach()
    +
+ + + +
    +
  • +

    detach

    +
    Tensor<T> detach()
    This method detaches this tensor from its underlying computation-graph or simply does nothing if no graph is present.
    - Nodes within a computation graph are instances of the "GraphNode" class which are also + Nodes within a computation graph are instances of the "GraphNode" class which are also simple components of the tensors they represent in the graph.
    - Therefore, "detaching" this tensor from the graph simply means removing its GraphNode component.
    -
    -
    Returns:
    + Therefore, "detaching" this tensor from the graph simply means removing its GraphNode component.
+
+
Returns:
This very instance in order to allows for a more streamline usage of this method.
- -
  • -
    -

    timesAssign

    -
    Tensor<T> timesAssign(Tensor<T> other)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      timesAssign

      +
      Tensor<T> timesAssign(Tensor<T> other)
      +
      +
      Parameters:
      other - The tensor whose elements ought to be multiplied and assigned to elements in this tensor.
      -
      Returns:
      +
      Returns:
      This instance where each value element was multiplied by the corresponding element in the provided tensor.
      -
    • -
  • -
    -

    timesAssign

    -
    Tensor<T> timesAssign(T other)
    -
    -
    Parameters:
    + + + + + + +
      +
    • +

      timesAssign

      +
      Tensor<T> timesAssign(T other)
      +
      +
      Parameters:
      other - The value which ought to be multiplied and assigned to each element in this tensor.
      -
      Returns:
      +
      Returns:
      This instance where each value element was multiplied by the provided element.
      -
    • -
  • -
    -

    divAssign

    -
    Tensor<T> divAssign(Tensor<T> other)
    -
    + + + + + + + + + + + + +
      +
    • +

      plusAssign

      +
      Tensor<T> plusAssign(Tensor<T> other)
      Performs an addition of the passed tensor to this tensor. The result of the addition will be stored in this tensor (inline operation).
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The tensor which ought to be added to this tensor.
      -
      Returns:
      +
      Returns:
      This tensor.
      -
      • -
    • -
      -

      minusAssign

      -
      Tensor<T> minusAssign(Tensor<T> other)
      -
      +
    + + + +
      +
    • +

      minusAssign

      +
      Tensor<T> minusAssign(Tensor<T> other)
      • -
    • -
      -

      minusAssign

      -
      Tensor<T> minusAssign(T other)
      -
      -
      Parameters:
      +
    + + + + + +
      +
    • +

      minusAssign

      +
      Tensor<T> minusAssign(T other)
      +
      +
      Parameters:
      other - The scalar value which should be subtracted from the values of this tensor.
      -
      Returns:
      +
      Returns:
      This tensor after the minus-assign inline operation was applied.
      -
      • -
    • -
      -

      assign

      -
      Tensor<T> assign(T other)
      -
      Description copied from interface: MutateNda
      +
    + + + + + +
      +
    • +

      assign

      +
      Tensor<T> assign(T other)
      +
      Description copied from interface: MutateNda
      Use this to assign the provided item to all elements of this nd-array! This method is an inline operation which changes the underlying data of the nd-array.
      -
      -
      Specified by:
      -
      assign in interface MutateNda<T>
      -
      Parameters:
      +
      +
      Specified by:
      +
      assign in interface MutateNda<T>
      +
      Parameters:
      other - The item which ought to be assigned to all elements of this nd-array.
      -
      Returns:
      +
      Returns:
      This very nd-array to enable method chaining.
      -
      • -
    • -
      -

      assign

      -
      Tensor<T> assign(Nda<T> other)
      -
      Description copied from interface: MutateNda
      +
    + + + +
      +
    • +

      assign

      +
      Tensor<T> assign(Nda<T> other)
      +
      Description copied from interface: MutateNda
      Use this to assign the provided nd-array to this nd-array! This method is an inline operation which changes the underlying data of the nd-array.
      -
      -
      Specified by:
      -
      assign in interface MutateNda<T>
      -
      Parameters:
      +
      +
      Specified by:
      +
      assign in interface MutateNda<T>
      +
      Parameters:
      other - The nd-array which ought to be assigned to this nd-array.
      -
      Returns:
      +
      Returns:
      This very nd-array to enable method chaining.
      -
      • -
    • -
      -

      label

      -
      Tensor<T> label(String label)
      +
    + + + +
      +
    • +

      label

      +
      Tensor<T> label(java.lang.String label)
      Sets the label of this nd-array. The label is a human-readable string which can be used to identify this nd-array for example in a pretty-printed output.
      -
      -
      Specified by:
      -
      label in interface MutateNda<T>
      -
      Parameters:
      +
      +
      Specified by:
      +
      label in interface MutateNda<T>
      +
      Parameters:
      label - The label of this nd-array.
      -
      Returns:
      +
      Returns:
      This very nd-array to enable method chaining.
      -
      • -
    • -
      -

      labelAxes

      -
      Tensor<T> labelAxes(String[]... labels)
      +
    + + + +
      +
    • +

      labelAxes

      +
      Tensor<T> labelAxes(java.lang.String[]... labels)
      This method receives a label for this tensor and a - nested String array which ought to contain a + nested String array which ought to contain a label for the index of this tensor. The index for a single element of this tensor would be an array of numbers as long as the rank where every number is @@ -887,21 +1091,24 @@

      labelAxes

      dim 0 : ["A", "B"]
      dim 1 : ["1", "2", "3"]

      -
      -
      Specified by:
      -
      labelAxes in interface MutateNda<T>
      -
      Parameters:
      +
      +
      Specified by:
      +
      labelAxes in interface MutateNda<T>
      +
      Parameters:
      labels - A nested String array containing labels for indexes of the tensor dimensions.
      -
      Returns:
      +
      Returns:
      This tensor (method chaining).
      -
      • -
    • -
      -

      labelAxes

      -
      Tensor<T> labelAxes(List<List<Object>> labels)
      -
      This method receives a nested String list which +
    + + + +
      +
    • +

      labelAxes

      +
      Tensor<T> labelAxes(java.util.List<java.util.List<java.lang.Object>> labels)
      +
      This method receives a nested String list which ought to contain a label for the index of this tensor. The index for a single element of this tensor would be an array of numbers as long as the rank where every number is @@ -913,20 +1120,23 @@

      labelAxes

      dim 0 : ["A", "B"]
      dim 1 : ["1", "2", "3"]

      -
      -
      Specified by:
      -
      labelAxes in interface MutateNda<T>
      -
      Parameters:
      +
      +
      Specified by:
      +
      labelAxes in interface MutateNda<T>
      +
      Parameters:
      labels - A nested String list containing labels for indexes of the tensor dimensions.
      -
      Returns:
      +
      Returns:
      This tensor (method chaining).
      -
      • -
    • -
      -

      labelAxes

      -
      Tensor<T> labelAxes(Map<Object,List<Object>> labels)
      +
    + + + +
      +
    • +

      labelAxes

      +
      Tensor<T> labelAxes(java.util.Map<java.lang.Object,java.util.List<java.lang.Object>> labels)
      This method provides the ability to label not only the indices of the shape of this tensor, but also the dimension of the shape. @@ -939,20 +1149,23 @@

      labelAxes

      "dim 1" : ["1", "2", "3"]
      ]

      -
      -
      Specified by:
      -
      labelAxes in interface MutateNda<T>
      -
      Parameters:
      +
      +
      Specified by:
      +
      labelAxes in interface MutateNda<T>
      +
      Parameters:
      labels - A map in which the keys are dimension labels and the values are lists of index labels for the dimension.
      -
      Returns:
      +
      Returns:
      This tensor (method chaining).
      -
      • -
    • -
      -

      setIsVirtual

      -
      Tensor<T> setIsVirtual(boolean isVirtual)
      +
    + + + +
      +
    • +

      setIsVirtual

      +
      Tensor<T> setIsVirtual(boolean isVirtual)
      Virtualizing is the opposite to actualizing a tensor. A tensor is virtual if the size of the underlying data is not actually equal to the number of elements which the tensor claims to store, aka its size. @@ -968,23 +1181,82 @@

      setIsVirtual

      This only makes sense for homogeneously populated tensors. Passing false to this method will "actualize" a "virtual" tensor. Meaning the underlying data array will at least become as large as the size of the tensor - as is defined by NDimensional.size().
      -
      -
      Parameters:
      + as is defined by NDimensional.size().
    • +
    +
    Parameters:
    isVirtual - The truth value determining if this tensor should be "virtual" or "actual".
    -
    Returns:
    +
    Returns:
    This concrete instance, to allow for method chaining.
    - - - + + + + - + +
    + + +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/MutateTsr.html b/docs/jdocs/neureka/MutateTsr.html deleted file mode 100644 index 51d5a71e1..000000000 --- a/docs/jdocs/neureka/MutateTsr.html +++ /dev/null @@ -1,1327 +0,0 @@ - - - - - -MutateTsr (neureka 0.21.0 API) - - - - - - - - - - - - - -
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    Package neureka
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    Interface MutateTsr<T>

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      All Superinterfaces:
      -
      MutateNda<T>
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      public interface MutateTsr<T>
-extends MutateNda<T>
      -
      Tensors should be considered immutable, however sometimes it - is important to mutate their state for performance reasons. - This interface exposes several methods for mutating the state of a tensor. - The usage of methods exposed by this API is generally discouraged - because the exposed state can easily lead to broken tensors and exceptions...
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        Method Summary

        - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        All Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethodDescription
        Tsr<T>addToGradient​(Tsr<T> error) -
        This method takes the provided Tsr instance and adds its - contents to the contents of the Tsr which is set as gradient of this very Tsr.
        -
        Tsr<T>assign​(Nda<T> other) -
        Use this to assign the provided nd-array to this nd-array! - This method is an inline operation which changes the underlying data of the nd-array.
        -
        Tsr<T>assign​(T other) -
        Use this to assign the provided item to all elements of this nd-array! - This method is an inline operation which changes the underlying data of the nd-array.
        -
        Tsr<T>delete() -
        Although tensors will be garbage collected when they are not strongly referenced, - there is also the option to manually free up the tensor and its associated data in a native environment.
        -
        Tsr<T>detach() -
        This method detaches this tensor from its underlying computation-graph - or simply does nothing if no graph is present.
        - Nodes within a computation graph are instances of the "GraphNode" class which are also - simple components of the tensors they represent in the graph.
        -
        Tsr<T>divAssign​(Tsr<T> other) 
        default <A> AgetDataForWriting​(java.lang.Class<A> arrayTypeClass) -
        Use this to access the underlying writable data of this tensor if - you want to modify it.
        -
        Tsr<T>incrementVersion​(ExecutionCall<?> call) -
        This method is responsible for incrementing - the "_version" field variable which represents the version of the data of this tensor.
        -
        Tsr<T>label​(java.lang.String label) -
        Sets the label of this nd-array.
        -
        Tsr<T>labelAxes​(java.lang.String[]... labels) -
        This method receives a label for this tensor and a - nested String array which ought to contain a - label for the index of this tensor.
        -
        Tsr<T>labelAxes​(java.util.List<java.util.List<java.lang.Object>> labels) -
        This method receives a nested String list which - ought to contain a label for the index of this tensor.
        -
        Tsr<T>labelAxes​(java.util.Map<java.lang.Object,​java.util.List<java.lang.Object>> labels) -
        This method provides the ability to - label not only the indices of the shape of this tensor, but also - the dimension of the shape.
        -
        Tsr<T>minusAssign​(Tsr<T> other) 
        Tsr<T>minusAssign​(T other) 
        Tsr<T>modAssign​(Tsr<T> other) 
        Tsr<T>plusAssign​(Tsr<T> other) -
        Performs an addition of the passed tensor to this tensor.
        -
        Tsr<T>putAt​(int[] indices, - T value) -
        Use this to put a single item at a particular - position within this nd-array.
        -
        Tsr<T>putAt​(int index, - T value) -
        Individual entries for value items in this nd-array can be set - via this method.
        -
        Tsr<T>putAt​(java.util.List<?> key, - Nda<T> value) -
        This method enables injecting slices of nd-array to be assigned into this nd-array! - It takes a key of various types which is used to configure a slice - nd-array sharing the same underlying data as the original nd-array.
        -
        Tsr<T>putAt​(java.util.List<?> indices, - T value) -
        Use this to place a single item at a particular position within this nd-array!
        -
        Tsr<T>putAt​(java.util.Map<?,​java.lang.Integer> key, - Nda<T> value) -
        This method enables assigning a provided nd-array to be a subset/slice of this nd-array! - It takes a key which is used to configure a slice - sharing the same underlying data as the original nd-array.
        -
        default Tsr<T>set​(int[] indices, - T value) -
        Use this to place a single item at a particular position within this nd-array!
        -
        default Tsr<T>set​(int i0, - int i1, - int i2, - T value)
        default Tsr<T>set​(int i0, - int i1, - T value)
        default Tsr<T>set​(int index, - T value) -
        Individual entries for value items in this nd-array can be set - via this method.
        -
        Tsr<T>setData​(Data<T> data) -
        At the heart of every tensor is the Data object, which holds the actual data array, - a sequence of values of the same type.
        -
        Tsr<T>setDataAt​(int i, - T o) -
        A tensor ought to have some way to selectively modify its underlying data array.
        -
        Tsr<T>setIsIntermediate​(boolean isIntermediate) -
        Intermediate tensors are internal non-user tensors which may be eligible - for deletion when further consumed by a Function.
        -
        Tsr<T>setIsVirtual​(boolean isVirtual) -
        Virtualizing is the opposite to actualizing a tensor.
        -
        Tsr<T>setItemAt​(int i, - T o) -
        An NDArray implementation ought to have some way to selectively modify its underlying value.
        -
        Tsr<T>setItems​(java.lang.Object value) -
        This method will receive an object an try to interpret - it or its contents to be set as value for this nd-array.
        -
        Tsr<T>setNDConf​(NDConfiguration configuration) -
        This method sets the NDConfiguration of this NDArray.
        -
        Tsr<T>timesAssign​(Tsr<T> other) 
        Tsr<T>timesAssign​(T other) 
        Tsr<T>toLayout​(NDConfiguration.Layout layout) -
        This method allows you to modify the data-layout of this AbstractNda.
        -
        <V> Tsr<V>toType​(java.lang.Class<V> typeClass) -
        This method is an inline operation which changes the underlying data of this tensor.
        -
        <U> Tsr<U>upcast​(java.lang.Class<U> superType) -
        Use this to do a runtime checked upcast of the type parameter of the tensor.
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        Method Detail

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          putAt

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          Tsr<T> putAt​(java.util.Map<?,​java.lang.Integer> key,
-             Nda<T> value)
          -
          This method enables assigning a provided nd-array to be a subset/slice of this nd-array! - It takes a key which is used to configure a slice - sharing the same underlying data as the original nd-array. - This slice is then used to assign the second argument value to it. - The usage of this method is especially powerful when used in Groovy.
          - The following code illustrates this very well: - 
          
-      a[[[0..0]:1, [0..0]:1, [0..3]:2]] = b
-
          - Here a single argument with the format '[i..j]:k' is equivalent - to pythons 'i:j:k' syntax for indexing! (numpy)
          - i... start indexAlias.
          - j... end indexAlias. (inclusive!)
          - k... step size.
          -
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          Specified by:
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          putAt in interface MutateNda<T>
          -
          Parameters:
          -
          key - This object is a map defining a step and a targeted index or range of indices...
          -
          value - The nd-array which ought to be assigned into a slice of this nd-array.
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          Returns:
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          A slice nd-array or scalar value.
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          putAt

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          Tsr<T> putAt​(int[] indices,
-             T value)
          -
          Use this to put a single item at a particular - position within this nd-array.
          -
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          Specified by:
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          putAt in interface MutateNda<T>
          -
          Parameters:
          -
          indices - The indices of the nd-position where the provided item should be placed.
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          value - The item which should be placed at the position defined by the provided indices.
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          Returns:
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          This nd-array itself.
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          set

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          default Tsr<T> set​(int[] indices,
-                   T value)
          -
          Use this to place a single item at a particular position within this nd-array!
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          Specified by:
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          set in interface MutateNda<T>
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          Parameters:
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          indices - An array of indices targeting a particular position in this nd-array...
          -
          value - the value which ought to be placed at the targeted position.
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          Returns:
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          This very nd-array in order to enable method chaining...
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          set

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          default Tsr<T> set​(int i0,
-                   int i1,
-                   T value)
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          Specified by:
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          set in interface MutateNda<T>
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          set

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          default Tsr<T> set​(int i0,
-                   int i1,
-                   int i2,
-                   T value)
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          Specified by:
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          set in interface MutateNda<T>
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          putAt

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          Tsr<T> putAt​(int index,
-             T value)
          -
          Individual entries for value items in this nd-array can be set - via this method.
          -
          -
          Specified by:
          -
          putAt in interface MutateNda<T>
          -
          Parameters:
          -
          index - The scalar index targeting a specific value position within this nd-array - which ought to be replaced by the one provided by the second parameter - of this method.
          -
          value - The item which ought to be placed at the targeted position.
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          Returns:
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          This very nd-array in order to enable method chaining...
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          set

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          default Tsr<T> set​(int index,
-                   T value)
          -
          Individual entries for value items in this nd-array can be set - via this method.
          -
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          Specified by:
          -
          set in interface MutateNda<T>
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          Parameters:
          -
          index - The scalar index targeting a specific value position within this nd-array - which ought to be replaced by the one provided by the second parameter - of this method.
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          value - The item which ought to be placed at the targeted position.
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          Returns:
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          This very nd-array in order to enable method chaining...
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          putAt

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          Tsr<T> putAt​(java.util.List<?> key,
-             Nda<T> value)
          -
          This method enables injecting slices of nd-array to be assigned into this nd-array! - It takes a key of various types which is used to configure a slice - nd-array sharing the same underlying data as the original nd-array. - This slice is then used to assign the second argument to it, namely - the "value" argument.
          -
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          Specified by:
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          putAt in interface MutateNda<T>
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          Parameters:
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          key - This object is a list defining a targeted index or range of indices...
          -
          value - the nd-array which ought to be assigned to a slice of this nd-array.
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          Returns:
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          This very nd-array in order to enable method chaining...
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          putAt

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          Tsr<T> putAt​(java.util.List<?> indices,
-             T value)
          -
          Use this to place a single item at a particular position within this nd-array!
          -
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          Specified by:
          -
          putAt in interface MutateNda<T>
          -
          Parameters:
          -
          indices - A list of indices targeting a particular position in this nd-array...
          -
          value - the value which ought to be placed at the targeted position.
          -
          Returns:
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          This very nd-array in order to enable method chaining...
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          setItemAt

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          Tsr<T> setItemAt​(int i,
-                 T o)
          -
          An NDArray implementation ought to have some way to selectively modify its underlying value. - This method simply overrides an element within this data array sitting at position "i".
          -
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          Specified by:
          -
          setItemAt in interface MutateNda<T>
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          Parameters:
          -
          i - The index of the value array entry which ought to be addressed.
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          o - The object which ought to be placed at the requested position.
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          Returns:
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          This very nd-array in order to enable method chaining.
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          setItems

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          Tsr<T> setItems​(java.lang.Object value)
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          This method will receive an object an try to interpret - it or its contents to be set as value for this nd-array. - It will not necessarily replace the underlying data array object of this - nd-array itself, but also try to convert and copy the provided value - into the data array of this nd-array.
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          Specified by:
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          setItems in interface MutateNda<T>
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          Parameters:
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          value - The value which may be a scalar or array and will be used to populate this nd-array.
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          Returns:
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          This very nd-array to enable method chaining.
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          addToGradient

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          Tsr<T> addToGradient​(Tsr<T> error)
          -
          This method takes the provided Tsr instance and adds its - contents to the contents of the Tsr which is set as gradient of this very Tsr.
          -
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          Parameters:
          -
          error - The error gradient which ought to be added to the gradient of this tensor.
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          Returns:
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          This very tensor instance to enable method chaining.
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          setNDConf

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          Tsr<T> setNDConf​(NDConfiguration configuration)
          -
          This method sets the NDConfiguration of this NDArray. - Therefore, it should not be used lightly as it can cause major internal inconsistencies.
          -
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          Parameters:
          -
          configuration - The new NDConfiguration instance which ought to be set.
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          Returns:
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          The final instance type of this class which enables method chaining.
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          toType

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          <V> Tsr<V> toType​(java.lang.Class<V> typeClass)
          -
          This method is an inline operation which changes the underlying data of this tensor. - It converts the data types of the elements of this tensor to the specified type!
          -
          - WARNING : The usage of this method is discouraged for the following reasons:
          -
          - 1. Inline operations are inherently error-prone for most use cases.
          - 2. This inline operation in particular has no safety net, - meaning that there is no implementation of version mismatch detection - like there is for those operations present in the standard operation backend... - No exceptions will be thrown during backpropagation!
          - 3. This method has not yet been implemented to also handle instances which - are slices of parent tensors! - Therefore, there might be unexpected performance penalties or side effects - associated with this method.
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          Specified by:
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          toType in interface MutateNda<T>
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          Type Parameters:
          -
          V - The type parameter for the returned tensor.
          -
          Parameters:
          -
          typeClass - The target type class for elements of this tensor.
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          Returns:
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          The same tensor instance whose data has been converted to hold a different type.
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          upcast

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          <U> Tsr<U> upcast​(java.lang.Class<U> superType)
          -
          Use this to do a runtime checked upcast of the type parameter of the tensor. - This is unsafe because it is in conflict with the Nda.itemType() - method.
          -
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          Type Parameters:
          -
          U - The super type parameter of the value type of the tensor.
          -
          Parameters:
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          superType - The class of the super type of the tensor's value type.
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          Returns:
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          A tensor whose type parameter is upcast.
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          toLayout

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          Tsr<T> toLayout​(NDConfiguration.Layout layout)
          -
          This method allows you to modify the data-layout of this AbstractNda. - Warning! The method should not be used unless absolutely necessary. - This is because it can cause unpredictable side effects especially for certain - operations expecting a particular data layout (like for example matrix multiplication). -
          -
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          Parameters:
          -
          layout - The layout of the data array (row or column major).
          -
          Returns:
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          The final instance type of this class which enables method chaining.
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          incrementVersion

          -
          Tsr<T> incrementVersion​(ExecutionCall<?> call)
          -
          This method is responsible for incrementing - the "_version" field variable which represents the version of the data of this tensor. - Meaning : - Every time the underlying data (_value) changes this version ought to increment alongside. - The method is called during the execution procedure.
          -
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          Parameters:
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          call - The context object containing all relevant information that defines a call for tensor execution.
          -
          Returns:
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          This very tensor instance. (factory pattern)
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          setIsIntermediate

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          Tsr<T> setIsIntermediate​(boolean isIntermediate)
          -
          Intermediate tensors are internal non-user tensors which may be eligible - for deletion when further consumed by a Function. - For the casual user of Neureka, this flag should always be false!
          -
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          Parameters:
          -
          isIntermediate - The truth value determining if this tensor is not a user tensor but an internal - tensor which may be eligible for deletion by Functions consuming it.
          -
          Returns:
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          The tensor to which this unsafe API belongs.
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          delete

          -
          Tsr<T> delete()
          -
          Although tensors will be garbage collected when they are not strongly referenced, - there is also the option to manually free up the tensor and its associated data in a native environment. - This is especially useful when tensors are stored on a device like the OpenCLDevice. - In that case calling this method will free the memory reserved for this tensor on the device. - This manual memory freeing through this method can be faster than waiting for - the garbage collector to kick in at a latr point in time...
          -
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          Returns:
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          The tensor wo which this unsafe API belongs to allow for method chaining.
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          setDataAt

          -
          Tsr<T> setDataAt​(int i,
-                 T o)
          -
          A tensor ought to have some way to selectively modify its underlying data array. - This method simply overrides an element within this data array sitting at position "i".
          -
          -
          Parameters:
          -
          i - The index of the data array entry which ought to be addressed.
          -
          o - The object which ought to be placed at the requested position.
          -
          Returns:
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          This very tensor in order to enable method chaining.
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          setData

          -
          Tsr<T> setData​(Data<T> data)
          -
          At the heart of every tensor is the Data object, which holds the actual data array, - a sequence of values of the same type. - This method allows you to set the data of this tensor to a new data object. - Changing the data object of a tensor will not change the shape of the tensor and how - nd-indices are mapped to the data array. -

          - Warning! This method should not be used unless absolutely necessary. - This is because it can cause unpredictable side effects especially for certain - operations expecting a particular data layout (like for example matrix multiplication).

          -
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          Parameters:
          -
          data - The new data object which ought to be set.
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          Returns:
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          The tensor in question, to allow for method chaining.
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          getDataForWriting

          -
          default <A> A getDataForWriting​(java.lang.Class<A> arrayTypeClass)
          -
          Use this to access the underlying writable data of this tensor if - you want to modify it. - This method will ensure that you receive an instance of whatever array type you provide - or throw descriptive exceptions to make sure that any unwanted behaviour does not - spread further in the backend.
          -
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          Type Parameters:
          -
          A - The type parameter of the provided type class.
          -
          Parameters:
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          arrayTypeClass - The expected array type underlying the tensor.
          -
          Returns:
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          The underlying data array of this tensor.
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          detach

          -
          Tsr<T> detach()
          -
          This method detaches this tensor from its underlying computation-graph - or simply does nothing if no graph is present.
          - Nodes within a computation graph are instances of the "GraphNode" class which are also - simple components of the tensors they represent in the graph.
          - Therefore, "detaching" this tensor from the graph simply means removing its GraphNode component.
          -
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          Returns:
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          This very instance in order to allows for a more streamline usage of this method.
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          timesAssign

          -
          Tsr<T> timesAssign​(Tsr<T> other)
          -
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          Parameters:
          -
          other - The tensor whose elements ought to be multiplied and assigned to elements in this tensor.
          -
          Returns:
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          This instance where each value element was multiplied by the corresponding element in the provided tensor.
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          timesAssign

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          Tsr<T> timesAssign​(T other)
          -
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          Parameters:
          -
          other - The value which ought to be multiplied and assigned to each element in this tensor.
          -
          Returns:
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          This instance where each value element was multiplied by the provided element.
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          divAssign

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          Tsr<T> divAssign​(Tsr<T> other)
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          modAssign

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          Tsr<T> modAssign​(Tsr<T> other)
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          plusAssign

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          Tsr<T> plusAssign​(Tsr<T> other)
          -
          Performs an addition of the passed tensor to this tensor. - The result of the addition will be stored in this tensor (inline operation).
          -
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          Parameters:
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          other - The tensor which ought to be added to this tensor.
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          Returns:
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          This tensor.
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          minusAssign

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          Tsr<T> minusAssign​(Tsr<T> other)
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        - - - - - -
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          minusAssign

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          Tsr<T> minusAssign​(T other)
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          Parameters:
          -
          other - The scalar value which should be subtracted from the values of this tensor.
          -
          Returns:
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          This tensor after the minus-assign inline operation was applied.
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          assign

          -
          Tsr<T> assign​(T other)
          -
          Description copied from interface: MutateNda
          -
          Use this to assign the provided item to all elements of this nd-array! - This method is an inline operation which changes the underlying data of the nd-array.
          -
          -
          Specified by:
          -
          assign in interface MutateNda<T>
          -
          Parameters:
          -
          other - The item which ought to be assigned to all elements of this nd-array.
          -
          Returns:
          -
          This very nd-array to enable method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          assign

          -
          Tsr<T> assign​(Nda<T> other)
          -
          Description copied from interface: MutateNda
          -
          Use this to assign the provided nd-array to this nd-array! - This method is an inline operation which changes the underlying data of the nd-array.
          -
          -
          Specified by:
          -
          assign in interface MutateNda<T>
          -
          Parameters:
          -
          other - The nd-array which ought to be assigned to this nd-array.
          -
          Returns:
          -
          This very nd-array to enable method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          label

          -
          Tsr<T> label​(java.lang.String label)
          -
          Sets the label of this nd-array. - The label is a human-readable string which can be used to identify this nd-array - for example in a pretty-printed output.
          -
          -
          Specified by:
          -
          label in interface MutateNda<T>
          -
          Parameters:
          -
          label - The label of this nd-array.
          -
          Returns:
          -
          This very nd-array to enable method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          labelAxes

          -
          Tsr<T> labelAxes​(java.lang.String[]... labels)
          -
          This method receives a label for this tensor and a - nested String array which ought to contain a - label for the index of this tensor. - The index for a single element of this tensor would be an array - of numbers as long as the rank where every number is - in the range of the corresponding shape dimension... - Labeling an index means that for every dimension there - must be a label for elements in this range array!
          - For example the shape (2,3) could be labeled as follows:
          -
          - dim 0 : ["A", "B"]
          - dim 1 : ["1", "2", "3"]
          -
          -
          -
          Specified by:
          -
          labelAxes in interface MutateNda<T>
          -
          Parameters:
          -
          labels - A nested String array containing labels for indexes of the tensor dimensions.
          -
          Returns:
          -
          This tensor (method chaining).
          -
          -
          • -
        - - - -
          -
        • -

          labelAxes

          -
          Tsr<T> labelAxes​(java.util.List<java.util.List<java.lang.Object>> labels)
          -
          This method receives a nested String list which - ought to contain a label for the index of this tensor. - The index for a single element of this tensor would be an array - of numbers as long as the rank where every number is - in the range of the corresponding shape dimension... - Labeling an index means that for every dimension there - must be a label for elements in this range array!
          - For example the shape (2,3) could be labeled as follows:
          -
          - dim 0 : ["A", "B"]
          - dim 1 : ["1", "2", "3"]
          -
          -
          -
          Specified by:
          -
          labelAxes in interface MutateNda<T>
          -
          Parameters:
          -
          labels - A nested String list containing labels for indexes of the tensor dimensions.
          -
          Returns:
          -
          This tensor (method chaining).
          -
          -
          • -
        - - - -
          -
        • -

          labelAxes

          -
          Tsr<T> labelAxes​(java.util.Map<java.lang.Object,​java.util.List<java.lang.Object>> labels)
          -
          This method provides the ability to - label not only the indices of the shape of this tensor, but also - the dimension of the shape. - The first and only argument of the method expects a map instance - where keys are the objects which ought to act as dimension labels - and the values are lists of labels for the indices of said dimensions. - For example the shape (2,3) could be labeled as follows:
          - [
          - "dim 0" : ["A", "B"],
          - "dim 1" : ["1", "2", "3"]
          - ]
          -
          -
          -
          Specified by:
          -
          labelAxes in interface MutateNda<T>
          -
          Parameters:
          -
          labels - A map in which the keys are dimension labels and the values are lists of index labels for the dimension.
          -
          Returns:
          -
          This tensor (method chaining).
          -
          -
          • -
        - - - -
          -
        • -

          setIsVirtual

          -
          Tsr<T> setIsVirtual​(boolean isVirtual)
          -
          Virtualizing is the opposite to actualizing a tensor. - A tensor is virtual if the size of the underlying data is not actually equal to - the number of elements which the tensor claims to store, aka its size. - This is for example the case when initializing a tensor filled with a single - value continuously. In that case the tensor will flag itself as virtual and only allocate the - underlying data array to hold a single item even though the tensor might actually hold - many more items. - The reasons for this feature is that it greatly improves performance in certain cases. - In essence this feature is a form of lazy loading. -

          - WARNING! Virtualizing is the process of compacting the underlying data array - down to an array holding a single value item. - This only makes sense for homogeneously populated tensors. - Passing false to this method will "actualize" a "virtual" tensor. - Meaning the underlying data array will at least become as large as the size of the tensor - as is defined by NDimensional.size().
          -
          -
          Parameters:
          -
          isVirtual - The truth value determining if this tensor should be "virtual" or "actual".
          -
          Returns:
          -
          This concrete instance, to allow for method chaining.
          -
          -
          • -
        -
        • -
      -
      -
      • -
    -
    -
    -
    - -
    - -
    - - diff --git a/docs/jdocs/neureka/Nda.Item.html b/docs/jdocs/neureka/Nda.Item.html index c72f45da2..5e2804314 100644 --- a/docs/jdocs/neureka/Nda.Item.html +++ b/docs/jdocs/neureka/Nda.Item.html @@ -1,246 +1,366 @@ - + + - -Nda.Item (neureka 1.0.0 API) - - - - + +Nda.Item (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Interface Nda.Item<V>

    +
    neureka
    +

    Interface Nda.Item<V>

    -
    -
    -
    Type Parameters:
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The type of the items of this nd-array.
      -
      +
      All Known Subinterfaces:
      -
      MutateNda.Item<V>
      +
      MutateNda.Item<V>
      -
      +
      Enclosing interface:
      -
      Nda<V>
      +
      Nda<V>
      -
      public static interface Nda.Item<V>
      -
      Instances of this are being returned by the Nda.at(int...) method, +
      +
      public static interface Nda.Item<V>
      +
      Instances of this are being returned by the Nda.at(int...) method, and they allow you to get individual nd-array items
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      default boolean
      -
      doesNotExist()
      -
       
      -
      default boolean
      -
      exists()
      -
       
      -
      default V
      -
      get()
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethod and Description
        default booleandoesNotExist() 
        default booleanexists() 
        default Vget()
        Get the value at the targeted position or throw an exception if the item does not exist.
        - -
        default Optional<V>
        -
        map(Function<V,V> mapper)
        -
        +
        default java.util.Optional<V>map(java.util.function.Function<V,V> mapper)
        Maps this item to an optional value based on the provided lambda.
        - -
        default V
        -
        orElse(V defaultValue)
        -
        +
        default VorElse(V defaultValue)
        Get the value at the targeted position or return the provided default value if the item does not exist.
        - -
        V
        -
        orElseNull()
        -
        +
        VorElseNull()
        Get the value at the targeted position or return null if the item does not exist.
        - -
        default Optional<V>
        -
        toOptional()
        -
        +
        default java.util.Optional<V>toOptional()
        Converts this item into an optional value.
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        get

        -
        default V get()
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            get

            +
            default V get()
            Get the value at the targeted position or throw an exception if the item does not exist.
            -
            -
            Returns:
            +
            +
            Returns:
            The value at the targeted position.
            -
        • -
      • -
        -

        orElse

        -
        default V orElse(V defaultValue)
        +
      + + + + + +
        +
      • +

        orElse

        +
        default V orElse(V defaultValue)
        Get the value at the targeted position or return the provided default value if the item does not exist.
        -
        -
        Parameters:
        +
        +
        Parameters:
        defaultValue - The default value to return if the item does not exist.
        -
        Returns:
        +
        Returns:
        The value at the targeted position or the provided default value.
        -
        Throws:
        -
        IllegalArgumentException - If the provided default value is null.
        +
        Throws:
        +
        java.lang.IllegalArgumentException - If the provided default value is null.
        -
      • -
    • -
      -

      orElseNull

      -
      V orElseNull()
      +
    + + + +
      +
    • +

      orElseNull

      +
      V orElseNull()
      Get the value at the targeted position or return null if the item does not exist.
      -
      -
      Returns:
      +
      +
      Returns:
      The value at the targeted position or null.
      -
      • -
    • -
      -

      toOptional

      -
      default Optional<V> toOptional()
      +
    + + + +
      +
    • +

      toOptional

      +
      default java.util.Optional<V> toOptional()
      Converts this item into an optional value. If the item exists, the resulting optional will contain the value. Otherwise, the resulting optional will be empty.
      -
      -
      Returns:
      +
      +
      Returns:
      An optional value.
      -
      • -
    • -
      -

      map

      -
      default Optional<V> map(Function<V,V> mapper)
      +
    + + + +
      +
    • +

      map

      +
      default java.util.Optional<V> map(java.util.function.Function<V,V> mapper)
      Maps this item to an optional value based on the provided lambda. The lambda will be executed if the item exists. If the lambda returns null the resulting optional will be empty. Otherwise, the resulting optional will contain the value returned by the lambda.
      -
      -
      Parameters:
      +
      +
      Parameters:
      mapper - The lambda which maps the item to an optional value.
      -
      Returns:
      +
      Returns:
      An optional value based on the provided lambda.
      -
      • -
    • -
      -

      exists

      -
      default boolean exists()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      exists

      +
      default boolean exists()
      +
      +
      Returns:
      true if the item exists, false otherwise.
      -
      • -
    • -
      -

      doesNotExist

      -
      default boolean doesNotExist()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      doesNotExist

      +
      default boolean doesNotExist()
      +
      +
      Returns:
      true if the item does not exist, false otherwise.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/Nda.html b/docs/jdocs/neureka/Nda.html index 807b9cd79..803c910c9 100644 --- a/docs/jdocs/neureka/Nda.html +++ b/docs/jdocs/neureka/Nda.html @@ -1,683 +1,823 @@ - + + - -Nda (neureka 1.0.0 API) - - - - + +Nda (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Interface Nda<V>

    -
    -
    -
    -
    Type Parameters:
    -
    V - The type of the items stored in the Nda.
    -
    -
    +
    neureka
    +

    Interface Nda<V>

    +
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      +
      V - The type of the items stored in the Nda.
      +
      +
      All Superinterfaces:
      -
      Iterable<V>, NDimensional
      +
      java.lang.Iterable<V>, NDimensional
      -
      +
      All Known Subinterfaces:
      -
      Tensor<V>
      +
      Tensor<V>
      -
      public interface Nda<V> -extends NDimensional, Iterable<V>
      -
      Nda, which is an abbreviation of 'N-Dimensional-Array', represents +
      +
      public interface Nda<V>
+extends NDimensional, java.lang.Iterable<V>
      +
      Nda, which is an abbreviation of 'N-Dimensional-Array', represents a multidimensional, homogeneously filled fixed-size array of items.

      - Ndas should be constructed using the fluent builder API exposed by of(Class).

      - -
      -
        + Ndas should be constructed using the fluent builder API exposed by of(Class).
      +
      • +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Interface
      -
      Description
      -
      static interface 
      -
      Nda.Item<V>
      -
      -
      Instances of this are being returned by the at(int...) method, +
        +
      • + + +

        Nested Class Summary

        + + + + + + + + + + +
        Nested Classes 
        Modifier and TypeInterface and Description
        static interface Nda.Item<V> +
        Instances of this are being returned by the at(int...) method, and they allow you to get individual nd-array items
        - - - +
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      default boolean
      -
      any(Predicate<V> predicate)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethod and Description
        default booleanany(java.util.function.Predicate<V> predicate)
        Iterates over every element of this nd-array, and checks whether any element matches the provided lambda.
        - -
        Nda.Item<V>
        -
        at(int... indices)
        -
        -
        This method exposes the Nda.Item API which allows you to get or set +
        Nda.Item<V>at(int... indices) +
        This method exposes the Nda.Item API which allows you to get or set individual items within this nd-array targeted by an array of provided indices.
        - -
        Nda<V>
        -
        concatAt(int axis, - Nda<V> other)
        -
        +
        Nda<V>concatAt(int axis, + Nda<V> other)
        This method concatenates the provided nd-array together with this nd-array along a specified axis.
        - -
        Nda<V>
        -
        concatAt(int axis, - Nda<V> other, - Nda<V>... ndArrays)
        -
        +
        Nda<V>concatAt(int axis, + Nda<V> other, + Nda<V>... ndArrays)
        This method concatenates the provided nd-arrays together with this nd-array along a specified axis.
        - -
        default int
        -
        count(Predicate<V> predicate)
        -
        +
        default intcount(java.util.function.Predicate<V> predicate)
        Iterates over every element of this nd-array, and counts the number of times the provided lambda matches the items of this array.
        - -
        Nda<V>
        -
        deepCopy()
        -
        +
        Nda<V>deepCopy()
        This method creates and returns a new nd-array instance which is not only a copy of the configuration of this nd-array but also a copy of the underlying data array.
        - -
        default boolean
        -
        every(Predicate<V> predicate)
        -
        +
        default booleanevery(java.util.function.Predicate<V> predicate)
        Iterates over every element of this nd-array, and checks whether all elements are true according to the provided lambda.
        - -
        default Stream<V>
        -
        filter(Predicate<V> predicate)
        -
        +
        default java.util.stream.Stream<V>filter(java.util.function.Predicate<V> predicate)
        A convenience method for stream().filter( predicate ).
        - -
        default <R> Stream<R>
        -
        flatMap(Function<V,Stream<R>> mapper)
        -
        +
        default <R> java.util.stream.Stream<R>flatMap(java.util.function.Function<V,java.util.stream.Stream<R>> mapper)
        A convenience method for nda.stream().flatMap( mapper ), - which turns this Nda into a Stream of its items.
        - -
        Nda<V>
        -
        get(int i)
        -
        -
        This getter method creates and returns a slice of the original nd-array.
        -
        -
        Nda<V>
        -
        get(int... indices)
        -
        + which turns this Nda into a Stream of its items.
        +
        Nda<V>get(int... indices)
        The following method enables access to specific scalar elements within the nd-array.
        - -
        Nda<V>
        -
        get(Number i)
        -
        +
        Nda<V>get(int i)
        This getter method creates and returns a slice of the original nd-array.
        - -
        Nda<V>
        -
        get(Object key)
        -
        +
        Nda<V>get(java.lang.Number i) +
        This getter method creates and returns a slice of the original nd-array.
        +
        Nda<V>get(java.lang.Object... args) +
        The following method enables the creation of nd-array slices which access + the same underlying data (possibly from a different view).
        +
        Nda<V>get(java.lang.Object key)
        This method enables nd-array slicing! It takes a key of various types and configures a slice nd-array which shares the same underlying data as the original nd-array.
        - -
        Nda<V>
        -
        get(Object... args)
        -
        -
        The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view).
        -
        -
        Nda<V>
        -
        getAt(int i)
        -
        -
        This getter method creates and returns a slice of the original nd-array.
        -
        -
        Nda<V>
        -
        getAt(int... indices)
        -
        +
        Nda<V>getAt(int... indices)
        The following method enables access to specific scalar elements within the nd-array.
        - -
        Nda<V>
        -
        getAt(Number i)
        -
        +
        Nda<V>getAt(int i)
        This getter method creates and returns a slice of the original nd-array.
        - -
        Nda<V>
        -
        getAt(Object... args)
        -
        -
        The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view).
        -
        -
        Nda<V>
        -
        getAt(List<?> key)
        -
        +
        Nda<V>getAt(java.util.List<?> key)
        This method enables nd-array slicing! It takes a key of various types and configures a slice nd-array which shares the same underlying data as the original nd-array.
        - -
        Nda<V>
        -
        getAt(Map<?,Integer> rangToSteps)
        -
        +
        Nda<V>getAt(java.util.Map<?,java.lang.Integer> rangToSteps)
        This method is most useful when used in Groovy where defining maps is done through square brackets, making it possible to slice nd-arrays like so:
        - -
        default <A> A
        -
        getDataAs(Class<A> arrayTypeClass)
        -
        -
        Use this to get the items of the underlying Data buffer +
        Nda<V>getAt(java.lang.Number i) +
        This getter method creates and returns a slice of the original nd-array.
        +
        Nda<V>getAt(java.lang.Object... args) +
        The following method enables the creation of nd-array slices which access + the same underlying data (possibly from a different view).
        +
        default <A> AgetDataAs(java.lang.Class<A> arrayTypeClass) +
        Use this to get the items of the underlying Data buffer of this nd-array as a primitive array of the specified type.
        - -
        V
        -
        getDataAt(int i)
        -
        +
        VgetDataAt(int i)
        Use this to access elements of the underlying data array without any index transformation applied to it.
        - -
        default V
        -
        getItem()
        -
        -
        Equivalent to the #item(0) and item().
        -
        -
        default List<V>
        -
        getItems()
        -
        -
        A more verbose version of the items() method (best used by JVM languages with property support).
        -
        -
        default <A> A
        -
        getItemsAs(Class<A> arrayTypeClass)
        -
        +
        default VgetItem() +
        Equivalent to the #item(0) and item().
        +
        default java.util.List<V>getItems() +
        A more verbose version of the items() method (best used by JVM languages with property support).
        +
        default <A> AgetItemsAs(java.lang.Class<A> arrayTypeClass)
        Use this to get the items of this nd-array as a primitive array of the specified type.
        - -
        Class<V>
        -
        getItemType()
        -
         
        -
        default String
        -
        getLabel()
        -
        +
        java.lang.Class<V>getItemType() 
        default java.lang.StringgetLabel()
        A nd-array can have a label.
        - -
        MutateNda<V>
        -
        getMut()
        -
        +
        MutateNda<V>getMut()
        This method exposes an API for mutating the state of this tensor.
        - -
        Object
        -
        getRawData()
        -
        +
        java.lang.ObjectgetRawData()
        This returns an unprocessed version of the underlying data of this nd-array.
        - -
        Object
        -
        getRawItems()
        -
         
        -
        default boolean
        -
        isFullSlice()
        -
        +
        java.lang.ObjectgetRawItems() 
        default booleanisFullSlice()
        If this nd-array is a full slice of a parent nd-array then this method will yield true.
        - -
        boolean
        -
        isPartialSlice()
        -
        +
        booleanisPartialSlice()
        If this nd-array is a partial slice of a parent nd-array then this method will yield true.
        - -
        boolean
        -
        isShallowCopy()
        -
        +
        booleanisShallowCopy()
        If this nd-array is a shallow copy of a parent nd-array then this method will yield true.
        - -
        boolean
        -
        isSlice()
        -
        +
        booleanisSlice()
        If this nd-array is a slice of a parent nd-array then this method will yield true.
        - -
        boolean
        -
        isSliceParent()
        -
        +
        booleanisSliceParent()
        If slices have been derived from this nd-array then it is a "slice parent".
        - -
        default V
        -
        item()
        -
        -
        Equivalent to the #item(0) and getItem().
        -
        -
        default V
        -
        item(int i)
        -
        -
        The following method returns a single item within this nd-array - targeted by the provided integer index.
        -
        -
        default V
        -
        item(int... indices)
        -
        +
        default Vitem() +
        Equivalent to the #item(0) and getItem().
        +
        default Vitem(int... indices)
        This method returns a raw value item within this nd-array targeted by an index array which is expected to hold an index for every dimension of the shape of this nd-array.
        - -
        default List<V>
        -
        items()
        -
        -
        A more concise version of the getItems() method.
        -
        -
        default Class<V>
        -
        itemType()
        -
         
        -
        default String
        -
        label()
        -
        +
        default Vitem(int i) +
        The following method returns a single item within this nd-array + targeted by the provided integer index.
        +
        default java.util.List<V>items() +
        A more concise version of the getItems() method.
        +
        default java.lang.Class<V>itemType() 
        default java.lang.Stringlabel()
        A nd-array can have a label.
        - -
        Nda<V>
        -
        map(Function<V,V> mapper)
        -
        +
        Nda<V>map(java.util.function.Function<V,V> mapper)
        This method is a convenience method for mapping the items of this nd-array to another nd-array of the same type based on the provided lambda function, which will be applied to all items of this nd-array individually (element-wise).
        - -
        <T> Nda<T>
        -
        mapTo(Class<T> typeClass, - Function<V,T> mapper)
        -
        +
        <T> Nda<T>mapTo(java.lang.Class<T> typeClass, + java.util.function.Function<V,T> mapper)
        This is a convenience method for mapping a nd-array to a nd-array of new type based on a provided target item type and mapping lambda.
        - -
        default V
        -
        maxItem(Comparator<V> comparator)
        -
        +
        default VmaxItem(java.util.Comparator<V> comparator)
        Returns the maximum item of this nd-array according to the provided - Comparator.
        - -
        default V
        -
        minItem(Comparator<V> comparator)
        -
        + Comparator.
        +
        default VminItem(java.util.Comparator<V> comparator)
        Returns the minimum item of this nd-array according to the provided - Comparator.
        - -
        default MutateNda<V>
        -
        mut()
        -
        + Comparator.
        +
        default MutateNda<V>mut()
        This method exposes an API for mutating the state of this tensor.
        - -
        default boolean
        -
        none(Predicate<V> predicate)
        -
        +
        default booleannone(java.util.function.Predicate<V> predicate)
        Iterates over every element of this nd-array, and checks whether none of the elements match the provided lambda.
        - -
        static Nda<Boolean>
        -
        of(boolean... value)
        -
        +
        static Nda<java.lang.Boolean>of(boolean... value)
        Constructs a vector of booleans based on the provided array.
        - -
        static Nda<Byte>
        -
        of(byte... value)
        -
        +
        static Nda<java.lang.Byte>of(byte... value)
        Constructs a vector of bytes based on the provided array.
        - -
        static Nda<Double>
        -
        of(double value)
        -
         
        -
        static Nda<Double>
        -
        of(double... value)
        -
        +
        static <V> WithShapeOrScalarOrVector<V>of(java.lang.Class<V> type) +
        This is the entry point to the fluent nd-array builder API for building + Nda instances in a readable and type safe fashion.
        +
        static Nda<java.lang.Double>of(double... value)
        Constructs a vector of doubles based on the provided array.
        - -
        static Nda<Float>
        -
        of(float... value)
        -
        +
        static Nda<java.lang.Double>of(double value) 
        static Nda<java.lang.Float>of(float... value)
        Constructs a vector of floats based on the provided array.
        - -
        static Nda<Integer>
        -
        of(int... value)
        -
        +
        static Nda<java.lang.Integer>of(int... value)
        Constructs a vector of ints based on the provided array.
        - -
        static Nda<Long>
        -
        of(long... value)
        -
        -
        Constructs a vector of longs based on the provided array.
        -
        -
        static Nda<Short>
        -
        of(short... value)
        -
        -
        Constructs a vector of shorts based on the provided array.
        -
        -
        static <V> WithShapeOrScalarOrVector<V>
        -
        of(Class<V> type)
        -
        -
        This is the entry point to the fluent nd-array builder API for building - Nda instances in a readable and type safe fashion.
        -
        -
        static <T> Nda<T>
        -
        of(Iterable<T> values)
        -
        +
        static <T> Nda<T>of(java.lang.Iterable<T> values)
        Constructs a vector of objects based on the provided iterable.
        - -
        static <T> Nda<T>
        -
        of(List<T> values)
        -
        +
        static <T> Nda<T>of(java.util.List<T> values)
        Constructs a vector of objects based on the provided list.
        - -
        static Nda<Boolean>
        -
        of(Shape shape, - boolean... values)
        -
        +
        static Nda<java.lang.Long>of(long... value) +
        Constructs a vector of longs based on the provided array.
        +
        static Nda<java.lang.Boolean>of(Shape shape, + boolean... values)
        Use this to construct and return a boolean based nd-array of the specified shape and initial values.
        - -
        static Nda<Byte>
        -
        of(Shape shape, - byte... values)
        -
        +
        static Nda<java.lang.Byte>of(Shape shape, + byte... values)
        Use this to construct and return a byte based nd-array of the specified shape and initial values.
        - -
        static Nda<Double>
        -
        of(Shape shape, - double... values)
        -
        +
        static Nda<java.lang.Double>of(Shape shape, + double... values)
        Use this to construct and return a double based nd-array of the specified shape and initial values.
        - -
        static Nda<Float>
        -
        of(Shape shape, - float... values)
        -
        +
        static Nda<java.lang.Float>of(Shape shape, + float... values)
        Use this to construct and return a float based nd-array of the specified shape and initial values.
        - -
        static Nda<Integer>
        -
        of(Shape shape, - int... values)
        -
        +
        static Nda<java.lang.Integer>of(Shape shape, + int... values)
        Use this to construct and return a int based nd-array of the specified shape and initial values.
        - -
        static Nda<Long>
        -
        of(Shape shape, - long... values)
        -
        +
        static Nda<java.lang.Long>of(Shape shape, + long... values)
        Use this to construct and return a long based nd-array of the specified shape and initial values.
        - -
        static Nda<Short>
        -
        of(Shape shape, - short... values)
        -
        +
        static Nda<java.lang.Short>of(Shape shape, + short... values)
        Use this to construct and return a short based nd-array of the specified shape and initial values.
        - -
        static <T> Nda<T>
        -
        of(Shape shape, - T... values)
        -
        +
        static <T> Nda<T>of(Shape shape, + T... values)
        Use this to construct and return an object based nd-array of the specified shape and initial values.
        - -
        static <T> Nda<T>
        -
        of(T... values)
        -
        +
        static Nda<java.lang.Short>of(short... value) +
        Constructs a vector of shorts based on the provided array.
        +
        static <T> Nda<T>of(T... values)
        Constructs a vector of objects based on the provided array.
        - -
        static WithShapeOrScalarOrVector<BigDecimal>
        -
        ofBigDecimals()
        -
        +
        static WithShapeOrScalarOrVector<java.math.BigDecimal>ofBigDecimals()
        This is a shortcut method for Nda.of(BigDecimal.class) - used to build Ndas storing BigDecimals.
        - -
        static WithShapeOrScalarOrVector<Boolean>
        -
        ofBooleans()
        -
        + used to build Ndas storing BigDecimals.
        +
        static WithShapeOrScalarOrVector<java.lang.Boolean>ofBooleans()
        This is a shortcut method for Nda.of(Boolean.class) - used to build Ndas storing Booleans.
        - -
        static WithShapeOrScalarOrVector<Byte>
        -
        ofBytes()
        -
        + used to build Ndas storing Booleans.
        +
        static WithShapeOrScalarOrVector<java.lang.Byte>ofBytes()
        This is a shortcut method for Nda.of(Byte.class) - used to build Ndas storing Bytes.
        - -
        static WithShapeOrScalarOrVector<Character>
        -
        ofChars()
        -
        + used to build Ndas storing Bytes.
        +
        static WithShapeOrScalarOrVector<java.lang.Character>ofChars()
        This is a shortcut method for Nda.of(Character.class) - used to build Ndas storing Characters.
        - -
        static WithShapeOrScalarOrVector<Double>
        -
        ofDoubles()
        -
        + used to build Ndas storing Characters.
        +
        static WithShapeOrScalarOrVector<java.lang.Double>ofDoubles()
        This is a shortcut method for Nda.of(Double.class) - used to build Ndas storing Doubles.
        - -
        static WithShapeOrScalarOrVector<Float>
        -
        ofFloats()
        -
        + used to build Ndas storing Doubles.
        +
        static WithShapeOrScalarOrVector<java.lang.Float>ofFloats()
        This is a shortcut method for Nda.of(Float.class) - used to build Ndas storing Floats.
        - -
        static WithShapeOrScalarOrVector<Integer>
        -
        ofInts()
        -
        + used to build Ndas storing Floats.
        +
        static WithShapeOrScalarOrVector<java.lang.Integer>ofInts()
        This is a shortcut method for Nda.of(Integer.class) - used to build Ndas storing Integers.
        - -
        static WithShapeOrScalarOrVector<Long>
        -
        ofLongs()
        -
        + used to build Ndas storing Integers.
        +
        static WithShapeOrScalarOrVector<java.lang.Long>ofLongs()
        This is a shortcut method for Nda.of(Long.class) - used to build Ndas storing Longs.
        - -
        static WithShapeOrScalarOrVector<Number>
        -
        ofNumbers()
        -
        + used to build Ndas storing Longs.
        +
        static WithShapeOrScalarOrVector<java.lang.Number>ofNumbers()
        This is a shortcut method for Nda.of(Number.class) - used to build Ndas storing Numbers.
        - -
        static WithShapeOrScalarOrVector<Object>
        -
        ofObjects()
        -
        + used to build Ndas storing Numbers.
        +
        static WithShapeOrScalarOrVector<java.lang.Object>ofObjects()
        This is a shortcut method for Nda.of(Object.class) - used to build Ndas storing Objects.
        - -
        static WithShapeOrScalarOrVector<Short>
        -
        ofShorts()
        -
        + used to build Ndas storing Objects.
        +
        static WithShapeOrScalarOrVector<java.lang.Short>ofShorts()
        This is a shortcut method for Nda.of(Short.class) - used to build Ndas storing Shorts.
        - -
        static WithShapeOrScalarOrVector<String>
        -
        ofStrings()
        -
        + used to build Ndas storing Shorts.
        +
        static WithShapeOrScalarOrVector<java.lang.String>ofStrings()
        This is a shortcut method for Nda.of(String.class) - used to build Ndas storing Strings.
        - -
        Nda<V>
        -
        permute(int... dims)
        -
        + used to build Ndas storing Strings.
        +
        Nda<V>permute(int... dims)
        Returns a view of the original tensor input with its dimensions permuted.
        Consider a 3-dimensional tensor x with shape (2×3×5), then calling x.permute(1, 0, 2) will return a 3-dimensional tensor of shape (3×2×5).
        - -
        Nda<V>
        -
        reshape(int... shape)
        -
        +
        Nda<V>reshape(int... shape)
        Returns a nd-array with the same data and number of elements as this nd-array, but with the specified shape.
        - -
        Nda<V>
        -
        shallowCopy()
        -
        +
        Nda<V>shallowCopy()
        This creates a copy where the underlying data is still the same.
        - -
        static <T> Collector<T,?,Nda<T>>
        -
        shaped(int... shape)
        -
        +
        static <T> java.util.stream.Collector<T,?,Nda<T>>shaped(int... shape)
        Returns a Collector that accumulates the input elements into a - new Nda with the specified shape.
        - -
        AxisOrGet<V>
        -
        slice()
        -
        -
        This method returns a SliceBuilder instance exposing a simple builder API + new Nda with the specified shape.
        +
        AxisOrGet<V>slice() +
        This method returns a SliceBuilder instance exposing a simple builder API which enables the configuration of a slice of the current nd-array via method chaining.
        - -
        int
        -
        sliceCount()
        -
        +
        intsliceCount()
        This method returns the number of slices which have been created from this nd-array.
        - -
        default Stream<V>
        -
        stream()
        -
         
        -
        String
        -
        toString()
        -
        -
        This method returns a String representation of this nd-array.
        -
        -
        default String
        -
        toString(Consumer<NDPrintSettings> config)
        -
        +
        default java.util.stream.Stream<V>stream() 
        java.lang.StringtoString() +
        This method returns a String representation of this nd-array.
        +
        default java.lang.StringtoString(java.util.function.Consumer<NDPrintSettings> config)
        This allows you to provide a lambda which configures how this nd-array should be - converted to String instances.
        - -
        default String
        -
        toString(NDPrintSettings config)
        -
        -
        Use this to turn this nd-array into a String instance based on the provided - NDPrintSettings instance, which allows you to configure things + converted to String instances.
        +
        default java.lang.StringtoString(NDPrintSettings config) +
        Use this to turn this nd-array into a String instance based on the provided + NDPrintSettings instance, which allows you to configure things like the number of chars per entry, delimiters, the number of items per line, etc.
        - -
        Nda<V>
        -
        transpose(int dim1, - int dim2)
        -
        +
        Nda<V>transpose(int dim1, + int dim2)
        Returns a view of the original tensor input the targeted axes are swapped / transposed.
        - -
        Nda<V>
        -
        withLabel(String label)
        -
         
        -
        Nda<V>
        -
        withLabels(String[]... labels)
        -
        -
        This method receives a nested String array which - ought to contain a label for the index of this nd-array.
        -
        -
        Nda<V>
        -
        withLabels(List<List<Object>> labels)
        -
        -
        This method receives a nested String list which +
        Nda<V>withLabel(java.lang.String label) 
        Nda<V>withLabels(java.util.List<java.util.List<java.lang.Object>> labels) +
        This method receives a nested String list which ought to contain a label for the index of this nd-array.
        - -
        Nda<V>
        -
        withLabels(Map<Object,List<Object>> labels)
        -
        +
        Nda<V>withLabels(java.util.Map<java.lang.Object,java.util.List<java.lang.Object>> labels)
        This method provides the ability to label not only the indices of the shape of this nd-array, but also the dimension of the shape.
        +
        Nda<V>withLabels(java.lang.String[]... labels) +
        This method receives a nested String array which + ought to contain a label for the index of this nd-array.
        +
        + +
          +
        • + + +

          Methods inherited from interface java.lang.Iterable

          +forEach, iterator, spliterator
          • +
        +
        • +
      +
      • +
    -
    -
    - -
    -

    Methods inherited from interface java.lang.Iterable

    -forEach, iterator, spliterator
    -
    -

    Methods inherited from interface neureka.ndim.NDimensional

    -getNDConf, getRank, getShape, getSize, indexOfIndex, indexOfIndices, indicesMap, indicesOfIndex, offset, rank, shape, shape, size, spread, strides
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Method Details

        -
          -
        • -
          -

          of

          -
          static <V> WithShapeOrScalarOrVector<V> of(Class<V> type)
          +
            +
          • + + +

            Method Detail

            + + + +
              +
            • +

              of

              +
              static <V> WithShapeOrScalarOrVector<V> of(java.lang.Class<V> type)
              This is the entry point to the fluent nd-array builder API for building - Nda instances in a readable and type safe fashion. - The returned WithShapeOrScalarOrVector is the next step in the - fluent Nda builder API which will lead to the creation + Nda instances in a readable and type safe fashion. + The returned WithShapeOrScalarOrVector is the next step in the + fluent Nda builder API which will lead to the creation of an nd-array storing values defined by the provided type class. A simple usage example would be: 
              
@@ -707,583 +847,708 @@ 
              of
              
    
              
        Nda.of(Byte.class).withShape(2, 3).andWhere( (i, indices) -> i * 5 - 30 )
              -
              -
              Parameters:
              +
              +
              Parameters:
              type - The type class of the items stored by the nd-array built by the exposed builder API.
              -
              Returns:
              -
              The next step of the Nda builder API which exposes methods for defining shapes.
              +
              Returns:
              +
              The next step of the Nda builder API which exposes methods for defining shapes.
              -
          • -
        • -
          -

          ofStrings

          -
          static WithShapeOrScalarOrVector<String> ofStrings()
          +
        + + + +
          +
        • +

          ofStrings

          +
          static WithShapeOrScalarOrVector<java.lang.String> ofStrings()
          This is a shortcut method for Nda.of(String.class) - used to build Ndas storing Strings.
          -
          -
          Returns:
          -
          The next step of the Nda builder API which exposes methods for defining shapes.
          + used to build Ndas storing Strings.
      +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    -
  • -
    -

    ofInts

    -
    static WithShapeOrScalarOrVector<Integer> ofInts()
    + + + + +
      +
    • +

      ofInts

      +
      static WithShapeOrScalarOrVector<java.lang.Integer> ofInts()
      This is a shortcut method for Nda.of(Integer.class) - used to build Ndas storing Integers.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      + used to build Ndas storing Integers. +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    • -
  • -
    -

    ofDoubles

    -
    static WithShapeOrScalarOrVector<Double> ofDoubles()
    + + + + +
      +
    • +

      ofDoubles

      +
      static WithShapeOrScalarOrVector<java.lang.Double> ofDoubles()
      This is a shortcut method for Nda.of(Double.class) - used to build Ndas storing Doubles.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      + used to build Ndas storing Doubles. +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    • -
  • -
    -

    ofFloats

    -
    static WithShapeOrScalarOrVector<Float> ofFloats()
    + + + + +
      +
    • +

      ofFloats

      +
      static WithShapeOrScalarOrVector<java.lang.Float> ofFloats()
      This is a shortcut method for Nda.of(Float.class) - used to build Ndas storing Floats.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      + used to build Ndas storing Floats. +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    • -
  • -
    -

    ofLongs

    -
    static WithShapeOrScalarOrVector<Long> ofLongs()
    + + + + +
      +
    • +

      ofLongs

      +
      static WithShapeOrScalarOrVector<java.lang.Long> ofLongs()
      This is a shortcut method for Nda.of(Long.class) - used to build Ndas storing Longs.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      + used to build Ndas storing Longs. +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    • -
  • -
    -

    ofBooleans

    -
    static WithShapeOrScalarOrVector<Boolean> ofBooleans()
    + + + + +
      +
    • +

      ofBooleans

      +
      static WithShapeOrScalarOrVector<java.lang.Boolean> ofBooleans()
      This is a shortcut method for Nda.of(Boolean.class) - used to build Ndas storing Booleans.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      + used to build Ndas storing Booleans. +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    • -
  • -
    -

    ofChars

    -
    static WithShapeOrScalarOrVector<Character> ofChars()
    + + + + +
      +
    • +

      ofChars

      +
      static WithShapeOrScalarOrVector<java.lang.Character> ofChars()
      This is a shortcut method for Nda.of(Character.class) - used to build Ndas storing Characters.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      + used to build Ndas storing Characters. +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    • -
  • -
    -

    ofBytes

    -
    static WithShapeOrScalarOrVector<Byte> ofBytes()
    + + + + +
      +
    • +

      ofBytes

      +
      static WithShapeOrScalarOrVector<java.lang.Byte> ofBytes()
      This is a shortcut method for Nda.of(Byte.class) - used to build Ndas storing Bytes.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      + used to build Ndas storing Bytes. +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    • -
  • -
    -

    ofShorts

    -
    static WithShapeOrScalarOrVector<Short> ofShorts()
    + + + + +
      +
    • +

      ofShorts

      +
      static WithShapeOrScalarOrVector<java.lang.Short> ofShorts()
      This is a shortcut method for Nda.of(Short.class) - used to build Ndas storing Shorts.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      + used to build Ndas storing Shorts. +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    • -
  • -
    -

    ofObjects

    -
    static WithShapeOrScalarOrVector<Object> ofObjects()
    + + + + +
      +
    • +

      ofObjects

      +
      static WithShapeOrScalarOrVector<java.lang.Object> ofObjects()
      This is a shortcut method for Nda.of(Object.class) - used to build Ndas storing Objects.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      + used to build Ndas storing Objects. +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    • -
  • -
    -

    ofNumbers

    -
    static WithShapeOrScalarOrVector<Number> ofNumbers()
    + + + + +
      +
    • +

      ofNumbers

      +
      static WithShapeOrScalarOrVector<java.lang.Number> ofNumbers()
      This is a shortcut method for Nda.of(Number.class) - used to build Ndas storing Numbers.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      + used to build Ndas storing Numbers. +
      +
      Returns:
      +
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
    • -
  • -
    -

    ofBigDecimals

    -
    static WithShapeOrScalarOrVector<BigDecimal> ofBigDecimals()
    + + + + +
      +
    • +

      ofBigDecimals

      +
      static WithShapeOrScalarOrVector<java.math.BigDecimal> ofBigDecimals()
      This is a shortcut method for Nda.of(BigDecimal.class) - used to build Ndas storing BigDecimals.
      -
      -
      Returns:
      -
      The next step of the Nda builder API which exposes methods for defining shapes.
      -
      -
    -
    • -
  • -
    -

    of

    -
    static Nda<Double> of(double value)
    -
    -
    Parameters:
    + used to build Ndas storing BigDecimals. +
    +
    Returns:
    +
    The next step of the Nda builder API which exposes methods for defining shapes.
    +
    +
    • + + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Double> of(double value)
      +
      +
      Parameters:
      value - The scalar value which ought to be represented as nd-array.
      -
      Returns:
      +
      Returns:
      A scalar double nd-array.
      -
      • -
    • -
      -

      of

      -
      static Nda<Float> of(float... value)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Float> of(float... value)
      Constructs a vector of floats based on the provided array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      value - The array of floats from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D nd-array of floats.
      -
      • -
    • -
      -

      of

      -
      static Nda<Double> of(double... value)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Double> of(double... value)
      Constructs a vector of doubles based on the provided array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      value - The array of doubles from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D nd-array of doubles.
      -
      • -
    • -
      -

      of

      -
      static Nda<Byte> of(byte... value)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Byte> of(byte... value)
      Constructs a vector of bytes based on the provided array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      value - The array of bytes from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D nd-array of bytes.
      -
      • -
    • -
      -

      of

      -
      static Nda<Integer> of(int... value)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Integer> of(int... value)
      Constructs a vector of ints based on the provided array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      value - The array of ints from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D nd-array of ints.
      -
      • -
    • -
      -

      of

      -
      static Nda<Long> of(long... value)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Long> of(long... value)
      Constructs a vector of longs based on the provided array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      value - The array of longs from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D nd-array of longs.
      -
      • -
    • -
      -

      of

      -
      static Nda<Short> of(short... value)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Short> of(short... value)
      Constructs a vector of shorts based on the provided array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      value - The array of shorts from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D nd-array of shorts.
      -
      • -
    • -
      -

      of

      -
      static Nda<Boolean> of(boolean... value)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Boolean> of(boolean... value)
      Constructs a vector of booleans based on the provided array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      value - The array of booleans from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D nd-array of shorts.
      -
      • -
    • -
      -

      of

      -
      @SafeVarargs -static <T> Nda<T> of(T... values)
      +
    + + + + + +
      +
    • +

      of

      +
      @SafeVarargs
+static <T> Nda<T> of(T... values)
      Constructs a vector of objects based on the provided array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      values - The array of objects from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D nd-array of objects.
      -
      • -
    • -
      -

      of

      -
      static Nda<Double> of(Shape shape, - double... values)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Double> of(Shape shape,
+                                double... values)
      Use this to construct and return a double based nd-array of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the nd-array will be populated based on repeated iteration over the provided double array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The shape of the resulting nd-array consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Nda<Float> of(Shape shape, - float... values)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Float> of(Shape shape,
+                               float... values)
      Use this to construct and return a float based nd-array of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the nd-array will be populated based on repeated iteration over the provided float array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The shape of the resulting nd-array consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Nda<Byte> of(Shape shape, - byte... values)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Byte> of(Shape shape,
+                              byte... values)
      Use this to construct and return a byte based nd-array of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the nd-array will be populated based on repeated iteration over the provided byte array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The shape of the resulting nd-array consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Nda<Integer> of(Shape shape, - int... values)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Integer> of(Shape shape,
+                                 int... values)
      Use this to construct and return a int based nd-array of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the nd-array will be populated based on repeated iteration over the provided int array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The shape of the resulting nd-array consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Nda<Long> of(Shape shape, - long... values)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Long> of(Shape shape,
+                              long... values)
      Use this to construct and return a long based nd-array of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the nd-array will be populated based on repeated iteration over the provided long array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The shape of the resulting nd-array consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Nda<Short> of(Shape shape, - short... values)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Short> of(Shape shape,
+                               short... values)
      Use this to construct and return a short based nd-array of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the nd-array will be populated based on repeated iteration over the provided short array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The shape of the resulting nd-array consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Nda<Boolean> of(Shape shape, - boolean... values)
      +
    + + + +
      +
    • +

      of

      +
      static Nda<java.lang.Boolean> of(Shape shape,
+                                 boolean... values)
      Use this to construct and return a boolean based nd-array of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the nd-array will be populated based on repeated iteration over the provided boolean array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The shape of the resulting nd-array consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      @SafeVarargs -static <T> Nda<T> of(Shape shape, - T... values)
      +
    + + + + + +
      +
    • +

      of

      +
      @SafeVarargs
+static <T> Nda<T> of(Shape shape,
+                                  T... values)
      Use this to construct and return an object based nd-array of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the nd-array will be populated based on repeated iteration over the provided object array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The shape of the resulting nd-array consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static <T> Nda<T> of(Iterable<T> values)
      +
    + + + +
      +
    • +

      of

      +
      static <T> Nda<T> of(java.lang.Iterable<T> values)
      Constructs a vector of objects based on the provided iterable.
      -
      -
      Parameters:
      +
      +
      Parameters:
      values - The iterable of objects from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D nd-array of objects.
      -
      • -
    • -
      -

      of

      -
      static <T> Nda<T> of(List<T> values)
      +
    + + + +
      +
    • +

      of

      +
      static <T> Nda<T> of(java.util.List<T> values)
      Constructs a vector of objects based on the provided list.
      -
      -
      Parameters:
      +
      +
      Parameters:
      values - The list of objects from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D nd-array of objects.
      -
      • -
    • -
      -

      getLabel

      -
      default String getLabel()
      +
    + + + +
      +
    • +

      getLabel

      +
      default java.lang.String getLabel()
      A nd-array can have a label. This label is used for example when printing the nd-array. When loading a CSV file for example the label of the nd-array will be taken from the cell where the header row and the first column intersect.
      -
      -
      Returns:
      +
      +
      Returns:
      The label/name of the nd-array.
      -
      • -
    • -
      -

      label

      -
      default String label()
      +
    + + + +
      +
    • +

      label

      +
      default java.lang.String label()
      A nd-array can have a label. This label is used for example when printing the nd-array. When loading a CSV file for example the label of the nd-array will be taken from the cell where the header row and the first column intersect. - This is a shorter version of getLabel().
      -
      -
      Returns:
      + This is a shorter version of getLabel(). +
      +
      Returns:
      The label/name of the nd-array.
      -
      • -
    • -
      -

      isSlice

      -
      boolean isSlice()
      +
    + + + +
      +
    • +

      isSlice

      +
      boolean isSlice()
      If this nd-array is a slice of a parent nd-array then this method will yield true. - Slices can be created by calling the variations of the "getAt(int...)" method.
      -
      -
      Returns:
      + Slices can be created by calling the variations of the "getAt(int...)" method. +
      +
      Returns:
      The truth value determining if this nd-array is a slice of another nd-array.
      -
      See Also:
      -
      - -
      +
      See Also:
      +
      getAt(int...), +slice()
      -
      • -
    • -
      -

      isShallowCopy

      -
      boolean isShallowCopy()
      +
    + + + +
      +
    • +

      isShallowCopy

      +
      boolean isShallowCopy()
      If this nd-array is a shallow copy of a parent nd-array then this method will yield true. - Shallow copies can be created by calling the "shallowCopy()" method.
      -
      -
      Returns:
      + Shallow copies can be created by calling the "shallowCopy()" method. +
      +
      Returns:
      The truth value determining if this nd-array is a shallow copy of another nd-array.
      -
      See Also:
      -
      - -
      +
      See Also:
      +
      shallowCopy()
      -
      • -
    • -
      -

      isPartialSlice

      -
      boolean isPartialSlice()
      +
    + + + +
      +
    • +

      isPartialSlice

      +
      boolean isPartialSlice()
      If this nd-array is a partial slice of a parent nd-array then this method will yield true. A partial slice is a slice which does not view all the parents items. - Partial slices can be created by calling the variations of the "getAt(int...)" method. - This is the inverse of isFullSlice().
      -
      -
      Returns:
      + Partial slices can be created by calling the variations of the "getAt(int...)" method. + This is the inverse of isFullSlice(). +
      +
      Returns:
      The truth value determining if this nd-array is a partial slice of another nd-array.
      -
      • -
    • -
      -

      isFullSlice

      -
      default boolean isFullSlice()
      +
    + + + +
      +
    • +

      isFullSlice

      +
      default boolean isFullSlice()
      If this nd-array is a full slice of a parent nd-array then this method will yield true. A full slice is a slice which views all the parents items. - Full slices can be created by calling the variations of the "getAt(int...)" method. - This is the inverse of isPartialSlice().
      -
      -
      Returns:
      + Full slices can be created by calling the variations of the "getAt(int...)" method. + This is the inverse of isPartialSlice(). +
      +
      Returns:
      The truth value determining if this nd-array is a full slice of another nd-array.
      -
      • -
    • -
      -

      sliceCount

      -
      int sliceCount()
      +
    + + + +
      +
    • +

      sliceCount

      +
      int sliceCount()
      This method returns the number of slices which have been created from this nd-array. - It does so by accessing the Relation component if present + It does so by accessing the Relation component if present which internally keeps track of slices via weak references.
      -
      -
      Returns:
      +
      +
      Returns:
      The number of slices derived from this nd-array.
      -
      • -
    • -
      -

      isSliceParent

      -
      boolean isSliceParent()
      +
    + + + +
      +
    • +

      isSliceParent

      +
      boolean isSliceParent()
      If slices have been derived from this nd-array then it is a "slice parent". This is what this method will determine, in which case, it will return true.
      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if slices have been derived from this nd-array.
      -
      • -
    • -
      -

      getItemType

      -
      Class<V> getItemType()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      getItemType

      +
      java.lang.Class<V> getItemType()
      +
      +
      Returns:
      The type class of individual value items within this nd-array.
      -
      • -
    • -
      -

      itemType

      -
      default Class<V> itemType()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      itemType

      +
      default java.lang.Class<V> itemType()
      +
      +
      Returns:
      The type class of individual value items within this nd-array.
      -
      • -
    • -
      -

      withLabel

      -
      Nda<V> withLabel(String label)
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      withLabel

      +
      Nda<V> withLabel(java.lang.String label)
      +
      +
      Returns:
      A new nd-array which is a shallow copy of this nd-array but with a different label.
      -
      • -
    • -
      -

      withLabels

      -
      Nda<V> withLabels(String[]... labels)
      -
      This method receives a nested String array which +
    + + + +
      +
    • +

      withLabels

      +
      Nda<V> withLabels(java.lang.String[]... labels)
      +
      This method receives a nested String array which ought to contain a label for the index of this nd-array. The index for a single element of this nd-array would be an array of numbers as long as the rank where every number is @@ -1295,19 +1560,22 @@

      withLabels

      dim 0 : ["A", "B"]
      dim 1 : ["1", "2", "3"]

      -
      -
      Parameters:
      +
      +
      Parameters:
      labels - A nested String array containing labels for indexes of the nd-array dimensions.
      -
      Returns:
      +
      Returns:
      This nd-array (method chaining).
      -
      • -
    • -
      -

      withLabels

      -
      Nda<V> withLabels(List<List<Object>> labels)
      -
      This method receives a nested String list which +
    + + + +
      +
    • +

      withLabels

      +
      Nda<V> withLabels(java.util.List<java.util.List<java.lang.Object>> labels)
      +
      This method receives a nested String list which ought to contain a label for the index of this nd-array. The index for a single element of this nd-array would be an array of numbers as long as the rank where every number is @@ -1319,18 +1587,21 @@

      withLabels

      dim 0 : ["A", "B"]
      dim 1 : ["1", "2", "3"]

      -
      -
      Parameters:
      +
      +
      Parameters:
      labels - A nested String list containing labels for indexes of the nd-array dimensions.
      -
      Returns:
      +
      Returns:
      This nd-array (method chaining).
      -
      • -
    • -
      -

      withLabels

      -
      Nda<V> withLabels(Map<Object,List<Object>> labels)
      +
    + + + +
      +
    • +

      withLabels

      +
      Nda<V> withLabels(java.util.Map<java.lang.Object,java.util.List<java.lang.Object>> labels)
      This method provides the ability to label not only the indices of the shape of this nd-array, but also the dimension of the shape. @@ -1343,125 +1614,149 @@

      withLabels

      "dim 1" : ["1", "2", "3"]
      ]

      -
      -
      Parameters:
      +
      +
      Parameters:
      labels - A map in which the keys are dimension labels and the values are lists of index labels for the dimension.
      -
      Returns:
      +
      Returns:
      This nd-array (method chaining).
      -
      • -
    • -
      -

      stream

      -
      default Stream<V> stream()
      -
      -
      Returns:
      -
      A Stream of the items in this Nda.
      +
    + + + +
      +
    • +

      stream

      +
      default java.util.stream.Stream<V> stream()
      +
      +
      Returns:
      +
      A Stream of the items in this Nda.
      -
      • -
    • -
      -

      filter

      -
      default Stream<V> filter(Predicate<V> predicate)
      +
    + + + +
      +
    • +

      filter

      +
      default java.util.stream.Stream<V> filter(java.util.function.Predicate<V> predicate)
      A convenience method for stream().filter( predicate ).
      -
      -
      Parameters:
      -
      predicate - The predicate to filter the items of this Nda.
      -
      Returns:
      -
      A Stream of the items in this Nda which match the predicate.
      -
      - -
      • -
    • -
      -

      flatMap

      -
      default <R> Stream<R> flatMap(Function<V,Stream<R>> mapper)
      +
      +
      Parameters:
      +
      predicate - The predicate to filter the items of this Nda.
      +
      Returns:
      +
      A Stream of the items in this Nda which match the predicate.
      +
      +
      • +
    + + + +
      +
    • +

      flatMap

      +
      default <R> java.util.stream.Stream<R> flatMap(java.util.function.Function<V,java.util.stream.Stream<R>> mapper)
      A convenience method for nda.stream().flatMap( mapper ), - which turns this Nda into a Stream of its items.
      + which turns this Nda into a Stream of its items.
      Here an example of how to use this method : 
      
     var nda = Nda.of( -2, -1, 0, 1, 2 );
     var list = nda.flatMap( i -> Stream.of( i * 2, i * 3 ) ).toList();
     // list = [-4, -6, -2, -3, 0, 0, 2, 3, 4, 6, 6, 9]
      -
      -
      Parameters:
      -
      mapper - The mapper to map the items of this Nda.
      -
      Returns:
      -
      A Stream of the items in this Nda which match the predicate.
      -
      - -
      • -
    • -
      -

      shaped

      -
      static <T> Collector<T,?,Nda<T>> shaped(int... shape)
      +
      +
      Parameters:
      +
      mapper - The mapper to map the items of this Nda.
      +
      Returns:
      +
      A Stream of the items in this Nda which match the predicate.
      +
      +
      • +
    + + + +
      +
    • +

      shaped

      +
      static <T> java.util.stream.Collector<T,?,Nda<T>> shaped(int... shape)
      Returns a Collector that accumulates the input elements into a - new Nda with the specified shape.
      + new Nda with the specified shape.
      Usage example : 
      
     var nda = Stream.of( 1, 2, 3, 4, 5, 6 )
                       .collect( Nda.shaped( 2, 3 ) );
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - the type of the input elements
      -
      Parameters:
      +
      Parameters:
      shape - The shape of the nd-array to be returned.
      -
      Returns:
      +
      Returns:
      a Collector which collects all the input elements into a - Nda, in encounter order.
      + Nda, in encounter order.
      -
      • -
    • -
      -

      every

      -
      default boolean every(Predicate<V> predicate)
      +
    + + + +
      +
    • +

      every

      +
      default boolean every(java.util.function.Predicate<V> predicate)
      Iterates over every element of this nd-array, and checks whether all elements are true according to the provided lambda.
      -
      -
      Parameters:
      +
      +
      Parameters:
      predicate - The lambda to check each element against.
      -
      Returns:
      +
      Returns:
      true if every item in the nd-array matches the predicate, false otherwise.
      -
      • -
    • -
      -

      any

      -
      default boolean any(Predicate<V> predicate)
      +
    + + + +
      +
    • +

      any

      +
      default boolean any(java.util.function.Predicate<V> predicate)
      Iterates over every element of this nd-array, and checks whether any element matches the provided lambda.
      -
      -
      Parameters:
      +
      +
      Parameters:
      predicate - The lambda to check each element against.
      -
      Returns:
      +
      Returns:
      true if any item in the nd-array matches the predicate, false otherwise.
      -
      • -
    • -
      -

      none

      -
      default boolean none(Predicate<V> predicate)
      +
    + + + +
      +
    • +

      none

      +
      default boolean none(java.util.function.Predicate<V> predicate)
      Iterates over every element of this nd-array, and checks whether none of the elements match the provided lambda.
      -
      -
      Parameters:
      +
      +
      Parameters:
      predicate - The lambda to check each element against.
      -
      Returns:
      +
      Returns:
      true if none of the items in the nd-array match the predicate, false otherwise.
      -
      • -
    • -
      -

      count

      -
      default int count(Predicate<V> predicate)
      +
    + + + +
      +
    • +

      count

      +
      default int count(java.util.function.Predicate<V> predicate)
      Iterates over every element of this nd-array, and counts the number of times the provided lambda matches the items of this array.

      @@ -1472,201 +1767,243 @@

      count

      var count = nda.count( i -> i > 5 ); System.out.println( count ); // prints 5
      -
      -
      Parameters:
      +
      +
      Parameters:
      predicate - The lambda to check each element against.
      -
      Returns:
      +
      Returns:
      The number of items in the nd-array that match the predicate.
      -
      • -
    • -
      -

      minItem

      -
      default V minItem(Comparator<V> comparator)
      +
    + + + +
      +
    • +

      minItem

      +
      default V minItem(java.util.Comparator<V> comparator)
      Returns the minimum item of this nd-array according to the provided - Comparator. This is a special case of a reduction.
      -
      -
      Parameters:
      -
      comparator - The Comparator to use to determine the order of the items in the nd-array.
      -
      Returns:
      + Comparator. This is a special case of a reduction. +
      +
      Parameters:
      +
      comparator - The Comparator to use to determine the order of the items in the nd-array.
      +
      Returns:
      The minimum value in the nd-array.
      -
      • -
    • -
      -

      maxItem

      -
      default V maxItem(Comparator<V> comparator)
      +
    + + + +
      +
    • +

      maxItem

      +
      default V maxItem(java.util.Comparator<V> comparator)
      Returns the maximum item of this nd-array according to the provided - Comparator. This is a special case of a reduction.
      -
      -
      Parameters:
      -
      comparator - The Comparator to use to determine the order of the items in the nd-array.
      -
      Returns:
      + Comparator. This is a special case of a reduction. +
      +
      Parameters:
      +
      comparator - The Comparator to use to determine the order of the items in the nd-array.
      +
      Returns:
      The maximum value in the nd-array.
      -
      • -
    • -
      -

      getRawData

      -
      Object getRawData()
      +
    + + + +
      +
    • +

      getRawData

      +
      java.lang.Object getRawData()
      This returns an unprocessed version of the underlying data of this nd-array. If this nd-array is outsourced (stored on a device), then the data will be loaded into an array and returned by this method. Do not expect the returned array to be actually stored within the nd-array itself! - Contrary to the getItems() method, this one will - return the data in an unbiased form, where for example a virtual (see Tensor.isVirtual()) + Contrary to the getItems() method, this one will + return the data in an unbiased form, where for example a virtual (see Tensor.isVirtual()) nd-array will have this method return an array of length 1.
      -
      -
      Returns:
      +
      +
      Returns:
      An unbiased copy of the underlying data of this nd-array.
      -
      • -
    • -
      -

      getDataAt

      -
      V getDataAt(int i)
      +
    + + + +
      +
    • +

      getDataAt

      +
      V getDataAt(int i)
      Use this to access elements of the underlying data array without any index - transformation applied to it. This is usually similar to the item(int) method, + transformation applied to it. This is usually similar to the item(int) method, however for nd-arrays which are sliced or permuted views of the data of another nd-array, this method will always be unbiased access of the raw data...
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The position of the targeted item within the raw data array of an NDArray implementation.
      -
      Returns:
      +
      Returns:
      The found object sitting at the specified index position.
      -
      • -
    • -
      -

      getItems

      -
      default List<V> getItems()
      -
      A more verbose version of the items() method (best used by JVM languages with property support).
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      getItems

      +
      default java.util.List<V> getItems()
      +
      A more verbose version of the items() method (best used by JVM languages with property support).
      +
      +
      Returns:
      A list of the items in this nd-array.
      -
      • -
    • -
      -

      items

      -
      default List<V> items()
      -
      A more concise version of the getItems() method.
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      items

      +
      default java.util.List<V> items()
      +
      A more concise version of the getItems() method.
      +
      +
      Returns:
      A list of the items in this nd-array.
      -
      • -
    • -
      -

      getRawItems

      -
      Object getRawItems()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      getRawItems

      +
      java.lang.Object getRawItems()
      +
      +
      Returns:
      The items of this nd-array as a (if possible) primitive array.
      -
      • -
    • -
      -

      item

      -
      default V item(int i)
      +
    + + + +
      +
    • +

      item

      +
      default V item(int i)
      The following method returns a single item within this nd-array targeted by the provided integer index.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The scalar index of the item which should be returned by the method.
      -
      Returns:
      +
      Returns:
      The item found at the targeted index.
      -
      • -
    • -
      -

      item

      -
      default V item(int... indices)
      +
    + + + +
      +
    • +

      item

      +
      default V item(int... indices)
      This method returns a raw value item within this nd-array targeted by an index array which is expected to hold an index for every dimension of the shape of this nd-array. So the provided array must have the same length as the rank of this nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The index array which targets a single value item within this nd-array.
      -
      Returns:
      +
      Returns:
      The found raw value item targeted by the provided index array.
      -
      • -
    • -
      -

      item

      -
      default V item()
      -
      Equivalent to the #item(0) and getItem().
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      item

      +
      default V item()
      +
      Equivalent to the #item(0) and getItem().
      +
      +
      Returns:
      The first item of this nd-array.
      -
      • -
    • -
      -

      getItem

      -
      default V getItem()
      -
      Equivalent to the #item(0) and item().
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      getItem

      +
      default V getItem()
      +
      Equivalent to the #item(0) and item().
      +
      +
      Returns:
      The first item of this nd-array.
      -
      • -
    • -
      -

      getItemsAs

      -
      default <A> A getItemsAs(Class<A> arrayTypeClass)
      +
    + + + +
      +
    • +

      getItemsAs

      +
      default <A> A getItemsAs(java.lang.Class<A> arrayTypeClass)
      Use this to get the items of this nd-array as a primitive array of the specified type.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      A - The type of the array to return.
      -
      Parameters:
      +
      Parameters:
      arrayTypeClass - The class of the array type to return.
      -
      Returns:
      +
      Returns:
      The items of this nd-array as a primitive array of the specified type.
      -
      • -
    • -
      -

      getDataAs

      -
      default <A> A getDataAs(Class<A> arrayTypeClass)
      -
      Use this to get the items of the underlying Data buffer +
    + + + +
      +
    • +

      getDataAs

      +
      default <A> A getDataAs(java.lang.Class<A> arrayTypeClass)
      +
      Use this to get the items of the underlying Data buffer of this nd-array as a primitive array of the specified type. Note that the length of the returned array may be different from the size of this nd-array. This is the case if this nd-array is a slice of another larger nd-array.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      A - The type of the array to return.
      -
      Parameters:
      +
      Parameters:
      arrayTypeClass - The class of the array type to return.
      -
      Returns:
      +
      Returns:
      The items of this nd-array as a primitive array of the specified type.
      -
      • -
    • -
      -

      slice

      -
      AxisOrGet<V> slice()
      -
      This method returns a SliceBuilder instance exposing a simple builder API +
    + + + +
      +
    • +

      slice

      +
      AxisOrGet<V> slice()
      +
      This method returns a SliceBuilder instance exposing a simple builder API which enables the configuration of a slice of the current nd-array via method chaining.
      The following code snippet slices a 3-dimensional nd-array into a nd-array of shape (2x1x3) 
      
@@ -1676,187 +2013,223 @@ 
      slice
      
           .axis().from(0).to(2)
           .get();
      -
      -
      Returns:
      -
      An instance of the SliceBuilder class exposing a readable builder API for creating slices.
      -
      - -
      • -
    • -
      -

      concatAt

      -
      Nda<V> concatAt(int axis, - Nda<V> other, - Nda<V>... ndArrays)
      +
      +
      Returns:
      +
      An instance of the SliceBuilder class exposing a readable builder API for creating slices.
      +
      +
      • +
    + + + +
      +
    • +

      concatAt

      +
      Nda<V> concatAt(int axis,
+                Nda<V> other,
+                Nda<V>... ndArrays)
      This method concatenates the provided nd-arrays together with this nd-array along a specified axis. The provided nd-arrays must have the same shape and data type as the current nd-array, except for the specified axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      axis - The axis along which the provided nd-arrays should be concatenated. The axis must be within the range of the rank of the current nd-array.
      other - The other nd-arrays which should be concatenated with this nd-array.
      ndArrays - The non-null, non-empty nd-arrays which should be concatenated together with this and the other nd-array. The nd-arrays all must have the same shape as this nd-array, except for the specified axis. Also, it must have the same data type as the current nd-array.
      -
      Returns:
      +
      Returns:
      A new nd-array which is the concatenation of the current nd-array and the provided nd-arrays.
      -
      • -
    • -
      -

      concatAt

      -
      Nda<V> concatAt(int axis, - Nda<V> other)
      +
    + + + +
      +
    • +

      concatAt

      +
      Nda<V> concatAt(int axis,
+                Nda<V> other)
      This method concatenates the provided nd-array together with this nd-array along a specified axis. The provided nd-array must have the same shape and data type as this nd-array, except for the specified axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      axis - The axis along which the provided nd-arrays should be concatenated. The axis must be within the range of the rank of the current nd-array.
      other - The other nd-arrays which should be concatenated with this nd-array.
      -
      Returns:
      +
      Returns:
      A new nd-array which is the concatenation of the current nd-array and the provided nd-arrays.
      -
      • -
    • -
      -

      getAt

      -
      Nda<V> getAt(int... indices)
      +
    + + + +
      +
    • +

      getAt

      +
      Nda<V> getAt(int... indices)
      The following method enables access to specific scalar elements within the nd-array. The method name also translates to the subscription operator in Groovy.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The index array of the element which should be returned.
      -
      Returns:
      +
      Returns:
      An element located at the provided index.
      -
      • -
    • -
      -

      getAt

      -
      Nda<V> getAt(Number i)
      +
    + + + +
      +
    • +

      getAt

      +
      Nda<V> getAt(java.lang.Number i)
      This getter method creates and returns a slice of the original nd-array. The returned slice is a scalar nd-array wrapping a single value element which is being targeted by the provided integer index.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the value item which should be returned as a nd-array instance.
      -
      Returns:
      +
      Returns:
      A nd-array holding a single value element which is internally still residing in the original nd-array.
      -
      • -
    • -
      -

      get

      -
      Nda<V> get(int... indices)
      +
    + + + +
      +
    • +

      get

      +
      Nda<V> get(int... indices)
      The following method enables access to specific scalar elements within the nd-array. The method name also translates to the subscription operator in Groovy.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The index array of the element which should be returned.
      -
      Returns:
      +
      Returns:
      An element located at the provided index.
      -
      • -
    • -
      -

      getAt

      -
      Nda<V> getAt(Object... args)
      +
    + + + +
      +
    • +

      getAt

      +
      Nda<V> getAt(java.lang.Object... args)
      The following method enables the creation of nd-array slices which access the same underlying data (possibly from a different view). The method name also translates to the subscription operator in Groovy.
      -
      -
      Parameters:
      +
      +
      Parameters:
      args - An arbitrary number of arguments which can be used for slicing.
      -
      Returns:
      +
      Returns:
      A slice nd-array created based on the passed keys.
      -
      • -
    • -
      -

      get

      -
      Nda<V> get(Object... args)
      +
    + + + +
      +
    • +

      get

      +
      Nda<V> get(java.lang.Object... args)
      The following method enables the creation of nd-array slices which access the same underlying data (possibly from a different view). The method name also translates to the subscription operator in Groovy.
      -
      -
      Parameters:
      +
      +
      Parameters:
      args - An arbitrary number of arguments which can be used for slicing.
      -
      Returns:
      +
      Returns:
      A slice nd-array created based on the passed keys.
      -
      • -
    • -
      -

      getAt

      -
      Nda<V> getAt(int i)
      +
    + + + +
      +
    • +

      getAt

      +
      Nda<V> getAt(int i)
      This getter method creates and returns a slice of the original nd-array. The returned slice is a scalar nd-array wrapping a single value element which is being targeted by the provided integer index.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the value item which should be returned as a nd-array instance.
      -
      Returns:
      +
      Returns:
      A nd-array holding a single value element which is internally still residing in the original nd-array.
      -
      • -
    • -
      -

      get

      -
      Nda<V> get(int i)
      +
    + + + +
      +
    • +

      get

      +
      Nda<V> get(int i)
      This getter method creates and returns a slice of the original nd-array. The returned slice is a scalar nd-array wrapping a single value element which is being targeted by the provided integer index.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the value item which should be returned as a nd-array instance.
      -
      Returns:
      +
      Returns:
      A nd-array holding a single value element which is internally still residing in the original nd-array.
      -
      • -
    • -
      -

      get

      -
      Nda<V> get(Number i)
      +
    + + + +
      +
    • +

      get

      +
      Nda<V> get(java.lang.Number i)
      This getter method creates and returns a slice of the original nd-array. The returned slice is a scalar nd-array wrapping a single value element which is being targeted by the provided integer index.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the value item which should be returned as a nd-array instance.
      -
      Returns:
      +
      Returns:
      A nd-array holding a single value element which is internally still residing in the original nd-array.
      -
      • -
    • -
      -

      get

      -
      Nda<V> get(Object key)
      +
    + + + +
      +
    • +

      get

      +
      Nda<V> get(java.lang.Object key)
      This method enables nd-array slicing! It takes a key of various types and configures a slice nd-array which shares the same underlying data as the original nd-array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      key - This object might be a wide range of objects including maps, lists or arrays...
      -
      Returns:
      +
      Returns:
      A slice nd-array or scalar value.
      -
      • -
    • -
      -

      getAt

      -
      Nda<V> getAt(Map<?,Integer> rangToSteps)
      +
    + + + +
      +
    • +

      getAt

      +
      Nda<V> getAt(java.util.Map<?,java.lang.Integer> rangToSteps)
      This method is most useful when used in Groovy where defining maps is done through square brackets, making it possible to slice nd-arrays like so:
      @@ -1868,35 +2241,41 @@

      getAt

      i... start indexAlias.
      j... end indexAlias. (inclusive!)
      k... step size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      rangToSteps - A map where the keys define where axes should be sliced and values which define the steps for the specific axis.
      -
      Returns:
      +
      Returns:
      A nd-array slice with an offset based on the provided map keys and steps based on the provided map values.
      -
      • -
    • -
      -

      getAt

      -
      Nda<V> getAt(List<?> key)
      +
    + + + +
      +
    • +

      getAt

      +
      Nda<V> getAt(java.util.List<?> key)
      This method enables nd-array slicing! It takes a key of various types and configures a slice nd-array which shares the same underlying data as the original nd-array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      key - This object might be a wide range of objects including maps, lists or arrays...
      -
      Returns:
      +
      Returns:
      A slice nd-array or scalar value.
      -
      • -
    • -
      -

      mapTo

      -
      <T> Nda<T> mapTo(Class<T> typeClass, - Function<V,T> mapper)
      +
    + + + +
      +
    • +

      mapTo

      +
      <T> Nda<T> mapTo(java.lang.Class<T> typeClass,
+                 java.util.function.Function<V,T> mapper)

      This is a convenience method for mapping a nd-array to a nd-array of new type based on a provided target item type and mapping lambda. @@ -1914,21 +2293,24 @@

      mapTo

      where ever it may reside back to the JVM, execute the mapping lambda, and then transfer the result back to the original location.

      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter of the items of the returned nd-array.
      -
      Parameters:
      +
      Parameters:
      typeClass - The class of the item type to which the items of this nd-array should be mapped.
      mapper - The lambda which maps the items of this nd-array to a new one.
      -
      Returns:
      +
      Returns:
      A new nd-array of type T.
      -
      • -
    • -
      -

      map

      -
      Nda<V> map(Function<V,V> mapper)
      +
    + + + +
      +
    • +

      map

      +
      Nda<V> map(java.util.function.Function<V,V> mapper)

      This method is a convenience method for mapping the items of this nd-array to another nd-array of the same type based on the provided lambda function, which will be applied @@ -1944,44 +2326,53 @@

      map

      This is a problem if this nd-array lives somewhere other than the JVM. So, therefore, this method will temporally transfer this nd-array from where ever it may reside back to the JVM, execute the mapping lambda, and then transfer the result back to the original location.
      -
      -
      Parameters:
      +
      +
      Parameters:
      mapper - The lambda which maps the items of this nd-array to a new one.
      -
      Returns:
      +
      Returns:
      A new nd-array of type V.
      -
      • -
    • -
      -

      deepCopy

      -
      Nda<V> deepCopy()
      +
    + + + +
      +
    • +

      deepCopy

      +
      Nda<V> deepCopy()
      This method creates and returns a new nd-array instance which is not only a copy of the configuration of this nd-array but also a copy of the underlying data array.
      (Note: the underlying nd-array will not be attached to any kind of computation graph)
      -
      -
      Returns:
      +
      +
      Returns:
      A new nd-array instance which is a deep copy of this nd-array.
      -
      • -
    • -
      -

      shallowCopy

      -
      Nda<V> shallowCopy()
      +
    + + + +
      +
    • +

      shallowCopy

      +
      Nda<V> shallowCopy()
      This creates a copy where the underlying data is still the same.
      (Note: the underlying nd-array will not be attached to any kind of computation graph)
      -
      -
      Returns:
      +
      +
      Returns:
      A shallow copy where the underlying data is shared with this nd-array.
      -
      • -
    • -
      -

      getMut

      -
      MutateNda<V> getMut()
      +
    + + + +
      +
    • +

      getMut

      +
      MutateNda<V> getMut()
      This method exposes an API for mutating the state of this tensor. The usage of methods exposed by this API is generally discouraged because the exposed state can easily lead to broken tensors and exceptional situations!
      @@ -1991,16 +2382,19 @@

      getMut

      performance is critical!
      (Like in custom backend extensions for example)
      -
      -
      Returns:
      +
      +
      Returns:
      The unsafe API exposes methods for mutating the state of the tensor.
      -
      • -
    • -
      -

      mut

      -
      default MutateNda<V> mut()
      +
    + + + +
      +
    • +

      mut

      +
      default MutateNda<V> mut()
      This method exposes an API for mutating the state of this tensor. The usage of methods exposed by this API is generally discouraged because the exposed state can easily lead to broken tensors and exceptional situations!
      @@ -2010,16 +2404,19 @@

      mut

      performance is critical!
      (Like custom backend extensions for example)
      -
      -
      Returns:
      +
      +
      Returns:
      The unsafe API exposes methods for mutating the state of the tensor.
      -
      • -
    • -
      -

      reshape

      -
      Nda<V> reshape(int... shape)
      +
    + + + +
      +
    • +

      reshape

      +
      Nda<V> reshape(int... shape)
      Returns a nd-array with the same data and number of elements as this nd-array, but with the specified shape. When possible, the returned nd-array will be a view of this nd-array. @@ -2029,75 +2426,90 @@

      reshape

      Keep in mind that the new shape must have the same number of elements as the original shape.

      This operation supports autograd.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The new shape of the returned nd-array.
      -
      Returns:
      +
      Returns:
      A new nd-array instance with the same underlying data (~shallow copy) but with a different shape.
      -
      • -
    • -
      -

      permute

      -
      Nda<V> permute(int... dims)
      +
    + + + +
      +
    • +

      permute

      +
      Nda<V> permute(int... dims)
      Returns a view of the original tensor input with its dimensions permuted.
      Consider a 3-dimensional tensor x with shape (2×3×5), then calling x.permute(1, 0, 2) will return a 3-dimensional tensor of shape (3×2×5).
      -
      -
      Parameters:
      +
      +
      Parameters:
      dims - The desired ordering of dimensions
      -
      Returns:
      +
      Returns:
      A new nd-array instance with the same underlying data (~shallow copy) but with a different shape.
      -
      • -
    • -
      -

      transpose

      -
      Nda<V> transpose(int dim1, - int dim2)
      +
    + + + +
      +
    • +

      transpose

      +
      Nda<V> transpose(int dim1,
+                 int dim2)
      Returns a view of the original tensor input the targeted axes are swapped / transposed.
      -
      -
      Parameters:
      +
      +
      Parameters:
      dim1 - The first dimension to be swapped.
      dim2 - The second dimension to be swapped.
      -
      Returns:
      +
      Returns:
      A new nd-array instance with the same underlying data (~shallow copy) but with a different shape.
      -
      • -
    • -
      -

      at

      -
      Nda.Item<V> at(int... indices)
      -
      This method exposes the Nda.Item API which allows you to get or set +
    + + + +
      +
    • +

      at

      +
      Nda.Item<V> at(int... indices)
      +
      This method exposes the Nda.Item API which allows you to get or set individual items within this nd-array targeted by an array of provided indices.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - An array of indices targeting a particular position in this nd-array...
      -
      Returns:
      +
      Returns:
      An object which allows you to get or set individual items within this nd-array.
      -
      • -
    • -
      -

      toString

      -
      default String toString(NDPrintSettings config)
      -
      Use this to turn this nd-array into a String instance based on the provided - NDPrintSettings instance, which allows you to configure things +
    + + + +
      +
    • +

      toString

      +
      default java.lang.String toString(NDPrintSettings config)
      +
      Use this to turn this nd-array into a String instance based on the provided + NDPrintSettings instance, which allows you to configure things like the number of chars per entry, delimiters, the number of items per line, etc.
      -
      • -
    • -
      -

      toString

      -
      default String toString(Consumer<NDPrintSettings> config)
      +
    + + + +
      +
    • +

      toString

      +
      default java.lang.String toString(java.util.function.Consumer<NDPrintSettings> config)
      This allows you to provide a lambda which configures how this nd-array should be - converted to String instances. - The provided Consumer will receive a NDPrintSettings instance + converted to String instances. + The provided Consumer will receive a NDPrintSettings instance which allows you to change various settings with the help of method chaining.
      Here is an example: 
      
@@ -2109,36 +2521,98 @@ 
      toString
      
              .setCellSize(15)
           );
      -
      -
      Parameters:
      -
      config - A consumer of the NDPrintSettings ready to be configured.
      -
      Returns:
      -
      The String representation of this nd-array.
      -
      - -
      • -
    • -
      -

      toString

      -
      String toString()
      -
      This method returns a String representation of this nd-array. +
      +
      Parameters:
      +
      config - A consumer of the NDPrintSettings ready to be configured.
      +
      Returns:
      +
      The String representation of this nd-array.
      +
      +
      • +
    + + + +
      +
    • +

      toString

      +
      java.lang.String toString()
      +
      This method returns a String representation of this nd-array. The default settings are used for the conversion.
      -
      -
      Overrides:
      -
      toString in class Object
      -
      Returns:
      -
      The String representation of this nd-array.
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      +
      Returns:
      +
      The String representation of this nd-array.
      -
    - - + + + + - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
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    + diff --git a/docs/jdocs/neureka/Neureka.Settings.AutoGrad.html b/docs/jdocs/neureka/Neureka.Settings.AutoGrad.html index beb662a57..c59937571 100644 --- a/docs/jdocs/neureka/Neureka.Settings.AutoGrad.html +++ b/docs/jdocs/neureka/Neureka.Settings.AutoGrad.html @@ -1,225 +1,290 @@ - + + - -Neureka.Settings.AutoGrad (neureka 1.0.0 API) - - - - + +Neureka.Settings.AutoGrad (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Class Neureka.Settings.AutoGrad

    -
    -
    java.lang.Object -
    neureka.Neureka.Settings.AutoGrad
    +
    neureka
    +

    Class Neureka.Settings.AutoGrad

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.Neureka.Settings.AutoGrad
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      Neureka.Settings
      +
      Neureka.Settings
      -
      public class Neureka.Settings.AutoGrad -extends Object
      +
      +
      public class Neureka.Settings.AutoGrad
+extends java.lang.Object
      This class contains settings which are related to the automatic differentiation of tensors.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      AutoGrad()
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        AutoGrad

        -
        public AutoGrad()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            AutoGrad

            +
            public AutoGrad()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      isPreventingInlineOperations

      -
      public boolean isPreventingInlineOperations()
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          isPreventingInlineOperations

          +
          public boolean isPreventingInlineOperations()
          Inline operations are operations where the data of a tensor passed into an operation is being modified. Usually the result of an operation is stored inside a new tensor. Use this flag to detect if an operation is an inline operation.
          -
      • -
    • -
      -

      setIsPreventingInlineOperations

      -
      public void setIsPreventingInlineOperations(boolean prevent)
      +
    + + + +
      +
    • +

      setIsPreventingInlineOperations

      +
      public void setIsPreventingInlineOperations(boolean prevent)
      Inline operations are operations where the data of a tensor passed into an operation is being modified. Usually the result of an operation is stored inside a new tensor. Use this flag to detect if an operation is an inline operation.
      -
    • -
  • -
    -

    isRetainingPendingErrorForJITProp

    -
    public boolean isRetainingPendingErrorForJITProp()
    + + + + +
      +
    • +

      isRetainingPendingErrorForJITProp

      +
      public boolean isRetainingPendingErrorForJITProp()
      This flag enables an optimization technique which only propagates error values to gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them at divergent differentiation paths within the computation graph.
      @@ -228,12 +293,15 @@

      isRetainingPendingErrorForJITProp

      This technique however uses more memory but will improve performance for some networks substantially. The technique is termed JIT-Propagation.
      -
    • -
  • -
    -

    setIsRetainingPendingErrorForJITProp

    -
    public void setIsRetainingPendingErrorForJITProp(boolean retain)
    + + + + +
      +
    • +

      setIsRetainingPendingErrorForJITProp

      +
      public void setIsRetainingPendingErrorForJITProp(boolean retain)
      This flag enables an optimization technique which only propagates error values to gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them at divergent differentiation paths within the computation graph.
      @@ -242,40 +310,49 @@

      setIsRetainingPendingErrorForJITProp

      This technique however uses more memory but will improve performance for some networks substantially. The technique is termed JIT-Propagation.
      -
    • -
  • -
    -

    isApplyingGradientWhenTensorIsUsed

    -
    public boolean isApplyingGradientWhenTensorIsUsed()
    + + + + +
      +
    • +

      isApplyingGradientWhenTensorIsUsed

      +
      public boolean isApplyingGradientWhenTensorIsUsed()
      Gradients will automatically be applied (or JITed) to tensors as soon as - they are being used for calculation (GraphNode instantiation). + they are being used for calculation (GraphNode instantiation). This feature works well with JIT-Propagation.
      -
    • -
  • -
    -

    setIsApplyingGradientWhenTensorIsUsed

    -
    public void setIsApplyingGradientWhenTensorIsUsed(boolean apply)
    + + + + +
      +
    • +

      setIsApplyingGradientWhenTensorIsUsed

      +
      public void setIsApplyingGradientWhenTensorIsUsed(boolean apply)
      Gradients will automatically be applied (or JITed) to tensors as soon as - they are being used for calculation (GraphNode instantiation). + they are being used for calculation (GraphNode instantiation). This feature works well with JIT-Propagation.
      -
      -
      Parameters:
      +
      +
      Parameters:
      apply - The flag determining if gradients should be applied when their tensors are used.
      -
    • -
  • -
    -

    isApplyingGradientWhenRequested

    -
    public boolean isApplyingGradientWhenRequested()
    + + + + +
      +
    • +

      isApplyingGradientWhenRequested

      +
      public boolean isApplyingGradientWhenRequested()
      Gradients will only be applied if requested. Usually this happens immediately, however if the flag 'applyGradientWhenTensorIsUsed' is set to true, then the tensor will only be updated by its gradient if requested AND the tensor is used for calculation! - (GraphNode instantiation).

      + (GraphNode instantiation).

      This flag works alongside two other autograd features which can be enabled by flipping the feature flags
      'isApplyingGradientWhenRequested' and 'isApplyingGradientWhenTensorIsUsed'
      @@ -286,44 +363,109 @@

      isApplyingGradientWhenRequested

      Setting both flags to true will inhibit the effect of the second setting 'isApplyingGradientWhenTensorIsUsed' unless a form of "permission" is being signaled to the autograd system. This signal comes in the form of a "request" flag which marks a tensor as allowed to - be updated by its gradient. This request can be dispatched to a Tensor - by setting Tensor.setGradientApplyRequested(boolean) to true.
      -
      -
      Returns:
      + be updated by its gradient. This request can be dispatched to a Tensor + by setting Tensor.setGradientApplyRequested(boolean) to true.
    • +
    +
    Returns:
    The truth value determining if gradients should be applied upon request.
    - -
  • -
    -

    setIsApplyingGradientWhenRequested

    -
    public void setIsApplyingGradientWhenRequested(boolean apply)
    + + + + +
      +
    • +

      setIsApplyingGradientWhenRequested

      +
      public void setIsApplyingGradientWhenRequested(boolean apply)
      Gradients will only be applied if requested. Usually this happens immediately, however if the flag 'applyGradientWhenTensorIsUsed' is set to true, then the tensor will only be updated by its gradient if requested AND the tensor is used for calculation! - (GraphNode instantiation).
      -
    + (GraphNode instantiation).
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • - - + + + + - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/Neureka.Settings.DType.html b/docs/jdocs/neureka/Neureka.Settings.DType.html index ec84a05f3..92041b51a 100644 --- a/docs/jdocs/neureka/Neureka.Settings.DType.html +++ b/docs/jdocs/neureka/Neureka.Settings.DType.html @@ -1,235 +1,365 @@ - + + - -Neureka.Settings.DType (neureka 1.0.0 API) - - - - + +Neureka.Settings.DType (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Class Neureka.Settings.DType

    +
    neureka
    +

    Class Neureka.Settings.DType

    -
    java.lang.Object -
    neureka.Neureka.Settings.DType
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.Neureka.Settings.DType
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      DType()
      -
       
      +
      +
      public class Neureka.Settings.DType
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        DType

        -
        public DType()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            DType

            +
            public DType()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getDefaultDataTypeClass

      -
      public Class<?> getDefaultDataTypeClass()
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getDefaultDataTypeClass

          +
          public java.lang.Class<?> getDefaultDataTypeClass()
          The default data type is not relevant most of the time. However, if a tensor is being constructed without providing a type class, then this property will be used.
          -
      • -
    • -
      -

      getDefaultDataType

      -
      public DataType<?> getDefaultDataType()
      -
      +
    + + + +
      +
    • +

      getDefaultDataType

      +
      public DataType<?> getDefaultDataType()
      • -
    • -
      -

      setDefaultDataTypeClass

      -
      public void setDefaultDataTypeClass(Class<?> dtype)
      +
    + + + +
      +
    • +

      setDefaultDataTypeClass

      +
      public void setDefaultDataTypeClass(java.lang.Class<?> dtype)
      The default data type is not relevant most of the time. However, if a tensor is being constructed without providing a type class, then this property will be used.
      -
    • -
  • -
    -

    getIsAutoConvertingExternalDataToJVMTypes

    -
    public boolean getIsAutoConvertingExternalDataToJVMTypes()
    + + + + +
      +
    • +

      getIsAutoConvertingExternalDataToJVMTypes

      +
      public boolean getIsAutoConvertingExternalDataToJVMTypes()
      This flag will determine if foreign data types will be converted into the next best fit (in terms of bits) or if it should be converted into something that does not mess with the representation of the data. For example an unsigned int can be converted bit-wise into a JVM int, or it could be converted to a JVM long type in order to be compatible with JVM operations...
      -
    • -
  • -
    -

    setIsAutoConvertingExternalDataToJVMTypes

    -
    public void setIsAutoConvertingExternalDataToJVMTypes(boolean autoConvert)
    + + + + +
      +
    • +

      setIsAutoConvertingExternalDataToJVMTypes

      +
      public void setIsAutoConvertingExternalDataToJVMTypes(boolean autoConvert)
      This flag will determine if foreign data types will be converted into the next best fit (in terms of bits) or if it should be converted into something that does not mess with the representation of the data. For example an unsigned int can be converted bit-wise into a JVM int, or it could be converted to a JVM long type in order to be compatible with JVM operations...
      -
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/Neureka.Settings.Debug.html b/docs/jdocs/neureka/Neureka.Settings.Debug.html index 49057a68e..dba10fe3e 100644 --- a/docs/jdocs/neureka/Neureka.Settings.Debug.html +++ b/docs/jdocs/neureka/Neureka.Settings.Debug.html @@ -1,174 +1,229 @@ - + + - -Neureka.Settings.Debug (neureka 1.0.0 API) - - - - + +Neureka.Settings.Debug (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Class Neureka.Settings.Debug

    +
    neureka
    +

    Class Neureka.Settings.Debug

    -
    java.lang.Object -
    neureka.Neureka.Settings.Debug
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.Neureka.Settings.Debug
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      Debug()
      -
       
      +
      +
      public class Neureka.Settings.Debug
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Debug

        -
        public Debug()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Debug

            +
            public Debug()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      isKeepingDerivativeTargetPayloads

      -
      public boolean isKeepingDerivativeTargetPayloads()
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          isKeepingDerivativeTargetPayloads

          +
          public boolean isKeepingDerivativeTargetPayloads()
          Every derivative is calculated with respect to some graph node. Graph nodes contain payload tensors. A tensor might not always be used for backpropagation, @@ -182,12 +237,15 @@

          isKeepingDerivativeTargetPayloads

          The flag determines this behavior with respect to target nodes. It is used in the test suit to validate that the right tensors were calculated. This flag should not be modified in production! (memory leak)
          -
      • -
    • -
      -

      setIsKeepingDerivativeTargetPayloads

      -
      public void setIsKeepingDerivativeTargetPayloads(boolean keep)
      +
    + + + +
      +
    • +

      setIsKeepingDerivativeTargetPayloads

      +
      public void setIsKeepingDerivativeTargetPayloads(boolean keep)
      Every derivative is calculated with respect to some graph node. Graph nodes contain payload tensors. A tensor might not always be used for backpropagation, @@ -201,52 +259,120 @@

      setIsKeepingDerivativeTargetPayloads

      The flag determines this behavior with respect to target nodes. It is used in the test suit to validate that the right tensors were calculated. This flag should not be modified in production! (memory leak)
      -
    • -
  • -
    -

    isDeletingIntermediateTensors

    -
    public boolean isDeletingIntermediateTensors()
    -
    Function instances will produce hidden intermediate results + + + + +
      +
    • +

      isDeletingIntermediateTensors

      +
      public boolean isDeletingIntermediateTensors()
      +
      Function instances will produce hidden intermediate results when executing an array of inputs. These tensors might not always be used for backpropagation, which means they will be deleted if possible. Tensors are not deleted of they are leave tensors (They are created by the user or require gradients) or they are angle points between forward- and reverse-mode-AutoDiff! This flag should not be modified in production! (memory leak)
      -
    • -
  • -
    -

    setIsDeletingIntermediateTensors

    -
    public void setIsDeletingIntermediateTensors(boolean delete)
    -
    Function instances will produce hidden intermediate results + + + + +
      +
    • +

      setIsDeletingIntermediateTensors

      +
      public void setIsDeletingIntermediateTensors(boolean delete)
      +
      Function instances will produce hidden intermediate results when executing an array of inputs. These tensors might not always be used for backpropagation, which means they will be deleted if possible. Tensors are not deleted of they are leave tensors (They are created by the user or require gradients) or they are angle points between forward- and reverse-mode-AutoDiff! This flag should not be modified in production! (memory leak)
      -
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/Neureka.Settings.NDim.html b/docs/jdocs/neureka/Neureka.Settings.NDim.html index ea9a125f1..d35a5fbb7 100644 --- a/docs/jdocs/neureka/Neureka.Settings.NDim.html +++ b/docs/jdocs/neureka/Neureka.Settings.NDim.html @@ -1,207 +1,325 @@ - + + - -Neureka.Settings.NDim (neureka 1.0.0 API) - - - - + +Neureka.Settings.NDim (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Class Neureka.Settings.NDim

    -
    -
    java.lang.Object -
    neureka.Neureka.Settings.NDim
    +
    neureka
    +

    Class Neureka.Settings.NDim

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.Neureka.Settings.NDim
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      Neureka.Settings
      +
      Neureka.Settings
      -
      public class Neureka.Settings.NDim -extends Object
      +
      +
      public class Neureka.Settings.NDim
+extends java.lang.Object
      Settings for configuring the access pattern of nd-arrays/tensors.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      NDim()
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        NDim

        -
        public NDim()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            NDim

            +
            public NDim()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      isOnlyUsingDefaultNDConfiguration

      -
      public boolean isOnlyUsingDefaultNDConfiguration()
      -
      This flag determines which NDConfiguration implementations +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          isOnlyUsingDefaultNDConfiguration

          +
          public boolean isOnlyUsingDefaultNDConfiguration()
          +
          This flag determines which NDConfiguration implementations should be used for nd-arrays/tensors. - If this flag is set to true, then the less performant general purpose NDConfiguration + If this flag is set to true, then the less performant general purpose NDConfiguration will be used for all nd-arrays/tensors.
          -
          -
          Returns:
          -
          The truth value determining if only the default SlicedNDConfiguration should be used.
          +
          +
          Returns:
          +
          The truth value determining if only the default SlicedNDConfiguration should be used.
          -
      • -
    • -
      -

      setIsOnlyUsingDefaultNDConfiguration

      -
      public void setIsOnlyUsingDefaultNDConfiguration(boolean enabled)
      -
      Setting this flag determines which NDConfiguration implementations +
    + + + +
      +
    • +

      setIsOnlyUsingDefaultNDConfiguration

      +
      public void setIsOnlyUsingDefaultNDConfiguration(boolean enabled)
      +
      Setting this flag determines which NDConfiguration implementations should be used for nd-arrays/tensors. - If this flag is set to true, then the less performant general purpose NDConfiguration + If this flag is set to true, then the less performant general purpose NDConfiguration will be used for all nd-arrays/tensors.
      -
      -
      Parameters:
      -
      enabled - The truth value determining if only the default SlicedNDConfiguration should be used.
      +
      +
      Parameters:
      +
      enabled - The truth value determining if only the default SlicedNDConfiguration should be used.
      -
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/Neureka.Settings.View.html b/docs/jdocs/neureka/Neureka.Settings.View.html index 8506ab503..d722e2e0a 100644 --- a/docs/jdocs/neureka/Neureka.Settings.View.html +++ b/docs/jdocs/neureka/Neureka.Settings.View.html @@ -1,172 +1,283 @@ - + + - -Neureka.Settings.View (neureka 1.0.0 API) - - - - + +Neureka.Settings.View (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Class Neureka.Settings.View

    +
    neureka
    +

    Class Neureka.Settings.View

    -
    java.lang.Object -
    neureka.Neureka.Settings.View
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.Neureka.Settings.View
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      NDPrintSettings
      -
      getNDPrintSettings()
      -
      -
      Settings for configuring how tensors should be converted to String representations.
      +
      +
      public class Neureka.Settings.View
+extends java.lang.Object
      +
      Settings for configuring how objects should be converted to String representations.
      +
      • +
    -
    void
    -
    ndArrays(Consumer<NDPrintSettings> should)
    -
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        getNDPrintSettings

        -
        public NDPrintSettings getNDPrintSettings()
        -
        Settings for configuring how tensors should be converted to String representations.
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            getNDPrintSettings

            +
            public NDPrintSettings getNDPrintSettings()
            +
            Settings for configuring how tensors should be converted to String representations.
            • -
          • -
            -

            ndArrays

            -
            public void ndArrays(Consumer<NDPrintSettings> should)
            +
          + + + +
            +
          • +

            ndArrays

            +
            public void ndArrays(java.util.function.Consumer<NDPrintSettings> should)
            This allows you to provide a lambda to configure how tensors should be - converted to String instances. - The provided Consumer will receive a NDPrintSettings instance + converted to String instances. + The provided Consumer will receive a NDPrintSettings instance which allows you to change various settings with the help of method chaining.
            -
            -
            Parameters:
            -
            should - A consumer of the NDPrintSettings ready to be configured.
            +
            +
            Parameters:
            +
            should - A consumer of the NDPrintSettings ready to be configured.
            -
      • -
    • -
      -

      toString

      -
      public String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/Neureka.Settings.html b/docs/jdocs/neureka/Neureka.Settings.html index 450924993..7daccec18 100644 --- a/docs/jdocs/neureka/Neureka.Settings.html +++ b/docs/jdocs/neureka/Neureka.Settings.html @@ -1,304 +1,463 @@ - + + - -Neureka.Settings (neureka 1.0.0 API) - - - - + +Neureka.Settings (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Class Neureka.Settings

    -
    -
    java.lang.Object -
    neureka.Neureka.Settings
    +
    neureka
    +

    Class Neureka.Settings

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.Neureka.Settings
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      Neureka
      +
      Neureka
      -
      public class Neureka.Settings -extends Object
      -
      This class hosts the settings of the Neureka instance which will be used throughout the library.
      -
    -
    -
      +
      +
      public class Neureka.Settings
+extends java.lang.Object
      +
      This class hosts the settings of the Neureka instance which will be used throughout the library.
      + +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + +
      +
    • +

      dtype

      +
      public Neureka.Settings.DType dtype(java.lang.Object closure)
      This allows you to configure Neureka using a Groovy DSL.
      -
      • -
    • -
      -

      isLocked

      -
      public boolean isLocked()
      +
    + + + +
      +
    • +

      isLocked

      +
      public boolean isLocked()
      Locked settings can only be read but not written to. - Trying to write to a locked Neureka.Settings instance will not have an effect. + Trying to write to a locked Neureka.Settings instance will not have an effect. The attempt, however, will be logged.
      -
      • -
    • -
      -

      setIsLocked

      -
      public void setIsLocked(boolean locked)
      -
      Can be used to lock or unlock the settings of the current thread-local Neureka instance. +
    + + + +
      +
    • +

      setIsLocked

      +
      public void setIsLocked(boolean locked)
      +
      Can be used to lock or unlock the settings of the current thread-local Neureka instance. Locked settings can only be read but not written to. - Trying to write to a locked Neureka.Settings instance will not have an effect. + Trying to write to a locked Neureka.Settings instance will not have an effect. The attempt, however, will be logged.
      -
      • -
    • -
      -

      toString

      -
      public String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/Neureka.Utility.html b/docs/jdocs/neureka/Neureka.Utility.html index 27689a897..6746a8c1b 100644 --- a/docs/jdocs/neureka/Neureka.Utility.html +++ b/docs/jdocs/neureka/Neureka.Utility.html @@ -1,174 +1,284 @@ - + + - -Neureka.Utility (neureka 1.0.0 API) - - - - + +Neureka.Utility (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Class Neureka.Utility

    +
    neureka
    +

    Class Neureka.Utility

    -
    java.lang.Object -
    neureka.Neureka.Utility
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.Neureka.Utility
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      Utility()
      -
       
      +
      +
      public static class Neureka.Utility
+extends java.lang.Object
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        Utility() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      String
      -
      readResource(String path)
      -
      + -
      -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Utility

        -
        public Utility()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Utility

            +
            public Utility()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      readResource

      -
      public String readResource(String path)
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          readResource

          +
          public java.lang.String readResource(java.lang.String path)
          Helper method which reads the file with the given name and returns the contents of this file as a String. Will exit the application if the file can not be read.
          -
          -
          Parameters:
          +
          +
          Parameters:
          path - The path to the jar resource.
          -
          Returns:
          +
          Returns:
          The contents of the file
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/Neureka.html b/docs/jdocs/neureka/Neureka.html index 1afce9168..cbfcd8d9e 100644 --- a/docs/jdocs/neureka/Neureka.html +++ b/docs/jdocs/neureka/Neureka.html @@ -1,354 +1,514 @@ - + + - -Neureka (neureka 1.0.0 API) - - - - + +Neureka (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Class Neureka

    -
    -
    java.lang.Object -
    neureka.Neureka
    +
    neureka
    +

    Class Neureka

    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.Neureka
        • +
      +
      • +
    +
    +
      +
    • -
      public final class Neureka -extends Object
      -
      Neureka is the key access point for thread local / global library settings ( seeNeureka.Settings) - as well as execution contexts (see BackendContext) - and pre-instantiated Functions. - Neureka exposes the execution context via the backend() method, +
      +
      public final class Neureka
+extends java.lang.Object
      +
      Neureka is the key access point for thread local / global library settings ( seeNeureka.Settings) + as well as execution contexts (see BackendContext) + and pre-instantiated Functions. + Neureka exposes the execution context via the backend() method, the library settings which govern the behaviour of various library components - can be accessed via the settings() method. - Common functions can be accessed within a given BackendContext instance based on which they were built. + can be accessed via the settings() method. + Common functions can be accessed within a given BackendContext instance based on which they were built. If one wishes to modify the default library settings it is possible to do so by editing the "library_settings.groovy" DSL file.
      -
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Class
      -
      Description
      -
      class 
      -
      Neureka.Settings
      -
      -
      This class hosts the settings of the Neureka instance which will be used throughout the library.
      -
      -
      static class 
      -
      Neureka.Utility
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        backend

        -
        public BackendContext backend()
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            backend

            +
            public BackendContext backend()
            • -
          • -
            -

            get

            -
            public static Neureka get()
            -
            The Neureka class represents the configuration of this library. +
          + + + +
            +
          • +

            get

            +
            public static Neureka get()
            +
            The Neureka class represents the configuration of this library. Instances of this configuration are stored local to every thread in order to make both the library settings and the execution context threadsafe! - This method will return the Neureka instance which corresponds to the thread calling it.
            -
            -
            Returns:
            -
            The thread local library configuration state called Neureka.
            + This method will return the Neureka instance which corresponds to the thread calling it.
    +
    +
    Returns:
    +
    The thread local library configuration state called Neureka.
    - -
  • -
    -

    set

    -
    public static void set(Neureka instance)
    -
    Neureka is a thread local singleton. - Therefore, this method will only set the provided Neureka instance + + + + +
      +
    • +

      set

      +
      public static void set(Neureka instance)
      +
      Neureka is a thread local singleton. + Therefore, this method will only set the provided Neureka instance for the thread which is calling this method. - Other threads calling the get() method to retrieve the instance + Other threads calling the get() method to retrieve the instance will get their own instance... (This can theoretically be bypassed by sharing instances)
      -
      -
      Parameters:
      -
      instance - The Neureka instance which ought to be set as thread local singleton.
      +
      +
      Parameters:
      +
      instance - The Neureka instance which ought to be set as thread local singleton.
      -
    • -
  • -
    -

    configure

    -
    public static Neureka configure(Object closure)
    + + + + +
      +
    • +

      configure

      +
      public static Neureka configure(java.lang.Object closure)
      This allows you to configure Neureka using a Groovy DSL.
      -
      -
      Parameters:
      +
      +
      Parameters:
      closure - A Groovy closure to allow for DSL type configuring.
      -
      Returns:
      -
      The thread-local Neureka singleton instance.
      +
      Returns:
      +
      The thread-local Neureka singleton instance.
      -
    • -
  • -
    -

    canAccessOpenCL

    -
    public boolean canAccessOpenCL()
    -
    -
    Returns:
    + + + + +
      +
    • +

      canAccessOpenCL

      +
      public boolean canAccessOpenCL()
      +
      +
      Returns:
      The truth value determining if OpenCL is accessible.
      -
    • -
  • -
    -

    canAccessOpenCLDevice

    -
    public boolean canAccessOpenCLDevice()
    -
    -
    Returns:
    -
    The truth value determining if at least 1 OpenCLDevice is accessible.
    + + + + +
      +
    • +

      canAccessOpenCLDevice

      +
      public boolean canAccessOpenCLDevice()
      +
      +
      Returns:
      +
      The truth value determining if at least 1 OpenCLDevice is accessible.
      -
    • -
  • -
    -

    settings

    -
    public Neureka.Settings settings()
    -
    -
    Returns:
    -
    An instance of library wide Neureka.Settings determining the behaviour of many classes...
    + + + + +
    • -
  • -
    -

    settings

    -
    public Neureka.Settings settings(Object closure)
    + + + + +
      +
    • +

      settings

      +
      public Neureka.Settings settings(java.lang.Object closure)
      This allows you to configure Neureka using a Groovy DSL.
      -
    • -
  • -
    -

    utility

    -
    public Neureka.Utility utility()
    -
    -
    Returns:
    + + + + +
      +
    • +

      utility

      +
      public Neureka.Utility utility()
      +
      +
      Returns:
      An instance of an utility class useful for loading resources or checking if they are even available.
      -
    • -
  • -
    -

    version

    -
    public static String version()
    -
    -
    Returns:
    + + + + +
      +
    • +

      version

      +
      public static java.lang.String version()
      +
      +
      Returns:
      The semantic version of the Neureka library.
      -
    • -
  • -
    -

    reset

    -
    public void reset()
    + + + + +
      +
    • +

      reset

      +
      public void reset()
      This method will try to reload the "library_settings.groovy" script - which will re-configure the library wide Neureka.Settings instance nested inside Neureka. + which will re-configure the library wide Neureka.Settings instance nested inside Neureka. If the execution of this file fails then the settings will be reverted to a hardcoded default state.
      -
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • -
  • -
    -

    getBackend

    -
    public BackendContext getBackend()
    -
    -
    Returns:
    + + + + +
      +
    • +

      getBackend

      +
      public BackendContext getBackend()
      +
      +
      Returns:
      A context object which is expected to host all the tensor operations...
      -
    • -
  • -
    -

    setBackend

    -
    public void setBackend(BackendContext backendContext)
    + + + + +
      +
    • +

      setBackend

      +
      public void setBackend(BackendContext backendContext)
      Use this method to attach a backend context (for operations) to this thread local library context.
      -
      -
      Parameters:
      -
      backendContext - The BackendContext which should be set for this thread local library context.
      +
      +
      Parameters:
      +
      backendContext - The BackendContext which should be set for this thread local library context.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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    + + + +
    + +
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      +
    • Summary: 
      • +
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      • +
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      • +
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      • +
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    • Detail: 
      • +
    • Field | 
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/Shape.html b/docs/jdocs/neureka/Shape.html index b018bd051..67140b29d 100644 --- a/docs/jdocs/neureka/Shape.html +++ b/docs/jdocs/neureka/Shape.html @@ -1,406 +1,568 @@ - + + - -Shape (neureka 1.0.0 API) - - - - + +Shape (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Interface Shape

    +
    neureka
    +

    Interface Shape

    -
    -
    +
    +
    +
      +
    • +
      All Superinterfaces:
      -
      Iterable<Integer>
      +
      java.lang.Iterable<java.lang.Integer>
      -
      public interface Shape -extends Iterable<Integer>
      +
      +
      public interface Shape
+extends java.lang.Iterable<java.lang.Integer>
      Basically a tuple of integers which is used to describe the shape of an array. The shape of an array is the number of elements in each dimension. - A Shape is an immutable monadic type, which means that you can transform - a Shape into another Shape by applying a function to it, like for example - through the map(java.util.function.Function) method.
      -
    -
    -
    + + +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      default boolean
      -
      any(Predicate<Integer> predicate)
      -
       
      -
      default int
      -
      count(Predicate<Integer> predicate)
      -
       
      -
      default int
      -
      elements()
      -
       
      -
      default boolean
      -
      every(Predicate<Integer> predicate)
      -
       
      -
      default Shape
      -
      filter(Predicate<Integer> predicate)
      -
       
      -
      int
      -
      get(int i)
      -
       
      -
      default Iterator<Integer>
      -
      iterator()
      -
       
      -
      default Shape
      -
      map(Function<Integer,Integer> mapper)
      -
      -
      This method is used to transform a Shape into another Shape +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethod and Description
        default booleanany(java.util.function.Predicate<java.lang.Integer> predicate) 
        default intcount(java.util.function.Predicate<java.lang.Integer> predicate) 
        default intelements() 
        default booleanevery(java.util.function.Predicate<java.lang.Integer> predicate) 
        default Shapefilter(java.util.function.Predicate<java.lang.Integer> predicate) 
        intget(int i) 
        default java.util.Iterator<java.lang.Integer>iterator() 
        default Shapemap(java.util.function.Function<java.lang.Integer,java.lang.Integer> mapper) +
        This method is used to transform a Shape into another Shape by applying a function to it.
        - -
        static Shape
        -
        of(int... shape)
        -
        -
        This method is used to create a Shape instance from an array of integers.
        -
        -
        static Shape
        -
        of(Iterable<? extends Number> shape)
        -
        -
        This method is used to create a Shape instance from an iterable of numbers +
        static Shapeof(int... shape) +
        This method is used to create a Shape instance from an array of integers.
        +
        static Shapeof(java.lang.Iterable<? extends java.lang.Number> shape) +
        This method is used to create a Shape instance from an iterable of numbers whose integer values are used to describe the shape of a nd-array.
        - -
        static Shape
        -
        of(List<? extends Number> shape)
        -
        -
        This method is used to create a Shape instance from a list of numbers +
        static Shapeof(java.util.List<? extends java.lang.Number> shape) +
        This method is used to create a Shape instance from a list of numbers whose integer values are used to describe the shape of a nd-array.
        - -
        static Shape
        -
        of(Stream<? extends Number> shape)
        -
        -
        This method is used to create a Shape instance from a stream of numbers +
        static Shapeof(java.util.stream.Stream<? extends java.lang.Number> shape) +
        This method is used to create a Shape instance from a stream of numbers whose integer values are used to describe the shape of a nd-array.
        - -
        int
        -
        size()
        -
         
        -
        default Shape
        -
        slice(int start)
        -
         
        -
        default Shape
        -
        slice(int start, - int end)
        -
         
        -
        default Stream<Integer>
        -
        stream()
        -
         
        -
        default int[]
        -
        toIntArray()
        -
         
        - - - -
        -

        Methods inherited from interface java.lang.Iterable

        -forEach, spliterator
        - +
        intsize() 
        default Shapeslice(int start) 
        default Shapeslice(int start, + int end) 
        default java.util.stream.Stream<java.lang.Integer>stream() 
        default int[]toIntArray() 
        +
          +
        • + + +

          Methods inherited from interface java.lang.Iterable

          +forEach, spliterator
          • +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        static Shape of(List<? extends Number> shape)
        -
        This method is used to create a Shape instance from a list of numbers +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            of

            +
            static Shape of(java.util.List<? extends java.lang.Number> shape)
            +
            This method is used to create a Shape instance from a list of numbers whose integer values are used to describe the shape of a nd-array. The shape of an array is the number of elements in each dimension.
            -
            -
            Parameters:
            +
            +
            Parameters:
            shape - The list of integers which is used to describe the shape of an array.
            -
            Returns:
            -
            A Shape instance which is created from the given array of integers.
            +
            Returns:
            +
            A Shape instance which is created from the given array of integers.
            -
        • -
      • -
        -

        of

        -
        static Shape of(Stream<? extends Number> shape)
        -
        This method is used to create a Shape instance from a stream of numbers +
      + + + +
        +
      • +

        of

        +
        static Shape of(java.util.stream.Stream<? extends java.lang.Number> shape)
        +
        This method is used to create a Shape instance from a stream of numbers whose integer values are used to describe the shape of a nd-array. The shape of an array is the number of elements in each dimension.
        -
        -
        Parameters:
        +
        +
        Parameters:
        shape - The stream of integers which is used to describe the shape of an array.
        -
        Returns:
        -
        A Shape instance which is created from the given array of integers.
        +
        Returns:
        +
        A Shape instance which is created from the given array of integers.
        -
      • -
    • -
      -

      of

      -
      static Shape of(Iterable<? extends Number> shape)
      -
      This method is used to create a Shape instance from an iterable of numbers +
    + + + +
      +
    • +

      of

      +
      static Shape of(java.lang.Iterable<? extends java.lang.Number> shape)
      +
      This method is used to create a Shape instance from an iterable of numbers whose integer values are used to describe the shape of a nd-array. The shape of an array is the number of elements in each dimension.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The iterable of integers which is used to describe the shape of an array.
      -
      Returns:
      -
      A Shape instance which is created from the given array of integers.
      +
      Returns:
      +
      A Shape instance which is created from the given array of integers.
      -
      • -
    • -
      -

      of

      -
      static Shape of(int... shape)
      -
      This method is used to create a Shape instance from an array of integers. +
    + + + +
      +
    • +

      of

      +
      static Shape of(int... shape)
      +
      This method is used to create a Shape instance from an array of integers. The array of integers is used to describe the shape of an array. The shape of an array is the number of elements in each dimension.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The array of integers which is used to describe the shape of an array.
      -
      Returns:
      -
      A Shape instance which is created from the given array of integers.
      +
      Returns:
      +
      A Shape instance which is created from the given array of integers.
      -
      • -
    • -
      -

      size

      -
      int size()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      size

      +
      int size()
      +
      +
      Returns:
      The number of dimensions of the shape.
      -
      • -
    • -
      -

      elements

      -
      default int elements()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      elements

      +
      default int elements()
      +
      +
      Returns:
      The number of elements in the shape.
      -
      • -
    • -
      -

      get

      -
      int get(int i)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      get

      +
      int get(int i)
      +
      +
      Parameters:
      i - The index of the dimension/axis.
      -
      Returns:
      +
      Returns:
      The number of elements in the dimension/axis at the given index.
      -
      • -
    • -
      -

      toIntArray

      -
      default int[] toIntArray()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      toIntArray

      +
      default int[] toIntArray()
      +
      +
      Returns:
      This shape as an array of integers.
      -
      • -
    • -
      -

      map

      -
      default Shape map(Function<Integer,Integer> mapper)
      -
      This method is used to transform a Shape into another Shape +
    + + + +
      +
    • +

      map

      +
      default Shape map(java.util.function.Function<java.lang.Integer,java.lang.Integer> mapper)
      +
      This method is used to transform a Shape into another Shape by applying a function to it.
      -
      -
      Parameters:
      -
      mapper - The function which is used to transform the Shape.
      -
      Returns:
      -
      A new Shape instance which is the result of the transformation.
      +
      +
      Parameters:
      +
      mapper - The function which is used to transform the Shape.
      +
      Returns:
      +
      A new Shape instance which is the result of the transformation.
      -
      • -
    • -
      -

      stream

      -
      default Stream<Integer> stream()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      stream

      +
      default java.util.stream.Stream<java.lang.Integer> stream()
      +
      +
      Returns:
      This shape as a stream of integers.
      -
      • -
    • -
      -

      slice

      -
      default Shape slice(int start, - int end)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      slice

      +
      default Shape slice(int start,
+                    int end)
      +
      +
      Parameters:
      start - The start index of the slice, inclusive.
      end - The end index of the slice, exclusive.
      -
      Returns:
      +
      Returns:
      A slice of this shape starting at the given start index and ending at the given end index.
      -
      • -
    • -
      -

      slice

      -
      default Shape slice(int start)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      slice

      +
      default Shape slice(int start)
      +
      +
      Parameters:
      start - The start index of the slice, inclusive.
      -
      Returns:
      +
      Returns:
      A slice of this shape starting at the given start index and ending at the end of the shape.
      -
      • -
    • -
      -

      filter

      -
      default Shape filter(Predicate<Integer> predicate)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      filter

      +
      default Shape filter(java.util.function.Predicate<java.lang.Integer> predicate)
      +
      +
      Parameters:
      predicate - The predicate which is used to filter the shape.
      -
      Returns:
      +
      Returns:
      A new shape which is the result of filtering this shape with the given predicate.
      -
      • -
    • -
      -

      count

      -
      default int count(Predicate<Integer> predicate)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      count

      +
      default int count(java.util.function.Predicate<java.lang.Integer> predicate)
      +
      +
      Parameters:
      predicate - The predicate which is used to count the elements of the shape for which it is true.
      -
      Returns:
      +
      Returns:
      The number of elements in the shape which satisfy the given predicate.
      -
      • -
    • -
      -

      every

      -
      default boolean every(Predicate<Integer> predicate)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      every

      +
      default boolean every(java.util.function.Predicate<java.lang.Integer> predicate)
      +
      +
      Parameters:
      predicate - The predicate which is used to test the elements of the shape.
      -
      Returns:
      +
      Returns:
      True if the given predicate is true for all elements of the shape.
      -
      • -
    • -
      -

      any

      -
      default boolean any(Predicate<Integer> predicate)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      any

      +
      default boolean any(java.util.function.Predicate<java.lang.Integer> predicate)
      +
      +
      Parameters:
      predicate - The predicate which is used to test the elements of the shape.
      -
      Returns:
      +
      Returns:
      True if the given predicate is true for at least one element of the shape.
      -
      • -
    • -
      -

      iterator

      -
      default Iterator<Integer> iterator()
      -
      -
      Specified by:
      -
      iterator in interface Iterable<Integer>
      -
      Returns:
      +
    + + + +
      +
    • +

      iterator

      +
      default java.util.Iterator<java.lang.Integer> iterator()
      +
      +
      Specified by:
      +
      iterator in interface java.lang.Iterable<java.lang.Integer>
      +
      Returns:
      An iterator over the shape.
      -
    - - + + +
    + - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
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      • +
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      • +
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      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/Tensor.ImageType.html b/docs/jdocs/neureka/Tensor.ImageType.html index 6c8695869..ad5fc0b45 100644 --- a/docs/jdocs/neureka/Tensor.ImageType.html +++ b/docs/jdocs/neureka/Tensor.ImageType.html @@ -1,342 +1,514 @@ - + + - -Tensor.ImageType (neureka 1.0.0 API) - - - - + +Tensor.ImageType (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Enum Class Tensor.ImageType

    -
    -
    java.lang.Object -
    java.lang.Enum<Tensor.ImageType> -
    neureka.Tensor.ImageType
    -
    +
    neureka
    +

    Enum Tensor.ImageType

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • + +
      • +
    +
    +
    -
    -
    - +
    + - -
    -
      +
    +
    + -
  • -
    -

    Field Details

    -
      -
    • -
      -

      bufferType

      -
      public final int bufferType
      -
      +
        +
      • + + +

        Field Detail

        + + + +
          +
        • +

          bufferType

          +
          public final int bufferType
          • -
        • -
          -

          dataType

          -
          public final DataType<?> dataType
          -
          +
        + + + +
          +
        • +

          dataType

          +
          public final DataType<?> dataType
          • -
        • -
          -

          numberOfChannels

          -
          public final int numberOfChannels
          -
          +
        + + + +
          +
        • +

          numberOfChannels

          +
          public final int numberOfChannels
        -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static Tensor.ImageType[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static Tensor.ImageType[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (Tensor.ImageType c : Tensor.ImageType.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static Tensor.ImageType valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static Tensor.ImageType valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/Tensor.html b/docs/jdocs/neureka/Tensor.html index 7cbe39373..9f27a48c7 100644 --- a/docs/jdocs/neureka/Tensor.html +++ b/docs/jdocs/neureka/Tensor.html @@ -1,89 +1,115 @@ - + + - -Tensor (neureka 1.0.0 API) - - - - + +Tensor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka
    -

    Interface Tensor<V>

    -
    -
    -
    -
    Type Parameters:
    +
    neureka
    +

    Interface Tensor<V>

    +
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The type parameter for the individual value items within this tensor.
      -
      +
      All Superinterfaces:
      -
      Component<Tensor<V>>, ComponentOwner<Tensor<V>>, Iterable<V>, Nda<V>, NDimensional
      +
      Component<Tensor<V>>, ComponentOwner<Tensor<V>>, java.lang.Iterable<V>, Nda<V>, NDimensional
      -
      public interface Tensor<V> -extends Nda<V>, Component<Tensor<V>>, ComponentOwner<Tensor<V>>
      -
      A Tensor is a mathematical concept and type of multidimensional +
      +
      public interface Tensor<V>
+extends Nda<V>, Component<Tensor<V>>, ComponentOwner<Tensor<V>>
      +
      A Tensor is a mathematical concept and type of multidimensional data-structure with certain transformation properties. Technically however, it is mostly a simple container / data-structure which can house data indexed by N dimensions. Therefore, it is often also described as a nd-array. @@ -98,1329 +124,1563 @@

      Interface Tensor<V>

      Such operations might be simple element-wise operations or more complex linear operations like the dot-product, matrix- or even tensor multiplications.

      - -
      -
        + +
      +
      +
      +
        +
      • -
      • -
        -

        Nested Class Summary

        -
        Nested Classes
        -
        -
        Modifier and Type
        -
        Interface
        -
        Description
        -
        static enum 
        -
        Tensor.ImageType
        -
        -
        Use this enum as argument for the asImage(Tensor.ImageType) method to + -
      • -
        -

        Method Summary

        -
        -
        -
        -
        -
        Modifier and Type
        -
        Method
        -
        Description
        -
        default Tensor<V>
        -
        abs()
        -
        +
          +
        • + + +

          Method Summary

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          All Methods Static Methods Instance Methods Abstract Methods Default Methods 
          Modifier and TypeMethod and Description
          default Tensor<V>abs()
          This method is a functionally identical to the following alternatives:
          - -
          default void
          -
          applyGradient()
          -
          +
          default voidapplyGradient()
          If this tensor owns a gradient tensor as component, then it can be applied by this method.
          - -
          BufferedImage
          -
          asImage(Tensor.ImageType type)
          -
          -
          Turns this tensor into a BufferedImage based on the provided - Tensor.ImageType formatting choice.
          -
          -
          <T> T
          -
          asType(Class<T> typeClass)
          -
           
          -
          default Tensor<V>
          -
          backward()
          -
          +
          java.awt.image.BufferedImageasImage(Tensor.ImageType type) +
          Turns this tensor into a BufferedImage based on the provided + Tensor.ImageType formatting choice.
          +
          <T> TasType(java.lang.Class<T> typeClass) 
          default Tensor<V>backward()
          Use this to back-propagate an error signal of 1.0 through the recorded computation graph.
          - -
          default Tensor<V>
          -
          backward(double value)
          -
          -
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
          -
          -
          default Tensor<V>
          -
          backward(Tensor<V> error)
          -
          -
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
          -
          -
          default boolean
          -
          belongsToGraph()
          -
          -
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
          -
          -
          default Tensor<V>
          -
          cbrt()
          -
          +
          default Tensor<V>backward(double value) +
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
          +
          default Tensor<V>backward(Tensor<V> error) +
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
          +
          default booleanbelongsToGraph() +
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
          +
          default Tensor<V>cbrt()
          This method is a functionally identical to the following alternatives:
          - -
          default Tensor<V>
          -
          concatAt(int axis, - Nda<V> other)
          -
          +
          default Tensor<V>concatAt(int axis, + Nda<V> other)
          This method concatenates the provided nd-array together with this nd-array along a specified axis.
          - -
          default Tensor<V>
          -
          concatAt(int axis, - Nda<V> other, - Nda<V>... ndArrays)
          -
          +
          default Tensor<V>concatAt(int axis, + Nda<V> other, + Nda<V>... ndArrays)
          This method concatenates the provided nd-arrays together with this nd-array along a specified axis.
          - -
          default boolean
          -
          contains(Tensor<V> other)
          -
          +
          default booleancontains(Tensor<V> other)
          This method name translates to the "in" keyword in Kotlin! The same is true for the "isCase" method in Groovy.
          - -
          default Tensor<V>
          -
          conv(Tensor<V> other)
          -
          +
          default Tensor<V>conv(Tensor<V> other)
          This method performs convolution between this tensor and the one passed as argument.
          - -
          default Tensor<V>
          -
          convDot(Tensor<V> other)
          -
          +
          default Tensor<V>convDot(Tensor<V> other)
          This method performs a convolutional based dot product between the last dimension of this tensor and the first dimension of the passed tensor.
          - -
          default Tensor<V>
          -
          cos()
          -
          +
          default Tensor<V>cos()
          This method is a functionally identical to the following alternatives:
          - -
          Tensor<V>
          -
          deepClone()
          -
          -
          This is almost identical to the deepCopy() method except that +
          Tensor<V>deepClone() +
          This is almost identical to the deepCopy() method except that the returned tensor will have autograd support, meaning that the cloning will be part of the autograd computation graph, and backpropagation will traverse the cloned tensor as well.
          - -
          Tensor<V>
          -
          deepCopy()
          -
          +
          Tensor<V>deepCopy()
          This method creates and returns a new nd-array instance which is not only a copy of the configuration of this nd-array but also a copy of the underlying data array.
          - -
          default Tensor<V>
          -
          detached()
          -
          +
          default Tensor<V>detached()
          This method returns a new tensor detached from any underlying computation-graph or simply does nothing if no graph is present.
          - Nodes within a computation graph are instances of the "GraphNode" class which are also + Nodes within a computation graph are instances of the "GraphNode" class which are also simple components of the tensors they represent in the graph.
          - -
          default Tensor<V>
          -
          dimtrim()
          -
          -
          This creates a new tensor with the same underlying Data and whose shape is trimmed.
          -
          -
          default Tensor<V>
          -
          div(Tensor<V> other)
          -
          +
          default Tensor<V>dimtrim() +
          This creates a new tensor with the same underlying Data and whose shape is trimmed.
          +
          default Tensor<V>div(Tensor<V> other)
          This method will produce the quotient of two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method.
          - -
          default Tensor<V>
          -
          div(V value)
          -
           
          -
          default Tensor<V>
          -
          dot(Tensor<V> other)
          -
          +
          default Tensor<V>div(V value) 
          default Tensor<V>dot(Tensor<V> other)
          Performs a dot product between the last dimension of this tensor and the first dimension of the provided tensor.
          - -
          default Tensor<V>
          -
          exp()
          -
          +
          default Tensor<V>exp()
          This method is a functionally identical to the following alternatives:
          - -
          default Optional<NDFrame<V>>
          -
          frame()
          -
          -
          This is a functionally identical alternative to getFrame().
          -
          -
          default Tensor<V>
          -
          get(int i)
          -
          -
          This getter method creates and returns a slice of the original nd-array.
          -
          -
          default Tensor<V>
          -
          get(int... indices)
          -
          +
          default java.util.Optional<NDFrame<V>>frame() +
          This is a functionally identical alternative to getFrame().
          +
          default Tensor<V>get(int... indices)
          The following method enables access to specific scalar elements within the nd-array.
          - -
          default Tensor<V>
          -
          get(Number i)
          -
          +
          default Tensor<V>get(int i)
          This getter method creates and returns a slice of the original nd-array.
          - -
          default Tensor<V>
          -
          get(Object key)
          -
          +
          default Tensor<V>get(java.lang.Number i) +
          This getter method creates and returns a slice of the original nd-array.
          +
          default Tensor<V>get(java.lang.Object... args) +
          The following method enables the creation of nd-array slices which access + the same underlying data (possibly from a different view).
          +
          default Tensor<V>get(java.lang.Object key)
          This method enables nd-array slicing! It takes a key of various types and configures a slice nd-array which shares the same underlying data as the original nd-array.
          - -
          default Tensor<V>
          -
          get(Object... args)
          -
          -
          The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view).
          -
          -
          default Tensor<V>
          -
          getAt(int i)
          -
          -
          This getter method creates and returns a slice of the original nd-array.
          -
          -
          Tensor<V>
          -
          getAt(int... indices)
          -
          +
          Tensor<V>getAt(int... indices)
          The following method enables access to specific scalar elements within the nd-array.
          - -
          default Tensor<V>
          -
          getAt(Number i)
          -
          +
          default Tensor<V>getAt(int i)
          This getter method creates and returns a slice of the original nd-array.
          - -
          default Tensor<V>
          -
          getAt(Object... args)
          -
          -
          The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view).
          -
          -
          Tensor<V>
          -
          getAt(List<?> key)
          -
          +
          Tensor<V>getAt(java.util.List<?> key)
          This method enables nd-array slicing! It takes a key of various types and configures a slice nd-array which shares the same underlying data as the original nd-array.
          - -
          Tensor<V>
          -
          getAt(Map<?,Integer> rangToSteps)
          -
          +
          Tensor<V>getAt(java.util.Map<?,java.lang.Integer> rangToSteps)
          This method is most useful when used in Groovy where defining maps is done through square brackets, making it possible to slice nd-arrays like so:
          - -
          DataType<V>
          -
          getDataType()
          -
          -
          This method returns the DataType instance of this Tensor, which is +
          default Tensor<V>getAt(java.lang.Number i) +
          This getter method creates and returns a slice of the original nd-array.
          +
          default Tensor<V>getAt(java.lang.Object... args) +
          The following method enables the creation of nd-array slices which access + the same underlying data (possibly from a different view).
          +
          DataType<V>getDataType() +
          This method returns the DataType instance of this Tensor, which is a wrapper object for the actual type class representing the value items stored inside the underlying data array of this tensor.
          - -
          default Device<V>
          -
          getDevice()
          -
           
          -
          default Optional<NDFrame<V>>
          -
          getFrame()
          -
           
          -
          default Optional<Tensor<V>>
          -
          getGradient()
          -
           
          -
          default Optional<GraphNode<V>>
          -
          getGraphNode()
          -
           
          -
          MutateTensor<V>
          -
          getMut()
          -
          +
          default Device<V>getDevice() 
          default java.util.Optional<NDFrame<V>>getFrame() 
          default java.util.Optional<Tensor<V>>getGradient() 
          default java.util.Optional<GraphNode<V>>getGraphNode() 
          MutateTensor<V>getMut()
          This method exposes an API for mutating the state of this tensor.
          - -
          Class<?>
          -
          getRepresentativeItemClass()
          -
          -
          The Class returned by this method is the representative Class of the - value items of a concrete AbstractNda but not necessarily the actual Class of +
          java.lang.Class<?>getRepresentativeItemClass() +
          The Class returned by this method is the representative Class of the + value items of a concrete AbstractNda but not necessarily the actual Class of a given value item, this is especially true for numeric types, which are represented by - implementations of the NumericType interface.
          - -
          default Tensor<V>
          -
          getT()
          -
          -
          A method which returns a new Tensor instance which is a transposed twin of this instance.
          - This is an alternative to the functionally identical T() method.
          -
          -
          int
          -
          getVersion()
          -
          + implementations of the NumericType interface.
          +
          default Tensor<V>getT() +
          A method which returns a new Tensor instance which is a transposed twin of this instance.
          + This is an alternative to the functionally identical T() method.
          +
          intgetVersion()
          The version number is tracking how often this tensor has been mutated.
          - -
          default Optional<Tensor<V>>
          -
          gradient()
          -
          -
          This is a functionally identical alternative to the getGradient() method.
          -
          -
          boolean
          -
          gradientApplyRequested()
          -
          +
          default java.util.Optional<Tensor<V>>gradient() +
          This is a functionally identical alternative to the getGradient() method.
          +
          booleangradientApplyRequested()
          This flag works alongside two autograd features which can be enabled inside the library settings.
          - -
          default Optional<GraphNode<V>>
          -
          graphNode()
          -
          -
          This is a functionally identical alternative to getGraphNode().
          -
          -
          default boolean
          -
          hasGradient()
          -
          +
          default java.util.Optional<GraphNode<V>>graphNode() +
          This is a functionally identical alternative to getGraphNode().
          +
          default booleanhasGradient()
          Tensors can be components of other tensors which makes the implicitly their gradients.
          - -
          boolean
          -
          is(Class<?> typeClass)
          -
          +
          booleanis(java.lang.Class<?> typeClass)
          This method compares the passed class with the underlying data-type of this NDArray.
          - -
          default boolean
          -
          isBranch()
          -
          -
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
          -
          -
          boolean
          -
          isCase(Tensor<V> other)
          -
          +
          default booleanisBranch() +
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
          +
          booleanisCase(Tensor<V> other)
          This method name translates to the "in" keyword in Groovy! The same is true for the "contains" method in Kotlin.
          - -
          boolean
          -
          isDeleted()
          -
          -
          This will check if the MutateTensor.delete() method was previously called on this tensor.
          -
          -
          default boolean
          -
          isEmpty()
          -
          -
          A tensor is empty if it's Data storage is null.
          -
          -
          boolean
          -
          isIntermediate()
          -
          +
          booleanisDeleted() +
          This will check if the MutateTensor.delete() method was previously called on this tensor.
          +
          default booleanisEmpty() +
          A tensor is empty if it's Data storage is null.
          +
          booleanisIntermediate()
          Intermediate tensors are internal non-user tensors which may be eligible - for deletion when further consumed by a Function.
          - -
          default boolean
          -
          isLeave()
          -
          -
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
          -
          -
          default boolean
          -
          isOutsourced()
          -
          -
          Outsourced means that the tensor is stored on a Device implementation instance which is not the CPU.
          -
          -
          default boolean
          -
          isPartialSlice()
          -
          + for deletion when further consumed by a Function.
          +
          default booleanisLeave() +
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
          +
          default booleanisOutsourced() +
          Outsourced means that the tensor is stored on a Device implementation instance which is not the CPU.
          +
          default booleanisPartialSlice()
          If this nd-array is a partial slice of a parent nd-array then this method will yield true.
          - -
          default boolean
          -
          isShallowCopy()
          -
          +
          default booleanisShallowCopy()
          If this nd-array is a shallow copy of a parent nd-array then this method will yield true.
          - -
          default boolean
          -
          isSlice()
          -
          +
          default booleanisSlice()
          If this nd-array is a slice of a parent nd-array then this method will yield true.
          - -
          default boolean
          -
          isSliceParent()
          -
          +
          default booleanisSliceParent()
          If slices have been derived from this nd-array then it is a "slice parent".
          - -
          default boolean
          -
          isUndefined()
          -
          -
          A tensor is "undefined" if it has either no NDConfiguration implementation instance - or this instance does not have a shape set for this Tensor which is needed for +
          default booleanisUndefined() +
          A tensor is "undefined" if it has either no NDConfiguration implementation instance + or this instance does not have a shape set for this Tensor which is needed for a tensor to also have a rank and dimensionality...
          - -
          boolean
          -
          isVirtual()
          -
          +
          booleanisVirtual()
          A Virtual tensor is a tensor whose underlying data array is of size 1, holding only a single value.
          - -
          static <V> IterByOrIterFromOrAllTensor<V>
          -
          like(Tensor<V> template)
          -
          +
          static <V> IterByOrIterFromOrAllTensor<V>like(Tensor<V> template)
          Use this factory method to instantiate a new tensor with the same data type, shape - and memory location (Device instance) as the provided template tensor.
          - -
          default Tensor<V>
          -
          ln()
          -
          + and memory location (Device instance) as the provided template tensor.
          +
          default Tensor<V>ln()
          This method is a functionally identical to the following alternatives:
          - -
          default Tensor<V>
          -
          log10()
          -
          +
          default Tensor<V>log10()
          This method is a functionally identical to the following alternatives:
          - -
          default Tensor<V>
          -
          map(Function<V,V> mapper)
          -
          +
          default Tensor<V>map(java.util.function.Function<V,V> mapper)
          This method is a convenience method for mapping the items of this nd-array to another nd-array of the same type based on the provided lambda function, which will be applied to all items of this nd-array individually (element-wise).
          - -
          default <T> Tensor<T>
          -
          mapTo(Class<T> typeClass, - Function<V,T> mapper)
          -
          +
          default <T> Tensor<T>mapTo(java.lang.Class<T> typeClass, + java.util.function.Function<V,T> mapper)
          This is a convenience method for mapping a nd-array to a nd-array of new type based on a provided target item type and mapping lambda.
          - -
          default Tensor<V>
          -
          matMul(Tensor<V> other)
          -
          +
          default Tensor<V>matMul(Tensor<V> other)
          This will produce the matrix product of - two tensors with rank 2 (matrices), where the left operand is this Tensor + two tensors with rank 2 (matrices), where the left operand is this Tensor instance and the right operand is the argument passed to the method.
          - -
          default Tensor<V>
          -
          max()
          -
          +
          default Tensor<V>max()
          Calculate the max value of all values within this tensor and returns it in the form of a scalar tensor.
          - -
          default Tensor<V>
          -
          mean()
          -
          +
          default Tensor<V>mean()
          Calculate the mean value of all values within this tensor and returns it in the form of a scalar tensor.
          - -
          default Tensor<V>
          -
          min()
          -
          +
          default Tensor<V>min()
          Calculate the min value of all values within this tensor and returns it in the form of a scalar tensor.
          - -
          default Tensor<V>
          -
          minus(Tensor<V> other)
          -
          +
          default Tensor<V>minus(Tensor<V> other)
          Performs subtraction on two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method.
          - -
          default Tensor<V>
          -
          minus(V other)
          -
          -
          This method will create a new Tensor - with the provided item subtracted from all elements of this Tensor.
          -
          -
          default Tensor<V>
          -
          mod(int other)
          -
           
          -
          default Tensor<V>
          -
          mod(Tensor<V> other)
          -
          +
          default Tensor<V>minus(V other) +
          This method will create a new Tensor + with the provided item subtracted from all elements of this Tensor.
          +
          default Tensor<V>mod(int other) 
          default Tensor<V>mod(Tensor<V> other)
          Produces the modulus of two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method.
          - -
          default Tensor<V>
          -
          multiply(double value)
          -
           
          -
          default Tensor<V>
          -
          multiply(Tensor<V> other)
          -
          -
          This method is synonymous to the times(Tensor) method.
          -
          -
          default Tensor<V>
          -
          multiply(V other)
          -
           
          -
          default MutateTensor<V>
          -
          mut()
          -
          +
          default Tensor<V>multiply(double value) 
          default Tensor<V>multiply(Tensor<V> other) +
          This method is synonymous to the times(Tensor) method.
          +
          default Tensor<V>multiply(V other) 
          default MutateTensor<V>mut()
          This method exposes an API for mutating the state of this tensor.
          - -
          default Tensor<V>
          -
          neg()
          -
          +
          default Tensor<V>neg()
          This method is a functionally identical to the following alternatives:
          - -
          default Tensor<V>
          -
          negative()
          -
           
          -
          static Tensor<Object>
          -
          newInstance()
          -
          +
          default Tensor<V>negative() 
          static Tensor<java.lang.Object>newInstance()
          This static factory method creates and return a completely empty and undefined tensor which is void of any contents and meaning.
          - -
          static Tensor<Boolean>
          -
          of(boolean... value)
          -
          +
          static Tensor<java.lang.Boolean>of(boolean... value)
          Constructs a vector of booleans based on the provided array.
          - -
          static Tensor<Byte>
          -
          of(byte value)
          -
          +
          static Tensor<java.lang.Byte>of(byte... value)
          Constructs a vector of bytes based on the provided array.
          - -
          static Tensor<Byte>
          -
          of(byte... value)
          -
          -
          Constructs a vector of bytes based on the provided array.
          -
          -
          static Tensor<Double>
          -
          of(double value)
          -
           
          -
          static Tensor<Double>
          -
          of(double... value)
          -
          -
          Constructs a vector of doubles based on the provided array.
          -
          -
          static Tensor<Float>
          -
          of(float value)
          -
          -
          Constructs a vector of floats based on the provided array.
          -
          -
          static Tensor<Float>
          -
          of(float... value)
          -
          -
          Constructs a vector of floats based on the provided array.
          -
          -
          static Tensor<Integer>
          -
          of(int value)
          -
          -
          Constructs a vector of ints based on the provided array.
          -
          -
          static Tensor<Integer>
          -
          of(int... value)
          -
          -
          Constructs a vector of ints based on the provided array.
          -
          -
          static Tensor<Long>
          -
          of(long value)
          -
          -
          Constructs a vector of longs based on the provided array.
          -
          -
          static Tensor<Long>
          -
          of(long... value)
          -
          -
          Constructs a vector of longs based on the provided array.
          -
          -
          static Tensor<Short>
          -
          of(short value)
          -
          -
          Constructs a vector of shorts based on the provided array.
          -
          -
          static Tensor<Short>
          -
          of(short... value)
          -
          -
          Constructs a vector of shorts based on the provided array.
          -
          -
          static <T> Tensor<T>
          -
          of(Class<T> type, - List<Object> conf)
          -
          -
          This factory method will turn a list of values or nested lists of values into a Tensor +
          static Tensor<java.lang.Byte>of(byte value) 
          static <T> Tensor<T>of(java.lang.Class<T> type, + java.util.List<java.lang.Object> conf) +
          This factory method will turn a list of values or nested lists of values into a Tensor instance with the corresponding rank and shape and whose values are of the provided type.
          - -
          static <T> Tensor<T>
          -
          of(Class<T> type, - Shape shape, - Filler<T> filler)
          -
          +
          static <T> Tensor<T>of(java.lang.Class<T> type, + Shape shape, + Filler<T> filler)
          This factory method allows the creation of tensors with an additional initialization lambda for filling the underlying data array with desired values.
          - -
          static <V> WithShapeOrScalarOrVectorOnDevice<V>
          -
          of(Class<V> type)
          -
          +
          static <V> WithShapeOrScalarOrVectorOnDevice<V>of(java.lang.Class<V> type)
          This is the entry point to the fluent tensor builder API for building - Tensor instances in a readable and type safe fashion.
          - -
          static <V> Tensor<V>
          -
          of(Class<V> type, - List<Integer> shape, - Object data)
          -
          + Tensor instances in a readable and type safe fashion.
          +
          static <V> Tensor<V>of(java.lang.Class<V> type, + java.util.List<java.lang.Integer> shape, + java.util.List<V> data)
          Use this to construct and return a tensor of the specified type, shape and data object.
          - -
          static <V> Tensor<V>
          -
          of(Class<V> type, - List<Integer> shape, - List<V> data)
          -
          +
          static <V> Tensor<V>of(java.lang.Class<V> type, + java.util.List<java.lang.Integer> shape, + java.lang.Object data)
          Use this to construct and return a tensor of the specified type, shape and data object.
          - -
          static <V extends Number>
          Tensor<V>
          -
          of(Class<V> type, - Shape shape, - Number data)
          -
          +
          static <V> Tensor<V>of(java.lang.Class<V> valueType, + Shape shape, + Arg.Seed seed) +
          Use this to construct and return a seeded tensor of the specified type.
          +
          static <V> Tensor<V>of(java.lang.Class<V> type, + Shape shape, + java.util.List<V> data) +
          Use this to construct and return a tensor of the specified type, shape and list of items.
          +
          static <V extends java.lang.Number>
          Tensor<V>          		of(java.lang.Class<V> type, + Shape shape, + java.lang.Number data)
          Use this to construct and return a tensor of the specified type, shape and number.
          - -
          static <V> Tensor<V>
          -
          of(Class<V> type, - Shape shape, - Object data)
          -
          +
          static <V> Tensor<V>of(java.lang.Class<V> type, + Shape shape, + java.lang.Object data)
          Use this to construct and return a tensor of the specified type, shape and data object.
          - -
          static <V> Tensor<V>
          -
          of(Class<V> type, - Shape shape, - List<V> data)
          -
          -
          Use this to construct and return a tensor of the specified type, shape and list of items.
          -
          -
          static <V> Tensor<V>
          -
          of(Class<V> valueType, - Shape shape, - Arg.Seed seed)
          -
          -
          Use this to construct and return a seeded tensor of the specified type.
          -
          -
          static <T> Tensor<T>
          -
          of(Iterable<T> iterable)
          -
          -
          Constructs a vector of objects based on the provided iterable.
          -
          -
          static <T> Tensor<T>
          -
          of(Object... args)
          -
          -
          This static Tensor factory method tries to interpret the provided - arguments to create the instance the use might wants.
          -
          -
          static <V> Tensor<V>
          -
          of(String expression, - boolean doAD, - List<Tensor<V>> tensors)
          -
          -
          This method takes a list of tensors and a String expression describing - operations which ought to be applied to the tensors in said list.
          -
          -
          static <V> Tensor<V>
          -
          of(String expression, - boolean doAD, - Tensor<V>... tensors)
          -
          -
          This method takes an array of tensors and a String expression describing - operations which ought to be applied to the tensors in said array.
          -
          -
          static <V> Tensor<V>
          -
          of(String expression, - List<Tensor<V>> inputs)
          -
          -
          This factory method allows for the creation and execution of Function instances - without actually instantiating them manually, - where the result will then be returned by this factory method.
          -
          -
          static <T> Tensor<T>
          -
          of(String e1, - Tensor<T> a, - char o, - Tensor<T> b, - String e2)
          -
          -
          Use this to conveniently operate on 2 tensors.
          -
          -
          static <T> Tensor<T>
          -
          of(String e1, - Tensor<T> a, - String e2)
          -
          -
          Use this to conveniently operate on a tensor.
          -
          -
          static <T> Tensor<T>
          -
          of(String e1, - Tensor<T> a, - String e2, - Tensor<T> b, - String e3, - Tensor<T> c, - String e4)
          -
          -
          Use this to conveniently operate on 3 tensors.
          -
          -
          static <V> Tensor<V>
          -
          of(String expression, - Tensor<V> tensor)
          -
          -
          This method takes a tensor and a String expression describing - operations which ought to be applied to said tensor.
          -
          -
          static <V> Tensor<V>
          -
          of(String expression, - Tensor<V>... tensors)
          -
          -
          This method takes an array of tensors and a String expression describing - operations which ought to be applied to the tensors in said array.
          -
          -
          static <V extends Number>
          Tensor<V>
          -
          of(String expression, - V... inputs)
          -
          -
          This factory method allows for the creation and execution of Function instances - without actually instantiating them manually, - where the result will then be returned by this factory method.
          -
          -
          static Tensor<Double>
          -
          of(List<? extends Number> shape, - String seed)
          -
          -
          This factory method will create and return a Tensor instance - based on a list of Number instances whose rounded values will be interpreted as - the shape of this new Tensor instance and a seed which will serve - as a source of pseudo randomness to generate the values for the new instance.
          -
          -
          static <V> Tensor<V>
          -
          of(List<? extends Number> shape, - List<V> items)
          -
          -
          Creates a new Tensor instance based on a list of numbers representing the shape, - and a list of values representing the value of the resulting tensor.
          -
          -
          static <T> Tensor<T>
          -
          of(List<Integer> shape, - T item)
          -
          -
          This is a convenient factory method for creating Tensor instances for - values of type Tensor based on a list of integers - defining a shape made up of axes sizes as well as a scalar value of type Tensor - which will fill out the data array spanned by the provided shape information.
          -
          -
          static Tensor<Object>
          -
          of(List<Object> conf)
          -
          -
          This factory method will turn a list of values or nested lists of values into a Tensor - instance with the corresponding rank and shape.
          -
          -
          static <T> Tensor<T>
          -
          of(DataType<T> type, - List<Integer> shape, - Filler<T> filler)
          -
          +
          static <T> Tensor<T>of(DataType<T> type, + java.util.List<java.lang.Integer> shape, + Filler<T> filler)
          This factory method allows the creation of tensors with an additional initialization lambda for filling the underlying data array with desired values.
          - -
          static <T> Tensor<T>
          -
          of(DataType<T> type, - Shape shape, - Filler<T> filler)
          -
          +
          static <T> Tensor<T>of(DataType<T> type, + Shape shape, + Filler<T> filler)
          This factory method allows the creation of tensors with an additional initialization lambda for filling the underlying data array with desired values.
          - -
          static <V> Tensor<V>
          -
          of(DataType<V> dataType, - List<Integer> shape, - List<V> data)
          -
          +
          static <V extends N,N>
          Tensor<V>          		of(DataType<V> dataType, + Device<N> device, + Shape shape, + java.lang.Object data) +
          This factory method is among the most flexible and forgiving ways to create a Tensor instance.
          +
          static <V> Tensor<V>of(DataType<V> dataType, + java.util.List<java.lang.Integer> shape, + java.util.List<V> data)
          Use this to construct and return a tensor of the specified type, shape and data object.
          - -
          static <V extends N, -N>
          Tensor<V>
          -
          of(DataType<V> dataType, - Device<N> device, - Shape shape, - Object data)
          -
          -
          This factory method is among the most flexible and forgiving ways to create a Tensor instance.
          -
          -
          static <V> Tensor<V>
          -
          of(DataType<V> dataType, - NDConstructor ndConstructor, - Data<V> data)
          -
          +
          static <V> Tensor<V>of(DataType<V> dataType, + NDConstructor ndConstructor, + Data<V> data)
          This factory method a raw tensor constructor which will not perform any type checking or data conversion on the data provided to it.
          - -
          static <V> Tensor<V>
          -
          of(DataType<V> type, - Shape shape)
          -
          +
          static <V> Tensor<V>of(DataType<V> type, + Shape shape)
          Use this to construct and return a tensor of the specified type and shape.
          - -
          static <V> Tensor<V>
          -
          of(DataType<V> dataType, - Shape shape, - Object data)
          -
          -
          This factory method is among the most flexible and forgiving ways to create a Tensor instance.
          -
          -
          static <V> Tensor<V>
          -
          of(DataType<V> dataType, - Shape shape, - List<V> data)
          -
          +
          static <V> Tensor<V>of(DataType<V> dataType, + Shape shape, + java.util.List<V> data)
          Use this to construct and return a tensor of the specified type, shape and a list of items.
          - -
          static Tensor<Boolean>
          -
          of(Shape shape, - boolean[] values)
          -
          +
          static <V> Tensor<V>of(DataType<V> dataType, + Shape shape, + java.lang.Object data) +
          This factory method is among the most flexible and forgiving ways to create a Tensor instance.
          +
          static Tensor<java.lang.Double>of(double... value) +
          Constructs a vector of doubles based on the provided array.
          +
          static Tensor<java.lang.Double>of(double value) 
          static Tensor<java.lang.Float>of(float... value) +
          Constructs a vector of floats based on the provided array.
          +
          static Tensor<java.lang.Float>of(float value) 
          static Tensor<java.lang.Integer>of(int... value) +
          Constructs a vector of ints based on the provided array.
          +
          static Tensor<java.lang.Integer>of(int value) 
          static <T> Tensor<T>of(java.lang.Iterable<T> iterable) +
          Constructs a vector of objects based on the provided iterable.
          +
          static <V> Tensor<V>of(java.util.List<? extends java.lang.Number> shape, + java.util.List<V> items) +
          Creates a new Tensor instance based on a list of numbers representing the shape, + and a list of values representing the value of the resulting tensor.
          +
          static Tensor<java.lang.Double>of(java.util.List<? extends java.lang.Number> shape, + java.lang.String seed) +
          This factory method will create and return a Tensor instance + based on a list of Number instances whose rounded values will be interpreted as + the shape of this new Tensor instance and a seed which will serve + as a source of pseudo randomness to generate the values for the new instance.
          +
          static <T> Tensor<T>of(java.util.List<java.lang.Integer> shape, + T item) +
          This is a convenient factory method for creating Tensor instances for + values of type T based on a list of integers + defining a shape made up of axes sizes as well as a scalar value of type T + which will fill out the data array spanned by the provided shape information.
          +
          static Tensor<java.lang.Object>of(java.util.List<java.lang.Object> conf) +
          This factory method will turn a list of values or nested lists of values into a Tensor + instance with the corresponding rank and shape.
          +
          static Tensor<java.lang.Long>of(long... value) +
          Constructs a vector of longs based on the provided array.
          +
          static Tensor<java.lang.Long>of(long value) 
          static <T> Tensor<T>of(java.lang.Object... args) +
          This static Tensor factory method tries to interpret the provided + arguments to create the instance the use might wants.
          +
          static Tensor<java.lang.Boolean>of(Shape shape, + boolean[] values)
          Use this to construct and return a boolean tensor of the specified shape and initial values.
          - -
          static Tensor<Byte>
          -
          of(Shape shape, - byte[] values)
          -
          +
          static Tensor<java.lang.Byte>of(Shape shape, + byte[] values)
          Use this to construct and return a byte tensor of the specified shape and initial values.
          - -
          static Tensor<Double>
          -
          of(Shape shape, - double value)
          -
          +
          static <V> Tensor<V>of(Shape shape, + Data<V> data) +
          Use this to construct and return a tensor of the specified shape and data object.
          + This method is typically used like this:
          +
          static Tensor<java.lang.Double>of(Shape shape, + double value)
          Use this to construct and return a homogeneously populated double tensor of the specified shape.
          - -
          static Tensor<Double>
          -
          of(Shape shape, - double[] values)
          -
          +
          static Tensor<java.lang.Double>of(Shape shape, + double[] values)
          Use this to construct and return a double tensor of the specified shape and initial values.
          - -
          static Tensor<Float>
          -
          of(Shape shape, - float value)
          -
          +
          static Tensor<java.lang.Float>of(Shape shape, + float value)
          Use this to construct and return a homogeneously populated float tensor of the specified shape.
          - -
          static Tensor<Float>
          -
          of(Shape shape, - float[] values)
          -
          +
          static Tensor<java.lang.Float>of(Shape shape, + float[] values)
          Use this to construct and return a float tensor of the specified shape and initial values.
          - -
          static Tensor<Integer>
          -
          of(Shape shape, - int[] values)
          -
          +
          static Tensor<java.lang.Integer>of(Shape shape, + int[] values)
          Use this to construct and return an int tensor of the specified shape and initial values.
          - -
          static Tensor<Long>
          -
          of(Shape shape, - long[] values)
          -
          +
          static <V> Tensor<V>of(Shape shape, + java.util.List<V> items) +
          Creates a new Tensor instance based on a shape tuple of numbers representing the nd-array shape, + and a list of items representing the value of the resulting tensor.
          +
          static Tensor<java.lang.Long>of(Shape shape, + long[] values)
          Use this to construct and return a long tensor of the specified shape and initial values.
          - -
          static Tensor<Short>
          -
          of(Shape shape, - short[] values)
          -
          +
          static Tensor<java.lang.Short>of(Shape shape, + short[] values)
          Use this to construct and return a short tensor of the specified shape and initial values.
          - -
          static <V> Tensor<V>
          -
          of(Shape shape, - List<V> items)
          -
          -
          Creates a new Tensor instance based on a shape tuple of numbers representing the nd-array shape, - and a list of items representing the value of the resulting tensor.
          -
          -
          static <V> Tensor<V>
          -
          of(Shape shape, - Data<V> data)
          -
          -
          Use this to construct and return a tensor of the specified shape and data object.
          - This method is typically used like this:
          -
          -
          static <T> Tensor<T>
          -
          of(Shape shape, - T value)
          -
          -
          This is a convenient factory method for creating Tensor instances for - representing items of type Tensor.
          -
          -
          static <T> Tensor<T>
          -
          of(Tensor<T> a, - char o, - Tensor<T> b)
          -
          +
          static <T> Tensor<T>of(Shape shape, + T value) +
          This is a convenient factory method for creating Tensor instances for + representing items of type T.
          +
          static Tensor<java.lang.Short>of(short... value) +
          Constructs a vector of shorts based on the provided array.
          +
          static Tensor<java.lang.Short>of(short value) 
          static <V> Tensor<V>of(java.lang.String expression, + boolean doAD, + java.util.List<Tensor<V>> tensors) +
          This method takes a list of tensors and a String expression describing + operations which ought to be applied to the tensors in said list.
          +
          static <V> Tensor<V>of(java.lang.String expression, + boolean doAD, + Tensor<V>... tensors) +
          This method takes an array of tensors and a String expression describing + operations which ought to be applied to the tensors in said array.
          +
          static <V> Tensor<V>of(java.lang.String expression, + java.util.List<Tensor<V>> inputs) +
          This factory method allows for the creation and execution of Function instances + without actually instantiating them manually, + where the result will then be returned by this factory method.
          +
          static <T> Tensor<T>of(java.lang.String e1, + Tensor<T> a, + char o, + Tensor<T> b, + java.lang.String e2)
          Use this to conveniently operate on 2 tensors.
          - -
          static <T> Tensor<T>
          -
          of(Tensor<T> a, - char o1, - Tensor<T> b, - char o2, - Tensor<T> c)
          -
          +
          static <T> Tensor<T>of(java.lang.String e1, + Tensor<T> a, + java.lang.String e2) +
          Use this to conveniently operate on a tensor.
          +
          static <T> Tensor<T>of(java.lang.String e1, + Tensor<T> a, + java.lang.String e2, + Tensor<T> b, + java.lang.String e3, + Tensor<T> c, + java.lang.String e4)
          Use this to conveniently operate on 3 tensors.
          - -
          static <V> Tensor<V>
          -
          ofAny(Class<V> type, - Shape shape, - Object data)
          -
          +
          static <V> Tensor<V>of(java.lang.String expression, + Tensor<V>... tensors) +
          This method takes an array of tensors and a String expression describing + operations which ought to be applied to the tensors in said array.
          +
          static <V> Tensor<V>of(java.lang.String expression, + Tensor<V> tensor) +
          This method takes a tensor and a String expression describing + operations which ought to be applied to said tensor.
          +
          static <V extends java.lang.Number>
          Tensor<V>          		of(java.lang.String expression, + V... inputs) +
          This factory method allows for the creation and execution of Function instances + without actually instantiating them manually, + where the result will then be returned by this factory method.
          +
          static <T> Tensor<T>of(Tensor<T> a, + char o, + Tensor<T> b) +
          Use this to conveniently operate on 2 tensors.
          +
          static <T> Tensor<T>of(Tensor<T> a, + char o1, + Tensor<T> b, + char o2, + Tensor<T> c) +
          Use this to conveniently operate on 3 tensors.
          +
          static <V> Tensor<V>ofAny(java.lang.Class<V> type, + Shape shape, + java.lang.Object data)
          Use this to construct and return a tensor of the specified type, shape and data object.
          - -
          static WithShapeOrScalarOrVectorOnDevice<Byte>
          -
          ofBytes()
          -
          -
          This is a simple convenience method which is simply calling the of(Class) +
          static WithShapeOrScalarOrVectorOnDevice<java.lang.Byte>ofBytes() +
          This is a simple convenience method which is simply calling the of(Class) method like so: of(Byte.class).
          - -
          static WithShapeOrScalarOrVectorOnDevice<Double>
          -
          ofDoubles()
          -
          -
          This is a simple convenience method which is simply calling the of(Class) +
          static WithShapeOrScalarOrVectorOnDevice<java.lang.Double>ofDoubles() +
          This is a simple convenience method which is simply calling the of(Class) method like so: of(Double.class).
          - -
          static WithShapeOrScalarOrVectorOnDevice<Float>
          -
          ofFloats()
          -
          -
          This is a simple convenience method which is simply calling the of(Class) +
          static WithShapeOrScalarOrVectorOnDevice<java.lang.Float>ofFloats() +
          This is a simple convenience method which is simply calling the of(Class) method like so: of(Float.class).
          - -
          static WithShapeOrScalarOrVectorOnDevice<Integer>
          -
          ofInts()
          -
          -
          This is a simple convenience method which is simply calling the of(Class) +
          static WithShapeOrScalarOrVectorOnDevice<java.lang.Integer>ofInts() +
          This is a simple convenience method which is simply calling the of(Class) method like so: of(Integer.class).
          - -
          static <V> Tensor<V>
          -
          ofRandom(Class<V> valueTypeClass, - int... shape)
          -
          +
          static <V> Tensor<V>ofRandom(java.lang.Class<V> valueTypeClass, + int... shape)
          This factory method produces a randomly populated tensor of the provided type and shape using a hard coded default seed.
          - -
          static WithShapeOrScalarOrVectorOnDevice<Short>
          -
          ofShorts()
          -
          -
          This is a simple convenience method which is simply calling the of(Class) +
          static WithShapeOrScalarOrVectorOnDevice<java.lang.Short>ofShorts() +
          This is a simple convenience method which is simply calling the of(Class) method like so: of(Short.class).
          - -
          default Tensor<V>
          -
          permute(int... dims)
          -
          +
          default Tensor<V>permute(int... dims)
          Returns a view of the original tensor input with its dimensions permuted.
          Consider a 3-dimensional tensor x with shape (2×3×5), then calling x.permute(1, 0, 2) will return a 3-dimensional tensor of shape (3×2×5).
          - -
          default Tensor<V>
          -
          plus(Tensor<V> other)
          -
          +
          default Tensor<V>plus(Tensor<V> other)
          This method will produce the addition of two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method.
          - -
          default Tensor<V>
          -
          plus(V value)
          -
          -
          This method will create a new Tensor - with the provided double scalar added to all elements of this Tensor.
          -
          -
          default Tensor<V>
          -
          power(Tensor<V> other)
          -
          +
          default Tensor<V>plus(V value) +
          This method will create a new Tensor + with the provided double scalar added to all elements of this Tensor.
          +
          default Tensor<V>power(Tensor<V> other)
          This will produce the power of two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method.
          - -
          default Tensor<V>
          -
          power(V value)
          -
          +
          default Tensor<V>power(V value)
          Raises all items of this tensor to the power of the provided value.
          - -
          default Tensor<V>
          -
          relu()
          -
          +
          default Tensor<V>relu()
          This method is a functionally identical to the following alternatives:
          - -
          default Tensor<V>
          -
          rem(int other)
          -
          -
          This method is synonymous to the mod(int) method.
          -
          -
          default Tensor<V>
          -
          reshape(int... shape)
          -
          +
          default Tensor<V>rem(int other) +
          This method is synonymous to the mod(int) method.
          +
          default Tensor<V>reshape(int... shape)
          Returns a nd-array with the same data and number of elements as this nd-array, but with the specified shape.
          - -
          boolean
          -
          rqsGradient()
          -
          +
          booleanrqsGradient()
          This flag will indirectly trigger the activation of the autograd / auto-differentiation system of this library! If the flag is set to 'true' and the tensor is used for computation then - it will also receive gradients when the backward() method is being called + it will also receive gradients when the backward() method is being called on any descendant tensor within the computation graph.
          - -
          default Tensor<V>
          -
          set(OptimizerFactory optimizerFactory)
          -
          -
          Configures an Optimizer for this tensor based on the given OptimizerFactory - which will be used to create a new Optimizer instance specific to this tensor.
          -
          -
          Tensor<V>
          -
          setGradientApplyRequested(boolean applyRequested)
          -
          +
          default Tensor<V>set(OptimizerFactory optimizerFactory) +
          Configures an Optimizer for this tensor based on the given OptimizerFactory + which will be used to create a new Optimizer instance specific to this tensor.
          +
          Tensor<V>setGradientApplyRequested(boolean applyRequested)
          This flag works alongside two autograd features which can be enabled inside the library settings.
          - -
          Tensor<V>
          -
          setRqsGradient(boolean rqsGradient)
          -
          +
          Tensor<V>setRqsGradient(boolean rqsGradient)
          Setting this flag to true will tell the autograd system to accumulate gradients at this tensor.
          - -
          default Tensor<V>
          -
          shallowClone()
          -
           
          -
          default Tensor<V>
          -
          shallowCopy()
          -
          +
          default Tensor<V>shallowClone() 
          default Tensor<V>shallowCopy()
          This creates a copy where the underlying data is still the same.
          - -
          static <T> Collector<T,?,Tensor<T>>
          -
          shaped(int... shape)
          -
          +
          static <T> java.util.stream.Collector<T,?,Tensor<T>>shaped(int... shape)
          Returns a Collector that accumulates the input elements into a - new Tensor with the specified shape.
          - -
          static <T> Collector<T,?,Tensor<T>>
          -
          shaped(Shape shape)
          -
          + new Tensor with the specified shape.
          +
          static <T> java.util.stream.Collector<T,?,Tensor<T>>shaped(Shape shape)
          Returns a Collector that accumulates the input elements into a - new Tensor with the specified shape.
          - -
          default Tensor<V>
          -
          sig()
          -
          + new Tensor with the specified shape.
          +
          default Tensor<V>sig()
          This method is a functionally identical to the following alternatives:
          - -
          default Tensor<V>
          -
          sigmoid()
          -
           
          -
          default Tensor<V>
          -
          sin()
          -
          +
          default Tensor<V>sigmoid() 
          default Tensor<V>sin()
          This method is a functionally identical to the following alternatives:
          - -
          AxisOrGetTensor<V>
          -
          slice()
          -
          -
          This method returns a SliceBuilder instance exposing a simple builder API +
          AxisOrGetTensor<V>slice() +
          This method returns a SliceBuilder instance exposing a simple builder API which enables the configuration of a slice of the current nd-array via method chaining.
          - -
          default int
          -
          sliceCount()
          -
          +
          default intsliceCount()
          This method returns the number of slices which have been created from this nd-array.
          - -
          default Tensor<V>
          -
          softmax()
          -
           
          -
          default Tensor<V>
          -
          softmax(int axis)
          -
           
          -
          default Tensor<V>
          -
          softmax(int... axes)
          -
          +
          default Tensor<V>softmax() 
          default Tensor<V>softmax(int... axes)
          Calculates the softmax function along the specified axes.
          - -
          default Tensor<V>
          -
          softplus()
          -
          +
          default Tensor<V>softmax(int axis) 
          default Tensor<V>softplus()
          This method is a functionally identical to the following alternatives:
          - -
          default Tensor<V>
          -
          sqrt()
          -
          +
          default Tensor<V>sqrt()
          This method is a functionally identical to the following alternatives:
          - -
          default Tensor<V>
          -
          sum()
          -
          +
          default Tensor<V>sum()
          Calculate the sum value of all values within this tensor and returns it in the form of a scalar tensor.
          - -
          default Tensor<V>
          -
          sum(int axis)
          -
          +
          default Tensor<V>sum(int... axes)
          Calculate the sum value of all values - within this tensor along the specified axis and returns it + within this tensor along the specified axes and returns it in the form of a tensor.
          - -
          default Tensor<V>
          -
          sum(int... axes)
          -
          +
          default Tensor<V>sum(int axis)
          Calculate the sum value of all values - within this tensor along the specified axes and returns it + within this tensor along the specified axis and returns it in the form of a tensor.
          - -
          default Tensor<V>
          -
          T()
          -
          -
          Creates and returns a new Tensor instance which is a transposed twin of this instance.
          - This is a shorter alternative to the functionally identical getT() method.
          -
          -
          default Tensor<V>
          -
          tanh()
          -
          +
          default Tensor<V>T() +
          Creates and returns a new Tensor instance which is a transposed twin of this instance.
          + This is a shorter alternative to the functionally identical getT() method.
          +
          default Tensor<V>tanh()
          This method is a functionally identical to the following alternatives:
          - -
          default Tensor<V>
          -
          times(Tensor<V> other)
          -
          -
          This is a functionally identical synonym to the multiply(Tensor) method.
          -
          -
          default Tensor<V>
          -
          times(V other)
          -
           
          -
          default Tensor<V>
          -
          to(String deviceType)
          -
           
          -
          Tensor<V>
          -
          to(Device<?> device)
          -
          -
          This method takes a Device and tries to migrate the contents of this Tensor - instance to that Device!
          -
          -
          default String
          -
          toString(String conf)
          -
           
          -
          default String
          -
          toString(Consumer<NDPrintSettings> configurator)
          -
          +
          default Tensor<V>times(Tensor<V> other) +
          This is a functionally identical synonym to the multiply(Tensor) method.
          +
          default Tensor<V>times(V other) 
          Tensor<V>to(Device<?> device) +
          This method takes a Device and tries to migrate the contents of this Tensor + instance to that Device!
          +
          default Tensor<V>to(java.lang.String deviceType) 
          default java.lang.StringtoString(java.util.function.Consumer<NDPrintSettings> configurator)
          This allows you to provide a lambda which configures how this nd-array should be - converted to String instances.
          - -
          default String
          -
          toString(NDPrintSettings config)
          -
          -
          Use this to turn this nd-array into a String instance based on the provided - NDPrintSettings instance, which allows you to configure things + converted to String instances.
          +
          default java.lang.StringtoString(NDPrintSettings config) +
          Use this to turn this nd-array into a String instance based on the provided + NDPrintSettings instance, which allows you to configure things like the number of chars per entry, delimiters, the number of items per line, etc.
          - -
          default Tensor<V>
          -
          transpose(int dim1, - int dim2)
          -
          +
          default java.lang.StringtoString(java.lang.String conf) 
          default Tensor<V>transpose(int dim1, + int dim2)
          Returns a view of the original tensor input the targeted axes are swapped / transposed.
          - -
          default boolean
          -
          update(Component.OwnerChangeRequest<Tensor<V>> changeRequest)
          -
          -
          Important : Components of type Tensor are simply gradients! +
          default booleanupdate(Component.OwnerChangeRequest<Tensor<V>> changeRequest) +
          Important : Components of type Tensor are simply gradients! Currently, this method is used only to catch illegal arguments which is for example the case when trying to attach a gradient with a different shape...
          - -
          Tensor<V>
          -
          withLabel(String label)
          -
          -
          Tensor<V>
          -
          withLabels(String[]... labels)
          -
          -
          This method receives a nested String array which - ought to contain a label for the index of this nd-array.
          -
          -
          Tensor<V>
          -
          withLabels(List<List<Object>> labels)
          -
          -
          This method receives a nested String list which +
          Tensor<V>withLabel(java.lang.String label)
          Tensor<V>withLabels(java.util.List<java.util.List<java.lang.Object>> labels) +
          This method receives a nested String list which ought to contain a label for the index of this nd-array.
          - -
          Tensor<V>
          -
          withLabels(Map<Object,List<Object>> labels)
          -
          +
          Tensor<V>withLabels(java.util.Map<java.lang.Object,java.util.List<java.lang.Object>> labels)
          This method provides the ability to label not only the indices of the shape of this nd-array, but also the dimension of the shape.
          +
          Tensor<V>withLabels(java.lang.String[]... labels) +
          This method receives a nested String array which + ought to contain a label for the index of this nd-array.
          +
          default Tensor<V>xor(double value) +
          This method is a functionally identical synonym to the power(Tensor) method.
          +
          default Tensor<V>xor(Tensor<V> other) +
          This method is a functionally identical synonym to the power(Tensor) method.
          +
          + + +
            +
          • + + +

            Methods inherited from interface java.lang.Iterable

            +forEach, iterator, spliterator
            • +
          + +
          • +
        +
        • +
      -
      default Tensor<V>
      -
      xor(double value)
      -
      -
      This method is a functionally identical synonym to the power(Tensor) method.
      -
      -
      default Tensor<V>
      -
      xor(Tensor<V> other)
      -
      -
      This method is a functionally identical synonym to the power(Tensor) method.
      -
      -
    -
    -
    -
    -

    Methods inherited from interface neureka.common.composition.ComponentOwner

    -find, get, getAll, has, remove, set
    -
    -

    Methods inherited from interface java.lang.Iterable

    -forEach, iterator, spliterator
    -
    -

    Methods inherited from interface neureka.Nda

    -any, at, count, every, filter, flatMap, getDataAs, getDataAt, getItem, getItems, getItemsAs, getItemType, getLabel, getRawData, getRawItems, isFullSlice, item, item, item, items, itemType, label, maxItem, minItem, none, stream, toString
    -
    -

    Methods inherited from interface neureka.ndim.NDimensional

    -getNDConf, getRank, getShape, getSize, indexOfIndex, indexOfIndices, indicesMap, indicesOfIndex, offset, rank, shape, shape, size, spread, strides
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Method Details

        -
          -
        • -
          -

          newInstance

          -
          static Tensor<Object> newInstance()
          +
            +
          • + + +

            Method Detail

            + + + +
              +
            • +

              newInstance

              +
              static Tensor<java.lang.Object> newInstance()
              This static factory method creates and return a completely empty and undefined tensor which is void of any contents and meaning. - The use case for this would be to use the produced Tensor + The use case for this would be to use the produced Tensor instance as a target for an inline operations which fills the instance with an actual value.
              - An example of this approach would be to call the MutateTensor.putAt(List, Nda) method with an empty list as key. + An example of this approach would be to call the MutateTensor.putAt(List, Nda) method with an empty list as key. This will be interpreted as an inline copy of the contents of the - second parameter into this Tensor instance.
              -
              -
              Returns:
              -
              A new and completely empty / uninitialized Tensor instance.
              -
              -
          -
          • -
        • -
          -

          of

          -
          static <T> Tensor<T> of(Tensor<T> a, - char o, - Tensor<T> b)
          + second parameter into this Tensor instance.
      +
      +
      Returns:
      +
      A new and completely empty / uninitialized Tensor instance.
      +
      + +
    + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(Tensor<T> a,
+                        char o,
+                        Tensor<T> b)
      Use this to conveniently operate on 2 tensors. A simple example would be: of(a,'*',b).
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The value item type parameter for the involved tensors.
      -
      Parameters:
      +
      Parameters:
      a - The left operand.
      o - The operator, which may be '+', '-', '*'...
      b - The right operand.
      -
      Returns:
      +
      Returns:
      The result of the operation defined by the provided character.
      -
    -
  • -
    -

    of

    -
    static <T> Tensor<T> of(Tensor<T> a, - char o1, - Tensor<T> b, - char o2, - Tensor<T> c)
    + + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(Tensor<T> a,
+                        char o1,
+                        Tensor<T> b,
+                        char o2,
+                        Tensor<T> c)
      Use this to conveniently operate on 3 tensors. A simple example would be: of(a,'*',b,'+',c).
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The value item type parameter for the involved tensors.
      -
      Parameters:
      +
      Parameters:
      a - The first and left most operand.
      o1 - The first operator, which may be '+', '-', '*'...
      b - The second operand.
      o2 - The second operator, which may also be '+', '-', '*'...
      c - The third and last operand.
      -
      Returns:
      +
      Returns:
      The result of the operations defined by the 2 provided characters.
      -
    • -
  • -
    -

    of

    -
    static <T> Tensor<T> of(String e1, - Tensor<T> a, - String e2)
    + + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(java.lang.String e1,
+                        Tensor<T> a,
+                        java.lang.String e2)
      Use this to conveniently operate on a tensor. A simple example would be: of("sig(tanh(",a,"))").
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The value item type parameter for the involved tensor.
      -
      Parameters:
      +
      Parameters:
      e1 - The first part of the string expression defining how the provided tensor should be processed.
      a - The tensor which ought to be sent to whatever is defined by the provided expressions.
      e2 - The latter part of the expression defining how the provided tensor should be executed.
      -
      Returns:
      +
      Returns:
      The result of the operation(s) defined by the provided strings.
      -
    • -
  • -
    -

    of

    -
    static <T> Tensor<T> of(String e1, - Tensor<T> a, - char o, - Tensor<T> b, - String e2)
    + + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(java.lang.String e1,
+                        Tensor<T> a,
+                        char o,
+                        Tensor<T> b,
+                        java.lang.String e2)
      Use this to conveniently operate on 2 tensors. A simple example would be: of("relu(",a,'-',b,")*2").
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The value item type parameter for the involved tensor.
      -
      Parameters:
      +
      Parameters:
      e1 - The first part of the string expression defining how the provided tensor should be processed.
      a - The first tensor which ought to be sent to whatever function is defined by the provided expressions.
      o - An operator combining both a and b to form a result.
      b - The second tensor and right operand which ought to be sent to whatever function is defined by the provided expressions.
      e2 - The latter part of the expression defining how the provided tensor should be executed.
      -
      Returns:
      +
      Returns:
      The result of the operation(s) defined by the provided strings.
      -
    -
    • -
  • -
    -

    of

    -
    static <T> Tensor<T> of(String e1, - Tensor<T> a, - String e2, - Tensor<T> b, - String e3, - Tensor<T> c, - String e4)
    +
    • + + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(java.lang.String e1,
+                        Tensor<T> a,
+                        java.lang.String e2,
+                        Tensor<T> b,
+                        java.lang.String e3,
+                        Tensor<T> c,
+                        java.lang.String e4)
      Use this to conveniently operate on 3 tensors. A simple example would be: of("abs((",a,"-",b,") * ",c,")").
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter for the involved tensors.
      -
      Parameters:
      +
      Parameters:
      e1 - The first part of the expression which would typically be used to define a function name.
      a - The first argument.
      e2 - The second part of the expression, which might be an operation.
      @@ -1428,178 +1688,216 @@

      of

      e3 - The third part of the expression...
      c - The third argument.
      e4 - The last part of the expression which should syntactically match the other expression...
      -
      Returns:
      +
      Returns:
      The result of the calculation defined by the provided expressions and arguments.
      -
      • -
    • -
      -

      of

      -
      static <T> Tensor<T> of(Object... args)
      -
      This static Tensor factory method tries to interpret the provided +
    + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(java.lang.Object... args)
      +
      This static Tensor factory method tries to interpret the provided arguments to create the instance the use might wants.
      -
      -
      Parameters:
      +
      +
      Parameters:
      args - The arguments which ought to be interpreted.
      -
      Returns:
      -
      The result of the interpretation in the form of a Tensor instance of typ Object.
      +
      Returns:
      +
      The result of the interpretation in the form of a Tensor instance of typ Object.
      -
      • -
    • -
      -

      of

      -
      static <T> Tensor<T> of(Iterable<T> iterable)
      +
    + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(java.lang.Iterable<T> iterable)
      Constructs a vector of objects based on the provided iterable.
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      iterable - The iterable of objects from which a 1D nd-array ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D tensor of objects.
      -
      • -
    • -
      -

      of

      -
      static <T> Tensor<T> of(List<Integer> shape, - T item)
      -
      This is a convenient factory method for creating Tensor instances for - values of type Tensor based on a list of integers - defining a shape made up of axes sizes as well as a scalar value of type Tensor - which will fill out the data array spanned by the provided shape information.
      -
      -
      Parameters:
      -
      shape - A list of integers whose values ought to define the size of the axes of the shape of the new Tensor.
      -
      item - An object of type Tensor which will populate the data array of the new instance.
      -
      Returns:
      -
      A new Tensor instance for the generic type Tensor.
      -
      -
      -
      • -
    • -
      -

      of

      -
      static <T> Tensor<T> of(Shape shape, - T value)
      -
      This is a convenient factory method for creating Tensor instances for - representing items of type Tensor. The factory method - instantiates tensors based on a Shape tuple of integers - defining axes sizes, and a scalar item of type Tensor +
    + + + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(java.util.List<java.lang.Integer> shape,
+                        T item)
      +
      This is a convenient factory method for creating Tensor instances for + values of type T based on a list of integers + defining a shape made up of axes sizes as well as a scalar value of type T + which will fill out the data array spanned by the provided shape information.
      +
      +
      Parameters:
      +
      shape - A list of integers whose values ought to define the size of the axes of the shape of the new Tensor.
      +
      item - An object of type T which will populate the data array of the new instance.
      +
      Returns:
      +
      A new Tensor instance for the generic type T.
      +
      +
      • +
    + + + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(Shape shape,
+                        T value)
      +
      This is a convenient factory method for creating Tensor instances for + representing items of type T. The factory method + instantiates tensors based on a Shape tuple of integers + defining axes sizes, and a scalar item of type T which will fill out the data array spanned by the provided shape information. A simple usage example would be: 
      
      Tensor.of(Shape.of( 4, 3, 6 ), 42);
      -
      -
      Parameters:
      -
      shape - An immutable tuple of integers whose values ought to define the size of the axes of the shape of the new Tensor.
      -
      value - An object of type Tensor which will populate the data array of the new instance.
      -
      Returns:
      -
      A new Tensor instance for the generic type Tensor.
      -
      - -
      • -
    • -
      -

      of

      -
      static Tensor<Double> of(List<? extends Number> shape, - String seed)
      -
      This factory method will create and return a Tensor instance - based on a list of Number instances whose rounded values will be interpreted as - the shape of this new Tensor instance and a seed which will serve +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      +
      shape - An immutable tuple of integers whose values ought to define the size of the axes of the shape of the new Tensor.
      +
      value - An object of type T which will populate the data array of the new instance.
      +
      Returns:
      +
      A new Tensor instance for the generic type T.
      +
      +
      • +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Double> of(java.util.List<? extends java.lang.Number> shape,
+                                   java.lang.String seed)
      +
      This factory method will create and return a Tensor instance + based on a list of Number instances whose rounded values will be interpreted as + the shape of this new Tensor instance and a seed which will serve as a source of pseudo randomness to generate the values for the new instance.
      -
      -
      Parameters:
      -
      shape - A list of Number instances which will be interpreted as a shape array.
      -
      seed - A source of pseudo randomness for the Tensor instance created by this method.
      -
      Returns:
      -
      A new Tensor instance created based on a shape and a seed.
      -
      - -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(List<? extends Number> shape, - List<V> items)
      -
      Creates a new Tensor instance based on a list of numbers representing the shape, +
      +
      Parameters:
      +
      shape - A list of Number instances which will be interpreted as a shape array.
      +
      seed - A source of pseudo randomness for the Tensor instance created by this method.
      +
      Returns:
      +
      A new Tensor instance created based on a shape and a seed.
      +
      +
      • +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(java.util.List<? extends java.lang.Number> shape,
+                        java.util.List<V> items)
      +
      Creates a new Tensor instance based on a list of numbers representing the shape, and a list of values representing the value of the resulting tensor.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of the value list and returned tensor.
      -
      Parameters:
      -
      shape - A list of numbers whose integer values will be used to form the shape of the resulting Tensor.
      -
      items - A list of values which will be used to populate the data array of the resulting Tensor.
      -
      Returns:
      -
      A new Tensor instance constructed based on the provided shape and value list.
      -
      - -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(Shape shape, - List<V> items)
      -
      Creates a new Tensor instance based on a shape tuple of numbers representing the nd-array shape, +
      Parameters:
      +
      shape - A list of numbers whose integer values will be used to form the shape of the resulting Tensor.
      +
      items - A list of values which will be used to populate the data array of the resulting Tensor.
      +
      Returns:
      +
      A new Tensor instance constructed based on the provided shape and value list.
      + +
      • +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(Shape shape,
+                        java.util.List<V> items)
      +
      Creates a new Tensor instance based on a shape tuple of numbers representing the nd-array shape, and a list of items representing the value of the resulting tensor.
      A simple usage example would be: 
      
      Tensor.of(Shape.of( 2, 3, 4 ), myListOfItems);
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of the value list and returned tensor.
      -
      Parameters:
      -
      shape - A shape tuple of numbers whose integer values will be used to form the shape of the resulting Tensor.
      -
      items - A list of values which will be used to populate the data array of the resulting Tensor.
      -
      Returns:
      -
      A new Tensor instance constructed based on the provided shape and value list.
      -
      - -
      • -
    • -
      -

      of

      -
      static Tensor<Object> of(List<Object> conf)
      -
      This factory method will turn a list of values or nested lists of values into a Tensor +
      Parameters:
      +
      shape - A shape tuple of numbers whose integer values will be used to form the shape of the resulting Tensor.
      +
      items - A list of values which will be used to populate the data array of the resulting Tensor.
      +
      Returns:
      +
      A new Tensor instance constructed based on the provided shape and value list.
      + +
      • +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Object> of(java.util.List<java.lang.Object> conf)
      +
      This factory method will turn a list of values or nested lists of values into a Tensor instance with the corresponding rank and shape.
      -
      -
      Parameters:
      +
      +
      Parameters:
      conf - A list of either values or nested lists which are themselves either or.
      -
      Returns:
      -
      A new Tensor instance whose shape and data is based on the provided list structure.
      +
      Returns:
      +
      A new Tensor instance whose shape and data is based on the provided list structure.
      -
      • -
    • -
      -

      of

      -
      static <T> Tensor<T> of(Class<T> type, - List<Object> conf)
      -
      This factory method will turn a list of values or nested lists of values into a Tensor +
    + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(java.lang.Class<T> type,
+                        java.util.List<java.lang.Object> conf)
      +
      This factory method will turn a list of values or nested lists of values into a Tensor instance with the corresponding rank and shape and whose values are of the provided type.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter of the tensor returned by this factory method.
      -
      Parameters:
      +
      Parameters:
      type - The type of the tensor produced by this factory method.
      conf - A list of either values or nested lists which are themselves either or.
      -
      Returns:
      -
      A new Tensor instance whose shape and data is based on the provided list structure.
      +
      Returns:
      +
      A new Tensor instance whose shape and data is based on the provided list structure.
      -
      • -
    • -
      -

      of

      -
      static <V> WithShapeOrScalarOrVectorOnDevice<V> of(Class<V> type)
      +
    + + + + + + + +
      +
    • +

      ofDoubles

      +
      static WithShapeOrScalarOrVectorOnDevice<java.lang.Double> ofDoubles()
      +
      This is a simple convenience method which is simply calling the of(Class) method like so: of(Double.class). - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tensor builder API which in this case will lead to the creation + The returned WithShapeOrScalarOrVector is the next step in the + fluent Tensor builder API which in this case will lead to the creation of a tensor storing doubles.
      A simple usage example would be: 
      
@@ -1655,20 +1958,25 @@ 
      ofDoubles
      
            .withShape( 2, 3, 4 )
            .andFill( 5d, 3d, 5d )
      -
      -
      Returns:
      -
      The next step of the Tensor builder API which exposes methods for defining shapes.
      +
      +
      Specified by:
      +
      ofDoubles in interface Nda<V>
      +
      Returns:
      +
      The next step of the Tensor builder API which exposes methods for defining shapes.
      -
      • -
    • -
      -

      ofFloats

      -
      static WithShapeOrScalarOrVectorOnDevice<Float> ofFloats()
      -
      This is a simple convenience method which is simply calling the of(Class) +
    + + + +
      +
    • +

      ofFloats

      +
      static WithShapeOrScalarOrVectorOnDevice<java.lang.Float> ofFloats()
      +
      This is a simple convenience method which is simply calling the of(Class) method like so: of(Float.class). - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tensor builder API which in this case will lead to the creation + The returned WithShapeOrScalarOrVector is the next step in the + fluent Tensor builder API which in this case will lead to the creation of a tensor storing floats.
      A simple usage example would be: 
      
@@ -1676,663 +1984,810 @@ 
      ofFloats
      
            .withShape( 2, 3, 4 )
            .andFill( 5f, 7f, 11f )
      -
      -
      Returns:
      -
      The next step of the Tensor builder API which exposes methods for defining shapes.
      +
      +
      Specified by:
      +
      ofFloats in interface Nda<V>
      +
      Returns:
      +
      The next step of the Tensor builder API which exposes methods for defining shapes.
      -
      • -
    • -
      -

      ofInts

      -
      static WithShapeOrScalarOrVectorOnDevice<Integer> ofInts()
      -
      This is a simple convenience method which is simply calling the of(Class) +
    + + + +
      +
    • +

      ofInts

      +
      static WithShapeOrScalarOrVectorOnDevice<java.lang.Integer> ofInts()
      +
      This is a simple convenience method which is simply calling the of(Class) method like so: of(Integer.class). - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tensor builder API which in this case will lead to the creation + The returned WithShapeOrScalarOrVector is the next step in the + fluent Tensor builder API which in this case will lead to the creation of a tensor storing integers.
      -
      -
      Returns:
      -
      The next step of the Tensor builder API which exposes methods for defining shapes.
      +
      +
      Specified by:
      +
      ofInts in interface Nda<V>
      +
      Returns:
      +
      The next step of the Tensor builder API which exposes methods for defining shapes.
      -
      • -
    • -
      -

      ofShorts

      -
      static WithShapeOrScalarOrVectorOnDevice<Short> ofShorts()
      -
      This is a simple convenience method which is simply calling the of(Class) +
    + + + +
      +
    • +

      ofShorts

      +
      static WithShapeOrScalarOrVectorOnDevice<java.lang.Short> ofShorts()
      +
      This is a simple convenience method which is simply calling the of(Class) method like so: of(Short.class). - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tensor builder API which in this case will lead to the creation + The returned WithShapeOrScalarOrVector is the next step in the + fluent Tensor builder API which in this case will lead to the creation of a tensor storing shorts.
      -
      -
      Returns:
      -
      The next step of the Tensor builder API which exposes methods for defining shapes.
      +
      +
      Specified by:
      +
      ofShorts in interface Nda<V>
      +
      Returns:
      +
      The next step of the Tensor builder API which exposes methods for defining shapes.
      -
      • -
    • -
      -

      ofBytes

      -
      static WithShapeOrScalarOrVectorOnDevice<Byte> ofBytes()
      -
      This is a simple convenience method which is simply calling the of(Class) +
    + + + +
      +
    • +

      ofBytes

      +
      static WithShapeOrScalarOrVectorOnDevice<java.lang.Byte> ofBytes()
      +
      This is a simple convenience method which is simply calling the of(Class) method like so: of(Byte.class). - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tensor builder API which in this case will lead to the creation + The returned WithShapeOrScalarOrVector is the next step in the + fluent Tensor builder API which in this case will lead to the creation of a tensor storing bytes.
      -
      -
      Returns:
      -
      The next step of the Tensor builder API which exposes methods for defining shapes.
      +
      +
      Specified by:
      +
      ofBytes in interface Nda<V>
      +
      Returns:
      +
      The next step of the Tensor builder API which exposes methods for defining shapes.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Double> of(double... value)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Double> of(double... value)
      Constructs a vector of doubles based on the provided array.
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      value - The array of doubles from which a 1D tensor ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D tensor of doubles.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Double> of(double value)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Double> of(double value)
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      value - The scalar value which ought to be represented as tensor.
      -
      Returns:
      +
      Returns:
      A scalar double tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Float> of(float... value)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Float> of(float... value)
      Constructs a vector of floats based on the provided array.
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      value - The array of floats from which a 1D tensor ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D tensor of floats.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Float> of(float value)
      -
      Description copied from interface: Nda
      -
      Constructs a vector of floats based on the provided array.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Float> of(float value)
      +
      +
      Parameters:
      value - The scalar value which ought to be represented as tensor.
      -
      Returns:
      +
      Returns:
      A scalar float tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Byte> of(byte... value)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Byte> of(byte... value)
      Constructs a vector of bytes based on the provided array.
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      value - The array of bytes from which a 1D tensor ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D tensor of bytes.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Byte> of(byte value)
      -
      Description copied from interface: Nda
      -
      Constructs a vector of bytes based on the provided array.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Byte> of(byte value)
      +
      +
      Parameters:
      value - The scalar value which ought to be represented as tensor.
      -
      Returns:
      +
      Returns:
      A scalar byte tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Integer> of(int... value)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Integer> of(int... value)
      Constructs a vector of ints based on the provided array.
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      value - The array of ints from which a 1D tensor ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D tensor of ints.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Integer> of(int value)
      -
      Description copied from interface: Nda
      -
      Constructs a vector of ints based on the provided array.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Integer> of(int value)
      +
      +
      Parameters:
      value - The scalar value which ought to be represented as tensor.
      -
      Returns:
      +
      Returns:
      A scalar int tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Long> of(long... value)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Long> of(long... value)
      Constructs a vector of longs based on the provided array.
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      value - The array of longs from which a 1D tensor ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D tensor of longs.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Long> of(long value)
      -
      Description copied from interface: Nda
      -
      Constructs a vector of longs based on the provided array.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Long> of(long value)
      +
      +
      Parameters:
      value - The scalar value which ought to be represented as tensor.
      -
      Returns:
      +
      Returns:
      A scalar long tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Short> of(short... value)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Short> of(short... value)
      Constructs a vector of shorts based on the provided array.
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      value - The array of shorts from which a 1D tensor ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D tensor of shorts.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Short> of(short value)
      -
      Description copied from interface: Nda
      -
      Constructs a vector of shorts based on the provided array.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Short> of(short value)
      +
      +
      Parameters:
      value - The scalar value which ought to be represented as tensor.
      -
      Returns:
      +
      Returns:
      A scalar short tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Boolean> of(boolean... value)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Boolean> of(boolean... value)
      Constructs a vector of booleans based on the provided array.
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      value - The array of booleans from which a 1D tensor ought to be constructed.
      -
      Returns:
      +
      Returns:
      A vector / 1D tensor of shorts.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(Class<V> valueType, - Shape shape, - Arg.Seed seed)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(java.lang.Class<V> valueType,
+                        Shape shape,
+                        Arg.Seed seed)
      Use this to construct and return a seeded tensor of the specified type.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      valueType - The type class of the items stored by the resulting tensor.
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      -
      seed - An arbitrary String whose hash will be used to as a seed.
      -
      Returns:
      +
      seed - An arbitrary String whose hash will be used to as a seed.
      +
      Returns:
      A newly created and seeded tensor of the provided type and shape.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Double> of(Shape shape, - double value)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Double> of(Shape shape,
+                                   double value)
      Use this to construct and return a homogeneously populated double tensor of the specified shape.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      value - The value which ought to be used to populate the tensor homogeneously.
      -
      Returns:
      +
      Returns:
      A new tensor instance with the provided shape and initial value.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Double> of(Shape shape, - double[] values)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Double> of(Shape shape,
+                                   double[] values)
      Use this to construct and return a double tensor of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the tensor will be populated based on repeated iteration over the provided double array.
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Integer> of(Shape shape, - int[] values)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Integer> of(Shape shape,
+                                    int[] values)
      Use this to construct and return an int tensor of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the tensor will be populated based on repeated iteration over the provided int array.
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Byte> of(Shape shape, - byte[] values)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Byte> of(Shape shape,
+                                 byte[] values)
      Use this to construct and return a byte tensor of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the tensor will be populated based on repeated iteration over the provided byte array..
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Long> of(Shape shape, - long[] values)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Long> of(Shape shape,
+                                 long[] values)
      Use this to construct and return a long tensor of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the tensor will be populated based on repeated iteration over the provided long array..
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Short> of(Shape shape, - short[] values)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Short> of(Shape shape,
+                                  short[] values)
      Use this to construct and return a short tensor of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the tensor will be populated based on repeated iteration over the provided short array..
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Float> of(Shape shape, - float[] values)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Float> of(Shape shape,
+                                  float[] values)
      Use this to construct and return a float tensor of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the tensor will be populated based on repeated iteration over the provided float array..
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Float> of(Shape shape, - float value)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Float> of(Shape shape,
+                                  float value)
      Use this to construct and return a homogeneously populated float tensor of the specified shape.
      -
      -
      Parameters:
      +
      +
      Parameters:
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      value - The value which ought to be used to populate the tensor homogeneously.
      -
      Returns:
      +
      Returns:
      A new tensor instance with the provided shape and initial value.
      -
      • -
    • -
      -

      of

      -
      static Tensor<Boolean> of(Shape shape, - boolean[] values)
      +
    + + + +
      +
    • +

      of

      +
      static Tensor<java.lang.Boolean> of(Shape shape,
+                                    boolean[] values)
      Use this to construct and return a boolean tensor of the specified shape and initial values. The length of the provided array does not have to match the number of elements defined by the provided shape, the tensor will be populated based on repeated iteration over the provided boolean array..
      -
      -
      Parameters:
      +
      +
      Specified by:
      +
      of in interface Nda<V>
      +
      Parameters:
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      values - The values which ought to be used to populate the tensor.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(Shape shape, - Data<V> data)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(Shape shape,
+                        Data<V> data)
      Use this to construct and return a tensor of the specified shape and data object.
      This method is typically used like this:
      
       Tsr<Integer> tensor = Tsr.of( Shape.of(2,3), Data.of(1,2,3,4,5,6) );
      The resulting tensor will have the shape [2,3] and the values [1,2,3,4,5,6].
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      data - The data object which contains the values to be used to populate the tensor.
      -
      Returns:
      +
      Returns:
      A newly created tensor of the provided shape and data.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(DataType<V> type, - Shape shape)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(DataType<V> type,
+                        Shape shape)
      Use this to construct and return a tensor of the specified type and shape.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      type - The type of the items stored by the resulting tensor.
      shape - The shape of the resulting tensor consisting of any number of axis-sizes.
      -
      Returns:
      +
      Returns:
      A newly created tensor of the provided type and shape.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(Class<V> type, - Shape shape, - Object data)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(java.lang.Class<V> type,
+                        Shape shape,
+                        java.lang.Object data)
      Use this to construct and return a tensor of the specified type, shape and data object.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      type - The type of the items stored by the resulting tensor.
      shape - The shape of the resulting tensor consisting of an array of axis-sizes.
      data - The data object which will be used to populate the tensor.
      -
      Returns:
      +
      Returns:
      A newly created tensor of the provided type, shape and data.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(Class<V> type, - List<Integer> shape, - Object data)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(java.lang.Class<V> type,
+                        java.util.List<java.lang.Integer> shape,
+                        java.lang.Object data)
      Use this to construct and return a tensor of the specified type, shape and data object.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      type - The type of the items stored by the resulting tensor.
      shape - The shape of the resulting tensor consisting of list of axis-sizes.
      data - The data object which will be used to populate the tensor.
      -
      Returns:
      +
      Returns:
      A newly created tensor of the provided type, shape and data.
      -
      • -
    • -
      -

      of

      -
      static <V extends Number> Tensor<V> of(Class<V> type, - Shape shape, - Number data)
      +
    + + + +
      +
    • +

      of

      +
      static <V extends java.lang.Number> Tensor<V> of(java.lang.Class<V> type,
+                                                 Shape shape,
+                                                 java.lang.Number data)
      Use this to construct and return a tensor of the specified type, shape and number.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      type - The type of the items stored by the resulting tensor.
      shape - The shape of the resulting tensor consisting of a immutable tuple of axis-sizes.
      data - The data object which will be used to populate the tensor.
      -
      Returns:
      +
      Returns:
      A newly created tensor of the provided type, shape and data.
      -
      • -
    • -
      -

      ofAny

      -
      static <V> Tensor<V> ofAny(Class<V> type, - Shape shape, - Object data)
      +
    + + + +
      +
    • +

      ofAny

      +
      static <V> Tensor<V> ofAny(java.lang.Class<V> type,
+                           Shape shape,
+                           java.lang.Object data)
      Use this to construct and return a tensor of the specified type, shape and data object.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      type - The type of the items stored by the resulting tensor.
      shape - The shape of the resulting tensor consisting of a immutable tuple of axis-sizes.
      data - The data object which will be used to populate the tensor.
      -
      Returns:
      +
      Returns:
      A newly created tensor of the provided type, shape and data.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(Class<V> type, - List<Integer> shape, - List<V> data)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(java.lang.Class<V> type,
+                        java.util.List<java.lang.Integer> shape,
+                        java.util.List<V> data)
      Use this to construct and return a tensor of the specified type, shape and data object.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      type - The type of the items stored by the resulting tensor.
      shape - The shape of the resulting tensor consisting of a list of axis-sizes.
      data - The list of items which will be used to populate the tensor.
      -
      Returns:
      +
      Returns:
      A newly created tensor of the provided type, shape and data.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(Class<V> type, - Shape shape, - List<V> data)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(java.lang.Class<V> type,
+                        Shape shape,
+                        java.util.List<V> data)
      Use this to construct and return a tensor of the specified type, shape and list of items. Here a simple usage example: 
      
       Tsr<Float> tensor = Tsr.of( Float.class, Shape.of(2,3), List.of(1f,2f,3f,4f,5f,6f) );
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      type - The type of the items stored by the resulting tensor.
      shape - The shape of the resulting tensor consisting of an immutable tuple of axis-sizes.
      data - The list of items which will be used to populate the tensor.
      -
      Returns:
      +
      Returns:
      A newly created tensor of the provided type, shape and data.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(DataType<V> dataType, - List<Integer> shape, - List<V> data)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(DataType<V> dataType,
+                        java.util.List<java.lang.Integer> shape,
+                        java.util.List<V> data)
      Use this to construct and return a tensor of the specified type, shape and data object.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      dataType - The type of the items stored by the resulting tensor.
      shape - The shape of the resulting tensor consisting of a list of axis-sizes.
      data - The data object which will be used to populate the tensor.
      -
      Returns:
      +
      Returns:
      A newly created tensor of the provided type, shape and data.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(DataType<V> dataType, - Shape shape, - List<V> data)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(DataType<V> dataType,
+                        Shape shape,
+                        java.util.List<V> data)
      Use this to construct and return a tensor of the specified type, shape and a list of items. Here a simple usage example: 
      
       Tsr<Integer> tensor = Tsr.of( DataType.F32, Shape.of(2,3), List.of(1,2,3,4,5,6) );
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      +
      Parameters:
      dataType - The type of the items stored by the resulting tensor.
      shape - The shape of the resulting tensor consisting of an immutable tuple of axis-sizes.
      data - The list of items which will be used to populate the tensor.
      -
      Returns:
      +
      Returns:
      A newly created tensor of the provided type, shape and data.
      - -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(DataType<V> dataType, - Shape shape, - Object data)
      -
      This factory method is among the most flexible and forgiving ways to create a Tensor instance. - It receives a DataType for type safety and to ensure that the produced Tensor instance - will contain elements of the correct type, and a Shape tuple which stores the sizes of the axes that the - instance ought to possess, and finally it receives a data Object which can be anything ranging from - a List to an array or simply a single value which ought to fill out the entire Tensor.
      -
      -
      Parameters:
      -
      dataType - The data type of the data represented by Tensor instance created by this method.
      -
      shape - An immutable tuple of axis sizes describing the dimensionality of the Tensor created by this method.
      -
      data - The data for the Tensor that is about to be created, which can be a list, an array or scalar.
      -
      Returns:
      -
      A new Tensor instance of the specified type, shape and containing the provided data.
      -
      -
      -
      • -
    • -
      -

      of

      -
      static <V extends N, -N> Tensor<V> of(DataType<V> dataType, - Device<N> device, - Shape shape, - Object data)
      -
      This factory method is among the most flexible and forgiving ways to create a Tensor instance. - It receives a DataType for type safety and to ensure that the produced Tensor instance - will contain elements of the correct type, and a Shape tuple which stores the sizes of the axes that the - instance ought to possess, and finally it receives a data Object which can be anything ranging from - a List to an array or simply a single value which ought to fill out the entire Tensor.
      -
      -
      Parameters:
      -
      dataType - The data type of the data represented by Tensor instance created by this method.
      +
      • +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(DataType<V> dataType,
+                        Shape shape,
+                        java.lang.Object data)
      +
      This factory method is among the most flexible and forgiving ways to create a Tensor instance. + It receives a DataType for type safety and to ensure that the produced Tensor instance + will contain elements of the correct type, and a Shape tuple which stores the sizes of the axes that the + instance ought to possess, and finally it receives a data Object which can be anything ranging from + a List to an array or simply a single value which ought to fill out the entire Tensor.
      +
      +
      Parameters:
      +
      dataType - The data type of the data represented by Tensor instance created by this method.
      +
      shape - An immutable tuple of axis sizes describing the dimensionality of the Tensor created by this method.
      +
      data - The data for the Tensor that is about to be created, which can be a list, an array or scalar.
      +
      Returns:
      +
      A new Tensor instance of the specified type, shape and containing the provided data.
      +
      +
      • +
    + + + +
      +
    • +

      of

      +
      static <V extends N,N> Tensor<V> of(DataType<V> dataType,
+                                    Device<N> device,
+                                    Shape shape,
+                                    java.lang.Object data)
      +
      This factory method is among the most flexible and forgiving ways to create a Tensor instance. + It receives a DataType for type safety and to ensure that the produced Tensor instance + will contain elements of the correct type, and a Shape tuple which stores the sizes of the axes that the + instance ought to possess, and finally it receives a data Object which can be anything ranging from + a List to an array or simply a single value which ought to fill out the entire Tensor.
      +
      +
      Parameters:
      +
      dataType - The data type of the data represented by Tensor instance created by this method.
      device - The device on which the tensor will be stored.
      -
      shape - An immutable tuple of axis sizes describing the dimensionality of the Tensor created by this method.
      -
      data - The data for the Tensor that is about to be created, which can be a list, an array or scalar.
      -
      Returns:
      -
      A new Tensor instance of the specified type, shape and containing the provided data.
      -
      - -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(DataType<V> dataType, - NDConstructor ndConstructor, - Data<V> data)
      +
      shape - An immutable tuple of axis sizes describing the dimensionality of the Tensor created by this method.
      +
      data - The data for the Tensor that is about to be created, which can be a list, an array or scalar.
      +
      Returns:
      +
      A new Tensor instance of the specified type, shape and containing the provided data.
      + +
      • +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(DataType<V> dataType,
+                        NDConstructor ndConstructor,
+                        Data<V> data)
      This factory method a raw tensor constructor which will not perform any type checking or data conversion on the data provided to it. It constructs the tensor expecting that the data provided to it is of the correct type and an array of axis sizes.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of individual tensor items.
      -
      Parameters:
      -
      dataType - The data type of the data represented by Tensor instance created by this method.
      -
      ndConstructor - The NDConstructor that will be used to construct the Tensor instance.
      -
      data - The data for the Tensor that is about to be created, which is expected to be an array.
      -
      Returns:
      -
      A new Tensor instance of the specified type, shape and containing the provided data.
      -
      - -
      • -
    • -
      -

      of

      -
      static <T> Tensor<T> of(DataType<T> type, - List<Integer> shape, - Filler<T> filler)
      +
      Parameters:
      +
      dataType - The data type of the data represented by Tensor instance created by this method.
      +
      ndConstructor - The NDConstructor that will be used to construct the Tensor instance.
      +
      data - The data for the Tensor that is about to be created, which is expected to be an array.
      +
      Returns:
      +
      A new Tensor instance of the specified type, shape and containing the provided data.
      + +
      • +
    + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(DataType<T> type,
+                        java.util.List<java.lang.Integer> shape,
+                        Filler<T> filler)
      This factory method allows the creation of tensors with an additional initialization lambda for filling the underlying data array with desired values. Other than regular numeric types it is also possible to initialize the @@ -2341,22 +2796,25 @@

      of

      Therefore the constructor requires not only a shape as argument but also the data type which ought to be allocated as well as the initialization lambda which will be called iteratively.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter for the actual data array items.
      -
      Parameters:
      +
      Parameters:
      type - The data type this tensor ought to have.
      shape - The shape of this new tensor ought to have.
      filler - The lambda Object which ought to fill this tensor with the appropriate data.
      -
      • -
    • -
      -

      of

      -
      static <T> Tensor<T> of(DataType<T> type, - Shape shape, - Filler<T> filler)
      +
    + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(DataType<T> type,
+                        Shape shape,
+                        Filler<T> filler)
      This factory method allows the creation of tensors with an additional initialization lambda for filling the underlying data array with desired values. Other than regular numeric types it is also possible to initialize the @@ -2369,22 +2827,25 @@

      of

      
       Tsr<Double> tensor = Tsr.of( DataType.F64, Shape.of(2, 3), (i, j) -> i + j );
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter for the actual data array items.
      -
      Parameters:
      +
      Parameters:
      type - The data type this tensor ought to have.
      shape - The shape of this new tensor ought to have.
      filler - The lambda Object which ought to fill this tensor with the appropriate data.
      -
      • -
    • -
      -

      of

      -
      static <T> Tensor<T> of(Class<T> type, - Shape shape, - Filler<T> filler)
      +
    + + + +
      +
    • +

      of

      +
      static <T> Tensor<T> of(java.lang.Class<T> type,
+                        Shape shape,
+                        Filler<T> filler)
      This factory method allows the creation of tensors with an additional initialization lambda for filling the underlying data array with desired values. Other than regular numeric types it is also possible to initialize the @@ -2393,29 +2854,34 @@

      of

      Therefore the constructor requires not only a shape as argument but also the data type which ought to be allocated as well as the initialization lambda which will be called iteratively.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter for the actual data array items.
      -
      Parameters:
      +
      Parameters:
      type - The data type class the items of this tensor ought to have.
      shape - The shape of this new tensor ought to have.
      filler - The lambda Object which ought to fill this tensor with the appropriate data.
      -
      • -
    • -
      -

      of

      -
      @SafeVarargs -static <V extends Number> Tensor<V> of(String expression, - V... inputs)
      -
      This factory method allows for the creation and execution of Function instances +
    + + + + + +
      +
    • +

      of

      +
      @SafeVarargs
+static <V extends java.lang.Number> Tensor<V> of(java.lang.String expression,
+                                                              V... inputs)
      +
      This factory method allows for the creation and execution of Function instances without actually instantiating them manually, where the result will then be returned by this factory method.

      - The passed String will be parsed into a Function AST which will be cached + The passed String will be parsed into a Function AST which will be cached using the expression as key in case it will be used in future constructor calls like this one, or elsewhere... - The created / retrieved Function will then be called with the supplied input list + The created / retrieved Function will then be called with the supplied input list in order to trigger an execution. The result of which will be used for the population of the fields of this very instance.
      @@ -2423,25 +2889,28 @@

      of

      • 'var a = Tsr.of( "sin( I[0] ) / I[1]", 12f, -6.34f )'
      -
      -
      Parameters:
      +
      +
      Parameters:
      expression - A String which will be used for parsing a Function AST.
      inputs - An array of inputs which can be tensors or numeric types.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(String expression, - List<Tensor<V>> inputs)
      -
      This factory method allows for the creation and execution of Function instances +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(java.lang.String expression,
+                        java.util.List<Tensor<V>> inputs)
      +
      This factory method allows for the creation and execution of Function instances without actually instantiating them manually, where the result will then be returned by this factory method.

      - The passed String will be parsed into a Function AST which will be cached + The passed String will be parsed into a Function AST which will be cached using the expression as key in case it will be used in future constructor calls like this one, or elsewhere... - The created / retrieved Function will then be called with the supplied input list + The created / retrieved Function will then be called with the supplied input list in order to trigger an execution. The result of which will be used for the population of the fields of this very instance.
      @@ -2449,24 +2918,27 @@

      of

      • 'var a = Tsr.of( "sin( I[0] ) / I[1]", List.of(b, c) )'
      -
      -
      Parameters:
      +
      +
      Parameters:
      expression - A String which will be used for parsing a Function AST.
      inputs - A list of inputs which can be tensors or numeric types.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(String expression, - boolean doAD, - List<Tensor<V>> tensors)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(java.lang.String expression,
+                        boolean doAD,
+                        java.util.List<Tensor<V>> tensors)
      This method takes a list of tensors and a String expression describing operations which ought to be applied to the tensors in said list. It also receives a boolean flag which determines if the defined function should be executed with autograd enabled. - The provided expression will be parsed to a Function instance expecting as many inputs + The provided expression will be parsed to a Function instance expecting as many inputs as there are array entries, namely : "I[0]", "I[1]", "I[2]", ...
      An example would be the following :
        @@ -2475,25 +2947,28 @@

        of

        Which takes the tensor 'b' and 'c' and applies the function "f(x,y) = sin(x) / y" element-wise to produce a new tensor 'a'! Additionally, there is a helpful flag which allows one to specify if the - parsed Function instance emerging from the provided expression - should also allow the tracking of computations via a computation graph (GraphNode instances). + parsed Function instance emerging from the provided expression + should also allow the tracking of computations via a computation graph (GraphNode instances). This history tracking then enables auto-differentiation.
      -
      -
      Parameters:
      +
      +
      Parameters:
      expression - The expression describing operations applied to the provided tensors.
      doAD - A flag which when set to true commands the creation of a computation graph during operation execution.
      tensors - A list of tensors used as inputs to the Function instance parsed from the provided expression.
      -
      • -
    • -
      -

      of

      -
      static <V> Tensor<V> of(String expression, - Tensor<V> tensor)
      +
    + + + +
      +
    • +

      of

      +
      static <V> Tensor<V> of(java.lang.String expression,
+                        Tensor<V> tensor)
      This method takes a tensor and a String expression describing operations which ought to be applied to said tensor. - This expression will be parsed to a Function instance expecting one input, + This expression will be parsed to a Function instance expecting one input, namely : "I[0]"
      An example would be the following :
        @@ -2503,22 +2978,25 @@

        of

        Which takes the tensor 'b' and applies the function "f(x) = sin(x) * 2" element-wise to produce a new tensor 'a'!

      -
      -
      Parameters:
      +
      +
      Parameters:
      tensor - A tensor which serves as input to the Function instance parsed from the given expression.
      expression - The expression describing operations applied to the provided tensor.
      -
      • -
    • -
      -

      of

      -
      @SafeVarargs -static <V> Tensor<V> of(String expression, - Tensor<V>... tensors)
      +
    + + + +
      +
    • +

      of

      +
      @SafeVarargs
+static <V> Tensor<V> of(java.lang.String expression,
+                                     Tensor<V>... tensors)
      This method takes an array of tensors and a String expression describing operations which ought to be applied to the tensors in said array. - This expression will be parsed to a Function instance expecting as many inputs + This expression will be parsed to a Function instance expecting as many inputs as there are array entries, namely : "I[0]", "I[1]", "I[2]", ...
      An example would be the following :
        @@ -2527,25 +3005,28 @@

        of

        Which takes the tensor 'b' and 'c' and applies the function "f(x,y) = sin(x) / y" element-wise to produce a new tensor 'a'!
      -
      -
      Parameters:
      +
      +
      Parameters:
      expression - The expression describing operations applied to the provided tensors.
      tensors - An array of tensors used as inputs to the Function instance parsed from the provided expression.
      -
      • -
    • -
      -

      of

      -
      @SafeVarargs -static <V> Tensor<V> of(String expression, - boolean doAD, - Tensor<V>... tensors)
      +
    + + + +
      +
    • +

      of

      +
      @SafeVarargs
+static <V> Tensor<V> of(java.lang.String expression,
+                                     boolean doAD,
+                                     Tensor<V>... tensors)
      This method takes an array of tensors and a String expression describing operations which ought to be applied to the tensors in said array. It also receives a boolean flag which determines if the defined function should be executed with autograd enabled. - The provided expression will be parsed to a Function instance expecting as many inputs + The provided expression will be parsed to a Function instance expecting as many inputs as there are array entries, namely : "I[0]", "I[1]", "I[2]", ...
      An example would be the following :
        @@ -2554,163 +3035,192 @@

        of

        Which takes the tensor 'b' and 'c' and applies the function "f(x,y) = sin(x) / y" element-wise to produce a new tensor 'a'! Additionally, there is a helpful flag which allows one to specify if the - parsed Function instance emerging from the provided expression - should also allow the tracking of computations via a computation graph (GraphNode instances). + parsed Function instance emerging from the provided expression + should also allow the tracking of computations via a computation graph (GraphNode instances). This history tracking then enables auto-differentiation.
      -
      -
      Parameters:
      +
      +
      Parameters:
      expression - The expression describing operations applied to the provided tensors.
      doAD - A flag which when set to true commands the creation of a computation graph during operation execution.
      tensors - An array of tensors used as inputs to the Function instance parsed from the provided expression.
      -
      • -
    • -
      -

      ofRandom

      -
      static <V> Tensor<V> ofRandom(Class<V> valueTypeClass, - int... shape)
      +
    + + + +
      +
    • +

      ofRandom

      +
      static <V> Tensor<V> ofRandom(java.lang.Class<V> valueTypeClass,
+                              int... shape)
      This factory method produces a randomly populated tensor of the provided type and shape using a hard coded default seed. If the provided type class is representing a - floating point number type (like Double or Float) then the random numbers will + floating point number type (like Double or Float) then the random numbers will be gaussian ("normally") distributed values with mean 0.0 and standard deviation 1.0.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      V - The type parameter of the values stored by the returned tensor.
      -
      Parameters:
      +
      Parameters:
      valueTypeClass - The type class of the values stored by the returned tensor.
      shape - The shape of the tensor produced by this factory method.
      -
      Returns:
      +
      Returns:
      A randomly filled tensor of the provided type.
      -
      • -
    • -
      -

      like

      -
      static <V> IterByOrIterFromOrAllTensor<V> like(Tensor<V> template)
      +
    + + + +
      +
    • +

      like

      +
      static <V> IterByOrIterFromOrAllTensor<V> like(Tensor<V> template)
      Use this factory method to instantiate a new tensor with the same data type, shape - and memory location (Device instance) as the provided template tensor.
      -
      -
      Type Parameters:
      + and memory location (Device instance) as the provided template tensor. +
      +
      Type Parameters:
      V - The type parameter defining the value type of the provided as well as returned tensor.
      -
      Parameters:
      +
      Parameters:
      template - The template tensor whose type, shape and location should be taken to construct a new tensor.
      -
      Returns:
      -
      A new Tensor instance with the same data type, shape and memory location as the provided template.
      +
      Returns:
      +
      A new Tensor instance with the same data type, shape and memory location as the provided template.
      -
      • -
    • -
      -

      shaped

      -
      static <T> Collector<T,?,Tensor<T>> shaped(int... shape)
      +
    + + + +
      +
    • +

      shaped

      +
      static <T> java.util.stream.Collector<T,?,Tensor<T>> shaped(int... shape)
      Returns a Collector that accumulates the input elements into a - new Tensor with the specified shape.
      + new Tensor with the specified shape.
      Usage example : 
      
     var tensor = Stream.of( 1, 2, 3, 4, 5, 6 )
                       .collect( Tsr.shaped( 2, 3 ) );
      -
      -
      Type Parameters:
      +
      +
      Specified by:
      +
      shaped in interface Nda<V>
      +
      Type Parameters:
      T - the type of the input elements
      -
      Parameters:
      +
      Parameters:
      shape - The shape of the tensor to be returned.
      -
      Returns:
      +
      Returns:
      a Collector which collects all the input elements into a - Tensor, in encounter order.
      + Tensor, in encounter order.
      -
      • -
    • -
      -

      shaped

      -
      static <T> Collector<T,?,Tensor<T>> shaped(Shape shape)
      +
    + + + +
      +
    • +

      shaped

      +
      static <T> java.util.stream.Collector<T,?,Tensor<T>> shaped(Shape shape)
      Returns a Collector that accumulates the input elements into a - new Tensor with the specified shape.
      + new Tensor with the specified shape.
      Usage example : 
      
     var tensor = Stream.of( 1, 2, 3, 4, 5, 6 )
                       .collect( Tsr.shaped( otherTensor.shape() ) );
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - the type of the input elements
      -
      Parameters:
      +
      Parameters:
      shape - The shape of the tensor to be returned.
      -
      Returns:
      +
      Returns:
      a Collector which collects all the input elements into a - Tensor, in encounter order.
      + Tensor, in encounter order.
      -
      • -
    • -
      -

      setRqsGradient

      -
      Tensor<V> setRqsGradient(boolean rqsGradient)
      +
    + + + +
      +
    • +

      setRqsGradient

      +
      Tensor<V> setRqsGradient(boolean rqsGradient)
      Setting this flag to true will tell the autograd system to accumulate gradients at this tensor. This is achieved by allowing for the recording of a computation graph for when this tensor is used in any autograd supporting operations. This allows the autograd / auto-differentiation system to traverse said graph - for when the backward() method is called + for when the backward() method is called on any descendant tensor at the most recent end of the computation graph.
      -
      -
      Parameters:
      +
      +
      Parameters:
      rqsGradient - The truth value determining if this tensor ought to receive gradients via the built-in automatic backpropagation system.
      -
      Returns:
      -
      This very Tensor instance in order to enable method chaining.
      +
      Returns:
      +
      This very Tensor instance in order to enable method chaining.
      -
      • -
    • -
      -

      rqsGradient

      -
      boolean rqsGradient()
      +
    + + + +
      +
    • +

      rqsGradient

      +
      boolean rqsGradient()
      This flag will indirectly trigger the activation of the autograd / auto-differentiation system of this library! If the flag is set to 'true' and the tensor is used for computation then - it will also receive gradients when the backward() method is being called + it will also receive gradients when the backward() method is being called on any descendant tensor within the computation graph.
      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if this tensor ought to receive gradients via the built-in automatic backpropagation system.
      -
      • -
    • -
      -

      isIntermediate

      -
      boolean isIntermediate()
      +
    + + + +
      +
    • +

      isIntermediate

      +
      boolean isIntermediate()
      Intermediate tensors are internal non-user tensors which may be eligible - for deletion when further consumed by a Function. + for deletion when further consumed by a Function. For the casual user of Neureka, this flag should always be false!
      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if this tensor is not a user tensor but an internal - tensor which may be eligible for deletion by Functions consuming it.
      + tensor which may be eligible for deletion by Functions consuming it.
      -
      • -
    • -
      -

      isOutsourced

      -
      default boolean isOutsourced()
      -
      Outsourced means that the tensor is stored on a Device implementation instance which is not the CPU.
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      isOutsourced

      +
      default boolean isOutsourced()
      +
      Outsourced means that the tensor is stored on a Device implementation instance which is not the CPU.
      +
      +
      Returns:
      The truth value determining if the data of this tensor is not actually stored inside it in the form of a traditional primitive JVM array!
      -
      • -
    • -
      -

      isVirtual

      -
      boolean isVirtual()
      +
    + + + +
      +
    • +

      isVirtual

      +
      boolean isVirtual()
      A Virtual tensor is a tensor whose underlying data array is of size 1, holding only a single value.
      This only makes sense for homogeneously populated tensors. An example of such a tensor would be:
      @@ -2718,202 +3228,233 @@

      isVirtual

      The reasons for this feature is that it greatly improves performance in certain cases. In essence this feature is a form of lazy loading.

      - Use MutateTensor.setIsVirtual(boolean) to "actualize" a "virtual" tensor, and vise versa.

      -
      -
      Returns:
      + Use MutateTensor.setIsVirtual(boolean) to "actualize" a "virtual" tensor, and vise versa. +
      +
      Returns:
      The truth value determining if this tensor is "virtual" or "actual".
      -
      • -
    • -
      -

      isDeleted

      -
      boolean isDeleted()
      -
      This will check if the MutateTensor.delete() method was previously called on this tensor. +
    + + + +
      +
    • +

      isDeleted

      +
      boolean isDeleted()
      +
      This will check if the MutateTensor.delete() method was previously called on this tensor. This means that the tensor data was freed on every device and any references inside the tensor are null (to be eligable for garbage collection).
      -
      -
      Returns:
      -
      The truth value determining if the MutateTensor.delete() method has been called oin this instance.
      +
      +
      Returns:
      +
      The truth value determining if the MutateTensor.delete() method has been called oin this instance.
      -
      • -
    • -
      -

      isEmpty

      -
      default boolean isEmpty()
      -
      A tensor is empty if it's Data storage is null. +
    + + + +
      +
    • +

      isEmpty

      +
      default boolean isEmpty()
      +
      A tensor is empty if it's Data storage is null. This is true for deleted tensors or tensors which have not been initialized yet.
      -
      -
      Returns:
      -
      The truth value determining if this tensor has no Data.
      -
      - -
      • -
    • -
      -

      isUndefined

      -
      default boolean isUndefined()
      -
      A tensor is "undefined" if it has either no NDConfiguration implementation instance - or this instance does not have a shape set for this Tensor which is needed for +
      +
      Returns:
      +
      The truth value determining if this tensor has no Data.
      +
      +
      • +
    + + + +
      +
    • +

      isUndefined

      +
      default boolean isUndefined()
      +
      A tensor is "undefined" if it has either no NDConfiguration implementation instance + or this instance does not have a shape set for this Tensor which is needed for a tensor to also have a rank and dimensionality...
      -
      -
      Returns:
      -
      The truth value determining if this tensor has an NDConfiguration stored internally.
      +
      +
      Returns:
      +
      The truth value determining if this tensor has an NDConfiguration stored internally.
      -
      • -
    • -
      -

      isSlice

      -
      default boolean isSlice()
      +
    + + + +
      +
    • +

      isSlice

      +
      default boolean isSlice()
      If this nd-array is a slice of a parent nd-array then this method will yield true. - Slices can be created by calling the variations of the "Nda.getAt(int...)" method.
      -
      -
      Specified by:
      -
      isSlice in interface Nda<V>
      -
      Returns:
      + Slices can be created by calling the variations of the "Nda.getAt(int...)" method. +
      +
      Specified by:
      +
      isSlice in interface Nda<V>
      +
      Returns:
      The truth value determining if this nd-array is a slice of another nd-array.
      -
      See Also:
      -
      - -
      +
      See Also:
      +
      Nda.getAt(int...), +Nda.slice()
      -
      • -
    • -
      -

      isShallowCopy

      -
      default boolean isShallowCopy()
      +
    + + + +
      +
    • +

      isShallowCopy

      +
      default boolean isShallowCopy()
      If this nd-array is a shallow copy of a parent nd-array then this method will yield true. - Shallow copies can be created by calling the "Nda.shallowCopy()" method.
      -
      -
      Specified by:
      -
      isShallowCopy in interface Nda<V>
      -
      Returns:
      + Shallow copies can be created by calling the "Nda.shallowCopy()" method. +
      +
      Specified by:
      +
      isShallowCopy in interface Nda<V>
      +
      Returns:
      The truth value determining if this nd-array is a shallow copy of another nd-array.
      -
      See Also:
      -
      - -
      +
      See Also:
      +
      Nda.shallowCopy()
      -
      • -
    • -
      -

      isPartialSlice

      -
      default boolean isPartialSlice()
      +
    + + + +
      +
    • +

      isPartialSlice

      +
      default boolean isPartialSlice()
      If this nd-array is a partial slice of a parent nd-array then this method will yield true. A partial slice is a slice which does not view all the parents items. - Partial slices can be created by calling the variations of the "Nda.getAt(int...)" method. - This is the inverse of Nda.isFullSlice().
      -
      -
      Specified by:
      -
      isPartialSlice in interface Nda<V>
      -
      Returns:
      + Partial slices can be created by calling the variations of the "Nda.getAt(int...)" method. + This is the inverse of Nda.isFullSlice(). +
      +
      Specified by:
      +
      isPartialSlice in interface Nda<V>
      +
      Returns:
      The truth value determining if this nd-array is a partial slice of another nd-array.
      -
      • -
    • -
      -

      sliceCount

      -
      default int sliceCount()
      +
    + + + +
      +
    • +

      sliceCount

      +
      default int sliceCount()
      This method returns the number of slices which have been created from this nd-array. - It does so by accessing the Relation component if present + It does so by accessing the Relation component if present which internally keeps track of slices via weak references.
      -
      -
      Specified by:
      -
      sliceCount in interface Nda<V>
      -
      Returns:
      +
      +
      Specified by:
      +
      sliceCount in interface Nda<V>
      +
      Returns:
      The number of slices derived from this nd-array.
      -
      • -
    • -
      -

      isSliceParent

      -
      default boolean isSliceParent()
      +
    + + + +
      +
    • +

      isSliceParent

      +
      default boolean isSliceParent()
      If slices have been derived from this nd-array then it is a "slice parent". This is what this method will determine, in which case, it will return true.
      -
      -
      Specified by:
      -
      isSliceParent in interface Nda<V>
      -
      Returns:
      +
      +
      Specified by:
      +
      isSliceParent in interface Nda<V>
      +
      Returns:
      The truth value determining if slices have been derived from this nd-array.
      -
      • -
    • -
      -

      belongsToGraph

      -
      default boolean belongsToGraph()
      -
      Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. +
    + + + +
      +
    • +

      belongsToGraph

      +
      default boolean belongsToGraph()
      +
      Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. This is because autograd requires recording a computation graph for back-prop traversal. - This autograd system however, will only be triggered by Function implementations which - are not "detached", meaning they have their "Function.isDoingAD()" flags set to true!
      - Detached functions (like those pre-instantiated in Function.Detached.*) will not attach GraphNode + This autograd system however, will only be triggered by Function implementations which + are not "detached", meaning they have their "Function.isDoingAD()" flags set to true!
      + Detached functions (like those pre-instantiated in Function.Detached.*) will not attach GraphNode instances to involved tensors which will prevent the formation of a computation graph.
      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if this tensor belongs to a recorded computation graph.
      -
      • -
    • -
      -

      isLeave

      -
      default boolean isLeave()
      -
      Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. +
    + + + +
      +
    • +

      isLeave

      +
      default boolean isLeave()
      +
      Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. This is because autograd requires recording a computation graph for back-prop traversal. - This autograd system however, will only be triggered by Function implementations which - are not "detached", meaning they have their "Function.isDoingAD()" flags set to true!
      + This autograd system however, will only be triggered by Function implementations which + are not "detached", meaning they have their "Function.isDoingAD()" flags set to true!
      A tensor is a leave if it is attached to a computation graph in which it is not an intermediate / branch node but input / branch node.
      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if this tensor is attached to a computation graph as leave node.
      -
      • -
    • -
      -

      isBranch

      -
      default boolean isBranch()
      -
      Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. +
    + + + +
      +
    • +

      isBranch

      +
      default boolean isBranch()
      +
      Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. This is because autograd requires recording a computation graph for back-prop traversal. - This autograd system however, will only be triggered by Function implementations which - are not "detached", meaning they have their "Function.isDoingAD()" flags set to true!
      + This autograd system however, will only be triggered by Function implementations which + are not "detached", meaning they have their "Function.isDoingAD()" flags set to true!
      A tensor is a branch if it is attached to a computation graph in which it is not an input / leave node but intermediate / branch node.
      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if this tensor is attached to a computation graph as branch node.
      -
      • -
    • -
      -

      hasGradient

      -
      default boolean hasGradient()
      +
    + + + +
      +
    • +

      hasGradient

      +
      default boolean hasGradient()
      Tensors can be components of other tensors which makes the implicitly their gradients.
      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if this tensor has another tensor attached to it (which is its gradient).
      -
      • -
    • -
      -

      gradientApplyRequested

      -
      boolean gradientApplyRequested()
      +
    + + + +
      +
    • +

      gradientApplyRequested

      +
      boolean gradientApplyRequested()
      This flag works alongside two autograd features which can be enabled inside the library settings. They will come into effect when flipping their feature flags,
      namely: 'isApplyingGradientWhenRequested' and 'isApplyingGradientWhenTensorIsUsed'
      @@ -2926,16 +3467,19 @@

      gradientApplyRequested

      This signal comes in the form of a "request" flag which marks a tensor as allowed to be updated by its gradient.

      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if the application of the gradient of this tensor is requested.
      -
      • -
    • -
      -

      setGradientApplyRequested

      -
      Tensor<V> setGradientApplyRequested(boolean applyRequested)
      +
    + + + +
      +
    • +

      setGradientApplyRequested

      +
      Tensor<V> setGradientApplyRequested(boolean applyRequested)
      This flag works alongside two autograd features which can be enabled inside the library settings. They will come into effect when flipping their feature flags,
      namely: 'isApplyingGradientWhenRequested' and 'isApplyingGradientWhenTensorIsUsed'
      @@ -2948,77 +3492,92 @@

      setGradientApplyRequested

      This signal comes in the form of a "request" flag which marks a tensor as allowed to be updated by its gradient.

      -
      -
      Parameters:
      +
      +
      Parameters:
      applyRequested - The truth value determining if the application of the gradient of this tensor is requested.
      -
      Returns:
      +
      Returns:
      This very tensor instance in order to enable method chaining.
      -
      • -
    • -
      -

      update

      -
      default boolean update(Component.OwnerChangeRequest<Tensor<V>> changeRequest)
      -
      Important : Components of type Tensor are simply gradients! +
    + + + +
      +
    • +

      update

      +
      default boolean update(Component.OwnerChangeRequest<Tensor<V>> changeRequest)
      +
      Important : Components of type Tensor are simply gradients! Currently, this method is used only to catch illegal arguments which is for example the case when trying to attach a gradient with a different shape... (Otherwise the gradient tensor "does not mind" an owner change...)
      -
      -
      Specified by:
      -
      update in interface Component<V>
      -
      Parameters:
      -
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      -
      Returns:
      +
      +
      Specified by:
      +
      update in interface Component<Tensor<V>>
      +
      Parameters:
      +
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      +
      Returns:
      The truth value determining if the state change should be aborted or not.
      -
      • -
    • -
      -

      getVersion

      -
      int getVersion()
      +
    + + + +
      +
    • +

      getVersion

      +
      int getVersion()
      The version number is tracking how often this tensor has been mutated. This is especially useful for checking the correcting of auto-grad!
      -
      • -
    • -
      -

      getDataType

      -
      DataType<V> getDataType()
      -
      This method returns the DataType instance of this Tensor, which is +
    + + + +
      +
    • +

      getDataType

      +
      DataType<V> getDataType()
      +
      This method returns the DataType instance of this Tensor, which is a wrapper object for the actual type class representing the value items stored inside the underlying data array of this tensor.
      -
      -
      Returns:
      -
      The DataType instance of this Tensor storing important type information.
      -
      - -
      • -
    • -
      -

      getRepresentativeItemClass

      -
      Class<?> getRepresentativeItemClass()
      -
      The Class returned by this method is the representative Class of the - value items of a concrete AbstractNda but not necessarily the actual Class of +
      +
      Returns:
      +
      The DataType instance of this Tensor storing important type information.
      +
      +
      • +
    + + + +
      +
    • +

      getRepresentativeItemClass

      +
      java.lang.Class<?> getRepresentativeItemClass()
      +
      The Class returned by this method is the representative Class of the + value items of a concrete AbstractNda but not necessarily the actual Class of a given value item, this is especially true for numeric types, which are represented by - implementations of the NumericType interface.
      - For example in the case of a tensor of type Double, this method would - return F64 which is the representative class of Double.
      - Calling the Nda.getItemType() method instead of this method would return the actual value - type class, namely: Double.
      -
      -
      Returns:
      + implementations of the NumericType interface.
      + For example in the case of a tensor of type Double, this method would + return F64 which is the representative class of Double.
      + Calling the Nda.getItemType() method instead of this method would return the actual value + type class, namely: Double. +
      +
      Returns:
      The representative type class of individual value items within this concrete AbstractNda - extension instance which might also be subclasses of the NumericType interface + extension instance which might also be subclasses of the NumericType interface to model unsigned types or other JVM foreign numeric concepts.
      -
      • -
    • -
      -

      getMut

      -
      MutateTensor<V> getMut()
      +
    + + + +
      +
    • +

      getMut

      +
      MutateTensor<V> getMut()
      This method exposes an API for mutating the state of this tensor. The usage of methods exposed by this API is generally discouraged because the exposed state can easily lead to broken tensors and exceptional situations!
      @@ -3028,18 +3587,21 @@

      getMut

      performance is critical!
      (Like in custom backend extensions for example)
      -
      -
      Specified by:
      -
      getMut in interface Nda<V>
      -
      Returns:
      +
      +
      Specified by:
      +
      getMut in interface Nda<V>
      +
      Returns:
      The unsafe API exposes methods for mutating the state of the tensor.
      -
      • -
    • -
      -

      mut

      -
      default MutateTensor<V> mut()
      +
    + + + +
      +
    • +

      mut

      +
      default MutateTensor<V> mut()
      This method exposes an API for mutating the state of this tensor. The usage of methods exposed by this API is generally discouraged because the exposed state can easily lead to broken tensors and exceptional situations!
      @@ -3049,18 +3611,21 @@

      mut

      performance is critical!
      (Like custom backend extensions for example)
      -
      -
      Specified by:
      -
      mut in interface Nda<V>
      -
      Returns:
      +
      +
      Specified by:
      +
      mut in interface Nda<V>
      +
      Returns:
      The unsafe API exposes methods for mutating the state of the tensor.
      -
      • -
    • -
      -

      reshape

      -
      default Tensor<V> reshape(int... shape)
      +
    + + + +
      +
    • +

      reshape

      +
      default Tensor<V> reshape(int... shape)
      Returns a nd-array with the same data and number of elements as this nd-array, but with the specified shape. When possible, the returned nd-array will be a view of this nd-array. @@ -3070,124 +3635,145 @@

      reshape

      Keep in mind that the new shape must have the same number of elements as the original shape.

      This operation supports autograd.
      -
      -
      Specified by:
      -
      reshape in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      reshape in interface Nda<V>
      +
      Parameters:
      shape - The new shape of the returned nd-array.
      -
      Returns:
      +
      Returns:
      A new nd-array instance with the same underlying data (~shallow copy) but with a different shape.
      -
      • -
    • -
      -

      permute

      -
      default Tensor<V> permute(int... dims)
      +
    + + + +
      +
    • +

      permute

      +
      default Tensor<V> permute(int... dims)
      Returns a view of the original tensor input with its dimensions permuted.
      Consider a 3-dimensional tensor x with shape (2×3×5), then calling x.permute(1, 0, 2) will return a 3-dimensional tensor of shape (3×2×5).
      -
      -
      Specified by:
      -
      permute in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      permute in interface Nda<V>
      +
      Parameters:
      dims - The desired ordering of dimensions
      -
      Returns:
      +
      Returns:
      A new nd-array instance with the same underlying data (~shallow copy) but with a different shape.
      -
      • -
    • -
      -

      transpose

      -
      default Tensor<V> transpose(int dim1, - int dim2)
      +
    + + + +
      +
    • +

      transpose

      +
      default Tensor<V> transpose(int dim1,
+                            int dim2)
      Returns a view of the original tensor input the targeted axes are swapped / transposed.
      -
      -
      Specified by:
      -
      transpose in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      transpose in interface Nda<V>
      +
      Parameters:
      dim1 - The first dimension to be swapped.
      dim2 - The second dimension to be swapped.
      -
      Returns:
      +
      Returns:
      A new nd-array instance with the same underlying data (~shallow copy) but with a different shape.
      - -
      • -
    • -
      -

      to

      -
      Tensor<V> to(Device<?> device)
      -
      This method takes a Device and tries to migrate the contents of this Tensor - instance to that Device!
      -
      -
      Parameters:
      -
      device - The Device which should host this Tensor as well as be added to its components list.
      -
      Returns:
      +
      • +
    + + + +
      +
    • +

      to

      +
      Tensor<V> to(Device<?> device)
      +
      This method takes a Device and tries to migrate the contents of this Tensor + instance to that Device!
      +
      +
      Parameters:
      +
      device - The Device which should host this Tensor as well as be added to its components list.
      +
      Returns:
      This very class to enable method chaining.
      -
      • -
    • -
      -

      to

      -
      default Tensor<V> to(String deviceType)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      to

      +
      default Tensor<V> to(java.lang.String deviceType)
      +
      +
      Parameters:
      deviceType - A search key identifying the device onto which this tensor should be stored.
      -
      Returns:
      +
      Returns:
      This very tensor instance in order to enable method chaining.
      -
      • -
    • -
      -

      set

      -
      default Tensor<V> set(OptimizerFactory optimizerFactory)
      -
      Configures an Optimizer for this tensor based on the given OptimizerFactory - which will be used to create a new Optimizer instance specific to this tensor. - The Optimizer instance will be attached to this tensor as a component - and then called to perform the actual optimization when the applyGradient() method is called. +
    + + + +
      +
    • +

      set

      +
      default Tensor<V> set(OptimizerFactory optimizerFactory)
      +
      Configures an Optimizer for this tensor based on the given OptimizerFactory + which will be used to create a new Optimizer instance specific to this tensor. + The Optimizer instance will be attached to this tensor as a component + and then called to perform the actual optimization when the applyGradient() method is called.

      Here a simple example of how to use this method: 

      
   var t = Tsr.of( 1.0, 2.0, 3.0 ).set( Optimizer.ADAM );

      - As you can see, the Optimizer interface exposes various types of popular + As you can see, the Optimizer interface exposes various types of popular optimization algorithm factories which can be used to quickly and conveniently create - an Optimizer instance for a particular tensor.

      -
      -
      Parameters:
      -
      optimizerFactory - The OptimizerFactory which will be used to create a new Optimizer instance.
      -
      Returns:
      + an Optimizer instance for a particular tensor. +
      +
      Parameters:
      +
      optimizerFactory - The OptimizerFactory which will be used to create a new Optimizer instance.
      +
      Returns:
      This tensor instance to allow for method chaining.
      -
      • -
    • -
      -

      backward

      -
      default Tensor<V> backward(Tensor<V> error)
      -
      Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. +
    + + + +
      +
    • +

      backward

      +
      default Tensor<V> backward(Tensor<V> error)
      +
      Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. This is because autograd requires recording a computation graph for back-prop traversal. If this tensor is part of a computation graph then this method will traverse an error backward in the recorded history towards tensors which require the accumulation of gradients.
      -
      -
      Parameters:
      +
      +
      Parameters:
      error - A tensor which is back-propagated to gradients. Must match the size og this tensor.
      -
      Returns:
      +
      Returns:
      This tensor, to allow for method chaining.
      -
      • -
    • -
      -

      backward

      -
      default Tensor<V> backward(double value)
      -
      Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. +
    + + + +
      +
    • +

      backward

      +
      default Tensor<V> backward(double value)
      +
      Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. This is because autograd requires recording a computation graph for back-prop traversal. If this tensor is part of a computation graph then this method will traverse an error backward in the recorded history towards tensors which require @@ -3197,21 +3783,24 @@

      backward

      turns it into a matching tensor ( with the same shape) which will then be back-propagated through the recorded computation graph.
      -
      -
      Parameters:
      +
      +
      Parameters:
      value - A scalar which is back-propagated to gradients. Must match the size og this tensor.
      -
      Returns:
      +
      Returns:
      The tensor, to allow for method chaining.
      -
      • -
    • -
      -

      backward

      -
      default Tensor<V> backward()
      +
    + + + +
      +
    • +

      backward

      +
      default Tensor<V> backward()
      Use this to back-propagate an error signal of 1.0 through the recorded computation graph. Tensors which are used or produced by operations supporting the autograd system - will have this graph defined by GraphNode components attached to them. + will have this graph defined by GraphNode components attached to them. This is because autograd requires recording a computation graph for back-prop traversal. If this tensor is part of a computation graph then this method will traverse an error backward in the recorded history towards tensors which require @@ -3220,129 +3809,162 @@

      backward

      This method assumes that the user wants to back-propagate an error of "1" having the same shape as this tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      This tensor to allow for method chaining.
      -
      • -
    • -
      -

      getGradient

      -
      default Optional<Tensor<V>> getGradient()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      getGradient

      +
      default java.util.Optional<Tensor<V>> getGradient()
      +
      +
      Returns:
      The gradient of this tensor which is internally stored as component.
      -
      • -
    • -
      -

      gradient

      -
      default Optional<Tensor<V>> gradient()
      -
      This is a functionally identical alternative to the getGradient() method.
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      gradient

      +
      default java.util.Optional<Tensor<V>> gradient()
      +
      This is a functionally identical alternative to the getGradient() method.
      +
      +
      Returns:
      The gradient of this tensor which is internally stored as component.
      -
      • -
    • -
      -

      applyGradient

      -
      default void applyGradient()
      +
    + + + +
      +
    • +

      applyGradient

      +
      default void applyGradient()
      If this tensor owns a gradient tensor as component, then it can be applied by this method.
      "Applying" a gradient to a tensor simply means adding the values inside the gradient element-wise to the owning host tensor via an inline operation.
      - -
      • -
    • -
      -

      getDevice

      -
      default Device<V> getDevice()
      -
      -
      Returns:
      -
      The device on which this tensor is stored or CPU if it is not outsourced.
      -
      -
      -
      • -
    • -
      -

      getGraphNode

      -
      default Optional<GraphNode<V>> getGraphNode()
      -
      -
      Returns:
      +
      • +
    + + + +
      +
    • +

      getDevice

      +
      default Device<V> getDevice()
      +
      +
      Returns:
      +
      The device on which this tensor is stored or CPU if it is not outsourced.
      +
      +
      • +
    + + + +
      +
    • +

      getGraphNode

      +
      default java.util.Optional<GraphNode<V>> getGraphNode()
      +
      +
      Returns:
      The graph node optional of the computation graph to which this tensor belongs or an empty optional if not part of a graph.
      -
      • -
    • -
      -

      graphNode

      -
      default Optional<GraphNode<V>> graphNode()
      -
      This is a functionally identical alternative to getGraphNode().
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      graphNode

      +
      default java.util.Optional<GraphNode<V>> graphNode()
      +
      This is a functionally identical alternative to getGraphNode().
      +
      +
      Returns:
      The graph node optional of the computation graph to which this tensor belongs or an empty optional if not part of a graph.
      - -
      • -
    • -
      -

      getFrame

      -
      default Optional<NDFrame<V>> getFrame()
      -
      -
      Returns:
      -
      An instance of the NDFrame component if present.
      -
      -
      -
      • -
    • -
      -

      frame

      -
      default Optional<NDFrame<V>> frame()
      -
      This is a functionally identical alternative to getFrame().
      -
      -
      Returns:
      -
      An instance of the NDFrame component if present.
      -
      -
      -
      • -
    • -
      -

      detached

      -
      default Tensor<V> detached()
      +
      • +
    + + + +
      +
    • +

      getFrame

      +
      default java.util.Optional<NDFrame<V>> getFrame()
      +
      +
      Returns:
      +
      An instance of the NDFrame component if present.
      +
      +
      • +
    + + + +
      +
    • +

      frame

      +
      default java.util.Optional<NDFrame<V>> frame()
      +
      This is a functionally identical alternative to getFrame().
      +
      +
      Returns:
      +
      An instance of the NDFrame component if present.
      +
      +
      • +
    + + + +
      +
    • +

      detached

      +
      default Tensor<V> detached()
      This method returns a new tensor detached from any underlying computation-graph or simply does nothing if no graph is present.
      - Nodes within a computation graph are instances of the "GraphNode" class which are also + Nodes within a computation graph are instances of the "GraphNode" class which are also simple components of the tensors they represent in the graph.
      Therefore, a "detached" clone of this tensor is - simply a tensor without a GraphNode component.
      -
      -
      Returns:
      + simply a tensor without a GraphNode component. +
      +
      Returns:
      This very instance in order to allow for a more streamline usage of this method.
      -
      • -
    • -
      -

      withLabel

      -
      Tensor<V> withLabel(String label)
      -
      -
      Specified by:
      -
      withLabel in interface Nda<V>
      -
      Returns:
      +
    + + + +
      +
    • +

      withLabel

      +
      Tensor<V> withLabel(java.lang.String label)
      +
      +
      Specified by:
      +
      withLabel in interface Nda<V>
      +
      Returns:
      A new nd-array which is a shallow copy of this nd-array but with a different label.
      -
      • -
    • -
      -

      withLabels

      -
      Tensor<V> withLabels(String[]... labels)
      -
      This method receives a nested String array which +
    + + + +
      +
    • +

      withLabels

      +
      Tensor<V> withLabels(java.lang.String[]... labels)
      +
      This method receives a nested String array which ought to contain a label for the index of this nd-array. The index for a single element of this nd-array would be an array of numbers as long as the rank where every number is @@ -3354,21 +3976,24 @@

      withLabels

      dim 0 : ["A", "B"]
      dim 1 : ["1", "2", "3"]

      -
      -
      Specified by:
      -
      withLabels in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      withLabels in interface Nda<V>
      +
      Parameters:
      labels - A nested String array containing labels for indexes of the nd-array dimensions.
      -
      Returns:
      +
      Returns:
      This nd-array (method chaining).
      -
      • -
    • -
      -

      withLabels

      -
      Tensor<V> withLabels(List<List<Object>> labels)
      -
      This method receives a nested String list which +
    + + + +
      +
    • +

      withLabels

      +
      Tensor<V> withLabels(java.util.List<java.util.List<java.lang.Object>> labels)
      +
      This method receives a nested String list which ought to contain a label for the index of this nd-array. The index for a single element of this nd-array would be an array of numbers as long as the rank where every number is @@ -3380,20 +4005,23 @@

      withLabels

      dim 0 : ["A", "B"]
      dim 1 : ["1", "2", "3"]

      -
      -
      Specified by:
      -
      withLabels in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      withLabels in interface Nda<V>
      +
      Parameters:
      labels - A nested String list containing labels for indexes of the nd-array dimensions.
      -
      Returns:
      +
      Returns:
      This nd-array (method chaining).
      -
      • -
    • -
      -

      withLabels

      -
      Tensor<V> withLabels(Map<Object,List<Object>> labels)
      +
    + + + +
      +
    • +

      withLabels

      +
      Tensor<V> withLabels(java.util.Map<java.lang.Object,java.util.List<java.lang.Object>> labels)
      This method provides the ability to label not only the indices of the shape of this nd-array, but also the dimension of the shape. @@ -3406,38 +4034,44 @@

      withLabels

      "dim 1" : ["1", "2", "3"]
      ]

      -
      -
      Specified by:
      -
      withLabels in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      withLabels in interface Nda<V>
      +
      Parameters:
      labels - A map in which the keys are dimension labels and the values are lists of index labels for the dimension.
      -
      Returns:
      +
      Returns:
      This nd-array (method chaining).
      -
      • -
    • -
      -

      is

      -
      boolean is(Class<?> typeClass)
      +
    + + + +
      +
    • +

      is

      +
      boolean is(java.lang.Class<?> typeClass)
      This method compares the passed class with the underlying data-type of this NDArray. If the data-type of this NDArray is equivalent to the passed class then the returned boolean will be true, otherwise the method returns false.
      -
      -
      Parameters:
      +
      +
      Parameters:
      typeClass - The class which ought to be compared to the underlying data-type of this NDArray.
      -
      Returns:
      +
      Returns:
      The truth value of the question: Does this NDArray implementation hold the data of the passed type?
      -
      • -
    • -
      -

      plus

      -
      default Tensor<V> plus(Tensor<V> other)
      +
    + + + +
      +
    • +

      plus

      +
      default Tensor<V> plus(Tensor<V> other)
      This method will produce the addition of two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method. If the shapes of both of the involved tensors is identical then the result will be a regular element-wise addition. @@ -3446,38 +4080,46 @@

      plus

      Either the dimensions have the same size or one of them has size 1.
      Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
      And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The right operand of the addition.
      -
      Returns:
      -
      The sum of this instance as the left and the passed Tensor instance as right operand.
      +
      Returns:
      +
      The sum of this instance as the left and the passed Tensor instance as right operand.
      -
      • -
    • -
      -

      plus

      -
      default Tensor<V> plus(V value)
      -
      This method will create a new Tensor - with the provided double scalar added to all elements of this Tensor. +
    + + + + + +
      +
    • +

      plus

      +
      default Tensor<V> plus(V value)
      +
      This method will create a new Tensor + with the provided double scalar added to all elements of this Tensor.

      The shapes of this tensor is irrelevant as the provided value will simply be broadcast to any possible shape.

      -
      -
      Parameters:
      +
      +
      Parameters:
      value - The right operand of the addition.
      -
      Returns:
      +
      Returns:
      The sum between this instance as the left and the passed double as right operand.
      -
      • -
    • -
      -

      minus

      -
      default Tensor<V> minus(Tensor<V> other)
      +
    + + + +
      +
    • +

      minus

      +
      default Tensor<V> minus(Tensor<V> other)
      Performs subtraction on two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method. If the shapes of both of the involved tensors are identical then the result will be a regular element-wise subtraction. @@ -3486,97 +4128,120 @@

      minus

      Either the dimensions have the same size or one of them has size 1.
      Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
      And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The right operand of the subtraction.
      -
      Returns:
      -
      The difference between this instance as the left and the passed Tensor instance as right operand.
      +
      Returns:
      +
      The difference between this instance as the left and the passed Tensor instance as right operand.
      -
      • -
    • -
      -

      minus

      -
      default Tensor<V> minus(V other)
      -
      This method will create a new Tensor - with the provided item subtracted from all elements of this Tensor. +
    + + + + + +
      +
    • +

      minus

      +
      default Tensor<V> minus(V other)
      +
      This method will create a new Tensor + with the provided item subtracted from all elements of this Tensor.

      The shapes of this tensor is irrelevant as the provided item will simply be broadcast to all items od this tensor, irrespective of any shape.

      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The right operand of the subtraction, which is an item of the same type as this tensor.
      -
      Returns:
      +
      Returns:
      The difference between this instance as the left and the passed item as right operand.
      -
      • -
    • -
      -

      negative

      -
      default Tensor<V> negative()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      negative

      +
      default Tensor<V> negative()
      +
      +
      Returns:
      A clone of this tensor where the signs of all elements are flipped.
      - -
      • -
    • -
      -

      T

      -
      default Tensor<V> T()
      -
      Creates and returns a new Tensor instance which is a transposed twin of this instance.
      - This is a shorter alternative to the functionally identical getT() method.
      -
      -
      Returns:
      -
      A new transposed tensor with the same underlying Data as this tensor.
      -
      -
      -
      • -
    • -
      -

      getT

      -
      default Tensor<V> getT()
      -
      A method which returns a new Tensor instance which is a transposed twin of this instance.
      - This is an alternative to the functionally identical T() method.
      -
      -
      Returns:
      -
      A new transposed tensor with the same underlying Data as this tensor.
      -
      -
      -
      • -
    • -
      -

      mean

      -
      default Tensor<V> mean()
      +
      • +
    + + + +
      +
    • +

      T

      +
      default Tensor<V> T()
      +
      Creates and returns a new Tensor instance which is a transposed twin of this instance.
      + This is a shorter alternative to the functionally identical getT() method.
      +
      +
      Returns:
      +
      A new transposed tensor with the same underlying Data as this tensor.
      +
      +
      • +
    + + + +
      +
    • +

      getT

      +
      default Tensor<V> getT()
      +
      A method which returns a new Tensor instance which is a transposed twin of this instance.
      + This is an alternative to the functionally identical T() method.
      +
      +
      Returns:
      +
      A new transposed tensor with the same underlying Data as this tensor.
      +
      +
      • +
    + + + +
      +
    • +

      mean

      +
      default Tensor<V> mean()
      Calculate the mean value of all values within this tensor and returns it in the form of a scalar tensor.
      This operation supports autograd.
      -
      -
      Returns:
      +
      +
      Returns:
      A scalar tensor which wraps the mean value of all values of this tensor.
      -
      • -
    • -
      -

      sum

      -
      default Tensor<V> sum()
      +
    + + + +
      +
    • +

      sum

      +
      default Tensor<V> sum()
      Calculate the sum value of all values within this tensor and returns it in the form of a scalar tensor.
      This operation supports autograd.
      -
      -
      Returns:
      +
      +
      Returns:
      A scalar tensor which wraps the sum of all values of this tensor.
      -
      • -
    • -
      -

      sum

      -
      default Tensor<V> sum(int axis)
      +
    + + + +
      +
    • +

      sum

      +
      default Tensor<V> sum(int axis)
      Calculate the sum value of all values within this tensor along the specified axis and returns it in the form of a tensor.
      @@ -3585,18 +4250,21 @@

      sum

      sum of all values along the axis 1 is a single value for each of the two first dimensions.
      This operation supports autograd.
      -
      -
      Parameters:
      +
      +
      Parameters:
      axis - The axis along which the sum should be calculated.
      -
      Returns:
      +
      Returns:
      A tensor which wraps the sum of all values of this tensor along the specified axis.
      -
      • -
    • -
      -

      sum

      -
      default Tensor<V> sum(int... axes)
      +
    + + + +
      +
    • +

      sum

      +
      default Tensor<V> sum(int... axes)
      Calculate the sum value of all values within this tensor along the specified axes and returns it in the form of a tensor.
      @@ -3605,157 +4273,187 @@

      sum

      sum of all values along the axis 1 and 2 is a single value for each of the two first dimensions.
      This operation supports autograd.
      -
      -
      Parameters:
      +
      +
      Parameters:
      axes - The axes along which the sum should be calculated.
      -
      Returns:
      +
      Returns:
      A tensor which wraps the sum of all values of this tensor along the specified axes.
      -
      • -
    • -
      -

      min

      -
      default Tensor<V> min()
      +
    + + + +
      +
    • +

      min

      +
      default Tensor<V> min()
      Calculate the min value of all values within this tensor and returns it in the form of a scalar tensor.
      This operation supports autograd.
      -
      -
      Returns:
      +
      +
      Returns:
      A scalar tensor which wraps the smallest of all values of this tensor.
      -
      • -
    • -
      -

      max

      -
      default Tensor<V> max()
      +
    + + + +
      +
    • +

      max

      +
      default Tensor<V> max()
      Calculate the max value of all values within this tensor and returns it in the form of a scalar tensor.
      This operation supports autograd.
      -
      -
      Returns:
      +
      +
      Returns:
      A scalar tensor which wraps the largest of all values of this tensor.
      -
      • -
    • -
      -

      convDot

      -
      default Tensor<V> convDot(Tensor<V> other)
      +
    + + + +
      +
    • +

      convDot

      +
      default Tensor<V> convDot(Tensor<V> other)
      This method performs a convolutional based dot product between the last dimension of this tensor and the first dimension of the passed tensor.
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The tensor which is the right part of the dot product operation.
      -
      Returns:
      +
      Returns:
      A new tensor which is the dot product of this tensor and the passed one.
      -
      • -
    • -
      -

      dot

      -
      default Tensor<V> dot(Tensor<V> other)
      +
    + + + +
      +
    • +

      dot

      +
      default Tensor<V> dot(Tensor<V> other)
      Performs a dot product between the last dimension of this tensor and the first dimension of the provided tensor. However, currently this method can only handle matrices which means - that it is functionally completely identical to the matMul(Tensor) method.
      -
      -
      Parameters:
      + that it is functionally completely identical to the matMul(Tensor) method. +
      +
      Parameters:
      other - The tensor which is the right part of the dot product operation.
      -
      Returns:
      +
      Returns:
      A new tensor which is the dot product of this tensor and the passed one.
      -
      • -
    • -
      -

      matMul

      -
      default Tensor<V> matMul(Tensor<V> other)
      +
    + + + +
      +
    • +

      matMul

      +
      default Tensor<V> matMul(Tensor<V> other)
      This will produce the matrix product of - two tensors with rank 2 (matrices), where the left operand is this Tensor + two tensors with rank 2 (matrices), where the left operand is this Tensor instance and the right operand is the argument passed to the method.
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The right operand of the matrix multiplication.
      -
      Returns:
      -
      The matrix product of this instance as the left and the passed Tensor instance as right operand.
      +
      Returns:
      +
      The matrix product of this instance as the left and the passed Tensor instance as right operand.
      -
      • -
    • -
      -

      conv

      -
      default Tensor<V> conv(Tensor<V> other)
      +
    + + + +
      +
    • +

      conv

      +
      default Tensor<V> conv(Tensor<V> other)
      This method performs convolution between this tensor and the one passed as argument. - The convolution is performed by the Function which is registered under the name "conv".
      -
      -
      Parameters:
      + The convolution is performed by the Function which is registered under the name "conv". +
      +
      Parameters:
      other - The tensor which is the right operand of the convolutional operation.
      -
      Returns:
      +
      Returns:
      A new tensor which is the result of the convolutional operation.
      -
      • -
    • -
      -

      dimtrim

      -
      default Tensor<V> dimtrim()
      -
      This creates a new tensor with the same underlying Data and whose shape is trimmed. +
    + + + +
      +
    • +

      dimtrim

      +
      default Tensor<V> dimtrim()
      +
      This creates a new tensor with the same underlying Data and whose shape is trimmed. A trimmed shape is simply a shape without preceding and trailing ones.
      For example the shape (1x4x1x2x1) would be trimmed to (4x1x2). The underlying operation does not perform a removal of redundant ones all together. Only ones at the start and the beginning will be removed. A scalar tensor will not be affected by this operation.
      -
      -
      Returns:
      +
      +
      Returns:
      A tensor with the same underlying data but possibly trimmed shape without preceding or trailing ones.
      -
      • -
    • -
      -

      isCase

      -
      boolean isCase(Tensor<V> other)
      +
    + + + +
      +
    • +

      isCase

      +
      boolean isCase(Tensor<V> other)
      This method name translates to the "in" keyword in Groovy! The same is true for the "contains" method in Kotlin. Both methods do the exact same thing, however they exist for better language support.
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The tensor which will be checked.
      -
      Returns:
      +
      Returns:
      The answer to the following question: Is the data of the provided tensor a subset of the data of this tensor?
      -
      • -
    • -
      -

      contains

      -
      default boolean contains(Tensor<V> other)
      +
    + + + +
      +
    • +

      contains

      +
      default boolean contains(Tensor<V> other)
      This method name translates to the "in" keyword in Kotlin! The same is true for the "isCase" method in Groovy. Both methods do the exact same thing, however they exist for better language support.
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The tensor which will be checked.
      -
      Returns:
      +
      Returns:
      The answer to the following question: Is the data of the provided tensor a subset of the data of this tensor?
      -
      • -
    • -
      -

      multiply

      -
      default Tensor<V> multiply(Tensor<V> other)
      -
      This method is synonymous to the times(Tensor) method. +
    + + + +
      +
    • +

      multiply

      +
      default Tensor<V> multiply(Tensor<V> other)
      +
      This method is synonymous to the times(Tensor) method. Both of which will produce the product of two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method. If the shapes of both of the involved tensors is identical then the result will be a regular element-wise product. @@ -3764,34 +4462,42 @@

      multiply

      Either the dimensions have the same size or one of them has size 1.
      Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
      And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The right operand of the multiplication.
      -
      Returns:
      -
      The product of this instance as the left and the passed Tensor instance as right operand.
      +
      Returns:
      +
      The product of this instance as the left and the passed Tensor instance as right operand.
      -
      • -
    • -
      -

      multiply

      -
      default Tensor<V> multiply(V other)
      -
      -
      Parameters:
      +
    + + + + + +
      +
    • +

      multiply

      +
      default Tensor<V> multiply(V other)
      +
      +
      Parameters:
      other - The value which should be broadcast to all elements of a clone of this tensor.
      -
      Returns:
      +
      Returns:
      A new tensor where all elements are multiplied by the provided value.
      -
      • -
    • -
      -

      times

      -
      default Tensor<V> times(Tensor<V> other)
      -
      This is a functionally identical synonym to the multiply(Tensor) method. +
    + + + +
      +
    • +

      times

      +
      default Tensor<V> times(Tensor<V> other)
      +
      This is a functionally identical synonym to the multiply(Tensor) method. Both of which will produce the product of two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method. If the shapes of both of the involved tensors is identical then the result will be a regular element-wise product. @@ -3800,45 +4506,56 @@

      times

      Either the dimensions have the same size or one of them has size 1.
      Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
      And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The right operand of the multiplication.
      -
      Returns:
      -
      The product of this instance as the left and the passed Tensor instance as right operand.
      +
      Returns:
      +
      The product of this instance as the left and the passed Tensor instance as right operand.
      -
      • -
    • -
      -

      times

      -
      default Tensor<V> times(V other)
      -
      -
      Parameters:
      +
    + + + + + +
      +
    • +

      times

      +
      default Tensor<V> times(V other)
      +
      +
      Parameters:
      other - The value which should be broadcast to all elements of a clone of this tensor.
      -
      Returns:
      +
      Returns:
      A new tensor where all elements are multiplied by the provided value.
      -
      • -
    • -
      -

      multiply

      -
      default Tensor<V> multiply(double value)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      multiply

      +
      default Tensor<V> multiply(double value)
      +
      +
      Parameters:
      value - The value which should be broadcast to all elements of a clone of this tensor.
      -
      Returns:
      +
      Returns:
      A new tensor where all elements are multiplied by the provided value.
      -
      • -
    • -
      -

      div

      -
      default Tensor<V> div(Tensor<V> other)
      +
    + + + +
      +
    • +

      div

      +
      default Tensor<V> div(Tensor<V> other)
      This method will produce the quotient of two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method. If the shapes of both of the involved tensors are identical then the result will be a regular element-wise division. @@ -3847,27 +4564,35 @@

      div

      Either the dimensions have the same size or one of them has size 1.
      Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
      And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The right operand of the division.
      -
      Returns:
      -
      The quotient of this instance as the left and the passed Tensor instance as right operand.
      +
      Returns:
      +
      The quotient of this instance as the left and the passed Tensor instance as right operand.
      -
      • -
    • -
      -

      div

      -
      default Tensor<V> div(V value)
      -
      +
    + + + + + + + + + +
      +
    • +

      mod

      +
      default Tensor<V> mod(Tensor<V> other)
      Produces the modulus of two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method. If the shapes of these 2 tensors are identical then the result will be a regular element-wise modulo operation. @@ -3876,41 +4601,50 @@

      mod

      Either the dimensions have the same size or one of them has size 1.
      Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
      And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The right operand of the modulo operation.
      -
      Returns:
      -
      The modulus of this instance as the left and the passed Tensor instance as right operand.
      +
      Returns:
      +
      The modulus of this instance as the left and the passed Tensor instance as right operand.
      -
      • -
    • -
      -

      mod

      -
      default Tensor<V> mod(int other)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      mod

      +
      default Tensor<V> mod(int other)
      +
      +
      Parameters:
      other - The value which should be broadcast to all elements of a clone of this tensor.
      -
      Returns:
      +
      Returns:
      A new tensor where the modulo operation is applied to all elements using the provided int as right operand.
      -
      • -
    • -
      -

      rem

      -
      default Tensor<V> rem(int other)
      -
      This method is synonymous to the mod(int) method.
      -
      +
    + + + +
      +
    • +

      rem

      +
      default Tensor<V> rem(int other)
      +
      This method is synonymous to the mod(int) method.
      • -
    • -
      -

      power

      -
      default Tensor<V> power(Tensor<V> other)
      +
    + + + +
      +
    • +

      power

      +
      default Tensor<V> power(Tensor<V> other)
      This will produce the power of two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tensor + where the left operand is this Tensor instance and the right operand is the tensor passed to the method. If the shapes of the involved tensors are identical then the result will be a regular element-wise exponentiation. @@ -3919,46 +4653,60 @@

      power

      Either the dimensions have the same size or one of them has size 1.
      Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
      And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
      -
      -
      Parameters:
      +
      +
      Parameters:
      other - The right operand, also known as exponent, of the exponentiation.
      -
      Returns:
      -
      The power of this instance as the left and the passed Tensor instance as right operand.
      +
      Returns:
      +
      The power of this instance as the left and the passed Tensor instance as right operand.
      -
      • -
    • -
      -

      power

      -
      default Tensor<V> power(V value)
      +
    + + + + + +
      +
    • +

      power

      +
      default Tensor<V> power(V value)
      Raises all items of this tensor to the power of the provided value. The returned tensor is a new instance which will have the same shape as this tensor.
      -
      -
      Parameters:
      +
      +
      Parameters:
      value - The value which should be used to raise all items of this tensor to the power of.
      -
      Returns:
      +
      Returns:
      A new tensor where all items are raised to the power of the provided value.
      - -
      • -
    • -
      -

      xor

      -
      default Tensor<V> xor(Tensor<V> other)
      -
      This method is a functionally identical synonym to the power(Tensor) method.
      -
      -
      • -
    • -
      -

      xor

      -
      default Tensor<V> xor(double value)
      -
      This method is a functionally identical synonym to the power(Tensor) method.
      -
      -
      • -
    • -
      -

      sig

      -
      default Tensor<V> sig()
      +
      • +
    + + + + + + + +
      +
    • +

      xor

      +
      default Tensor<V> xor(double value)
      +
      This method is a functionally identical synonym to the power(Tensor) method.
      +
      • +
    + + + +
      +
    • +

      sig

      +
      default Tensor<V> sig()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -3966,16 +4714,19 @@ 
      sig
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("sig(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the sigmoid function applied to the items of this tensor.
      -
      • -
    • -
      -

      tanh

      -
      default Tensor<V> tanh()
      +
    + + + +
      +
    • +

      tanh

      +
      default Tensor<V> tanh()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -3983,16 +4734,19 @@ 
      tanh
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("tanh(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the tanh function applied to the items of this tensor.
      -
      • -
    • -
      -

      relu

      -
      default Tensor<V> relu()
      +
    + + + +
      +
    • +

      relu

      +
      default Tensor<V> relu()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4000,16 +4754,19 @@ 
      relu
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("relu(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the relu function applied to the items of this tensor.
      -
      • -
    • -
      -

      sin

      -
      default Tensor<V> sin()
      +
    + + + +
      +
    • +

      sin

      +
      default Tensor<V> sin()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4017,16 +4774,19 @@ 
      sin
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("sin(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the sin function applied to the items of this tensor.
      -
      • -
    • -
      -

      cos

      -
      default Tensor<V> cos()
      +
    + + + +
      +
    • +

      cos

      +
      default Tensor<V> cos()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4034,16 +4794,19 @@ 
      cos
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("cos(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the cos function applied to the items of this tensor.
      -
      • -
    • -
      -

      ln

      -
      default Tensor<V> ln()
      +
    + + + +
      +
    • +

      ln

      +
      default Tensor<V> ln()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4051,16 +4814,19 @@ 
      ln
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("ln(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the ln function applied to the items of this tensor.
      -
      • -
    • -
      -

      softplus

      -
      default Tensor<V> softplus()
      +
    + + + +
      +
    • +

      softplus

      +
      default Tensor<V> softplus()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4068,16 +4834,19 @@ 
      softplus
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("softplus(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the softplus function applied to the items of this tensor.
      -
      • -
    • -
      -

      exp

      -
      default Tensor<V> exp()
      +
    + + + +
      +
    • +

      exp

      +
      default Tensor<V> exp()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4085,16 +4854,19 @@ 
      exp
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("exp(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the exp function applied to the items of this tensor.
      -
      • -
    • -
      -

      sqrt

      -
      default Tensor<V> sqrt()
      +
    + + + +
      +
    • +

      sqrt

      +
      default Tensor<V> sqrt()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4102,16 +4874,19 @@ 
      sqrt
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("sqrt(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the sqrt function applied to the items of this tensor.
      -
      • -
    • -
      -

      log10

      -
      default Tensor<V> log10()
      +
    + + + +
      +
    • +

      log10

      +
      default Tensor<V> log10()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4119,16 +4894,19 @@ 
      log10
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("log10(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the log10 function applied to the items of this tensor.
      -
      • -
    • -
      -

      cbrt

      -
      default Tensor<V> cbrt()
      +
    + + + +
      +
    • +

      cbrt

      +
      default Tensor<V> cbrt()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4136,16 +4914,19 @@ 
      cbrt
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("cbrt(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the cbrt function applied to the items of this tensor.
      -
      • -
    • -
      -

      abs

      -
      default Tensor<V> abs()
      +
    + + + +
      +
    • +

      abs

      +
      default Tensor<V> abs()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4153,16 +4934,19 @@ 
      abs
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("abs(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the abs function applied to the items of this tensor.
      -
      • -
    • -
      -

      neg

      -
      default Tensor<V> neg()
      +
    + + + +
      +
    • +

      neg

      +
      default Tensor<V> neg()
      This method is a functionally identical to the following alternatives: 
      
       // Pre-instantiated:
@@ -4170,65 +4954,80 @@ 
      neg
      
       // Dynamically parsed and instantiated:
       var out2 = Function.of("neg(I[0])").call(myTensor);
      -
      -
      Returns:
      +
      +
      Returns:
      A new tensor whose items are the result of the neg function applied to the items of this tensor.
      -
      • -
    • -
      -

      softmax

      -
      default Tensor<V> softmax()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      softmax

      +
      default Tensor<V> softmax()
      +
      +
      Returns:
      A new tensor whose items are the result of the softmax function applied to the items of this tensor.
      -
      • -
    • -
      -

      softmax

      -
      default Tensor<V> softmax(int axis)
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      softmax

      +
      default Tensor<V> softmax(int axis)
      +
      +
      Returns:
      A new tensor whose items are the result of the softmax function applied to the items of this tensor.
      -
      • -
    • -
      -

      softmax

      -
      default Tensor<V> softmax(int... axes)
      +
    + + + +
      +
    • +

      softmax

      +
      default Tensor<V> softmax(int... axes)
      Calculates the softmax function along the specified axes.
      For example, if this tensor has a shape of (2, 3, 4) and the axes 0 and 2 are chosen, then the result will be a tensor of the same size where all elements summed up alongside axis 0 and 2 would be 1. Ao calling sum(0, 2) would in this example be a tensor of shape of (1, 3, 1) where every item is 1.
      This operation supports autograd.
      -
      -
      Parameters:
      +
      +
      Parameters:
      axes - The axes along which the softmax function should be applied.
      -
      Returns:
      +
      Returns:
      A new tensor whose items are the result of the softmax function applied to the items of this tensor.
      -
      • -
    • -
      -

      sigmoid

      -
      default Tensor<V> sigmoid()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      sigmoid

      +
      default Tensor<V> sigmoid()
      +
      +
      Returns:
      A new tensor whose items are the result of the sigmoid function applied to the items of this tensor.
      -
      • -
    • -
      -

      slice

      -
      AxisOrGetTensor<V> slice()
      -
      This method returns a SliceBuilder instance exposing a simple builder API +
    + + + +
      +
    • +

      slice

      +
      AxisOrGetTensor<V> slice()
      +
      This method returns a SliceBuilder instance exposing a simple builder API which enables the configuration of a slice of the current nd-array via method chaining.
      The following code snippet slices a 3-dimensional nd-array into a nd-array of shape (2x1x3) 
      
@@ -4238,211 +5037,247 @@ 
      slice
      
           .axis().from(0).to(2)
           .get();
      -
      -
      Specified by:
      -
      slice in interface Nda<V>
      -
      Returns:
      -
      An instance of the SliceBuilder class exposing a readable builder API for creating slices.
      -
      - -
      • -
    • -
      -

      concatAt

      -
      default Tensor<V> concatAt(int axis, - Nda<V> other, - Nda<V>... ndArrays)
      +
      +
      Specified by:
      +
      slice in interface Nda<V>
      +
      Returns:
      +
      An instance of the SliceBuilder class exposing a readable builder API for creating slices.
      +
      +
      • +
    + + + +
      +
    • +

      concatAt

      +
      default Tensor<V> concatAt(int axis,
+                           Nda<V> other,
+                           Nda<V>... ndArrays)
      This method concatenates the provided nd-arrays together with this nd-array along a specified axis. The provided nd-arrays must have the same shape and data type as the current nd-array, except for the specified axis.
      -
      -
      Specified by:
      -
      concatAt in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      concatAt in interface Nda<V>
      +
      Parameters:
      axis - The axis along which the provided nd-arrays should be concatenated. The axis must be within the range of the rank of the current nd-array.
      other - The other nd-arrays which should be concatenated with this nd-array.
      ndArrays - The non-null, non-empty nd-arrays which should be concatenated together with this and the other nd-array. The nd-arrays all must have the same shape as this nd-array, except for the specified axis. Also, it must have the same data type as the current nd-array.
      -
      Returns:
      +
      Returns:
      A new nd-array which is the concatenation of the current nd-array and the provided nd-arrays.
      -
      • -
    • -
      -

      concatAt

      -
      default Tensor<V> concatAt(int axis, - Nda<V> other)
      +
    + + + +
      +
    • +

      concatAt

      +
      default Tensor<V> concatAt(int axis,
+                           Nda<V> other)
      This method concatenates the provided nd-array together with this nd-array along a specified axis. The provided nd-array must have the same shape and data type as this nd-array, except for the specified axis.
      -
      -
      Specified by:
      -
      concatAt in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      concatAt in interface Nda<V>
      +
      Parameters:
      axis - The axis along which the provided nd-arrays should be concatenated. The axis must be within the range of the rank of the current nd-array.
      other - The other nd-arrays which should be concatenated with this nd-array.
      -
      Returns:
      +
      Returns:
      A new nd-array which is the concatenation of the current nd-array and the provided nd-arrays.
      -
      • -
    • -
      -

      getAt

      -
      Tensor<V> getAt(int... indices)
      +
    + + + +
      +
    • +

      getAt

      +
      Tensor<V> getAt(int... indices)
      The following method enables access to specific scalar elements within the nd-array. The method name also translates to the subscription operator in Groovy.
      -
      -
      Specified by:
      -
      getAt in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      getAt in interface Nda<V>
      +
      Parameters:
      indices - The index array of the element which should be returned.
      -
      Returns:
      +
      Returns:
      An element located at the provided index.
      -
      • -
    • -
      -

      getAt

      -
      default Tensor<V> getAt(Number i)
      +
    + + + +
      +
    • +

      getAt

      +
      default Tensor<V> getAt(java.lang.Number i)
      This getter method creates and returns a slice of the original nd-array. The returned slice is a scalar nd-array wrapping a single value element which is being targeted by the provided integer index.
      -
      -
      Specified by:
      -
      getAt in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      getAt in interface Nda<V>
      +
      Parameters:
      i - The index of the value item which should be returned as a nd-array instance.
      -
      Returns:
      +
      Returns:
      A nd-array holding a single value element which is internally still residing in the original nd-array.
      -
      • -
    • -
      -

      get

      -
      default Tensor<V> get(int... indices)
      +
    + + + +
      +
    • +

      get

      +
      default Tensor<V> get(int... indices)
      The following method enables access to specific scalar elements within the nd-array. The method name also translates to the subscription operator in Groovy.
      -
      -
      Specified by:
      -
      get in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      get in interface Nda<V>
      +
      Parameters:
      indices - The index array of the element which should be returned.
      -
      Returns:
      +
      Returns:
      An element located at the provided index.
      -
      • -
    • -
      -

      getAt

      -
      default Tensor<V> getAt(Object... args)
      +
    + + + +
      +
    • +

      getAt

      +
      default Tensor<V> getAt(java.lang.Object... args)
      The following method enables the creation of nd-array slices which access the same underlying data (possibly from a different view). The method name also translates to the subscription operator in Groovy.
      -
      -
      Specified by:
      -
      getAt in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      getAt in interface Nda<V>
      +
      Parameters:
      args - An arbitrary number of arguments which can be used for slicing.
      -
      Returns:
      +
      Returns:
      A slice nd-array created based on the passed keys.
      -
      • -
    • -
      -

      get

      -
      default Tensor<V> get(Object... args)
      +
    + + + +
      +
    • +

      get

      +
      default Tensor<V> get(java.lang.Object... args)
      The following method enables the creation of nd-array slices which access the same underlying data (possibly from a different view). The method name also translates to the subscription operator in Groovy.
      -
      -
      Specified by:
      -
      get in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      get in interface Nda<V>
      +
      Parameters:
      args - An arbitrary number of arguments which can be used for slicing.
      -
      Returns:
      +
      Returns:
      A slice nd-array created based on the passed keys.
      -
      • -
    • -
      -

      getAt

      -
      default Tensor<V> getAt(int i)
      +
    + + + +
      +
    • +

      getAt

      +
      default Tensor<V> getAt(int i)
      This getter method creates and returns a slice of the original nd-array. The returned slice is a scalar nd-array wrapping a single value element which is being targeted by the provided integer index.
      -
      -
      Specified by:
      -
      getAt in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      getAt in interface Nda<V>
      +
      Parameters:
      i - The index of the value item which should be returned as a nd-array instance.
      -
      Returns:
      +
      Returns:
      A nd-array holding a single value element which is internally still residing in the original nd-array.
      -
      • -
    • -
      -

      get

      -
      default Tensor<V> get(int i)
      +
    + + + +
      +
    • +

      get

      +
      default Tensor<V> get(int i)
      This getter method creates and returns a slice of the original nd-array. The returned slice is a scalar nd-array wrapping a single value element which is being targeted by the provided integer index.
      -
      -
      Specified by:
      -
      get in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      get in interface Nda<V>
      +
      Parameters:
      i - The index of the value item which should be returned as a nd-array instance.
      -
      Returns:
      +
      Returns:
      A nd-array holding a single value element which is internally still residing in the original nd-array.
      -
      • -
    • -
      -

      get

      -
      default Tensor<V> get(Number i)
      +
    + + + +
      +
    • +

      get

      +
      default Tensor<V> get(java.lang.Number i)
      This getter method creates and returns a slice of the original nd-array. The returned slice is a scalar nd-array wrapping a single value element which is being targeted by the provided integer index.
      -
      -
      Specified by:
      -
      get in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      get in interface Nda<V>
      +
      Parameters:
      i - The index of the value item which should be returned as a nd-array instance.
      -
      Returns:
      +
      Returns:
      A nd-array holding a single value element which is internally still residing in the original nd-array.
      -
      • -
    • -
      -

      get

      -
      default Tensor<V> get(Object key)
      +
    + + + +
      +
    • +

      get

      +
      default Tensor<V> get(java.lang.Object key)
      This method enables nd-array slicing! It takes a key of various types and configures a slice nd-array which shares the same underlying data as the original nd-array.
      -
      -
      Specified by:
      -
      get in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      get in interface Nda<V>
      +
      Parameters:
      key - This object might be a wide range of objects including maps, lists or arrays...
      -
      Returns:
      +
      Returns:
      A slice nd-array or scalar value.
      -
      • -
    • -
      -

      getAt

      -
      Tensor<V> getAt(Map<?,Integer> rangToSteps)
      +
    + + + +
      +
    • +

      getAt

      +
      Tensor<V> getAt(java.util.Map<?,java.lang.Integer> rangToSteps)
      This method is most useful when used in Groovy where defining maps is done through square brackets, making it possible to slice nd-arrays like so:
      @@ -4454,39 +5289,45 @@

      getAt

      i... start indexAlias.
      j... end indexAlias. (inclusive!)
      k... step size.
      -
      -
      Specified by:
      -
      getAt in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      getAt in interface Nda<V>
      +
      Parameters:
      rangToSteps - A map where the keys define where axes should be sliced and values which define the steps for the specific axis.
      -
      Returns:
      +
      Returns:
      A nd-array slice with an offset based on the provided map keys and steps based on the provided map values.
      -
      • -
    • -
      -

      getAt

      -
      Tensor<V> getAt(List<?> key)
      +
    + + + +
      +
    • +

      getAt

      +
      Tensor<V> getAt(java.util.List<?> key)
      This method enables nd-array slicing! It takes a key of various types and configures a slice nd-array which shares the same underlying data as the original nd-array.
      -
      -
      Specified by:
      -
      getAt in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      getAt in interface Nda<V>
      +
      Parameters:
      key - This object might be a wide range of objects including maps, lists or arrays...
      -
      Returns:
      +
      Returns:
      A slice nd-array or scalar value.
      -
      • -
    • -
      -

      mapTo

      -
      default <T> Tensor<T> mapTo(Class<T> typeClass, - Function<V,T> mapper)
      +
    + + + +
      +
    • +

      mapTo

      +
      default <T> Tensor<T> mapTo(java.lang.Class<T> typeClass,
+                            java.util.function.Function<V,T> mapper)

      This is a convenience method for mapping a nd-array to a nd-array of new type based on a provided target item type and mapping lambda. @@ -4504,23 +5345,26 @@

      mapTo

      where ever it may reside back to the JVM, execute the mapping lambda, and then transfer the result back to the original location.

      -
      -
      Specified by:
      -
      mapTo in interface Nda<V>
      -
      Type Parameters:
      +
      +
      Specified by:
      +
      mapTo in interface Nda<V>
      +
      Type Parameters:
      T - The type parameter of the items of the returned nd-array.
      -
      Parameters:
      +
      Parameters:
      typeClass - The class of the item type to which the items of this nd-array should be mapped.
      mapper - The lambda which maps the items of this nd-array to a new one.
      -
      Returns:
      +
      Returns:
      A new nd-array of type T.
      -
      • -
    • -
      -

      map

      -
      default Tensor<V> map(Function<V,V> mapper)
      +
    + + + +
      +
    • +

      map

      +
      default Tensor<V> map(java.util.function.Function<V,V> mapper)

      This method is a convenience method for mapping the items of this nd-array to another nd-array of the same type based on the provided lambda function, which will be applied @@ -4536,70 +5380,85 @@

      map

      This is a problem if this nd-array lives somewhere other than the JVM. So, therefore, this method will temporally transfer this nd-array from where ever it may reside back to the JVM, execute the mapping lambda, and then transfer the result back to the original location.
      -
      -
      Specified by:
      -
      map in interface Nda<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      map in interface Nda<V>
      +
      Parameters:
      mapper - The lambda which maps the items of this nd-array to a new one.
      -
      Returns:
      +
      Returns:
      A new nd-array of type V.
      -
      • -
    • -
      -

      asImage

      -
      BufferedImage asImage(Tensor.ImageType type)
      -
      Turns this tensor into a BufferedImage based on the provided - Tensor.ImageType formatting choice.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      asImage

      +
      java.awt.image.BufferedImage asImage(Tensor.ImageType type)
      +
      Turns this tensor into a BufferedImage based on the provided + Tensor.ImageType formatting choice.
      +
      +
      Parameters:
      type - The type of format used to create the buffered image.
      -
      Returns:
      -
      A BufferedImage populated with the contents of this tensor.
      +
      Returns:
      +
      A BufferedImage populated with the contents of this tensor.
      -
      • -
    • -
      -

      asType

      -
      <T> T asType(Class<T> typeClass)
      -
      -
      Type Parameters:
      +
    + + + +
      +
    • +

      asType

      +
      <T> T asType(java.lang.Class<T> typeClass)
      +
      +
      Type Parameters:
      T - The type parameter of the type that will be returned.
      -
      Parameters:
      +
      Parameters:
      typeClass - The class which is the target of the type conversion.
      -
      Returns:
      +
      Returns:
      An instance of the supplied type class.
      -
      • -
    • -
      -

      toString

      -
      default String toString(String conf)
      -
      +
    + + + +
      +
    • +

      toString

      +
      default java.lang.String toString(java.lang.String conf)
      • -
    • -
      -

      toString

      -
      default String toString(NDPrintSettings config)
      -
      Use this to turn this nd-array into a String instance based on the provided - NDPrintSettings instance, which allows you to configure things +
    + + + +
      +
    • +

      toString

      +
      default java.lang.String toString(NDPrintSettings config)
      +
      Use this to turn this nd-array into a String instance based on the provided + NDPrintSettings instance, which allows you to configure things like the number of chars per entry, delimiters, the number of items per line, etc.
      -
      -
      Specified by:
      -
      toString in interface Nda<V>
      +
      +
      Specified by:
      +
      toString in interface Nda<V>
      -
      • -
    • -
      -

      toString

      -
      default String toString(Consumer<NDPrintSettings> configurator)
      +
    + + + +
      +
    • +

      toString

      +
      default java.lang.String toString(java.util.function.Consumer<NDPrintSettings> configurator)
      This allows you to provide a lambda which configures how this nd-array should be - converted to String instances. - The provided Consumer will receive a NDPrintSettings instance + converted to String instances. + The provided Consumer will receive a NDPrintSettings instance which allows you to change various settings with the help of method chaining.
      Here is an example: 
      
@@ -4611,78 +5470,149 @@ 
      toString
      
              .setCellSize(15)
           );
      -
      -
      Specified by:
      -
      toString in interface Nda<V>
      -
      Parameters:
      -
      configurator - A consumer of the NDPrintSettings ready to be configured.
      -
      Returns:
      -
      The String representation of this nd-array.
      -
      - -
      • -
    • -
      -

      deepCopy

      -
      Tensor<V> deepCopy()
      +
      +
      Specified by:
      +
      toString in interface Nda<V>
      +
      Parameters:
      +
      configurator - A consumer of the NDPrintSettings ready to be configured.
      +
      Returns:
      +
      The String representation of this nd-array.
      +
      +
      • +
    + + + +
      +
    • +

      deepCopy

      +
      Tensor<V> deepCopy()
      This method creates and returns a new nd-array instance which is not only a copy of the configuration of this nd-array but also a copy of the underlying data array.
      (Note: the underlying nd-array will not be attached to any kind of computation graph)
      -
      -
      Specified by:
      -
      deepCopy in interface Nda<V>
      -
      Returns:
      +
      +
      Specified by:
      +
      deepCopy in interface Nda<V>
      +
      Returns:
      A new nd-array instance which is a deep copy of this nd-array.
      -
      • -
    • -
      -

      shallowCopy

      -
      default Tensor<V> shallowCopy()
      +
    + + + +
      +
    • +

      shallowCopy

      +
      default Tensor<V> shallowCopy()
      This creates a copy where the underlying data is still the same.
      (Note: the underlying nd-array will not be attached to any kind of computation graph)
      -
      -
      Specified by:
      -
      shallowCopy in interface Nda<V>
      -
      Returns:
      +
      +
      Specified by:
      +
      shallowCopy in interface Nda<V>
      +
      Returns:
      A shallow copy where the underlying data is shared with this nd-array.
      -
      • -
    • -
      -

      deepClone

      -
      Tensor<V> deepClone()
      -
      This is almost identical to the deepCopy() method except that +
    + + + +
      +
    • +

      deepClone

      +
      Tensor<V> deepClone()
      +
      This is almost identical to the deepCopy() method except that the returned tensor will have autograd support, meaning that the cloning will be part of the autograd computation graph, and backpropagation will traverse the cloned tensor as well.
      -
      -
      Returns:
      +
      +
      Returns:
      A deep clone of this tensor with autograd support.
      -
      • -
    • -
      -

      shallowClone

      -
      default Tensor<V> shallowClone()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      shallowClone

      +
      default Tensor<V> shallowClone()
      +
      +
      Returns:
      A shallow copy of this tensor with autograd support.
      -
    - - + + + + - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
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      • +
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      • +
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      +
    • Detail: 
      • +
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    + diff --git a/docs/jdocs/neureka/Tsr.ImageType.html b/docs/jdocs/neureka/Tsr.ImageType.html deleted file mode 100644 index 5e224806c..000000000 --- a/docs/jdocs/neureka/Tsr.ImageType.html +++ /dev/null @@ -1,568 +0,0 @@ - - - - - -Tsr.ImageType (neureka 0.21.0 API) - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    Package neureka
    -

    Enum Tsr.ImageType

    -
    -
    -
      -
    • java.lang.Object
      • -
    • - -
      • -
    -
    -
      -
    • -
      -
      All Implemented Interfaces:
      -
      java.io.Serializable, java.lang.Comparable<Tsr.ImageType>
      -
      -
      -
      Enclosing interface:
      -
      Tsr<V>
      -
      -
      -
      public static enum Tsr.ImageType
-extends java.lang.Enum<Tsr.ImageType>
      -
      Use this enum as argument for the Tsr.asImage(Tsr.ImageType) method to - specify the type of image that should be returned.
      -
      • -
    -
    -
    - -
    -
    -
      -
    • - -
      - -
      - -
      -
        -
      • - - -

        Field Detail

        - - - -
          -
        • -

          bufferType

          -
          public final int bufferType
          -
          • -
        - - - -
          -
        • -

          dataType

          -
          public final DataType<?> dataType
          -
          • -
        - - - -
          -
        • -

          numberOfChannels

          -
          public final int numberOfChannels
          -
          • -
        -
        • -
      -
      - -
      -
        -
      • - - -

        Method Detail

        - - - -
          -
        • -

          values

          -
          public static Tsr.ImageType[] values()
          -
          Returns an array containing the constants of this enum type, in -the order they are declared. This method may be used to iterate -over the constants as follows: -
          -for (Tsr.ImageType c : Tsr.ImageType.values())
-    System.out.println(c);
-
          -
          -
          Returns:
          -
          an array containing the constants of this enum type, in the order they are declared
          -
          -
          • -
        - - - -
          -
        • -

          valueOf

          -
          public static Tsr.ImageType valueOf​(java.lang.String name)
          -
          Returns the enum constant of this type with the specified name. -The string must match exactly an identifier used to declare an -enum constant in this type. (Extraneous whitespace characters are -not permitted.)
          -
          -
          Parameters:
          -
          name - the name of the enum constant to be returned.
          -
          Returns:
          -
          the enum constant with the specified name
          -
          Throws:
          -
          java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
          -
          java.lang.NullPointerException - if the argument is null
          -
          -
          • -
        -
        • -
      -
      -
      • -
    -
    -
    -
    - - - - diff --git a/docs/jdocs/neureka/Tsr.html b/docs/jdocs/neureka/Tsr.html deleted file mode 100644 index f4ee6eb82..000000000 --- a/docs/jdocs/neureka/Tsr.html +++ /dev/null @@ -1,5845 +0,0 @@ - - - - - -Tsr (neureka 0.21.0 API) - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    Package neureka
    -

    Interface Tsr<V>

    -
    -
    -
    -
      -
    • -
      -
      Type Parameters:
      -
      V - The type parameter for the individual value items within this tensor.
      -
      -
      -
      All Superinterfaces:
      -
      Component<Tsr<V>>, ComponentOwner<Tsr<V>>, java.lang.Iterable<V>, Nda<V>, NDimensional
      -
      -
      -
      public interface Tsr<V>
-extends Nda<V>, Component<Tsr<V>>, ComponentOwner<Tsr<V>>
      -
      Tsr is a 3 letter abbreviation of the word "tensor", a mathematical concept. - A tensor is a type of multidimensional data-structure with certain transformation properties. - Technically however, it is mostly a simple container / data-structure which can house data indexed by N dimensions. - Therefore, it is often also described as a nd-array. - Elements of a tensor are also mostly numeric.
      - This means that:
      - ...a tensor of rank 0 is a scalar, a tensor of rank 1 is a vector and a tensor of rank 2 is a matrix, etc... -

      - Consequently, tensors are a perfect fit for applying various operations on them. - Such operations might be simple element-wise operations or more complex linear operations like - the dot-product, matrix- or even tensor multiplications.
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        Method Summary

        - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        All Methods Static Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethodDescription
        default Tsr<V>abs() -
        This method is a functionally identical to the following alternatives:
        -
        default voidapplyGradient() -
        If this tensor owns a gradient tensor as component, then it can be applied by this method.
        -
        java.awt.image.BufferedImageasImage​(Tsr.ImageType type) -
        Turns this tensor into a BufferedImage based on the provided - Tsr.ImageType formatting choice.
        -
        <T> TasType​(java.lang.Class<T> typeClass) 
        default Tsr<V>backward() -
        Use this to back-propagate an error signal of 1.0 through the recorded computation graph.
        -
        default Tsr<V>backward​(double value) -
        Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
        -
        default Tsr<V>backward​(Tsr<V> error) -
        Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
        -
        default booleanbelongsToGraph() -
        Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
        -
        default Tsr<V>cbrt() -
        This method is a functionally identical to the following alternatives:
        -
        default Tsr<V>concatAt​(int axis, - Nda<V> other) -
        This method concatenates the provided nd-array together with this nd-array along a specified axis.
        -
        default Tsr<V>concatAt​(int axis, - Nda<V> other, - Nda<V>... ndArrays) -
        This method concatenates the provided nd-arrays together with this nd-array along a specified axis.
        -
        default booleancontains​(Tsr<V> other) -
        This method name translates to the "in" keyword in Kotlin! - The same is true for the "isCase" method in Groovy.
        -
        default Tsr<V>conv​(Tsr<V> other) -
        This method performs convolution between this tensor and the one passed as argument.
        -
        default Tsr<V>convDot​(Tsr<V> other) -
        This method performs a convolutional based dot product between the last dimension of this tensor - and the first dimension of the passed tensor.
        -
        default Tsr<V>cos() -
        This method is a functionally identical to the following alternatives:
        -
        Tsr<V>deepClone() -
        This is almost identical to the deepCopy() method except that - the returned tensor will have autograd support, meaning that the cloning - will be part of the autograd computation graph, and backpropagation - will traverse the cloned tensor as well.
        -
        Tsr<V>deepCopy() -
        This method creates and returns a new nd-array instance - which is not only a copy of the configuration of this nd-array but also a copy of - the underlying data array.
        -
        default Tsr<V>detached() -
        This method returns a new tensor detached from any underlying computation-graph - or simply does nothing if no graph is present.
        - Nodes within a computation graph are instances of the "GraphNode" class which are also - simple components of the tensors they represent in the graph.
        -
        default Tsr<V>dimtrim() -
        This creates a new tensor with the same underlying Data and whose shape is trimmed.
        -
        default Tsr<V>div​(Tsr<V> other) -
        This method will produce the quotient of - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method.
        -
        default Tsr<V>div​(V value) 
        default Tsr<V>dot​(Tsr<V> other) -
        Performs a dot product between the last dimension of this tensor - and the first dimension of the provided tensor.
        -
        default Tsr<V>exp() -
        This method is a functionally identical to the following alternatives:
        -
        default java.util.Optional<NDFrame<V>>frame() -
        This is a functionally identical alternative to getFrame().
        -
        default Tsr<V>get​(int i) -
        This getter method creates and returns a slice of the original nd-array.
        -
        default Tsr<V>get​(int... indices) -
        The following method enables access to specific scalar elements within the nd-array.
        -
        default Tsr<V>get​(java.lang.Number i) -
        This getter method creates and returns a slice of the original nd-array.
        -
        default Tsr<V>get​(java.lang.Object key) -
        This method enables nd-array slicing! - It takes a key of various types and configures a slice - nd-array which shares the same underlying data as the original nd-array.
        -
        default Tsr<V>get​(java.lang.Object... args) -
        The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view).
        -
        default Tsr<V>getAt​(int i) -
        This getter method creates and returns a slice of the original nd-array.
        -
        Tsr<V>getAt​(int... indices) -
        The following method enables access to specific scalar elements within the nd-array.
        -
        default Tsr<V>getAt​(java.lang.Number i) -
        This getter method creates and returns a slice of the original nd-array.
        -
        default Tsr<V>getAt​(java.lang.Object... args) -
        The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view).
        -
        Tsr<V>getAt​(java.util.List<?> key) -
        This method enables nd-array slicing! - It takes a key of various types and configures a slice - nd-array which shares the same underlying data as the original nd-array.
        -
        Tsr<V>getAt​(java.util.Map<?,​java.lang.Integer> rangToSteps) -
        This method is most useful when used in Groovy - where defining maps is done through square brackets, - making it possible to slice nd-arrays like so:
        -
        DataType<V>getDataType() -
        This method returns the DataType instance of this Tsr, which is - a wrapper object for the actual type class representing the value items stored inside - the underlying data array of this tensor.
        -
        default Device<V>getDevice() 
        default java.util.Optional<NDFrame<V>>getFrame() 
        default java.util.Optional<Tsr<V>>getGradient() 
        default java.util.Optional<GraphNode<V>>getGraphNode() 
        MutateTsr<V>getMut() -
        This method exposes an API for mutating the state of this tensor.
        -
        java.lang.Class<?>getRepresentativeItemClass() -
        The Class returned by this method is the representative Class of the - value items of a concrete AbstractNda but not necessarily the actual Class of - a given value item, this is especially true for numeric types, which are represented by - implementations of the NumericType interface.
        -
        default Tsr<V>getT() -
        A method which returns a new Tsr instance which is a transposed twin of this instance.
        - This is an alternative to the functionally identical T() method.
        -
        intgetVersion() -
        The version number is tracking how often this tensor has been mutated.
        -
        default java.util.Optional<Tsr<V>>gradient() -
        This is a functionally identical alternative to the getGradient() method.
        -
        booleangradientApplyRequested() -
        This flag works alongside two autograd features which can be enabled inside the library settings.
        -
        default java.util.Optional<GraphNode<V>>graphNode() -
        This is a functionally identical alternative to getGraphNode().
        -
        default booleanhasGradient() -
        Tensors can be components of other tensors which makes the - implicitly their gradients.
        -
        booleanis​(java.lang.Class<?> typeClass) -
        This method compares the passed class with the underlying data-type of this NDArray.
        -
        default booleanisBranch() -
        Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
        -
        booleanisCase​(Tsr<V> other) -
        This method name translates to the "in" keyword in Groovy! - The same is true for the "contains" method in Kotlin.
        -
        booleanisDeleted() -
        This will check if the MutateTsr.delete() method was previously called on this tensor.
        -
        default booleanisEmpty() -
        A tensor is empty if it's Data storage is null.
        -
        booleanisIntermediate() -
        Intermediate tensors are internal non-user tensors which may be eligible - for deletion when further consumed by a Function.
        -
        default booleanisLeave() -
        Tensors which are used or produced by the autograd system will have a GraphNode component attached to them.
        -
        default booleanisOutsourced() -
        Outsourced means that the tensor is stored on a Device implementation instance which is not the CPU.
        -
        default booleanisPartialSlice() -
        If this nd-array is a partial slice of a parent nd-array then this method will yield true.
        -
        default booleanisShallowCopy() -
        If this nd-array is a shallow copy of a parent nd-array then this method will yield true.
        -
        default booleanisSlice() -
        If this nd-array is a slice of a parent nd-array then this method will yield true.
        -
        default booleanisSliceParent() -
        If slices have been derived from this nd-array then it is a "slice parent".
        -
        default booleanisUndefined() -
        A tensor is "undefined" if it has either no NDConfiguration implementation instance - or this instance does not have a shape set for this Tsr which is needed for - a tensor to also have a rank and dimensionality...
        -
        booleanisVirtual() -
        A Virtual tensor is a tensor whose underlying data array is of size 1, holding only a single value.
        -
        static <V> IterByOrIterFromOrAllTsr<V>like​(Tsr<V> template) -
        Use this factory method to instantiate a new tensor with the same data type, shape - and memory location (Device instance) as the provided template tensor.
        -
        default Tsr<V>ln() -
        This method is a functionally identical to the following alternatives:
        -
        default Tsr<V>log10() -
        This method is a functionally identical to the following alternatives:
        -
        default Tsr<V>map​(java.util.function.Function<V,​V> mapper) -
        - This method is a convenience method for mapping the items of this nd-array to another - nd-array of the same type based on the provided lambda function, which will be applied - to all items of this nd-array individually (element-wise).
        -
        default <T> Tsr<T>mapTo​(java.lang.Class<T> typeClass, - java.util.function.Function<V,​T> mapper) -
        - This is a convenience method for mapping a nd-array to a nd-array of new type - based on a provided target item type and mapping lambda.
        -
        default Tsr<V>matMul​(Tsr<V> other) -
        This will produce the matrix product of - two tensors with rank 2 (matrices), where the left operand is this Tsr - instance and the right operand is the argument passed to the method.
        -
        default Tsr<V>max() -
        Calculate the max value of all values - within this tensor and returns it - in the form of a scalar tensor.
        -
        default Tsr<V>mean() -
        Calculate the mean value of all values - within this tensor and returns it - in the form of a scalar tensor.
        -
        default Tsr<V>min() -
        Calculate the min value of all values - within this tensor and returns it - in the form of a scalar tensor.
        -
        default Tsr<V>minus​(Tsr<V> other) -
        Performs subtraction on - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method.
        -
        default Tsr<V>minus​(V other) -
        This method will create a new Tsr - with the provided item subtracted from all elements of this Tsr.
        -
        default Tsr<V>mod​(int other) 
        default Tsr<V>mod​(Tsr<V> other) -
        Produces the modulus of - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method.
        -
        default Tsr<V>multiply​(double value) 
        default Tsr<V>multiply​(Tsr<V> other) -
        This method is synonymous to the times(Tsr) method.
        -
        default Tsr<V>multiply​(V other) 
        default MutateTsr<V>mut() -
        This method exposes an API for mutating the state of this tensor.
        -
        default Tsr<V>neg() -
        This method is a functionally identical to the following alternatives:
        -
        default Tsr<V>negative() 
        static Tsr<java.lang.Object>newInstance() -
        This static factory method creates and return a completely empty and undefined tensor - which is void of any contents and meaning.
        -
        static Tsr<java.lang.Boolean>of​(boolean... value) -
        Constructs a vector of booleans based on the provided array.
        -
        static Tsr<java.lang.Byte>of​(byte value) -
        Constructs a vector of bytes based on the provided array.
        -
        static Tsr<java.lang.Byte>of​(byte... value) -
        Constructs a vector of bytes based on the provided array.
        -
        static Tsr<java.lang.Double>of​(double value) 
        static Tsr<java.lang.Double>of​(double... value) -
        Constructs a vector of doubles based on the provided array.
        -
        static Tsr<java.lang.Float>of​(float value) -
        Constructs a vector of floats based on the provided array.
        -
        static Tsr<java.lang.Float>of​(float... value) -
        Constructs a vector of floats based on the provided array.
        -
        static Tsr<java.lang.Integer>of​(int value) -
        Constructs a vector of ints based on the provided array.
        -
        static Tsr<java.lang.Integer>of​(int... value) -
        Constructs a vector of ints based on the provided array.
        -
        static Tsr<java.lang.Long>of​(long value) -
        Constructs a vector of longs based on the provided array.
        -
        static Tsr<java.lang.Long>of​(long... value) -
        Constructs a vector of longs based on the provided array.
        -
        static Tsr<java.lang.Short>of​(short value) -
        Constructs a vector of shorts based on the provided array.
        -
        static Tsr<java.lang.Short>of​(short... value) -
        Constructs a vector of shorts based on the provided array.
        -
        static <T> Tsr<T>of​(java.lang.Class<T> type, - java.util.List<java.lang.Object> conf) -
        This factory method will turn a list of values or nested lists of values into a Tsr - instance with the corresponding rank and shape and whose values - are of the provided type.
        -
        static <V> WithShapeOrScalarOrVectorOnDevice<V>of​(java.lang.Class<V> type) -
        This is the entry point to the fluent tensor builder API for building - Tsr instances in a readable and type safe fashion.
        -
        static <V> Tsr<V>of​(java.lang.Class<V> type, - java.util.List<java.lang.Integer> shape, - java.lang.Object data) -
        Use this to construct and return a tensor of the specified type, shape and data object.
        -
        static <V> Tsr<V>of​(java.lang.Class<V> type, - java.util.List<java.lang.Integer> shape, - java.util.List<V> data) -
        Use this to construct and return a tensor of the specified type, shape and data object.
        -
        static <V extends java.lang.Number>
        Tsr<V>        		of​(java.lang.Class<V> type, - Shape shape, - java.lang.Number data) -
        Use this to construct and return a tensor of the specified type, shape and number.
        -
        static <V> Tsr<V>of​(java.lang.Class<V> type, - Shape shape, - java.lang.Object data) -
        Use this to construct and return a tensor of the specified type, shape and data object.
        -
        static <V> Tsr<V>of​(java.lang.Class<V> type, - Shape shape, - java.util.List<V> data) -
        Use this to construct and return a tensor of the specified type, shape and list of items.
        -
        static <V> Tsr<V>of​(java.lang.Class<V> valueType, - Shape shape, - Arg.Seed seed) -
        Use this to construct and return a seeded tensor of the specified type.
        -
        static <T> Tsr<T>of​(java.lang.Iterable<T> iterable) -
        Constructs a vector of objects based on the provided iterable.
        -
        static <T> Tsr<T>of​(java.lang.Object... args) -
        This static Tsr factory method tries to interpret the provided - arguments to create the instance the use might wants.
        -
        static <V> Tsr<V>of​(java.lang.String expression, - boolean doAD, - java.util.List<Tsr<V>> tensors) -
        This method takes a list of tensors and a String expression describing - operations which ought to be applied to the tensors in said list.
        -
        static <V> Tsr<V>of​(java.lang.String expression, - boolean doAD, - Tsr<V>... tensors) -
        This method takes an array of tensors and a String expression describing - operations which ought to be applied to the tensors in said array.
        -
        static <V> Tsr<V>of​(java.lang.String expression, - java.util.List<Tsr<V>> inputs) -
        This factory method allows for the creation and execution of Function instances - without actually instantiating them manually, - where the result will then be returned by this factory method.
        -
        static <T> Tsr<T>of​(java.lang.String e1, - Tsr<T> a, - char o, - Tsr<T> b, - java.lang.String e2) -
        Use this to conveniently operate on 2 tensors.
        -
        static <T> Tsr<T>of​(java.lang.String e1, - Tsr<T> a, - java.lang.String e2) -
        Use this to conveniently operate on a tensor.
        -
        static <T> Tsr<T>of​(java.lang.String e1, - Tsr<T> a, - java.lang.String e2, - Tsr<T> b, - java.lang.String e3, - Tsr<T> c, - java.lang.String e4) -
        Use this to conveniently operate on 3 tensors.
        -
        static <V> Tsr<V>of​(java.lang.String expression, - Tsr<V> tensor) -
        This method takes a tensor and a String expression describing - operations which ought to be applied to said tensor.
        -
        static <V> Tsr<V>of​(java.lang.String expression, - Tsr<V>... tensors) -
        This method takes an array of tensors and a String expression describing - operations which ought to be applied to the tensors in said array.
        -
        static <V extends java.lang.Number>
        Tsr<V>        		of​(java.lang.String expression, - V... inputs) -
        This factory method allows for the creation and execution of Function instances - without actually instantiating them manually, - where the result will then be returned by this factory method.
        -
        static Tsr<java.lang.Double>of​(java.util.List<? extends java.lang.Number> shape, - java.lang.String seed) -
        This factory method will create and return a Tsr instance - based on a list of Number instances whose rounded values will be interpreted as - the shape of this new Tsr instance and a seed which will serve - as a source of pseudo randomness to generate the values for the new instance.
        -
        static <V> Tsr<V>of​(java.util.List<? extends java.lang.Number> shape, - java.util.List<V> items) -
        Creates a new Tsr instance based on a list of numbers representing the shape, - and a list of values representing the value of the resulting tensor.
        -
        static <T> Tsr<T>of​(java.util.List<java.lang.Integer> shape, - T item) -
        This is a convenient factory method for creating Tsr instances for - values of type Tsr based on a list of integers - defining a shape made up of axes sizes as well as a scalar value of type Tsr - which will fill out the data array spanned by the provided shape information.
        -
        static Tsr<java.lang.Object>of​(java.util.List<java.lang.Object> conf) -
        This factory method will turn a list of values or nested lists of values into a Tsr - instance with the corresponding rank and shape.
        -
        static <T> Tsr<T>of​(DataType<T> type, - int[] shape, - Filler<T> filler) -
        This factory method allows the creation of tensors with an additional initialization - lambda for filling the underlying data array with desired values.
        -
        static <T> Tsr<T>of​(DataType<T> type, - java.util.List<java.lang.Integer> shape, - Filler<T> filler) -
        This factory method allows the creation of tensors with an additional initialization - lambda for filling the underlying data array with desired values.
        -
        static <T> Tsr<T>of​(DataType<T> type, - Shape shape, - Filler<T> filler) -
        This factory method allows the creation of tensors with an additional initialization - lambda for filling the underlying data array with desired values.
        -
        static <V> Tsr<V>of​(DataType<V> dataType, - int[] shape, - java.lang.Object data) -
        This factory method is among the most flexible and forgiving ways to create a Tsr instance.
        -
        static <V> Tsr<V>of​(DataType<V> dataType, - java.util.List<java.lang.Integer> shape, - java.util.List<V> data) -
        Use this to construct and return a tensor of the specified type, shape and data object.
        -
        static <V extends N,​N>
        Tsr<V>        		of​(DataType<V> dataType, - Device<N> device, - Shape shape, - java.lang.Object data) -
        This factory method is among the most flexible and forgiving ways to create a Tsr instance.
        -
        static <V> Tsr<V>of​(DataType<V> dataType, - NDConstructor ndConstructor, - Data<V> data) -
        This factory method a raw tensor constructor which will not perform any type checking - or data conversion on the data provided to it.
        -
        static <V> Tsr<V>of​(DataType<V> type, - Shape shape) -
        Use this to construct and return a tensor of the specified type and shape.
        -
        static <V> Tsr<V>of​(DataType<V> dataType, - Shape shape, - java.lang.Object data) -
        This factory method is among the most flexible and forgiving ways to create a Tsr instance.
        -
        static <V> Tsr<V>of​(DataType<V> dataType, - Shape shape, - java.util.List<V> data) -
        Use this to construct and return a tensor of the specified type, shape and a list of items.
        -
        static Tsr<java.lang.Boolean>of​(Shape shape, - boolean[] values) -
        Use this to construct and return a boolean tensor of the specified shape and initial values.
        -
        static Tsr<java.lang.Byte>of​(Shape shape, - byte[] values) -
        Use this to construct and return a byte tensor of the specified shape and initial values.
        -
        static Tsr<java.lang.Double>of​(Shape shape, - double value) -
        Use this to construct and return a homogeneously populated double tensor of the specified shape.
        -
        static Tsr<java.lang.Double>of​(Shape shape, - double[] values) -
        Use this to construct and return a double tensor of the specified shape and initial values.
        -
        static Tsr<java.lang.Float>of​(Shape shape, - float value) -
        Use this to construct and return a homogeneously populated float tensor of the specified shape.
        -
        static Tsr<java.lang.Float>of​(Shape shape, - float[] values) -
        Use this to construct and return a float tensor of the specified shape and initial values.
        -
        static Tsr<java.lang.Integer>of​(Shape shape, - int[] values) -
        Use this to construct and return an int tensor of the specified shape and initial values.
        -
        static Tsr<java.lang.Long>of​(Shape shape, - long[] values) -
        Use this to construct and return a long tensor of the specified shape and initial values.
        -
        static Tsr<java.lang.Short>of​(Shape shape, - short[] values) -
        Use this to construct and return a short tensor of the specified shape and initial values.
        -
        static <V> Tsr<V>of​(Shape shape, - java.util.List<V> items) -
        Creates a new Tsr instance based on a shape tuple of numbers representing the nd-array shape, - and a list of items representing the value of the resulting tensor.
        -
        static <V> Tsr<V>of​(Shape shape, - Data<V> data) -
        Use this to construct and return a tensor of the specified shape and data object.
        - This method is typically used like this:
        -
        static <T> Tsr<T>of​(Shape shape, - T value) -
        This is a convenient factory method for creating Tsr instances for - representing items of type Tsr.
        -
        static <T> Tsr<T>of​(Tsr<T> a, - char o, - Tsr<T> b) -
        Use this to conveniently operate on 2 tensors.
        -
        static <T> Tsr<T>of​(Tsr<T> a, - char o1, - Tsr<T> b, - char o2, - Tsr<T> c) -
        Use this to conveniently operate on 3 tensors.
        -
        static <V> Tsr<V>ofAny​(java.lang.Class<V> type, - Shape shape, - java.lang.Object data) -
        Use this to construct and return a tensor of the specified type, shape and data object.
        -
        static WithShapeOrScalarOrVectorOnDevice<java.lang.Byte>ofBytes() -
        This is a simple convenience method which is simply calling the of(Class) - method like so: of(Byte.class).
        -
        static WithShapeOrScalarOrVectorOnDevice<java.lang.Double>ofDoubles() -
        This is a simple convenience method which is simply calling the of(Class) - method like so: of(Double.class).
        -
        static WithShapeOrScalarOrVectorOnDevice<java.lang.Float>ofFloats() -
        This is a simple convenience method which is simply calling the of(Class) - method like so: of(Float.class).
        -
        static WithShapeOrScalarOrVectorOnDevice<java.lang.Integer>ofInts() -
        This is a simple convenience method which is simply calling the of(Class) - method like so: of(Integer.class).
        -
        static <V> Tsr<V>ofRandom​(java.lang.Class<V> valueTypeClass, - int... shape) -
        This factory method produces a randomly populated tensor of the provided - type and shape using a hard coded default seed.
        -
        static WithShapeOrScalarOrVectorOnDevice<java.lang.Short>ofShorts() -
        This is a simple convenience method which is simply calling the of(Class) - method like so: of(Short.class).
        -
        default Tsr<V>permute​(int... dims) -
        Returns a view of the original tensor input with its dimensions permuted.
        - Consider a 3-dimensional tensor x with shape (2×3×5), - then calling x.permute(1, 0, 2) will return a 3-dimensional tensor of shape (3×2×5).
        -
        default Tsr<V>plus​(Tsr<V> other) -
        This method will produce the addition of - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method.
        -
        default Tsr<V>plus​(V value) -
        This method will create a new Tsr - with the provided double scalar added to all elements of this Tsr.
        -
        default Tsr<V>power​(Tsr<V> other) -
        This will produce the power of - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method.
        -
        default Tsr<V>power​(V value) -
        Raises all items of this tensor to the power of the provided value.
        -
        default Tsr<V>relu() -
        This method is a functionally identical to the following alternatives:
        -
        default Tsr<V>rem​(int other) -
        This method is synonymous to the mod(int) method.
        -
        default Tsr<V>reshape​(int... shape) -
        Returns a nd-array with the same data and number of elements as this nd-array, but with the specified shape.
        -
        booleanrqsGradient() -
        This flag will indirectly trigger the activation of the autograd / auto-differentiation system of this library! - If the flag is set to 'true' and the tensor is used for computation then - it will also receive gradients when the backward() method is being called - on any descendant tensor within the computation graph.
        -
        default Tsr<V>set​(OptimizerFactory optimizerFactory) -
        Configures an Optimizer for this tensor based on the given OptimizerFactory - which will be used to create a new Optimizer instance specific to this tensor.
        -
        Tsr<V>setGradientApplyRequested​(boolean applyRequested) -
        This flag works alongside two autograd features which can be enabled inside the library settings.
        -
        Tsr<V>setRqsGradient​(boolean rqsGradient) -
        Setting this flag to true will tell the autograd system to accumulate gradients at this tensor.
        -
        default Tsr<V>shallowClone() 
        default Tsr<V>shallowCopy() -
        This creates a copy where the underlying data is still the same.
        -
        static <T> java.util.stream.Collector<T,​?,​Tsr<T>>shaped​(int... shape) -
        Returns a Collector that accumulates the input elements into a - new Tsr with the specified shape.
        -
        static <T> java.util.stream.Collector<T,​?,​Tsr<T>>shaped​(Shape shape) -
        Returns a Collector that accumulates the input elements into a - new Tsr with the specified shape.
        -
        default Tsr<V>sig() -
        This method is a functionally identical to the following alternatives:
        -
        default Tsr<V>sigmoid() 
        default Tsr<V>sin() -
        This method is a functionally identical to the following alternatives:
        -
        AxisOrGetTsr<V>slice() -
        This method returns a SliceBuilder instance exposing a simple builder API - which enables the configuration of a slice of the current nd-array via method chaining.
        -
        default intsliceCount() -
        This method returns the number of slices which have been - created from this nd-array.
        -
        default Tsr<V>softmax() 
        default Tsr<V>softmax​(int axis) 
        default Tsr<V>softmax​(int... axes) -
        Calculates the softmax function along the specified axes.
        -
        default Tsr<V>softplus() -
        This method is a functionally identical to the following alternatives:
        -
        default Tsr<V>sqrt() -
        This method is a functionally identical to the following alternatives:
        -
        default Tsr<V>sum() -
        Calculate the sum value of all values - within this tensor and returns it - in the form of a scalar tensor.
        -
        default Tsr<V>sum​(int axis) -
        Calculate the sum value of all values - within this tensor along the specified axis and returns it - in the form of a tensor.
        -
        default Tsr<V>sum​(int... axes) -
        Calculate the sum value of all values - within this tensor along the specified axes and returns it - in the form of a tensor.
        -
        default Tsr<V>T() -
        Creates and returns a new Tsr instance which is a transposed twin of this instance.
        - This is a shorter alternative to the functionally identical getT() method.
        -
        default Tsr<V>tanh() -
        This method is a functionally identical to the following alternatives:
        -
        default Tsr<V>times​(Tsr<V> other) -
        This is a functionally identical synonym to the multiply(Tsr) method.
        -
        default Tsr<V>times​(V other) 
        default Tsr<V>to​(java.lang.String deviceType) 
        Tsr<V>to​(Device<?> device) -
        This method takes a Device and tries to migrate the contents of this Tsr - instance to that Device!
        -
        default java.lang.StringtoString​(java.lang.String conf) 
        default java.lang.StringtoString​(java.util.function.Consumer<NDPrintSettings> configurator) -
        This allows you to provide a lambda which configures how this nd-array should be - converted to String instances.
        -
        default java.lang.StringtoString​(NDPrintSettings config) -
        Use this to turn this nd-array into a String instance based on the provided - NDPrintSettings instance, which allows you to configure things - like the number of chars per entry, delimiters, the number of items per line, etc.
        -
        default Tsr<V>transpose​(int dim1, - int dim2) -
        Returns a view of the original tensor input the targeted - axes are swapped / transposed.
        -
        default booleanupdate​(Component.OwnerChangeRequest<Tsr<V>> changeRequest) -
        Important : Components of type Tsr are simply gradients! - Currently, this method is used only to catch illegal arguments which - is for example the case when trying to attach a gradient with a different shape...
        -
        Tsr<V>withLabel​(java.lang.String label)
        Tsr<V>withLabels​(java.lang.String[]... labels) -
        This method receives a nested String array which - ought to contain a label for the index of this nd-array.
        -
        Tsr<V>withLabels​(java.util.List<java.util.List<java.lang.Object>> labels) -
        This method receives a nested String list which - ought to contain a label for the index of this nd-array.
        -
        Tsr<V>withLabels​(java.util.Map<java.lang.Object,​java.util.List<java.lang.Object>> labels) -
        This method provides the ability to - label not only the indices of the shape of this nd-array, but also - the dimension of the shape.
        -
        default Tsr<V>xor​(double value) -
        This method is a functionally identical synonym to the power(Tsr) method.
        -
        default Tsr<V>xor​(Tsr<V> other) -
        This method is a functionally identical synonym to the power(Tsr) method.
        -
        - -
          -
        • - - -

          Methods inherited from interface java.lang.Iterable

          -forEach, iterator, spliterator
          • -
        - - -
        • -
      -
      -
      • -
    -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Detail

        - - - -
          -
        • -

          newInstance

          -
          static Tsr<java.lang.Object> newInstance()
          -
          This static factory method creates and return a completely empty and undefined tensor - which is void of any contents and meaning. - The use case for this would be to use the produced Tsr - instance as a target for an inline operations which fills the instance with an actual value.
          - An example of this approach would be to call the MutateTsr.putAt(List, Nda) method with an empty list as key. - This will be interpreted as an inline copy of the contents of the - second parameter into this Tsr instance.
          -
          -
          Returns:
          -
          A new and completely empty / uninitialized Tsr instance.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(Tsr<T> a,
-                     char o,
-                     Tsr<T> b)
          -
          Use this to conveniently operate on 2 tensors. - A simple example would be: of(a,'*',b).
          -
          -
          Type Parameters:
          -
          T - The value item type parameter for the involved tensors.
          -
          Parameters:
          -
          a - The left operand.
          -
          o - The operator, which may be '+', '-', '*'...
          -
          b - The right operand.
          -
          Returns:
          -
          The result of the operation defined by the provided character.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(Tsr<T> a,
-                     char o1,
-                     Tsr<T> b,
-                     char o2,
-                     Tsr<T> c)
          -
          Use this to conveniently operate on 3 tensors. - A simple example would be: of(a,'*',b,'+',c).
          -
          -
          Type Parameters:
          -
          T - The value item type parameter for the involved tensors.
          -
          Parameters:
          -
          a - The first and left most operand.
          -
          o1 - The first operator, which may be '+', '-', '*'...
          -
          b - The second operand.
          -
          o2 - The second operator, which may also be '+', '-', '*'...
          -
          c - The third and last operand.
          -
          Returns:
          -
          The result of the operations defined by the 2 provided characters.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(java.lang.String e1,
-                     Tsr<T> a,
-                     java.lang.String e2)
          -
          Use this to conveniently operate on a tensor. - A simple example would be: of("sig(tanh(",a,"))").
          -
          -
          Type Parameters:
          -
          T - The value item type parameter for the involved tensor.
          -
          Parameters:
          -
          e1 - The first part of the string expression defining how the provided tensor should be processed.
          -
          a - The tensor which ought to be sent to whatever is defined by the provided expressions.
          -
          e2 - The latter part of the expression defining how the provided tensor should be executed.
          -
          Returns:
          -
          The result of the operation(s) defined by the provided strings.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(java.lang.String e1,
-                     Tsr<T> a,
-                     char o,
-                     Tsr<T> b,
-                     java.lang.String e2)
          -
          Use this to conveniently operate on 2 tensors. - A simple example would be: of("relu(",a,'-',b,")*2").
          -
          -
          Type Parameters:
          -
          T - The value item type parameter for the involved tensor.
          -
          Parameters:
          -
          e1 - The first part of the string expression defining how the provided tensor should be processed.
          -
          a - The first tensor which ought to be sent to whatever function is defined by the provided expressions.
          -
          o - An operator combining both a and b to form a result.
          -
          b - The second tensor and right operand which ought to be sent to whatever function is defined by the provided expressions.
          -
          e2 - The latter part of the expression defining how the provided tensor should be executed.
          -
          Returns:
          -
          The result of the operation(s) defined by the provided strings.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(java.lang.String e1,
-                     Tsr<T> a,
-                     java.lang.String e2,
-                     Tsr<T> b,
-                     java.lang.String e3,
-                     Tsr<T> c,
-                     java.lang.String e4)
          -
          Use this to conveniently operate on 3 tensors. - A simple example would be: - of("abs((",a,"-",b,") * ",c,")").
          -
          -
          Type Parameters:
          -
          T - The type parameter for the involved tensors.
          -
          Parameters:
          -
          e1 - The first part of the expression which would typically be used to define a function name.
          -
          a - The first argument.
          -
          e2 - The second part of the expression, which might be an operation.
          -
          b - The second argument.
          -
          e3 - The third part of the expression...
          -
          c - The third argument.
          -
          e4 - The last part of the expression which should syntactically match the other expression...
          -
          Returns:
          -
          The result of the calculation defined by the provided expressions and arguments.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(java.lang.Object... args)
          -
          This static Tsr factory method tries to interpret the provided - arguments to create the instance the use might wants.
          -
          -
          Parameters:
          -
          args - The arguments which ought to be interpreted.
          -
          Returns:
          -
          The result of the interpretation in the form of a Tsr instance of typ Object.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(java.lang.Iterable<T> iterable)
          -
          Constructs a vector of objects based on the provided iterable.
          -
          -
          Parameters:
          -
          iterable - The iterable of objects from which a 1D nd-array ought to be constructed.
          -
          Returns:
          -
          A vector / 1D tensor of objects.
          -
          -
          • -
        - - - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(java.util.List<java.lang.Integer> shape,
-                     T item)
          -
          This is a convenient factory method for creating Tsr instances for - values of type Tsr based on a list of integers - defining a shape made up of axes sizes as well as a scalar value of type Tsr - which will fill out the data array spanned by the provided shape information.
          -
          -
          Parameters:
          -
          shape - A list of integers whose values ought to define the size of the axes of the shape of the new Tsr.
          -
          item - An object of type Tsr which will populate the data array of the new instance.
          -
          Returns:
          -
          A new Tsr instance for the generic type Tsr.
          -
          -
          • -
        - - - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(Shape shape,
-                     T value)
          -
          This is a convenient factory method for creating Tsr instances for - representing items of type Tsr. The factory method - instantiates tensors based on a Shape tuple of integers - defining axes sizes, and a scalar item of type Tsr - which will fill out the data array spanned by the provided shape information. - A simple usage example would be: - 
          
-     Tsr.of(Shape.of( 4, 3, 6 ), 42);
-
          -
          -
          Parameters:
          -
          shape - An immutable tuple of integers whose values ought to define the size of the axes of the shape of the new Tsr.
          -
          value - An object of type Tsr which will populate the data array of the new instance.
          -
          Returns:
          -
          A new Tsr instance for the generic type Tsr.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Double> of​(java.util.List<? extends java.lang.Number> shape,
-                                java.lang.String seed)
          -
          This factory method will create and return a Tsr instance - based on a list of Number instances whose rounded values will be interpreted as - the shape of this new Tsr instance and a seed which will serve - as a source of pseudo randomness to generate the values for the new instance.
          -
          -
          Parameters:
          -
          shape - A list of Number instances which will be interpreted as a shape array.
          -
          seed - A source of pseudo randomness for the Tsr instance created by this method.
          -
          Returns:
          -
          A new Tsr instance created based on a shape and a seed.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(java.util.List<? extends java.lang.Number> shape,
-                     java.util.List<V> items)
          -
          Creates a new Tsr instance based on a list of numbers representing the shape, - and a list of values representing the value of the resulting tensor.
          -
          -
          Type Parameters:
          -
          V - The type parameter of the value list and returned tensor.
          -
          Parameters:
          -
          shape - A list of numbers whose integer values will be used to form the shape of the resulting Tsr.
          -
          items - A list of values which will be used to populate the data array of the resulting Tsr.
          -
          Returns:
          -
          A new Tsr instance constructed based on the provided shape and value list.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(Shape shape,
-                     java.util.List<V> items)
          -
          Creates a new Tsr instance based on a shape tuple of numbers representing the nd-array shape, - and a list of items representing the value of the resulting tensor.
          - A simple usage example would be: - 
          
-     Tsr.of(Shape.of( 2, 3, 4 ), myListOfItems);
-
          -
          -
          Type Parameters:
          -
          V - The type parameter of the value list and returned tensor.
          -
          Parameters:
          -
          shape - A shape tuple of numbers whose integer values will be used to form the shape of the resulting Tsr.
          -
          items - A list of values which will be used to populate the data array of the resulting Tsr.
          -
          Returns:
          -
          A new Tsr instance constructed based on the provided shape and value list.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Object> of​(java.util.List<java.lang.Object> conf)
          -
          This factory method will turn a list of values or nested lists of values into a Tsr - instance with the corresponding rank and shape.
          -
          -
          Parameters:
          -
          conf - A list of either values or nested lists which are themselves either or.
          -
          Returns:
          -
          A new Tsr instance whose shape and data is based on the provided list structure.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(java.lang.Class<T> type,
-                     java.util.List<java.lang.Object> conf)
          -
          This factory method will turn a list of values or nested lists of values into a Tsr - instance with the corresponding rank and shape and whose values - are of the provided type.
          -
          -
          Type Parameters:
          -
          T - The type parameter of the tensor returned by this factory method.
          -
          Parameters:
          -
          type - The type of the tensor produced by this factory method.
          -
          conf - A list of either values or nested lists which are themselves either or.
          -
          Returns:
          -
          A new Tsr instance whose shape and data is based on the provided list structure.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> WithShapeOrScalarOrVectorOnDevice<V> of​(java.lang.Class<V> type)
          -
          This is the entry point to the fluent tensor builder API for building - Tsr instances in a readable and type safe fashion. - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tsr builder API which will lead to the creation - of a tensor storing values defined by the provided type class. - A simple usage example would be: - 
          
-      Tsr.of(Double.class)
-            .withShape( 2, 3, 4 )
-            .andFill( 5, 3, 5 )
-
          - - It is also possible to define a range using the API to populate the tensor with values: - 
          
-      Tsr.of(Double.class)
-            .withShape( 2, 3, 4 )
-            .andFillFrom( 2 ).to( 9 ).step( 2 )
-
          - - If one needs a simple scalar then the following shortcut is possible: - 
          
-      Tsr.of(Float.class).scalar( 3f )
-
          - - This principle works for vectors as well: - 
          
-       Tsr.of(Byte.class).vector( 2, 5, 6, 7, 8 )
-
          - For more fine-grained control over the initialization one can - pass an initialization lambda to the API: - 
          
-       Tsr.of(Byte.class).withShape(2, 3).andWhere( (i, indices) -> i * 5 - 30 )
-
          -
          - Consider using the following convenience methods: - ofFloats(), ofDoubles(), ofInts(), ofBytes(), ofShorts()
          -
          -
          Parameters:
          -
          type - The type class of the items stored by the tensor built by the exposed builder API.
          -
          Returns:
          -
          The next step of the Tsr builder API which exposes methods for defining shapes.
          -
          -
          • -
        - - - -
          -
        • -

          ofDoubles

          -
          static WithShapeOrScalarOrVectorOnDevice<java.lang.Double> ofDoubles()
          -
          This is a simple convenience method which is simply calling the of(Class) - method like so: of(Double.class). - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tsr builder API which in this case will lead to the creation - of a tensor storing doubles.
          - A simple usage example would be: - 
          
-     Tsr.ofDoubles()
-           .withShape( 2, 3, 4 )
-           .andFill( 5d, 3d, 5d )
-
          -
          -
          Returns:
          -
          The next step of the Tsr builder API which exposes methods for defining shapes.
          -
          -
          • -
        - - - -
          -
        • -

          ofFloats

          -
          static WithShapeOrScalarOrVectorOnDevice<java.lang.Float> ofFloats()
          -
          This is a simple convenience method which is simply calling the of(Class) - method like so: of(Float.class). - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tsr builder API which in this case will lead to the creation - of a tensor storing floats.
          - A simple usage example would be: - 
          
-     Tsr.ofFloats()
-           .withShape( 2, 3, 4 )
-           .andFill( 5f, 7f, 11f )
-
          -
          -
          Returns:
          -
          The next step of the Tsr builder API which exposes methods for defining shapes.
          -
          -
          • -
        - - - -
          -
        • -

          ofInts

          -
          static WithShapeOrScalarOrVectorOnDevice<java.lang.Integer> ofInts()
          -
          This is a simple convenience method which is simply calling the of(Class) - method like so: of(Integer.class). - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tsr builder API which in this case will lead to the creation - of a tensor storing integers.
          -
          -
          Returns:
          -
          The next step of the Tsr builder API which exposes methods for defining shapes.
          -
          -
          • -
        - - - -
          -
        • -

          ofShorts

          -
          static WithShapeOrScalarOrVectorOnDevice<java.lang.Short> ofShorts()
          -
          This is a simple convenience method which is simply calling the of(Class) - method like so: of(Short.class). - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tsr builder API which in this case will lead to the creation - of a tensor storing shorts.
          -
          -
          Returns:
          -
          The next step of the Tsr builder API which exposes methods for defining shapes.
          -
          -
          • -
        - - - -
          -
        • -

          ofBytes

          -
          static WithShapeOrScalarOrVectorOnDevice<java.lang.Byte> ofBytes()
          -
          This is a simple convenience method which is simply calling the of(Class) - method like so: of(Byte.class). - The returned WithShapeOrScalarOrVector is the next step in the - fluent Tsr builder API which in this case will lead to the creation - of a tensor storing bytes.
          -
          -
          Returns:
          -
          The next step of the Tsr builder API which exposes methods for defining shapes.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Double> of​(double... value)
          -
          Constructs a vector of doubles based on the provided array.
          -
          -
          Parameters:
          -
          value - The array of doubles from which a 1D tensor ought to be constructed.
          -
          Returns:
          -
          A vector / 1D tensor of doubles.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Double> of​(double value)
          -
          -
          Parameters:
          -
          value - The scalar value which ought to be represented as tensor.
          -
          Returns:
          -
          A scalar double tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Float> of​(float... value)
          -
          Constructs a vector of floats based on the provided array.
          -
          -
          Parameters:
          -
          value - The array of floats from which a 1D tensor ought to be constructed.
          -
          Returns:
          -
          A vector / 1D tensor of floats.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Float> of​(float value)
          -
          Description copied from interface: Nda
          -
          Constructs a vector of floats based on the provided array.
          -
          -
          Parameters:
          -
          value - The scalar value which ought to be represented as tensor.
          -
          Returns:
          -
          A scalar float tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Byte> of​(byte... value)
          -
          Constructs a vector of bytes based on the provided array.
          -
          -
          Parameters:
          -
          value - The array of bytes from which a 1D tensor ought to be constructed.
          -
          Returns:
          -
          A vector / 1D tensor of bytes.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Byte> of​(byte value)
          -
          Description copied from interface: Nda
          -
          Constructs a vector of bytes based on the provided array.
          -
          -
          Parameters:
          -
          value - The scalar value which ought to be represented as tensor.
          -
          Returns:
          -
          A scalar byte tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Integer> of​(int... value)
          -
          Constructs a vector of ints based on the provided array.
          -
          -
          Parameters:
          -
          value - The array of ints from which a 1D tensor ought to be constructed.
          -
          Returns:
          -
          A vector / 1D tensor of ints.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Integer> of​(int value)
          -
          Description copied from interface: Nda
          -
          Constructs a vector of ints based on the provided array.
          -
          -
          Parameters:
          -
          value - The scalar value which ought to be represented as tensor.
          -
          Returns:
          -
          A scalar int tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Long> of​(long... value)
          -
          Constructs a vector of longs based on the provided array.
          -
          -
          Parameters:
          -
          value - The array of longs from which a 1D tensor ought to be constructed.
          -
          Returns:
          -
          A vector / 1D tensor of longs.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Long> of​(long value)
          -
          Description copied from interface: Nda
          -
          Constructs a vector of longs based on the provided array.
          -
          -
          Parameters:
          -
          value - The scalar value which ought to be represented as tensor.
          -
          Returns:
          -
          A scalar long tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Short> of​(short... value)
          -
          Constructs a vector of shorts based on the provided array.
          -
          -
          Parameters:
          -
          value - The array of shorts from which a 1D tensor ought to be constructed.
          -
          Returns:
          -
          A vector / 1D tensor of shorts.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Short> of​(short value)
          -
          Description copied from interface: Nda
          -
          Constructs a vector of shorts based on the provided array.
          -
          -
          Parameters:
          -
          value - The scalar value which ought to be represented as tensor.
          -
          Returns:
          -
          A scalar short tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Boolean> of​(boolean... value)
          -
          Constructs a vector of booleans based on the provided array.
          -
          -
          Parameters:
          -
          value - The array of booleans from which a 1D tensor ought to be constructed.
          -
          Returns:
          -
          A vector / 1D tensor of shorts.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(java.lang.Class<V> valueType,
-                     Shape shape,
-                     Arg.Seed seed)
          -
          Use this to construct and return a seeded tensor of the specified type.
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          valueType - The type class of the items stored by the resulting tensor.
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          seed - An arbitrary String whose hash will be used to as a seed.
          -
          Returns:
          -
          A newly created and seeded tensor of the provided type and shape.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Double> of​(Shape shape,
-                                double value)
          -
          Use this to construct and return a homogeneously populated double tensor of the specified shape.
          -
          -
          Parameters:
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          value - The value which ought to be used to populate the tensor homogeneously.
          -
          Returns:
          -
          A new tensor instance with the provided shape and initial value.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Double> of​(Shape shape,
-                                double[] values)
          -
          Use this to construct and return a double tensor of the specified shape and initial values. - The length of the provided array does not have to match the number of elements - defined by the provided shape, the tensor will be populated based on repeated iteration over the - provided double array.
          -
          -
          Parameters:
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          values - The values which ought to be used to populate the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Integer> of​(Shape shape,
-                                 int[] values)
          -
          Use this to construct and return an int tensor of the specified shape and initial values. - The length of the provided array does not have to match the number of elements - defined by the provided shape, the tensor will be populated based on repeated iteration over the - provided int array.
          -
          -
          Parameters:
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          values - The values which ought to be used to populate the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Byte> of​(Shape shape,
-                              byte[] values)
          -
          Use this to construct and return a byte tensor of the specified shape and initial values. - The length of the provided array does not have to match the number of elements - defined by the provided shape, the tensor will be populated based on repeated iteration over the - provided byte array..
          -
          -
          Parameters:
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          values - The values which ought to be used to populate the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Long> of​(Shape shape,
-                              long[] values)
          -
          Use this to construct and return a long tensor of the specified shape and initial values. - The length of the provided array does not have to match the number of elements - defined by the provided shape, the tensor will be populated based on repeated iteration over the - provided long array..
          -
          -
          Parameters:
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          values - The values which ought to be used to populate the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Short> of​(Shape shape,
-                               short[] values)
          -
          Use this to construct and return a short tensor of the specified shape and initial values. - The length of the provided array does not have to match the number of elements - defined by the provided shape, the tensor will be populated based on repeated iteration over the - provided short array..
          -
          -
          Parameters:
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          values - The values which ought to be used to populate the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Float> of​(Shape shape,
-                               float[] values)
          -
          Use this to construct and return a float tensor of the specified shape and initial values. - The length of the provided array does not have to match the number of elements - defined by the provided shape, the tensor will be populated based on repeated iteration over the - provided float array..
          -
          -
          Parameters:
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          values - The values which ought to be used to populate the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Float> of​(Shape shape,
-                               float value)
          -
          Use this to construct and return a homogeneously populated float tensor of the specified shape.
          -
          -
          Parameters:
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          value - The value which ought to be used to populate the tensor homogeneously.
          -
          Returns:
          -
          A new tensor instance with the provided shape and initial value.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static Tsr<java.lang.Boolean> of​(Shape shape,
-                                 boolean[] values)
          -
          Use this to construct and return a boolean tensor of the specified shape and initial values. - The length of the provided array does not have to match the number of elements - defined by the provided shape, the tensor will be populated based on repeated iteration over the - provided boolean array..
          -
          -
          Parameters:
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          values - The values which ought to be used to populate the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(Shape shape,
-                     Data<V> data)
          -
          Use this to construct and return a tensor of the specified shape and data object.
          - This method is typically used like this:
          - 
          
-      Tsr<Integer> tensor = Tsr.of( Shape.of(2,3), Data.of(1,2,3,4,5,6) );
-
          - The resulting tensor will have the shape [2,3] and the values [1,2,3,4,5,6].
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          data - The data object which contains the values to be used to populate the tensor.
          -
          Returns:
          -
          A newly created tensor of the provided shape and data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(DataType<V> type,
-                     Shape shape)
          -
          Use this to construct and return a tensor of the specified type and shape.
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          type - The type of the items stored by the resulting tensor.
          -
          shape - The shape of the resulting tensor consisting of any number of axis-sizes.
          -
          Returns:
          -
          A newly created tensor of the provided type and shape.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(java.lang.Class<V> type,
-                     Shape shape,
-                     java.lang.Object data)
          -
          Use this to construct and return a tensor of the specified type, shape and data object.
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          type - The type of the items stored by the resulting tensor.
          -
          shape - The shape of the resulting tensor consisting of an array of axis-sizes.
          -
          data - The data object which will be used to populate the tensor.
          -
          Returns:
          -
          A newly created tensor of the provided type, shape and data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(java.lang.Class<V> type,
-                     java.util.List<java.lang.Integer> shape,
-                     java.lang.Object data)
          -
          Use this to construct and return a tensor of the specified type, shape and data object.
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          type - The type of the items stored by the resulting tensor.
          -
          shape - The shape of the resulting tensor consisting of list of axis-sizes.
          -
          data - The data object which will be used to populate the tensor.
          -
          Returns:
          -
          A newly created tensor of the provided type, shape and data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V extends java.lang.Number> Tsr<V> of​(java.lang.Class<V> type,
-                                              Shape shape,
-                                              java.lang.Number data)
          -
          Use this to construct and return a tensor of the specified type, shape and number.
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          type - The type of the items stored by the resulting tensor.
          -
          shape - The shape of the resulting tensor consisting of a immutable tuple of axis-sizes.
          -
          data - The data object which will be used to populate the tensor.
          -
          Returns:
          -
          A newly created tensor of the provided type, shape and data.
          -
          -
          • -
        - - - -
          -
        • -

          ofAny

          -
          static <V> Tsr<V> ofAny​(java.lang.Class<V> type,
-                        Shape shape,
-                        java.lang.Object data)
          -
          Use this to construct and return a tensor of the specified type, shape and data object.
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          type - The type of the items stored by the resulting tensor.
          -
          shape - The shape of the resulting tensor consisting of a immutable tuple of axis-sizes.
          -
          data - The data object which will be used to populate the tensor.
          -
          Returns:
          -
          A newly created tensor of the provided type, shape and data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(java.lang.Class<V> type,
-                     java.util.List<java.lang.Integer> shape,
-                     java.util.List<V> data)
          -
          Use this to construct and return a tensor of the specified type, shape and data object.
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          type - The type of the items stored by the resulting tensor.
          -
          shape - The shape of the resulting tensor consisting of a list of axis-sizes.
          -
          data - The list of items which will be used to populate the tensor.
          -
          Returns:
          -
          A newly created tensor of the provided type, shape and data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(java.lang.Class<V> type,
-                     Shape shape,
-                     java.util.List<V> data)
          -
          Use this to construct and return a tensor of the specified type, shape and list of items. - Here a simple usage example:
          - 
          
-      Tsr<Float> tensor = Tsr.of( Float.class, Shape.of(2,3), List.of(1f,2f,3f,4f,5f,6f) );
-
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          type - The type of the items stored by the resulting tensor.
          -
          shape - The shape of the resulting tensor consisting of an immutable tuple of axis-sizes.
          -
          data - The list of items which will be used to populate the tensor.
          -
          Returns:
          -
          A newly created tensor of the provided type, shape and data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(DataType<V> dataType,
-                     java.util.List<java.lang.Integer> shape,
-                     java.util.List<V> data)
          -
          Use this to construct and return a tensor of the specified type, shape and data object.
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          dataType - The type of the items stored by the resulting tensor.
          -
          shape - The shape of the resulting tensor consisting of a list of axis-sizes.
          -
          data - The data object which will be used to populate the tensor.
          -
          Returns:
          -
          A newly created tensor of the provided type, shape and data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(DataType<V> dataType,
-                     Shape shape,
-                     java.util.List<V> data)
          -
          Use this to construct and return a tensor of the specified type, shape and a list of items. - Here a simple usage example:
          - 
          
-      Tsr<Integer> tensor = Tsr.of( DataType.F32, Shape.of(2,3), List.of(1,2,3,4,5,6) );
-
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          dataType - The type of the items stored by the resulting tensor.
          -
          shape - The shape of the resulting tensor consisting of an immutable tuple of axis-sizes.
          -
          data - The list of items which will be used to populate the tensor.
          -
          Returns:
          -
          A newly created tensor of the provided type, shape and data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(DataType<V> dataType,
-                     int[] shape,
-                     java.lang.Object data)
          -
          This factory method is among the most flexible and forgiving ways to create a Tsr instance. - It receives a DataType for type safety and to ensure that the produced Tsr instance - will contain elements of the correct type, a shape array which stores the sizes of the axes that the - instance ought to possess, and finally it receives a data Object which can be anything ranging from - a List to an array or simply a single value which ought to fill out the entire Tsr.
          -
          -
          Parameters:
          -
          dataType - The data type of the data represented by Tsr instance created by this method.
          -
          shape - An array of axis sizes describing the dimensionality of the Tsr created by this method.
          -
          data - The data for the Tsr that is about to be created, which can be a list, an array or scalar.
          -
          Returns:
          -
          A new Tsr instance of the specified type, shape and containing the provided data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(DataType<V> dataType,
-                     Shape shape,
-                     java.lang.Object data)
          -
          This factory method is among the most flexible and forgiving ways to create a Tsr instance. - It receives a DataType for type safety and to ensure that the produced Tsr instance - will contain elements of the correct type, and a Shape tuple which stores the sizes of the axes that the - instance ought to possess, and finally it receives a data Object which can be anything ranging from - a List to an array or simply a single value which ought to fill out the entire Tsr.
          -
          -
          Parameters:
          -
          dataType - The data type of the data represented by Tsr instance created by this method.
          -
          shape - An immutable tuple of axis sizes describing the dimensionality of the Tsr created by this method.
          -
          data - The data for the Tsr that is about to be created, which can be a list, an array or scalar.
          -
          Returns:
          -
          A new Tsr instance of the specified type, shape and containing the provided data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V extends N,​N> Tsr<V> of​(DataType<V> dataType,
-                                       Device<N> device,
-                                       Shape shape,
-                                       java.lang.Object data)
          -
          This factory method is among the most flexible and forgiving ways to create a Tsr instance. - It receives a DataType for type safety and to ensure that the produced Tsr instance - will contain elements of the correct type, and a Shape tuple which stores the sizes of the axes that the - instance ought to possess, and finally it receives a data Object which can be anything ranging from - a List to an array or simply a single value which ought to fill out the entire Tsr.
          -
          -
          Parameters:
          -
          dataType - The data type of the data represented by Tsr instance created by this method.
          -
          device - The device on which the tensor will be stored.
          -
          shape - An immutable tuple of axis sizes describing the dimensionality of the Tsr created by this method.
          -
          data - The data for the Tsr that is about to be created, which can be a list, an array or scalar.
          -
          Returns:
          -
          A new Tsr instance of the specified type, shape and containing the provided data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(DataType<V> dataType,
-                     NDConstructor ndConstructor,
-                     Data<V> data)
          -
          This factory method a raw tensor constructor which will not perform any type checking - or data conversion on the data provided to it. - It constructs the tensor expecting that the data provided to it is of the correct type - and an array of axis sizes.
          -
          -
          Type Parameters:
          -
          V - The type parameter of individual tensor items.
          -
          Parameters:
          -
          dataType - The data type of the data represented by Tsr instance created by this method.
          -
          ndConstructor - The NDConstructor that will be used to construct the Tsr instance.
          -
          data - The data for the Tsr that is about to be created, which is expected to be an array.
          -
          Returns:
          -
          A new Tsr instance of the specified type, shape and containing the provided data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(DataType<T> type,
-                     java.util.List<java.lang.Integer> shape,
-                     Filler<T> filler)
          -
          This factory method allows the creation of tensors with an additional initialization - lambda for filling the underlying data array with desired values. - Other than regular numeric types it is also possible to initialize the - tensor with regular Objects like String instances or custom data types like complex - numbers for example...
          - Therefore the constructor requires not only a shape as argument but also - the data type which ought to be allocated as well as the initialization - lambda which will be called iteratively.
          -
          -
          Type Parameters:
          -
          T - The type parameter for the actual data array items.
          -
          Parameters:
          -
          type - The data type this tensor ought to have.
          -
          shape - The shape of this new tensor ought to have.
          -
          filler - The lambda Object which ought to fill this tensor with the appropriate data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(DataType<T> type,
-                     Shape shape,
-                     Filler<T> filler)
          -
          This factory method allows the creation of tensors with an additional initialization - lambda for filling the underlying data array with desired values. - Other than regular numeric types it is also possible to initialize the - tensor with regular Objects like String instances or custom data types like complex - numbers for example...
          - Therefore the constructor requires not only a shape as argument but also - the data type which ought to be allocated as well as the initialization - lambda which will be called iteratively. - Here a simple usage example:
          - 
          
-      Tsr<Double> tensor = Tsr.of( DataType.F64, Shape.of(2, 3), (i, j) -> i + j );
-
          -
          -
          Type Parameters:
          -
          T - The type parameter for the actual data array items.
          -
          Parameters:
          -
          type - The data type this tensor ought to have.
          -
          shape - The shape of this new tensor ought to have.
          -
          filler - The lambda Object which ought to fill this tensor with the appropriate data.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <T> Tsr<T> of​(DataType<T> type,
-                     int[] shape,
-                     Filler<T> filler)
          -
          This factory method allows the creation of tensors with an additional initialization - lambda for filling the underlying data array with desired values. - Besides regular numeric types it is also possible to initialize the - tensor with regular objects like String instances or custom data types like complex - numbers for example...
          - Therefore the constructor requires not only a shape as argument but also - the data type which ought to be allocated as well as the initialization - lambda which will be called iteratively.
          -
          -
          Type Parameters:
          -
          T - The type parameter for the actual data array items.
          -
          Parameters:
          -
          type - The data type this tensor ought to have.
          -
          shape - The shape of this new tensor ought to have.
          -
          filler - The lambda Object which ought to fill this tensor with the appropriate data.
          -
          -
          • -
        - - - - - -
          -
        • -

          of

          -
          @SafeVarargs
-static <V extends java.lang.Number> Tsr<V> of​(java.lang.String expression,
-                                              V... inputs)
          -
          This factory method allows for the creation and execution of Function instances - without actually instantiating them manually, - where the result will then be returned by this factory method.

          - The passed String will be parsed into a Function AST which will be cached - using the expression as key in case it will be used in future constructor calls - like this one, or elsewhere... - The created / retrieved Function will then be called with the supplied input list - in order to trigger an execution. - The result of which will be used for the population of the fields of this - very instance.
          - An example would be the following :
          -
            -
          • 'var a = Tsr.of( "sin( I[0] ) / I[1]", 12f, -6.34f )'
            • -
          -
          -
          Parameters:
          -
          expression - A String which will be used for parsing a Function AST.
          -
          inputs - An array of inputs which can be tensors or numeric types.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(java.lang.String expression,
-                     java.util.List<Tsr<V>> inputs)
          -
          This factory method allows for the creation and execution of Function instances - without actually instantiating them manually, - where the result will then be returned by this factory method.

          - The passed String will be parsed into a Function AST which will be cached - using the expression as key in case it will be used in future constructor calls - like this one, or elsewhere... - The created / retrieved Function will then be called with the supplied input list - in order to trigger an execution. - The result of which will be used for the population of the fields of this - very instance.
          - An example would be the following :
          -
            -
          • 'var a = Tsr.of( "sin( I[0] ) / I[1]", List.of(b, c) )'
            • -
          -
          -
          Parameters:
          -
          expression - A String which will be used for parsing a Function AST.
          -
          inputs - A list of inputs which can be tensors or numeric types.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(java.lang.String expression,
-                     boolean doAD,
-                     java.util.List<Tsr<V>> tensors)
          -
          This method takes a list of tensors and a String expression describing - operations which ought to be applied to the tensors in said list. - It also receives a boolean flag which determines if the defined function - should be executed with autograd enabled. - The provided expression will be parsed to a Function instance expecting as many inputs - as there are array entries, namely : "I[0]", "I[1]", "I[2]", ...
          - An example would be the following :
          -
            -
          • 'var a = Tsr.of( "sin( I[0] ) / I[1]", true, List.of(b, c) )'
            • -
          - Which takes the tensor 'b' and 'c' and applies the function "f(x,y) = sin(x) / y" - element-wise to produce a new tensor 'a'! - Additionally, there is a helpful flag which allows one to specify if the - parsed Function instance emerging from the provided expression - should also allow the tracking of computations via a computation graph (GraphNode instances). - This history tracking then enables auto-differentiation.
          -
          -
          Parameters:
          -
          expression - The expression describing operations applied to the provided tensors.
          -
          doAD - A flag which when set to true commands the creation of a computation graph during operation execution.
          -
          tensors - A list of tensors used as inputs to the Function instance parsed from the provided expression.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          static <V> Tsr<V> of​(java.lang.String expression,
-                     Tsr<V> tensor)
          -
          This method takes a tensor and a String expression describing - operations which ought to be applied to said tensor. - This expression will be parsed to a Function instance expecting one input, - namely : "I[0]"
          - An example would be the following : -
            -
          • 'var a = Tsr.of( "sin( I[0] ) * 2", b )'
            • -
          - - Which takes the tensor 'b' and applies the function "f(x) = sin(x) * 2" - element-wise to produce a new tensor 'a'!
          -
          -
          -
          Parameters:
          -
          tensor - A tensor which serves as input to the Function instance parsed from the given expression.
          -
          expression - The expression describing operations applied to the provided tensor.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          @SafeVarargs
-static <V> Tsr<V> of​(java.lang.String expression,
-                     Tsr<V>... tensors)
          -
          This method takes an array of tensors and a String expression describing - operations which ought to be applied to the tensors in said array. - This expression will be parsed to a Function instance expecting as many inputs - as there are array entries, namely : "I[0]", "I[1]", "I[2]", ...
          - An example would be the following : -
            -
          • 'var a = Tsr.of( "sin( I[0] ) / I[1]", b, c )'
            • -
          - - Which takes the tensor 'b' and 'c' and applies the function "f(x,y) = sin(x) / y" - element-wise to produce a new tensor 'a'!
          -
          -
          Parameters:
          -
          expression - The expression describing operations applied to the provided tensors.
          -
          tensors - An array of tensors used as inputs to the Function instance parsed from the provided expression.
          -
          -
          • -
        - - - -
          -
        • -

          of

          -
          @SafeVarargs
-static <V> Tsr<V> of​(java.lang.String expression,
-                     boolean doAD,
-                     Tsr<V>... tensors)
          -
          This method takes an array of tensors and a String expression describing - operations which ought to be applied to the tensors in said array. - It also receives a boolean flag which determines if the defined function - should be executed with autograd enabled. - The provided expression will be parsed to a Function instance expecting as many inputs - as there are array entries, namely : "I[0]", "I[1]", "I[2]", ...
          - An example would be the following :
          -
            -
          • 'var a = Tsr.of( "sin( I[0] ) / I[1]", true, b, c )'
            • -
          - Which takes the tensor 'b' and 'c' and applies the function "f(x,y) = sin(x) / y" - element-wise to produce a new tensor 'a'! - Additionally, there is a helpful flag which allows one to specify if the - parsed Function instance emerging from the provided expression - should also allow the tracking of computations via a computation graph (GraphNode instances). - This history tracking then enables auto-differentiation.
          -
          -
          Parameters:
          -
          expression - The expression describing operations applied to the provided tensors.
          -
          doAD - A flag which when set to true commands the creation of a computation graph during operation execution.
          -
          tensors - An array of tensors used as inputs to the Function instance parsed from the provided expression.
          -
          -
          • -
        - - - -
          -
        • -

          ofRandom

          -
          static <V> Tsr<V> ofRandom​(java.lang.Class<V> valueTypeClass,
-                           int... shape)
          -
          This factory method produces a randomly populated tensor of the provided - type and shape using a hard coded default seed. - If the provided type class is representing a - floating point number type (like Double or Float) then the random numbers will - be gaussian ("normally") distributed values with mean 0.0 and standard - deviation 1.0.
          -
          -
          Type Parameters:
          -
          V - The type parameter of the values stored by the returned tensor.
          -
          Parameters:
          -
          valueTypeClass - The type class of the values stored by the returned tensor.
          -
          shape - The shape of the tensor produced by this factory method.
          -
          Returns:
          -
          A randomly filled tensor of the provided type.
          -
          -
          • -
        - - - -
          -
        • -

          like

          -
          static <V> IterByOrIterFromOrAllTsr<V> like​(Tsr<V> template)
          -
          Use this factory method to instantiate a new tensor with the same data type, shape - and memory location (Device instance) as the provided template tensor.
          -
          -
          Type Parameters:
          -
          V - The type parameter defining the value type of the provided as well as returned tensor.
          -
          Parameters:
          -
          template - The template tensor whose type, shape and location should be taken to construct a new tensor.
          -
          Returns:
          -
          A new Tsr instance with the same data type, shape and memory location as the provided template.
          -
          -
          • -
        - - - -
          -
        • -

          shaped

          -
          static <T> java.util.stream.Collector<T,​?,​Tsr<T>> shaped​(int... shape)
          -
          Returns a Collector that accumulates the input elements into a - new Tsr with the specified shape.
          - Usage example :
          - 
          
-    var tensor = Stream.of( 1, 2, 3, 4, 5, 6 )
-                      .collect( Tsr.shaped( 2, 3 ) );
-
          -
          -
          Type Parameters:
          -
          T - the type of the input elements
          -
          Parameters:
          -
          shape - The shape of the tensor to be returned.
          -
          Returns:
          -
          a Collector which collects all the input elements into a - Tsr, in encounter order.
          -
          -
          • -
        - - - -
          -
        • -

          shaped

          -
          static <T> java.util.stream.Collector<T,​?,​Tsr<T>> shaped​(Shape shape)
          -
          Returns a Collector that accumulates the input elements into a - new Tsr with the specified shape.
          - Usage example :
          - 
          
-    var tensor = Stream.of( 1, 2, 3, 4, 5, 6 )
-                      .collect( Tsr.shaped( otherTensor.shape() ) );
-
          -
          -
          Type Parameters:
          -
          T - the type of the input elements
          -
          Parameters:
          -
          shape - The shape of the tensor to be returned.
          -
          Returns:
          -
          a Collector which collects all the input elements into a - Tsr, in encounter order.
          -
          -
          • -
        - - - -
          -
        • -

          setRqsGradient

          -
          Tsr<V> setRqsGradient​(boolean rqsGradient)
          -
          Setting this flag to true will tell the autograd system to accumulate gradients at this tensor. - This is achieved by allowing for the recording of a computation graph - for when this tensor is used in any autograd supporting operations. - This allows the autograd / auto-differentiation system to traverse said graph - for when the backward() method is called - on any descendant tensor at the most recent end of the computation graph.
          -
          -
          Parameters:
          -
          rqsGradient - The truth value determining if this tensor ought to receive gradients via - the built-in automatic backpropagation system.
          -
          Returns:
          -
          This very Tsr instance in order to enable method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          rqsGradient

          -
          boolean rqsGradient()
          -
          This flag will indirectly trigger the activation of the autograd / auto-differentiation system of this library! - If the flag is set to 'true' and the tensor is used for computation then - it will also receive gradients when the backward() method is being called - on any descendant tensor within the computation graph.
          -
          -
          Returns:
          -
          The truth value determining if this tensor ought to receive gradients via - the built-in automatic backpropagation system.
          -
          -
          • -
        - - - -
          -
        • -

          isIntermediate

          -
          boolean isIntermediate()
          -
          Intermediate tensors are internal non-user tensors which may be eligible - for deletion when further consumed by a Function. - For the casual user of Neureka, this flag should always be false!
          -
          -
          Returns:
          -
          The truth value determining if this tensor is not a user tensor but an internal - tensor which may be eligible for deletion by Functions consuming it.
          -
          -
          • -
        - - - -
          -
        • -

          isOutsourced

          -
          default boolean isOutsourced()
          -
          Outsourced means that the tensor is stored on a Device implementation instance which is not the CPU.
          -
          -
          Returns:
          -
          The truth value determining if the data of this tensor is not actually stored inside it - in the form of a traditional primitive JVM array!
          -
          -
          • -
        - - - -
          -
        • -

          isVirtual

          -
          boolean isVirtual()
          -
          A Virtual tensor is a tensor whose underlying data array is of size 1, holding only a single value.
          - This only makes sense for homogeneously populated tensors. - An example of such a tensor would be:
          - Tsr.ofInts().withShape(x,y).all(n)

          - The reasons for this feature is that it greatly improves performance in certain cases. - In essence this feature is a form of lazy loading. -

          - Use MutateTsr.setIsVirtual(boolean) to "actualize" a "virtual" tensor, and vise versa.

          -
          -
          Returns:
          -
          The truth value determining if this tensor is "virtual" or "actual".
          -
          -
          • -
        - - - -
          -
        • -

          isDeleted

          -
          boolean isDeleted()
          -
          This will check if the MutateTsr.delete() method was previously called on this tensor. - This means that the tensor data was freed on every device - and any references inside the tensor are null (to be eligable for garbage collection).
          -
          -
          Returns:
          -
          The truth value determining if the MutateTsr.delete() method has been called oin this instance.
          -
          -
          • -
        - - - -
          -
        • -

          isEmpty

          -
          default boolean isEmpty()
          -
          A tensor is empty if it's Data storage is null. - This is true for deleted tensors or tensors which have not been initialized yet.
          -
          -
          Returns:
          -
          The truth value determining if this tensor has no Data.
          -
          -
          • -
        - - - -
          -
        • -

          isUndefined

          -
          default boolean isUndefined()
          -
          A tensor is "undefined" if it has either no NDConfiguration implementation instance - or this instance does not have a shape set for this Tsr which is needed for - a tensor to also have a rank and dimensionality...
          -
          -
          Returns:
          -
          The truth value determining if this tensor has an NDConfiguration stored internally.
          -
          -
          • -
        - - - -
          -
        • -

          isSlice

          -
          default boolean isSlice()
          -
          If this nd-array is a slice of a parent nd-array then this method will yield true. - Slices can be created by calling the variations of the "Nda.getAt(int...)" method.
          -
          -
          Specified by:
          -
          isSlice in interface Nda<V>
          -
          Returns:
          -
          The truth value determining if this nd-array is a slice of another nd-array.
          -
          See Also:
          -
          Nda.getAt(int...), -Nda.slice()
          -
          -
          • -
        - - - -
          -
        • -

          isShallowCopy

          -
          default boolean isShallowCopy()
          -
          If this nd-array is a shallow copy of a parent nd-array then this method will yield true. - Shallow copies can be created by calling the "Nda.shallowCopy()" method.
          -
          -
          Specified by:
          -
          isShallowCopy in interface Nda<V>
          -
          Returns:
          -
          The truth value determining if this nd-array is a shallow copy of another nd-array.
          -
          See Also:
          -
          Nda.shallowCopy()
          -
          -
          • -
        - - - -
          -
        • -

          isPartialSlice

          -
          default boolean isPartialSlice()
          -
          If this nd-array is a partial slice of a parent nd-array then this method will yield true. - A partial slice is a slice which does not view all the parents items. - Partial slices can be created by calling the variations of the "Nda.getAt(int...)" method. - This is the inverse of Nda.isFullSlice().
          -
          -
          Specified by:
          -
          isPartialSlice in interface Nda<V>
          -
          Returns:
          -
          The truth value determining if this nd-array is a partial slice of another nd-array.
          -
          -
          • -
        - - - -
          -
        • -

          sliceCount

          -
          default int sliceCount()
          -
          This method returns the number of slices which have been - created from this nd-array. - It does so by accessing the Relation component if present - which internally keeps track of slices via weak references.
          -
          -
          Specified by:
          -
          sliceCount in interface Nda<V>
          -
          Returns:
          -
          The number of slices derived from this nd-array.
          -
          -
          • -
        - - - -
          -
        • -

          isSliceParent

          -
          default boolean isSliceParent()
          -
          If slices have been derived from this nd-array then it is a "slice parent". - This is what this method will determine, in which case, it will return true.
          -
          -
          Specified by:
          -
          isSliceParent in interface Nda<V>
          -
          Returns:
          -
          The truth value determining if slices have been derived from this nd-array.
          -
          -
          • -
        - - - -
          -
        • -

          belongsToGraph

          -
          default boolean belongsToGraph()
          -
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. - This is because autograd requires recording a computation graph for back-prop traversal. - This autograd system however, will only be triggered by Function implementations which - are not "detached", meaning they have their "Function.isDoingAD()" flags set to true!
          - Detached functions (like those pre-instantiated in Function.Detached.*) will not attach GraphNode - instances to involved tensors which will prevent the formation of a computation graph.
          -
          -
          Returns:
          -
          The truth value determining if this tensor belongs to a recorded computation graph.
          -
          -
          • -
        - - - -
          -
        • -

          isLeave

          -
          default boolean isLeave()
          -
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. - This is because autograd requires recording a computation graph for back-prop traversal. - This autograd system however, will only be triggered by Function implementations which - are not "detached", meaning they have their "Function.isDoingAD()" flags set to true!
          - A tensor is a leave if it is attached to a computation graph in which it is not an intermediate / branch node - but input / branch node.
          -
          -
          Returns:
          -
          The truth value determining if this tensor is attached to a computation graph as leave node.
          -
          -
          • -
        - - - -
          -
        • -

          isBranch

          -
          default boolean isBranch()
          -
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. - This is because autograd requires recording a computation graph for back-prop traversal. - This autograd system however, will only be triggered by Function implementations which - are not "detached", meaning they have their "Function.isDoingAD()" flags set to true!
          - A tensor is a branch if it is attached to a computation graph in which it is not an input / leave node - but intermediate / branch node.
          -
          -
          Returns:
          -
          The truth value determining if this tensor is attached to a computation graph as branch node.
          -
          -
          • -
        - - - -
          -
        • -

          hasGradient

          -
          default boolean hasGradient()
          -
          Tensors can be components of other tensors which makes the - implicitly their gradients.
          -
          -
          Returns:
          -
          The truth value determining if this tensor has another tensor attached to it (which is its gradient).
          -
          -
          • -
        - - - -
          -
        • -

          gradientApplyRequested

          -
          boolean gradientApplyRequested()
          -
          This flag works alongside two autograd features which can be enabled inside the library settings. - They will come into effect when flipping their feature flags,
          - namely: 'isApplyingGradientWhenRequested' and 'isApplyingGradientWhenTensorIsUsed'
          - As the first flag name suggests gradients will be applied to their tensors when it is set to true, - however this will only happen when the second flag is set to true as well, because otherwise gradients - wouldn't be applied to their tensors automatically in the first place...
          -
          - Setting both flags to true will inhibit the effect of the second setting 'isApplyingGradientWhenTensorIsUsed' - unless a form of "permission" is being signaled to the autograd system. - This signal comes in the form of a "request" flag which marks a tensor as allowed to - be updated by its gradient.
          -
          -
          -
          Returns:
          -
          The truth value determining if the application of the gradient of this tensor is requested.
          -
          -
          • -
        - - - -
          -
        • -

          setGradientApplyRequested

          -
          Tsr<V> setGradientApplyRequested​(boolean applyRequested)
          -
          This flag works alongside two autograd features which can be enabled inside the library settings. - They will come into effect when flipping their feature flags,
          - namely: 'isApplyingGradientWhenRequested' and 'isApplyingGradientWhenTensorIsUsed'
          - As the first flag name suggests gradients will be applied to their tensors when it is set to true, - however this will only happen when the second flag is set to true as well, because otherwise gradients - wouldn't be applied to their tensors automatically in the first place...
          -
          - Setting both flags to true will inhibit effect of the second setting 'isApplyingGradientWhenTensorIsUsed' - unless a form of "permission" is being signaled to the autograd system. - This signal comes in the form of a "request" flag which marks a tensor as allowed to - be updated by its gradient.
          -
          -
          -
          Parameters:
          -
          applyRequested - The truth value determining if the application of the gradient of this tensor is requested.
          -
          Returns:
          -
          This very tensor instance in order to enable method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          update

          -
          default boolean update​(Component.OwnerChangeRequest<Tsr<V>> changeRequest)
          -
          Important : Components of type Tsr are simply gradients! - Currently, this method is used only to catch illegal arguments which - is for example the case when trying to attach a gradient with a different shape... - (Otherwise the gradient tensor "does not mind" an owner change...)
          -
          -
          Specified by:
          -
          update in interface Component<V>
          -
          Parameters:
          -
          changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
          -
          Returns:
          -
          The truth value determining if the state change should be aborted or not.
          -
          -
          • -
        - - - -
          -
        • -

          getVersion

          -
          int getVersion()
          -
          The version number is tracking how often this tensor has been mutated. - This is especially useful for checking the correcting of auto-grad!
          -
          • -
        - - - -
          -
        • -

          getDataType

          -
          DataType<V> getDataType()
          -
          This method returns the DataType instance of this Tsr, which is - a wrapper object for the actual type class representing the value items stored inside - the underlying data array of this tensor.
          -
          -
          Returns:
          -
          The DataType instance of this Tsr storing important type information.
          -
          -
          • -
        - - - -
          -
        • -

          getRepresentativeItemClass

          -
          java.lang.Class<?> getRepresentativeItemClass()
          -
          The Class returned by this method is the representative Class of the - value items of a concrete AbstractNda but not necessarily the actual Class of - a given value item, this is especially true for numeric types, which are represented by - implementations of the NumericType interface.
          - For example in the case of a tensor of type Double, this method would - return F64 which is the representative class of Double.
          - Calling the Nda.getItemType() method instead of this method would return the actual value - type class, namely: Double.
          -
          -
          Returns:
          -
          The representative type class of individual value items within this concrete AbstractNda - extension instance which might also be subclasses of the NumericType interface - to model unsigned types or other JVM foreign numeric concepts.
          -
          -
          • -
        - - - -
          -
        • -

          getMut

          -
          MutateTsr<V> getMut()
          -
          This method exposes an API for mutating the state of this tensor. - The usage of methods exposed by this API is generally discouraged - because the exposed state can easily lead to broken tensors and exceptional situations!
          -
          -

          - Only use this if you know what you are doing and - performance is critical!
          -           - (Like in custom backend extensions for example)

          -
          -
          Specified by:
          -
          getMut in interface Nda<V>
          -
          Returns:
          -
          The unsafe API exposes methods for mutating the state of the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          mut

          -
          default MutateTsr<V> mut()
          -
          This method exposes an API for mutating the state of this tensor. - The usage of methods exposed by this API is generally discouraged - because the exposed state can easily lead to broken tensors and exceptional situations!
          -
          -

          - Only use this if you know what you are doing and - performance is critical!
          -           - (Like custom backend extensions for example)

          -
          -
          Specified by:
          -
          mut in interface Nda<V>
          -
          Returns:
          -
          The unsafe API exposes methods for mutating the state of the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          reshape

          -
          default Tsr<V> reshape​(int... shape)
          -
          Returns a nd-array with the same data and number of elements as this nd-array, but with the specified shape. - When possible, the returned nd-array will be a view of this nd-array. - - A single dimension may be -1, in which case it’s inferred from the remaining - dimensions and the number of elements in input. - - Keep in mind that the new shape must have the same number of elements as the original shape.
          -
          - This operation supports autograd.
          -
          -
          Specified by:
          -
          reshape in interface Nda<V>
          -
          Parameters:
          -
          shape - The new shape of the returned nd-array.
          -
          Returns:
          -
          A new nd-array instance with the same underlying data (~shallow copy) but with a different shape.
          -
          -
          • -
        - - - -
          -
        • -

          permute

          -
          default Tsr<V> permute​(int... dims)
          -
          Returns a view of the original tensor input with its dimensions permuted.
          - Consider a 3-dimensional tensor x with shape (2×3×5), - then calling x.permute(1, 0, 2) will return a 3-dimensional tensor of shape (3×2×5).
          -
          -
          Specified by:
          -
          permute in interface Nda<V>
          -
          Parameters:
          -
          dims - The desired ordering of dimensions
          -
          Returns:
          -
          A new nd-array instance with the same underlying data (~shallow copy) but with a different shape.
          -
          -
          • -
        - - - -
          -
        • -

          transpose

          -
          default Tsr<V> transpose​(int dim1,
-                         int dim2)
          -
          Returns a view of the original tensor input the targeted - axes are swapped / transposed.
          -
          -
          Specified by:
          -
          transpose in interface Nda<V>
          -
          Parameters:
          -
          dim1 - The first dimension to be swapped.
          -
          dim2 - The second dimension to be swapped.
          -
          Returns:
          -
          A new nd-array instance with the same underlying data (~shallow copy) but with a different shape.
          -
          -
          • -
        - - - -
          -
        • -

          to

          -
          Tsr<V> to​(Device<?> device)
          -
          This method takes a Device and tries to migrate the contents of this Tsr - instance to that Device!
          -
          -
          Parameters:
          -
          device - The Device which should host this Tsr as well as be added to its components list.
          -
          Returns:
          -
          This very class to enable method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          to

          -
          default Tsr<V> to​(java.lang.String deviceType)
          -
          -
          Parameters:
          -
          deviceType - A search key identifying the device onto which this tensor should be stored.
          -
          Returns:
          -
          This very tensor instance in order to enable method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          set

          -
          default Tsr<V> set​(OptimizerFactory optimizerFactory)
          -
          Configures an Optimizer for this tensor based on the given OptimizerFactory - which will be used to create a new Optimizer instance specific to this tensor. - The Optimizer instance will be attached to this tensor as a component - and then called to perform the actual optimization when the applyGradient() method is called. -

          - Here a simple example of how to use this method: - 

          
-  var t = Tsr.of( 1.0, 2.0, 3.0 ).set( Optimizer.ADAM );
-
          -

          - As you can see, the Optimizer interface exposes various types of popular - optimization algorithm factories which can be used to quickly and conveniently create - an Optimizer instance for a particular tensor.

          -
          -
          Parameters:
          -
          optimizerFactory - The OptimizerFactory which will be used to create a new Optimizer instance.
          -
          Returns:
          -
          This tensor instance to allow for method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          backward

          -
          default Tsr<V> backward​(Tsr<V> error)
          -
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. - This is because autograd requires recording a computation graph for back-prop traversal. - If this tensor is part of a computation graph then this method - will traverse an error backward in the recorded history towards tensors which require - the accumulation of gradients.
          -
          -
          Parameters:
          -
          error - A tensor which is back-propagated to gradients. Must match the size og this tensor.
          -
          Returns:
          -
          This tensor, to allow for method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          backward

          -
          default Tsr<V> backward​(double value)
          -
          Tensors which are used or produced by the autograd system will have a GraphNode component attached to them. - This is because autograd requires recording a computation graph for back-prop traversal. - If this tensor is part of a computation graph then this method - will traverse an error backward in the recorded history towards tensors which require - the accumulation of gradients.
          -
          - This method turns the given scalar value and - turns it into a matching tensor ( with the same shape) - which will then be back-propagated through the - recorded computation graph.
          -
          -
          Parameters:
          -
          value - A scalar which is back-propagated to gradients. Must match the size og this tensor.
          -
          Returns:
          -
          The tensor, to allow for method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          backward

          -
          default Tsr<V> backward()
          -
          Use this to back-propagate an error signal of 1.0 through the recorded computation graph. - Tensors which are used or produced by operations supporting the autograd system - will have this graph defined by GraphNode components attached to them. - This is because autograd requires recording a computation graph for back-prop traversal. - If this tensor is part of a computation graph then this method - will traverse an error backward in the recorded history towards tensors which require - the accumulation of gradients.
          -
          - This method assumes that the user wants to back-propagate - an error of "1" having the same shape as - this tensor.
          -
          -
          Returns:
          -
          This tensor to allow for method chaining.
          -
          -
          • -
        - - - -
          -
        • -

          getGradient

          -
          default java.util.Optional<Tsr<V>> getGradient()
          -
          -
          Returns:
          -
          The gradient of this tensor which is internally stored as component.
          -
          -
          • -
        - - - -
          -
        • -

          gradient

          -
          default java.util.Optional<Tsr<V>> gradient()
          -
          This is a functionally identical alternative to the getGradient() method.
          -
          -
          Returns:
          -
          The gradient of this tensor which is internally stored as component.
          -
          -
          • -
        - - - -
          -
        • -

          applyGradient

          -
          default void applyGradient()
          -
          If this tensor owns a gradient tensor as component, then it can be applied by this method.
          - "Applying" a gradient to a tensor simply means adding the values inside the gradient element-wise - to the owning host tensor via an inline operation.
          -
          • -
        - - - -
          -
        • -

          getDevice

          -
          default Device<V> getDevice()
          -
          -
          Returns:
          -
          The device on which this tensor is stored or CPU if it is not outsourced.
          -
          -
          • -
        - - - -
          -
        • -

          getGraphNode

          -
          default java.util.Optional<GraphNode<V>> getGraphNode()
          -
          -
          Returns:
          -
          The graph node optional of the computation graph to which this tensor belongs - or an empty optional if not part of a graph.
          -
          -
          • -
        - - - -
          -
        • -

          graphNode

          -
          default java.util.Optional<GraphNode<V>> graphNode()
          -
          This is a functionally identical alternative to getGraphNode().
          -
          -
          Returns:
          -
          The graph node optional of the computation graph to which this tensor belongs - or an empty optional if not part of a graph.
          -
          -
          • -
        - - - -
          -
        • -

          getFrame

          -
          default java.util.Optional<NDFrame<V>> getFrame()
          -
          -
          Returns:
          -
          An instance of the NDFrame component if present.
          -
          -
          • -
        - - - -
          -
        • -

          frame

          -
          default java.util.Optional<NDFrame<V>> frame()
          -
          This is a functionally identical alternative to getFrame().
          -
          -
          Returns:
          -
          An instance of the NDFrame component if present.
          -
          -
          • -
        - - - -
          -
        • -

          detached

          -
          default Tsr<V> detached()
          -
          This method returns a new tensor detached from any underlying computation-graph - or simply does nothing if no graph is present.
          - Nodes within a computation graph are instances of the "GraphNode" class which are also - simple components of the tensors they represent in the graph.
          - Therefore, a "detached" clone of this tensor is - simply a tensor without a GraphNode component.
          -
          -
          Returns:
          -
          This very instance in order to allow for a more streamline usage of this method.
          -
          -
          • -
        - - - -
          -
        • -

          withLabel

          -
          Tsr<V> withLabel​(java.lang.String label)
          -
          -
          Specified by:
          -
          withLabel in interface Nda<V>
          -
          Returns:
          -
          A new nd-array which is a shallow copy of this nd-array but with a different label.
          -
          -
          • -
        - - - -
          -
        • -

          withLabels

          -
          Tsr<V> withLabels​(java.lang.String[]... labels)
          -
          This method receives a nested String array which - ought to contain a label for the index of this nd-array. - The index for a single element of this nd-array would be an array - of numbers as long as the rank where every number is - in the range of the corresponding shape dimension... - Labeling an index means that for every dimension there - must be a label for elements in this range array!
          - For example the shape (2,3) could be labeled as follows:
          -
          - dim 0 : ["A", "B"]
          - dim 1 : ["1", "2", "3"]
          -
          -
          -
          Specified by:
          -
          withLabels in interface Nda<V>
          -
          Parameters:
          -
          labels - A nested String array containing labels for indexes of the nd-array dimensions.
          -
          Returns:
          -
          This nd-array (method chaining).
          -
          -
          • -
        - - - -
          -
        • -

          withLabels

          -
          Tsr<V> withLabels​(java.util.List<java.util.List<java.lang.Object>> labels)
          -
          This method receives a nested String list which - ought to contain a label for the index of this nd-array. - The index for a single element of this nd-array would be an array - of numbers as long as the rank where every number is - in the range of the corresponding shape dimension... - Labeling an index means that for every dimension there - must be a label for elements in this range array!
          - For example the shape (2,3) could be labeled as follows:
          -
          - dim 0 : ["A", "B"]
          - dim 1 : ["1", "2", "3"]
          -
          -
          -
          Specified by:
          -
          withLabels in interface Nda<V>
          -
          Parameters:
          -
          labels - A nested String list containing labels for indexes of the nd-array dimensions.
          -
          Returns:
          -
          This nd-array (method chaining).
          -
          -
          • -
        - - - -
          -
        • -

          withLabels

          -
          Tsr<V> withLabels​(java.util.Map<java.lang.Object,​java.util.List<java.lang.Object>> labels)
          -
          This method provides the ability to - label not only the indices of the shape of this nd-array, but also - the dimension of the shape. - The first and only argument of the method expects a map instance - where keys are the objects which ought to act as dimension labels - and the values are lists of labels for the indices of said dimensions. - For example the shape (2,3) could be labeled as follows:
          - [
          - "dim 0" : ["A", "B"],
          - "dim 1" : ["1", "2", "3"]
          - ]
          -
          -
          -
          Specified by:
          -
          withLabels in interface Nda<V>
          -
          Parameters:
          -
          labels - A map in which the keys are dimension labels and the values are lists of index labels for the dimension.
          -
          Returns:
          -
          This nd-array (method chaining).
          -
          -
          • -
        - - - -
          -
        • -

          is

          -
          boolean is​(java.lang.Class<?> typeClass)
          -
          This method compares the passed class with the underlying data-type of this NDArray. - If the data-type of this NDArray is equivalent to the passed class then the returned - boolean will be true, otherwise the method returns false.
          -
          -
          Parameters:
          -
          typeClass - The class which ought to be compared to the underlying data-type of this NDArray.
          -
          Returns:
          -
          The truth value of the question: Does this NDArray implementation hold the data of the passed type?
          -
          -
          • -
        - - - -
          -
        • -

          plus

          -
          default Tsr<V> plus​(Tsr<V> other)
          -
          This method will produce the addition of - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method. - If the shapes of both of the involved tensors is identical then - the result will be a regular element-wise addition. - Otherwise, the method will also be able to perform broadcasting, however only if - for every pair of shape dimensions the following is true: - Either the dimensions have the same size or one of them has size 1.
          - Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
          - And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
          -
          -
          Parameters:
          -
          other - The right operand of the addition.
          -
          Returns:
          -
          The sum of this instance as the left and the passed Tsr instance as right operand.
          -
          -
          • -
        - - - - - -
          -
        • -

          plus

          -
          default Tsr<V> plus​(V value)
          -
          This method will create a new Tsr - with the provided double scalar added to all elements of this Tsr. - - The shapes of this tensor is irrelevant as the provided value will - simply be broadcast to any possible shape.
          -
          -
          Parameters:
          -
          value - The right operand of the addition.
          -
          Returns:
          -
          The sum between this instance as the left and the passed double as right operand.
          -
          -
          • -
        - - - -
          -
        • -

          minus

          -
          default Tsr<V> minus​(Tsr<V> other)
          -
          Performs subtraction on - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method. - If the shapes of both of the involved tensors are identical then - the result will be a regular element-wise subtraction. - Otherwise, the method will also be able to perform broadcasting, however only if - for every pair of shape dimensions the following is true: - Either the dimensions have the same size or one of them has size 1.
          - Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
          - And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
          -
          -
          Parameters:
          -
          other - The right operand of the subtraction.
          -
          Returns:
          -
          The difference between this instance as the left and the passed Tsr instance as right operand.
          -
          -
          • -
        - - - - - -
          -
        • -

          minus

          -
          default Tsr<V> minus​(V other)
          -
          This method will create a new Tsr - with the provided item subtracted from all elements of this Tsr. - - The shapes of this tensor is irrelevant as the provided item will - simply be broadcast to all items od this tensor, irrespective of any shape.
          -
          -
          Parameters:
          -
          other - The right operand of the subtraction, which is an item of the same type as this tensor.
          -
          Returns:
          -
          The difference between this instance as the left and the passed item as right operand.
          -
          -
          • -
        - - - -
          -
        • -

          negative

          -
          default Tsr<V> negative()
          -
          -
          Returns:
          -
          A clone of this tensor where the signs of all elements are flipped.
          -
          -
          • -
        - - - -
          -
        • -

          T

          -
          default Tsr<V> T()
          -
          Creates and returns a new Tsr instance which is a transposed twin of this instance.
          - This is a shorter alternative to the functionally identical getT() method.
          -
          -
          Returns:
          -
          A new transposed tensor with the same underlying Data as this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          getT

          -
          default Tsr<V> getT()
          -
          A method which returns a new Tsr instance which is a transposed twin of this instance.
          - This is an alternative to the functionally identical T() method.
          -
          -
          Returns:
          -
          A new transposed tensor with the same underlying Data as this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          mean

          -
          default Tsr<V> mean()
          -
          Calculate the mean value of all values - within this tensor and returns it - in the form of a scalar tensor.
          - This operation supports autograd.
          -
          -
          Returns:
          -
          A scalar tensor which wraps the mean value of all values of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          sum

          -
          default Tsr<V> sum()
          -
          Calculate the sum value of all values - within this tensor and returns it - in the form of a scalar tensor.
          - This operation supports autograd.
          -
          -
          Returns:
          -
          A scalar tensor which wraps the sum of all values of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          sum

          -
          default Tsr<V> sum​(int axis)
          -
          Calculate the sum value of all values - within this tensor along the specified axis and returns it - in the form of a tensor.
          - For example, if this tensor has a shape of (2, 3, 4) and the axis is 1, - then the result will be a tensor with a shape of (2, 1, 4) because the - sum of all values along the axis 1 is a single value for each of the two - first dimensions.
          - This operation supports autograd.
          -
          -
          Parameters:
          -
          axis - The axis along which the sum should be calculated.
          -
          Returns:
          -
          A tensor which wraps the sum of all values of this tensor along the specified axis.
          -
          -
          • -
        - - - -
          -
        • -

          sum

          -
          default Tsr<V> sum​(int... axes)
          -
          Calculate the sum value of all values - within this tensor along the specified axes and returns it - in the form of a tensor.
          - For example, if this tensor has a shape of (2, 3, 4) and the axes are 1 and 2, - then the result will be a tensor with a shape of (2, 1, 1) because the - sum of all values along the axis 1 and 2 is a single value for each of the two - first dimensions.
          - This operation supports autograd.
          -
          -
          Parameters:
          -
          axes - The axes along which the sum should be calculated.
          -
          Returns:
          -
          A tensor which wraps the sum of all values of this tensor along the specified axes.
          -
          -
          • -
        - - - -
          -
        • -

          min

          -
          default Tsr<V> min()
          -
          Calculate the min value of all values - within this tensor and returns it - in the form of a scalar tensor.
          - This operation supports autograd.
          -
          -
          Returns:
          -
          A scalar tensor which wraps the smallest of all values of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          max

          -
          default Tsr<V> max()
          -
          Calculate the max value of all values - within this tensor and returns it - in the form of a scalar tensor.
          - This operation supports autograd.
          -
          -
          Returns:
          -
          A scalar tensor which wraps the largest of all values of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          convDot

          -
          default Tsr<V> convDot​(Tsr<V> other)
          -
          This method performs a convolutional based dot product between the last dimension of this tensor - and the first dimension of the passed tensor.
          -
          -
          Parameters:
          -
          other - The tensor which is the right part of the dot product operation.
          -
          Returns:
          -
          A new tensor which is the dot product of this tensor and the passed one.
          -
          -
          • -
        - - - -
          -
        • -

          dot

          -
          default Tsr<V> dot​(Tsr<V> other)
          -
          Performs a dot product between the last dimension of this tensor - and the first dimension of the provided tensor. - However, currently this method can only handle matrices which means - that it is functionally completely identical to the matMul(Tsr) method.
          -
          -
          Parameters:
          -
          other - The tensor which is the right part of the dot product operation.
          -
          Returns:
          -
          A new tensor which is the dot product of this tensor and the passed one.
          -
          -
          • -
        - - - -
          -
        • -

          matMul

          -
          default Tsr<V> matMul​(Tsr<V> other)
          -
          This will produce the matrix product of - two tensors with rank 2 (matrices), where the left operand is this Tsr - instance and the right operand is the argument passed to the method.
          -
          -
          Parameters:
          -
          other - The right operand of the matrix multiplication.
          -
          Returns:
          -
          The matrix product of this instance as the left and the passed Tsr instance as right operand.
          -
          -
          • -
        - - - -
          -
        • -

          conv

          -
          default Tsr<V> conv​(Tsr<V> other)
          -
          This method performs convolution between this tensor and the one passed as argument. - The convolution is performed by the Function which is registered under the name "conv".
          -
          -
          Parameters:
          -
          other - The tensor which is the right operand of the convolutional operation.
          -
          Returns:
          -
          A new tensor which is the result of the convolutional operation.
          -
          -
          • -
        - - - -
          -
        • -

          dimtrim

          -
          default Tsr<V> dimtrim()
          -
          This creates a new tensor with the same underlying Data and whose shape is trimmed. - A trimmed shape is simply a shape without preceding and trailing ones.
          - For example the shape (1x4x1x2x1) would be trimmed to (4x1x2). - The underlying operation does not perform a removal of redundant ones all together. - Only ones at the start and the beginning will be removed. - A scalar tensor will not be affected by this operation.
          -
          -
          Returns:
          -
          A tensor with the same underlying data but possibly trimmed shape without preceding or trailing ones.
          -
          -
          • -
        - - - -
          -
        • -

          isCase

          -
          boolean isCase​(Tsr<V> other)
          -
          This method name translates to the "in" keyword in Groovy! - The same is true for the "contains" method in Kotlin. - Both methods do the exact same thing, however they exist - for better language support.
          -
          -
          Parameters:
          -
          other - The tensor which will be checked.
          -
          Returns:
          -
          The answer to the following question: Is the data of the provided tensor a subset of the data of this tensor?
          -
          -
          • -
        - - - -
          -
        • -

          contains

          -
          default boolean contains​(Tsr<V> other)
          -
          This method name translates to the "in" keyword in Kotlin! - The same is true for the "isCase" method in Groovy. - Both methods do the exact same thing, however they exist - for better language support.
          -
          -
          Parameters:
          -
          other - The tensor which will be checked.
          -
          Returns:
          -
          The answer to the following question: Is the data of the provided tensor a subset of the data of this tensor?
          -
          -
          • -
        - - - -
          -
        • -

          multiply

          -
          default Tsr<V> multiply​(Tsr<V> other)
          -
          This method is synonymous to the times(Tsr) method. - Both of which will produce the product of - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method. - If the shapes of both of the involved tensors is identical then - the result will be a regular element-wise product. - Otherwise, the method will also be able to perform broadcasting, however only if - for every pair of shape dimensions the following is true: - Either the dimensions have the same size or one of them has size 1.
          - Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
          - And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
          -
          -
          Parameters:
          -
          other - The right operand of the multiplication.
          -
          Returns:
          -
          The product of this instance as the left and the passed Tsr instance as right operand.
          -
          -
          • -
        - - - - - -
          -
        • -

          multiply

          -
          default Tsr<V> multiply​(V other)
          -
          -
          Parameters:
          -
          other - The value which should be broadcast to all elements of a clone of this tensor.
          -
          Returns:
          -
          A new tensor where all elements are multiplied by the provided value.
          -
          -
          • -
        - - - -
          -
        • -

          times

          -
          default Tsr<V> times​(Tsr<V> other)
          -
          This is a functionally identical synonym to the multiply(Tsr) method. - Both of which will produce the product of - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method. - If the shapes of both of the involved tensors is identical then - the result will be a regular element-wise product. - Otherwise, the method will also be able to perform broadcasting, however only if - for every pair of shape dimensions the following is true: - Either the dimensions have the same size or one of them has size 1.
          - Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
          - And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
          -
          -
          Parameters:
          -
          other - The right operand of the multiplication.
          -
          Returns:
          -
          The product of this instance as the left and the passed Tsr instance as right operand.
          -
          -
          • -
        - - - - - -
          -
        • -

          times

          -
          default Tsr<V> times​(V other)
          -
          -
          Parameters:
          -
          other - The value which should be broadcast to all elements of a clone of this tensor.
          -
          Returns:
          -
          A new tensor where all elements are multiplied by the provided value.
          -
          -
          • -
        - - - -
          -
        • -

          multiply

          -
          default Tsr<V> multiply​(double value)
          -
          -
          Parameters:
          -
          value - The value which should be broadcast to all elements of a clone of this tensor.
          -
          Returns:
          -
          A new tensor where all elements are multiplied by the provided value.
          -
          -
          • -
        - - - -
          -
        • -

          div

          -
          default Tsr<V> div​(Tsr<V> other)
          -
          This method will produce the quotient of - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method. - If the shapes of both of the involved tensors are identical then - the result will be a regular element-wise division. - Otherwise, the method will also be able to perform broadcasting, however only if - for every pair of shape dimensions the following is true: - Either the dimensions have the same size or one of them has size 1.
          - Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
          - And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
          -
          -
          Parameters:
          -
          other - The right operand of the division.
          -
          Returns:
          -
          The quotient of this instance as the left and the passed Tsr instance as right operand.
          -
          -
          • -
        - - - - - -
          -
        • -

          div

          -
          default Tsr<V> div​(V value)
          -
          • -
        - - - -
          -
        • -

          mod

          -
          default Tsr<V> mod​(Tsr<V> other)
          -
          Produces the modulus of - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method. - If the shapes of these 2 tensors are identical then - the result will be a regular element-wise modulo operation. - Otherwise, the method will also be able to perform broadcasting, however only if - for every pair of shape dimensions the following is true: - Either the dimensions have the same size or one of them has size 1.
          - Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
          - And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
          -
          -
          Parameters:
          -
          other - The right operand of the modulo operation.
          -
          Returns:
          -
          The modulus of this instance as the left and the passed Tsr instance as right operand.
          -
          -
          • -
        - - - -
          -
        • -

          mod

          -
          default Tsr<V> mod​(int other)
          -
          -
          Parameters:
          -
          other - The value which should be broadcast to all elements of a clone of this tensor.
          -
          Returns:
          -
          A new tensor where the modulo operation is applied to all - elements using the provided int as right operand.
          -
          -
          • -
        - - - -
          -
        • -

          rem

          -
          default Tsr<V> rem​(int other)
          -
          This method is synonymous to the mod(int) method.
          -
          • -
        - - - -
          -
        • -

          power

          -
          default Tsr<V> power​(Tsr<V> other)
          -
          This will produce the power of - two tensors with the same rank (or two ranks which can be made compatible with padding ones), - where the left operand is this Tsr - instance and the right operand is the tensor passed to the method. - If the shapes of the involved tensors are identical then - the result will be a regular element-wise exponentiation. - Otherwise, the method will also be able to perform broadcasting, however only if - for every pair of shape dimensions the following is true: - Either the dimensions have the same size or one of them has size 1.
          - Here is an example of 2 matching shapes: (1, 4, 1) and (3, 4, 1)
          - And here is an example of a mismatch: (2, 4, 1) and (3, 4, 1)
          -
          -
          Parameters:
          -
          other - The right operand, also known as exponent, of the exponentiation.
          -
          Returns:
          -
          The power of this instance as the left and the passed Tsr instance as right operand.
          -
          -
          • -
        - - - - - -
          -
        • -

          power

          -
          default Tsr<V> power​(V value)
          -
          Raises all items of this tensor to the power of the provided value. - The returned tensor is a new instance which will have the same shape as this tensor.
          -
          -
          Parameters:
          -
          value - The value which should be used to raise all items of this tensor to the power of.
          -
          Returns:
          -
          A new tensor where all items are raised to the power of the provided value.
          -
          -
          • -
        - - - -
          -
        • -

          xor

          -
          default Tsr<V> xor​(Tsr<V> other)
          -
          This method is a functionally identical synonym to the power(Tsr) method.
          -
          • -
        - - - -
          -
        • -

          xor

          -
          default Tsr<V> xor​(double value)
          -
          This method is a functionally identical synonym to the power(Tsr) method.
          -
          • -
        - - - -
          -
        • -

          sig

          -
          default Tsr<V> sig()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().sigmoid().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("sig(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the sigmoid function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          tanh

          -
          default Tsr<V> tanh()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().tanh().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("tanh(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the tanh function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          relu

          -
          default Tsr<V> relu()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().relu().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("relu(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the relu function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          sin

          -
          default Tsr<V> sin()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().sin().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("sin(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the sin function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          cos

          -
          default Tsr<V> cos()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().cos().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("cos(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the cos function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          ln

          -
          default Tsr<V> ln()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().ln().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("ln(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the ln function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          softplus

          -
          default Tsr<V> softplus()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().softplus().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("softplus(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the softplus function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          exp

          -
          default Tsr<V> exp()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().exp().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("exp(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the exp function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          sqrt

          -
          default Tsr<V> sqrt()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().sqrt().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("sqrt(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the sqrt function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          log10

          -
          default Tsr<V> log10()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().log10().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("log10(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the log10 function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          cbrt

          -
          default Tsr<V> cbrt()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().cbrt().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("cbrt(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the cbrt function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          abs

          -
          default Tsr<V> abs()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().abs().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("abs(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the abs function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          neg

          -
          default Tsr<V> neg()
          -
          This method is a functionally identical to the following alternatives: - 
          
-      // Pre-instantiated:
-      var out1 = Neureka.get().backend().getAutogradFunction().neg().call( myTensor );
-      // Dynamically parsed and instantiated:
-      var out2 = Function.of("neg(I[0])").call(myTensor);
-
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the neg function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          softmax

          -
          default Tsr<V> softmax()
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the softmax function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          softmax

          -
          default Tsr<V> softmax​(int axis)
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the softmax function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          softmax

          -
          default Tsr<V> softmax​(int... axes)
          -
          Calculates the softmax function along the specified axes.
          - For example, if this tensor has a shape of (2, 3, 4) and the axes 0 and 2 are chosen, - then the result will be a tensor of the same size where all elements summed up alongside - axis 0 and 2 would be 1. - Ao calling sum(0, 2) would in this example be a tensor of shape of (1, 3, 1) where every item is 1.
          - This operation supports autograd.
          -
          -
          Parameters:
          -
          axes - The axes along which the softmax function should be applied.
          -
          Returns:
          -
          A new tensor whose items are the result of the softmax function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          sigmoid

          -
          default Tsr<V> sigmoid()
          -
          -
          Returns:
          -
          A new tensor whose items are the result of the sigmoid function applied to the items of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          slice

          -
          AxisOrGetTsr<V> slice()
          -
          This method returns a SliceBuilder instance exposing a simple builder API - which enables the configuration of a slice of the current nd-array via method chaining.
          - The following code snippet slices a 3-dimensional nd-array into a nd-array of shape (2x1x3)
          - 
          
-  myArray.slice()
-          .axis(0).from(0).to(1)
-          .axis(1).at(5) // equivalent to '.from(5).to(5)'
-          .axis().from(0).to(2)
-          .get();
-
          -
          -
          Specified by:
          -
          slice in interface Nda<V>
          -
          Returns:
          -
          An instance of the SliceBuilder class exposing a readable builder API for creating slices.
          -
          -
          • -
        - - - -
          -
        • -

          concatAt

          -
          default Tsr<V> concatAt​(int axis,
-                        Nda<V> other,
-                        Nda<V>... ndArrays)
          -
          This method concatenates the provided nd-arrays together with this nd-array along a specified axis. - The provided nd-arrays must have the same shape and data type as the current nd-array, except for the specified axis.
          -
          -
          Specified by:
          -
          concatAt in interface Nda<V>
          -
          Parameters:
          -
          axis - The axis along which the provided nd-arrays should be concatenated. - The axis must be within the range of the rank of the current nd-array.
          -
          other - The other nd-arrays which should be concatenated with this nd-array.
          -
          ndArrays - The non-null, non-empty nd-arrays which should be concatenated together with this and the other nd-array. - The nd-arrays all must have the same shape as this nd-array, except for the specified axis. - Also, it must have the same data type as the current nd-array.
          -
          Returns:
          -
          A new nd-array which is the concatenation of the current nd-array and the provided nd-arrays.
          -
          -
          • -
        - - - -
          -
        • -

          concatAt

          -
          default Tsr<V> concatAt​(int axis,
-                        Nda<V> other)
          -
          This method concatenates the provided nd-array together with this nd-array along a specified axis. - The provided nd-array must have the same shape and data type as this nd-array, except for the specified axis.
          -
          -
          Specified by:
          -
          concatAt in interface Nda<V>
          -
          Parameters:
          -
          axis - The axis along which the provided nd-arrays should be concatenated. - The axis must be within the range of the rank of the current nd-array.
          -
          other - The other nd-arrays which should be concatenated with this nd-array.
          -
          Returns:
          -
          A new nd-array which is the concatenation of the current nd-array and the provided nd-arrays.
          -
          -
          • -
        - - - -
          -
        • -

          getAt

          -
          Tsr<V> getAt​(int... indices)
          -
          The following method enables access to specific scalar elements within the nd-array. - The method name also translates to the subscription operator in Groovy.
          -
          -
          Specified by:
          -
          getAt in interface Nda<V>
          -
          Parameters:
          -
          indices - The index array of the element which should be returned.
          -
          Returns:
          -
          An element located at the provided index.
          -
          -
          • -
        - - - -
          -
        • -

          getAt

          -
          default Tsr<V> getAt​(java.lang.Number i)
          -
          This getter method creates and returns a slice of the original nd-array. - The returned slice is a scalar nd-array wrapping a single value element which - is being targeted by the provided integer index.
          -
          -
          Specified by:
          -
          getAt in interface Nda<V>
          -
          Parameters:
          -
          i - The index of the value item which should be returned as a nd-array instance.
          -
          Returns:
          -
          A nd-array holding a single value element which is internally still residing in the original nd-array.
          -
          -
          • -
        - - - -
          -
        • -

          get

          -
          default Tsr<V> get​(int... indices)
          -
          The following method enables access to specific scalar elements within the nd-array. - The method name also translates to the subscription operator in Groovy.
          -
          -
          Specified by:
          -
          get in interface Nda<V>
          -
          Parameters:
          -
          indices - The index array of the element which should be returned.
          -
          Returns:
          -
          An element located at the provided index.
          -
          -
          • -
        - - - -
          -
        • -

          getAt

          -
          default Tsr<V> getAt​(java.lang.Object... args)
          -
          The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view). - The method name also translates to the subscription operator in Groovy.
          -
          -
          Specified by:
          -
          getAt in interface Nda<V>
          -
          Parameters:
          -
          args - An arbitrary number of arguments which can be used for slicing.
          -
          Returns:
          -
          A slice nd-array created based on the passed keys.
          -
          -
          • -
        - - - -
          -
        • -

          get

          -
          default Tsr<V> get​(java.lang.Object... args)
          -
          The following method enables the creation of nd-array slices which access - the same underlying data (possibly from a different view). - The method name also translates to the subscription operator in Groovy.
          -
          -
          Specified by:
          -
          get in interface Nda<V>
          -
          Parameters:
          -
          args - An arbitrary number of arguments which can be used for slicing.
          -
          Returns:
          -
          A slice nd-array created based on the passed keys.
          -
          -
          • -
        - - - -
          -
        • -

          getAt

          -
          default Tsr<V> getAt​(int i)
          -
          This getter method creates and returns a slice of the original nd-array. - The returned slice is a scalar nd-array wrapping a single value element which - is being targeted by the provided integer index.
          -
          -
          Specified by:
          -
          getAt in interface Nda<V>
          -
          Parameters:
          -
          i - The index of the value item which should be returned as a nd-array instance.
          -
          Returns:
          -
          A nd-array holding a single value element which is internally still residing in the original nd-array.
          -
          -
          • -
        - - - -
          -
        • -

          get

          -
          default Tsr<V> get​(int i)
          -
          This getter method creates and returns a slice of the original nd-array. - The returned slice is a scalar nd-array wrapping a single value element which - is being targeted by the provided integer index.
          -
          -
          Specified by:
          -
          get in interface Nda<V>
          -
          Parameters:
          -
          i - The index of the value item which should be returned as a nd-array instance.
          -
          Returns:
          -
          A nd-array holding a single value element which is internally still residing in the original nd-array.
          -
          -
          • -
        - - - -
          -
        • -

          get

          -
          default Tsr<V> get​(java.lang.Number i)
          -
          This getter method creates and returns a slice of the original nd-array. - The returned slice is a scalar nd-array wrapping a single value element which - is being targeted by the provided integer index.
          -
          -
          Specified by:
          -
          get in interface Nda<V>
          -
          Parameters:
          -
          i - The index of the value item which should be returned as a nd-array instance.
          -
          Returns:
          -
          A nd-array holding a single value element which is internally still residing in the original nd-array.
          -
          -
          • -
        - - - -
          -
        • -

          get

          -
          default Tsr<V> get​(java.lang.Object key)
          -
          This method enables nd-array slicing! - It takes a key of various types and configures a slice - nd-array which shares the same underlying data as the original nd-array.
          -
          -
          Specified by:
          -
          get in interface Nda<V>
          -
          Parameters:
          -
          key - This object might be a wide range of objects including maps, lists or arrays...
          -
          Returns:
          -
          A slice nd-array or scalar value.
          -
          -
          • -
        - - - -
          -
        • -

          getAt

          -
          Tsr<V> getAt​(java.util.Map<?,​java.lang.Integer> rangToSteps)
          -
          This method is most useful when used in Groovy - where defining maps is done through square brackets, - making it possible to slice nd-arrays like so:
          - 
          
-      var b = a[[[0..0]:1, [0..0]:1, [0..3]:2]]
-
          - Here a single argument with the format '[i..j]:k' is equivalent - to Pythons 'i:j:k' syntax for indexing! (numpy)
          - i... start indexAlias.
          - j... end indexAlias. (inclusive!)
          - k... step size.
          -
          -
          Specified by:
          -
          getAt in interface Nda<V>
          -
          Parameters:
          -
          rangToSteps - A map where the keys define where axes should be sliced and values which define the steps for the specific axis.
          -
          Returns:
          -
          A nd-array slice with an offset based on the provided map keys and - steps based on the provided map values.
          -
          -
          • -
        - - - -
          -
        • -

          getAt

          -
          Tsr<V> getAt​(java.util.List<?> key)
          -
          This method enables nd-array slicing! - It takes a key of various types and configures a slice - nd-array which shares the same underlying data as the original nd-array.
          -
          -
          Specified by:
          -
          getAt in interface Nda<V>
          -
          Parameters:
          -
          key - This object might be a wide range of objects including maps, lists or arrays...
          -
          Returns:
          -
          A slice nd-array or scalar value.
          -
          -
          • -
        - - - -
          -
        • -

          mapTo

          -
          default <T> Tsr<T> mapTo​(java.lang.Class<T> typeClass,
-                         java.util.function.Function<V,​T> mapper)
          -

          - This is a convenience method for mapping a nd-array to a nd-array of new type - based on a provided target item type and mapping lambda. - Here a simple example: -

          - 
          
-     Nda<String>  a = Nda.of(String.class).vector("1", "2", "3");
-     Nda<Integer> b = a.mapTo(Integer.class, s -> Integer.parseInt(s));
-
          -

          - Note:
          - The provided lambda cannot be executed anywhere else but the CPU. - This is a problem if this nd-array lives somewhere other than the JVM. - So, therefore, this method will temporally transfer this nd-array from - where ever it may reside back to the JVM, execute the mapping lambda, and - then transfer the result back to the original location. -

          -
          -
          Specified by:
          -
          mapTo in interface Nda<V>
          -
          Type Parameters:
          -
          T - The type parameter of the items of the returned nd-array.
          -
          Parameters:
          -
          typeClass - The class of the item type to which the items of this nd-array should be mapped.
          -
          mapper - The lambda which maps the items of this nd-array to a new one.
          -
          Returns:
          -
          A new nd-array of type T.
          -
          -
          • -
        - - - -
          -
        • -

          map

          -
          default Tsr<V> map​(java.util.function.Function<V,​V> mapper)
          -

          - This method is a convenience method for mapping the items of this nd-array to another - nd-array of the same type based on the provided lambda function, which will be applied - to all items of this nd-array individually (element-wise). -

          - Here a simple example: - 
          
-  Nda<String> a = Nda.of(String.class).vector("1", "2", "3");
-  Nda<String> b = a.map( s -> s + "!" );
-
          - Note:
          - The provided lambda cannot be executed anywhere else but the CPU. - This is a problem if this nd-array lives somewhere other than the JVM. - So, therefore, this method will temporally transfer this nd-array from where ever it may reside - back to the JVM, execute the mapping lambda, and then transfer the result back to the original location.
          -
          -
          Specified by:
          -
          map in interface Nda<V>
          -
          Parameters:
          -
          mapper - The lambda which maps the items of this nd-array to a new one.
          -
          Returns:
          -
          A new nd-array of type V.
          -
          -
          • -
        - - - -
          -
        • -

          asImage

          -
          java.awt.image.BufferedImage asImage​(Tsr.ImageType type)
          -
          Turns this tensor into a BufferedImage based on the provided - Tsr.ImageType formatting choice.
          -
          -
          Parameters:
          -
          type - The type of format used to create the buffered image.
          -
          Returns:
          -
          A BufferedImage populated with the contents of this tensor.
          -
          -
          • -
        - - - -
          -
        • -

          asType

          -
          <T> T asType​(java.lang.Class<T> typeClass)
          -
          -
          Type Parameters:
          -
          T - The type parameter of the type that will be returned.
          -
          Parameters:
          -
          typeClass - The class which is the target of the type conversion.
          -
          Returns:
          -
          An instance of the supplied type class.
          -
          -
          • -
        - - - -
          -
        • -

          toString

          -
          default java.lang.String toString​(java.lang.String conf)
          -
          • -
        - - - -
          -
        • -

          toString

          -
          default java.lang.String toString​(NDPrintSettings config)
          -
          Use this to turn this nd-array into a String instance based on the provided - NDPrintSettings instance, which allows you to configure things - like the number of chars per entry, delimiters, the number of items per line, etc.
          -
          -
          Specified by:
          -
          toString in interface Nda<V>
          -
          -
          • -
        - - - -
          -
        • -

          toString

          -
          default java.lang.String toString​(java.util.function.Consumer<NDPrintSettings> configurator)
          -
          This allows you to provide a lambda which configures how this nd-array should be - converted to String instances. - The provided Consumer will receive a NDPrintSettings instance - which allows you to change various settings with the help of method chaining.
          - Here is an example: - 
          
-       t.toString(it ->
-           it.setHasSlimNumbers(false)
-             .setIsScientific(true)
-             .setIsCellBound(true)
-             .setIsMultiline(true)
-             .setCellSize(15)
-          );
-
          -
          -
          Specified by:
          -
          toString in interface Nda<V>
          -
          Parameters:
          -
          configurator - A consumer of the NDPrintSettings ready to be configured.
          -
          Returns:
          -
          The String representation of this nd-array.
          -
          -
          • -
        - - - -
          -
        • -

          deepCopy

          -
          Tsr<V> deepCopy()
          -
          This method creates and returns a new nd-array instance - which is not only a copy of the configuration of this nd-array but also a copy of - the underlying data array.
          - (Note: the underlying nd-array will not be attached to any kind of computation graph)
          -
          -
          Specified by:
          -
          deepCopy in interface Nda<V>
          -
          Returns:
          -
          A new nd-array instance which is a deep copy of this nd-array.
          -
          -
          • -
        - - - -
          -
        • -

          shallowCopy

          -
          default Tsr<V> shallowCopy()
          -
          This creates a copy where the underlying data is still the same.
          - (Note: the underlying nd-array will not be attached to any kind of computation graph)
          -
          -
          Specified by:
          -
          shallowCopy in interface Nda<V>
          -
          Returns:
          -
          A shallow copy where the underlying data is shared with this nd-array.
          -
          -
          • -
        - - - -
          -
        • -

          deepClone

          -
          Tsr<V> deepClone()
          -
          This is almost identical to the deepCopy() method except that - the returned tensor will have autograd support, meaning that the cloning - will be part of the autograd computation graph, and backpropagation - will traverse the cloned tensor as well.
          -
          -
          Returns:
          -
          A deep clone of this tensor with autograd support.
          -
          -
          • -
        - - - -
          -
        • -

          shallowClone

          -
          default Tsr<V> shallowClone()
          -
          -
          Returns:
          -
          A shallow copy of this tensor with autograd support.
          -
          -
          • -
        -
        • -
      -
      -
      • -
    -
    -
    -
    - -
    - -
    - - diff --git a/docs/jdocs/neureka/autograd/ADAction.html b/docs/jdocs/neureka/autograd/ADAction.html index 1016fa819..f878e9104 100644 --- a/docs/jdocs/neureka/autograd/ADAction.html +++ b/docs/jdocs/neureka/autograd/ADAction.html @@ -1,202 +1,312 @@ - + + - -ADAction (neureka 1.0.0 API) - - - - + +ADAction (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.autograd
    -

    Interface ADAction

    +
    neureka.autograd
    +

    Interface ADAction

    -
    -
    +
    +
    +
      +
    • +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public interface ADAction
      +
      +
      @FunctionalInterface
+public interface ADAction
      This interface is the declaration for - lambda actions for both the act(ADTarget) method of the ADAction interface.

      + lambda actions for both the act(ADTarget) method of the ADAction interface.

      Implementations of this perform auto-differentiation forwards or backwards along the computation graph. - These differentiation actions are performed through the "act(ADTarget)" + These differentiation actions are performed through the "act(ADTarget)" method which are being called - by instances of the GraphNode class during propagation. - An ADAction may also wrap and expose a partial derivative + by instances of the GraphNode class during propagation. + An ADAction may also wrap and expose a partial derivative which may or may not be present for certain operations. - Said derivative must be tracked and flagged as derivative by a GraphNode + Said derivative must be tracked and flagged as derivative by a GraphNode to make sure that it will not be deleted after a forward pass.

      - Note: Do not access the GraphNode.getPayload() of the GraphNode + Note: Do not access the GraphNode.getPayload() of the GraphNode passed to implementation of this. The payload is weakly referenced, meaning that this method can return null!

      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        static ADAction of(ADAction action)
        -
        +
          +
        • + + +

          Method Detail

          + + + + + + + + + + + +
            +
          • +

            act

            +
            Tensor<?> act(ADTarget<?> target)
            The auto-differentiation forward or backward pass of an ADAction propagate partial differentiations forward along the computation graph.
            -
            -
            Parameters:
            -
            target - A wrapper for the GraphNode at which the differentiation ought to +
            +
            Parameters:
            +
            target - A wrapper for the GraphNode at which the differentiation ought to be performed and error which ought to be used for the forward or backward differentiation.
            -
            Returns:
            +
            Returns:
            The result of a forward or backward mode auto differentiation.
            -
      • -
    • -
      -

      findCaptured

      -
      default Tensor<?>[] findCaptured()
      -
      Finds captured Tensor instances in this current action +
    + + + +
      +
    • +

      findCaptured

      +
      default Tensor<?>[] findCaptured()
      +
      Finds captured Tensor instances in this current action using reflection (This is usually a partial derivative).
      -
      -
      Returns:
      -
      The captured Tensor instances.
      +
      +
      Returns:
      +
      The captured Tensor instances.
      -
      • -
    • -
      -

      partialDerivative

      -
      default Optional<Tensor<?>> partialDerivative()
      -
      +
    + + + +
      +
    • +

      partialDerivative

      +
      default java.util.Optional<Tensor<?>> partialDerivative()
      +
      • +
    - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/autograd/ADTarget.html b/docs/jdocs/neureka/autograd/ADTarget.html index 682836e19..f6187b326 100644 --- a/docs/jdocs/neureka/autograd/ADTarget.html +++ b/docs/jdocs/neureka/autograd/ADTarget.html @@ -1,165 +1,276 @@ - + + - -ADTarget (neureka 1.0.0 API) - - - - + +ADTarget (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.autograd
    -

    Class ADTarget<V>

    +
    neureka.autograd
    +

    Class ADTarget<V>

    -
    java.lang.Object -
    neureka.autograd.ADTarget<V>
    -
    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.autograd.ADTarget<V>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The data type of the tensor.
      -
      public final class ADTarget<V> -extends Object
      +
      +
      public final class ADTarget<V>
+extends java.lang.Object
      This is simply a wrapper for useful information needed by implementations of - the ADAction and ADAction interfaces to perform error propagation. + the ADAction and ADAction interfaces to perform error propagation. The class exposes the targeted index and graph node of the input towards a provided error should be propagated.
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      Tensor<V>
      -
      error()
      -
       
      -
      int
      -
      inputIndex()
      -
       
      -
      GraphNode<V>
      -
      node()
      -
       
      -
      -
      +
      • +
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        inputIndex

        -
        public int inputIndex()
        -
        -
        Returns:
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            inputIndex

            +
            public int inputIndex()
            +
            +
            Returns:
            The index of the input targeted for propagation.
            -
        • -
      • -
        -

        node

        -
        public GraphNode<V> node()
        -
        -
        Returns:
        +
      + + + +
        +
      • +

        node

        +
        public GraphNode<V> node()
        +
        +
        Returns:
        The targeted graph node of the tensor towards the error should be propagated.
        -
      • -
    • -
      -

      error

      -
      public Tensor<V> error()
      -
      +
    + + + +
      +
    • +

      error

      +
      public Tensor<V> error()
      +
      • +
    - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/autograd/GraphNode.Print.html b/docs/jdocs/neureka/autograd/GraphNode.Print.html index 84740d6b4..527d7ea12 100644 --- a/docs/jdocs/neureka/autograd/GraphNode.Print.html +++ b/docs/jdocs/neureka/autograd/GraphNode.Print.html @@ -1,229 +1,354 @@ - + + - -GraphNode.Print (neureka 1.0.0 API) - - - - + +GraphNode.Print (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.autograd
    -

    Enum Class GraphNode.Print

    -
    -
    java.lang.Object -
    java.lang.Enum<GraphNode.Print> -
    neureka.autograd.GraphNode.Print
    -
    +
    neureka.autograd
    +

    Enum GraphNode.Print

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • java.lang.Enum<GraphNode.Print>
        • +
      • +
          +
        • neureka.autograd.GraphNode.Print
          • +
        +
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from class java.lang.Enum

      -Enum.EnumDesc<E extends Enum<E>>
      -
      +
      +
      public static enum GraphNode.Print
+extends java.lang.Enum<GraphNode.Print>
      • - -
    • -
      -

      Enum Constant Summary

      -
      Enum Constants
      -
      -
      Enum Constant
      -
      Description
      -
      COMPACT
      -
       
      -
      FANCY
      -
       
      -
      SIMPLE
      -
       
      +
    - +
    + - -
    -
      +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static GraphNode.Print[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static GraphNode.Print[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (GraphNode.Print c : GraphNode.Print.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static GraphNode.Print valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static GraphNode.Print valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/autograd/GraphNode.html b/docs/jdocs/neureka/autograd/GraphNode.html index 6ad0acc33..9e1c34fb6 100644 --- a/docs/jdocs/neureka/autograd/GraphNode.html +++ b/docs/jdocs/neureka/autograd/GraphNode.html @@ -1,91 +1,122 @@ - + + - -GraphNode (neureka 1.0.0 API) - - - - + +GraphNode (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.autograd
    -

    Class GraphNode<V>

    +
    neureka.autograd
    +

    Class GraphNode<V>

    -
    java.lang.Object -
    neureka.autograd.GraphNode<V>
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.autograd.GraphNode<V>
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Component<Tensor<V>>
      +
      Component<Tensor<V>>
      -
      public class GraphNode<V> -extends Object -implements Component<Tensor<V>>
      -
      Instances of the GraphNode class are components of tensors (Tensor instances) +
      +
      public class GraphNode<V>
+extends java.lang.Object
+implements Component<Tensor<V>>
      +
      Instances of the GraphNode class are components of tensors (Tensor instances) which model and record computations / operations between them. - GraphNodes form a computation graph when operations are applied to tensors. + GraphNodes form a computation graph when operations are applied to tensors. This graph can then later on be used for traversal by an important algorithm implemented inside this class, namely: backpropagation. This algorithm is more generally known as reverse mode auto differentiation. @@ -95,311 +126,388 @@

      Class GraphNode<V>

      Children are weakly referenced so that abandoned / detached graph branches (child nodes) can be garbage collected... ...whereas parents are strongly referenced in order to grant successful traversal.
      -
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Class
      -
      Description
      -
      static enum 
      -
      GraphNode.Print
      -
       
      +
      • +
    -
    -

    Nested classes/interfaces inherited from interface neureka.common.composition.Component

    -Component.IsBeing, Component.OwnerChangeRequest<O>
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        GraphNode

        -
        public GraphNode(Function function, - ExecutionCall<Device<?>> call, - Supplier<Result> payloadSupplier)
        -
        -
        Parameters:
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            GraphNode

            +
            public GraphNode(Function function,
+                 ExecutionCall<Device<?>> call,
+                 java.util.function.Supplier<Result> payloadSupplier)
            +
            +
            Parameters:
            function - Is the function that lead to the creation of this node.
            call - The execution call, or null if the node is not a result of an execution call (a leave).
            payloadSupplier - Provides the payload of this node.
            -
      -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      usesAD

      -
      public boolean usesAD()
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          usesAD

          +
          public boolean usesAD()
          This gradient node is involved in auto-differentiation.
          -
          -
          Returns:
          +
          +
          Returns:
          boolean
          -
      • -
    • -
      -

      usesForwardAD

      -
      public boolean usesForwardAD()
      +
    + + + +
      +
    • +

      usesForwardAD

      +
      public boolean usesForwardAD()
      This node propagates forward.
      -
      -
      Returns:
      +
      +
      Returns:
      boolean
      -
    • -
  • -
    -

    usesReverseAD

    -
    public boolean usesReverseAD()
    + + + + +
      +
    • +

      usesReverseAD

      +
      public boolean usesReverseAD()
      This node propagates _backward.
      -
      -
      Returns:
      +
      +
      Returns:
      boolean
      -
    • -
  • -
    -

    isLeave

    -
    public boolean isLeave()
    + + + + +
      +
    • +

      isLeave

      +
      public boolean isLeave()
      This node (and the corresponding tensor) was not created by a function! (it's a leave tensor)
      -
      -
      Returns:
      +
      +
      Returns:
      boolean
      -
    • -
  • -
    -

    isGraphLeave

    -
    public boolean isGraphLeave()
    -
    + + + + +
      +
    • +

      isGraphLeave

      +
      public boolean isGraphLeave()
      • -
    • -
      -

      getPayloadShape

      -
      public List<Integer> getPayloadShape()
      +
    + + + +
      +
    • +

      getPayloadShape

      +
      public java.util.List<java.lang.Integer> getPayloadShape()
      Note: This method will never return null even if the actual payload tensor was garbage collected. - This is because the GraphNode will remember the shape of the tensor.
      -
      -
      Returns:
      -
      The shape of the payload tensor represented by this GraphNode.
      + This is because the GraphNode will remember the shape of the tensor.
    • +
    +
    Returns:
    +
    The shape of the payload tensor represented by this GraphNode.
    - -
  • -
    -

    getPayload

    -
    public Optional<Tensor<V>> getPayload()
    + + + + +
      +
    • +

      getPayload

      +
      public java.util.Optional<Tensor<V>> getPayload()
      The value of a graph node is the tensor to which it belongs (is a component of).

      - Meaning it is the tensor owning this GraphNode component. + Meaning it is the tensor owning this GraphNode component. It is referenced weakly because it might not be needed any more (Not referenced inside AD-Agent for example) and can therefore be garbage collected.

      Warning: This method might return null because the payload is weakly referenced! Meaning that it might get garbage collected.

      -
      -
      Returns:
      +
      +
      Returns:
      The tensor payload of this graph-node.
      -
    • -
  • -
    -

    update

    -
    public boolean update(Component.OwnerChangeRequest<Tensor<V>> changeRequest)
    -
    Description copied from interface: Component
    + + + + +
      +
    • +

      update

      +
      public boolean update(Component.OwnerChangeRequest<Tensor<V>> changeRequest)
      +
      Description copied from interface: Component
      Components are not the slaves of their owners. If the owner registers any state changes related to a given component, then said component will be informed by the owner about the change as well as receive @@ -409,53 +517,62 @@

      update

      is being added to, or removed from, its current owner. If components hold references to their owners then this method gives them the ability to update said reference when a new owner takes over the components of an old one. - The Component.OwnerChangeRequest implementation instance passed to this method - informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). + The Component.OwnerChangeRequest implementation instance passed to this method + informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). If this method returns false then this means that this component rejects the proposed update. The component owner will then abort the proposed change.
      -
      -
      Specified by:
      -
      update in interface Component<V>
      -
      Parameters:
      -
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      -
      Returns:
      +
      +
      Specified by:
      +
      update in interface Component<Tensor<V>>
      +
      Parameters:
      +
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      +
      Returns:
      The truth value determining if the state change should be aborted or not.
      -
    • -
  • -
    -

    getAndRemovePendingError

    -
    public neureka.autograd.PendingError<V> getAndRemovePendingError()
    + + + + +
      +
    • +

      getAndRemovePendingError

      +
      public neureka.autograd.PendingError<V> getAndRemovePendingError()
      This method is called by the JITProp component. A pending should only ever be retrieved from a GraphNode once because afterward the accumulated error is about to be back-propagated. Therefore, this method nulls the reference when returning the PendingError instance.
      -
      -
      Returns:
      +
      +
      Returns:
      Returns an instance of the PendingError class containing a error accumulation.
      -
    • -
  • -
    -

    backward

    -
    public void backward(Tensor<V> error)
    + + + + +
      +
    • +

      backward

      +
      public void backward(Tensor<V> error)
      This method is the entry-point for the back-propagation process. It sets up a key/value map which stores nodes and their intermediate error accumulations. Accumulation occurs inside the private '_backward' method which traverses the computation graph recursively, halts when errors can be accumulated, adds a PendingError and returns to the method below! Here all the nodes and error values will then be carried (propagated) to the gradients!
      -
      -
      Parameters:
      +
      +
      Parameters:
      error - The current error which is created by multiplying it with current size and traversing it.
      -
    • -
  • -
    -

    backwardJIT

    -
    public void backwardJIT(Tensor<V> error)
    + + + + +
      +
    • +

      backwardJIT

      +
      public void backwardJIT(Tensor<V> error)
      This method is called only when JITProp is active. If an error has accumulated inside a JITProp component and the component is triggered to continue pending backward calls @@ -465,87 +582,105 @@

      backwardJIT

      in order to mark this error source as 'done' so that other JITProp components do not propagate this 'source' node multiple times.
      -
      -
      Parameters:
      +
      +
      Parameters:
      error - The error which ought to be back-propagated just-in-time.
      -
    • -
  • -
    -

    has

    -
    public boolean has(GraphNode<V> target)
    + + + + +
      +
    • +

      has

      +
      public boolean has(GraphNode<V> target)
      This method checks if a given graph node is an AD target of this node. This would mean that this node contains an AD-action for the given GraphNode (target).
      -
      -
      Parameters:
      +
      +
      Parameters:
      target - The targeted derivation graph node reference.
      -
      Returns:
      +
      Returns:
      boolean
      -
    • -
  • -
    -

    size

    -
    public int size()
    + + + + +
      +
    • +

      size

      +
      public int size()
      This is the number of AD-actions stored inside this node. It can be interpreted as the 'number of AD paths'.
      -
      -
      Returns:
      +
      +
      Returns:
      int
      -
    • -
  • -
    -

    forEachDerivative

    -
    public void forEachDerivative(BiConsumer<GraphNode<V>,ADAction> action)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      forEachDerivative

      +
      public void forEachDerivative(java.util.function.BiConsumer<GraphNode<V>,ADAction> action)
      +
      +
      Parameters:
      action - The lambda performing an action on all targeted nodes and their agents.
      -
    • -
  • -
    -

    forEachTarget

    -
    public void forEachTarget(Consumer<GraphNode<V>> action)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      forEachTarget

      +
      public void forEachTarget(java.util.function.Consumer<GraphNode<V>> action)
      +
      +
      Parameters:
      action - An action which should be applied to the graph nodes of all the partial derivatives.
      -
    • -
  • -
    -

    hasDerivatives

    -
    public boolean hasDerivatives()
    -
    -
    Returns:
    + + + + +
      +
    • +

      hasDerivatives

      +
      public boolean hasDerivatives()
      +
      +
      Returns:
      Checks if this node stores target / AD-action (usually derivatives) pairs.
      -
    • -
  • -
    -

    getMode

    -
    public int getMode()
    -
    This is the getter for an important GraphNode property which + + + + +
      +
    • +

      getMode

      +
      public int getMode()
      +
      This is the getter for an important GraphNode property which holds the auto-differentiation mode used by this instance to decide if a given error should be forward propagated backward propagated or not propagated at all. - If the mode is greater than 0, then this means this GraphNode + If the mode is greater than 0, then this means this GraphNode will perform forward propagation. In this case the mode number is also the cumulative number of forward propagation steps - in the tree of source GraphNode instances. + in the tree of source GraphNode instances. If the mode is below 0, then this means this instance will perform reverse mode differentiation (back-propagation). The absolute of a negative mode represents the number of referenced source nodes which have a mode state other than zero. This means that they directly or indirectly reference - a GraphNode instance which represents a Tensor instance - having the Tensor.rqsGradient() flag set to true! + a GraphNode instance which represents a Tensor instance + having the Tensor.rqsGradient() flag set to true!
      Mode state meaning:
      -----------------------------------------------------------
      @@ -555,134 +690,229 @@

      getMode

      -----------------------------------------------------------
      | mode lesser 0 | backward Auto-Differentiation
      -----------------------------------------------------------

      -
      -
      Returns:
      +
      +
      Returns:
      The differentiation mode represented as an integer which encodes 3 distinct states.
      -
    • -
  • -
    -

    isReliesOnJustInTimeProp

    -
    public boolean isReliesOnJustInTimeProp()
    + + + + +
      +
    • +

      isReliesOnJustInTimeProp

      +
      public boolean isReliesOnJustInTimeProp()
      This flag is used for a performance optimization feature namely 'Just In Time Propagation'. This feature accumulates errors and continues propagation as soon as they are needed. (At the end of 'backward()' or when the tensor is used again). - If the flag Neureka.Settings.AutoGrad.isRetainingPendingErrorForJITProp() is set to true + If the flag Neureka.Settings.AutoGrad#isRetainingPendingErrorForJITProp() is set to true then error values will accumulate whenever it makes sense. This technique however uses more memory but will improve performance for some networks substantially.

      All nodes between a Pending-Error and those requiring gradients will be marked with '_relies_on_JIPProp=true'!

      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if this graph node relies on just in time propagation.
      -
    • -
  • -
    -

    getPendingError

    -
    public Optional<neureka.autograd.PendingError<V>> getPendingError()
    + + + + +
      +
    • +

      getPendingError

      +
      public java.util.Optional<neureka.autograd.PendingError<V>> getPendingError()
      Used by the Just-In-Time back-prop component.
      -
    • -
  • -
    -

    isUsedAsDerivative

    -
    public boolean isUsedAsDerivative()
    + + + + +
      +
    • +

      isUsedAsDerivative

      +
      public boolean isUsedAsDerivative()
      The chain-rule states that the derivative of f(x) = h(g(x)) with respect to x is: g'(x) * h'(g(x)) An example would be: f(x) = ((x*y)*z) f'(x) = (1*y) * (1*z) = z*y The values z,y or z*y must not be deleted as they are needed for back-propagation!
      -
    • -
  • -
    -

    getFunction

    -
    public Optional<Function> getFunction()
    -
    Recorded Function which produced this GraphNode.
    -
    + + + + +
      +
    • +

      getFunction

      +
      public java.util.Optional<Function> getFunction()
      +
      Recorded Function which produced this GraphNode.
      • -
    • -
      -

      getParents

      -
      public List<GraphNode<V>> getParents()
      -
      +
    + + + +
      +
    • +

      getParents

      +
      public java.util.List<GraphNode<V>> getParents()
      • -
    • -
      -

      getPayloadReferenceVersion

      -
      public int getPayloadReferenceVersion()
      +
    + + + +
      +
    • +

      getPayloadReferenceVersion

      +
      public int getPayloadReferenceVersion()
      This variable holds a copy of the version of the payload tensor recorded when this GraphNode instance is instantiated. It must be treated as final and should never be modified. However, it can be read freely in order to check that the version of the payload hasn't changed.
      -
      • -
    • -
      -

      getPayloadDataType

      -
      public DataType<V> getPayloadDataType()
      -
      +
    + + + + + + + +
      +
    • +

      getChildren

      +
      public java.util.List<java.lang.ref.WeakReference<GraphNode<V>>> getChildren()
      +
      The children are GraphNode instances which represent computations + involving the payload of this very GraphNode instance.
      • -
    • -
      -

      canBeDeleted

      -
      public boolean canBeDeleted()
      -
      +
    + + + +
      +
    • +

      canBeDeleted

      +
      public boolean canBeDeleted()
      • -
    • -
      -

      type

      -
      public String type()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      type

      +
      public java.lang.String type()
      +
      +
      Returns:
      Returns the type of the node as descriptive String in capital letters.
      -
      • -
    • -
      -

      toString

      -
      public String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
      • -
    • -
      -

      toString

      -
      public String toString(GraphNode.Print mode)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      toString

      +
      public java.lang.String toString(GraphNode.Print mode)
      +
      +
      Parameters:
      mode - The format of the string representation.
      -
      Returns:
      +
      Returns:
      Returns a String representation of this node.
      -
    -
    • - + + +
    + - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/autograd/JITProp.html b/docs/jdocs/neureka/autograd/JITProp.html index c73c86fac..fa5ff11b1 100644 --- a/docs/jdocs/neureka/autograd/JITProp.html +++ b/docs/jdocs/neureka/autograd/JITProp.html @@ -1,262 +1,406 @@ - + + - -JITProp (neureka 1.0.0 API) - - - - + +JITProp (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.autograd
    -

    Class JITProp<V>

    +
    neureka.autograd
    +

    Class JITProp<V>

    -
    java.lang.Object -
    neureka.autograd.JITProp<V>
    -
    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.autograd.JITProp<V>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The type parameter of the involved tensors.
      -
      +
      All Implemented Interfaces:
      -
      Component<Tensor<V>>
      +
      Component<Tensor<V>>
      -
      public final class JITProp<V> -extends Object -implements Component<Tensor<V>>
      +
      +
      public final class JITProp<V>
+extends java.lang.Object
+implements Component<Tensor<V>>
      This class keeps track of graph nodes which require back-propagation in order to be able to continue the process at a later point in time (based on some configurable conditions).
      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        JITProp

        -
        public JITProp(Set<GraphNode<V>> pending)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            JITProp

            +
            public JITProp(java.util.Set<GraphNode<V>> pending)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      addPending

      -
      public void addPending(Set<GraphNode<V>> pending)
      -
      -
      Parameters:
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          addPending

          +
          public void addPending(java.util.Set<GraphNode<V>> pending)
          +
          +
          Parameters:
          pending - A set of GraphNode<V> instance which are saved for future backprop continuation.
          -
      • -
    • -
      -

      noteFinished

      -
      public void noteFinished(GraphNode<V> finishedJITProps)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      noteFinished

      +
      public void noteFinished(GraphNode<V> finishedJITProps)
      +
      +
      Parameters:
      finishedJITProps - The reference to a GraphNote which has finished (JITed) backpropation.
      -
    • -
  • -
    -

    finishedCount

    -
    public int finishedCount()
    -
    + + + + +
      +
    • +

      finishedCount

      +
      public int finishedCount()
      • -
    • -
      -

      pendingCount

      -
      public int pendingCount()
      -
      +
    + + + +
      +
    • +

      pendingCount

      +
      public int pendingCount()
      • -
    • -
      -

      execute

      -
      public void execute()
      +
    + + + +
      +
    • +

      execute

      +
      public void execute()
      This method triggers the continuation of the back-propagation which has been put on hold by saving the pending graph nodes inside this class.
      The execution request happens when gradients are immediately required by a tensor, which is the case when the tensor is about to apply its gradients.
      However because the gradient has not yet been fully calculated this method will be called first (assuming the tensor has a JITProp component stored).
      -
      • -
    • -
      -

      isDone

      -
      public boolean isDone()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      isDone

      +
      public boolean isDone()
      +
      +
      Returns:
      The truth value determining if the back-propagation has been completed.
      -
      • -
    • -
      -

      toString

      -
      public String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/autograd/package-frame.html b/docs/jdocs/neureka/autograd/package-frame.html new file mode 100644 index 000000000..0631549a8 --- /dev/null +++ b/docs/jdocs/neureka/autograd/package-frame.html @@ -0,0 +1,29 @@ + + + + + +neureka.autograd (neureka 1.0.1 API) + + + + +

    neureka.autograd

    +
    +

    Interfaces

    + +

    Classes

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/autograd/package-summary.html b/docs/jdocs/neureka/autograd/package-summary.html index 85d4c6b43..fefa65846 100644 --- a/docs/jdocs/neureka/autograd/package-summary.html +++ b/docs/jdocs/neureka/autograd/package-summary.html @@ -1,121 +1,191 @@ - + + - -neureka.autograd (neureka 1.0.0 API) - - - - + +neureka.autograd (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.autograd

    -
    -
    -
    package neureka.autograd
    -
    -
      -
    • - -
      • -
    • -
      -
      -
      -
      -
      Class
      -
      Description
      -
      ADAction
      -
      +

      Package neureka.autograd

      +
      +
      +
        +
      • + + + + + + + + + + + + +
        Interface Summary 
        InterfaceDescription
        ADAction
        This interface is the declaration for - lambda actions for both the ADAction.act(ADTarget) method of the ADAction interface.
        - -
        ADTarget<V>
        -
        + lambda actions for both the ADAction.act(ADTarget) method of the ADAction interface.
        +
        +
        • +
      • + + + + + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        ADTarget<V>
        This is simply a wrapper for useful information needed by implementations of - the ADAction and ADAction interfaces to perform error propagation.
        - -
        GraphNode<V>
        -
        -
        Instances of the GraphNode class are components of tensors (Tensor instances) + the ADAction and ADAction interfaces to perform error propagation.
        +
        GraphNode<V> +
        Instances of the GraphNode class are components of tensors (Tensor instances) which model and record computations / operations between them.
        - -
        GraphNode.Print
        -
         
        -
        JITProp<V>
        -
        +
        JITProp<V>
        This class keeps track of graph nodes which require back-propagation in order to be able to continue the process at a later point in time (based on some configurable conditions).
        - - - - +
        +
        • +
      • + + + + + + + + + + + + +
        Enum Summary 
        EnumDescription
        GraphNode.Print 
      -
    -
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/autograd/package-tree.html b/docs/jdocs/neureka/autograd/package-tree.html index 4f10b7819..b8078208a 100644 --- a/docs/jdocs/neureka/autograd/package-tree.html +++ b/docs/jdocs/neureka/autograd/package-tree.html @@ -1,93 +1,152 @@ - + + - -neureka.autograd Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.autograd Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.autograd

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -

    Interface Hierarchy

    -
    -
    -

    Enum Class Hierarchy

    +

    Enum Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Algorithm.html b/docs/jdocs/neureka/backend/api/Algorithm.html index 2e2073467..bb7499d53 100644 --- a/docs/jdocs/neureka/backend/api/Algorithm.html +++ b/docs/jdocs/neureka/backend/api/Algorithm.html @@ -1,196 +1,312 @@ - + + - -Algorithm (neureka 1.0.0 API) - - - - + +Algorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface Algorithm

    +
    neureka.backend.api
    +

    Interface Algorithm

    -
    -
    +
    +
    +
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        withName

        -
        static FunAlgorithm withName(String name)
        -
        This is a factory method for creating a new instance of this FunAlgorithm class.
        -
        -
        Parameters:
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            withName

            +
            static FunAlgorithm withName(java.lang.String name)
            +
            This is a factory method for creating a new instance of this FunAlgorithm class.
            +
            +
            Parameters:
            name - The name of the functional algorithm.
            -
            Returns:
            -
            A new FunAlgorithm with the provided name.
            +
            Returns:
            +
            A new FunAlgorithm with the provided name.
            -
        • -
      • -
        -

        getName

        -
        String getName()
        -
        The name of an Algorithm may be used for OpenCL kernel compilation or simply +
      + + + +
        +
      • +

        getName

        +
        java.lang.String getName()
        +
        The name of an Algorithm may be used for OpenCL kernel compilation or simply for debugging purposes to identify which type of algorithm is being executed at any given time...
        -
        -
        Returns:
        -
        The name of this Algorithm.
        +
        +
        Returns:
        +
        The name of this Algorithm.
        -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/AutoDiffMode.html b/docs/jdocs/neureka/backend/api/AutoDiffMode.html index 0d3fcc7aa..636bc7ea2 100644 --- a/docs/jdocs/neureka/backend/api/AutoDiffMode.html +++ b/docs/jdocs/neureka/backend/api/AutoDiffMode.html @@ -1,251 +1,388 @@ - + + - -AutoDiffMode (neureka 1.0.0 API) - - - - + +AutoDiffMode (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Enum Class AutoDiffMode

    -
    -
    java.lang.Object -
    java.lang.Enum<AutoDiffMode> -
    neureka.backend.api.AutoDiffMode
    -
    +
    neureka.backend.api
    +

    Enum AutoDiffMode

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • java.lang.Enum<AutoDiffMode>
        • +
      • +
          +
        • neureka.backend.api.AutoDiffMode
          • +
        +
        • +
      +
      • +
    +
    +
    -
    -
    - +
    +
      +
    • + + -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      boolean
      -
      allowsBackward()
      -
       
      -
      boolean
      -
      allowsForward()
      -
       
      -
      static AutoDiffMode
      -
      valueOf(String name)
      -
      -
      Returns the enum constant of this class with the specified name.
      -
      -
      static AutoDiffMode[]
      -
      values()
      -
      -
      Returns an array containing the constants of this enum class, in +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        booleanallowsBackward() 
        booleanallowsForward() 
        static AutoDiffModevalueOf(java.lang.String name) +
        Returns the enum constant of this type with the specified name.
        +
        static AutoDiffMode[]values() +
        Returns an array containing the constants of this enum type, in the order they are declared.
        +
        +
          +
        • + + +

          Methods inherited from class java.lang.Enum

          +clone, compareTo, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf
          • +
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +getClass, notify, notifyAll, wait, wait, wait
          • +
        +
        • +
      +
      • +
    -
    -
    -
    -
    -

    Methods inherited from class java.lang.Enum

    -clone, compareTo, describeConstable, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf
    -
    -

    Methods inherited from class java.lang.Object

    -getClass, notify, notifyAll, wait, wait, wait
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Enum Constant Details

        -
          -
        • -
          -

          FORWARD_ONLY

          -
          public static final AutoDiffMode FORWARD_ONLY
          -
          +
            +
          • + + +

            Enum Constant Detail

            + + + +
              +
            • +

              FORWARD_ONLY

              +
              public static final AutoDiffMode FORWARD_ONLY
              • -
            • -
              -

              BACKWARD_ONLY

              -
              public static final AutoDiffMode BACKWARD_ONLY
              -
              +
            + + + +
              +
            • +

              BACKWARD_ONLY

              +
              public static final AutoDiffMode BACKWARD_ONLY
              • -
            • -
              -

              FORWARD_AND_BACKWARD

              -
              public static final AutoDiffMode FORWARD_AND_BACKWARD
              -
              +
            + + + +
              +
            • +

              FORWARD_AND_BACKWARD

              +
              public static final AutoDiffMode FORWARD_AND_BACKWARD
              • -
            • -
              -

              NOT_SUPPORTED

              -
              public static final AutoDiffMode NOT_SUPPORTED
              -
              +
            + + + +
              +
            • +

              NOT_SUPPORTED

              +
              public static final AutoDiffMode NOT_SUPPORTED
            -
        • +
      -
    • -
      -

      Method Details

      -
        -
      • -
        -

        values

        -
        public static AutoDiffMode[] values()
        -
        Returns an array containing the constants of this enum class, in -the order they are declared.
        -
        -
        Returns:
        -
        an array containing the constants of this enum class, in the order they are declared
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            values

            +
            public static AutoDiffMode[] values()
            +
            Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
            +for (AutoDiffMode c : AutoDiffMode.values())
+    System.out.println(c);
+
            +
            +
            Returns:
            +
            an array containing the constants of this enum type, in the order they are declared
            -
        • -
      • -
        -

        valueOf

        -
        public static AutoDiffMode valueOf(String name)
        -
        Returns the enum constant of this class with the specified name. +
      + + + +
        +
      • +

        valueOf

        +
        public static AutoDiffMode valueOf(java.lang.String name)
        +
        Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
        -
        -
        Parameters:
        +
        +
        Parameters:
        name - the name of the enum constant to be returned.
        -
        Returns:
        +
        Returns:
        the enum constant with the specified name
        -
        Throws:
        -
        IllegalArgumentException - if this enum class has no constant with the specified name
        -
        NullPointerException - if the argument is null
        +
        Throws:
        +
        java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
        +
        java.lang.NullPointerException - if the argument is null
        -
      • -
    • -
      -

      allowsForward

      -
      public boolean allowsForward()
      -
      +
    + + + +
      +
    • +

      allowsForward

      +
      public boolean allowsForward()
      • -
    • -
      -

      allowsBackward

      -
      public boolean allowsBackward()
      -
      +
    + + + +
      +
    • +

      allowsBackward

      +
      public boolean allowsBackward()
      +
      • +
    -
    - + + - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/BackendContext.Runner.html b/docs/jdocs/neureka/backend/api/BackendContext.Runner.html index 2845b05dd..fc2585595 100644 --- a/docs/jdocs/neureka/backend/api/BackendContext.Runner.html +++ b/docs/jdocs/neureka/backend/api/BackendContext.Runner.html @@ -1,244 +1,359 @@ - + + - -BackendContext.Runner (neureka 1.0.0 API) - - - - + +BackendContext.Runner (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class BackendContext.Runner

    -
    -
    java.lang.Object -
    neureka.backend.api.BackendContext.Runner
    +
    neureka.backend.api
    +

    Class BackendContext.Runner

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.BackendContext.Runner
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      BackendContext
      +
      BackendContext
      -
      public static class BackendContext.Runner -extends Object
      +
      +
      public static class BackendContext.Runner
+extends java.lang.Object
      This is a very simple class with a single purpose, namely it exposes methods which receive lambda instances in order to then execute them - in a given BackendContext, just to then switch back to the original context again. - Switching a context simply means that the BackendContext which produced this BackendContext.Runner + in a given BackendContext, just to then switch back to the original context again. + Switching a context simply means that the BackendContext which produced this BackendContext.Runner will temporarily be set as execution context for the current thread - local Neureka instance.

      + local Neureka instance.

      - A BackendContext.Runner wraps both the called context as well as the context of the caller in order - to perform this temporary context switching throughout the execution of the lambdas passed to the BackendContext.Runner. + A BackendContext.Runner wraps both the called context as well as the context of the caller in order + to perform this temporary context switching throughout the execution of the lambdas passed to the BackendContext.Runner. After a given lambda was executed, the original context will be restored in the current thread - local Neureka instance through the Neureka.setBackend(BackendContext)) method.
      -
    -
    -
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    - +
    +
      +
    • + +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        <T> Tcall(java.util.function.Supplier<T> contextSpecificAction) +
        Use this method to supply a lambda which will be executed in the BackendContext + which produced this very BackendContext.Runner instance.
        +
        <T> Tinvoke(java.util.function.Supplier<T> contextSpecificAction) +
        Use this method to supply a lambda which will be executed in the BackendContext + which produced this very BackendContext.Runner instance.
        +
        BackendContext.Runnerrun(java.lang.Runnable contextSpecificAction) +
        Use this method to supply a lambda which will be executed in the BackendContext + which produced this very BackendContext.Runner instance.
        +
        <T> TrunAndGet(java.util.function.Supplier<T> contextSpecificAction) +
        Use this method to supply a lambda which will be executed in the BackendContext + which produced this very BackendContext.Runner instance.
        +
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      - + + + +
        +
      • +

        runAndGet

        +
        public <T> T runAndGet(java.util.function.Supplier<T> contextSpecificAction)
        +
        Use this method to supply a lambda which will be executed in the BackendContext + which produced this very BackendContext.Runner instance. + After the lambda finished execution successfully the original BackendContext will be restored. + This method distinguishes itself from the run(Runnable) method because the lambda supplied to this method is expected to return something. What may be returned is up to the user, one might want to return the result of a tensor operation which might be exclusively available in the used context.
        -
        -
        Type Parameters:
        +
        +
        Type Parameters:
        T - The return type of the supplied context action which will also be returned by this method.
        -
        Parameters:
        -
        contextSpecificAction - The context specific action which will be execute in the BackendContext which produced this BackendContext.Runner.
        -
        Returns:
        +
        Parameters:
        +
        contextSpecificAction - The context specific action which will be execute in the BackendContext which produced this BackendContext.Runner.
        +
        Returns:
        The result of the supplied context action.
        -
      • -
    • -
      -

      call

      -
      public <T> T call(Supplier<T> contextSpecificAction)
      -
      Use this method to supply a lambda which will be executed in the BackendContext - which produced this very BackendContext.Runner instance. - After the lambda finished execution successfully the original BackendContext will be restored. - This method distinguishes itself from the run(Runnable) method because the +
    + + + +
      +
    • +

      call

      +
      public <T> T call(java.util.function.Supplier<T> contextSpecificAction)
      +
      Use this method to supply a lambda which will be executed in the BackendContext + which produced this very BackendContext.Runner instance. + After the lambda finished execution successfully the original BackendContext will be restored. + This method distinguishes itself from the run(Runnable) method because the lambda supplied to this method is expected to return something.
      What may be returned is up to the user, one might want to return the result of a tensor operation which might be exclusively available in the used context. - This method is doing the exact same thing as the runAndGet(Supplier) method, + This method is doing the exact same thing as the runAndGet(Supplier) method, however its name is shorter and it can even be omitted entirely when using Groovy.

      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The return type of the supplied context action which will also be returned by this method.
      -
      Parameters:
      -
      contextSpecificAction - The context specific action which will be execute in the BackendContext which produced this BackendContext.Runner.
      -
      Returns:
      +
      Parameters:
      +
      contextSpecificAction - The context specific action which will be execute in the BackendContext which produced this BackendContext.Runner.
      +
      Returns:
      The result of the supplied context action.
      -
      • -
    • -
      -

      invoke

      -
      public <T> T invoke(Supplier<T> contextSpecificAction)
      -
      Use this method to supply a lambda which will be executed in the BackendContext - which produced this very BackendContext.Runner instance. - After the lambda finished execution successfully the original BackendContext will be restored. - This method distinguishes itself from the run(Runnable) method because the +
    + + + +
      +
    • +

      invoke

      +
      public <T> T invoke(java.util.function.Supplier<T> contextSpecificAction)
      +
      Use this method to supply a lambda which will be executed in the BackendContext + which produced this very BackendContext.Runner instance. + After the lambda finished execution successfully the original BackendContext will be restored. + This method distinguishes itself from the run(Runnable) method because the lambda supplied to this method is expected to return something.
      What may be returned is up to the user, one might want to return the result of a tensor operation which might be exclusively available in the used context. - This method is doing the exact same thing as the runAndGet(Supplier) method, + This method is doing the exact same thing as the runAndGet(Supplier) method, however its name is shorter and it can even be omitted entirely when using Kotlin.

      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The return type of the supplied context action which will also be returned by this method.
      -
      Parameters:
      -
      contextSpecificAction - The context specific action which will be execute in the BackendContext which produced this BackendContext.Runner.
      -
      Returns:
      +
      Parameters:
      +
      contextSpecificAction - The context specific action which will be execute in the BackendContext which produced this BackendContext.Runner.
      +
      Returns:
      The result of the supplied context action.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/BackendContext.html b/docs/jdocs/neureka/backend/api/BackendContext.html index 6dcdc15e8..0e172556b 100644 --- a/docs/jdocs/neureka/backend/api/BackendContext.html +++ b/docs/jdocs/neureka/backend/api/BackendContext.html @@ -1,523 +1,709 @@ - + + - -BackendContext (neureka 1.0.0 API) - - - - + +BackendContext (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class BackendContext

    +
    neureka.backend.api
    +

    Class BackendContext

    -
    java.lang.Object -
    neureka.backend.api.BackendContext
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.BackendContext
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Cloneable
      +
      java.lang.Cloneable
      -
      public final class BackendContext -extends Object -implements Cloneable
      -
      Instances of this class are execution contexts hosting Operation instances which receive Tensor +
      +
      public final class BackendContext
+extends java.lang.Object
+implements java.lang.Cloneable
      +
      Instances of this class are execution contexts hosting Operation instances which receive Tensor instances for execution. - BackendContexts are managed by Neureka, a (thread-local) Singleton / Multiton library context.
      + BackendContexts are managed by Neureka, a (thread-local) Singleton / Multiton library context.
      Contexts are cloneable for testing purposes and to enable extending the backend dynamically. - A given instance also hosts a reference to a Functions instance which exposes commonly used - pre-instantiated Function implementation instances. + A given instance also hosts a reference to a Functions instance which exposes commonly used + pre-instantiated Function implementation instances.

      - The BackendContext initializes and stores Operation instances in various data structures - for fast access and querying (Mostly used by the ParseUtil and FunctionParser). + The BackendContext initializes and stores Operation instances in various data structures + for fast access and querying (Mostly used by the ParseUtil and FunctionParser).
      - Operations are stored in simple list and map collections, + Operations are stored in simple list and map collections, namely:
      The "_instances" list and the "_lookup" map as declared below.

      - During class initialization concrete classes extending the Operation class - are being instantiated in the static block below via a ServiceLoader. - BackendContext instances expose a useful class called BackendContext.Runner, + During class initialization concrete classes extending the Operation class + are being instantiated in the static block below via a ServiceLoader. + BackendContext instances expose a useful class called BackendContext.Runner, which performs temporary context switching between the caller's context and this context during the execution of provided lambdas.
      -
    -
    -
      + +
    +
    +
    + + + + +
      +
    • +

      getOperationLookupMap

      +
      public java.util.Map<java.lang.String,Operation> getOperationLookupMap()
      +
      This method returns an unmodifiable view of the mapping between the Operation.getIdentifier() / Operation.getOperator() properties + and the Operation implementation instances to which they belong. Query operations on the returned map "read through" to the specified map, and attempts to modify the returned map, whether direct or via its collection views, - result in an UnsupportedOperationException.
      -
      -
      Returns:
      -
      An unmodifiable mapping of Operation properties to the Operation instances to which they belong.
      + result in an UnsupportedOperationException.
    +
    +
    Returns:
    +
    An unmodifiable mapping of Operation properties to the Operation instances to which they belong.
    - -
  • -
    -

    getOperations

    -
    public List<Operation> getOperations()
    + + + + +
      +
    • +

      getOperations

      +
      public java.util.List<Operation> getOperations()
      This method returns an unmodifiable view of the - list of Operation implementation instances managed by this context. + list of Operation implementation instances managed by this context. Query operations on the returned map "read through" to the specified map, and attempts to modify the returned map, whether direct or via its collection views, - result in an UnsupportedOperationException.
      -
      -
      Returns:
      -
      An unmodifiable view of the list of Operation implementation instances managed by this context
      + result in an UnsupportedOperationException.
    • +
    +
    Returns:
    +
    An unmodifiable view of the list of Operation implementation instances managed by this context
    - -
  • -
    -

    size

    -
    public int size()
    -
    -
    Returns:
    -
    The number of Operation instances stored on this BackendContext.
    + + + + +
    • -
  • -
    -

    getFunctionCache

    -
    public FunctionCache getFunctionCache()
    -
    -
    Returns:
    -
    The Function and Tensor cache of this BackendContext
    + + + + +
    • -
  • -
    -

    getFunction

    -
    public Functions getFunction()
    -
    This method returns a Functions instance which wraps pre-instantiated - Function instances which are configured to not track their computational history. + + + + +
      +
    • +

      getFunction

      +
      public Functions getFunction()
      +
      This method returns a Functions instance which wraps pre-instantiated + Function instances which are configured to not track their computational history. This means that no computation graph will be built by these instances. - ( Computation graphs in Neureka are made of instances of the GraphNode class... )
      -
    + ( Computation graphs in Neureka are made of instances of the GraphNode class... )
    • -
  • -
    -

    getAutogradFunction

    -
    public Functions getAutogradFunction()
    -
    This method returns a Functions instance which wraps pre-instantiated - Function instances which are configured to track their computational history. + + + + +
      +
    • +

      getAutogradFunction

      +
      public Functions getAutogradFunction()
      +
      This method returns a Functions instance which wraps pre-instantiated + Function instances which are configured to track their computational history. This means that a computation graph will be built by these instances. - ( Computation graphs in Neureka are made of instances of the GraphNode class... )
      -
      -
      Returns:
      + ( Computation graphs in Neureka are made of instances of the GraphNode class... )
    +
    +
    Returns:
    A container object which exposes various types of functions with autograd support.
    -
    • -
  • -
    -

    addOperation

    -
    public BackendContext addOperation(Operation operation)
    -
    This method registers Operation implementation instances in this BackendContext - which is the thread local execution context receiving and processing Tensor instances...

    -
    -
    Parameters:
    -
    operation - The Operation instance which ought to be registered as part of this execution context.
    -
    Returns:
    + + + + +
      +
    • +

      addOperation

      +
      public BackendContext addOperation(Operation operation)
      +
      This method registers Operation implementation instances in this BackendContext + which is the thread local execution context receiving and processing Tensor instances...

      +
      +
      Parameters:
      +
      operation - The Operation instance which ought to be registered as part of this execution context.
      +
      Returns:
      This very context instance to allow for method chaining.
      -
    • -
  • -
    -

    hasOperation

    -
    public boolean hasOperation(Operation operation)
    -
    -
    Parameters:
    -
    operation - The Operation which may or may not be part of this BackendContext.
    -
    Returns:
    -
    The truth value determining if the provided Operation is part of this BackendContext.
    + + + + +
    • -
  • -
    -

    hasOperation

    -
    public boolean hasOperation(String operationIdentifier)
    -
    -
    Parameters:
    -
    operationIdentifier - The Operation identifier which may be the function name or operator if present.
    -
    Returns:
    -
    The truth value determining if the provided Operation is part of this BackendContext.
    + + + + +
      +
    • +

      hasOperation

      +
      public boolean hasOperation(java.lang.String operationIdentifier)
      +
      +
      Parameters:
      +
      operationIdentifier - The Operation identifier which may be the function name or operator if present.
      +
      Returns:
      +
      The truth value determining if the provided Operation is part of this BackendContext.
      -
    • -
  • -
    -

    getOperation

    -
    public Operation getOperation(int index)
    -
    This method queries the operations in this BackendContext - by a provided index integer targeting an entry in the list of Operation implementation instances + + + + +
      +
    • +

      getOperation

      +
      public Operation getOperation(int index)
      +
      This method queries the operations in this BackendContext + by a provided index integer targeting an entry in the list of Operation implementation instances sitting in this execution context.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The index of the operation.
      -
      Returns:
      +
      Returns:
      The found Operation instance or null.
      -
    • -
  • -
    -

    getOperation

    -
    public Operation getOperation(String identifier)
    + + + + +
      +
    • +

      getOperation

      +
      public Operation getOperation(java.lang.String identifier)
      This method queries the operations in this BackendContext by a provided identifier which has to match the name of an existing operation.
      -
      -
      Parameters:
      +
      +
      Parameters:
      identifier - The operation identifier, aka: its name.
      -
      Returns:
      +
      Returns:
      The requested Operation or null.
      -
    • -
  • -
    -

    clone

    -
    public BackendContext clone()
    -
    This method produces a shallow copy of this BackendContext. + + + + +
      +
    • +

      clone

      +
      public BackendContext clone()
      +
      This method produces a shallow copy of this BackendContext. This is useful for debugging, testing and extending contexts during runtime without side effects!
      -
      -
      Overrides:
      -
      clone in class Object
      -
      Returns:
      +
      +
      Overrides:
      +
      clone in class java.lang.Object
      +
      Returns:
      A shallow copy of this operation / execution context.
      -
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • -
  • -
    -

    has

    -
    public <E extends BackendExtension> boolean has(Class<E> extensionClass)
    -
    Checks if this context has an instance of the provided BackendExtension type.
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      has

      +
      public <E extends BackendExtension> boolean has(java.lang.Class<E> extensionClass)
      +
      Checks if this context has an instance of the provided BackendExtension type.
      +
      +
      Type Parameters:
      E - The type parameter of the provided type class which requires the type to be an extension.
      -
      Parameters:
      +
      Parameters:
      extensionClass - The type class of the extensions whose presents should be checked.
      -
      Returns:
      +
      Returns:
      The truth value determining if the provided type is present.
      -
    • -
  • -
    -

    find

    -
    public <E extends BackendExtension> Optional<E> find(Class<E> componentClass)
    -
    Returns an Optional instance of the provided BackendExtension type - or an empty Optional if no extension of that type was found.
    -
    + + + + +
      +
    • +

      find

      +
      public <E extends BackendExtension> java.util.Optional<E> find(java.lang.Class<E> componentClass)
      +
      Returns an Optional instance of the provided BackendExtension type + or an empty Optional if no extension of that type was found.
      • -
    • -
      -

      getExtensions

      -
      public List<BackendExtension> getExtensions()
      -
      -
      Returns:
      -
      A list of all BackendExtension instances.
      +
    + + + + + + + + -
    • - + + + + - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/BackendExtension.DeviceOption.html b/docs/jdocs/neureka/backend/api/BackendExtension.DeviceOption.html index b8e96c606..5b954d40f 100644 --- a/docs/jdocs/neureka/backend/api/BackendExtension.DeviceOption.html +++ b/docs/jdocs/neureka/backend/api/BackendExtension.DeviceOption.html @@ -1,184 +1,298 @@ - + + - -BackendExtension.DeviceOption (neureka 1.0.0 API) - - - - + +BackendExtension.DeviceOption (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class BackendExtension.DeviceOption

    +
    neureka.backend.api
    +

    Class BackendExtension.DeviceOption

    -
    java.lang.Object -
    neureka.backend.api.BackendExtension.DeviceOption
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.BackendExtension.DeviceOption
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      DeviceOption(Device<?> device, - double confidence)
      -
       
      +
      +
      public static class BackendExtension.DeviceOption
+extends java.lang.Object
      +
      This class describes an available Device implementation found for a given BackendExtension. + It exists because a typical BackendExtension will most likely also have a + custom Device implementation exposing a specific API for executing tensors on them...
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        DeviceOption

        -
        public DeviceOption(Device<?> device, - double confidence)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            DeviceOption

            +
            public DeviceOption(Device<?> device,
+                    double confidence)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      device

      -
      public Device<?> device()
      -
      -
      Returns:
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          device

          +
          public Device<?> device()
          +
          +
          Returns:
          The device which fits a given key word best.
          -
      • -
    • -
      -

      confidence

      -
      public double confidence()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      confidence

      +
      public double confidence()
      +
      +
      Returns:
      The confidence level determining how well a given search key matches the wrapped device.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/BackendExtension.html b/docs/jdocs/neureka/backend/api/BackendExtension.html index 77215cb8e..d66680a83 100644 --- a/docs/jdocs/neureka/backend/api/BackendExtension.html +++ b/docs/jdocs/neureka/backend/api/BackendExtension.html @@ -1,209 +1,327 @@ - + + - -BackendExtension (neureka 1.0.0 API) - - - - + +BackendExtension (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface BackendExtension

    +
    neureka.backend.api
    +

    Interface BackendExtension

    -
    -
    +
    +
    +
    -
    -
    -
    -

    Nested classes/interfaces inherited from interface neureka.common.composition.Component

    -Component.IsBeing, Component.OwnerChangeRequest<O>
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        find

        -
        BackendExtension.DeviceOption find(String searchKey)
        -
        The BackendContext does not handle Device instances directly. - Instead, the task of instantiating and exposing Device implementations - should be carried by BackendExtension implementations. +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            find

            +
            BackendExtension.DeviceOption find(java.lang.String searchKey)
            +
            The BackendContext does not handle Device instances directly. + Instead, the task of instantiating and exposing Device implementations + should be carried by BackendExtension implementations. One extension might be implementing CUDA operations, - therefore, the extension should also deal with some sort of CUDADevice implementation.
            -
            -
            Parameters:
            -
            searchKey - The search key used to find a suitable Device implementation in this extension.
            -
            Returns:
            -
            A suitable BackendExtension.DeviceOption or null if nothing was found.
            + therefore, the extension should also deal with some sort of CUDADevice implementation.
        +
        +
        Parameters:
        +
        searchKey - The search key used to find a suitable Device implementation in this extension.
        +
        Returns:
        +
        A suitable BackendExtension.DeviceOption or null if nothing was found.
        -
        • -
      • -
        -

        reset

        -
        default void reset()
        -
        This will indirectly be called through the Neureka.reset() method, +
      + + + +
        +
      • +

        reset

        +
        default void reset()
        +
        This will indirectly be called through the Neureka.reset() method, which is responsible for resetting the library settings.
        -
      • -
    • -
      -

      dispose

      -
      void dispose()
      +
    + + + +
      +
    • +

      dispose

      +
      void dispose()
      Tells this extension to dispose itself. - One should not use a BackendExtension after it was disposed!
      - + One should not use a BackendExtension after it was disposed!
    + + + + + + - - +
    + - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.Builder.html b/docs/jdocs/neureka/backend/api/Call.Builder.html index 2fde64b67..47cdcbd5e 100644 --- a/docs/jdocs/neureka/backend/api/Call.Builder.html +++ b/docs/jdocs/neureka/backend/api/Call.Builder.html @@ -1,163 +1,278 @@ - + + - -Call.Builder (neureka 1.0.0 API) - - - - + +Call.Builder (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class Call.Builder<V,T extends Device<V>>

    +
    neureka.backend.api
    +

    Class Call.Builder<V,T extends Device<V>>

    -
    java.lang.Object -
    neureka.backend.api.Call.Builder<V,T>
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.Call.Builder<V,T>
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      Call<D>
      +
      Call<D>
      -
      public static class Call.Builder<V,T extends Device<V>> -extends Object
      -
    -
    -
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    - +
    + - -
    -
      +
    +
    + - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.DeviceCondition.html b/docs/jdocs/neureka/backend/api/Call.DeviceCondition.html index 6bea9c787..bf794dc7b 100644 --- a/docs/jdocs/neureka/backend/api/Call.DeviceCondition.html +++ b/docs/jdocs/neureka/backend/api/Call.DeviceCondition.html @@ -1,128 +1,222 @@ - + + - -Call.DeviceCondition (neureka 1.0.0 API) - - - - + +Call.DeviceCondition (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface Call.DeviceCondition

    +
    neureka.backend.api
    +

    Interface Call.DeviceCondition

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      Call<D>
      +
      Call<D>
      -
      public static interface Call.DeviceCondition
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      boolean
      -
      check(Device<?> device)
      -
       
      -
      -
      +
      +
      public static interface Call.DeviceCondition
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        check

        -
        boolean check(Device<?> device)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            check

            +
            boolean check(Device<?> device)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.Else.html b/docs/jdocs/neureka/backend/api/Call.Else.html index 00683a9ce..de1c28a64 100644 --- a/docs/jdocs/neureka/backend/api/Call.Else.html +++ b/docs/jdocs/neureka/backend/api/Call.Else.html @@ -1,128 +1,224 @@ - + + - -Call.Else (neureka 1.0.0 API) - - - - + +Call.Else (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface Call.Else<T>

    +
    neureka.backend.api
    +

    Interface Call.Else<T>

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      Call<D>
      +
      Call<D>
      -
      public static interface Call.Else<T>
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      T
      -
      orElse(T value)
      -
       
      -
      -
      +
      +
      public static interface Call.Else<T>
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        orElse

        -
        T orElse(T value)
        -
        + -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.OperationCondition.html b/docs/jdocs/neureka/backend/api/Call.OperationCondition.html index 1902fa85e..4b4f6ac7b 100644 --- a/docs/jdocs/neureka/backend/api/Call.OperationCondition.html +++ b/docs/jdocs/neureka/backend/api/Call.OperationCondition.html @@ -1,128 +1,222 @@ - + + - -Call.OperationCondition (neureka 1.0.0 API) - - - - + +Call.OperationCondition (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface Call.OperationCondition

    +
    neureka.backend.api
    +

    Interface Call.OperationCondition

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      Call<D>
      +
      Call<D>
      -
      public static interface Call.OperationCondition
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      boolean
      -
      check(Operation type)
      -
       
      -
      -
      +
      +
      public static interface Call.OperationCondition
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.TensorCompare.html b/docs/jdocs/neureka/backend/api/Call.TensorCompare.html index 4bbf7791f..1887c02f6 100644 --- a/docs/jdocs/neureka/backend/api/Call.TensorCompare.html +++ b/docs/jdocs/neureka/backend/api/Call.TensorCompare.html @@ -1,130 +1,224 @@ - + + - -Call.TensorCompare (neureka 1.0.0 API) - - - - + +Call.TensorCompare (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface Call.TensorCompare

    +
    neureka.backend.api
    +

    Interface Call.TensorCompare

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      Call<D>
      +
      Call<D>
      -
      public static interface Call.TensorCompare
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      boolean
      -
      check(Tensor<?> first, - Tensor<?> second)
      -
       
      -
      -
      +
      +
      public static interface Call.TensorCompare
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        check

        -
        boolean check(Tensor<?> first, - Tensor<?> second)
        -
        + -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.TensorCondition.html b/docs/jdocs/neureka/backend/api/Call.TensorCondition.html index d49f5dd6c..61342cef0 100644 --- a/docs/jdocs/neureka/backend/api/Call.TensorCondition.html +++ b/docs/jdocs/neureka/backend/api/Call.TensorCondition.html @@ -1,128 +1,222 @@ - + + - -Call.TensorCondition (neureka 1.0.0 API) - - - - + +Call.TensorCondition (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface Call.TensorCondition

    +
    neureka.backend.api
    +

    Interface Call.TensorCondition

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      Call<D>
      +
      Call<D>
      -
      public static interface Call.TensorCondition
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      boolean
      -
      check(Tensor<?> tensor)
      -
       
      -
      -
      +
      +
      public static interface Call.TensorCondition
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        check

        -
        boolean check(Tensor<?> tensor)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            check

            +
            boolean check(Tensor<?> tensor)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.TensorProperty.html b/docs/jdocs/neureka/backend/api/Call.TensorProperty.html index 4123ec386..dd0d62795 100644 --- a/docs/jdocs/neureka/backend/api/Call.TensorProperty.html +++ b/docs/jdocs/neureka/backend/api/Call.TensorProperty.html @@ -1,128 +1,222 @@ - + + - -Call.TensorProperty (neureka 1.0.0 API) - - - - + +Call.TensorProperty (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface Call.TensorProperty

    +
    neureka.backend.api
    +

    Interface Call.TensorProperty

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      Call<D>
      +
      Call<D>
      -
      public static interface Call.TensorProperty
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      Object
      -
      propertyOf(Tensor<?> tensor)
      -
       
      -
      -
      +
      +
      public static interface Call.TensorProperty
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        propertyOf

        -
        Object propertyOf(Tensor<?> tensor)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            propertyOf

            +
            java.lang.Object propertyOf(Tensor<?> tensor)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.TensorsCondition.html b/docs/jdocs/neureka/backend/api/Call.TensorsCondition.html index 0640b7203..c98b222b1 100644 --- a/docs/jdocs/neureka/backend/api/Call.TensorsCondition.html +++ b/docs/jdocs/neureka/backend/api/Call.TensorsCondition.html @@ -1,128 +1,222 @@ - + + - -Call.TensorsCondition (neureka 1.0.0 API) - - - - + +Call.TensorsCondition (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface Call.TensorsCondition

    +
    neureka.backend.api
    +

    Interface Call.TensorsCondition

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      Call<D>
      +
      Call<D>
      -
      public static interface Call.TensorsCondition
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      boolean
      -
      check(Tensor<?>[] tensors)
      -
       
      -
      -
      +
      +
      public static interface Call.TensorsCondition
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        check

        -
        boolean check(Tensor<?>[] tensors)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            check

            +
            boolean check(Tensor<?>[] tensors)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.Validator.Estimator.html b/docs/jdocs/neureka/backend/api/Call.Validator.Estimator.html index 7efe29cd6..88e49a672 100644 --- a/docs/jdocs/neureka/backend/api/Call.Validator.Estimator.html +++ b/docs/jdocs/neureka/backend/api/Call.Validator.Estimator.html @@ -1,234 +1,376 @@ - + + - -Call.Validator.Estimator (neureka 1.0.0 API) - - - - + +Call.Validator.Estimator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class Call.Validator.Estimator

    +
    neureka.backend.api
    +

    Class Call.Validator.Estimator

    -
    java.lang.Object -
    neureka.backend.api.Call.Validator.Estimator
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.Call.Validator.Estimator
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      Estimator(boolean isValid)
      -
       
      +
      +
      public class Call.Validator.Estimator
+extends java.lang.Object
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Estimator

        -
        public Estimator(boolean isValid)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Estimator

            +
            public Estimator(boolean isValid)
          -
      • +
    -
  • -
    -

    Method Details

    -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.Validator.html b/docs/jdocs/neureka/backend/api/Call.Validator.html index 696e22c91..8a9162d4d 100644 --- a/docs/jdocs/neureka/backend/api/Call.Validator.html +++ b/docs/jdocs/neureka/backend/api/Call.Validator.html @@ -1,319 +1,491 @@ - + + - -Call.Validator (neureka 1.0.0 API) - - - - + +Call.Validator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class Call.Validator

    -
    -
    java.lang.Object -
    neureka.backend.api.Call.Validator
    +
    neureka.backend.api
    +

    Class Call.Validator

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.Call.Validator
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      Call<D>
      +
      Call<D>
      -
      public class Call.Validator -extends Object
      +
      +
      public class Call.Validator
+extends java.lang.Object
      This is a simple nested class offering various lambda based methods - for validating the tensor arguments stored inside this ExecutionCall. + for validating the tensor arguments stored inside this ExecutionCall. It is a useful tool readable as well as concise validation of a given request for execution, that is primarily used inside implementations of the middle - layer of the backend-API architecture (SuitabilityPredicate.isSuitableFor(ExecutionCall)).
      -
    -
    -
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Validator

        -
        public Validator()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Validator

            +
            public Validator()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      isValid

      -
      public boolean isValid()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          isValid

          +
          public boolean isValid()
          • -
        • -
          -

          ifValid

          -
          public <T> Call.Else<T> ifValid(T value)
          -
          +
        + + + + + +
          +
        • +

          ifValid

          +
          public <T> Call.Else<T> ifValid(T value)
          • -
        • -
          -

          basicSuitability

          -
          public float basicSuitability()
          +
        + + + +
          +
        • +

          basicSuitability

          +
          public float basicSuitability()
          The validity as float being >0/true and 0/false. - If the Call is valid then a suitability estimation of 0.9f + If the Call is valid then a suitability estimation of 0.9f will be returned simply because a suitability of 1 would mean that no other algorithm could ever compete with this one if if was faster or simply better suited!
          -
          -
          Returns:
          +
          +
          Returns:
          The current validity of this Validator as float value.
          -
    • -
  • -
    -

    suitabilityIfValid

    -
    public float suitabilityIfValid(float estimationIfValid)
    -
    + + + + +
      +
    • +

      suitabilityIfValid

      +
      public float suitabilityIfValid(float estimationIfValid)
      • -
    • -
      -

      getEstimator

      -
      public Call<D>.Validator.Estimator getEstimator()
      -
      +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
    • +

      allShare

      +
      public <T> Call.Validator allShare(java.util.function.Function<Tensor<?>,T> propertyProvider)
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Call.html b/docs/jdocs/neureka/backend/api/Call.html index f7d27e517..5afad2e1e 100644 --- a/docs/jdocs/neureka/backend/api/Call.html +++ b/docs/jdocs/neureka/backend/api/Call.html @@ -1,415 +1,597 @@ - + + - -Call (neureka 1.0.0 API) - - - - + +Call (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class Call<D>

    -
    -
    java.lang.Object -
    neureka.backend.api.Call<D>
    +
    neureka.backend.api
    +

    Class Call<D>

    -
    -
    -
    Type Parameters:
    -
    D - The type parameter which defines the Device targeted by this Call.
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.Call<D>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      +
      D - The type parameter which defines the Device targeted by this Call.
      -
      +
      Direct Known Subclasses:
      -
      ExecutionCall
      +
      ExecutionCall
      -
      public class Call<D> -extends Object
      +
      +
      public class Call<D>
+extends java.lang.Object
      Instances of this class model simple execution calls to the backend. - They can be passed to Function instances in order to get full - control over the execution via the use of call Args. - This class is the precursor class of ExecutionCall which is a more complete + They can be passed to Function instances in order to get full + control over the execution via the use of call Args. + This class is the precursor class of ExecutionCall which is a more complete execution state bundle used inside the backend.
      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Field Details

      -
        -
      • -
        -

        _device

        -
        protected final D _device
        +
          +
        • + + +

          Field Detail

          + + + +
            +
          • +

            _device

            +
            protected final D _device
            This field references the device on which this ExecutionCall should be executed.
            -
        • -
      • -
        -

        _arguments

        -
        protected final Args _arguments
        +
      + + + +
        +
      • +

        _arguments

        +
        protected final Args _arguments
        Meta arguments which are usually specific to certain operations.
        -
      • -
    • -
      -

      _inputs

      -
      protected final Tensor<?>[] _inputs
      +
    + + + +
      +
    • +

      _inputs

      +
      protected final Tensor<?>[] _inputs
      The tensor arguments from which an operation will either read or to which it will write.
      The first entry of this array is usually containing the output tensor, however this is not a necessity. Some operation algorithms might use multiple argument entries as output tensors.
      -
    - + -
  • -
    -

    Constructor Details

    -
      -
    • -
      -

      Call

      -
      protected Call(Tensor<?>[] tensors, - D device, - List<Arg> arguments)
      -
      +
        +
      • + + +

        Constructor Detail

        + + + + + +
          +
        • +

          Call

          +
          protected Call(Tensor<?>[] tensors,
+               D device,
+               java.util.List<Arg> arguments)
        -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      to

      -
      public static <V, -T extends Device<V>> -Call.Builder<V,T> to(T device)
      -
      +
        +
      • + + +

        Method Detail

        + + + + + +
          +
        • +

          to

          +
          public static <V,T extends Device<V>> Call.Builder<V,T> to(T device)
          • -
        • -
          -

          getDevice

          -
          public D getDevice()
          -
          -
          Returns:
          +
        + + + +
          +
        • +

          getDevice

          +
          public D getDevice()
          +
          +
          Returns:
          The device targeted by this call for execution.
          -
    • -
  • -
    -

    inputs

    -
    public Tensor<?>[] inputs()
    -
    -
    Returns:
    -
    The Tensor parameters of this Call for execution.
    + + + + +
      +
    • +

      inputs

      +
      public Tensor<?>[] inputs()
      +
      +
      Returns:
      +
      The Tensor parameters of this Call for execution.
      -
    • -
  • -
    -

    arity

    -
    public int arity()
    -
    -
    Returns:
    + + + + +
      +
    • +

      arity

      +
      public int arity()
      +
      +
      Returns:
      The number of input tensors.
      -
    • -
  • -
    -

    input

    -
    public Tensor<?> input(int i)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      input

      +
      public Tensor<?> input(int i)
      +
      +
      Parameters:
      i - The index of the tensor argument which should be returned.
      -
      Returns:
      -
      The i'th Tensor parameter of this Call for execution.
      +
      Returns:
      +
      The i'th Tensor parameter of this Call for execution.
      -
    • -
  • -
    -

    rearrangeInputs

    -
    public void rearrangeInputs(int... indices)
    -
    + + + + +
      +
    • +

      rearrangeInputs

      +
      public void rearrangeInputs(int... indices)
      • -
    • -
      -

      getDeviceFor

      -
      public <T> Device<T> getDeviceFor(Class<T> supportCheck)
      -
      +
    + + + +
      +
    • +

      getDeviceFor

      +
      public <T> Device<T> getDeviceFor(java.lang.Class<T> supportCheck)
      • -
    • -
      -

      allMetaArgs

      -
      public List<Arg> allMetaArgs()
      -
      +
    + + + +
      +
    • +

      allMetaArgs

      +
      public java.util.List<Arg> allMetaArgs()
      • -
    • -
      -

      get

      -
      public <V, -T extends Arg<V>> T get(Class<T> argumentClass)
      -
      +
    + + + +
      +
    • +

      get

      +
      public <V,T extends Arg<V>> T get(java.lang.Class<T> argumentClass)
      • -
    • -
      -

      getValOf

      -
      public <V, -T extends Arg<V>> V getValOf(Class<T> argumentClass)
      -
      +
    + + + +
      +
    • +

      getValOf

      +
      public <V,T extends Arg<V>> V getValOf(java.lang.Class<T> argumentClass)
      • -
    • -
      -

      getDerivativeIndex

      -
      public int getDerivativeIndex()
      -
      +
    + + + +
      +
    • +

      getDerivativeIndex

      +
      public int getDerivativeIndex()
      • -
    • -
      -

      input

      -
      public <V> Tensor<V> input(Class<V> valueTypeClass, - int i)
      -
      +
    + + + +
      +
    • +

      input

      +
      public <V> Tensor<V> input(java.lang.Class<V> valueTypeClass,
+                           int i)
      • -
    • -
      -

      validate

      -
      public Call<D>.Validator validate()
      -
      +
    + + + + -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/DeviceAlgorithm.html b/docs/jdocs/neureka/backend/api/DeviceAlgorithm.html index 4f4f197b7..7af9dd6dd 100644 --- a/docs/jdocs/neureka/backend/api/DeviceAlgorithm.html +++ b/docs/jdocs/neureka/backend/api/DeviceAlgorithm.html @@ -1,258 +1,395 @@ - + + - -DeviceAlgorithm (neureka 1.0.0 API) - - - - + +DeviceAlgorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface DeviceAlgorithm<C extends DeviceAlgorithm<C>>

    +
    neureka.backend.api
    +

    Interface DeviceAlgorithm<C extends DeviceAlgorithm<C>>

    -
    -
    -
    Type Parameters:
    +
    +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      - + + + + + +
        +
      • +

        setImplementationFor

        +
        <D extends Device<?>,I extends ImplementationFor<D>> C setImplementationFor(java.lang.Class<D> deviceClass,
+                                                                            I implementation)
        +
        Implementations of the DeviceAlgorithm interface ought to express a compositional design pattern.
        This means that concrete implementations of an algorithm for a device are not extending an Algorithm, they are components of it instead.
        These components can be stored on an Algorithm by passing a Device class as key and an ImplementationFor instance as value.
        -
        -
        Type Parameters:
        -
        D - The type parameter of the Device type for which - an implementation should be set in this Device.
        -
        I - The type of the ImplementationFor the provided Device type.
        -
        Parameters:
        -
        deviceClass - The class of the Device for which an implementation should be set.
        -
        implementation - The ImplementationFor the provided Device type.
        -
        Returns:
        -
        This very Algorithm instance to allow for method chaining.
        +
        +
        Type Parameters:
        +
        D - The type parameter of the Device type for which + an implementation should be set in this Device.
        +
        I - The type of the ImplementationFor the provided Device type.
        +
        Parameters:
        +
        deviceClass - The class of the Device for which an implementation should be set.
        +
        implementation - The ImplementationFor the provided Device type.
        +
        Returns:
        +
        This very Algorithm instance to allow for method chaining.
        -
      • -
    • -
      -

      getImplementationFor

      -
      <D extends Device<?>> ImplementationFor<D> getImplementationFor(Class<D> deviceClass)
      -
      An ImplementationFor a specific Device can be accessed by passing the class of - the Device for which an implementation should be returned. - An Algorithm instance ought to contain a collection of these Device specific +
    + + + +
      +
    • +

      getImplementationFor

      +
      <D extends Device<?>> ImplementationFor<D> getImplementationFor(java.lang.Class<D> deviceClass)
      +
      An ImplementationFor a specific Device can be accessed by passing the class of + the Device for which an implementation should be returned. + An Algorithm instance ought to contain a collection of these Device specific implementations...
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      D - The type parameter which has to be a class extending the Device interface.
      -
      Parameters:
      +
      Parameters:
      deviceClass - The class of the device for which the stored algorithm implementation should be returned.
      -
      Returns:
      +
      Returns:
      The implementation for the passed device type class.
      -
      • -
    • -
      -

      getImplementationFor

      -
      default <D extends Device<?>> ImplementationFor<D> getImplementationFor(D device)
      -
      An ImplementationFor a specific Device can be accessed by passing - the Device for which an implementation should be returned. - An Algorithm instance ought to contain a collection of these Device specific +
    + + + + + +
      +
    • +

      getImplementationFor

      +
      default <D extends Device<?>> ImplementationFor<D> getImplementationFor(D device)
      +
      An ImplementationFor a specific Device can be accessed by passing + the Device for which an implementation should be returned. + An Algorithm instance ought to contain a collection of these Device specific implementations...
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      D - type parameter which has to be a class extending the Device interface.
      -
      Parameters:
      +
      Parameters:
      device - The device for which the stored algorithm implementation should be returned.
      -
      Returns:
      +
      Returns:
      The implementation for the passed device type class.
      -
      • -
    • -
      -

      hasImplementationFor

      -
      default <D extends Device<?>> boolean hasImplementationFor(D device)
      -
      +
    + + + + + +
      +
    • +

      hasImplementationFor

      +
      default <D extends Device<?>> boolean hasImplementationFor(D device)
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/ExecutionCall.Builder.html b/docs/jdocs/neureka/backend/api/ExecutionCall.Builder.html index 536470bf1..228ee06bb 100644 --- a/docs/jdocs/neureka/backend/api/ExecutionCall.Builder.html +++ b/docs/jdocs/neureka/backend/api/ExecutionCall.Builder.html @@ -1,166 +1,283 @@ - + + - -ExecutionCall.Builder (neureka 1.0.0 API) - - - - + +ExecutionCall.Builder (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class ExecutionCall.Builder<D extends Device<?>>

    +
    neureka.backend.api
    +

    Class ExecutionCall.Builder<D extends Device<?>>

    -
    java.lang.Object -
    neureka.backend.api.ExecutionCall.Builder<D>
    -
    -
    -
    -
    Type Parameters:
    -
    D - The type parameter for the device targeted by the ExecutionCall built by this builder.
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.ExecutionCall.Builder<D>
        • +
      +
      • +
    +
    +
    -
    -
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    - +
    + - -
    -
      +
    +
    + - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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      +
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/ExecutionCall.html b/docs/jdocs/neureka/backend/api/ExecutionCall.html index 9d0c388b7..9685e0d85 100644 --- a/docs/jdocs/neureka/backend/api/ExecutionCall.html +++ b/docs/jdocs/neureka/backend/api/ExecutionCall.html @@ -1,92 +1,126 @@ - + + - -ExecutionCall (neureka 1.0.0 API) - - - - + +ExecutionCall (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class ExecutionCall<D extends Device<?>>

    -
    -
    java.lang.Object -
    neureka.backend.api.Call<D> -
    neureka.backend.api.ExecutionCall<D>
    -
    +
    neureka.backend.api
    +

    Class ExecutionCall<D extends Device<?>>

    -
    -
    -
    Type Parameters:
    +
    + +
    +
      +
    • +
      +
      Type Parameters:
      D - The Device implementation targeted by an instance of this ExecutionCall!
      -
      public class ExecutionCall<D extends Device<?>> -extends Call<D>
      +
      +
      public class ExecutionCall<D extends Device<?>>
+extends Call<D>
      This class is a simple container holding references to a targeted - Device, Operation and maybe some case specific - meta Args needed to execute + Device, Operation and maybe some case specific + meta Args needed to execute an array of input tensors which are also wrapped by this.

      This class is technically immutable, however the contents @@ -94,253 +128,386 @@

      Class ExecutionCall<D extends < The meta arguments wrapped by this are responsible for storing operation specific variables like for example an input index for calculating a partial derivative. Certain operations might require other unique types of arguments...

      -
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Class
      -
      Description
      -
      static class 
      -
      ExecutionCall.Builder<D extends Device<?>>
      -
       
      +
      • +
    -
    -

    Nested classes/interfaces inherited from class neureka.backend.api.Call

    -Call.DeviceCondition, Call.Else<T>, Call.OperationCondition, Call.TensorCompare, Call.TensorCondition, Call.TensorProperty, Call.TensorsCondition, Call.Validator
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      - + + + +
        +
      • +

        checkArity

        +
        public void checkArity()
        • -
      • -
        -

        getOperation

        -
        public Operation getOperation()
        +
      + + + +
        +
      • +

        getOperation

        +
        public Operation getOperation()
        This returns the operation which will ultimately process this execution call. It contains multiple algorithms and device specific implementations, one of which might be applicable to this call...

        -
        -
        Returns:
        -
        The Operation targeted by this call for execution.
        +
        +
        Returns:
        +
        The Operation targeted by this call for execution.
        -
      • -
    • -
      -

      withInputs

      -
      public ExecutionCall<D> withInputs(Tensor<?>... inputs)
      +
    + + + +
      +
    • +

      withInputs

      +
      public ExecutionCall<D> withInputs(Tensor<?>... inputs)
      Use this to produce a clone with a new array of input tensors.
      -
      -
      Parameters:
      -
      inputs - The new array of input tensors for the new ExecutionCall returned by this.
      -
      Returns:
      -
      A new ExecutionCall instance with the provided array of input tensors.
      +
      +
      Parameters:
      +
      inputs - The new array of input tensors for the new ExecutionCall returned by this.
      +
      Returns:
      +
      A new ExecutionCall instance with the provided array of input tensors.
      -
      • -
    • -
      -

      withAddedInputAt

      -
      public ExecutionCall<D> withAddedInputAt(int index, - Tensor<?> added)
      -
      +
    + + + + + + + +
      +
    • +

      withInputAt

      +
      public ExecutionCall<D> withInputAt(int index,
+                                    Tensor<?> replacement)
      • -
    • -
      -

      withRemovedInputAt

      -
      public ExecutionCall<D> withRemovedInputAt(int index)
      -
      +
    + + + + + + + + + + + +
      +
    • +

      withArgs

      +
      public ExecutionCall<D> withArgs(Arg<?>... args)
      Use this to produce a clone with a new set of meta arguments.
      -
      -
      Parameters:
      -
      args - The new set of meta args for the new ExecutionCall returned by this.
      -
      Returns:
      -
      A new ExecutionCall instance with the provided set of meta arguments.
      +
      +
      Parameters:
      +
      args - The new set of meta args for the new ExecutionCall returned by this.
      +
      Returns:
      +
      A new ExecutionCall instance with the provided set of meta arguments.
      -
      • -
    • -
      -

      getAlgorithm

      -
      public Algorithm getAlgorithm()
      -
      An ExecutionCall will either already have a targeted Algorithm defined - at instantiation or otherwise it will query the associated Operation - for an Algorithm best suitable for the state of this ExecutionCall. +
    + + + +
      +
    • +

      getAlgorithm

      +
      public Algorithm getAlgorithm()
      +
      An ExecutionCall will either already have a targeted Algorithm defined + at instantiation or otherwise it will query the associated Operation + for an Algorithm best suitable for the state of this ExecutionCall. Generally speaking, this method should only very rarely return null, however, if it does, then this most definitely means that there is nor backend support for this call for execution...
      -
      -
      Returns:
      -
      The Algorithm suitable for this ExecutionCall.
      +
      +
      Returns:
      +
      The Algorithm suitable for this ExecutionCall.
      -
      • -
    • -
      -

      autogradMode

      -
      public AutoDiffMode autogradMode()
      -
      This method queries the underlying Operation for a suitable Algorithm - for this ExecutionCall to see what kind of auto differentiation can be performed.
      -
      -
      Returns:
      -
      The AutoDiffMode for this call.
      +
    + + + + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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    + + + +
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      +
    • Detail: 
      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Extensions.html b/docs/jdocs/neureka/backend/api/Extensions.html index 9d5e11d1d..cf5052598 100644 --- a/docs/jdocs/neureka/backend/api/Extensions.html +++ b/docs/jdocs/neureka/backend/api/Extensions.html @@ -1,173 +1,234 @@ - + + - -Extensions (neureka 1.0.0 API) - - - - + +Extensions (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class Extensions

    -
    -
    java.lang.Object -
    neureka.common.composition.AbstractComponentOwner<Extensions> -
    neureka.backend.api.Extensions
    +
    neureka.backend.api
    +

    Class Extensions

    -
    -
    -
    +
    + +
    +
    -
    -
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Extensions

        -
        public Extensions()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Extensions

            +
            public Extensions()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + + + +
      +
    • +

      _removeOrReject

      +
      protected <T extends Component<Extensions>> T _removeOrReject(T newComponent)
      +
      Description copied from class: AbstractComponentOwner
      An implementation of this method checks if the passed component should be removed from the component collection of this class or its removal should be "rejected". Rejection in this case simply means that it returns null instead of the passed component.
      -
      -
      Specified by:
      -
      _removeOrReject in class AbstractComponentOwner<Extensions>
      -
      Parameters:
      +
      +
      Specified by:
      +
      _removeOrReject in class AbstractComponentOwner<Extensions>
      +
      Parameters:
      newComponent - The component which should be removed from the components list.
      -
      Returns:
      +
      Returns:
      The same component or null if its removal has been rejected.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
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      • +
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      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/ImplementationFor.html b/docs/jdocs/neureka/backend/api/ImplementationFor.html index d0b5480be..91647f025 100644 --- a/docs/jdocs/neureka/backend/api/ImplementationFor.html +++ b/docs/jdocs/neureka/backend/api/ImplementationFor.html @@ -1,157 +1,251 @@ - + + - -ImplementationFor (neureka 1.0.0 API) - - - - + +ImplementationFor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface ImplementationFor<D extends Device<?>>

    +
    neureka.backend.api
    +

    Interface ImplementationFor<D extends Device<?>>

    -
    -
    -
    Type Parameters:
    +
    +
    +
    -
    -
      + of an execution procedure tailored to a specific Device (interface) instance + and Algorithm (interface) instance! + Instances of implementations of the ImplementationFor interface are components + of instances of implementations of the Algorithm interface, + which themselves are components of Operation implementation instances.
    + + +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      Tensor<?>
      -
      run(ExecutionCall<D> call)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        Tensor<?>run(ExecutionCall<D> call)
        This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented.
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        run

        -
        Tensor<?> run(ExecutionCall<D> call)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            run

            +
            Tensor<?> run(ExecutionCall<D> call)
            This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
            -
            -
            Parameters:
            +
            +
            Parameters:
            call - The call which ought to be executed on this implementation.
            -
      -
    - + + +
    + - + +
    + + +
    Skip navigation links
    + + + + +
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    • Summary: 
      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/LazyRef.html b/docs/jdocs/neureka/backend/api/LazyRef.html index 32aa43fde..120ee0898 100644 --- a/docs/jdocs/neureka/backend/api/LazyRef.html +++ b/docs/jdocs/neureka/backend/api/LazyRef.html @@ -1,161 +1,272 @@ - + + - -LazyRef (neureka 1.0.0 API) - - - - + +LazyRef (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class LazyRef<V>

    +
    neureka.backend.api
    +

    Class LazyRef<V>

    -
    java.lang.Object -
    neureka.backend.api.LazyRef<V>
    -
    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.LazyRef<V>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The value type parameter of the thing wrapped by this.
      -
      public final class LazyRef<V> -extends Object
      +
      +
      public final class LazyRef<V>
+extends java.lang.Object
      This will simply fetch a variable from a lambda once and then continuously return this one value. In a sense it is a lazy reference! This is an internal class, do not depend on this outside this package.
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      V
      -
      get()
      -
       
      -
      static <V> LazyRef<V>
      -
      of(Supplier<V> source)
      -
       
      -
      String
      -
      toString()
      -
       
      -
      -
      +
      • +
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
    - +
    +
      +
    • + +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        Vget() 
        static <V> LazyRef<V>of(java.util.function.Supplier<V> source) 
        java.lang.StringtoString() 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
          • +
      - -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        public static <V> LazyRef<V> of(Supplier<V> source)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            of

            +
            public static <V> LazyRef<V> of(java.util.function.Supplier<V> source)
            • -
          • -
            -

            get

            -
            public V get()
            -
            +
          + + + +
            +
          • +

            get

            +
            public V get()
            • -
          • -
            -

            toString

            -
            public String toString()
            -
            -
            Overrides:
            -
            toString in class Object
            +
          + + + +
            +
          • +

            toString

            +
            public java.lang.String toString()
            +
            +
            Overrides:
            +
            toString in class java.lang.Object
            -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
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      • +
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      • +
    +
      +
    • Detail: 
      • +
    • Field | 
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      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/Operation.html b/docs/jdocs/neureka/backend/api/Operation.html index 68b3ca0a3..88e8ae92f 100644 --- a/docs/jdocs/neureka/backend/api/Operation.html +++ b/docs/jdocs/neureka/backend/api/Operation.html @@ -1,443 +1,554 @@ - + + - -Operation (neureka 1.0.0 API) - - - - + +Operation (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Interface Operation

    +
    neureka.backend.api
    +

    Interface Operation

    -
    -
    +
    +
    +
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      String
      -
      asDerivative(Function[] children, - int derivationIndex)
      -
      -
      Operation implementations and Function implementations are in a tight relationship - where the Function describes an abstract syntax tree based on the syntactic information provided - by the Operation (through methods like getOperator() or getIdentifier()).
      + and optionally also an operator in the form of String instances. + Alongside there must be an implementation of the stringify(String[]) method, + which ought to generate a String view as part of a Function-AST.
      +
      • +
    -
    static OperationBuilder
    -
    builder()
    -
     
    -
    double
    -
    calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        builder

        -
        static OperationBuilder builder()
        -
        +
          +
        • + + +

          Method Detail

          + + + + + + + +
            +
          • +

            getAllAlgorithms

            +
            Algorithm[] getAllAlgorithms()
            • -
          • -
            -

            getAlgorithmFor

            -
            Algorithm getAlgorithmFor(ExecutionCall<?> call)
            -
            Alongside a component system made up of Algorithm instances, implementations - of this interface also ought to express a routing mechanism which finds the best Algorithm - for a given ExecutionCall instance. +
          + + + +
            +
          • +

            getAlgorithmFor

            +
            Algorithm getAlgorithmFor(ExecutionCall<?> call)
            +
            Alongside a component system made up of Algorithm instances, implementations + of this interface also ought to express a routing mechanism which finds the best Algorithm + for a given ExecutionCall instance. This method signature describes this requirement.
            -
            -
            Parameters:
            -
            call - The ExecutionCall instance which needs the best Algorithm for execution.
            -
            Returns:
            -
            The chosen Algorithm which ought to be fir for execution the provided call.
            +
            +
            Parameters:
            +
            call - The ExecutionCall instance which needs the best Algorithm for execution.
            +
            Returns:
            +
            The chosen Algorithm which ought to be fir for execution the provided call.
            -
      • -
    • -
      -

      setAlgorithm

      -
      <T extends Algorithm> Operation setAlgorithm(Class<T> type, - T instance)
      -
      Operation implementations embody a component system hosting unique Algorithm instances. - For a given class implementing the Algorithm class, there can only be a single - instance of it referenced (aka supported) by a given Operation instance. - This method enables the registration of Algorithm types in the component system of this Operation.
      -
      -
      Type Parameters:
      -
      T - The type parameter of the Algorithm type class.
      -
      Parameters:
      -
      type - The class of the type which implements Algorithm as key for the provided instance.
      -
      instance - The instance of the provided type class which ought to be referenced (supported) by this Operation.
      -
      Returns:
      -
      This very Operation instance to enable method chaining on it.
      +
    + + + + + +
      +
    • +

      setAlgorithm

      +
      <T extends Algorithm> Operation setAlgorithm(java.lang.Class<T> type,
+                                             T instance)
      +
      Operation implementations embody a component system hosting unique Algorithm instances. + For a given class implementing the Algorithm class, there can only be a single + instance of it referenced (aka supported) by a given Operation instance. + This method enables the registration of Algorithm types in the component system of this Operation.
      +
      +
      Type Parameters:
      +
      T - The type parameter of the Algorithm type class.
      +
      Parameters:
      +
      type - The class of the type which implements Algorithm as key for the provided instance.
      +
      instance - The instance of the provided type class which ought to be referenced (supported) by this Operation.
      +
      Returns:
      +
      This very Operation instance to enable method chaining on it.
      -
      • -
    • -
      -

      setAlgorithm

      -
      default <T extends Algorithm> Operation setAlgorithm(T instance)
      -
      +
    + + + + + +
      +
    • +

      setAlgorithm

      +
      default <T extends Algorithm> Operation setAlgorithm(T instance)
      • -
    • -
      -

      getAlgorithm

      -
      <T extends Algorithm> T getAlgorithm(Class<T> type)
      -
      Operation implementations embody a component system hosting unique Algorithm instances. - For a given class implementing the Algorithm class, there can only be a single - instance of it referenced (aka supported) by a given Operation instance. +
    + + + +
      +
    • +

      getAlgorithm

      +
      <T extends Algorithm> T getAlgorithm(java.lang.Class<T> type)
      +
      Operation implementations embody a component system hosting unique Algorithm instances. + For a given class implementing the Algorithm class, there can only be a single + instance of it referenced (aka supported) by a given Operation instance. This method ensures this in terms of read access by returning only a single instance or null - based on the provided class instance whose type extends the Algorithm interface.
      -
      -
      Type Parameters:
      -
      T - The type parameter of the Algorithm type class.
      -
      Parameters:
      -
      type - The class of the type which implements Algorithm as a key to get an existing instance.
      -
      Returns:
      -
      The instance of the specified type if any exists within this Operation.
      + based on the provided class instance whose type extends the Algorithm interface.
    +
    +
    Type Parameters:
    +
    T - The type parameter of the Algorithm type class.
    +
    Parameters:
    +
    type - The class of the type which implements Algorithm as a key to get an existing instance.
    +
    Returns:
    +
    The instance of the specified type if any exists within this Operation.
    - -
  • -
    -

    supportsAlgorithm

    -
    <T extends Algorithm> boolean supportsAlgorithm(Class<T> type)
    -
    This method checks if this Operation contains an instance of the - Algorithm implementation specified via its type class.
    -
    -
    Type Parameters:
    -
    T - The type parameter of the Algorithm type class.
    -
    Parameters:
    -
    type - The class of the type which implements Algorithm.
    -
    Returns:
    -
    The truth value determining if this Operation contains an instance of the specified Algorithm type.
    + + + + +
      +
    • +

      supportsAlgorithm

      +
      <T extends Algorithm> boolean supportsAlgorithm(java.lang.Class<T> type)
      +
      This method checks if this Operation contains an instance of the + Algorithm implementation specified via its type class.
      +
      +
      Type Parameters:
      +
      T - The type parameter of the Algorithm type class.
      +
      Parameters:
      +
      type - The class of the type which implements Algorithm.
      +
      Returns:
      +
      The truth value determining if this Operation contains an instance of the specified Algorithm type.
      -
    • -
  • -
    -

    getIdentifier

    -
    String getIdentifier()
    -
    Concrete Operation types ought to be representable by a function name. + + + + +
      +
    • +

      getIdentifier

      +
      java.lang.String getIdentifier()
      +
      Concrete Operation types ought to be representable by a function name. The following ensures that this contract is met when overriding the method.
      -
      -
      Returns:
      -
      the function name which serves as identifier when parsing Function instances.
      +
      +
      Returns:
      +
      the function name which serves as identifier when parsing Function instances.
      -
    • -
  • -
    -

    stringify

    -
    String stringify(String[] children)
    -
    + + + + +
      +
    • +

      stringify

      +
      java.lang.String stringify(java.lang.String[] children)
      • -
    • -
      -

      asDerivative

      -
      String asDerivative(Function[] children, - int derivationIndex)
      -
      Operation implementations and Function implementations are in a tight relationship - where the Function describes an abstract syntax tree based on the syntactic information provided - by the Operation (through methods like getOperator() or getIdentifier()). - One important feature of the Function is the ability to create - derivatives by calling the Function.getDerivative(int) method. - Implementations of this Function method ought to call the method defined below in order to - form the derivation based on the child nodes of the abstract syntax tree of the given Function node.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      asDerivative

      +
      java.lang.String asDerivative(Function[] children,
+                              int derivationIndex)
      +
      Operation implementations and Function implementations are in a tight relationship + where the Function describes an abstract syntax tree based on the syntactic information provided + by the Operation (through methods like getOperator() or getIdentifier()). + One important feature of the Function is the ability to create + derivatives by calling the Function.getDerivative(int) method. + Implementations of this Function method ought to call the method defined below in order to + form the derivation based on the child nodes of the abstract syntax tree of the given Function node.
      +
      +
      Parameters:
      children - The child nodes of a AST node referencing this operation.
      derivationIndex - The index of the input node which ought to be derived.
      -
      Returns:
      -
      The derivative as a String which should be parsable into yet another AST.
      +
      Returns:
      +
      The derivative as a String which should be parsable into yet another AST.
      -
      • -
    • -
      -

      getOperator

      -
      String getOperator()
      -
      +
    + + + +
      +
    • +

      getOperator

      +
      java.lang.String getOperator()
      • -
    • -
      -

      getArity

      -
      int getArity()
      +
    + + + +
      +
    • +

      getArity

      +
      int getArity()
      Arity is the number of arguments or operands that this function or operation takes.
      -
      -
      Returns:
      +
      +
      Returns:
      The number of arguments expected by this operation, or -1 if an arbitrary number is accepted.
      -
      • -
    • -
      -

      isOperator

      -
      boolean isOperator()
      +
    + + + +
      +
    • +

      isOperator

      +
      boolean isOperator()
      An operator is an alternative to a function like "sum()" or "prod()".
      Examples would be "+, -, * ..."!
      -
      -
      Returns:
      +
      +
      Returns:
      If this operation can be represented as operator like "+, -, * ..."!
      -
      • -
    • -
      -

      isIndexer

      -
      boolean isIndexer()
      -
      This boolean property tell the Function implementations that this Operation +
    + + + +
      +
    • +

      isIndexer

      +
      boolean isIndexer()
      +
      This boolean property tell the Function implementations that this Operation ought to be viewed as something to be indexed. - The Function will use this information to iterate over all the provided inputs and + The Function will use this information to iterate over all the provided inputs and then execute the function wile also passing the index to the function AST. - The resulting array will then be available to this Operation as argument list. - This feature works alongside the Function implementation found in - FunctionVariable, which represents an input indexed + The resulting array will then be available to this Operation as argument list. + This feature works alongside the Function implementation found in + FunctionVariable, which represents an input indexed by the identifier 'j'!
      -
      -
      Returns:
      +
      +
      Returns:
      If this operation is an indexer.
      -
      • -
    • -
      -

      isDifferentiable

      -
      @Deprecated -boolean isDifferentiable()
      -
      Deprecated.
      -
      +
    + + + +
      +
    • +

      isDifferentiable

      +
      @Deprecated
+boolean isDifferentiable()
      +
      Deprecated. 
      • -
    • -
      -

      isInline

      -
      boolean isInline()
      -
      This flag indicates that the implementation of this Operation +
    + + + +
      +
    • +

      isInline

      +
      boolean isInline()
      +
      This flag indicates that the implementation of this Operation performs an operation which modifies the inputs to that operation. An example of this would be an assignment operation which copies the contents of one nd-array / tensor into another tensor. This second tensor will then have changed its state. This can be dangerous when auto-differentiation is involved.
      -
      -
      Returns:
      -
      The truth value determining if this Operation changes the contents of inputs.
      +
      +
      Returns:
      +
      The truth value determining if this Operation changes the contents of inputs.
      -
      • -
    • -
      -

      supports

      -
      <T extends Algorithm> boolean supports(Class<T> implementation)
      -
      +
    + + + + + + + + + + + +
      +
    • +

      calculate

      +
      double calculate(double[] inputs,
+                 int j,
+                 int d,
+                 Function[] src)
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -453,25 +564,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/api/Result.html b/docs/jdocs/neureka/backend/api/Result.html index 43bfd92ad..756dcd0c4 100644 --- a/docs/jdocs/neureka/backend/api/Result.html +++ b/docs/jdocs/neureka/backend/api/Result.html @@ -1,169 +1,288 @@ - + + - -Result (neureka 1.0.0 API) - - - - + +Result (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api
    -

    Class Result

    -
    -
    java.lang.Object -
    neureka.backend.api.Result
    +
    neureka.backend.api
    +

    Class Result

    -
    -
    -
    public final class Result -extends Object
    -
    An immutable wrapper for a tensor as a result of anb Execution - as well as an ADActionSupplier for providing auto-differentiation support.
    -
    -
    -
    +
    + - -
    -
      +
    +
    + - + + +
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    + diff --git a/docs/jdocs/neureka/backend/api/fun/ADActionSupplier.html b/docs/jdocs/neureka/backend/api/fun/ADActionSupplier.html index a20b30333..baee4db35 100644 --- a/docs/jdocs/neureka/backend/api/fun/ADActionSupplier.html +++ b/docs/jdocs/neureka/backend/api/fun/ADActionSupplier.html @@ -1,162 +1,256 @@ - + + - -ADActionSupplier (neureka 1.0.0 API) - - - - + +ADActionSupplier (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.fun
    -

    Interface ADActionSupplier

    +
    neureka.backend.api.fun
    +

    Interface ADActionSupplier

    -
    -
    +
    +
    +
      +
    • +
      All Known Implementing Classes:
      -
      FallbackAlgorithm
      +
      FallbackAlgorithm
      -
      +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public interface ADActionSupplier
      +
      +
      @FunctionalInterface
+public interface ADActionSupplier
      Implementations of this functional interface ought to return a new instance - of the ADAction class responsible for performing automatic differentiation + of the ADAction class responsible for performing automatic differentiation both for forward and backward mode differentiation.
      - Therefore an ADAction exposes 2 different procedures.
      + Therefore an ADAction exposes 2 different procedures.
      One is the forward mode differentiation, and the other one
      is the backward mode differentiation which is more commonly known as back-propagation...
      Besides that it may also contain context information used
      to perform said procedures.
      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        supplyADActionFor

        -
        ADAction supplyADActionFor(Function function, - ExecutionCall<? extends Device<?>> call)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            supplyADActionFor

            +
            ADAction supplyADActionFor(Function function,
+                           ExecutionCall<? extends Device<?>> call)
            This method ought to return a new instance - if the ADAction class responsible for performing automatic differentiation + if the ADAction class responsible for performing automatic differentiation both for forward and backward mode differentiation.
            - Therefore an ADAction exposes 2 different procedures.
            + Therefore an ADAction exposes 2 different procedures.
            One is the forward mode differentiation, and the other one
            is the backward mode differentiation which is more commonly known as back-propagation...
            Besides that it may also contain context information used
            to perform said procedures.
            -
            -
            Parameters:
            +
            +
            Parameters:
            function - The function from where the request for auto differentiation originates.
            call - The execution call of the current execution which requires auto differentiation support.
            -
            Returns:
            -
            The resulting ADAction.
            +
            Returns:
            +
            The resulting ADAction.
            -
      -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
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    + diff --git a/docs/jdocs/neureka/backend/api/fun/ADSupportPredicate.html b/docs/jdocs/neureka/backend/api/fun/ADSupportPredicate.html index 04a38ca0e..2a575f9b5 100644 --- a/docs/jdocs/neureka/backend/api/fun/ADSupportPredicate.html +++ b/docs/jdocs/neureka/backend/api/fun/ADSupportPredicate.html @@ -1,151 +1,245 @@ - + + - -ADSupportPredicate (neureka 1.0.0 API) - - - - + +ADSupportPredicate (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.fun
    -

    Interface ADSupportPredicate

    +
    neureka.backend.api.fun
    +

    Interface ADSupportPredicate

    -
    -
    +
    +
    +
    -
    -
      +
      +
      @FunctionalInterface
+public interface ADSupportPredicate
      +
      A ADSupportPredicate lambda checks which auto differentiation mode + can be performed for a given ExecutionCall. + The analyzer returns a AutoDiffMode enum instance.
      + +
    +
    +
    + - -
    -
      +
    +
    +
    +
    +
    Parameters:
    +
    call - The ExecutionCall which should be checked.
    +
    Returns:
    +
    A AutoDiffMode enum instance describing what kind of differentiation can be performed.
    - - - + + +
    +
    - + +
    + + +
    Skip navigation links
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    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/fun/Execution.html b/docs/jdocs/neureka/backend/api/fun/Execution.html index 96aa5979d..5e43a8b96 100644 --- a/docs/jdocs/neureka/backend/api/fun/Execution.html +++ b/docs/jdocs/neureka/backend/api/fun/Execution.html @@ -1,162 +1,256 @@ - + + - -Execution (neureka 1.0.0 API) - - - - + +Execution (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.fun
    -

    Interface Execution

    +
    neureka.backend.api.fun
    +

    Interface Execution

    -
    -
    +
    +
    +
    -
    -
    - +
    + - -
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    + diff --git a/docs/jdocs/neureka/backend/api/fun/ExecutionPreparation.html b/docs/jdocs/neureka/backend/api/fun/ExecutionPreparation.html index fc98fd979..39454bba7 100644 --- a/docs/jdocs/neureka/backend/api/fun/ExecutionPreparation.html +++ b/docs/jdocs/neureka/backend/api/fun/ExecutionPreparation.html @@ -1,91 +1,117 @@ - + + - -ExecutionPreparation (neureka 1.0.0 API) - - - - + +ExecutionPreparation (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.fun
    -

    Interface ExecutionPreparation

    +
    neureka.backend.api.fun
    +

    Interface ExecutionPreparation

    -
    -
    +
    +
    +
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    - +
    + - -
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/fun/SuitabilityPredicate.html b/docs/jdocs/neureka/backend/api/fun/SuitabilityPredicate.html index e8c0869f8..44e2bc07e 100644 --- a/docs/jdocs/neureka/backend/api/fun/SuitabilityPredicate.html +++ b/docs/jdocs/neureka/backend/api/fun/SuitabilityPredicate.html @@ -1,337 +1,434 @@ - + + - -SuitabilityPredicate (neureka 1.0.0 API) - - - - + +SuitabilityPredicate (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.fun
    -

    Interface SuitabilityPredicate

    +
    neureka.backend.api.fun
    +

    Interface SuitabilityPredicate

    -
    -
    +
    +
    +
    -
    -
      - -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      static final float
      -
      BAD
      -
       
      -
      static final float
      -
      EXCELLENT
      -
       
      -
      static final float
      -
      GOOD
      -
       
      -
      static final float
      -
      NOT_GOOD
      -
       
      -
      static final float
      -
      OKAY
      -
       
      -
      static final float
      -
      PERFECT
      -
       
      -
      static final float
      -
      TERRIBLE
      -
       
      -
      static final float
      -
      UNSUITABLE
      -
       
      -
      static final float
      -
      VERY_GOOD
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Field Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Fields 
        Modifier and TypeField and Description
        static floatBAD 
        static floatEXCELLENT 
        static floatGOOD 
        static floatNOT_GOOD 
        static floatOKAY 
        static floatPERFECT 
        static floatTERRIBLE 
        static floatUNSUITABLE 
        static floatVERY_GOOD 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      float
      -
      isSuitableFor(ExecutionCall<? extends Device<?>> call)
      -
      -
      When an ExecutionCall instance has been formed then it will be routed by
      - the given Operation instance to their components, namely :
      - Algorithm instances !
      + -
      -
      -
        - -
      • -
        -

        Field Details

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    +
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    + diff --git a/docs/jdocs/neureka/backend/api/fun/package-frame.html b/docs/jdocs/neureka/backend/api/fun/package-frame.html new file mode 100644 index 000000000..509c4116c --- /dev/null +++ b/docs/jdocs/neureka/backend/api/fun/package-frame.html @@ -0,0 +1,23 @@ + + + + + +neureka.backend.api.fun (neureka 1.0.1 API) + + + + +

    neureka.backend.api.fun

    +
    +

    Interfaces

    + +
    + + diff --git a/docs/jdocs/neureka/backend/api/fun/package-summary.html b/docs/jdocs/neureka/backend/api/fun/package-summary.html index 35f4cc096..b1fd9a3d7 100644 --- a/docs/jdocs/neureka/backend/api/fun/package-summary.html +++ b/docs/jdocs/neureka/backend/api/fun/package-summary.html @@ -1,120 +1,172 @@ - + + - -neureka.backend.api.fun (neureka 1.0.0 API) - - - - + +neureka.backend.api.fun (neureka 1.0.1 API) - - - - - - + + -
    - -
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    Package neureka.backend.api.fun

    -
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    -
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    - -
    -

    Hierarchy For Package neureka.backend.api.fun

    -Package Hierarchies: +Package Hierarchies:
    -
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    Interface Hierarchy

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    + diff --git a/docs/jdocs/neureka/backend/api/ini/BackendLoader.html b/docs/jdocs/neureka/backend/api/ini/BackendLoader.html index 6e0d5ce54..9a2a828e6 100644 --- a/docs/jdocs/neureka/backend/api/ini/BackendLoader.html +++ b/docs/jdocs/neureka/backend/api/ini/BackendLoader.html @@ -1,124 +1,218 @@ - + + - -BackendLoader (neureka 1.0.0 API) - - - - + +BackendLoader (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.ini
    -

    Interface BackendLoader

    +
    neureka.backend.api.ini
    +

    Interface BackendLoader

    -
    +
    +
    +
      +
    • -
      public interface BackendLoader
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      void
      -
      load(BackendRegistry registry)
      -
       
      -
      -
      +
      +
      public interface BackendLoader
      +
      • +
    - +
    + - -
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      +
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    + diff --git a/docs/jdocs/neureka/backend/api/ini/BackendRegistry.html b/docs/jdocs/neureka/backend/api/ini/BackendRegistry.html index a8997e4c9..a680f448e 100644 --- a/docs/jdocs/neureka/backend/api/ini/BackendRegistry.html +++ b/docs/jdocs/neureka/backend/api/ini/BackendRegistry.html @@ -1,140 +1,247 @@ - + + - -BackendRegistry (neureka 1.0.0 API) - - - - + +BackendRegistry (neureka 1.0.1 API) - - - - - - + + -
    - -
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    neureka.backend.api.ini
    +

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    -
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    -
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    +
    neureka.backend.api.ini
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    - -
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    Package neureka.backend.api.ini
    -

    Interface ReceiveForDevice<D extends Device<?>>

    +
    neureka.backend.api.ini
    +

    Interface ReceiveForDevice<D extends Device<?>>

    -
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      +
    • -
      public interface ReceiveForDevice<D extends Device<?>>
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    - -
    -
    -
    Package neureka.backend.api.ini
    -

    Interface ReceiveForOperation<D extends Device<?>>

    +
    neureka.backend.api.ini
    +

    Interface ReceiveForOperation<D extends Device<?>>

    -
    +
    +
    +
      +
    • -
      public interface ReceiveForOperation<D extends Device<?>>
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    + diff --git a/docs/jdocs/neureka/backend/api/ini/package-frame.html b/docs/jdocs/neureka/backend/api/ini/package-frame.html new file mode 100644 index 000000000..a7721808c --- /dev/null +++ b/docs/jdocs/neureka/backend/api/ini/package-frame.html @@ -0,0 +1,27 @@ + + + + + +neureka.backend.api.ini (neureka 1.0.1 API) + + + + +

    neureka.backend.api.ini

    +
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    Interfaces

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    - -
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    Package neureka.backend.api.ini

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    -
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    + diff --git a/docs/jdocs/neureka/backend/api/template/algorithms/AbstractDeviceAlgorithm.html b/docs/jdocs/neureka/backend/api/template/algorithms/AbstractDeviceAlgorithm.html index e00571ab6..95780d19a 100644 --- a/docs/jdocs/neureka/backend/api/template/algorithms/AbstractDeviceAlgorithm.html +++ b/docs/jdocs/neureka/backend/api/template/algorithms/AbstractDeviceAlgorithm.html @@ -1,386 +1,564 @@ - + + - -AbstractDeviceAlgorithm (neureka 1.0.0 API) - - - - + +AbstractDeviceAlgorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.template.algorithms
    -

    Class AbstractDeviceAlgorithm<C extends DeviceAlgorithm<C>>

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C>
    +
    neureka.backend.api.template.algorithms
    +

    Class AbstractDeviceAlgorithm<C extends DeviceAlgorithm<C>>

    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C>
        • +
      +
      • +
    +
    +
    -
    -
    -
    -

    Fields inherited from interface neureka.backend.api.fun.SuitabilityPredicate

    -BAD, EXCELLENT, GOOD, NOT_GOOD, OKAY, PERFECT, TERRIBLE, UNSUITABLE, VERY_GOOD
    - +
    +
    +
    + -
  • -
    -

    Constructor Details

    -
      -
    • -
      -

      AbstractDeviceAlgorithm

      -
      public AbstractDeviceAlgorithm(String name)
      -
      +
        +
      • + + +

        Constructor Detail

        + + + +
          +
        • +

          AbstractDeviceAlgorithm

          +
          public AbstractDeviceAlgorithm(java.lang.String name)
        -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      setImplementationFor

      -
      public <D extends Device<?>, -E extends ImplementationFor<D>> -C setImplementationFor(Class<D> deviceClass, - E implementation)
      -
      Description copied from interface: DeviceAlgorithm
      -
      Implementations of the DeviceAlgorithm interface ought to express a compositional design pattern.
      +
        +
      • + + +

        Method Detail

        + + + + + +
          +
        • +

          setImplementationFor

          +
          public <D extends Device<?>,E extends ImplementationFor<D>> C setImplementationFor(java.lang.Class<D> deviceClass,
+                                                                                   E implementation)
          +
          Description copied from interface: DeviceAlgorithm
          +
          Implementations of the DeviceAlgorithm interface ought to express a compositional design pattern.
          This means that concrete implementations of an algorithm for a device are not extending an Algorithm, they are components of it instead.
          These components can be stored on an Algorithm by passing a Device class as key and an ImplementationFor instance as value.
          -
          -
          Specified by:
          -
          setImplementationFor in interface DeviceAlgorithm<C extends DeviceAlgorithm<C>>
          -
          Type Parameters:
          -
          D - The type parameter of the Device type for which - an implementation should be set in this Device.
          -
          E - The type of the ImplementationFor the provided Device type.
          -
          Parameters:
          -
          deviceClass - The class of the Device for which an implementation should be set.
          -
          implementation - The ImplementationFor the provided Device type.
          -
          Returns:
          -
          This very Algorithm instance to allow for method chaining.
          +
          +
          Specified by:
          +
          setImplementationFor in interface DeviceAlgorithm<C extends DeviceAlgorithm<C>>
          +
          Type Parameters:
          +
          D - The type parameter of the Device type for which + an implementation should be set in this Device.
          +
          E - The type of the ImplementationFor the provided Device type.
          +
          Parameters:
          +
          deviceClass - The class of the Device for which an implementation should be set.
          +
          implementation - The ImplementationFor the provided Device type.
          +
          Returns:
          +
          This very Algorithm instance to allow for method chaining.
          -
      • -
    • -
      -

      getImplementationFor

      -
      public <D extends Device<?>> ImplementationFor<D> getImplementationFor(Class<D> deviceClass)
      -
      Description copied from interface: DeviceAlgorithm
      -
      An ImplementationFor a specific Device can be accessed by passing the class of - the Device for which an implementation should be returned. - An Algorithm instance ought to contain a collection of these Device specific +
    + + + +
      +
    • +

      getImplementationFor

      +
      public <D extends Device<?>> ImplementationFor<D> getImplementationFor(java.lang.Class<D> deviceClass)
      +
      Description copied from interface: DeviceAlgorithm
      +
      An ImplementationFor a specific Device can be accessed by passing the class of + the Device for which an implementation should be returned. + An Algorithm instance ought to contain a collection of these Device specific implementations...
      -
      -
      Specified by:
      -
      getImplementationFor in interface DeviceAlgorithm<C extends DeviceAlgorithm<C>>
      -
      Type Parameters:
      +
      +
      Specified by:
      +
      getImplementationFor in interface DeviceAlgorithm<C extends DeviceAlgorithm<C>>
      +
      Type Parameters:
      D - The type parameter which has to be a class extending the Device interface.
      -
      Parameters:
      +
      Parameters:
      deviceClass - The class of the device for which the stored algorithm implementation should be returned.
      -
      Returns:
      +
      Returns:
      The implementation for the passed device type class.
      -
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • -
  • -
    -

    executeFor

    -
    public static Tensor<?> executeFor(Function caller, - ExecutionCall<? extends Device<?>> call, - FinalExecutor executor)
    -
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
    • +

      executeOnCommonDevice

      +
      public static <R> R executeOnCommonDevice(ExecutionCall<?> call,
+                                          java.util.function.Supplier<R> execution)
      • -
    • -
      -

      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
    + + + +
      +
    • +

      getName

      +
      public java.lang.String getName()
      +
      This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
      -
      -
      Specified by:
      -
      getName in interface Algorithm
      -
      Returns:
      -
      The name of this Algorithm.
      +
      +
      Specified by:
      +
      getName in interface Algorithm
      +
      Returns:
      +
      The name of this Algorithm.
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/algorithms/AbstractFunAlgorithm.html b/docs/jdocs/neureka/backend/api/template/algorithms/AbstractFunAlgorithm.html index 0861b20d8..0014d7a71 100644 --- a/docs/jdocs/neureka/backend/api/template/algorithms/AbstractFunAlgorithm.html +++ b/docs/jdocs/neureka/backend/api/template/algorithms/AbstractFunAlgorithm.html @@ -1,323 +1,474 @@ - + + - -AbstractFunAlgorithm (neureka 1.0.0 API) - - - - + +AbstractFunAlgorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.template.algorithms
    -

    Class AbstractFunAlgorithm

    +
    neureka.backend.api.template.algorithms
    +

    Class AbstractFunAlgorithm

    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractFunAlgorithm
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.template.algorithms.AbstractFunAlgorithm
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public class AbstractFunAlgorithm
+extends java.lang.Object
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        AbstractFunAlgorithm

        -
        protected AbstractFunAlgorithm(String name)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            AbstractFunAlgorithm

            +
            protected AbstractFunAlgorithm(java.lang.String name)
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    setIsSuitableFor

    -
    public final AbstractFunAlgorithm setIsSuitableFor(SuitabilityPredicate isSuitableFor)
    -
    The SuitabilityPredicate received by this method - checks if a given instance of an ExecutionCall is - suitable to be executed in ImplementationFor instances - residing in this Algorithm as components. - The lambda will be called by the isSuitableFor(ExecutionCall) method - by any given Operation instances this algorithm belongs to.
    -
    -
    Parameters:
    + + + + +
    • -
  • -
    -

    setAutogradModeFor

    -
    public final AbstractFunAlgorithm setAutogradModeFor(ADSupportPredicate autogradModeFor)
    -
    A ADSupportPredicate lambda checks what kind of auto differentiation mode an - Algorithm supports for a given ExecutionCall. - The lambda will be called by the autoDiffModeFrom(ExecutionCall) method - by any given Operation instances this algorithm belongs to.
    -
    -
    Parameters:
    + + + + +
    • -
  • -
    -

    autoDiffModeFrom

    -
    public AutoDiffMode autoDiffModeFrom(ExecutionCall<? extends Device<?>> call)
    -
    Description copied from interface: ADSupportPredicate
    + + + + +
    • +
    +
    Parameters:
    +
    call - The ExecutionCall which should be checked.
    +
    Returns:
    +
    A AutoDiffMode enum instance describing what kind of differentiation can be performed.
    - -
  • -
    -

    setExecution

    -
    public final AbstractFunAlgorithm setExecution(Execution execution)
    -
    + + + + + + + + + + + + +
      +
    • +

      getName

      +
      public java.lang.String getName()
      +
      This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
      -
      -
      Specified by:
      -
      getName in interface Algorithm
      -
      Returns:
      -
      The name of this Algorithm.
      +
      +
      Specified by:
      +
      getName in interface Algorithm
      +
      Returns:
      +
      The name of this Algorithm.
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/algorithms/AbstractFunDeviceAlgorithm.html b/docs/jdocs/neureka/backend/api/template/algorithms/AbstractFunDeviceAlgorithm.html index 7573b95a1..af88aa0df 100644 --- a/docs/jdocs/neureka/backend/api/template/algorithms/AbstractFunDeviceAlgorithm.html +++ b/docs/jdocs/neureka/backend/api/template/algorithms/AbstractFunDeviceAlgorithm.html @@ -1,335 +1,436 @@ - + + - -AbstractFunDeviceAlgorithm (neureka 1.0.0 API) - - - - + +AbstractFunDeviceAlgorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.template.algorithms
    -

    Class AbstractFunDeviceAlgorithm<C extends DeviceAlgorithm<C>>

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<C>
    -
    +
    neureka.backend.api.template.algorithms
    +

    Class AbstractFunDeviceAlgorithm<C extends DeviceAlgorithm<C>>

    -
    -
    -
    Type Parameters:
    +
    + +
    +
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        AbstractFunDeviceAlgorithm

        -
        public AbstractFunDeviceAlgorithm(String name)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            AbstractFunDeviceAlgorithm

            +
            public AbstractFunDeviceAlgorithm(java.lang.String name)
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    buildFunAlgorithm

    -
    public final C buildFunAlgorithm()
    -
    -
    Returns:
    -
    A new concrete implementation of the AbstractFunDeviceAlgorithm which - is fully built and ready to be used as an Operation component.
    + + + + +
      +
    • +

      buildFunAlgorithm

      +
      public final C buildFunAlgorithm()
      +
      +
      Returns:
      +
      A new concrete implementation of the AbstractFunDeviceAlgorithm which + is fully built and ready to be used as an Operation component.
      -
    • -
  • -
    -

    setIsSuitableFor

    -
    public final AbstractFunDeviceAlgorithm<C> setIsSuitableFor(SuitabilityPredicate isSuitableFor)
    -
    The SuitabilityPredicate received by this method - checks if a given instance of an ExecutionCall is - suitable to be executed in ImplementationFor instances - residing in this Algorithm as components. - The lambda will be called by the isSuitableFor(ExecutionCall) method - by any given Operation instances this algorithm belongs to.
    -
    -
    Parameters:
    + + + + +
    • -
  • -
    -

    setSupplyADActionFor

    -
    public final AbstractFunDeviceAlgorithm<C> setSupplyADActionFor(ADActionSupplier supplyADActionFor)
    -
    This method receives a ADActionSupplier which will supply - ADAction instances which can perform backward and forward auto differentiation.
    -
    -
    Parameters:
    -
    supplyADActionFor - A supplier for an ADAction containing implementation details for autograd.
    -
    Returns:
    + + + + +
      +
    • +

      setSupplyADActionFor

      +
      public final AbstractFunDeviceAlgorithm<C> setSupplyADActionFor(ADActionSupplier supplyADActionFor)
      +
      This method receives a ADActionSupplier which will supply + ADAction instances which can perform backward and forward auto differentiation.
      +
      +
      Parameters:
      +
      supplyADActionFor - A supplier for an ADAction containing implementation details for autograd.
      +
      Returns:
      This very instance to enable method chaining.
      -
    • -
  • -
    -

    setCallPreparation

    -
    public final AbstractFunDeviceAlgorithm<C> setCallPreparation(ExecutionPreparation instantiateNewTensorsForExecutionIn)
    -
    An Algorithm will produce a Result when executing an ExecutionCall. + + + + +
      +
    • +

      setCallPreparation

      +
      public final AbstractFunDeviceAlgorithm<C> setCallPreparation(ExecutionPreparation instantiateNewTensorsForExecutionIn)
      +
      An Algorithm will produce a Result when executing an ExecutionCall. This result must be created somehow. - A ExecutionPreparation implementation instance will do just that... + A ExecutionPreparation implementation instance will do just that... Often times the first entry in the array of tensors stored inside the call will be null to serve as a position for the output to be placed at. The creation of this output tensor is of course highly dependent on the type @@ -337,95 +438,169 @@

      setCallPreparation

      Element-wise operations for example will require the creation of an output tensor with the shape of the provided input tensors, whereas the execution of a linear operation like for example a broadcast operation will require a very different approach... - The lambda passed to this will be called by the prepare(ExecutionCall) method - by any given Operation instances this algorithm belongs to.
      -
      -
      Parameters:
      + The lambda passed to this will be called by the prepare(ExecutionCall) method + by any given Operation instances this algorithm belongs to.
    +
    +
    Parameters:
    instantiateNewTensorsForExecutionIn - A lambda which prepares the provided execution call (usually output instantiation).
    -
    Returns:
    +
    Returns:
    This very instance to enable method chaining.
    -
    • -
  • -
    -

    setAutogradModeFor

    -
    public final AbstractFunDeviceAlgorithm<C> setAutogradModeFor(ADSupportPredicate autogradModeFor)
    -
    A ADSupportPredicate lambda checks what kind of auto differentiation mode an - Algorithm supports for a given ExecutionCall. - The lambda will be called by the autoDiffModeFrom(ExecutionCall) method - by any given Operation instances this algorithm belongs to.
    -
    -
    Parameters:
    + + + + +
    • -
  • -
    -

    autoDiffModeFrom

    -
    public AutoDiffMode autoDiffModeFrom(ExecutionCall<? extends Device<?>> call)
    -
    Description copied from interface: ADSupportPredicate
    + + + + +
    • +
    +
    Specified by:
    +
    autoDiffModeFrom in interface ADSupportPredicate
    +
    Parameters:
    +
    call - The ExecutionCall which should be checked.
    +
    Returns:
    +
    A AutoDiffMode enum instance describing what kind of differentiation can be performed.
    - -
  • -
    -

    setExecution

    -
    public AbstractFunDeviceAlgorithm<C> setExecution(Execution execution)
    -
    + + + + + + + + + + + + +
      +
    • +

      getName

      +
      public java.lang.String getName()
      +
      This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
      -
      -
      Specified by:
      -
      getName in interface Algorithm
      -
      Returns:
      -
      The name of this Algorithm.
      +
      +
      Specified by:
      +
      getName in interface Algorithm
      +
      Returns:
      +
      The name of this Algorithm.
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/algorithms/FallbackAlgorithm.html b/docs/jdocs/neureka/backend/api/template/algorithms/FallbackAlgorithm.html index ee007661d..ca66ad08c 100644 --- a/docs/jdocs/neureka/backend/api/template/algorithms/FallbackAlgorithm.html +++ b/docs/jdocs/neureka/backend/api/template/algorithms/FallbackAlgorithm.html @@ -1,353 +1,516 @@ - + + - -FallbackAlgorithm (neureka 1.0.0 API) - - - - + +FallbackAlgorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.template.algorithms
    -

    Class FallbackAlgorithm

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<FallbackAlgorithm> -
    neureka.backend.api.template.algorithms.FallbackAlgorithm
    -
    +
    neureka.backend.api.template.algorithms
    +

    Class FallbackAlgorithm

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        FallbackAlgorithm

        -
        public FallbackAlgorithm(String name, - int arity, - Operation type)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            FallbackAlgorithm

            +
            public FallbackAlgorithm(java.lang.String name,
+                         int arity,
+                         Operation type)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      isSuitableFor

      -
      public float isSuitableFor(ExecutionCall<? extends Device<?>> call)
      -
      Description copied from interface: SuitabilityPredicate
      -
      When an ExecutionCall instance has been formed then it will be routed by
      - the given Operation instance to their components, namely :
      - Algorithm instances !
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          isSuitableFor

          +
          public float isSuitableFor(ExecutionCall<? extends Device<?>> call)
          +
          Description copied from interface: SuitabilityPredicate
          +
          When an ExecutionCall instance has been formed then it will be routed by
          + the given Operation instance to their components, namely :
          + Algorithm instances !
          - The ability to decide which algorithm is suitable for a given ExecutionCall instance
          + The ability to decide which algorithm is suitable for a given ExecutionCall instance
          is being granted by implementations of the following method.
          It returns a float representing the suitability of a given call.
          The float is expected to be between 0 and 1, where 0 means
          that the implementation is not suitable at all and 1 means that
          that it fits the call best!
          -
          -
          Specified by:
          -
          isSuitableFor in interface SuitabilityPredicate
          -
          Parameters:
          -
          call - The ExecutionCall whose suitability for execution on this Algorithm ought to be determined.
          -
          Returns:
          +
          +
          Specified by:
          +
          isSuitableFor in interface SuitabilityPredicate
          +
          Parameters:
          +
          call - The ExecutionCall whose suitability for execution on this Algorithm ought to be determined.
          +
          Returns:
          The suitability degree expressed by a float value between 0 and 1, where 0 means not suitable and 1 means suitable.
          -
      • -
    • -
      -

      supplyADActionFor

      -
      public ADAction supplyADActionFor(Function function, - ExecutionCall<? extends Device<?>> call)
      -
      Description copied from interface: ADActionSupplier
      +
    + + + +
      +
    • +

      supplyADActionFor

      +
      public ADAction supplyADActionFor(Function function,
+                                  ExecutionCall<? extends Device<?>> call)
      +
      Description copied from interface: ADActionSupplier
      This method ought to return a new instance - if the ADAction class responsible for performing automatic differentiation + if the ADAction class responsible for performing automatic differentiation both for forward and backward mode differentiation.
      - Therefore an ADAction exposes 2 different procedures.
      + Therefore an ADAction exposes 2 different procedures.
      One is the forward mode differentiation, and the other one
      is the backward mode differentiation which is more commonly known as back-propagation...
      Besides that it may also contain context information used
      to perform said procedures.
      -
      -
      Specified by:
      -
      supplyADActionFor in interface ADActionSupplier
      -
      Parameters:
      +
      +
      Specified by:
      +
      supplyADActionFor in interface ADActionSupplier
      +
      Parameters:
      function - The function from where the request for auto differentiation originates.
      call - The execution call of the current execution which requires auto differentiation support.
      -
      Returns:
      -
      The resulting ADAction.
      +
      Returns:
      +
      The resulting ADAction.
      -
    • -
  • -
    -

    ADAction

    -
    public static ADAction ADAction(Function function, - ExecutionCall<? extends Device<?>> call)
    -
    + + + + + + + + + + + + + + + + +
    • +
    +
    Specified by:
    +
    autoDiffModeFrom in interface ADSupportPredicate
    +
    Parameters:
    +
    call - The ExecutionCall which should be checked.
    +
    Returns:
    +
    A AutoDiffMode enum instance describing what kind of differentiation can be performed.
    - -
  • -
    -

    execute

    -
    public Result execute(Function caller, - ExecutionCall<? extends Device<?>> call)
    -
    -
    Specified by:
    -
    execute in interface Execution
    -
    Parameters:
    -
    caller - The caller Function from which the request for execution originated.
    -
    call - The ExecutionCall which should be executed.
    -
    Returns:
    -
    A Result instance wrapping a Tensor and optionally also an ADActionSupplier.
    + + + + +
    • -
  • -
    -

    getName

    -
    public String getName()
    -
    This method returns the name of this Algorithm + + + + +
      +
    • +

      getName

      +
      public java.lang.String getName()
      +
      This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
      -
      -
      Specified by:
      -
      getName in interface Algorithm
      -
      Returns:
      -
      The name of this Algorithm.
      +
      +
      Specified by:
      +
      getName in interface Algorithm
      +
      Returns:
      +
      The name of this Algorithm.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/algorithms/FunAlgorithm.html b/docs/jdocs/neureka/backend/api/template/algorithms/FunAlgorithm.html index 49f942842..8b238f281 100644 --- a/docs/jdocs/neureka/backend/api/template/algorithms/FunAlgorithm.html +++ b/docs/jdocs/neureka/backend/api/template/algorithms/FunAlgorithm.html @@ -1,189 +1,319 @@ - + + - -FunAlgorithm (neureka 1.0.0 API) - - - - + +FunAlgorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.template.algorithms
    -

    Class FunAlgorithm

    +
    neureka.backend.api.template.algorithms
    +

    Class FunAlgorithm

    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractFunAlgorithm -
    neureka.backend.api.template.algorithms.FunAlgorithm
    -
    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        FunAlgorithm

        -
        public FunAlgorithm(String name)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            FunAlgorithm

            +
            public FunAlgorithm(java.lang.String name)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/algorithms/FunDeviceAlgorithm.html b/docs/jdocs/neureka/backend/api/template/algorithms/FunDeviceAlgorithm.html index 2f313b604..f411797c0 100644 --- a/docs/jdocs/neureka/backend/api/template/algorithms/FunDeviceAlgorithm.html +++ b/docs/jdocs/neureka/backend/api/template/algorithms/FunDeviceAlgorithm.html @@ -1,203 +1,345 @@ - + + - -FunDeviceAlgorithm (neureka 1.0.0 API) - - - - + +FunDeviceAlgorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.template.algorithms
    -

    Class FunDeviceAlgorithm

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<FunDeviceAlgorithm> -
    neureka.backend.api.template.algorithms.FunDeviceAlgorithm
    -
    +
    neureka.backend.api.template.algorithms
    +

    Class FunDeviceAlgorithm

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        FunDeviceAlgorithm

        -
        public FunDeviceAlgorithm(String name)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            FunDeviceAlgorithm

            +
            public FunDeviceAlgorithm(java.lang.String name)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/algorithms/package-frame.html b/docs/jdocs/neureka/backend/api/template/algorithms/package-frame.html new file mode 100644 index 000000000..34f7b9d30 --- /dev/null +++ b/docs/jdocs/neureka/backend/api/template/algorithms/package-frame.html @@ -0,0 +1,24 @@ + + + + + +neureka.backend.api.template.algorithms (neureka 1.0.1 API) + + + + +

    neureka.backend.api.template.algorithms

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/api/template/algorithms/package-summary.html b/docs/jdocs/neureka/backend/api/template/algorithms/package-summary.html index e517eb85b..6f68ab429 100644 --- a/docs/jdocs/neureka/backend/api/template/algorithms/package-summary.html +++ b/docs/jdocs/neureka/backend/api/template/algorithms/package-summary.html @@ -1,97 +1,164 @@ - + + - -neureka.backend.api.template.algorithms (neureka 1.0.0 API) - - - - + +neureka.backend.api.template.algorithms (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.api.template.algorithms

    -
    -
    -
    package neureka.backend.api.template.algorithms
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/algorithms/package-tree.html b/docs/jdocs/neureka/backend/api/template/algorithms/package-tree.html index 72ce05245..74752500f 100644 --- a/docs/jdocs/neureka/backend/api/template/algorithms/package-tree.html +++ b/docs/jdocs/neureka/backend/api/template/algorithms/package-tree.html @@ -1,85 +1,148 @@ - + + - -neureka.backend.api.template.algorithms Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.api.template.algorithms Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.api.template.algorithms

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
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    + diff --git a/docs/jdocs/neureka/backend/api/template/implementations/AbstractImplementationFor.html b/docs/jdocs/neureka/backend/api/template/implementations/AbstractImplementationFor.html index 61c0d4e99..3c4a1322a 100644 --- a/docs/jdocs/neureka/backend/api/template/implementations/AbstractImplementationFor.html +++ b/docs/jdocs/neureka/backend/api/template/implementations/AbstractImplementationFor.html @@ -1,186 +1,296 @@ - + + - -AbstractImplementationFor (neureka 1.0.0 API) - - - - + +AbstractImplementationFor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.template.implementations
    -

    Class AbstractImplementationFor<D extends Device<?>>

    +
    neureka.backend.api.template.implementations
    +

    Class AbstractImplementationFor<D extends Device<?>>

    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<D>
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.template.implementations.AbstractImplementationFor<D>
        • +
      +
      • +
    +
    +
    -
    -
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        AbstractImplementationFor

        -
        public AbstractImplementationFor(ImplementationFor<D> implementationLambda, - int arity)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            AbstractImplementationFor

            +
            public AbstractImplementationFor(ImplementationFor<D> implementationLambda,
+                                 int arity)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      run

      -
      public Tensor<?> run(ExecutionCall<D> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<D> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<D extends Device<?>>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<D extends Device<?>>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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    • Prev Class
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    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
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      • +
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/implementations/package-frame.html b/docs/jdocs/neureka/backend/api/template/implementations/package-frame.html new file mode 100644 index 000000000..3b9f226f9 --- /dev/null +++ b/docs/jdocs/neureka/backend/api/template/implementations/package-frame.html @@ -0,0 +1,19 @@ + + + + + +neureka.backend.api.template.implementations (neureka 1.0.1 API) + + + + +

    neureka.backend.api.template.implementations

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/api/template/implementations/package-summary.html b/docs/jdocs/neureka/backend/api/template/implementations/package-summary.html index 2b4c1d8ef..e0d33a1c1 100644 --- a/docs/jdocs/neureka/backend/api/template/implementations/package-summary.html +++ b/docs/jdocs/neureka/backend/api/template/implementations/package-summary.html @@ -1,82 +1,139 @@ - + + - -neureka.backend.api.template.implementations (neureka 1.0.0 API) - - - - + +neureka.backend.api.template.implementations (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.api.template.implementations

    -
    -
    -
    package neureka.backend.api.template.implementations
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/implementations/package-tree.html b/docs/jdocs/neureka/backend/api/template/implementations/package-tree.html index 3a57b6b40..b122388f0 100644 --- a/docs/jdocs/neureka/backend/api/template/implementations/package-tree.html +++ b/docs/jdocs/neureka/backend/api/template/implementations/package-tree.html @@ -1,71 +1,134 @@ - + + - -neureka.backend.api.template.implementations Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.api.template.implementations Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.api.template.implementations

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/operations/AbstractOperation.html b/docs/jdocs/neureka/backend/api/template/operations/AbstractOperation.html index 5f3d3dd85..6db8ff1df 100644 --- a/docs/jdocs/neureka/backend/api/template/operations/AbstractOperation.html +++ b/docs/jdocs/neureka/backend/api/template/operations/AbstractOperation.html @@ -1,283 +1,362 @@ - + + - -AbstractOperation (neureka 1.0.0 API) - - - - + +AbstractOperation (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.template.operations
    -

    Class AbstractOperation

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation
    +
    neureka.backend.api.template.operations
    +

    Class AbstractOperation

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.api.template.operations.AbstractOperation
        • +
      +
      • +
    +
    +
    -
    -
      + +
    +
    +
    +
    -
    final <T extends Algorithm>
    Operation
    -
    setAlgorithm(Class<T> type, - T instance)
    -
    -
    Operation implementations embody a component system hosting unique Algorithm instances.
    -
    -
    String
    -
    stringify(String[] children)
    -
    -
    final <T extends Algorithm>
    boolean
    -
    supports(Class<T> implementation)
    -
     
    -
    final <T extends Algorithm>
    boolean
    -
    supportsAlgorithm(Class<T> type)
    -
    -
    This method checks if this Operation contains an instance of the - Algorithm implementation specified via its type class.
    -
    -
    final String
    -
    toString()
    -
     
    -
    -
    -
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.backend.api.Operation

    -calculate, execute, setAlgorithm
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Field Details

        -
          -
        • -
          -

          _function

          -
          protected final String _function
          +
            +
          • + + +

            Field Detail

            + + + +
              +
            • +

              _function

              +
              protected final java.lang.String _function
              An operation may have two ways in which it can describe itself as String within a Function AST. The first one is an operator style of representation and the second one a classical function. So for the 'Addition' operation the following two representations exist:
              @@ -286,12 +365,15 @@

              _function

            • Function: 'add'; Example: 'add( I[0], 3, 5*I[1] )'
            The following String is the latter way of representing the operation, namely: a functional way.
      -
    -
  • -
    -

    _operator

    -
    protected final String _operator
    + + + + +
      +
    • +

      _operator

      +
      protected final java.lang.String _operator
      An operation may have two ways in which it can describe itself as String within a Function AST. The first one is an operator style of representation and the second one a classical function. So for the 'Addition' operation the following two representations exist:
      @@ -300,346 +382,479 @@

      _operator

    • Function: 'add'; Example: 'add( I[0], 3, 5*I[1] )'
    The following String is the primary way of representing the operation, namely: as an operator.
    • - -
  • -
    -

    _arity

    -
    protected final int _arity
    + + + + +
      +
    • +

      _arity

      +
      protected final int _arity
      Arity is the number of arguments or operands that this function or operation takes.
      -
    • -
  • -
    -

    _isIndexer

    -
    protected final boolean _isIndexer
    + + + + +
      +
    • +

      _isIndexer

      +
      protected final boolean _isIndexer
      This flag determines if this operation is auto-indexing passed input arguments. Auto-indexing inputs means that for a given array of input arguments the wrapping Function instance will call its child nodes targeted via an index incrementally. The variable 'j' in a Functions expressions containing 'I[j]' will then be resolved to an actual input for a given indexer...
      -
    • -
  • -
    -

    _isDifferentiable

    -
    protected final boolean _isDifferentiable
    + + + + +
      +
    • +

      _isDifferentiable

      +
      protected final boolean _isDifferentiable
      Certain operations are not differentiable, meaning they cannot participate in neither forward nor reverse mode differentiation. In order to avoid error-prone behaviour trying to involve non- differentiable operations will yield proper exceptions.
      -
    • -
  • -
    -

    _isInline

    -
    protected final boolean _isInline
    + + + + +
      +
    • +

      _isInline

      +
      protected final boolean _isInline
      Inline operations are operations which change the state of the arguments passed to them.
      -
    • -
  • -
    -

    _isOperator

    -
    protected final boolean _isOperator
    -
    + + + + +
      +
    • +

      _isOperator

      +
      protected final boolean _isOperator
    -
    • + -
  • -
    -

    Constructor Details

    -
    • + -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      getAlgorithm

      +
      public final <T extends Algorithm> T getAlgorithm(java.lang.Class<T> type)
      +
      Operation implementations embody a component system hosting unique Algorithm instances. + For a given class implementing the Algorithm class, there can only be a single + instance of it referenced (aka supported) by a given Operation instance. This method ensures this in terms of read access by returning only a single instance or null - based on the provided class instance whose type extends the Algorithm interface.
      -
      -
      Specified by:
      -
      getAlgorithm in interface Operation
      -
      Type Parameters:
      -
      T - The type parameter of the Algorithm type class.
      -
      Parameters:
      -
      type - The class of the type which implements Algorithm as a key to get an existing instance.
      -
      Returns:
      -
      The instance of the specified type if any exists within this Operation.
      + based on the provided class instance whose type extends the Algorithm interface.
    • +
    +
    Specified by:
    +
    getAlgorithm in interface Operation
    +
    Type Parameters:
    +
    T - The type parameter of the Algorithm type class.
    +
    Parameters:
    +
    type - The class of the type which implements Algorithm as a key to get an existing instance.
    +
    Returns:
    +
    The instance of the specified type if any exists within this Operation.
    - -
  • -
    -

    supportsAlgorithm

    -
    public final <T extends Algorithm> boolean supportsAlgorithm(Class<T> type)
    -
    This method checks if this Operation contains an instance of the - Algorithm implementation specified via its type class.
    -
    -
    Specified by:
    -
    supportsAlgorithm in interface Operation
    -
    Type Parameters:
    -
    T - The type parameter of the Algorithm type class.
    -
    Parameters:
    -
    type - The class of the type which implements Algorithm.
    -
    Returns:
    -
    The truth value determining if this Operation contains an instance of the specified Algorithm type.
    + + + + +
      +
    • +

      supportsAlgorithm

      +
      public final <T extends Algorithm> boolean supportsAlgorithm(java.lang.Class<T> type)
      +
      This method checks if this Operation contains an instance of the + Algorithm implementation specified via its type class.
      +
      +
      Specified by:
      +
      supportsAlgorithm in interface Operation
      +
      Type Parameters:
      +
      T - The type parameter of the Algorithm type class.
      +
      Parameters:
      +
      type - The class of the type which implements Algorithm.
      +
      Returns:
      +
      The truth value determining if this Operation contains an instance of the specified Algorithm type.
      -
    • -
  • -
    -

    setAlgorithm

    -
    public final <T extends Algorithm> Operation setAlgorithm(Class<T> type, - T instance)
    -
    Operation implementations embody a component system hosting unique Algorithm instances. - For a given class implementing the Algorithm class, there can only be a single - instance of it referenced (aka supported) by a given Operation instance. - This method enables the registration of Algorithm types in the component system of this Operation.
    -
    -
    Specified by:
    -
    setAlgorithm in interface Operation
    -
    Type Parameters:
    -
    T - The type parameter of the Algorithm type class.
    -
    Parameters:
    -
    type - The class of the type which implements Algorithm as key for the provided instance.
    -
    instance - The instance of the provided type class which ought to be referenced (supported) by this Operation.
    -
    Returns:
    -
    This very Operation instance to enable method chaining on it.
    + + + + + + +
      +
    • +

      setAlgorithm

      +
      public final <T extends Algorithm> Operation setAlgorithm(java.lang.Class<T> type,
+                                                          T instance)
      +
      Operation implementations embody a component system hosting unique Algorithm instances. + For a given class implementing the Algorithm class, there can only be a single + instance of it referenced (aka supported) by a given Operation instance. + This method enables the registration of Algorithm types in the component system of this Operation.
      +
      +
      Specified by:
      +
      setAlgorithm in interface Operation
      +
      Type Parameters:
      +
      T - The type parameter of the Algorithm type class.
      +
      Parameters:
      +
      type - The class of the type which implements Algorithm as key for the provided instance.
      +
      instance - The instance of the provided type class which ought to be referenced (supported) by this Operation.
      +
      Returns:
      +
      This very Operation instance to enable method chaining on it.
      -
    • -
  • -
    -

    getAlgorithmFor

    -
    public final Algorithm getAlgorithmFor(ExecutionCall<?> call)
    -
    Description copied from interface: Operation
    -
    Alongside a component system made up of Algorithm instances, implementations - of this interface also ought to express a routing mechanism which finds the best Algorithm - for a given ExecutionCall instance. + + + + +
      +
    • +

      getAlgorithmFor

      +
      public final Algorithm getAlgorithmFor(ExecutionCall<?> call)
      +
      Description copied from interface: Operation
      +
      Alongside a component system made up of Algorithm instances, implementations + of this interface also ought to express a routing mechanism which finds the best Algorithm + for a given ExecutionCall instance. This method signature describes this requirement.
      -
      -
      Specified by:
      -
      getAlgorithmFor in interface Operation
      -
      Parameters:
      -
      call - The ExecutionCall instance which needs the best Algorithm for execution.
      -
      Returns:
      -
      The chosen Algorithm which ought to be fir for execution the provided call.
      +
      +
      Specified by:
      +
      getAlgorithmFor in interface Operation
      +
      Parameters:
      +
      call - The ExecutionCall instance which needs the best Algorithm for execution.
      +
      Returns:
      +
      The chosen Algorithm which ought to be fir for execution the provided call.
      -
    • -
  • -
    -

    supports

    -
    public final <T extends Algorithm> boolean supports(Class<T> implementation)
    -
    -
    Specified by:
    -
    supports in interface Operation
    + + + + +
      +
    • +

      supports

      +
      public final <T extends Algorithm> boolean supports(java.lang.Class<T> implementation)
      +
      +
      Specified by:
      +
      supports in interface Operation
      -
    • -
  • -
    -

    isOperator

    -
    public final boolean isOperator()
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      isOperator

      +
      public final boolean isOperator()
      +
      Description copied from interface: Operation
      An operator is an alternative to a function like "sum()" or "prod()".
      Examples would be "+, -, * ..."!
      -
      -
      Specified by:
      -
      isOperator in interface Operation
      -
      Returns:
      +
      +
      Specified by:
      +
      isOperator in interface Operation
      +
      Returns:
      If this operation can be represented as operator like "+, -, * ..."!
      -
    • -
  • -
    -

    getIdentifier

    -
    public String getIdentifier()
    -
    Description copied from interface: Operation
    -
    Concrete Operation types ought to be representable by a function name. + + + + +
      +
    • +

      getIdentifier

      +
      public java.lang.String getIdentifier()
      +
      Description copied from interface: Operation
      +
      Concrete Operation types ought to be representable by a function name. The following ensures that this contract is met when overriding the method.
      -
      -
      Specified by:
      -
      getIdentifier in interface Operation
      -
      Returns:
      -
      the function name which serves as identifier when parsing Function instances.
      +
      +
      Specified by:
      +
      getIdentifier in interface Operation
      +
      Returns:
      +
      the function name which serves as identifier when parsing Function instances.
      -
    • -
  • -
    -

    getOperator

    -
    public final String getOperator()
    -
    -
    Specified by:
    -
    getOperator in interface Operation
    + + + + +
      +
    • +

      getOperator

      +
      public final java.lang.String getOperator()
      +
      +
      Specified by:
      +
      getOperator in interface Operation
      -
    • -
  • -
    -

    getArity

    -
    public final int getArity()
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      getArity

      +
      public final int getArity()
      +
      Description copied from interface: Operation
      Arity is the number of arguments or operands that this function or operation takes.
      -
      -
      Specified by:
      -
      getArity in interface Operation
      -
      Returns:
      +
      +
      Specified by:
      +
      getArity in interface Operation
      +
      Returns:
      The number of arguments expected by this operation, or -1 if an arbitrary number is accepted.
      -
    • -
  • -
    -

    isIndexer

    -
    public final boolean isIndexer()
    -
    Description copied from interface: Operation
    -
    This boolean property tell the Function implementations that this Operation + + + + +
      +
    • +

      isIndexer

      +
      public final boolean isIndexer()
      +
      Description copied from interface: Operation
      +
      This boolean property tell the Function implementations that this Operation ought to be viewed as something to be indexed. - The Function will use this information to iterate over all the provided inputs and + The Function will use this information to iterate over all the provided inputs and then execute the function wile also passing the index to the function AST. - The resulting array will then be available to this Operation as argument list. - This feature works alongside the Function implementation found in - FunctionVariable, which represents an input indexed + The resulting array will then be available to this Operation as argument list. + This feature works alongside the Function implementation found in + FunctionVariable, which represents an input indexed by the identifier 'j'!
      -
      -
      Specified by:
      -
      isIndexer in interface Operation
      -
      Returns:
      +
      +
      Specified by:
      +
      isIndexer in interface Operation
      +
      Returns:
      If this operation is an indexer.
      -
    • -
  • -
    -

    isDifferentiable

    -
    public final boolean isDifferentiable()
    -
    -
    Specified by:
    -
    isDifferentiable in interface Operation
    + + + + +
      +
    • +

      isDifferentiable

      +
      public final boolean isDifferentiable()
      +
      +
      Specified by:
      +
      isDifferentiable in interface Operation
      -
    • -
  • -
    -

    isInline

    -
    public boolean isInline()
    -
    Description copied from interface: Operation
    -
    This flag indicates that the implementation of this Operation + + + + +
      +
    • +

      isInline

      +
      public boolean isInline()
      +
      Description copied from interface: Operation
      +
      This flag indicates that the implementation of this Operation performs an operation which modifies the inputs to that operation. An example of this would be an assignment operation which copies the contents of one nd-array / tensor into another tensor. This second tensor will then have changed its state. This can be dangerous when auto-differentiation is involved.
      -
      -
      Specified by:
      -
      isInline in interface Operation
      -
      Returns:
      -
      The truth value determining if this Operation changes the contents of inputs.
      +
      +
      Specified by:
      +
      isInline in interface Operation
      +
      Returns:
      +
      The truth value determining if this Operation changes the contents of inputs.
      -
    • -
  • -
    -

    getDefaultAlgorithm

    -
    public final FallbackAlgorithm getDefaultAlgorithm()
    -
    + + + + + + + + +
      +
    • +

      asDerivative

      +
      public java.lang.String asDerivative(Function[] children,
+                                     int derivationIndex)
      +
      Operation implementations and Function implementations are in a tight relationship + where the Function describes an abstract syntax tree based on the syntactic information provided + by the Operation (through methods like Operation.getOperator() or Operation.getIdentifier()). + One important feature of the Function is the ability to create + derivatives by calling the Function.getDerivative(int) method. + Implementations of this Function method ought to call the method defined below in order to + form the derivation based on the child nodes of the abstract syntax tree of the given Function node.
      +
      +
      Specified by:
      +
      asDerivative in interface Operation
      +
      Parameters:
      children - The child nodes of a AST node referencing this operation.
      derivationIndex - The index of the input node which ought to be derived.
      -
      Returns:
      -
      The derivative as a String which should be parsable into yet another AST.
      +
      Returns:
      +
      The derivative as a String which should be parsable into yet another AST.
      -
      • -
    • -
      -

      stringify

      -
      public String stringify(String[] children)
      -
      -
      Specified by:
      -
      stringify in interface Operation
      +
    + + + +
      +
    • +

      stringify

      +
      public java.lang.String stringify(java.lang.String[] children)
      +
      +
      Specified by:
      +
      stringify in interface Operation
      -
      • -
    • -
      -

      toString

      -
      public final String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public final java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
      • -
    • -
      -

      operationName

      -
      protected String operationName()
      +
    + + + +
      +
    • +

      operationName

      +
      protected java.lang.String operationName()
      Override this if you want your operation to have a string representation with a custom prefix which is something other than the simple class name!
      -
      -
      Returns:
      +
      +
      Returns:
      The simple class name, or something else if overridden.
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.Derivation.html b/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.Derivation.html index 2b16e8d46..10ea53a85 100644 --- a/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.Derivation.html +++ b/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.Derivation.html @@ -1,130 +1,224 @@ - + + - -OperationBuilder.Derivation (neureka 1.0.0 API) - - - - + +OperationBuilder.Derivation (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.template.operations
    -

    Interface OperationBuilder.Derivation

    +
    neureka.backend.api.template.operations
    +

    Interface OperationBuilder.Derivation

    -
    -
    +
    +
    +
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      String
      -
      asDerivative(Function[] children, - int d)
      -
       
      -
      -
      +
      +
      public static interface OperationBuilder.Derivation
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        asDerivative

        -
        String asDerivative(Function[] children, - int d)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            asDerivative

            +
            java.lang.String asDerivative(Function[] children,
+                              int d)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.Stringifier.html b/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.Stringifier.html index ce5845391..6d8984282 100644 --- a/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.Stringifier.html +++ b/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.Stringifier.html @@ -1,128 +1,222 @@ - + + - -OperationBuilder.Stringifier (neureka 1.0.0 API) - - - - + +OperationBuilder.Stringifier (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.api.template.operations
    -

    Interface OperationBuilder.Stringifier

    +
    neureka.backend.api.template.operations
    +

    Interface OperationBuilder.Stringifier

    -
    -
    +
    +
    +
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      String
      -
      stringify(String[] children)
      -
       
      -
      -
      +
      +
      public static interface OperationBuilder.Stringifier
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        stringify

        -
        String stringify(String[] children)
        -
        +
          +
        • + + +

          Method Detail

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    + diff --git a/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.html b/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.html index 29ff36c39..68d040b57 100644 --- a/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.html +++ b/docs/jdocs/neureka/backend/api/template/operations/OperationBuilder.html @@ -1,353 +1,544 @@ - + + - -OperationBuilder (neureka 1.0.0 API) - - - - + +OperationBuilder (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.api.template.operations
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    Class OperationBuilder

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    java.lang.Object -
    neureka.backend.api.template.operations.OperationBuilder
    +
    neureka.backend.api.template.operations
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    Class OperationBuilder

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      • neureka.backend.api.template.operations.OperationBuilder
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      public final class OperationBuilder -extends Object
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      This builder class builds instances of the Operation interface. - Implementing the Operation interface manually can result in a lot of boilerplate code. - A builder class is the perfect fit for the Operation because the interface mostly +
      +
      public final class OperationBuilder
+extends java.lang.Object
      +
      This builder class builds instances of the Operation interface. + Implementing the Operation interface manually can result in a lot of boilerplate code. + A builder class is the perfect fit for the Operation because the interface mostly defines simple properties...
      In order to ensure that all necessary properties have been set the builder keeps track of the passed parameters. If not all properties have been set, the builder will trow an exception.
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      Constructor Details

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          Constructor Detail

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            OperationBuilder

            +
            public OperationBuilder()
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    neureka.backend.api.template.operations

    +
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    Interfaces

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    Package neureka.backend.api.template.operations

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    Hierarchy For Package neureka.backend.api.template.operations

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    - -
    -
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    Package neureka.backend.cpu
    -

    Class CPUBackend

    -
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    java.lang.Object -
    neureka.backend.cpu.CPUBackend
    +
    neureka.backend.cpu
    +

    Class CPUBackend

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    +
    +
      +
    • java.lang.Object
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    • +
        +
      • neureka.backend.cpu.CPUBackend
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        CPUBackend

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        public CPUBackend()
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          Constructor Detail

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            +
          • +

            CPUBackend

            +
            public CPUBackend()
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    Method Details

    - + + + +
    • +
    +
    Specified by:
    +
    dispose in interface BackendExtension
    - -
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    getLoader

    -
    public BackendLoader getLoader()
    -
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    Specified by:
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    getLoader in interface BackendExtension
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    + diff --git a/docs/jdocs/neureka/backend/cpu/package-frame.html b/docs/jdocs/neureka/backend/cpu/package-frame.html new file mode 100644 index 000000000..caa883d4c --- /dev/null +++ b/docs/jdocs/neureka/backend/cpu/package-frame.html @@ -0,0 +1,19 @@ + + + + + +neureka.backend.cpu (neureka 1.0.1 API) + + + + +

    neureka.backend.cpu

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    Classes

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    Package neureka.backend.cpu

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      Class
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        Class Summary 
        ClassDescription
        CPUBackend
        This class loads the CPU operations into the Neureka library context.
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    Hierarchy For Package neureka.backend.cpu

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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/BiElementwise.html b/docs/jdocs/neureka/backend/main/algorithms/BiElementwise.html index b7ea06654..8d282a94c 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/BiElementwise.html +++ b/docs/jdocs/neureka/backend/main/algorithms/BiElementwise.html @@ -1,203 +1,345 @@ - + + - -BiElementwise (neureka 1.0.0 API) - - - - + +BiElementwise (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.algorithms
    -

    Class BiElementwise

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<BiElementwise> -
    neureka.backend.main.algorithms.BiElementwise
    -
    +
    neureka.backend.main.algorithms
    +

    Class BiElementwise

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      Constructor Details

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        BiElementwise

        -
        public BiElementwise()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            BiElementwise

            +
            public BiElementwise()
          -
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  • -
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    Method Details

    -
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    • -
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      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/BiScalarBroadcast.html b/docs/jdocs/neureka/backend/main/algorithms/BiScalarBroadcast.html index 34e7e1ea4..811af2328 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/BiScalarBroadcast.html +++ b/docs/jdocs/neureka/backend/main/algorithms/BiScalarBroadcast.html @@ -1,203 +1,345 @@ - + + - -BiScalarBroadcast (neureka 1.0.0 API) - - - - + +BiScalarBroadcast (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.algorithms
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    Class BiScalarBroadcast

    -
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    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<BiScalarBroadcast> -
    neureka.backend.main.algorithms.BiScalarBroadcast
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    +
    neureka.backend.main.algorithms
    +

    Class BiScalarBroadcast

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      Constructor Details

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      • -
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        BiScalarBroadcast

        -
        public BiScalarBroadcast()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            BiScalarBroadcast

            +
            public BiScalarBroadcast()
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    Method Details

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    • -
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      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/Broadcast.html b/docs/jdocs/neureka/backend/main/algorithms/Broadcast.html index 69f3e3bd2..469156840 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/Broadcast.html +++ b/docs/jdocs/neureka/backend/main/algorithms/Broadcast.html @@ -1,203 +1,345 @@ - + + - -Broadcast (neureka 1.0.0 API) - - - - + +Broadcast (neureka 1.0.1 API) - - - - - - + + -
    - -
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    Package neureka.backend.main.algorithms
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    Class Broadcast

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<Broadcast> -
    neureka.backend.main.algorithms.Broadcast
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    +
    neureka.backend.main.algorithms
    +

    Class Broadcast

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      Constructor Details

      -
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      • -
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        Broadcast

        -
        public Broadcast()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Broadcast

            +
            public Broadcast()
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  • -
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    Method Details

    -
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    • -
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      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
          -
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/DotProductAlgorithm.html b/docs/jdocs/neureka/backend/main/algorithms/DotProductAlgorithm.html index 4d4056b9f..3b253b42e 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/DotProductAlgorithm.html +++ b/docs/jdocs/neureka/backend/main/algorithms/DotProductAlgorithm.html @@ -1,203 +1,345 @@ - + + - -DotProductAlgorithm (neureka 1.0.0 API) - - - - + +DotProductAlgorithm (neureka 1.0.1 API) - - - - - - + + -
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    Class DotProductAlgorithm

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    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<DotProductAlgorithm> -
    neureka.backend.main.algorithms.DotProductAlgorithm
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    +
    neureka.backend.main.algorithms
    +

    Class DotProductAlgorithm

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      Constructor Details

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        DotProductAlgorithm

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        public DotProductAlgorithm()
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          Constructor Detail

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          • +

            DotProductAlgorithm

            +
            public DotProductAlgorithm()
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    Method Details

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      getName

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      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
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          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
          -
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/ElementwiseAlgorithm.html b/docs/jdocs/neureka/backend/main/algorithms/ElementwiseAlgorithm.html index ca2bb9a68..57422027b 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/ElementwiseAlgorithm.html +++ b/docs/jdocs/neureka/backend/main/algorithms/ElementwiseAlgorithm.html @@ -1,206 +1,348 @@ - + + - -ElementwiseAlgorithm (neureka 1.0.0 API) - - - - + +ElementwiseAlgorithm (neureka 1.0.1 API) - - - - - - + + -
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    -
    Package neureka.backend.main.algorithms
    -

    Class ElementwiseAlgorithm

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<ElementwiseAlgorithm> -
    neureka.backend.main.algorithms.ElementwiseAlgorithm
    -
    +
    neureka.backend.main.algorithms
    +

    Class ElementwiseAlgorithm

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      Constructor Details

      -
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      • -
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        ElementwiseAlgorithm

        -
        public ElementwiseAlgorithm()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ElementwiseAlgorithm

            +
            public ElementwiseAlgorithm()
          -
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  • -
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    Method Details

    -
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    • -
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      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
          -
    -
    • - + + +
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/MatMulAlgorithm.html b/docs/jdocs/neureka/backend/main/algorithms/MatMulAlgorithm.html index 8ccb22d0f..e9e224a1c 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/MatMulAlgorithm.html +++ b/docs/jdocs/neureka/backend/main/algorithms/MatMulAlgorithm.html @@ -1,203 +1,345 @@ - + + - -MatMulAlgorithm (neureka 1.0.0 API) - - - - + +MatMulAlgorithm (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.algorithms
    -

    Class MatMulAlgorithm

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<MatMulAlgorithm> -
    neureka.backend.main.algorithms.MatMulAlgorithm
    -
    +
    neureka.backend.main.algorithms
    +

    Class MatMulAlgorithm

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      Constructor Details

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        MatMulAlgorithm

        -
        public MatMulAlgorithm()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
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            MatMulAlgorithm

            +
            public MatMulAlgorithm()
          -
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    Method Details

    -
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    • -
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      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
          -
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    +
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    + + +
    Skip navigation links
    + + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/NDConvolution.html b/docs/jdocs/neureka/backend/main/algorithms/NDConvolution.html index d355aa2b7..b96d75bee 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/NDConvolution.html +++ b/docs/jdocs/neureka/backend/main/algorithms/NDConvolution.html @@ -1,203 +1,345 @@ - + + - -NDConvolution (neureka 1.0.0 API) - - - - + +NDConvolution (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.algorithms
    -

    Class NDConvolution

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<NDConvolution> -
    neureka.backend.main.algorithms.NDConvolution
    -
    +
    neureka.backend.main.algorithms
    +

    Class NDConvolution

    -
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      Constructor Details

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      • -
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        NDConvolution

        -
        public NDConvolution()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            NDConvolution

            +
            public NDConvolution()
          -
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  • -
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    Method Details

    -
      -
    • -
      -

      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
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    Skip navigation links
    + + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/ScalarAlgorithm.html b/docs/jdocs/neureka/backend/main/algorithms/ScalarAlgorithm.html index a6a64e2df..b32599ebb 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/ScalarAlgorithm.html +++ b/docs/jdocs/neureka/backend/main/algorithms/ScalarAlgorithm.html @@ -1,203 +1,345 @@ - + + - -ScalarAlgorithm (neureka 1.0.0 API) - - - - + +ScalarAlgorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
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    Package neureka.backend.main.algorithms
    -

    Class ScalarAlgorithm

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<ScalarAlgorithm> -
    neureka.backend.main.algorithms.ScalarAlgorithm
    -
    +
    neureka.backend.main.algorithms
    +

    Class ScalarAlgorithm

    -
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      Constructor Details

      -
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      • -
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        ScalarAlgorithm

        -
        public ScalarAlgorithm()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarAlgorithm

            +
            public ScalarAlgorithm()
          -
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  • -
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    Method Details

    -
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    • -
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      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
          -
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    • - + + +
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    Skip navigation links
    + + + +
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    + +
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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/ScalarBroadcast.html b/docs/jdocs/neureka/backend/main/algorithms/ScalarBroadcast.html index c6283ee0c..db1c4ef7b 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/ScalarBroadcast.html +++ b/docs/jdocs/neureka/backend/main/algorithms/ScalarBroadcast.html @@ -1,203 +1,345 @@ - + + - -ScalarBroadcast (neureka 1.0.0 API) - - - - + +ScalarBroadcast (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.algorithms
    -

    Class ScalarBroadcast

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<ScalarBroadcast> -
    neureka.backend.main.algorithms.ScalarBroadcast
    -
    +
    neureka.backend.main.algorithms
    +

    Class ScalarBroadcast

    -
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    • -
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      Constructor Details

      -
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      • -
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        ScalarBroadcast

        -
        public ScalarBroadcast(ScalarFun fun)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarBroadcast

            +
            public ScalarBroadcast(ScalarFun fun)
          -
      • +
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  • -
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    Method Details

    -
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    • -
      -

      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
          -
    -
    • - + + +
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    Skip navigation links
    + + + +
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    + +
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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/ScalarSumAlgorithm.html b/docs/jdocs/neureka/backend/main/algorithms/ScalarSumAlgorithm.html index 52b47fd3e..ff5cc6f3e 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/ScalarSumAlgorithm.html +++ b/docs/jdocs/neureka/backend/main/algorithms/ScalarSumAlgorithm.html @@ -1,189 +1,319 @@ - + + - -ScalarSumAlgorithm (neureka 1.0.0 API) - - - - + +ScalarSumAlgorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.algorithms
    -

    Class ScalarSumAlgorithm

    +
    neureka.backend.main.algorithms
    +

    Class ScalarSumAlgorithm

    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractFunAlgorithm -
    neureka.backend.main.algorithms.ScalarSumAlgorithm
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      Constructor Details

      -
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      • -
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        ScalarSumAlgorithm

        -
        public ScalarSumAlgorithm()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarSumAlgorithm

            +
            public ScalarSumAlgorithm()
          -
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  • -
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    Method Details

    -
      -
    • -
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      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/SumAlgorithm.html b/docs/jdocs/neureka/backend/main/algorithms/SumAlgorithm.html index 420904f74..e804b7bd2 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/SumAlgorithm.html +++ b/docs/jdocs/neureka/backend/main/algorithms/SumAlgorithm.html @@ -1,203 +1,345 @@ - + + - -SumAlgorithm (neureka 1.0.0 API) - - - - + +SumAlgorithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.algorithms
    -

    Class SumAlgorithm

    -
    -
    java.lang.Object -
    neureka.backend.api.template.algorithms.AbstractDeviceAlgorithm<C> -
    neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm<SumAlgorithm> -
    neureka.backend.main.algorithms.SumAlgorithm
    -
    +
    neureka.backend.main.algorithms
    +

    Class SumAlgorithm

    -
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    + +
    +
    -
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      +
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      Constructor Details

      -
        -
      • -
        -

        SumAlgorithm

        -
        public SumAlgorithm()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            SumAlgorithm

            +
            public SumAlgorithm()
          -
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    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getName

      -
      public String getName()
      -
      This method returns the name of this Algorithm +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          +
          This method returns the name of this Algorithm which may be used as variable names in OpenCL kernels or other backends. Therefore, this name is expected to be void of any spaces or non-numeric and alphabetic characters.
          -
          -
          Specified by:
          -
          getName in interface Algorithm
          -
          Returns:
          -
          The name of this Algorithm.
          +
          +
          Specified by:
          +
          getName in interface Algorithm
          +
          Returns:
          +
          The name of this Algorithm.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/Util.html b/docs/jdocs/neureka/backend/main/algorithms/Util.html index 9130a91ed..56dc65792 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/Util.html +++ b/docs/jdocs/neureka/backend/main/algorithms/Util.html @@ -1,158 +1,268 @@ - + + - -Util (neureka 1.0.0 API) - - - - + +Util (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.algorithms
    -

    Class Util

    +
    neureka.backend.main.algorithms
    +

    Class Util

    -
    java.lang.Object -
    neureka.backend.main.algorithms.Util
    -
    -
    -
    -
    public class Util -extends Object
    -
    -
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      - +
      +
        +
      • java.lang.Object
      • -
        -

        Constructor Summary

        -
        Constructors
        -
        -
        Constructor
        -
        Description
        -
        Util()
        -
         
        +
          +
        • neureka.backend.main.algorithms.Util
          • +
        +
        • +
      +
      +
        +
      • +
        +
        +
        public class Util
+extends java.lang.Object
        +
        • +
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      +
    • -
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      Constructor Details

      -
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      • -
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        Util

        -
        public Util()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Util

            +
            public Util()
          -
      • +
    -
  • -
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    Method Details

    -
      -
    • -
      -

      transpose

      -
      public static <T> Tensor<T> transpose(Tensor<T> t)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          transpose

          +
          public static <T> Tensor<T> transpose(Tensor<T> t)
        -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/internal/AndBackward.html b/docs/jdocs/neureka/backend/main/algorithms/internal/AndBackward.html index 46fccfa40..75b74ee76 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/internal/AndBackward.html +++ b/docs/jdocs/neureka/backend/main/algorithms/internal/AndBackward.html @@ -1,129 +1,225 @@ - + + - -AndBackward (neureka 1.0.0 API) - - - - + +AndBackward (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.algorithms.internal
    -

    Interface AndBackward<F>

    +
    neureka.backend.main.algorithms.internal
    +

    Interface AndBackward<F>

    -
    -
    +
    +
    +
      +
    • +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public interface AndBackward<F>
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      CLImplementation
      -
      and(F backward)
      -
       
      -
      -
      +
      +
      @FunctionalInterface
+public interface AndBackward<F>
      +
      • +
    - +
    + - -
    -
      +
    +
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    - + +
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    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
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    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/internal/WithForward.html b/docs/jdocs/neureka/backend/main/algorithms/internal/WithForward.html index 3d5ff65a5..a610d7526 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/internal/WithForward.html +++ b/docs/jdocs/neureka/backend/main/algorithms/internal/WithForward.html @@ -1,129 +1,225 @@ - + + - -WithForward (neureka 1.0.0 API) - - - - + +WithForward (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.algorithms.internal
    -

    Interface WithForward<F>

    +
    neureka.backend.main.algorithms.internal
    +

    Interface WithForward<F>

    -
    -
    +
    +
    +
      +
    • +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public interface WithForward<F>
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      AndBackward<F>
      -
      with(F forward)
      -
       
      -
      -
      +
      +
      @FunctionalInterface
+public interface WithForward<F>
      +
      • +
    - +
    + - -
    -
      +
    +
    + - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/internal/package-frame.html b/docs/jdocs/neureka/backend/main/algorithms/internal/package-frame.html new file mode 100644 index 000000000..1760866f4 --- /dev/null +++ b/docs/jdocs/neureka/backend/main/algorithms/internal/package-frame.html @@ -0,0 +1,20 @@ + + + + + +neureka.backend.main.algorithms.internal (neureka 1.0.1 API) + + + + +

    neureka.backend.main.algorithms.internal

    +
    +

    Interfaces

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/algorithms/internal/package-summary.html b/docs/jdocs/neureka/backend/main/algorithms/internal/package-summary.html index 4e78f7736..5603a64ed 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/internal/package-summary.html +++ b/docs/jdocs/neureka/backend/main/algorithms/internal/package-summary.html @@ -1,99 +1,143 @@ - + + - -neureka.backend.main.algorithms.internal (neureka 1.0.0 API) - - - - + +neureka.backend.main.algorithms.internal (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.algorithms.internal

    -
    -
    -
    package neureka.backend.main.algorithms.internal
    -
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    -
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/internal/package-tree.html b/docs/jdocs/neureka/backend/main/algorithms/internal/package-tree.html index e1d3df887..ae382aea5 100644 --- a/docs/jdocs/neureka/backend/main/algorithms/internal/package-tree.html +++ b/docs/jdocs/neureka/backend/main/algorithms/internal/package-tree.html @@ -1,68 +1,131 @@ - + + - -neureka.backend.main.algorithms.internal Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.algorithms.internal Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.algorithms.internal

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Interface Hierarchy

      -
    • neureka.backend.main.algorithms.internal.AndBackward<F>
      • -
    • neureka.backend.main.algorithms.internal.WithForward<F>
      • +
    • neureka.backend.main.algorithms.internal.AndBackward<F>
      • +
    • neureka.backend.main.algorithms.internal.WithForward<F>
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/algorithms/package-frame.html b/docs/jdocs/neureka/backend/main/algorithms/package-frame.html new file mode 100644 index 000000000..be89a779c --- /dev/null +++ b/docs/jdocs/neureka/backend/main/algorithms/package-frame.html @@ -0,0 +1,30 @@ + + + + + +neureka.backend.main.algorithms (neureka 1.0.1 API) + + + + +

    neureka.backend.main.algorithms

    +
    +

    Classes

    + +
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    Package neureka.backend.main.algorithms

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    Package neureka.backend.main.algorithms

    +
    Everything in this package should be considered library-private! DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! Code inside this package or any sub-packages might change frequently...
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    Package neureka.backend.main.algorithms Description

    +
    Everything in this package should be considered library-private! + DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! + Code inside this package or any sub-packages might change frequently...
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    Hierarchy For Package neureka.backend.main.algorithms

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    Package neureka.backend.main.implementations
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    Class CLImplementation

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    neureka.backend.main.implementations
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    Class CLImplementation

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    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation
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    Package neureka.backend.main.implementations
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    Interface CPUImplementation.AndImplementation

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    neureka.backend.main.implementations
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    Interface CPUImplementation.AndImplementation

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    Class CPUImplementation

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<CPU> -
    neureka.backend.main.implementations.CPUImplementation
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    neureka.backend.main.implementations
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    Class CPUImplementation

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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/ParsedCLImplementation.html b/docs/jdocs/neureka/backend/main/implementations/ParsedCLImplementation.html index 72621744f..4ade62fdb 100644 --- a/docs/jdocs/neureka/backend/main/implementations/ParsedCLImplementation.html +++ b/docs/jdocs/neureka/backend/main/implementations/ParsedCLImplementation.html @@ -1,194 +1,318 @@ - + + - -ParsedCLImplementation (neureka 1.0.0 API) - - - - + +ParsedCLImplementation (neureka 1.0.1 API) - - - - - - + + -
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    Class ParsedCLImplementation

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation
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    neureka.backend.main.implementations
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    Class ParsedCLImplementation

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    Class SimpleCLImplementation

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.SimpleCLImplementation
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    neureka.backend.main.implementations
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    Class SimpleCLImplementation

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        SimpleCLImplementation

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        protected SimpleCLImplementation(ImplementationFor<OpenCLDevice> execution, - int arity, - String kernelName, - String kernelSource)
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          Constructor Detail

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            SimpleCLImplementation

            +
            protected SimpleCLImplementation(ImplementationFor<OpenCLDevice> execution,
+                                 int arity,
+                                 java.lang.String kernelName,
+                                 java.lang.String kernelSource)
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    Package neureka.backend.main.implementations.broadcast
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    Class CLBroadcast

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLBroadcast
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    neureka.backend.main.implementations.broadcast
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    Class CLBroadcast

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        CLBroadcast

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            CLBroadcast

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            protected CLBroadcast(java.lang.String postfix,
+                      java.lang.String forward,
+                      java.lang.String backward)
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    Package neureka.backend.main.implementations.broadcast
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    Class CLBroadcastAddition

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLBroadcast -
    neureka.backend.main.implementations.broadcast.CLBroadcastAddition
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CLBroadcastAddition

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        CLBroadcastAddition

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          Constructor Detail

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            CLBroadcastAddition

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            public CLBroadcastAddition(java.lang.String id)
            +
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    Package neureka.backend.main.implementations.broadcast
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    Class CLBroadcastDivision

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLBroadcast -
    neureka.backend.main.implementations.broadcast.CLBroadcastDivision
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    neureka.backend.main.implementations.broadcast
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    Class CLBroadcastDivision

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        CLBroadcastDivision

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            CLBroadcastDivision

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            public CLBroadcastDivision(java.lang.String id)
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    Class CLBroadcastModulo

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    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
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    neureka.backend.main.implementations.broadcast.CLBroadcast -
    neureka.backend.main.implementations.broadcast.CLBroadcastModulo
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    neureka.backend.main.implementations.broadcast
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    Class CLBroadcastModulo

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        CLBroadcastModulo

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            CLBroadcastModulo

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            public CLBroadcastModulo(java.lang.String id)
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    Class CLBroadcastMultiplication

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    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
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    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLBroadcast -
    neureka.backend.main.implementations.broadcast.CLBroadcastMultiplication
    -
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    neureka.backend.main.implementations.broadcast
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    Class CLBroadcastMultiplication

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            CLBroadcastMultiplication

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            public CLBroadcastMultiplication(java.lang.String id)
            +
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    Class CLBroadcastPower

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
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    neureka.backend.main.implementations.broadcast.CLBroadcast -
    neureka.backend.main.implementations.broadcast.CLBroadcastPower
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    neureka.backend.main.implementations.broadcast
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    Class CLBroadcastPower

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        CLBroadcastPower

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        public CLBroadcastPower(String id)
        -
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          +
        • + + +

          Constructor Detail

          + + + +
            +
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            CLBroadcastPower

            +
            public CLBroadcastPower(java.lang.String id)
            +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLBroadcastSubtraction.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLBroadcastSubtraction.html index 3abc6c343..0bb5e4e67 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLBroadcastSubtraction.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLBroadcastSubtraction.html @@ -1,145 +1,272 @@ - + + - -CLBroadcastSubtraction (neureka 1.0.0 API) - - - - + +CLBroadcastSubtraction (neureka 1.0.1 API) - - - - - - + + -
    - -
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    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CLBroadcastSubtraction

    -
    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLBroadcast -
    neureka.backend.main.implementations.broadcast.CLBroadcastSubtraction
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CLBroadcastSubtraction

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    + +
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      Constructor Details

      -
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      • -
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        CLBroadcastSubtraction

        -
        public CLBroadcastSubtraction(String id)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLBroadcastSubtraction

            +
            public CLBroadcastSubtraction(java.lang.String id)
            +
            • +
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    • Summary: 
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcast.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcast.html index c0eb7c40b..ebdfa5764 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcast.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcast.html @@ -1,188 +1,315 @@ - + + - -CLScalarBroadcast (neureka 1.0.0 API) - - - - + +CLScalarBroadcast (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CLScalarBroadcast

    -
    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcast
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CLScalarBroadcast

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      Field Summary

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      +
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      public class CLScalarBroadcast
+extends ParsedCLImplementation
      +
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    Constructor Details

    -
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      CLScalarBroadcast

      -
      public CLScalarBroadcast(String postfix, - String activation, - String derivation)
      -
      +
        +
      • + + +

        Constructor Detail

        + + + +
          +
        • +

          CLScalarBroadcast

          +
          public CLScalarBroadcast(java.lang.String postfix,
+                         java.lang.String activation,
+                         java.lang.String derivation)
          +
          • +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastAddition.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastAddition.html index 7849821ed..bc2fdbc99 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastAddition.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastAddition.html @@ -1,201 +1,333 @@ - + + - -CLScalarBroadcastAddition (neureka 1.0.0 API) - - - - + +CLScalarBroadcastAddition (neureka 1.0.1 API) - - - - - - + + -
    - -
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    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CLScalarBroadcastAddition

    -
    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcastAddition
    -
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CLScalarBroadcastAddition

    -
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      Constructor Details

      -
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      • -
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        CLScalarBroadcastAddition

        -
        public CLScalarBroadcastAddition(String id)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLScalarBroadcastAddition

            +
            public CLScalarBroadcastAddition(java.lang.String id)
          -
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    Method Details

    - -
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    + + + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastDivision.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastDivision.html index f84bb0d29..0fa93eee7 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastDivision.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastDivision.html @@ -1,201 +1,333 @@ - + + - -CLScalarBroadcastDivision (neureka 1.0.0 API) - - - - + +CLScalarBroadcastDivision (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CLScalarBroadcastDivision

    -
    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcastDivision
    -
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CLScalarBroadcastDivision

    -
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    + +
    +
    -
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    -
      +
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      Constructor Details

      -
        -
      • -
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        CLScalarBroadcastDivision

        -
        public CLScalarBroadcastDivision(String id)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLScalarBroadcastDivision

            +
            public CLScalarBroadcastDivision(java.lang.String id)
          -
      • +
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  • -
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    Method Details

    - -
    • - + + +
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    Skip navigation links
    + + + + +
    +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastIdentity.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastIdentity.html index f53f4b3d5..11009e502 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastIdentity.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastIdentity.html @@ -1,201 +1,333 @@ - + + - -CLScalarBroadcastIdentity (neureka 1.0.0 API) - - - - + +CLScalarBroadcastIdentity (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CLScalarBroadcastIdentity

    -
    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcastIdentity
    -
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CLScalarBroadcastIdentity

    -
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      +
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      Constructor Details

      -
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      • -
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        CLScalarBroadcastIdentity

        -
        public CLScalarBroadcastIdentity(String id)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLScalarBroadcastIdentity

            +
            public CLScalarBroadcastIdentity(java.lang.String id)
          -
      • +
    -
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    Method Details

    - -
    • - + + +
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    Skip navigation links
    + + + + +
    +
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    + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastModulo.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastModulo.html index b78cd5b29..caa6baf78 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastModulo.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastModulo.html @@ -1,201 +1,333 @@ - + + - -CLScalarBroadcastModulo (neureka 1.0.0 API) - - - - + +CLScalarBroadcastModulo (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CLScalarBroadcastModulo

    -
    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcastModulo
    -
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CLScalarBroadcastModulo

    -
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      Constructor Details

      -
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      • -
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        CLScalarBroadcastModulo

        -
        public CLScalarBroadcastModulo(String id)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLScalarBroadcastModulo

            +
            public CLScalarBroadcastModulo(java.lang.String id)
          -
      • +
    -
  • -
    -

    Method Details

    - -
    • - + + +
    +
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    + + +
    Skip navigation links
    + + + + +
    +
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    + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastMultiplication.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastMultiplication.html index ce1492257..c40c79f42 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastMultiplication.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastMultiplication.html @@ -1,201 +1,333 @@ - + + - -CLScalarBroadcastMultiplication (neureka 1.0.0 API) - - - - + +CLScalarBroadcastMultiplication (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
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    Package neureka.backend.main.implementations.broadcast
    -

    Class CLScalarBroadcastMultiplication

    -
    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcastMultiplication
    -
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CLScalarBroadcastMultiplication

    -
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    +
    + +
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      Constructor Details

      -
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      • -
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        CLScalarBroadcastMultiplication

        -
        public CLScalarBroadcastMultiplication(String id)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLScalarBroadcastMultiplication

            +
            public CLScalarBroadcastMultiplication(java.lang.String id)
          -
      • +
    -
  • -
    -

    Method Details

    - -
    • - + + +
    +
    - + +
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    Skip navigation links
    + + + + +
    +
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    + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastPower.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastPower.html index 6f610be15..2a60eb311 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastPower.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastPower.html @@ -1,201 +1,333 @@ - + + - -CLScalarBroadcastPower (neureka 1.0.0 API) - - - - + +CLScalarBroadcastPower (neureka 1.0.1 API) - - - - - - + + -
    - -
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    Package neureka.backend.main.implementations.broadcast
    -

    Class CLScalarBroadcastPower

    -
    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcastPower
    -
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CLScalarBroadcastPower

    -
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      +
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      Constructor Details

      -
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      • -
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        CLScalarBroadcastPower

        -
        public CLScalarBroadcastPower(String id)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLScalarBroadcastPower

            +
            public CLScalarBroadcastPower(java.lang.String id)
          -
      • +
    -
  • -
    -

    Method Details

    - -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastSubtraction.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastSubtraction.html index 453309bf1..ea099db5f 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastSubtraction.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CLScalarBroadcastSubtraction.html @@ -1,201 +1,333 @@ - + + - -CLScalarBroadcastSubtraction (neureka 1.0.0 API) - - - - + +CLScalarBroadcastSubtraction (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CLScalarBroadcastSubtraction

    -
    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CLScalarBroadcastSubtraction
    -
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CLScalarBroadcastSubtraction

    -
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    +
    + +
    +
    -
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    +
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      +
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    • -
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      Constructor Details

      -
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      • -
        -

        CLScalarBroadcastSubtraction

        -
        public CLScalarBroadcastSubtraction(String id)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLScalarBroadcastSubtraction

            +
            public CLScalarBroadcastSubtraction(java.lang.String id)
          -
      • +
    -
  • -
    -

    Method Details

    - -
    • - + + +
    +
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    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcast.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcast.html index da090e558..c70693334 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcast.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcast.html @@ -1,213 +1,335 @@ - + + - -CPUBroadcast (neureka 1.0.0 API) - - - - + +CPUBroadcast (neureka 1.0.1 API) - - - - - - + + -
    - -
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    Package neureka.backend.main.implementations.broadcast
    -

    Class CPUBroadcast

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUBroadcast
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUBroadcast

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.broadcast.CPUBroadcast
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Modifier
      -
      Constructor
      -
      Description
      -
      protected
      -
      CPUBroadcast()
      -
       
      +
      +
      public abstract class CPUBroadcast
+extends java.lang.Object
+implements ImplementationFor<CPU>
      +
      • +
    - +
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      +
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      Constructor Details

      -
        -
      • -
        -

        CPUBroadcast

        -
        protected CPUBroadcast()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBroadcast

            +
            protected CPUBroadcast()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      _getFun

      -
      protected abstract CPUBiFun _getFun()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          _getFun

          +
          protected abstract CPUBiFun _getFun()
          • -
        • -
          -

          _getDeriveAt0

          -
          protected abstract CPUBiFun _getDeriveAt0()
          -
          +
        + + + +
          +
        • +

          _getDeriveAt0

          +
          protected abstract CPUBiFun _getDeriveAt0()
          • -
        • -
          -

          _getDeriveAt1

          -
          protected abstract CPUBiFun _getDeriveAt1()
          -
          +
        + + + + + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
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    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastAddition.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastAddition.html index 10ad3e57c..47114b436 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastAddition.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastAddition.html @@ -1,197 +1,322 @@ - + + - -CPUBroadcastAddition (neureka 1.0.0 API) - - - - + +CPUBroadcastAddition (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CPUBroadcastAddition

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUBroadcast -
    neureka.backend.main.implementations.broadcast.CPUBroadcastAddition
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUBroadcastAddition

    -
    -
    -
    +
    + +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CPUBroadcastAddition()
      -
       
      +
      +
      public class CPUBroadcastAddition
+extends CPUBroadcast
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUBroadcastAddition

        -
        public CPUBroadcastAddition()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBroadcastAddition

            +
            public CPUBroadcastAddition()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBroadcast
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
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    • Summary: 
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      • +
    • Constr | 
      • +
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      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastDivision.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastDivision.html index 983217c59..bb3aaf8cd 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastDivision.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastDivision.html @@ -1,197 +1,322 @@ - + + - -CPUBroadcastDivision (neureka 1.0.0 API) - - - - + +CPUBroadcastDivision (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CPUBroadcastDivision

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUBroadcast -
    neureka.backend.main.implementations.broadcast.CPUBroadcastDivision
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUBroadcastDivision

    -
    -
    -
    +
    + +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CPUBroadcastDivision()
      -
       
      +
      +
      public class CPUBroadcastDivision
+extends CPUBroadcast
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUBroadcastDivision

        -
        public CPUBroadcastDivision()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBroadcastDivision

            +
            public CPUBroadcastDivision()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBroadcast
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
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      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastModulo.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastModulo.html index 4739a6dce..526f909ae 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastModulo.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastModulo.html @@ -1,197 +1,322 @@ - + + - -CPUBroadcastModulo (neureka 1.0.0 API) - - - - + +CPUBroadcastModulo (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CPUBroadcastModulo

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUBroadcast -
    neureka.backend.main.implementations.broadcast.CPUBroadcastModulo
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUBroadcastModulo

    -
    -
    -
    +
    + +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CPUBroadcastModulo()
      -
       
      +
      +
      public class CPUBroadcastModulo
+extends CPUBroadcast
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUBroadcastModulo

        -
        public CPUBroadcastModulo()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBroadcastModulo

            +
            public CPUBroadcastModulo()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBroadcast
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastMultiplication.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastMultiplication.html index 172af7618..2d726b911 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastMultiplication.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastMultiplication.html @@ -1,197 +1,322 @@ - + + - -CPUBroadcastMultiplication (neureka 1.0.0 API) - - - - + +CPUBroadcastMultiplication (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CPUBroadcastMultiplication

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUBroadcast -
    neureka.backend.main.implementations.broadcast.CPUBroadcastMultiplication
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUBroadcastMultiplication

    -
    -
    -
    +
    + +
    +
    -
    -
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUBroadcastMultiplication

        -
        public CPUBroadcastMultiplication()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBroadcastMultiplication

            +
            public CPUBroadcastMultiplication()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBroadcast
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastPower.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastPower.html index 0f7873e28..806c43959 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastPower.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastPower.html @@ -1,197 +1,322 @@ - + + - -CPUBroadcastPower (neureka 1.0.0 API) - - - - + +CPUBroadcastPower (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CPUBroadcastPower

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUBroadcast -
    neureka.backend.main.implementations.broadcast.CPUBroadcastPower
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUBroadcastPower

    -
    -
    -
    +
    + +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CPUBroadcastPower()
      -
       
      +
      +
      public class CPUBroadcastPower
+extends CPUBroadcast
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUBroadcastPower

        -
        public CPUBroadcastPower()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBroadcastPower

            +
            public CPUBroadcastPower()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBroadcast
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastSubtraction.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastSubtraction.html index 100d219cf..691135e95 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastSubtraction.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastSubtraction.html @@ -1,197 +1,322 @@ - + + - -CPUBroadcastSubtraction (neureka 1.0.0 API) - - - - + +CPUBroadcastSubtraction (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CPUBroadcastSubtraction

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUBroadcast -
    neureka.backend.main.implementations.broadcast.CPUBroadcastSubtraction
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUBroadcastSubtraction

    -
    -
    -
    +
    + +
    +
    -
    -
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUBroadcastSubtraction

        -
        public CPUBroadcastSubtraction()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBroadcastSubtraction

            +
            public CPUBroadcastSubtraction()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBroadcast
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastSummation.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastSummation.html index 9ef01a4fd..dbbdfbe4a 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastSummation.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUBroadcastSummation.html @@ -1,197 +1,322 @@ - + + - -CPUBroadcastSummation (neureka 1.0.0 API) - - - - + +CPUBroadcastSummation (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CPUBroadcastSummation

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUBroadcast -
    neureka.backend.main.implementations.broadcast.CPUBroadcastSummation
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUBroadcastSummation

    -
    -
    -
    +
    + +
    +
    -
    -
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUBroadcastSummation

        -
        public CPUBroadcastSummation()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBroadcastSummation

            +
            public CPUBroadcastSummation()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBroadcast
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalaBroadcastPower.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalaBroadcastPower.html index 9d5241b70..fe1b0c035 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalaBroadcastPower.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalaBroadcastPower.html @@ -1,197 +1,322 @@ - + + - -CPUScalaBroadcastPower (neureka 1.0.0 API) - - - - + +CPUScalaBroadcastPower (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CPUScalaBroadcastPower

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CPUScalaBroadcastPower
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUScalaBroadcastPower

    -
    -
    -
    +
    + +
    +
    -
    -
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUScalaBroadcastPower

        -
        public CPUScalaBroadcastPower()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUScalaBroadcastPower

            +
            public CPUScalaBroadcastPower()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUScalarBroadcast
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcast.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcast.html index 21207a12a..b3c397526 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcast.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcast.html @@ -1,220 +1,346 @@ - + + - -CPUScalarBroadcast (neureka 1.0.0 API) - - - - + +CPUScalarBroadcast (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.broadcast
    -

    Class CPUScalarBroadcast

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUScalarBroadcast

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.broadcast.CPUScalarBroadcast
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CPUScalarBroadcast()
      -
       
      +
      +
      public abstract class CPUScalarBroadcast
+extends java.lang.Object
+implements ImplementationFor<CPU>
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUScalarBroadcast

        -
        public CPUScalarBroadcast()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUScalarBroadcast

            +
            public CPUScalarBroadcast()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      _getFun

      -
      protected abstract CPUBiFun _getFun()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          _getFun

          +
          protected abstract CPUBiFun _getFun()
          • -
        • -
          -

          _getDeriveAt0

          -
          protected abstract CPUBiFun _getDeriveAt0()
          -
          +
        + + + +
          +
        • +

          _getDeriveAt0

          +
          protected abstract CPUBiFun _getDeriveAt0()
          • -
        • -
          -

          _getDeriveAt1

          -
          protected abstract CPUBiFun _getDeriveAt1()
          -
          +
        + + + + + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    • -
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    _workloadFor

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    Package neureka.backend.main.implementations.broadcast
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    Class CPUScalarBroadcastAddition

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    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcastAddition
    -
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    neureka.backend.main.implementations.broadcast
    +

    Class CPUScalarBroadcastAddition

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        CPUScalarBroadcastAddition

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          Constructor Detail

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            CPUScalarBroadcastAddition

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            public CPUScalarBroadcastAddition()
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    Method Details

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    _getDeriveAt0

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    protected CPUBiFun _getDeriveAt0()
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    _getDeriveAt0 in class CPUScalarBroadcast
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    _getDeriveAt1

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    protected CPUBiFun _getDeriveAt1()
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    _getDeriveAt1 in class CPUScalarBroadcast
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    Package neureka.backend.main.implementations.broadcast
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    Class CPUScalarBroadcastDivision

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    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcastDivision
    +
    neureka.backend.main.implementations.broadcast
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    Class CPUScalarBroadcastDivision

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        CPUScalarBroadcastDivision

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          Constructor Detail

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            CPUScalarBroadcastDivision

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            public CPUScalarBroadcastDivision()
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    Method Details

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    _getDeriveAt1

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    protected CPUBiFun _getDeriveAt1()
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    _getDeriveAt1 in class CPUScalarBroadcast
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    Package neureka.backend.main.implementations.broadcast
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    Class CPUScalarBroadcastIdentity

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    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcastIdentity
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUScalarBroadcastIdentity

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        CPUScalarBroadcastIdentity

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          Constructor Detail

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            CPUScalarBroadcastIdentity

            +
            public CPUScalarBroadcastIdentity()
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    Method Details

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    _getDeriveAt1

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    protected CPUBiFun _getDeriveAt1()
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    _getDeriveAt1 in class CPUScalarBroadcast
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    Package neureka.backend.main.implementations.broadcast
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    Class CPUScalarBroadcastModulo

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    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcastModulo
    +
    neureka.backend.main.implementations.broadcast
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    Class CPUScalarBroadcastModulo

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        CPUScalarBroadcastModulo

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            CPUScalarBroadcastModulo

            +
            public CPUScalarBroadcastModulo()
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    Method Details

    - + + + +
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    _getDeriveAt1

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    protected CPUBiFun _getDeriveAt1()
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    _getDeriveAt1 in class CPUScalarBroadcast
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcastMultiplication.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcastMultiplication.html index 6058c126c..54bded20c 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcastMultiplication.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcastMultiplication.html @@ -1,225 +1,354 @@ - + + - -CPUScalarBroadcastMultiplication (neureka 1.0.0 API) - - - - + +CPUScalarBroadcastMultiplication (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.implementations.broadcast
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    Class CPUScalarBroadcastMultiplication

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    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcastMultiplication
    -
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUScalarBroadcastMultiplication

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        CPUScalarBroadcastMultiplication

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        public CPUScalarBroadcastMultiplication()
        -
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          Constructor Detail

          + + + +
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            CPUScalarBroadcastMultiplication

            +
            public CPUScalarBroadcastMultiplication()
          -
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    Method Details

    - + + + +
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    _getDeriveAt0

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    protected CPUBiFun _getDeriveAt0()
    -
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    _getDeriveAt0 in class CPUScalarBroadcast
    + + + + +
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    _getDeriveAt1

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    protected CPUBiFun _getDeriveAt1()
    -
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    _getDeriveAt1 in class CPUScalarBroadcast
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcastSubtraction.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcastSubtraction.html index 8841ea120..11e38fee7 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcastSubtraction.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/CPUScalarBroadcastSubtraction.html @@ -1,197 +1,322 @@ - + + - -CPUScalarBroadcastSubtraction (neureka 1.0.0 API) - - - - + +CPUScalarBroadcastSubtraction (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.implementations.broadcast
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    Class CPUScalarBroadcastSubtraction

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    java.lang.Object -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcast -
    neureka.backend.main.implementations.broadcast.CPUScalarBroadcastSubtraction
    +
    neureka.backend.main.implementations.broadcast
    +

    Class CPUScalarBroadcastSubtraction

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        CPUScalarBroadcastSubtraction

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        public CPUScalarBroadcastSubtraction()
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          Constructor Detail

          + + + +
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            CPUScalarBroadcastSubtraction

            +
            public CPUScalarBroadcastSubtraction()
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    _getDeriveAt1

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    protected CPUBiFun _getDeriveAt1()
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    _getDeriveAt1 in class CPUScalarBroadcast
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/package-frame.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/package-frame.html new file mode 100644 index 000000000..f9413f7fa --- /dev/null +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/package-frame.html @@ -0,0 +1,49 @@ + + + + + +neureka.backend.main.implementations.broadcast (neureka 1.0.1 API) + + + + +

    neureka.backend.main.implementations.broadcast

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/implementations/broadcast/package-summary.html b/docs/jdocs/neureka/backend/main/implementations/broadcast/package-summary.html index be9721541..ad2b88527 100644 --- a/docs/jdocs/neureka/backend/main/implementations/broadcast/package-summary.html +++ b/docs/jdocs/neureka/backend/main/implementations/broadcast/package-summary.html @@ -1,157 +1,259 @@ - + + - -neureka.backend.main.implementations.broadcast (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations.broadcast (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.implementations.broadcast

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    package neureka.backend.main.implementations.broadcast
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    - -
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    Hierarchy For Package neureka.backend.main.implementations.broadcast

    -Package Hierarchies: +Package Hierarchies:
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    Class Hierarchy

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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/convolution/AbstractCPUConvolution.html b/docs/jdocs/neureka/backend/main/implementations/convolution/AbstractCPUConvolution.html index e138db95a..33a734e46 100644 --- a/docs/jdocs/neureka/backend/main/implementations/convolution/AbstractCPUConvolution.html +++ b/docs/jdocs/neureka/backend/main/implementations/convolution/AbstractCPUConvolution.html @@ -1,193 +1,307 @@ - + + - -AbstractCPUConvolution (neureka 1.0.0 API) - - - - + +AbstractCPUConvolution (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.implementations.convolution
    -

    Class AbstractCPUConvolution

    +
    neureka.backend.main.implementations.convolution
    +

    Class AbstractCPUConvolution

    -
    java.lang.Object -
    neureka.backend.main.implementations.convolution.AbstractCPUConvolution
    -
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      +
    • java.lang.Object
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        +
      • neureka.backend.main.implementations.convolution.AbstractCPUConvolution
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      Constructor Summary

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      Constructor
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      Description
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      AbstractCPUConvolution()
      -
       
      +
      +
      public abstract class AbstractCPUConvolution
+extends java.lang.Object
+implements ImplementationFor<CPU>
      +
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        AbstractCPUConvolution

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          Constructor Detail

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            AbstractCPUConvolution

            +
            public AbstractCPUConvolution()
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    Method Details

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      _getFun

      -
      protected abstract CPUBiFun _getFun()
      -
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        +
      • + + +

        Method Detail

        + + + + + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    • - - + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/convolution/CLConvolution.html b/docs/jdocs/neureka/backend/main/implementations/convolution/CLConvolution.html index 84cd5bd2a..2d376f96b 100644 --- a/docs/jdocs/neureka/backend/main/implementations/convolution/CLConvolution.html +++ b/docs/jdocs/neureka/backend/main/implementations/convolution/CLConvolution.html @@ -1,143 +1,267 @@ - + + - -CLConvolution (neureka 1.0.0 API) - - - - + +CLConvolution (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.implementations.convolution
    -

    Class CLConvolution

    -
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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.convolution.CLConvolution
    +
    neureka.backend.main.implementations.convolution
    +

    Class CLConvolution

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      Constructor Details

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        CLConvolution

        -
        public CLConvolution(String id)
        -
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          +
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          Constructor Detail

          + + + +
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          • +

            CLConvolution

            +
            public CLConvolution(java.lang.String id)
          -
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    Skip navigation links
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    Package neureka.backend.main.implementations.convolution
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    Class CPUConvolution

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    neureka.backend.main.implementations.convolution.AbstractCPUConvolution -
    neureka.backend.main.implementations.convolution.CPUConvolution
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    neureka.backend.main.implementations.convolution
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    Class CPUConvolution

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            CPUConvolution

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            public CPUConvolution()
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    neureka.backend.main.implementations.convolution

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    Classes

    + +
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    Package neureka.backend.main.implementations.convolution

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    Hierarchy For Package neureka.backend.main.implementations.convolution

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    Class Hierarchy

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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CLBiElementwise.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CLBiElementwise.html index 354f010bf..e951e54a3 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CLBiElementwise.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CLBiElementwise.html @@ -1,151 +1,275 @@ - + + - -CLBiElementwise (neureka 1.0.0 API) - - - - + +CLBiElementwise (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.implementations.elementwise
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    Class CLBiElementwise

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.elementwise.CLBiElementwise
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    neureka.backend.main.implementations.elementwise
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    Class CLBiElementwise

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          Constructor Detail

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            CLBiElementwise

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            public CLBiElementwise(java.lang.String postfix,
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+                       java.lang.String differentiationSource)
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    Package neureka.backend.main.implementations.elementwise
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    Class CLBiElementwiseAddition

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.elementwise.CLBiElementwise -
    neureka.backend.main.implementations.elementwise.CLBiElementwiseAddition
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    neureka.backend.main.implementations.elementwise
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    Class CLBiElementwiseAddition

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            CLBiElementwiseAddition

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            public CLBiElementwiseAddition(java.lang.String postfix)
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    Package neureka.backend.main.implementations.elementwise
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    Class CLBiElementwiseDivision

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.ParsedCLImplementation -
    neureka.backend.main.implementations.elementwise.CLBiElementwise -
    neureka.backend.main.implementations.elementwise.CLBiElementwiseDivision
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    neureka.backend.main.implementations.elementwise
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    Class CLBiElementwiseDivision

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        CLBiElementwiseDivision

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            CLBiElementwiseDivision

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    Package neureka.backend.main.implementations.elementwise
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    Class CLBiElementwiseModulo

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    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
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    neureka.backend.main.implementations.elementwise.CLBiElementwise -
    neureka.backend.main.implementations.elementwise.CLBiElementwiseModulo
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    neureka.backend.main.implementations.elementwise
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    Class CLBiElementwiseModulo

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        CLBiElementwiseModulo

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            CLBiElementwiseModulo

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            public CLBiElementwiseModulo(java.lang.String postfix)
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    Package neureka.backend.main.implementations.elementwise
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    Class CLBiElementwiseMultiplication

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    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
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    neureka.backend.main.implementations.elementwise.CLBiElementwise -
    neureka.backend.main.implementations.elementwise.CLBiElementwiseMultiplication
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    neureka.backend.main.implementations.elementwise
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    Class CLBiElementwiseMultiplication

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        CLBiElementwiseMultiplication

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            CLBiElementwiseMultiplication

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            public CLBiElementwiseMultiplication(java.lang.String postfix)
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    Package neureka.backend.main.implementations.elementwise
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    Class CLBiElementwisePower

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    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
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    neureka.backend.main.implementations.elementwise.CLBiElementwise -
    neureka.backend.main.implementations.elementwise.CLBiElementwisePower
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    neureka.backend.main.implementations.elementwise
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    Class CLBiElementwisePower

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        CLBiElementwisePower

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          Constructor Detail

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            CLBiElementwisePower

            +
            public CLBiElementwisePower(java.lang.String postfix)
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    Class CLBiElementwiseSubtraction

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    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
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    neureka.backend.main.implementations.elementwise.CLBiElementwise -
    neureka.backend.main.implementations.elementwise.CLBiElementwiseSubtraction
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    neureka.backend.main.implementations.elementwise
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    Class CLBiElementwiseSubtraction

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        CLBiElementwiseSubtraction

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            CLBiElementwiseSubtraction

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            public CLBiElementwiseSubtraction(java.lang.String postfix)
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    Class CLElementwiseFunction

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    neureka.backend.main.implementations.elementwise.CLElementwiseFunction
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    neureka.backend.main.implementations.elementwise
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    Class CLElementwiseFunction

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        CLElementwiseFunction

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        public CLElementwiseFunction(ScalarFun fun)
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            CLElementwiseFunction

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            public CLElementwiseFunction(ScalarFun fun)
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CLRandomization.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CLRandomization.html index cb675d065..da36dc44a 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CLRandomization.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CLRandomization.html @@ -1,180 +1,290 @@ - + + - -CLRandomization (neureka 1.0.0 API) - - - - + +CLRandomization (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.elementwise
    -

    Class CLRandomization

    +
    neureka.backend.main.implementations.elementwise
    +

    Class CLRandomization

    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CLRandomization
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.elementwise.CLRandomization
        • +
      +
      • +
    +
    +
    -
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      +
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      Constructor Details

      -
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      • -
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        CLRandomization

        -
        public CLRandomization()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLRandomization

            +
            public CLRandomization()
          -
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  • -
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    Method Details

    -
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    • -
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      run

      -
      public Tensor<?> run(ExecutionCall<OpenCLDevice> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<OpenCLDevice> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<OpenCLDevice>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<OpenCLDevice>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    -
    • - + + +
    +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWise.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWise.html index b0560588c..0fcdc1459 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWise.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWise.html @@ -1,211 +1,333 @@ - + + - -CPUBiElementWise (neureka 1.0.0 API) - - - - + +CPUBiElementWise (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.elementwise
    -

    Class CPUBiElementWise

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CPUBiElementWise
    +
    neureka.backend.main.implementations.elementwise
    +

    Class CPUBiElementWise

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.elementwise.CPUBiElementWise
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      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CPUBiElementWise()
      -
       
      +
      +
      public abstract class CPUBiElementWise
+extends java.lang.Object
+implements ImplementationFor<CPU>
      +
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      Constructor Details

      -
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      • -
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        CPUBiElementWise

        -
        public CPUBiElementWise()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBiElementWise

            +
            public CPUBiElementWise()
          -
      • +
    -
  • -
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    Method Details

    -
      -
    • -
      -

      _getFun

      -
      protected abstract CPUBiFun _getFun()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          _getFun

          +
          protected abstract CPUBiFun _getFun()
          • -
        • -
          -

          _getDeriveAt0

          -
          protected abstract CPUBiFun _getDeriveAt0()
          -
          +
        + + + +
          +
        • +

          _getDeriveAt0

          +
          protected abstract CPUBiFun _getDeriveAt0()
          • -
        • -
          -

          _getDeriveAt1

          -
          protected abstract CPUBiFun _getDeriveAt1()
          -
          +
        + + + + + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    • - - + + +
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseAddition.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseAddition.html index 92b5bc3c4..009eb9ad0 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseAddition.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseAddition.html @@ -1,197 +1,322 @@ - + + - -CPUBiElementWiseAddition (neureka 1.0.0 API) - - - - + +CPUBiElementWiseAddition (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.elementwise
    -

    Class CPUBiElementWiseAddition

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CPUBiElementWise -
    neureka.backend.main.implementations.elementwise.CPUBiElementWiseAddition
    +
    neureka.backend.main.implementations.elementwise
    +

    Class CPUBiElementWiseAddition

    -
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    + +
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      Constructor Details

      -
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      • -
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        CPUBiElementWiseAddition

        -
        public CPUBiElementWiseAddition()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBiElementWiseAddition

            +
            public CPUBiElementWiseAddition()
          -
      • +
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  • -
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    Method Details

    - + + + +
    • -
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    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBiElementWise
    + + + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseDivision.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseDivision.html index 11cc2a58b..d4b624598 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseDivision.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseDivision.html @@ -1,197 +1,322 @@ - + + - -CPUBiElementWiseDivision (neureka 1.0.0 API) - - - - + +CPUBiElementWiseDivision (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.elementwise
    -

    Class CPUBiElementWiseDivision

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CPUBiElementWise -
    neureka.backend.main.implementations.elementwise.CPUBiElementWiseDivision
    +
    neureka.backend.main.implementations.elementwise
    +

    Class CPUBiElementWiseDivision

    -
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    + +
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      Constructor Details

      -
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      • -
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        CPUBiElementWiseDivision

        -
        public CPUBiElementWiseDivision()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBiElementWiseDivision

            +
            public CPUBiElementWiseDivision()
          -
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    Method Details

    - + + + +
    • -
  • -
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    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBiElementWise
    + + + + +
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    Skip navigation links
    + + + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseModulo.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseModulo.html index b0f652756..d3acfef34 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseModulo.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseModulo.html @@ -1,197 +1,322 @@ - + + - -CPUBiElementWiseModulo (neureka 1.0.0 API) - - - - + +CPUBiElementWiseModulo (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.elementwise
    -

    Class CPUBiElementWiseModulo

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CPUBiElementWise -
    neureka.backend.main.implementations.elementwise.CPUBiElementWiseModulo
    +
    neureka.backend.main.implementations.elementwise
    +

    Class CPUBiElementWiseModulo

    -
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    + +
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      Constructor Details

      -
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        CPUBiElementWiseModulo

        -
        public CPUBiElementWiseModulo()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBiElementWiseModulo

            +
            public CPUBiElementWiseModulo()
          -
      • +
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  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBiElementWise
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseMultiplication.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseMultiplication.html index a5691e1d7..0cd639997 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseMultiplication.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseMultiplication.html @@ -1,197 +1,322 @@ - + + - -CPUBiElementWiseMultiplication (neureka 1.0.0 API) - - - - + +CPUBiElementWiseMultiplication (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.elementwise
    -

    Class CPUBiElementWiseMultiplication

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CPUBiElementWise -
    neureka.backend.main.implementations.elementwise.CPUBiElementWiseMultiplication
    +
    neureka.backend.main.implementations.elementwise
    +

    Class CPUBiElementWiseMultiplication

    -
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    + +
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      Constructor Details

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        CPUBiElementWiseMultiplication

        -
        public CPUBiElementWiseMultiplication()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBiElementWiseMultiplication

            +
            public CPUBiElementWiseMultiplication()
          -
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  • -
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    Method Details

    - + + + +
    • -
  • -
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    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBiElementWise
    + + + + +
    • - - + + +
    +
    - + +
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWisePower.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWisePower.html index ddd3762aa..c2d7d4753 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWisePower.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWisePower.html @@ -1,197 +1,322 @@ - + + - -CPUBiElementWisePower (neureka 1.0.0 API) - - - - + +CPUBiElementWisePower (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.elementwise
    -

    Class CPUBiElementWisePower

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CPUBiElementWise -
    neureka.backend.main.implementations.elementwise.CPUBiElementWisePower
    +
    neureka.backend.main.implementations.elementwise
    +

    Class CPUBiElementWisePower

    -
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    + +
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      Constructor Details

      -
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        CPUBiElementWisePower

        -
        public CPUBiElementWisePower()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBiElementWisePower

            +
            public CPUBiElementWisePower()
          -
      • +
    -
  • -
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    Method Details

    - + + + +
    • -
  • -
    -

    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBiElementWise
    + + + + +
    • - - + + +
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseSubtraction.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseSubtraction.html index 402d9dc2b..7da26315d 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseSubtraction.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUBiElementWiseSubtraction.html @@ -1,197 +1,322 @@ - + + - -CPUBiElementWiseSubtraction (neureka 1.0.0 API) - - - - + +CPUBiElementWiseSubtraction (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
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    Package neureka.backend.main.implementations.elementwise
    -

    Class CPUBiElementWiseSubtraction

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CPUBiElementWise -
    neureka.backend.main.implementations.elementwise.CPUBiElementWiseSubtraction
    +
    neureka.backend.main.implementations.elementwise
    +

    Class CPUBiElementWiseSubtraction

    -
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    + +
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    -
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      Constructor Details

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      • -
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        CPUBiElementWiseSubtraction

        -
        public CPUBiElementWiseSubtraction()
        -
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          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUBiElementWiseSubtraction

            +
            public CPUBiElementWiseSubtraction()
          -
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    Method Details

    - + + + +
    • -
  • -
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    _getDeriveAt1

    -
    protected CPUBiFun _getDeriveAt1()
    -
    -
    Specified by:
    -
    _getDeriveAt1 in class CPUBiElementWise
    + + + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUElementwiseAssignFun.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUElementwiseAssignFun.html index fd0ba1fdb..7fd94f9b0 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUElementwiseAssignFun.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUElementwiseAssignFun.html @@ -1,183 +1,296 @@ - + + - -CPUElementwiseAssignFun (neureka 1.0.0 API) - - - - + +CPUElementwiseAssignFun (neureka 1.0.1 API) - - - - - - + + -
    - -
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    Package neureka.backend.main.implementations.elementwise
    -

    Class CPUElementwiseAssignFun

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CPUElementwiseFunction -
    neureka.backend.main.implementations.elementwise.CPUElementwiseAssignFun
    +
    neureka.backend.main.implementations.elementwise
    +

    Class CPUElementwiseAssignFun

    -
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        CPUElementwiseAssignFun

        -
        public CPUElementwiseAssignFun()
        -
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          Constructor Detail

          + + + +
            +
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            CPUElementwiseAssignFun

            +
            public CPUElementwiseAssignFun()
          -
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    Method Details

    -
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    • -
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      run

      -
      public Tensor<?> run(ExecutionCall<CPU> call)
      -
      Description copied from interface: ImplementationFor
      +
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      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
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          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Overrides:
          -
          run in class CPUElementwiseFunction
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Overrides:
          +
          run in class CPUElementwiseFunction
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUElementwiseFunction.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUElementwiseFunction.html index f7889d127..e0c430e53 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUElementwiseFunction.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPUElementwiseFunction.html @@ -1,184 +1,294 @@ - + + - -CPUElementwiseFunction (neureka 1.0.0 API) - - - - + +CPUElementwiseFunction (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.elementwise
    -

    Class CPUElementwiseFunction

    +
    neureka.backend.main.implementations.elementwise
    +

    Class CPUElementwiseFunction

    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CPUElementwiseFunction
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.elementwise.CPUElementwiseFunction
        • +
      +
      • +
    +
    +
    -
    -
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUElementwiseFunction

        -
        public CPUElementwiseFunction(ScalarFun fun)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUElementwiseFunction

            +
            public CPUElementwiseFunction(ScalarFun fun)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      run

      -
      public Tensor<?> run(ExecutionCall<CPU> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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      +
    • Summary: 
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    • Nested | 
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    • Constr | 
      • +
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPURandomization.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPURandomization.html index bfe19acb6..1e650bd13 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/CPURandomization.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/CPURandomization.html @@ -1,233 +1,369 @@ - + + - -CPURandomization (neureka 1.0.0 API) - - - - + +CPURandomization (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.elementwise
    -

    Class CPURandomization

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.elementwise.CPURandomization
    +
    neureka.backend.main.implementations.elementwise
    +

    Class CPURandomization

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.elementwise.CPURandomization
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CPURandomization()
      -
       
      +
      +
      public class CPURandomization
+extends java.lang.Object
+implements ImplementationFor<CPU>
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPURandomization

        -
        public CPURandomization()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPURandomization

            +
            public CPURandomization()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      run

      -
      public Tensor<?> run(ExecutionCall<CPU> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
      • -
    • -
      -

      fillRandomly

      -
      public static <T> T fillRandomly(T data, - Arg.Seed seed)
      -
      +
    + + + + + +
      +
    • +

      fillRandomly

      +
      public static <T> T fillRandomly(T data,
+                                 Arg.Seed seed)
      • -
    • -
      -

      fillRandomly

      -
      public static <T> T fillRandomly(T data, - String seed)
      -
      +
    + + + + + +
      +
    • +

      fillRandomly

      +
      public static <T> T fillRandomly(T data,
+                                 java.lang.String seed)
      • -
    • -
      -

      fillRandomly

      -
      public static <T> T fillRandomly(T data, - long seed)
      -
      +
    + + + + + +
      +
    • +

      fillRandomly

      +
      public static <T> T fillRandomly(T data,
+                                 long seed)
      • -
    • -
      -

      initialScramble

      -
      public static long initialScramble(long seed)
      -
      +
    + + + +
      +
    • +

      initialScramble

      +
      public static long initialScramble(long seed)
      • -
    • -
      -

      gaussianFrom

      -
      public static void gaussianFrom(long seed, - double[] out)
      -
      +
    + + + +
      +
    • +

      gaussianFrom

      +
      public static void gaussianFrom(long seed,
+                                double[] out)
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
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      +
    • Summary: 
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    • Nested | 
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/package-frame.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/package-frame.html new file mode 100644 index 000000000..c7449f10c --- /dev/null +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/package-frame.html @@ -0,0 +1,37 @@ + + + + + +neureka.backend.main.implementations.elementwise (neureka 1.0.1 API) + + + + +

    neureka.backend.main.implementations.elementwise

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/package-summary.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/package-summary.html index c1167e476..a2a116634 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/package-summary.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/package-summary.html @@ -1,133 +1,211 @@ - + + - -neureka.backend.main.implementations.elementwise (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations.elementwise (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.implementations.elementwise

    -
    -
    -
    package neureka.backend.main.implementations.elementwise
    -
    -
    -
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/elementwise/package-tree.html b/docs/jdocs/neureka/backend/main/implementations/elementwise/package-tree.html index 50ff2054e..fc0b10d99 100644 --- a/docs/jdocs/neureka/backend/main/implementations/elementwise/package-tree.html +++ b/docs/jdocs/neureka/backend/main/implementations/elementwise/package-tree.html @@ -1,110 +1,173 @@ - + + - -neureka.backend.main.implementations.elementwise Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations.elementwise Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.implementations.elementwise

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarAbsolute.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarAbsolute.html index fccb6c49c..e26e73db3 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarAbsolute.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarAbsolute.html @@ -1,231 +1,367 @@ - + + - -ScalarAbsolute (neureka 1.0.0 API) - - - - + +ScalarAbsolute (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarAbsolute

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarAbsolute
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarAbsolute

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarAbsolute
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class ScalarAbsolute
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarAbsolute

        -
        public ScalarAbsolute()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarAbsolute

            +
            public ScalarAbsolute()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarCbrt.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarCbrt.html index bd1cd407e..55468c7db 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarCbrt.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarCbrt.html @@ -1,231 +1,367 @@ - + + - -ScalarCbrt (neureka 1.0.0 API) - - - - + +ScalarCbrt (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarCbrt

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarCbrt
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarCbrt

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarCbrt
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public class ScalarCbrt
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarCbrt

        -
        public ScalarCbrt()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarCbrt

            +
            public ScalarCbrt()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
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    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarCosinus.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarCosinus.html index dd0d215f1..7cf02deba 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarCosinus.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarCosinus.html @@ -1,231 +1,367 @@ - + + - -ScalarCosinus (neureka 1.0.0 API) - - - - + +ScalarCosinus (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarCosinus

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarCosinus
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarCosinus

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarCosinus
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class ScalarCosinus
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarCosinus

        -
        public ScalarCosinus()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarCosinus

            +
            public ScalarCosinus()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarExp.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarExp.html index b042ebc90..5e25c2a09 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarExp.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarExp.html @@ -1,231 +1,367 @@ - + + - -ScalarExp (neureka 1.0.0 API) - - - - + +ScalarExp (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarExp

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarExp
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarExp

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarExp
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public class ScalarExp
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarExp

        -
        public ScalarExp()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarExp

            +
            public ScalarExp()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaSU.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaSU.html index 154072885..dd6b7cfb5 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaSU.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaSU.html @@ -1,237 +1,373 @@ - + + - -ScalarGaSU (neureka 1.0.0 API) - - - - + +ScalarGaSU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarGaSU

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarGaSU
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarGaSU

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarGaSU
        • +
      +
      • +
    +
    +
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarGaSU

        -
        public ScalarGaSU()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarGaSU

            +
            public ScalarGaSU()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaTU.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaTU.html index 1c17df784..4fcdb64b1 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaTU.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaTU.html @@ -1,237 +1,373 @@ - + + - -ScalarGaTU (neureka 1.0.0 API) - - - - + +ScalarGaTU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarGaTU

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarGaTU
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarGaTU

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarGaTU
        • +
      +
      • +
    +
    +
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarGaTU

        -
        public ScalarGaTU()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarGaTU

            +
            public ScalarGaTU()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaussian.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaussian.html index 69859aadf..34e08ccb8 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaussian.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaussian.html @@ -1,231 +1,367 @@ - + + - -ScalarGaussian (neureka 1.0.0 API) - - - - + +ScalarGaussian (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarGaussian

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarGaussian
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarGaussian

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarGaussian
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class ScalarGaussian
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarGaussian

        -
        public ScalarGaussian()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarGaussian

            +
            public ScalarGaussian()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaussianFast.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaussianFast.html index f514228e5..9cfa30dca 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaussianFast.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGaussianFast.html @@ -1,231 +1,367 @@ - + + - -ScalarGaussianFast (neureka 1.0.0 API) - - - - + +ScalarGaussianFast (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarGaussianFast

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarGaussianFast
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarGaussianFast

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarGaussianFast
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public class ScalarGaussianFast
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarGaussianFast

        -
        public ScalarGaussianFast()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarGaussianFast

            +
            public ScalarGaussianFast()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGeLU.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGeLU.html index 3d3c748e4..613e2230f 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGeLU.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarGeLU.html @@ -1,245 +1,385 @@ - + + - -ScalarGeLU (neureka 1.0.0 API) - - - - + +ScalarGeLU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarGeLU

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarGeLU
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarGeLU

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarGeLU
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      ScalarFun
      +
      ScalarFun
      -
      public class ScalarGeLU -extends Object -implements ScalarFun
      +
      +
      public class ScalarGeLU
+extends java.lang.Object
+implements ScalarFun
      The GELU activation function is based on the standard Gaussian cumulative distribution function and is defined as x Φ( x ) and implemented as x * sigmoid(x * 1.702). The GELU non-linearity weighs inputs by their percentile, rather than gates inputs by their sign as in ReLUs. Consequently, the GELU can be thought of as a smoother ReLU.
      -
    -
    -
      + +
    +
    +
    +
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.backend.main.implementations.fun.api.ScalarFun

    -calculate
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Constructor Details

        -
          -
        • -
          -

          ScalarGeLU

          -
          public ScalarGeLU()
          -
          +
            +
          • + + +

            Constructor Detail

            + + + +
              +
            • +

              ScalarGeLU

              +
              public ScalarGeLU()
            -
        • +
      -
    • -
      -

      Method Details

      - + + + +
        +
      • +

        activationCode

        +
        public java.lang.String activationCode()
        +
        +
        Specified by:
        +
        activationCode in interface ScalarFun
        -
      • -
    • -
      -

      derivationCode

      -
      public String derivationCode()
      -
      -
      Specified by:
      -
      derivationCode in interface ScalarFun
      +
    + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • -
  • -
    -

    gelu

    -
    public static double gelu(double x)
    -
    + + + + +
      +
    • +

      gelu

      +
      public static double gelu(double x)
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarIdentity.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarIdentity.html index 1a99a5f3a..0c8a3d03f 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarIdentity.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarIdentity.html @@ -1,231 +1,367 @@ - + + - -ScalarIdentity (neureka 1.0.0 API) - - - - + +ScalarIdentity (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarIdentity

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarIdentity
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarIdentity

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarIdentity
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class ScalarIdentity
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarIdentity

        -
        public ScalarIdentity()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarIdentity

            +
            public ScalarIdentity()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarLog10.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarLog10.html index f13024f48..5d71e98e2 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarLog10.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarLog10.html @@ -1,231 +1,367 @@ - + + - -ScalarLog10 (neureka 1.0.0 API) - - - - + +ScalarLog10 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarLog10

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarLog10
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarLog10

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarLog10
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public class ScalarLog10
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarLog10

        -
        public ScalarLog10()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarLog10

            +
            public ScalarLog10()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarLogarithm.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarLogarithm.html index 4af770de3..1dfee1de4 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarLogarithm.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarLogarithm.html @@ -1,231 +1,367 @@ - + + - -ScalarLogarithm (neureka 1.0.0 API) - - - - + +ScalarLogarithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarLogarithm

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarLogarithm
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarLogarithm

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarLogarithm
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class ScalarLogarithm
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarLogarithm

        -
        public ScalarLogarithm()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarLogarithm

            +
            public ScalarLogarithm()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarQuadratic.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarQuadratic.html index 44c14155a..6708e25f7 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarQuadratic.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarQuadratic.html @@ -1,231 +1,367 @@ - + + - -ScalarQuadratic (neureka 1.0.0 API) - - - - + +ScalarQuadratic (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarQuadratic

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarQuadratic
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarQuadratic

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarQuadratic
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class ScalarQuadratic
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarQuadratic

        -
        public ScalarQuadratic()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarQuadratic

            +
            public ScalarQuadratic()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarReLU.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarReLU.html index 691009e61..cd946c9ff 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarReLU.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarReLU.html @@ -1,231 +1,367 @@ - + + - -ScalarReLU (neureka 1.0.0 API) - - - - + +ScalarReLU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarReLU

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarReLU
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarReLU

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarReLU
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class ScalarReLU
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarReLU

        -
        public ScalarReLU()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarReLU

            +
            public ScalarReLU()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSeLU.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSeLU.html index 9ddef55bd..8e1103886 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSeLU.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSeLU.html @@ -1,249 +1,388 @@ - + + - -ScalarSeLU (neureka 1.0.0 API) - - - - + +ScalarSeLU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarSeLU

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarSeLU
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarSeLU

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarSeLU
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      ScalarFun
      +
      ScalarFun
      -
      public class ScalarSeLU -extends Object -implements ScalarFun
      +
      +
      public class ScalarSeLU
+extends java.lang.Object
+implements ScalarFun
      The Scaled Exponential Linear Unit, or SELU, is an activation function that induces self-normalizing properties. The SELU activation function is implemented as: - - if ( x > 0 ) return SCALE * x; + if ( x > 0 ) return SCALE * x; else if ( x <= 0 ) return SCALE * ALPHA * (Math.exp(x) - 1); else return Float.NaN;
      ...where ALPHA == 1.6733 and SCALE == 1.0507.
      -
    -
    -
      + +
    +
    +
    +
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.backend.main.implementations.fun.api.ScalarFun

    -calculate
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Constructor Details

        -
          -
        • -
          -

          ScalarSeLU

          -
          public ScalarSeLU()
          -
          +
            +
          • + + +

            Constructor Detail

            + + + +
              +
            • +

              ScalarSeLU

              +
              public ScalarSeLU()
            -
        • +
      -
    • -
      -

      Method Details

      - + + + +
        +
      • +

        activationCode

        +
        public java.lang.String activationCode()
        +
        +
        Specified by:
        +
        activationCode in interface ScalarFun
        -
      • -
    • -
      -

      derivationCode

      -
      public String derivationCode()
      -
      -
      Specified by:
      -
      derivationCode in interface ScalarFun
      +
    + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • -
  • -
    -

    selu

    -
    public static double selu(double x)
    -
    + + + + +
      +
    • +

      selu

      +
      public static double selu(double x)
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSiLU.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSiLU.html index 496f4e94e..a49e7c8b5 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSiLU.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSiLU.html @@ -1,244 +1,384 @@ - + + - -ScalarSiLU (neureka 1.0.0 API) - - - - + +ScalarSiLU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarSiLU

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarSiLU
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarSiLU

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarSiLU
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      ScalarFun
      +
      ScalarFun
      -
      public class ScalarSiLU -extends Object -implements ScalarFun
      +
      +
      public class ScalarSiLU
+extends java.lang.Object
+implements ScalarFun
      The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x). It is a smooth, non-monotonic function that consistently matches or outperforms ReLU on deep networks, it is unbounded above and bounded below.
      -
    -
    -
      + +
    +
    +
    +
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.backend.main.implementations.fun.api.ScalarFun

    -calculate
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Constructor Details

        -
          -
        • -
          -

          ScalarSiLU

          -
          public ScalarSiLU()
          -
          +
            +
          • + + +

            Constructor Detail

            + + + +
              +
            • +

              ScalarSiLU

              +
              public ScalarSiLU()
            -
        • +
      -
    • -
      -

      Method Details

      - + + + +
        +
      • +

        activationCode

        +
        public java.lang.String activationCode()
        +
        +
        Specified by:
        +
        activationCode in interface ScalarFun
        -
      • -
    • -
      -

      derivationCode

      -
      public String derivationCode()
      -
      -
      Specified by:
      -
      derivationCode in interface ScalarFun
      +
    + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • -
  • -
    -

    silu

    -
    public static double silu(double x)
    -
    + + + + +
      +
    • +

      silu

      +
      public static double silu(double x)
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSigmoid.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSigmoid.html index 0e860c203..781fc1670 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSigmoid.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSigmoid.html @@ -1,240 +1,380 @@ - + + - -ScalarSigmoid (neureka 1.0.0 API) - - - - + +ScalarSigmoid (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarSigmoid

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarSigmoid
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarSigmoid

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarSigmoid
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class ScalarSigmoid
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    +
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.backend.main.implementations.fun.api.ScalarFun

    -calculate
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Constructor Details

        -
          -
        • -
          -

          ScalarSigmoid

          -
          public ScalarSigmoid()
          -
          +
            +
          • + + +

            Constructor Detail

            + + + +
              +
            • +

              ScalarSigmoid

              +
              public ScalarSigmoid()
            -
        • +
      -
    • -
      -

      Method Details

      - + + + +
        +
      • +

        activationCode

        +
        public java.lang.String activationCode()
        +
        +
        Specified by:
        +
        activationCode in interface ScalarFun
        -
      • -
    • -
      -

      derivationCode

      -
      public String derivationCode()
      -
      -
      Specified by:
      -
      derivationCode in interface ScalarFun
      +
    + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • -
  • -
    -

    sig

    -
    public static double sig(double x)
    -
    + + + + +
      +
    • +

      sig

      +
      public static double sig(double x)
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSinus.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSinus.html index 74a89a0bf..b15d9a8b8 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSinus.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSinus.html @@ -1,231 +1,367 @@ - + + - -ScalarSinus (neureka 1.0.0 API) - - - - + +ScalarSinus (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarSinus

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarSinus
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarSinus

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarSinus
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class ScalarSinus
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarSinus

        -
        public ScalarSinus()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarSinus

            +
            public ScalarSinus()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSoftplus.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSoftplus.html index 0154b12c6..832709431 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSoftplus.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSoftplus.html @@ -1,233 +1,369 @@ - + + - -ScalarSoftplus (neureka 1.0.0 API) - - - - + +ScalarSoftplus (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarSoftplus

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarSoftplus
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarSoftplus

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarSoftplus
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      ScalarFun
      +
      ScalarFun
      -
      public final class ScalarSoftplus -extends Object -implements ScalarFun
      +
      +
      public final class ScalarSoftplus
+extends java.lang.Object
+implements ScalarFun
      SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.
      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarSoftplus

        -
        public ScalarSoftplus()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarSoftplus

            +
            public ScalarSoftplus()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSoftsign.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSoftsign.html index 9ebf83e1e..a5c19319a 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSoftsign.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSoftsign.html @@ -1,255 +1,399 @@ - + + - -ScalarSoftsign (neureka 1.0.0 API) - - - - + +ScalarSoftsign (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarSoftsign

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarSoftsign
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarSoftsign

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarSoftsign
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      ScalarFun
      +
      ScalarFun
      -
      public class ScalarSoftsign -extends Object -implements ScalarFun
      +
      +
      public class ScalarSoftsign
+extends java.lang.Object
+implements ScalarFun
      The softsign function, defined as x / ( 1 + Math.abs( x ) ), is a computationally cheap 0 centered activation function - which rescales the inputs between -1 and 1, very much like the ScalarTanh function. + which rescales the inputs between -1 and 1, very much like the ScalarTanh function. The softsign function converges polynomially and is computationally cheaper than the tanh function which converges exponentially. - This makes this function a computationally cheap non-exponential quasi ScalarTanh!
      -
    -
    -
      + This makes this function a computationally cheap non-exponential quasi ScalarTanh!
    + + +
    +
    +
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.backend.main.implementations.fun.api.ScalarFun

    -calculate
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Constructor Details

        -
          -
        • -
          -

          ScalarSoftsign

          -
          public ScalarSoftsign()
          -
          +
            +
          • + + +

            Constructor Detail

            + + + +
              +
            • +

              ScalarSoftsign

              +
              public ScalarSoftsign()
            -
        • +
      -
    • -
      -

      Method Details

      - + + + +
        +
      • +

        activationCode

        +
        public java.lang.String activationCode()
        +
        +
        Specified by:
        +
        activationCode in interface ScalarFun
        -
      • -
    • -
      -

      derivationCode

      -
      public String derivationCode()
      -
      -
      Specified by:
      -
      derivationCode in interface ScalarFun
      +
    + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • -
  • -
    -

    softsign

    -
    public static double softsign(double x)
    -
    + + + + +
      +
    • +

      softsign

      +
      public static double softsign(double x)
      • -
    • -
      -

      softsign

      -
      public static float softsign(float x)
      -
      +
    + + + +
      +
    • +

      softsign

      +
      public static float softsign(float x)
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSqrt.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSqrt.html index 5ba0b0224..69e9f5f7f 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSqrt.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarSqrt.html @@ -1,231 +1,367 @@ - + + - -ScalarSqrt (neureka 1.0.0 API) - - - - + +ScalarSqrt (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarSqrt

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarSqrt
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarSqrt

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarSqrt
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public class ScalarSqrt
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarSqrt

        -
        public ScalarSqrt()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarSqrt

            +
            public ScalarSqrt()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarTanh.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarTanh.html index 3f960b48d..5eb411dd0 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarTanh.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarTanh.html @@ -1,249 +1,393 @@ - + + - -ScalarTanh (neureka 1.0.0 API) - - - - + +ScalarTanh (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarTanh

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarTanh
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarTanh

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarTanh
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class ScalarTanh
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    +
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.backend.main.implementations.fun.api.ScalarFun

    -calculate
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Constructor Details

        -
          -
        • -
          -

          ScalarTanh

          -
          public ScalarTanh()
          -
          +
            +
          • + + +

            Constructor Detail

            + + + +
              +
            • +

              ScalarTanh

              +
              public ScalarTanh()
            -
        • +
      -
    • -
      -

      Method Details

      - + + + +
        +
      • +

        activationCode

        +
        public java.lang.String activationCode()
        +
        +
        Specified by:
        +
        activationCode in interface ScalarFun
        -
      • -
    • -
      -

      derivationCode

      -
      public String derivationCode()
      -
      -
      Specified by:
      -
      derivationCode in interface ScalarFun
      +
    + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • -
  • -
    -

    tanh

    -
    public static double tanh(double x)
    -
    + + + + +
      +
    • +

      tanh

      +
      public static double tanh(double x)
      • -
    • -
      -

      tanh

      -
      public static float tanh(float x)
      -
      +
    + + + +
      +
    • +

      tanh

      +
      public static float tanh(float x)
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarTanhFast.html b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarTanhFast.html index c973110c9..92bfafeeb 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/ScalarTanhFast.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/ScalarTanhFast.html @@ -1,231 +1,367 @@ - + + - -ScalarTanhFast (neureka 1.0.0 API) - - - - + +ScalarTanhFast (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun
    -

    Class ScalarTanhFast

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.fun.ScalarTanhFast
    +
    neureka.backend.main.implementations.fun
    +

    Class ScalarTanhFast

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.fun.ScalarTanhFast
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public class ScalarTanhFast
+extends java.lang.Object
+implements ScalarFun
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ScalarTanhFast

        -
        public ScalarTanhFast()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ScalarTanhFast

            +
            public ScalarTanhFast()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      activationCode

      +
      public java.lang.String activationCode()
      +
      +
      Specified by:
      +
      activationCode in interface ScalarFun
      -
    • -
  • -
    -

    derivationCode

    -
    public String derivationCode()
    -
    -
    Specified by:
    -
    derivationCode in interface ScalarFun
    + + + + +
      +
    • +

      derivationCode

      +
      public java.lang.String derivationCode()
      +
      +
      Specified by:
      +
      derivationCode in interface ScalarFun
      -
    • -
  • -
    -

    getActivation

    -
    public CPUFun getActivation()
    -
    -
    Specified by:
    -
    getActivation in interface ScalarFun
    + + + + +
    • -
  • -
    -

    getDerivative

    -
    public CPUFun getDerivative()
    -
    -
    Specified by:
    -
    getDerivative in interface ScalarFun
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/api/CPUBiFun.html b/docs/jdocs/neureka/backend/main/implementations/fun/api/CPUBiFun.html index 60d4254ee..09f09cf30 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/api/CPUBiFun.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/api/CPUBiFun.html @@ -1,214 +1,340 @@ - + + - -CPUBiFun (neureka 1.0.0 API) - - - - + +CPUBiFun (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.fun.api
    -

    Interface CPUBiFun

    +
    neureka.backend.main.implementations.fun.api
    +

    Interface CPUBiFun

    -
    +
    +
    +
      +
    • -
      public interface CPUBiFun
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      default boolean
      -
      invoke(boolean a, - boolean b)
      -
       
      -
      default byte
      -
      invoke(byte a, - byte b)
      -
       
      -
      default char
      -
      invoke(char a, - char b)
      -
       
      -
      double
      -
      invoke(double a, - double b)
      -
       
      -
      default float
      -
      invoke(float a, - float b)
      -
       
      -
      default int
      -
      invoke(int a, - int b)
      -
       
      -
      default long
      -
      invoke(long a, - long b)
      -
       
      -
      default short
      -
      invoke(short a, - short b)
      -
       
      -
      default Object
      -
      invoke(Object a, - Object b)
      -
       
      -
      -
      +
      +
      public interface CPUBiFun
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethod and Description
        default booleaninvoke(boolean a, + boolean b) 
        default byteinvoke(byte a, + byte b) 
        default charinvoke(char a, + char b) 
        doubleinvoke(double a, + double b) 
        default floatinvoke(float a, + float b) 
        default intinvoke(int a, + int b) 
        default longinvoke(long a, + long b) 
        default java.lang.Objectinvoke(java.lang.Object a, + java.lang.Object b) 
        default shortinvoke(short a, + short b) 
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        invoke

        -
        double invoke(double a, - double b)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            invoke

            +
            double invoke(double a,
+              double b)
            +
            • +
          + + + +
            +
          • +

            invoke

            +
            default float invoke(float a,
+                     float b)
            • -
          • -
            -

            invoke

            -
            default float invoke(float a, - float b)
            -
            +
          + + + +
            +
          • +

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    - -
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    Package neureka.backend.main.implementations.fun.api
    -

    Interface CPUFun

    +
    neureka.backend.main.implementations.fun.api
    +

    Interface CPUFun

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      public interface CPUFun
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    - -
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    Package neureka.backend.main.implementations.fun.api
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    Interface ScalarFun

    +
    neureka.backend.main.implementations.fun.api
    +

    Interface ScalarFun

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      + + + +
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        id

        +
        java.lang.String id()
        +
        • +
      + + + +
        +
      • +

        activationCode

        +
        java.lang.String activationCode()
        +
        • +
      + + + +
        +
      • +

        derivationCode

        +
        java.lang.String derivationCode()
        +
        • +
      + + + +
        +
      • +

        calculate

        +
        default double calculate(double input,
+                         boolean derive)
        +
        • +
      + + + +
        +
      • +

        getActivation

        +
        CPUFun getActivation()
        +
        • +
      + + + +
        +
      • +

        getDerivative

        +
        CPUFun getDerivative()
        +
        • +
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    neureka.backend.main.implementations.fun.api

    +
    +

    Interfaces

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/api/package-summary.html b/docs/jdocs/neureka/backend/main/implementations/fun/api/package-summary.html index 62d7158f3..350480b16 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/api/package-summary.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/api/package-summary.html @@ -1,97 +1,147 @@ - + + - -neureka.backend.main.implementations.fun.api (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations.fun.api (neureka 1.0.1 API) - - - - - - + + -
    - -
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    Package neureka.backend.main.implementations.fun.api

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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/api/package-tree.html b/docs/jdocs/neureka/backend/main/implementations/fun/api/package-tree.html index 114054eea..28bf8671d 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/api/package-tree.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/api/package-tree.html @@ -1,69 +1,132 @@ - + + - -neureka.backend.main.implementations.fun.api Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations.fun.api Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.implementations.fun.api

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Interface Hierarchy

      -
    • neureka.backend.main.implementations.fun.api.CPUBiFun
      • -
    • neureka.backend.main.implementations.fun.api.CPUFun
      • -
    • neureka.backend.main.implementations.fun.api.ScalarFun
      • +
    • neureka.backend.main.implementations.fun.api.CPUBiFun
      • +
    • neureka.backend.main.implementations.fun.api.CPUFun
      • +
    • neureka.backend.main.implementations.fun.api.ScalarFun
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    neureka.backend.main.implementations.fun

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/implementations/fun/package-summary.html b/docs/jdocs/neureka/backend/main/implementations/fun/package-summary.html index e406ff35a..06962817c 100644 --- a/docs/jdocs/neureka/backend/main/implementations/fun/package-summary.html +++ b/docs/jdocs/neureka/backend/main/implementations/fun/package-summary.html @@ -1,168 +1,252 @@ - + + - -neureka.backend.main.implementations.fun (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations.fun (neureka 1.0.1 API) - - - - - - + + -
    - -
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    -

    Package neureka.backend.main.implementations.fun

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    - -
    -

    Hierarchy For Package neureka.backend.main.implementations.fun

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
        -
      • neureka.backend.main.implementations.fun.ScalarAbsolute (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarCbrt (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarCosinus (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarExp (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarGaSU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarGaTU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarGaussian (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarGaussianFast (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarGeLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarIdentity (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarLog10 (implements neureka.backend.main.implementations.fun.api.ScalarFun)
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      • neureka.backend.main.implementations.fun.ScalarLogarithm (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarQuadratic (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarReLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarSeLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarSigmoid (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarSiLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarSinus (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarSoftplus (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarSoftsign (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarSqrt (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarTanh (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • -
      • neureka.backend.main.implementations.fun.ScalarTanhFast (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarAbsolute (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarCbrt (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarCosinus (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarExp (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarGaSU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarGaTU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarGaussian (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarGaussianFast (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarGeLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarIdentity (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarLog10 (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarLogarithm (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarQuadratic (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarReLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarSeLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarSigmoid (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarSiLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarSinus (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarSoftplus (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarSoftsign (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarSqrt (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarTanh (implements neureka.backend.main.implementations.fun.api.ScalarFun)
        • +
      • neureka.backend.main.implementations.fun.ScalarTanhFast (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    -
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/linear/CLDot.html b/docs/jdocs/neureka/backend/main/implementations/linear/CLDot.html index 0b01b5a42..176a516e0 100644 --- a/docs/jdocs/neureka/backend/main/implementations/linear/CLDot.html +++ b/docs/jdocs/neureka/backend/main/implementations/linear/CLDot.html @@ -1,181 +1,291 @@ - + + - -CLDot (neureka 1.0.0 API) - - - - + +CLDot (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.linear
    -

    Class CLDot

    +
    neureka.backend.main.implementations.linear
    +

    Class CLDot

    -
    java.lang.Object -
    neureka.backend.main.implementations.linear.CLDot
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.linear.CLDot
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CLDot()
      -
       
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CLDot

        -
        public CLDot()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLDot

            +
            public CLDot()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      run

      -
      public Tensor<?> run(ExecutionCall<OpenCLDevice> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<OpenCLDevice> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<OpenCLDevice>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<OpenCLDevice>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/linear/CPUDot.html b/docs/jdocs/neureka/backend/main/implementations/linear/CPUDot.html index a84fdeaa7..03ced7a97 100644 --- a/docs/jdocs/neureka/backend/main/implementations/linear/CPUDot.html +++ b/docs/jdocs/neureka/backend/main/implementations/linear/CPUDot.html @@ -1,180 +1,290 @@ - + + - -CPUDot (neureka 1.0.0 API) - - - - + +CPUDot (neureka 1.0.1 API) - - - - - - + + -
    - -
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    Package neureka.backend.main.implementations.linear
    -

    Class CPUDot

    +
    neureka.backend.main.implementations.linear
    +

    Class CPUDot

    -
    java.lang.Object -
    neureka.backend.main.implementations.linear.CPUDot
    -
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    -
    +
    +
      +
    • java.lang.Object
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      • neureka.backend.main.implementations.linear.CPUDot
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      Constructor Summary

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      Constructors
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      Constructor
      -
      Description
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      CPUDot()
      -
       
      +
      +
      public class CPUDot
+extends java.lang.Object
+implements ImplementationFor<CPU>
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        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        CPUDot() 
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      Method Summary

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      Modifier and Type
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      Method
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      Tensor<?>
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      run(ExecutionCall<CPU> call)
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      Constructor Details

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        CPUDot

        -
        public CPUDot()
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          Constructor Detail

          + + + +
            +
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            CPUDot

            +
            public CPUDot()
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    Method Details

    -
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      run

      -
      public Tensor<?> run(ExecutionCall<CPU> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    -
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      • +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/linear/package-frame.html b/docs/jdocs/neureka/backend/main/implementations/linear/package-frame.html new file mode 100644 index 000000000..5ac09b912 --- /dev/null +++ b/docs/jdocs/neureka/backend/main/implementations/linear/package-frame.html @@ -0,0 +1,20 @@ + + + + + +neureka.backend.main.implementations.linear (neureka 1.0.1 API) + + + + +

    neureka.backend.main.implementations.linear

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/implementations/linear/package-summary.html b/docs/jdocs/neureka/backend/main/implementations/linear/package-summary.html index c2cf00ad2..ee818146e 100644 --- a/docs/jdocs/neureka/backend/main/implementations/linear/package-summary.html +++ b/docs/jdocs/neureka/backend/main/implementations/linear/package-summary.html @@ -1,101 +1,145 @@ - + + - -neureka.backend.main.implementations.linear (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations.linear (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.implementations.linear

    +

    Package neureka.backend.main.implementations.linear

    -
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    package neureka.backend.main.implementations.linear
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      Class
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      Description
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      CLDot
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        Class Summary 
        ClassDescription
        CLDot
        Performs a dot product on two vectors using OpenCL.
        - -
        CPUDot
        -
         
        - - +
        CPUDot 
      -
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    + +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/linear/package-tree.html b/docs/jdocs/neureka/backend/main/implementations/linear/package-tree.html index 2934fe07f..bf47bdfd8 100644 --- a/docs/jdocs/neureka/backend/main/implementations/linear/package-tree.html +++ b/docs/jdocs/neureka/backend/main/implementations/linear/package-tree.html @@ -1,72 +1,135 @@ - + + - -neureka.backend.main.implementations.linear Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations.linear Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.implementations.linear

    -Package Hierarchies: +Package Hierarchies:
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    Class Hierarchy

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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/matmul/CLMatMul.html b/docs/jdocs/neureka/backend/main/implementations/matmul/CLMatMul.html index 446c910e0..ac7a4113b 100644 --- a/docs/jdocs/neureka/backend/main/implementations/matmul/CLMatMul.html +++ b/docs/jdocs/neureka/backend/main/implementations/matmul/CLMatMul.html @@ -1,143 +1,267 @@ - + + - -CLMatMul (neureka 1.0.0 API) - - - - + +CLMatMul (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.matmul
    -

    Class CLMatMul

    -
    -
    java.lang.Object -
    neureka.backend.api.template.implementations.AbstractImplementationFor<OpenCLDevice> -
    neureka.backend.main.implementations.CLImplementation -
    neureka.backend.main.implementations.SimpleCLImplementation -
    neureka.backend.main.implementations.matmul.CLMatMul
    +
    neureka.backend.main.implementations.matmul
    +

    Class CLMatMul

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      Constructor Details

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        CLMatMul

        -
        public CLMatMul()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLMatMul

            +
            public CLMatMul()
          -
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/matmul/CPUMatMul.html b/docs/jdocs/neureka/backend/main/implementations/matmul/CPUMatMul.html index fec11c2d6..cf9663ad6 100644 --- a/docs/jdocs/neureka/backend/main/implementations/matmul/CPUMatMul.html +++ b/docs/jdocs/neureka/backend/main/implementations/matmul/CPUMatMul.html @@ -1,265 +1,391 @@ - + + - -CPUMatMul (neureka 1.0.0 API) - - - - + +CPUMatMul (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.matmul
    -

    Class CPUMatMul

    -
    -
    java.lang.Object -
    neureka.backend.main.implementations.matmul.CPUMatMul
    +
    neureka.backend.main.implementations.matmul
    +

    Class CPUMatMul

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.implementations.matmul.CPUMatMul
        • +
      +
      • +
    +
    +
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      Constructor Summary

      -
      Constructors
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      -
      Constructor
      -
      Description
      -
      CPUMatMul()
      -
       
      +
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    +
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        +
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        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        CPUMatMul() 
        • +
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      Method Summary

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      Modifier and Type
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      Method
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      Description
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      static void
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      execute(boolean rowMajor, - double[] A, - double[] B, - double[] C, - int aRows, - int aCols, - int bCols)
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      static void
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      execute(boolean rowMajor, - float[] A, - float[] B, - float[] C, - int aRows, - int aCols, - int bCols)
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      -
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      execute(boolean rowMajor, - int[] A, - int[] B, - int[] C, - int aRows, - int aCols, - int bCols)
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      -
      static void
      -
      execute(boolean rowMajor, - long[] A, - long[] B, - long[] C, - int aRows, - int aCols, - int bCols)
      -
       
      -
      Tensor<?>
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      run(ExecutionCall<CPU> call)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static voidexecute(boolean rowMajor, + double[] A, + double[] B, + double[] C, + int aRows, + int aCols, + int bCols) 
        static voidexecute(boolean rowMajor, + float[] A, + float[] B, + float[] C, + int aRows, + int aCols, + int bCols) 
        static voidexecute(boolean rowMajor, + int[] A, + int[] B, + int[] C, + int aRows, + int aCols, + int bCols) 
        static voidexecute(boolean rowMajor, + long[] A, + long[] B, + long[] C, + int aRows, + int aCols, + int bCols) 
        Tensor<?>run(ExecutionCall<CPU> call)
        This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented.
        - - - - -
        -

        Methods inherited from class java.lang.Object

        -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
        - +
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
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      Constructor Details

      -
        -
      • -
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        CPUMatMul

        -
        public CPUMatMul()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUMatMul

            +
            public CPUMatMul()
          -
      • +
    -
  • -
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    Method Details

    -
      -
    • -
      -

      run

      -
      public Tensor<?> run(ExecutionCall<CPU> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
      • -
    • -
      -

      execute

      -
      public static void execute(boolean rowMajor, - double[] A, - double[] B, - double[] C, - int aRows, - int aCols, - int bCols)
      -
      +
    + + + +
      +
    • +

      execute

      +
      public static void execute(boolean rowMajor,
+                           double[] A,
+                           double[] B,
+                           double[] C,
+                           int aRows,
+                           int aCols,
+                           int bCols)
      • -
    • -
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      execute

      -
      public static void execute(boolean rowMajor, - float[] A, - float[] B, - float[] C, - int aRows, - int aCols, - int bCols)
      -
      +
    + + + +
      +
    • +

      execute

      +
      public static void execute(boolean rowMajor,
+                           float[] A,
+                           float[] B,
+                           float[] C,
+                           int aRows,
+                           int aCols,
+                           int bCols)
      • -
    • -
      -

      execute

      -
      public static void execute(boolean rowMajor, - long[] A, - long[] B, - long[] C, - int aRows, - int aCols, - int bCols)
      -
      +
    + + + +
      +
    • +

      execute

      +
      public static void execute(boolean rowMajor,
+                           long[] A,
+                           long[] B,
+                           long[] C,
+                           int aRows,
+                           int aCols,
+                           int bCols)
      • -
    • -
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      execute

      -
      public static void execute(boolean rowMajor, - int[] A, - int[] B, - int[] C, - int aRows, - int aCols, - int bCols)
      -
      +
    + + + +
      +
    • +

      execute

      +
      public static void execute(boolean rowMajor,
+                           int[] A,
+                           int[] B,
+                           int[] C,
+                           int aRows,
+                           int aCols,
+                           int bCols)
      +
      • +
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/matmul/package-frame.html b/docs/jdocs/neureka/backend/main/implementations/matmul/package-frame.html new file mode 100644 index 000000000..ec50456c7 --- /dev/null +++ b/docs/jdocs/neureka/backend/main/implementations/matmul/package-frame.html @@ -0,0 +1,20 @@ + + + + + +neureka.backend.main.implementations.matmul (neureka 1.0.1 API) + + + + +

    neureka.backend.main.implementations.matmul

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/implementations/matmul/package-summary.html b/docs/jdocs/neureka/backend/main/implementations/matmul/package-summary.html index ea5b26ff2..e3344fd91 100644 --- a/docs/jdocs/neureka/backend/main/implementations/matmul/package-summary.html +++ b/docs/jdocs/neureka/backend/main/implementations/matmul/package-summary.html @@ -1,101 +1,145 @@ - + + - -neureka.backend.main.implementations.matmul (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations.matmul (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.implementations.matmul

    +

    Package neureka.backend.main.implementations.matmul

    -
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    package neureka.backend.main.implementations.matmul
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      CLMatMul
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      CPUMatMul
      -
      +
      +
        +
      • + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        CLMatMul 
        CPUMatMul
        This is a library internal class, do not depend on this.
        - - - +
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    Hierarchy For Package neureka.backend.main.implementations.matmul

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    neureka.backend.main.implementations

    +
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    Interfaces

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    + + diff --git a/docs/jdocs/neureka/backend/main/implementations/package-summary.html b/docs/jdocs/neureka/backend/main/implementations/package-summary.html index b11f033c5..360021b67 100644 --- a/docs/jdocs/neureka/backend/main/implementations/package-summary.html +++ b/docs/jdocs/neureka/backend/main/implementations/package-summary.html @@ -1,135 +1,190 @@ - + + - -neureka.backend.main.implementations (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.implementations

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    package neureka.backend.main.implementations
    -
    +

    Package neureka.backend.main.implementations

    +
    Everything in this package should be considered library-private! DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! Code inside this package or any sub-packages might change frequently...
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    Package neureka.backend.main.implementations Description

    +
    Everything in this package should be considered library-private! + DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! + Code inside this package or any sub-packages might change frequently...
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/scalar/CLScalarFunction.html b/docs/jdocs/neureka/backend/main/implementations/scalar/CLScalarFunction.html index 2f4676afe..863d2bfec 100644 --- a/docs/jdocs/neureka/backend/main/implementations/scalar/CLScalarFunction.html +++ b/docs/jdocs/neureka/backend/main/implementations/scalar/CLScalarFunction.html @@ -1,180 +1,290 @@ - + + - -CLScalarFunction (neureka 1.0.0 API) - - - - + +CLScalarFunction (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.implementations.scalar
    -

    Class CLScalarFunction

    +
    neureka.backend.main.implementations.scalar
    +

    Class CLScalarFunction

    -
    java.lang.Object -
    neureka.backend.main.implementations.scalar.CLScalarFunction
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
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        +
      • neureka.backend.main.implementations.scalar.CLScalarFunction
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      Constructor Details

      -
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        CLScalarFunction

        -
        public CLScalarFunction(ScalarFun fun)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLScalarFunction

            +
            public CLScalarFunction(ScalarFun fun)
          -
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  • -
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    Method Details

    -
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      run

      -
      public Tensor<?> run(ExecutionCall<OpenCLDevice> call)
      -
      Description copied from interface: ImplementationFor
      +
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      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<OpenCLDevice> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<OpenCLDevice>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<OpenCLDevice>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
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    - -
    -
    -
    Package neureka.backend.main.implementations.scalar
    -

    Class CPUScalarBroadcastFunction

    +
    neureka.backend.main.implementations.scalar
    +

    Class CPUScalarBroadcastFunction

    -
    java.lang.Object -
    neureka.backend.main.implementations.scalar.CPUScalarBroadcastFunction
    -
    -
    -
    +
    +
      +
    • java.lang.Object
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      • neureka.backend.main.implementations.scalar.CPUScalarBroadcastFunction
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      Constructor Details

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        CPUScalarBroadcastFunction

        -
        public CPUScalarBroadcastFunction(ScalarFun fun)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUScalarBroadcastFunction

            +
            public CPUScalarBroadcastFunction(ScalarFun fun)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
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      run

      -
      public Tensor<?> run(ExecutionCall<CPU> call)
      -
      Description copied from interface: ImplementationFor
      +
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      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
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    + diff --git a/docs/jdocs/neureka/backend/main/implementations/scalar/CPUScalarFunction.html b/docs/jdocs/neureka/backend/main/implementations/scalar/CPUScalarFunction.html index a22cd99b9..5c5546b18 100644 --- a/docs/jdocs/neureka/backend/main/implementations/scalar/CPUScalarFunction.html +++ b/docs/jdocs/neureka/backend/main/implementations/scalar/CPUScalarFunction.html @@ -1,180 +1,290 @@ - + + - -CPUScalarFunction (neureka 1.0.0 API) - - - - + +CPUScalarFunction (neureka 1.0.1 API) - - - - - - + + -
    - -
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    -
    Package neureka.backend.main.implementations.scalar
    -

    Class CPUScalarFunction

    +
    neureka.backend.main.implementations.scalar
    +

    Class CPUScalarFunction

    -
    java.lang.Object -
    neureka.backend.main.implementations.scalar.CPUScalarFunction
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
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      • neureka.backend.main.implementations.scalar.CPUScalarFunction
        • +
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    • -
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      Constructor Details

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        CPUScalarFunction

        -
        public CPUScalarFunction(ScalarFun fun)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUScalarFunction

            +
            public CPUScalarFunction(ScalarFun fun)
          -
      • +
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  • -
    -

    Method Details

    -
      -
    • -
      -

      run

      -
      public Tensor<?> run(ExecutionCall<CPU> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
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    neureka.backend.main.implementations.scalar

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/implementations/scalar/package-summary.html b/docs/jdocs/neureka/backend/main/implementations/scalar/package-summary.html index 5320dd33d..e008f3e21 100644 --- a/docs/jdocs/neureka/backend/main/implementations/scalar/package-summary.html +++ b/docs/jdocs/neureka/backend/main/implementations/scalar/package-summary.html @@ -1,101 +1,147 @@ - + + - -neureka.backend.main.implementations.scalar (neureka 1.0.0 API) - - - - + +neureka.backend.main.implementations.scalar (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.implementations.scalar

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    package neureka.backend.main.implementations.scalar
    -
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    Hierarchy For Package neureka.backend.main.implementations.scalar

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    - -
    -
    -
    Package neureka.backend.main.internal
    -

    Interface FinalExecutor

    +
    neureka.backend.main.internal
    +

    Interface FinalExecutor

    -
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    +
      +
    • -
      public interface FinalExecutor
      -
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      Method Summary

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      Modifier and Type
      -
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      -
      Description
      -
      Tensor<?>
      -
      execute(ExecutionCall<? extends Device<?>> call)
      -
       
      -
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      +
      +
      public interface FinalExecutor
      +
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    neureka.backend.main.internal

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    Interfaces

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    + + diff --git a/docs/jdocs/neureka/backend/main/internal/package-summary.html b/docs/jdocs/neureka/backend/main/internal/package-summary.html index a2a1d3b48..57f3846fe 100644 --- a/docs/jdocs/neureka/backend/main/internal/package-summary.html +++ b/docs/jdocs/neureka/backend/main/internal/package-summary.html @@ -1,87 +1,152 @@ - + + - -neureka.backend.main.internal (neureka 1.0.0 API) - - - - + +neureka.backend.main.internal (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.backend.main.internal

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    package neureka.backend.main.internal
    -
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    Package neureka.backend.main.internal

    +
    Everything in this package should be considered library-private! DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! Code inside this package or any sub-packages might change frequently...
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    Package neureka.backend.main.internal Description

    +
    Everything in this package should be considered library-private! + DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! + Code inside this package or any sub-packages might change frequently...
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    + diff --git a/docs/jdocs/neureka/backend/main/memory/MemUtil.html b/docs/jdocs/neureka/backend/main/memory/MemUtil.html index deef64bbf..55abbbe79 100644 --- a/docs/jdocs/neureka/backend/main/memory/MemUtil.html +++ b/docs/jdocs/neureka/backend/main/memory/MemUtil.html @@ -1,217 +1,328 @@ - + + - -MemUtil (neureka 1.0.0 API) - - - - + +MemUtil (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.memory
    -

    Class MemUtil

    +
    neureka.backend.main.memory
    +

    Class MemUtil

    -
    java.lang.Object -
    neureka.backend.main.memory.MemUtil
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.memory.MemUtil
        • +
      +
      • +
    +
    +
      +
    • -
      public class MemUtil -extends Object
      +
      +
      public class MemUtil
+extends java.lang.Object
      Utility methods for deleting tensors or preventing thereof. In essence, it exposes convenience methods for setting and resetting - the Tensor.isIntermediate() flag or supplied tensors... + the Tensor.isIntermediate() flag or supplied tensors... This is an internal library class which should not be used anywhere but in Neurekas backend. Do not use this anywhere else!
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static void
      -
      autoDelete(Tensor<?>... tensors)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static voidautoDelete(Tensor<?>... tensors)
        This method will try to delete the provided array of tensors if the tensors are not important computation graph components (like derivatives for example).
        - -
        static <T> T
        -
        keep(Tensor<?>[] tensors, - Supplier<T> during)
        -
        +
        static <T> Tkeep(Tensor<?>[] tensors, + java.util.function.Supplier<T> during)
        This method makes sure that the provided tensors do not get deleted - by setting the Tensor.isIntermediate() flag to off - during the execution of the provided Supplier lambda! + by setting the Tensor.isIntermediate() flag to off + during the execution of the provided Supplier lambda! In said lambda the supplied thing will ultimately be returned by this method...
        - -
        static <T> T
        -
        keep(Tensor<?> a, - Tensor<?> b, - Supplier<T> during)
        -
        +
        static <T> Tkeep(Tensor<?> a, + Tensor<?> b, + java.util.function.Supplier<T> during)
        This method makes sure that the provided tensors do not get deleted - by setting the Tensor.isIntermediate() flag to off - during the execution of the provided Supplier lambda! + by setting the Tensor.isIntermediate() flag to off + during the execution of the provided Supplier lambda! In said lambda the supplied thing will ultimately be returned by this method...
        - - - - -
        -

        Methods inherited from class java.lang.Object

        -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
        - +
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
        +
        • +
    - -
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    • -
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      Method Details

      -
        -
      • -
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        autoDelete

        -
        public static void autoDelete(Tensor<?>... tensors)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            autoDelete

            +
            public static void autoDelete(Tensor<?>... tensors)
            This method will try to delete the provided array of tensors if the tensors are not important computation graph components (like derivatives for example).
            -
            -
            Parameters:
            +
            +
            Parameters:
            tensors - The tensors which should be deleted if possible.
            -
        • -
      • -
        -

        keep

        -
        public static <T> T keep(Tensor<?>[] tensors, - Supplier<T> during)
        +
      + + + +
        +
      • +

        keep

        +
        public static <T> T keep(Tensor<?>[] tensors,
+                         java.util.function.Supplier<T> during)
        This method makes sure that the provided tensors do not get deleted - by setting the Tensor.isIntermediate() flag to off - during the execution of the provided Supplier lambda! + by setting the Tensor.isIntermediate() flag to off + during the execution of the provided Supplier lambda! In said lambda the supplied thing will ultimately be returned by this method... - All provided tensors will have the Tensor.isIntermediate() flag + All provided tensors will have the Tensor.isIntermediate() flag set to their original state after execution.
        -
        -
        Type Parameters:
        +
        +
        Type Parameters:
        T - The type of the result produced by the provided lambda.
        -
        Parameters:
        +
        Parameters:
        tensors - An array of tensors which should not be deleted during the execution of the supplied lambda.
        during - A lambda producing a result during which the provided tensors should not be deleted.
        -
        Returns:
        +
        Returns:
        The result produced by the provided lambda.
        -
      • -
    • -
      -

      keep

      -
      public static <T> T keep(Tensor<?> a, - Tensor<?> b, - Supplier<T> during)
      +
    + + + +
      +
    • +

      keep

      +
      public static <T> T keep(Tensor<?> a,
+                         Tensor<?> b,
+                         java.util.function.Supplier<T> during)
      This method makes sure that the provided tensors do not get deleted - by setting the Tensor.isIntermediate() flag to off - during the execution of the provided Supplier lambda! + by setting the Tensor.isIntermediate() flag to off + during the execution of the provided Supplier lambda! In said lambda the supplied thing will ultimately be returned by this method... - Both of the provided tensors will have the Tensor.isIntermediate() flag + Both of the provided tensors will have the Tensor.isIntermediate() flag set to their original state after execution.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type of the result produced by the provided lambda.
      -
      Parameters:
      +
      Parameters:
      a - The first tensor which should not be deleted during the execution of the provided lambda.
      b - The second tensor which should not be deleted during the execution of the provided lambda.
      during - A lambda producing a result during whose execution the first to arguments should not be deleted.
      -
      Returns:
      +
      Returns:
      The result produced by the provided lambda.
      -
    - - + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/memory/MemValidator.html b/docs/jdocs/neureka/backend/main/memory/MemValidator.html index 8d3b814de..3e9ceaff0 100644 --- a/docs/jdocs/neureka/backend/main/memory/MemValidator.html +++ b/docs/jdocs/neureka/backend/main/memory/MemValidator.html @@ -1,187 +1,302 @@ - + + - -MemValidator (neureka 1.0.0 API) - - - - + +MemValidator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.memory
    -

    Class MemValidator

    +
    neureka.backend.main.memory
    +

    Class MemValidator

    -
    java.lang.Object -
    neureka.backend.main.memory.MemValidator
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.memory.MemValidator
        • +
      +
      • +
    +
    +
      +
    • -
      public class MemValidator -extends Object
      +
      +
      public class MemValidator
+extends java.lang.Object
      This class validates the states of tensors with respect to memory management before and after a lambda executes a function or some kind of algorithm on said tensors. - This validity refers to the Tensor.isIntermediate() flag, whose state should + This validity refers to the Tensor.isIntermediate() flag, whose state should adhere to strict rules in order to allow for safe deletion of tensors. - The lambda wrapped by this may be a Function call or a lower level - procedure defined a Algorithm implementation. + The lambda wrapped by this may be a Function call or a lower level + procedure defined a Algorithm implementation.

      Warning! This is an internal class. Do not depend on it.
      -
    -
    -
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        forInputs

        -
        public static MemValidator forInputs(Tensor<?>[] inputs, - Supplier<Result> resultProvider)
        -
        -
        Parameters:
        -
        inputs - The inputs used by the Supplier implementation to provide a result.
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            forInputs

            +
            public static MemValidator forInputs(Tensor<?>[] inputs,
+                                     java.util.function.Supplier<Result> resultProvider)
            +
            +
            Parameters:
            +
            inputs - The inputs used by the Supplier implementation to provide a result.
            resultProvider - The callback providing the result which ought to be validated.
            -
            Returns:
            -
            The MemValidator which ought to validate the provided result.
            +
            Returns:
            +
            The MemValidator which ought to validate the provided result.
            -
        • -
      • -
        -

        isWronglyIntermediate

        -
        public boolean isWronglyIntermediate()
        -
        -
        Returns:
        -
        Is true if the result tensor is wrongfully flagged as intermediate (see Tensor.isIntermediate()).
        +
      + + + +
        +
      • +

        isWronglyIntermediate

        +
        public boolean isWronglyIntermediate()
        +
        +
        Returns:
        +
        Is true if the result tensor is wrongfully flagged as intermediate (see Tensor.isIntermediate()).
        -
      • -
    • -
      -

      isWronglyNonIntermediate

      -
      public boolean isWronglyNonIntermediate()
      -
      -
      Returns:
      -
      Is true if the result tensor is wrongfully flagged as non-intermediate (see Tensor.isIntermediate()).
      +
    + + + +
      +
    • +

      isWronglyNonIntermediate

      +
      public boolean isWronglyNonIntermediate()
      +
      +
      Returns:
      +
      Is true if the result tensor is wrongfully flagged as non-intermediate (see Tensor.isIntermediate()).
      -
      • -
    • -
      -

      getResult

      -
      public Result getResult()
      -
      -
      Returns:
      -
      The result tensor returned by the Supplier lambda passed to this MemValidator.
      +
    + + + +
      +
    • +

      getResult

      +
      public Result getResult()
      +
      +
      Returns:
      +
      The result tensor returned by the Supplier lambda passed to this MemValidator.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/memory/package-frame.html b/docs/jdocs/neureka/backend/main/memory/package-frame.html new file mode 100644 index 000000000..7bbefeeef --- /dev/null +++ b/docs/jdocs/neureka/backend/main/memory/package-frame.html @@ -0,0 +1,20 @@ + + + + + +neureka.backend.main.memory (neureka 1.0.1 API) + + + + +

    neureka.backend.main.memory

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/memory/package-summary.html b/docs/jdocs/neureka/backend/main/memory/package-summary.html index ce251d41b..87081fb72 100644 --- a/docs/jdocs/neureka/backend/main/memory/package-summary.html +++ b/docs/jdocs/neureka/backend/main/memory/package-summary.html @@ -1,94 +1,161 @@ - + + - -neureka.backend.main.memory (neureka 1.0.0 API) - - - - + +neureka.backend.main.memory (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.memory

    -
    -
    -
    package neureka.backend.main.memory
    -
    +

    Package neureka.backend.main.memory

    +
    Everything in this package should be considered library-private! DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! Code inside this package or any sub-packages might change frequently...
    -
    -
    -
      -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      MemUtil
      -
      -
      Utility methods for deleting tensors or preventing thereof.
      -
      MemValidator
      -
      +

      See: Description

      +
      +
      +
        +
      • + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        MemUtil +
        Utility methods for deleting tensors or preventing thereof.
        +
        MemValidator
        This class validates the states of tensors with respect to memory management before and after a lambda executes a function or some kind of algorithm on said tensors.
        - - - +
      -
    -
    + + + +

    Package neureka.backend.main.memory Description

    +
    Everything in this package should be considered library-private! + DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! + Code inside this package or any sub-packages might change frequently...
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/memory/package-tree.html b/docs/jdocs/neureka/backend/main/memory/package-tree.html index d234f84b7..8719ec868 100644 --- a/docs/jdocs/neureka/backend/main/memory/package-tree.html +++ b/docs/jdocs/neureka/backend/main/memory/package-tree.html @@ -1,72 +1,135 @@ - + + - -neureka.backend.main.memory Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.memory Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.memory

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/ConvUtil.html b/docs/jdocs/neureka/backend/main/operations/ConvUtil.html index 53527927d..d762935ab 100644 --- a/docs/jdocs/neureka/backend/main/operations/ConvUtil.html +++ b/docs/jdocs/neureka/backend/main/operations/ConvUtil.html @@ -1,180 +1,298 @@ - + + - -ConvUtil (neureka 1.0.0 API) - - - - + +ConvUtil (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations
    -

    Class ConvUtil

    -
    -
    java.lang.Object -
    neureka.backend.main.operations.ConvUtil
    +
    neureka.backend.main.operations
    +

    Class ConvUtil

    -
    -
    -
    public class ConvUtil -extends Object
    -
    -
    -
      - +
      +
        +
      • java.lang.Object
      • -
        -

        Constructor Summary

        -
        Constructors
        -
        -
        Constructor
        -
        Description
        -
        ConvUtil()
        -
         
        +
          +
        • neureka.backend.main.operations.ConvUtil
          • +
        +
        • +
      +
      +
        +
      • +
        +
        +
        public class ConvUtil
+extends java.lang.Object
        +
        • +
      -
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ConvUtil

        -
        public ConvUtil()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ConvUtil

            +
            public ConvUtil()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      createDeconvolutionFor

      -
      public static NDConvolution createDeconvolutionFor(String op)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          createDeconvolutionFor

          +
          public static NDConvolution createDeconvolutionFor(java.lang.String op)
          • -
        • -
          -

          shapeOfCon

          -
          public static Shape shapeOfCon(int[] shape1, - int[] shape2)
          -
          +
        + + + +
          +
        • +

          shapeOfCon

          +
          public static Shape shapeOfCon(int[] shape1,
+                               int[] shape2)
          • -
        • -
          -

          executeRecursively

          -
          public static Tensor<?> executeRecursively(String op, - ExecutionCall<? extends Device<?>> call)
          -
          +
        + + + +
          +
        • +

          executeRecursively

          +
          public static Tensor<?> executeRecursively(java.lang.String op,
+                                           ExecutionCall<? extends Device<?>> call)
          +
          • +
      -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/ElemWiseUtil.html b/docs/jdocs/neureka/backend/main/operations/ElemWiseUtil.html index 82981c3b3..0290c505f 100644 --- a/docs/jdocs/neureka/backend/main/operations/ElemWiseUtil.html +++ b/docs/jdocs/neureka/backend/main/operations/ElemWiseUtil.html @@ -1,179 +1,293 @@ - + + - -ElemWiseUtil (neureka 1.0.0 API) - - - - + +ElemWiseUtil (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations
    -

    Class ElemWiseUtil

    +
    neureka.backend.main.operations
    +

    Class ElemWiseUtil

    -
    java.lang.Object -
    neureka.backend.main.operations.ElemWiseUtil
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.ElemWiseUtil
        • +
      +
      • +
    +
    +
      +
    • -
      public class ElemWiseUtil -extends Object
      -
      Methods inside this utility class execute only some ExecutionCall arguments +
      +
      public class ElemWiseUtil
+extends java.lang.Object
      +
      Methods inside this utility class execute only some ExecutionCall arguments in groups if their total number exceeds the arity of an operation.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      ElemWiseUtil()
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ElemWiseUtil

        -
        public ElemWiseUtil()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ElemWiseUtil

            +
            public ElemWiseUtil()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      newTensorLike

      -
      public static <V> Tensor<V> newTensorLike(Tensor<V> template, - double value)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          newTensorLike

          +
          public static <V> Tensor<V> newTensorLike(Tensor<V> template,
+                                          double value)
          • -
        • -
          -

          newTensorLike

          -
          public static <V> Tensor<V> newTensorLike(Class<V> type, - Shape shape, - boolean isOutsourced, - Device<Object> device, - double value)
          -
          +
        + + + +
          +
        • +

          newTensorLike

          +
          public static <V> Tensor<V> newTensorLike(java.lang.Class<V> type,
+                                          Shape shape,
+                                          boolean isOutsourced,
+                                          Device<java.lang.Object> device,
+                                          double value)
          +
          • +
      -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Absolute.html b/docs/jdocs/neureka/backend/main/operations/functions/Absolute.html index 1d5172c40..d4513d5d5 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Absolute.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Absolute.html @@ -1,210 +1,286 @@ - + + - -Absolute (neureka 1.0.0 API) - - - - + +Absolute (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Absolute

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Absolute
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Absolute

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Absolute

        -
        public Absolute()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Absolute

            +
            public Absolute()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Cbrt.html b/docs/jdocs/neureka/backend/main/operations/functions/Cbrt.html index 47981cf71..14df6a6c2 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Cbrt.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Cbrt.html @@ -1,210 +1,286 @@ - + + - -Cbrt (neureka 1.0.0 API) - - - - + +Cbrt (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Cbrt

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Cbrt
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Cbrt

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Cbrt

        -
        public Cbrt()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Cbrt

            +
            public Cbrt()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Cosinus.html b/docs/jdocs/neureka/backend/main/operations/functions/Cosinus.html index c1d7e773a..b6516ccd3 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Cosinus.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Cosinus.html @@ -1,210 +1,286 @@ - + + - -Cosinus (neureka 1.0.0 API) - - - - + +Cosinus (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Cosinus

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Cosinus
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Cosinus

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Cosinus

        -
        public Cosinus()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Cosinus

            +
            public Cosinus()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Exp.html b/docs/jdocs/neureka/backend/main/operations/functions/Exp.html index 463f0aa6e..334ad4449 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Exp.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Exp.html @@ -1,210 +1,286 @@ - + + - -Exp (neureka 1.0.0 API) - - - - + +Exp (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Exp

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Exp
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Exp

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Exp

        -
        public Exp()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Exp

            +
            public Exp()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/GaSU.html b/docs/jdocs/neureka/backend/main/operations/functions/GaSU.html index 4c3682a68..ab5133440 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/GaSU.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/GaSU.html @@ -1,216 +1,292 @@ - + + - -GaSU (neureka 1.0.0 API) - - - - + +GaSU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class GaSU

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.GaSU
    -
    +
    neureka.backend.main.operations.functions
    +

    Class GaSU

    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Operation
      +
      Operation
      -
      public class GaSU -extends AbstractOperation
      -
      The Self Gated Softsign Unit is based on the Softsign function - (a computationally cheap non-exponential quasi Tanh) - making it a polynomially based version of the GaTU function which - is itself based on the Tanh function. - Similar as the Softsign and Tanh function GaSU +
      +
      public class GaSU
+extends AbstractOperation
      +
      The Self Gated Softsign Unit is based on the Softsign function + (a computationally cheap non-exponential quasi Tanh) + making it a polynomially based version of the GaTU function which + is itself based on the Tanh function. + Similar as the Softsign and Tanh function GaSU is 0 centered and capped by -1 and +1.
      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        GaSU

        -
        public GaSU()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            GaSU

            +
            public GaSU()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -226,25 +302,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/GaTU.html b/docs/jdocs/neureka/backend/main/operations/functions/GaTU.html index 66d02f56c..e7a8e3fb0 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/GaTU.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/GaTU.html @@ -1,216 +1,292 @@ - + + - -GaTU (neureka 1.0.0 API) - - - - + +GaTU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class GaTU

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.GaTU
    -
    +
    neureka.backend.main.operations.functions
    +

    Class GaTU

    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Operation
      +
      Operation
      -
      public class GaTU -extends AbstractOperation
      -
      The Self Gated Tanh Unit is based on the Tanh - making it an exponentiation based version of the GaSU function which - is itself based on the Softsign function - (a computationally cheap non-exponential quasi Tanh). - Similar a the Softsign and Tanh function GaTU +
      +
      public class GaTU
+extends AbstractOperation
      +
      The Self Gated Tanh Unit is based on the Tanh + making it an exponentiation based version of the GaSU function which + is itself based on the Softsign function + (a computationally cheap non-exponential quasi Tanh). + Similar a the Softsign and Tanh function GaTU is 0 centered and caped by -1 and +1.
      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        GaTU

        -
        public GaTU()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            GaTU

            +
            public GaTU()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -226,25 +302,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Gaussian.html b/docs/jdocs/neureka/backend/main/operations/functions/Gaussian.html index 953e721a4..8403e5c17 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Gaussian.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Gaussian.html @@ -1,210 +1,286 @@ - + + - -Gaussian (neureka 1.0.0 API) - - - - + +Gaussian (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Gaussian

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Gaussian
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Gaussian

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Gaussian

        -
        public Gaussian()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Gaussian

            +
            public Gaussian()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/GaussianFast.html b/docs/jdocs/neureka/backend/main/operations/functions/GaussianFast.html index f0a495eb9..13ce49477 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/GaussianFast.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/GaussianFast.html @@ -1,210 +1,286 @@ - + + - -GaussianFast (neureka 1.0.0 API) - - - - + +GaussianFast (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class GaussianFast

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.GaussianFast
    -
    +
    neureka.backend.main.operations.functions
    +

    Class GaussianFast

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        GaussianFast

        -
        public GaussianFast()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            GaussianFast

            +
            public GaussianFast()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/GeLU.html b/docs/jdocs/neureka/backend/main/operations/functions/GeLU.html index 4cb7d160e..541cb0da5 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/GeLU.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/GeLU.html @@ -1,215 +1,291 @@ - + + - -GeLU (neureka 1.0.0 API) - - - - + +GeLU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class GeLU

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.GeLU
    -
    +
    neureka.backend.main.operations.functions
    +

    Class GeLU

    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Operation
      +
      Operation
      -
      public class GeLU -extends AbstractOperation
      +
      +
      public class GeLU
+extends AbstractOperation
      The GELU activation function is based on the standard Gaussian cumulative distribution function and is defined as x Φ( x ) and implemented as x * sigmoid(x * 1.702). The GELU non-linearity weighs inputs by their percentile, rather than gates inputs by their sign as in ReLUs. Consequently, the GELU can be thought of as a smoother ReLU.
      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        GeLU

        -
        public GeLU()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            GeLU

            +
            public GeLU()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -225,25 +301,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Identity.html b/docs/jdocs/neureka/backend/main/operations/functions/Identity.html index 2061250f1..c523e6861 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Identity.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Identity.html @@ -1,210 +1,286 @@ - + + - -Identity (neureka 1.0.0 API) - - - - + +Identity (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Identity

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Identity
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Identity

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Identity

        -
        public Identity()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Identity

            +
            public Identity()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Log10.html b/docs/jdocs/neureka/backend/main/operations/functions/Log10.html index 942108b1f..4d099297f 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Log10.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Log10.html @@ -1,210 +1,286 @@ - + + - -Log10 (neureka 1.0.0 API) - - - - + +Log10 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Log10

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Log10
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Log10

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Log10

        -
        public Log10()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Log10

            +
            public Log10()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Logarithm.html b/docs/jdocs/neureka/backend/main/operations/functions/Logarithm.html index ebbade591..02f194fc1 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Logarithm.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Logarithm.html @@ -1,244 +1,324 @@ - + + - -Logarithm (neureka 1.0.0 API) - - - - + +Logarithm (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Logarithm

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Logarithm
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Logarithm

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Logarithm

        -
        public Logarithm()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Logarithm

            +
            public Logarithm()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + + + + + +
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -254,25 +334,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Quadratic.html b/docs/jdocs/neureka/backend/main/operations/functions/Quadratic.html index 745636cd1..e4c7968cd 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Quadratic.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Quadratic.html @@ -1,210 +1,286 @@ - + + - -Quadratic (neureka 1.0.0 API) - - - - + +Quadratic (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Quadratic

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Quadratic
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Quadratic

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Quadratic

        -
        public Quadratic()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Quadratic

            +
            public Quadratic()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/ReLU.html b/docs/jdocs/neureka/backend/main/operations/functions/ReLU.html index f626d306b..c95c63114 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/ReLU.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/ReLU.html @@ -1,210 +1,286 @@ - + + - -ReLU (neureka 1.0.0 API) - - - - + +ReLU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class ReLU

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.ReLU
    -
    +
    neureka.backend.main.operations.functions
    +

    Class ReLU

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ReLU

        -
        public ReLU()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ReLU

            +
            public ReLU()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/SeLU.html b/docs/jdocs/neureka/backend/main/operations/functions/SeLU.html index 2977f23c5..2e3eded09 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/SeLU.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/SeLU.html @@ -1,219 +1,294 @@ - + + - -SeLU (neureka 1.0.0 API) - - - - + +SeLU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class SeLU

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.SeLU
    -
    +
    neureka.backend.main.operations.functions
    +

    Class SeLU

    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Operation
      +
      Operation
      -
      public class SeLU -extends AbstractOperation
      +
      +
      public class SeLU
+extends AbstractOperation
      The Scaled Exponential Linear Unit, or SELU, is an activation functions that induce self-normalizing properties. The SELU activation function is implemented as: - - if ( x > 0 ) return SCALE * x; + if ( x > 0 ) return SCALE * x; else if ( x <= 0 ) return SCALE * ALPHA * (Math.exp(x) - 1); else return Float.NaN;
      ...where ALPHA == 1.6733 and SCALE == 1.0507.
      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        SeLU

        -
        public SeLU()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            SeLU

            +
            public SeLU()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -229,25 +304,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/SiLU.html b/docs/jdocs/neureka/backend/main/operations/functions/SiLU.html index 99d0ec079..8245ab3d9 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/SiLU.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/SiLU.html @@ -1,214 +1,290 @@ - + + - -SiLU (neureka 1.0.0 API) - - - - + +SiLU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class SiLU

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.SiLU
    -
    +
    neureka.backend.main.operations.functions
    +

    Class SiLU

    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Operation
      +
      Operation
      -
      public class SiLU -extends AbstractOperation
      +
      +
      public class SiLU
+extends AbstractOperation
      The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x). It is a smooth, non-monotonic function that consistently matches or outperforms ReLU on deep networks, it is unbounded above and bounded below.
      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        SiLU

        -
        public SiLU()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            SiLU

            +
            public SiLU()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -224,25 +300,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Sigmoid.html b/docs/jdocs/neureka/backend/main/operations/functions/Sigmoid.html index 76bf808df..861fb54dc 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Sigmoid.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Sigmoid.html @@ -1,210 +1,286 @@ - + + - -Sigmoid (neureka 1.0.0 API) - - - - + +Sigmoid (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Sigmoid

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Sigmoid
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Sigmoid

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Sigmoid

        -
        public Sigmoid()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Sigmoid

            +
            public Sigmoid()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Sinus.html b/docs/jdocs/neureka/backend/main/operations/functions/Sinus.html index 5cc90162b..3982c21d6 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Sinus.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Sinus.html @@ -1,210 +1,286 @@ - + + - -Sinus (neureka 1.0.0 API) - - - - + +Sinus (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Sinus

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Sinus
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Sinus

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Sinus

        -
        public Sinus()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Sinus

            +
            public Sinus()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Softplus.html b/docs/jdocs/neureka/backend/main/operations/functions/Softplus.html index 8e8bfc007..9d5a8a7f3 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Softplus.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Softplus.html @@ -1,212 +1,288 @@ - + + - -Softplus (neureka 1.0.0 API) - - - - + +Softplus (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Softplus

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Softplus
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Softplus

    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Operation
      +
      Operation
      -
      public final class Softplus -extends AbstractOperation
      +
      +
      public final class Softplus
+extends AbstractOperation
      SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.
      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Softplus

        -
        public Softplus()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Softplus

            +
            public Softplus()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -222,25 +298,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Softsign.html b/docs/jdocs/neureka/backend/main/operations/functions/Softsign.html index aded5fb0f..501756ae9 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Softsign.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Softsign.html @@ -1,216 +1,292 @@ - + + - -Softsign (neureka 1.0.0 API) - - - - + +Softsign (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Softsign

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Softsign
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Softsign

    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Operation
      +
      Operation
      -
      public class Softsign -extends AbstractOperation
      +
      +
      public class Softsign
+extends AbstractOperation
      The softsign function, defined as x / ( 1 + Math.abs( x ) ), is a computationally cheap 0 centered activation function - which rescales the inputs between -1 and 1, very much like the Tanh function. + which rescales the inputs between -1 and 1, very much like the Tanh function. The softsign function converges polynomially and is computationally cheaper than the tanh function which converges exponentially. - This makes this function a computationally cheap non-exponential quasi Tanh!
      -
    -
    -
      + This makes this function a computationally cheap non-exponential quasi Tanh!
    + + +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Softsign

        -
        public Softsign()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Softsign

            +
            public Softsign()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -226,25 +302,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Sqrt.html b/docs/jdocs/neureka/backend/main/operations/functions/Sqrt.html index 564f0d3d0..b6ec57e87 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Sqrt.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Sqrt.html @@ -1,210 +1,286 @@ - + + - -Sqrt (neureka 1.0.0 API) - - - - + +Sqrt (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Sqrt

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Sqrt
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Sqrt

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Sqrt

        -
        public Sqrt()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Sqrt

            +
            public Sqrt()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/Tanh.html b/docs/jdocs/neureka/backend/main/operations/functions/Tanh.html index 280689e7b..7451503e0 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/Tanh.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/Tanh.html @@ -1,210 +1,286 @@ - + + - -Tanh (neureka 1.0.0 API) - - - - + +Tanh (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class Tanh

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.Tanh
    -
    +
    neureka.backend.main.operations.functions
    +

    Class Tanh

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Tanh

        -
        public Tanh()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Tanh

            +
            public Tanh()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/TanhFast.html b/docs/jdocs/neureka/backend/main/operations/functions/TanhFast.html index c2ac40173..4eac2a57f 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/TanhFast.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/TanhFast.html @@ -1,210 +1,286 @@ - + + - -TanhFast (neureka 1.0.0 API) - - - - + +TanhFast (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.functions
    -

    Class TanhFast

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.functions.TanhFast
    -
    +
    neureka.backend.main.operations.functions
    +

    Class TanhFast

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        TanhFast

        -
        public TanhFast()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            TanhFast

            +
            public TanhFast()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public final double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public final double calculate(double[] inputs,
+                              int j,
+                              int d,
+                              Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/package-frame.html b/docs/jdocs/neureka/backend/main/operations/functions/package-frame.html new file mode 100644 index 000000000..5aa92296c --- /dev/null +++ b/docs/jdocs/neureka/backend/main/operations/functions/package-frame.html @@ -0,0 +1,41 @@ + + + + + +neureka.backend.main.operations.functions (neureka 1.0.1 API) + + + + +

    neureka.backend.main.operations.functions

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/package-summary.html b/docs/jdocs/neureka/backend/main/operations/functions/package-summary.html index faa67ee1c..e27ce9336 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/package-summary.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/package-summary.html @@ -1,195 +1,265 @@ - + + - -neureka.backend.main.operations.functions (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.functions (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.operations.functions

    -
    -
    -
    package neureka.backend.main.operations.functions
    -
    -
    Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
    -
    -
    -
      -
    • -
      • -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      Absolute
      -
       
      -
      Cbrt
      -
       
      -
      Cosinus
      -
       
      -
      Exp
      -
       
      -
      GaSU
      -
      -
      The Self Gated Softsign Unit is based on the Softsign function - (a computationally cheap non-exponential quasi Tanh) - making it a polynomially based version of the GaTU function which - is itself based on the Tanh function.
      -
      -
      GaTU
      -
      -
      The Self Gated Tanh Unit is based on the Tanh - making it an exponentiation based version of the GaSU function which - is itself based on the Softsign function - (a computationally cheap non-exponential quasi Tanh).
      -
      -
      Gaussian
      -
       
      -
      GaussianFast
      -
       
      -
      GeLU
      -
      +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        Absolute 
        Cbrt 
        Cosinus 
        Exp 
        GaSU +
        The Self Gated Softsign Unit is based on the Softsign function + (a computationally cheap non-exponential quasi Tanh) + making it a polynomially based version of the GaTU function which + is itself based on the Tanh function.
        +
        GaTU +
        The Self Gated Tanh Unit is based on the Tanh + making it an exponentiation based version of the GaSU function which + is itself based on the Softsign function + (a computationally cheap non-exponential quasi Tanh).
        +
        Gaussian 
        GaussianFast 
        GeLU
        The GELU activation function is based on the standard Gaussian cumulative distribution function and is defined as x Φ( x ) and implemented as x * sigmoid(x * 1.702).
        - -
        Identity
        -
         
        -
        Log10
        -
         
        -
        Logarithm
        -
         
        -
        Quadratic
        -
         
        -
        ReLU
        -
         
        -
        SeLU
        -
        +
        Identity 
        Log10 
        Logarithm 
        Quadratic 
        ReLU 
        SeLU
        The Scaled Exponential Linear Unit, or SELU, is an activation functions that induce self-normalizing properties.
        - -
        Sigmoid
        -
         
        -
        SiLU
        -
        +
        Sigmoid 
        SiLU
        The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x).
        - -
        Sinus
        -
         
        -
        Softplus
        -
        +
        Sinus 
        Softplus
        SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.
        - -
        Softsign
        -
        +
        Softsign
        The softsign function, defined as x / ( 1 + Math.abs( x ) ), is a computationally cheap 0 centered activation function - which rescales the inputs between -1 and 1, very much like the Tanh function.
        - -
        Sqrt
        -
         
        -
        Tanh
        -
         
        -
        TanhFast
        -
         
        - - + which rescales the inputs between -1 and 1, very much like the Tanh function. +
        Sqrt 
        Tanh 
        TanhFast 
      -
    -
    + + + +

    Package neureka.backend.main.operations.functions Description

    +
    Everything in this package should be considered library-private! + DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! + Code inside this package or any sub-packages might change frequently...
    +
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/functions/package-tree.html b/docs/jdocs/neureka/backend/main/operations/functions/package-tree.html index a94c09ad4..1f4e607a5 100644 --- a/docs/jdocs/neureka/backend/main/operations/functions/package-tree.html +++ b/docs/jdocs/neureka/backend/main/operations/functions/package-tree.html @@ -1,97 +1,160 @@ - + + - -neureka.backend.main.operations.functions Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.functions Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.operations.functions

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
        -
      • neureka.backend.api.template.operations.AbstractOperation (implements neureka.backend.api.Operation) +
      • neureka.backend.api.template.operations.AbstractOperation (implements neureka.backend.api.Operation)
          -
        • neureka.backend.main.operations.functions.Absolute
          • -
        • neureka.backend.main.operations.functions.Cbrt
          • -
        • neureka.backend.main.operations.functions.Cosinus
          • -
        • neureka.backend.main.operations.functions.Exp
          • -
        • neureka.backend.main.operations.functions.GaSU
          • -
        • neureka.backend.main.operations.functions.GaTU
          • -
        • neureka.backend.main.operations.functions.Gaussian
          • -
        • neureka.backend.main.operations.functions.GaussianFast
          • -
        • neureka.backend.main.operations.functions.GeLU
          • -
        • neureka.backend.main.operations.functions.Identity
          • -
        • neureka.backend.main.operations.functions.Log10
          • -
        • neureka.backend.main.operations.functions.Logarithm
          • -
        • neureka.backend.main.operations.functions.Quadratic
          • -
        • neureka.backend.main.operations.functions.ReLU
          • -
        • neureka.backend.main.operations.functions.SeLU
          • -
        • neureka.backend.main.operations.functions.Sigmoid
          • -
        • neureka.backend.main.operations.functions.SiLU
          • -
        • neureka.backend.main.operations.functions.Sinus
          • -
        • neureka.backend.main.operations.functions.Softplus
          • -
        • neureka.backend.main.operations.functions.Softsign
          • -
        • neureka.backend.main.operations.functions.Sqrt
          • -
        • neureka.backend.main.operations.functions.Tanh
          • -
        • neureka.backend.main.operations.functions.TanhFast
          • +
        • neureka.backend.main.operations.functions.Absolute
          • +
        • neureka.backend.main.operations.functions.Cbrt
          • +
        • neureka.backend.main.operations.functions.Cosinus
          • +
        • neureka.backend.main.operations.functions.Exp
          • +
        • neureka.backend.main.operations.functions.GaSU
          • +
        • neureka.backend.main.operations.functions.GaTU
          • +
        • neureka.backend.main.operations.functions.Gaussian
          • +
        • neureka.backend.main.operations.functions.GaussianFast
          • +
        • neureka.backend.main.operations.functions.GeLU
          • +
        • neureka.backend.main.operations.functions.Identity
          • +
        • neureka.backend.main.operations.functions.Log10
          • +
        • neureka.backend.main.operations.functions.Logarithm
          • +
        • neureka.backend.main.operations.functions.Quadratic
          • +
        • neureka.backend.main.operations.functions.ReLU
          • +
        • neureka.backend.main.operations.functions.SeLU
          • +
        • neureka.backend.main.operations.functions.Sigmoid
          • +
        • neureka.backend.main.operations.functions.SiLU
          • +
        • neureka.backend.main.operations.functions.Sinus
          • +
        • neureka.backend.main.operations.functions.Softplus
          • +
        • neureka.backend.main.operations.functions.Softsign
          • +
        • neureka.backend.main.operations.functions.Sqrt
          • +
        • neureka.backend.main.operations.functions.Tanh
          • +
        • neureka.backend.main.operations.functions.TanhFast
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/indexer/Product.html b/docs/jdocs/neureka/backend/main/operations/indexer/Product.html index 63cf19313..535a8621d 100644 --- a/docs/jdocs/neureka/backend/main/operations/indexer/Product.html +++ b/docs/jdocs/neureka/backend/main/operations/indexer/Product.html @@ -1,205 +1,278 @@ - + + - -Product (neureka 1.0.0 API) - - - - + +Product (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.indexer
    -

    Class Product

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.indexer.Product
    -
    +
    neureka.backend.main.operations.indexer
    +

    Class Product

    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Operation
      +
      Operation
      -
      public final class Product -extends AbstractOperation
      +
      +
      public final class Product
+extends AbstractOperation
      This type of operation belongs to the same species as the - Summation operation. + Summation operation. It executes incoming calls so that the calling function will be executed with all input indices passed to it. The resulting array of tensors will then multiplied with each other - to produce the result of this operation, hence the name Product.
      -
    -
    -
      + to produce the result of this operation, hence the name Product.
    + + +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Product

        -
        public Product()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Product

            +
            public Product()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      execute

      -
      public Result execute(Function caller, - ExecutionCall<?> call)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          execute

          +
          public Result execute(Function caller,
+                      ExecutionCall<?> call)
          • -
        • -
          -

          calculate

          -
          public double calculate(double[] inputs, - int j, - int d, - Function[] src)
          -
          Description copied from interface: Operation
          +
        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -215,33 +288,95 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    • -
  • -
    -

    calculate

    -
    public static double calculate(double[] inputs, - int d, - Function[] src)
    -
    + + + + +
      +
    • +

      calculate

      +
      public static double calculate(double[] inputs,
+                               int d,
+                               Function[] src)
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/indexer/Summation.html b/docs/jdocs/neureka/backend/main/operations/indexer/Summation.html index 91853ca1a..c0731b0be 100644 --- a/docs/jdocs/neureka/backend/main/operations/indexer/Summation.html +++ b/docs/jdocs/neureka/backend/main/operations/indexer/Summation.html @@ -1,205 +1,278 @@ - + + - -Summation (neureka 1.0.0 API) - - - - + +Summation (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.indexer
    -

    Class Summation

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.indexer.Summation
    -
    +
    neureka.backend.main.operations.indexer
    +

    Class Summation

    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Operation
      +
      Operation
      -
      public final class Summation -extends AbstractOperation
      +
      +
      public final class Summation
+extends AbstractOperation
      This type of operation belongs to the same species as the - Product operation. + Product operation. It executes incoming calls so that the calling function will be executed with all input indices passed to it. The resulting array of tensors will then be summed - to produce the result of this operation, hence the name Summation.
      -
    -
    -
      + to produce the result of this operation, hence the name Summation.
    + + +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Summation

        -
        public Summation()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Summation

            +
            public Summation()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      execute

      -
      public Result execute(Function caller, - ExecutionCall<?> call)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          execute

          +
          public Result execute(Function caller,
+                      ExecutionCall<?> call)
          • -
        • -
          -

          calculate

          -
          public double calculate(double[] inputs, - int j, - int d, - Function[] src)
          -
          Description copied from interface: Operation
          +
        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -215,33 +288,95 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    • -
  • -
    -

    calculate

    -
    public static double calculate(double[] inputs, - int d, - Function[] src)
    -
    + + + + +
      +
    • +

      calculate

      +
      public static double calculate(double[] inputs,
+                               int d,
+                               Function[] src)
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/indexer/package-frame.html b/docs/jdocs/neureka/backend/main/operations/indexer/package-frame.html new file mode 100644 index 000000000..ff76af8d1 --- /dev/null +++ b/docs/jdocs/neureka/backend/main/operations/indexer/package-frame.html @@ -0,0 +1,20 @@ + + + + + +neureka.backend.main.operations.indexer (neureka 1.0.1 API) + + + + +

    neureka.backend.main.operations.indexer

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/operations/indexer/package-summary.html b/docs/jdocs/neureka/backend/main/operations/indexer/package-summary.html index 37348720c..278b89a5b 100644 --- a/docs/jdocs/neureka/backend/main/operations/indexer/package-summary.html +++ b/docs/jdocs/neureka/backend/main/operations/indexer/package-summary.html @@ -1,134 +1,162 @@ - + + - -neureka.backend.main.operations.indexer (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.indexer (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.operations.indexer

    -
    -
    -
    package neureka.backend.main.operations.indexer
    -
    -
    Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
    -
    -
    -
      -
    • -
      • -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      Product
      -
      +

      See: Description

      +
      +
      +
        +
      • + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        Product
        This type of operation belongs to the same species as the - Summation operation.
        - -
        Summation
        -
        + Summation operation.
        +
        Summation
        This type of operation belongs to the same species as the - Product operation.
        - - - + Product operation. +
      -
    -
    + + + +

    Package neureka.backend.main.operations.indexer Description

    +
    Everything in this package should be considered library-private! + DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! + Code inside this package or any sub-packages might change frequently...
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/indexer/package-tree.html b/docs/jdocs/neureka/backend/main/operations/indexer/package-tree.html index 3c787f2e8..68a31477f 100644 --- a/docs/jdocs/neureka/backend/main/operations/indexer/package-tree.html +++ b/docs/jdocs/neureka/backend/main/operations/indexer/package-tree.html @@ -1,76 +1,139 @@ - + + - -neureka.backend.main.operations.indexer Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.indexer Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.operations.indexer

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/Convolution.html b/docs/jdocs/neureka/backend/main/operations/linear/Convolution.html index 8de491c32..631c91203 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/Convolution.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/Convolution.html @@ -1,194 +1,266 @@ - + + - -Convolution (neureka 1.0.0 API) - - - - + +Convolution (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear
    -

    Class Convolution

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.linear.Convolution
    +
    neureka.backend.main.operations.linear
    +

    Class Convolution

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Convolution

        -
        public Convolution()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Convolution

            +
            public Convolution()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      execute

      -
      public Result execute(Function caller, - ExecutionCall<?> call)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          execute

          +
          public Result execute(Function caller,
+                      ExecutionCall<?> call)
          • -
        • -
          -

          calculate

          -
          public double calculate(double[] inputs, - int j, - int d, - Function[] src)
          -
          Description copied from interface: Operation
          +
        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -204,25 +276,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/DotProduct.html b/docs/jdocs/neureka/backend/main/operations/linear/DotProduct.html index c1788ec22..0b3d387fd 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/DotProduct.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/DotProduct.html @@ -1,183 +1,251 @@ - + + - -DotProduct (neureka 1.0.0 API) - - - - + +DotProduct (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear
    -

    Class DotProduct

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.linear.DotProduct
    +
    neureka.backend.main.operations.linear
    +

    Class DotProduct

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        DotProduct

        -
        public DotProduct()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            DotProduct

            +
            public DotProduct()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      calculate

      -
      public double calculate(double[] inputs, - int j, - int d, - Function[] src)
      -
      Description copied from interface: Operation
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -193,25 +261,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/MatMul.html b/docs/jdocs/neureka/backend/main/operations/linear/MatMul.html index 7aaf9612e..1d77c60d3 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/MatMul.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/MatMul.html @@ -1,183 +1,266 @@ - + + - -MatMul (neureka 1.0.0 API) - - - - + +MatMul (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear
    -

    Class MatMul

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.linear.MatMul
    +
    neureka.backend.main.operations.linear
    +

    Class MatMul

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        MatMul

        -
        public MatMul()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            MatMul

            +
            public MatMul()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      calculate

      -
      public double calculate(double[] inputs, - int j, - int d, - Function[] src)
      -
      Description copied from interface: Operation
      +
        +
      • + + +

        Method Detail

        + + + + + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -193,25 +276,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/XConvLeft.html b/docs/jdocs/neureka/backend/main/operations/linear/XConvLeft.html index 41ad1a0d9..79ad62e8d 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/XConvLeft.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/XConvLeft.html @@ -1,199 +1,271 @@ - + + - -XConvLeft (neureka 1.0.0 API) - - - - + +XConvLeft (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear
    -

    Class XConvLeft

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.linear.XConvLeft
    +
    neureka.backend.main.operations.linear
    +

    Class XConvLeft

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        XConvLeft

        -
        public XConvLeft()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            XConvLeft

            +
            public XConvLeft()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      calculate

      +
      public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -209,25 +281,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/XConvRight.html b/docs/jdocs/neureka/backend/main/operations/linear/XConvRight.html index 4126d1964..d02b65a5f 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/XConvRight.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/XConvRight.html @@ -1,199 +1,271 @@ - + + - -XConvRight (neureka 1.0.0 API) - - - - + +XConvRight (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear
    -

    Class XConvRight

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.linear.XConvRight
    +
    neureka.backend.main.operations.linear
    +

    Class XConvRight

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        XConvRight

        -
        public XConvRight()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            XConvRight

            +
            public XConvRight()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      calculate

      +
      public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -209,25 +281,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
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    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/AXPY.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/AXPY.html index 0ccc031df..4f73f8897 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/AXPY.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/AXPY.html @@ -1,193 +1,307 @@ - + + - -AXPY (neureka 1.0.0 API) - - - - + +AXPY (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Class AXPY

    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Class AXPY

    -
    java.lang.Object -
    neureka.backend.main.operations.linear.internal.blas.AXPY
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.linear.internal.blas.AXPY
        • +
      +
      • +
    +
    +
      +
    • -
      public final class AXPY -extends Object
      +
      +
      public final class AXPY
+extends java.lang.Object
      The ?axpy routines perform a vector-vector operation defined as y := a*x + y where: a is a scalar x and y are vectors each with a number of elements that equals n. y[] += a * x[]
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      AXPY()
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        AXPY() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static void
      -
      invoke(double[] y, - int yOffset, - double multiplier, - double[] x, - int xOffset, - int start, - int limit)
      -
       
      -
      static void
      -
      invoke(float[] y, - int basey, - float a, - float[] x, - int basex, - int first, - int limit)
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static voidinvoke(double[] y, + int yOffset, + double multiplier, + double[] x, + int xOffset, + int start, + int limit) 
        static voidinvoke(float[] y, + int basey, + float a, + float[] x, + int basex, + int first, + int limit) 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
        +
        • +
    - -
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      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        AXPY

        -
        public AXPY()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            AXPY

            +
            public AXPY()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      invoke

      -
      public static void invoke(double[] y, - int yOffset, - double multiplier, - double[] x, - int xOffset, - int start, - int limit)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          invoke

          +
          public static void invoke(double[] y,
+                          int yOffset,
+                          double multiplier,
+                          double[] x,
+                          int xOffset,
+                          int start,
+                          int limit)
          • -
        • -
          -

          invoke

          -
          public static void invoke(float[] y, - int basey, - float a, - float[] x, - int basex, - int first, - int limit)
          -
          +
        + + + +
          +
        • +

          invoke

          +
          public static void invoke(float[] y,
+                          int basey,
+                          float a,
+                          float[] x,
+                          int basex,
+                          int first,
+                          int limit)
          +
          • +
      -
    • - +
    +
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    + + +
    Skip navigation links
    + + + +
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      • +
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      • +
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/COPY.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/COPY.html index 2098211d3..7097f7e73 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/COPY.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/COPY.html @@ -1,159 +1,271 @@ - + + - -COPY (neureka 1.0.0 API) - - - - + +COPY (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Class COPY

    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Class COPY

    -
    java.lang.Object -
    neureka.backend.main.operations.linear.internal.blas.COPY
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.linear.internal.blas.COPY
        • +
      +
      • +
    +
    +
      +
    • -
      public final class COPY -extends Object
      +
      +
      public final class COPY
+extends java.lang.Object
      The ?copy routines perform a vector-vector operation defined as y = x, where x and y are vectors.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      COPY()
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        COPY() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static <T> T[]
      -
      copyOf(T[] original)
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static <T> T[]copyOf(T[] original) 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        COPY

        -
        public COPY()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            COPY

            +
            public COPY()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      copyOf

      -
      public static <T> T[] copyOf(T[] original)
      -
      +
        +
      • + + +

        Method Detail

        + + + + + +
          +
        • +

          copyOf

          +
          public static <T> T[] copyOf(T[] original)
        -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/DOT.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/DOT.html index 27cf9d613..9b9828cd4 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/DOT.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/DOT.html @@ -1,227 +1,349 @@ - + + - -DOT (neureka 1.0.0 API) - - - - + +DOT (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Class DOT

    -
    -
    java.lang.Object -
    neureka.backend.main.operations.linear.internal.blas.DOT
    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Class DOT

    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.linear.internal.blas.DOT
        • +
      +
      • +
    +
    +
      +
    • -
      public final class DOT -extends Object
      +
      +
      public final class DOT
+extends java.lang.Object
      The ?dot routines perform a vector-vector reduction operation defined as Equation where xi and yi are elements of vectors x and y.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      DOT()
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        DOT() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static double
      -
      invoke(double[] array1, - int offset1, - double[] array2, - int offset2, - int first, - int limit)
      -
       
      -
      static float
      -
      invoke(float[] array1, - int offset1, - float[] array2, - int offset2, - int first, - int limit)
      -
       
      -
      static int
      -
      invoke(int[] array1, - int offset1, - int[] array2, - int offset2, - int first, - int limit)
      -
       
      -
      static long
      -
      invoke(long[] array1, - int offset1, - long[] array2, - int offset2, - int first, - int limit)
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static doubleinvoke(double[] array1, + int offset1, + double[] array2, + int offset2, + int first, + int limit) 
        static floatinvoke(float[] array1, + int offset1, + float[] array2, + int offset2, + int first, + int limit) 
        static intinvoke(int[] array1, + int offset1, + int[] array2, + int offset2, + int first, + int limit) 
        static longinvoke(long[] array1, + int offset1, + long[] array2, + int offset2, + int first, + int limit) 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
      - -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        DOT

        -
        public DOT()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            DOT

            +
            public DOT()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      invoke

      -
      public static double invoke(double[] array1, - int offset1, - double[] array2, - int offset2, - int first, - int limit)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          invoke

          +
          public static double invoke(double[] array1,
+                            int offset1,
+                            double[] array2,
+                            int offset2,
+                            int first,
+                            int limit)
          • -
        • -
          -

          invoke

          -
          public static float invoke(float[] array1, - int offset1, - float[] array2, - int offset2, - int first, - int limit)
          -
          +
        + + + +
          +
        • +

          invoke

          +
          public static float invoke(float[] array1,
+                           int offset1,
+                           float[] array2,
+                           int offset2,
+                           int first,
+                           int limit)
          • -
        • -
          -

          invoke

          -
          public static long invoke(long[] array1, - int offset1, - long[] array2, - int offset2, - int first, - int limit)
          -
          +
        + + + +
          +
        • +

          invoke

          +
          public static long invoke(long[] array1,
+                          int offset1,
+                          long[] array2,
+                          int offset2,
+                          int first,
+                          int limit)
          • -
        • -
          -

          invoke

          -
          public static int invoke(int[] array1, - int offset1, - int[] array2, - int offset2, - int first, - int limit)
          -
          +
        + + + +
          +
        • +

          invoke

          +
          public static int invoke(int[] array1,
+                         int offset1,
+                         int[] array2,
+                         int offset2,
+                         int first,
+                         int limit)
        -
    • - + + +
    +
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    Skip navigation links
    + + + +
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      • +
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.VectorOperationF32.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.VectorOperationF32.html index 1231f5392..ac5adc3da 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.VectorOperationF32.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.VectorOperationF32.html @@ -1,139 +1,233 @@ - + + - -GEMM.VectorOperationF32 (neureka 1.0.0 API) - - - - + +GEMM.VectorOperationF32 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Interface GEMM.VectorOperationF32

    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Interface GEMM.VectorOperationF32

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      GEMM
      +
      GEMM
      -
      +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public static interface GEMM.VectorOperationF32
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      void
      -
      invoke(float[] product, - float[] left, - int complexity, - float[] right)
      -
       
      -
      -
      +
      +
      @FunctionalInterface
+public static interface GEMM.VectorOperationF32
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Method Summary

        + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        voidinvoke(float[] product, + float[] left, + int complexity, + float[] right) 
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        invoke

        -
        void invoke(float[] product, - float[] left, - int complexity, - float[] right)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            invoke

            +
            void invoke(float[] product,
+            float[] left,
+            int complexity,
+            float[] right)
            +
            • +
        -
    - +
    +
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    + + + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.VectorOperationF64.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.VectorOperationF64.html index 655c69139..cad96c09b 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.VectorOperationF64.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.VectorOperationF64.html @@ -1,139 +1,233 @@ - + + - -GEMM.VectorOperationF64 (neureka 1.0.0 API) - - - - + +GEMM.VectorOperationF64 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Interface GEMM.VectorOperationF64

    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Interface GEMM.VectorOperationF64

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      GEMM
      +
      GEMM
      -
      +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public static interface GEMM.VectorOperationF64
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      void
      -
      invoke(double[] product, - double[] left, - int complexity, - double[] right)
      -
       
      -
      -
      +
      +
      @FunctionalInterface
+public static interface GEMM.VectorOperationF64
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Method Summary

        + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        voidinvoke(double[] product, + double[] left, + int complexity, + double[] right) 
        +
        • +
    - -
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      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        invoke

        -
        void invoke(double[] product, - double[] left, - int complexity, - double[] right)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            invoke

            +
            void invoke(double[] product,
+            double[] left,
+            int complexity,
+            double[] right)
            +
            • +
        -
    - +
    +
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    Skip navigation links
    + + + + +
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      • +
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    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.html index c048efd47..25dfb8b8d 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/GEMM.html @@ -1,196 +1,315 @@ - + + - -GEMM (neureka 1.0.0 API) - - - - + +GEMM (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Class GEMM

    -
    -
    java.lang.Object -
    neureka.backend.main.operations.linear.internal.blas.GEMM
    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Class GEMM

    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.linear.internal.blas.GEMM
        • +
      +
      • +
    +
    +
      +
    • -
      public class GEMM -extends Object
      +
      +
      public class GEMM
+extends java.lang.Object
      A collection of primitive sub-routines for matrix multiplication performed on continuous arrays which are designed so that they can be vectorized by the JVMs JIT compiler (AVX instructions).
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    +
    +
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      +
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      Constructor Details

      -
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        GEMM

        -
        public GEMM()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            GEMM

            +
            public GEMM()
          -
      • +
    -
  • -
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    Method Details

    -
      -
    • -
      -

      operationForF32

      -
      public static GEMM.VectorOperationF32 operationForF32(boolean rowMajor, - long rows, - long columns)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          operationForF32

          +
          public static GEMM.VectorOperationF32 operationForF32(boolean rowMajor,
+                                                      long rows,
+                                                      long columns)
          • -
        • -
          -

          operationForF64

          -
          public static GEMM.VectorOperationF64 operationForF64(boolean rowMajor, - long rows, - long columns)
          -
          +
        + + + +
          +
        • +

          operationForF64

          +
          public static GEMM.VectorOperationF64 operationForF64(boolean rowMajor,
+                                                      long rows,
+                                                      long columns)
        -
    • - + + +
    +
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IAXPY.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IAXPY.html index f9e110de2..9044e56d3 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IAXPY.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IAXPY.html @@ -1,193 +1,307 @@ - + + - -IAXPY (neureka 1.0.0 API) - - - - + +IAXPY (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Class IAXPY

    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Class IAXPY

    -
    java.lang.Object -
    neureka.backend.main.operations.linear.internal.blas.IAXPY
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.linear.internal.blas.IAXPY
        • +
      +
      • +
    +
    +
      +
    • -
      public final class IAXPY -extends Object
      +
      +
      public final class IAXPY
+extends java.lang.Object
      The ?axpy routines perform a vector-vector operation defined as y := a*x + y where: a is a scalar x and y are vectors each with a number of elements that equals n. y[] += a * x[]
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      - -
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      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      IAXPY()
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        IAXPY() 
        • +
      -
    • -
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      Method Summary

      -
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      -
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      Modifier and Type
      -
      Method
      -
      Description
      -
      static void
      -
      invoke(int[] y, - int basey, - int a, - int[] x, - int basex, - int first, - int limit)
      -
       
      -
      static void
      -
      invoke(long[] y, - int yOffset, - long multiplier, - long[] x, - int xOffset, - int start, - int limit)
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static voidinvoke(int[] y, + int basey, + int a, + int[] x, + int basex, + int first, + int limit) 
        static voidinvoke(long[] y, + int yOffset, + long multiplier, + long[] x, + int xOffset, + int start, + int limit) 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
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        • +
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      +
    +
    +
      +
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      Constructor Details

      -
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        IAXPY

        -
        public IAXPY()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            IAXPY

            +
            public IAXPY()
          -
      • +
    -
  • -
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    Method Details

    -
      -
    • -
      -

      invoke

      -
      public static void invoke(long[] y, - int yOffset, - long multiplier, - long[] x, - int xOffset, - int start, - int limit)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          invoke

          +
          public static void invoke(long[] y,
+                          int yOffset,
+                          long multiplier,
+                          long[] x,
+                          int xOffset,
+                          int start,
+                          int limit)
          • -
        • -
          -

          invoke

          -
          public static void invoke(int[] y, - int basey, - int a, - int[] x, - int basex, - int first, - int limit)
          -
          +
        + + + +
          +
        • +

          invoke

          +
          public static void invoke(int[] y,
+                          int basey,
+                          int a,
+                          int[] x,
+                          int basex,
+                          int first,
+                          int limit)
          +
          • +
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    Skip navigation links
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    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IDOT.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IDOT.html index 328393573..09f423542 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IDOT.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IDOT.html @@ -1,189 +1,303 @@ - + + - -IDOT (neureka 1.0.0 API) - - - - + +IDOT (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Class IDOT

    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Class IDOT

    -
    java.lang.Object -
    neureka.backend.main.operations.linear.internal.blas.IDOT
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.linear.internal.blas.IDOT
        • +
      +
      • +
    +
    +
      +
    • -
      public final class IDOT -extends Object
      +
      +
      public final class IDOT
+extends java.lang.Object
      The ?dot routines perform a vector-vector reduction operation defined as Equation where xi and yi are elements of vectors x and y.
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      Constructor Summary

      -
      Constructors
      -
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      Constructor
      -
      Description
      -
      IDOT()
      -
       
      +
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    +
      +
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      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        IDOT() 
        • +
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      Method Summary

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      Modifier and Type
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      Method
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      static int
      -
      invoke(int[] array1, - int offset1, - int[] array2, - int offset2, - int first, - int limit)
      -
       
      -
      static long
      -
      invoke(long[] array1, - int offset1, - long[] array2, - int offset2, - int first, - int limit)
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static intinvoke(int[] array1, + int offset1, + int[] array2, + int offset2, + int first, + int limit) 
        static longinvoke(long[] array1, + int offset1, + long[] array2, + int offset2, + int first, + int limit) 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
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      Constructor Details

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        IDOT

        -
        public IDOT()
        -
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        • + + +

          Constructor Detail

          + + + +
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            IDOT

            +
            public IDOT()
          -
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  • -
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    Method Details

    -
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      invoke

      -
      public static long invoke(long[] array1, - int offset1, - long[] array2, - int offset2, - int first, - int limit)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          invoke

          +
          public static long invoke(long[] array1,
+                          int offset1,
+                          long[] array2,
+                          int offset2,
+                          int first,
+                          int limit)
          • -
        • -
          -

          invoke

          -
          public static int invoke(int[] array1, - int offset1, - int[] array2, - int offset2, - int first, - int limit)
          -
          +
        + + + +
          +
        • +

          invoke

          +
          public static int invoke(int[] array1,
+                         int offset1,
+                         int[] array2,
+                         int offset2,
+                         int first,
+                         int limit)
          +
          • +
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    + + +
    Skip navigation links
    + + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.VectorOperationI32.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.VectorOperationI32.html index 7e43056fb..6a33c4e07 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.VectorOperationI32.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.VectorOperationI32.html @@ -1,139 +1,233 @@ - + + - -IGEMM.VectorOperationI32 (neureka 1.0.0 API) - - - - + +IGEMM.VectorOperationI32 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Interface IGEMM.VectorOperationI32

    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Interface IGEMM.VectorOperationI32

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      IGEMM
      +
      IGEMM
      -
      +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public static interface IGEMM.VectorOperationI32
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
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      -
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      Modifier and Type
      -
      Method
      -
      Description
      -
      void
      -
      invoke(int[] product, - int[] left, - int complexity, - int[] right)
      -
       
      -
      -
      +
      +
      @FunctionalInterface
+public static interface IGEMM.VectorOperationI32
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Method Summary

        + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        voidinvoke(int[] product, + int[] left, + int complexity, + int[] right) 
        +
        • +
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      -
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        invoke

        -
        void invoke(int[] product, - int[] left, - int complexity, - int[] right)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            invoke

            +
            void invoke(int[] product,
+            int[] left,
+            int complexity,
+            int[] right)
            +
            • +
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    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.VectorOperationI64.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.VectorOperationI64.html index 3a8cb7257..e7657adc4 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.VectorOperationI64.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.VectorOperationI64.html @@ -1,139 +1,233 @@ - + + - -IGEMM.VectorOperationI64 (neureka 1.0.0 API) - - - - + +IGEMM.VectorOperationI64 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Interface IGEMM.VectorOperationI64

    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Interface IGEMM.VectorOperationI64

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      IGEMM
      +
      IGEMM
      -
      +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public static interface IGEMM.VectorOperationI64
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      void
      -
      invoke(long[] product, - long[] left, - int complexity, - long[] right)
      -
       
      -
      -
      +
      +
      @FunctionalInterface
+public static interface IGEMM.VectorOperationI64
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Method Summary

        + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        voidinvoke(long[] product, + long[] left, + int complexity, + long[] right) 
        +
        • +
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      -
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        invoke

        -
        void invoke(long[] product, - long[] left, - int complexity, - long[] right)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            invoke

            +
            void invoke(long[] product,
+            long[] left,
+            int complexity,
+            long[] right)
            +
            • +
        -
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    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.html index 00976f41b..631e1ccaa 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/IGEMM.html @@ -1,196 +1,315 @@ - + + - -IGEMM (neureka 1.0.0 API) - - - - + +IGEMM (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.blas
    -

    Class IGEMM

    -
    -
    java.lang.Object -
    neureka.backend.main.operations.linear.internal.blas.IGEMM
    +
    neureka.backend.main.operations.linear.internal.blas
    +

    Class IGEMM

    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.linear.internal.blas.IGEMM
        • +
      +
      • +
    +
    +
      +
    • -
      public class IGEMM -extends Object
      +
      +
      public class IGEMM
+extends java.lang.Object
      A collection of primitive sub-routines for matrix multiplication performed on continuous arrays which are designed so that they can be vectorized by the JVMs JIT compiler (AVX instructions).
      -
    -
    -
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        IGEMM

        -
        public IGEMM()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            IGEMM

            +
            public IGEMM()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      operationForI32

      -
      public static IGEMM.VectorOperationI32 operationForI32(boolean rowMajor, - long rows, - long columns)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          operationForI32

          +
          public static IGEMM.VectorOperationI32 operationForI32(boolean rowMajor,
+                                                       long rows,
+                                                       long columns)
          • -
        • -
          -

          operationForI64

          -
          public static IGEMM.VectorOperationI64 operationForI64(boolean rowMajor, - long rows, - long columns)
          -
          +
        + + + +
          +
        • +

          operationForI64

          +
          public static IGEMM.VectorOperationI64 operationForI64(boolean rowMajor,
+                                                       long rows,
+                                                       long columns)
        -
    • - + + +
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-frame.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-frame.html new file mode 100644 index 000000000..920a67c5b --- /dev/null +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-frame.html @@ -0,0 +1,32 @@ + + + + + +neureka.backend.main.operations.linear.internal.blas (neureka 1.0.1 API) + + + + +

    neureka.backend.main.operations.linear.internal.blas

    +
    +

    Interfaces

    + +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-summary.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-summary.html index 5f563bacd..9d1cc39ec 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-summary.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-summary.html @@ -1,134 +1,223 @@ - + + - -neureka.backend.main.operations.linear.internal.blas (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.linear.internal.blas (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.operations.linear.internal.blas

    -
    -
    -
    package neureka.backend.main.operations.linear.internal.blas
    -
    +

    Package neureka.backend.main.operations.linear.internal.blas

    +
    Everything in this package should be considered library-private! DO NOT USE CLASSES INSIDE THIS PACKAGE!
    -
    -
    -
      -
    • -
      -
      -
      -
      -
      Class
      -
      Description
      -
      AXPY
      -
      +
      +

      See: Description

      +
      +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + + + + + +
        Interface Summary 
        InterfaceDescription
        GEMM.VectorOperationF32 
        GEMM.VectorOperationF64 
        IGEMM.VectorOperationI32 
        IGEMM.VectorOperationI64 
        +
        • +
      • + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        AXPY
        The ?axpy routines perform a vector-vector operation defined as y := a*x + y where: a is a scalar x and y are vectors each with a number of elements that equals n.
        - -
        COPY
        -
        +
        COPY
        The ?copy routines perform a vector-vector operation defined as y = x, where x and y are vectors.
        - -
        DOT
        -
        +
        DOT
        The ?dot routines perform a vector-vector reduction operation defined as Equation where xi and yi are elements of vectors x and y.
        - -
        GEMM
        -
        +
        GEMM
        A collection of primitive sub-routines for matrix multiplication performed on continuous arrays which are designed so that they can be vectorized by the JVMs JIT compiler (AVX instructions).
        - -
        GEMM.VectorOperationF32
        -
         
        -
        GEMM.VectorOperationF64
        -
         
        -
        IAXPY
        -
        +
        IAXPY
        The ?axpy routines perform a vector-vector operation defined as y := a*x + y where: a is a scalar x and y are vectors each with a number of elements that equals n.
        - -
        IDOT
        -
        +
        IDOT
        The ?dot routines perform a vector-vector reduction operation defined as Equation where xi and yi are elements of vectors x and y.
        - -
        IGEMM
        -
        +
        IGEMM
        A collection of primitive sub-routines for matrix multiplication performed on continuous arrays which are designed so that they can be vectorized by the JVMs JIT compiler (AVX instructions).
        - -
        IGEMM.VectorOperationI32
        -
         
        -
        IGEMM.VectorOperationI64
        -
         
        - - - +
      -
    -
    + + + +

    Package neureka.backend.main.operations.linear.internal.blas Description

    +
    Everything in this package should be considered library-private! + DO NOT USE CLASSES INSIDE THIS PACKAGE!
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-tree.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-tree.html index 9f6c1e231..d813dbce9 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-tree.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/blas/package-tree.html @@ -1,86 +1,147 @@ - + + - -neureka.backend.main.operations.linear.internal.blas Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.linear.internal.blas Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.operations.linear.internal.blas

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
        -
      • neureka.backend.main.operations.linear.internal.blas.AXPY
        • -
      • neureka.backend.main.operations.linear.internal.blas.COPY
        • -
      • neureka.backend.main.operations.linear.internal.blas.DOT
        • -
      • neureka.backend.main.operations.linear.internal.blas.GEMM
        • -
      • neureka.backend.main.operations.linear.internal.blas.IAXPY
        • -
      • neureka.backend.main.operations.linear.internal.blas.IDOT
        • -
      • neureka.backend.main.operations.linear.internal.blas.IGEMM
        • +
      • neureka.backend.main.operations.linear.internal.blas.AXPY
        • +
      • neureka.backend.main.operations.linear.internal.blas.COPY
        • +
      • neureka.backend.main.operations.linear.internal.blas.DOT
        • +
      • neureka.backend.main.operations.linear.internal.blas.GEMM
        • +
      • neureka.backend.main.operations.linear.internal.blas.IAXPY
        • +
      • neureka.backend.main.operations.linear.internal.blas.IDOT
        • +
      • neureka.backend.main.operations.linear.internal.blas.IGEMM
    -
    -

    Interface Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLGEMM.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLGEMM.html index b04d5895c..5adc93c3b 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLGEMM.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLGEMM.html @@ -1,180 +1,290 @@ - + + - -CLGEMM (neureka 1.0.0 API) - - - - + +CLGEMM (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.opencl
    -

    Class CLGEMM

    +
    neureka.backend.main.operations.linear.internal.opencl
    +

    Class CLGEMM

    -
    java.lang.Object -
    neureka.backend.main.operations.linear.internal.opencl.CLGEMM
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.linear.internal.opencl.CLGEMM
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CLGEMM()
      -
       
      +
      +
      public class CLGEMM
+extends java.lang.Object
+implements ImplementationFor<OpenCLDevice>
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CLGEMM

        -
        public CLGEMM()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLGEMM

            +
            public CLGEMM()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      run

      -
      public Tensor<?> run(ExecutionCall<OpenCLDevice> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<OpenCLDevice> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<OpenCLDevice>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<OpenCLDevice>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLReduce.Type.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLReduce.Type.html index 897da5586..8300335b4 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLReduce.Type.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLReduce.Type.html @@ -1,221 +1,342 @@ - + + - -CLReduce.Type (neureka 1.0.0 API) - - - - + +CLReduce.Type (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.opencl
    -

    Enum Class CLReduce.Type

    -
    -
    java.lang.Object -
    java.lang.Enum<CLReduce.Type> -
    neureka.backend.main.operations.linear.internal.opencl.CLReduce.Type
    +
    neureka.backend.main.operations.linear.internal.opencl
    +

    Enum CLReduce.Type

    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • java.lang.Enum<CLReduce.Type>
        • +
      • +
          +
        • neureka.backend.main.operations.linear.internal.opencl.CLReduce.Type
          • +
        +
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from class java.lang.Enum

      -Enum.EnumDesc<E extends Enum<E>>
      -
      +
      +
      public static enum CLReduce.Type
+extends java.lang.Enum<CLReduce.Type>
      • - -
    • -
      -

      Enum Constant Summary

      -
      Enum Constants
      -
      -
      Enum Constant
      -
      Description
      -
      MAX
      -
       
      -
      MIN
      -
       
      +
    - +
    + - -
    -
      +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static CLReduce.Type[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static CLReduce.Type[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (CLReduce.Type c : CLReduce.Type.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static CLReduce.Type valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static CLReduce.Type valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLReduce.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLReduce.html index 18b7469ad..9f1152a30 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLReduce.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLReduce.html @@ -1,224 +1,345 @@ - + + - -CLReduce (neureka 1.0.0 API) - - - - + +CLReduce (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.opencl
    -

    Class CLReduce

    -
    -
    java.lang.Object -
    neureka.backend.main.operations.linear.internal.opencl.CLReduce
    +
    neureka.backend.main.operations.linear.internal.opencl
    +

    Class CLReduce

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.linear.internal.opencl.CLReduce
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Class
      -
      Description
      -
      static enum 
      -
      CLReduce.Type
      -
       
      +
      +
      public class CLReduce
+extends java.lang.Object
+implements ImplementationFor<OpenCLDevice>
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Field Details

      -
        -
      • -
        -

        INDICES_MAPPER_ID

        -
        public static String INDICES_MAPPER_ID
        -
        +
          +
        • + + +

          Field Detail

          + + + +
            +
          • +

            INDICES_MAPPER_ID

            +
            public static java.lang.String INDICES_MAPPER_ID
          -
      • +
    -
  • -
    -

    Constructor Details

    -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      run

      -
      public Tensor<Integer> run(ExecutionCall<OpenCLDevice> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<java.lang.Integer> run(ExecutionCall<OpenCLDevice> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<OpenCLDevice>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<OpenCLDevice>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLSum.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLSum.html index 98593314e..6a3f90e2f 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLSum.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/CLSum.html @@ -1,196 +1,310 @@ - + + - -CLSum (neureka 1.0.0 API) - - - - + +CLSum (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.linear.internal.opencl
    -

    Class CLSum

    -
    -
    java.lang.Object -
    neureka.backend.main.operations.linear.internal.opencl.CLSum
    +
    neureka.backend.main.operations.linear.internal.opencl
    +

    Class CLSum

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.linear.internal.opencl.CLSum
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CLSum()
      -
       
      +
      +
      public class CLSum
+extends java.lang.Object
+implements ImplementationFor<OpenCLDevice>
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CLSum

        -
        public CLSum()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLSum

            +
            public CLSum()
          -
      • +
    -
  • -
    -

    Method Details

    - + + + +
      +
    • +

      run

      +
      public static Tensor<java.lang.Float> run(Tensor<java.lang.Float> in,
+                                          OpenCLDevice device)
      This method compiles and executes the kernel that will return the sum of the elements in the in tensor.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-frame.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-frame.html new file mode 100644 index 000000000..7e73c2fde --- /dev/null +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-frame.html @@ -0,0 +1,25 @@ + + + + + +neureka.backend.main.operations.linear.internal.opencl (neureka 1.0.1 API) + + + + +

    neureka.backend.main.operations.linear.internal.opencl

    +
    +

    Classes

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-summary.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-summary.html index 4153479e3..6e7042196 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-summary.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-summary.html @@ -1,94 +1,162 @@ - + + - -neureka.backend.main.operations.linear.internal.opencl (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.linear.internal.opencl (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.operations.linear.internal.opencl

    -
    -
    -
    package neureka.backend.main.operations.linear.internal.opencl
    -
    -
      -
    • -
      -
      -
      -
      -
      Class
      -
      Description
      -
      CLGEMM
      -
       
      -
      CLReduce
      -
       
      -
      CLReduce.Type
      -
       
      -
      CLSum
      -
       
      -
      -
      +

      Package neureka.backend.main.operations.linear.internal.opencl

      +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        CLGEMM 
        CLReduce 
        CLSum 
        +
        • +
      • + + + + + + + + + + + + +
        Enum Summary 
        EnumDescription
        CLReduce.Type 
      -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-tree.html b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-tree.html index 387ea471d..4c1c633ca 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-tree.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/internal/opencl/package-tree.html @@ -1,87 +1,148 @@ - + + - -neureka.backend.main.operations.linear.internal.opencl Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.linear.internal.opencl Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.operations.linear.internal.opencl

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
        -
      • neureka.backend.main.operations.linear.internal.opencl.CLGEMM (implements neureka.backend.api.ImplementationFor<D>)
        • -
      • neureka.backend.main.operations.linear.internal.opencl.CLReduce (implements neureka.backend.api.ImplementationFor<D>)
        • -
      • neureka.backend.main.operations.linear.internal.opencl.CLSum (implements neureka.backend.api.ImplementationFor<D>)
        • +
      • neureka.backend.main.operations.linear.internal.opencl.CLGEMM (implements neureka.backend.api.ImplementationFor<D>)
        • +
      • neureka.backend.main.operations.linear.internal.opencl.CLReduce (implements neureka.backend.api.ImplementationFor<D>)
        • +
      • neureka.backend.main.operations.linear.internal.opencl.CLSum (implements neureka.backend.api.ImplementationFor<D>)
    -
    -
    -

    Enum Class Hierarchy

    +

    Enum Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
        -
      • java.lang.Enum<E> (implements java.lang.Comparable<T>, java.lang.constant.Constable, java.io.Serializable) +
      • java.lang.Enum<E> (implements java.lang.Comparable<T>, java.io.Serializable)
          -
        • neureka.backend.main.operations.linear.internal.opencl.CLReduce.Type
          • +
        • neureka.backend.main.operations.linear.internal.opencl.CLReduce.Type
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/package-frame.html b/docs/jdocs/neureka/backend/main/operations/linear/package-frame.html new file mode 100644 index 000000000..1cc319403 --- /dev/null +++ b/docs/jdocs/neureka/backend/main/operations/linear/package-frame.html @@ -0,0 +1,23 @@ + + + + + +neureka.backend.main.operations.linear (neureka 1.0.1 API) + + + + +

    neureka.backend.main.operations.linear

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/package-summary.html b/docs/jdocs/neureka/backend/main/operations/linear/package-summary.html index e9217220f..1ea1d4d98 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/package-summary.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/package-summary.html @@ -1,134 +1,168 @@ - + + - -neureka.backend.main.operations.linear (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.linear (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.operations.linear

    -
    -
    -
    package neureka.backend.main.operations.linear
    -
    -
    Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
    -
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    -
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    -
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/linear/package-tree.html b/docs/jdocs/neureka/backend/main/operations/linear/package-tree.html index eae54d362..9bc763ad2 100644 --- a/docs/jdocs/neureka/backend/main/operations/linear/package-tree.html +++ b/docs/jdocs/neureka/backend/main/operations/linear/package-tree.html @@ -1,79 +1,142 @@ - + + - -neureka.backend.main.operations.linear Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.linear Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.operations.linear

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/operator/Addition.html b/docs/jdocs/neureka/backend/main/operations/operator/Addition.html index ffe28c85a..9cd7abd41 100644 --- a/docs/jdocs/neureka/backend/main/operations/operator/Addition.html +++ b/docs/jdocs/neureka/backend/main/operations/operator/Addition.html @@ -1,233 +1,310 @@ - + + - -Addition (neureka 1.0.0 API) - - - - + +Addition (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.operator
    -

    Class Addition

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.operator.Addition
    -
    +
    neureka.backend.main.operations.operator
    +

    Class Addition

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    + +
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      +
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      Constructor Details

      -
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      • -
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        Addition

        -
        public Addition()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Addition

            +
            public Addition()
          -
      • +
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  • -
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    Method Details

    -
    • -
  • -
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    calculate

    -
    public double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -243,33 +320,95 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • -
  • -
    -

    calculate

    -
    public static double calculate(double[] inputs, - int d, - Function[] src)
    -
    + + + + +
      +
    • +

      calculate

      +
      public static double calculate(double[] inputs,
+                               int d,
+                               Function[] src)
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/operator/Division.html b/docs/jdocs/neureka/backend/main/operations/operator/Division.html index b48761867..c4aa16162 100644 --- a/docs/jdocs/neureka/backend/main/operations/operator/Division.html +++ b/docs/jdocs/neureka/backend/main/operations/operator/Division.html @@ -1,233 +1,310 @@ - + + - -Division (neureka 1.0.0 API) - - - - + +Division (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.operator
    -

    Class Division

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.operator.Division
    -
    +
    neureka.backend.main.operations.operator
    +

    Class Division

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      +
    • -
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      Constructor Details

      -
        -
      • -
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        Division

        -
        public Division()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Division

            +
            public Division()
          -
      • +
    -
  • -
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    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -243,33 +320,95 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • -
  • -
    -

    calculate

    -
    public static double calculate(double[] inputs, - int d, - Function[] src)
    -
    + + + + +
      +
    • +

      calculate

      +
      public static double calculate(double[] inputs,
+                               int d,
+                               Function[] src)
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/operator/Modulo.html b/docs/jdocs/neureka/backend/main/operations/operator/Modulo.html index 61013321a..37f8458ff 100644 --- a/docs/jdocs/neureka/backend/main/operations/operator/Modulo.html +++ b/docs/jdocs/neureka/backend/main/operations/operator/Modulo.html @@ -1,241 +1,321 @@ - + + - -Modulo (neureka 1.0.0 API) - - - - + +Modulo (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.operator
    -

    Class Modulo

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.operator.Modulo
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    +
    neureka.backend.main.operations.operator
    +

    Class Modulo

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      +
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      Constructor Details

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      • -
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        Modulo

        -
        public Modulo()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Modulo

            +
            public Modulo()
          -
      • +
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  • -
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    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -251,25 +331,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/operator/Multiplication.html b/docs/jdocs/neureka/backend/main/operations/operator/Multiplication.html index 969253bf7..68b9e39ec 100644 --- a/docs/jdocs/neureka/backend/main/operations/operator/Multiplication.html +++ b/docs/jdocs/neureka/backend/main/operations/operator/Multiplication.html @@ -1,246 +1,327 @@ - + + - -Multiplication (neureka 1.0.0 API) - - - - + +Multiplication (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.operator
    -

    Class Multiplication

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.operator.Multiplication
    -
    +
    neureka.backend.main.operations.operator
    +

    Class Multiplication

    -
    -
    +
    + +
    +
    -
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    + - -
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      +
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      +
    • -
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      Constructor Details

      -
        -
      • -
        -

        Multiplication

        -
        public Multiplication()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Multiplication

            +
            public Multiplication()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -256,33 +337,95 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • -
  • -
    -

    calculate

    -
    public static double calculate(double[] inputs, - int d, - Function[] src)
    -
    + + + + +
      +
    • +

      calculate

      +
      public static double calculate(double[] inputs,
+                               int d,
+                               Function[] src)
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/operator/Power.html b/docs/jdocs/neureka/backend/main/operations/operator/Power.html index e00d26df4..3e2b6bf05 100644 --- a/docs/jdocs/neureka/backend/main/operations/operator/Power.html +++ b/docs/jdocs/neureka/backend/main/operations/operator/Power.html @@ -1,233 +1,310 @@ - + + - -Power (neureka 1.0.0 API) - - - - + +Power (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.operator
    -

    Class Power

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.operator.Power
    -
    +
    neureka.backend.main.operations.operator
    +

    Class Power

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Power

        -
        public Power()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Power

            +
            public Power()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -243,33 +320,95 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • -
  • -
    -

    calculate

    -
    public static double calculate(double[] inputs, - int d, - Function[] src)
    -
    + + + + +
      +
    • +

      calculate

      +
      public static double calculate(double[] inputs,
+                               int d,
+                               Function[] src)
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/operator/Subtraction.html b/docs/jdocs/neureka/backend/main/operations/operator/Subtraction.html index 2dcf80c97..8c8c31fac 100644 --- a/docs/jdocs/neureka/backend/main/operations/operator/Subtraction.html +++ b/docs/jdocs/neureka/backend/main/operations/operator/Subtraction.html @@ -1,233 +1,310 @@ - + + - -Subtraction (neureka 1.0.0 API) - - - - + +Subtraction (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.operator
    -

    Class Subtraction

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.operator.Subtraction
    -
    +
    neureka.backend.main.operations.operator
    +

    Class Subtraction

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Subtraction

        -
        public Subtraction()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Subtraction

            +
            public Subtraction()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -243,33 +320,95 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • -
  • -
    -

    calculate

    -
    public static double calculate(double[] inputs, - int d, - Function[] src)
    -
    + + + + +
      +
    • +

      calculate

      +
      public static double calculate(double[] inputs,
+                               int d,
+                               Function[] src)
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/operator/package-frame.html b/docs/jdocs/neureka/backend/main/operations/operator/package-frame.html new file mode 100644 index 000000000..279856bb2 --- /dev/null +++ b/docs/jdocs/neureka/backend/main/operations/operator/package-frame.html @@ -0,0 +1,24 @@ + + + + + +neureka.backend.main.operations.operator (neureka 1.0.1 API) + + + + +

    neureka.backend.main.operations.operator

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/operations/operator/package-summary.html b/docs/jdocs/neureka/backend/main/operations/operator/package-summary.html index e0d6b5b47..fe2921adb 100644 --- a/docs/jdocs/neureka/backend/main/operations/operator/package-summary.html +++ b/docs/jdocs/neureka/backend/main/operations/operator/package-summary.html @@ -1,136 +1,172 @@ - + + - -neureka.backend.main.operations.operator (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.operator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.operations.operator

    -
    -
    -
    package neureka.backend.main.operations.operator
    -
    -
    Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
    -
    -
    -
    -
    -
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/operator/package-tree.html b/docs/jdocs/neureka/backend/main/operations/operator/package-tree.html index 3c8b5b69a..1961f2eba 100644 --- a/docs/jdocs/neureka/backend/main/operations/operator/package-tree.html +++ b/docs/jdocs/neureka/backend/main/operations/operator/package-tree.html @@ -1,80 +1,143 @@ - + + - -neureka.backend.main.operations.operator Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.operator Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.operations.operator

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
        -
      • neureka.backend.api.template.operations.AbstractOperation (implements neureka.backend.api.Operation) +
      • neureka.backend.api.template.operations.AbstractOperation (implements neureka.backend.api.Operation)
          -
        • neureka.backend.main.operations.operator.Addition
          • -
        • neureka.backend.main.operations.operator.Division
          • -
        • neureka.backend.main.operations.operator.Modulo
          • -
        • neureka.backend.main.operations.operator.Multiplication
          • -
        • neureka.backend.main.operations.operator.Power
          • -
        • neureka.backend.main.operations.operator.Subtraction
          • +
        • neureka.backend.main.operations.operator.Addition
          • +
        • neureka.backend.main.operations.operator.Division
          • +
        • neureka.backend.main.operations.operator.Modulo
          • +
        • neureka.backend.main.operations.operator.Multiplication
          • +
        • neureka.backend.main.operations.operator.Power
          • +
        • neureka.backend.main.operations.operator.Subtraction
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/AssignLeft.html b/docs/jdocs/neureka/backend/main/operations/other/AssignLeft.html index 8f28477c8..2664cc8ab 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/AssignLeft.html +++ b/docs/jdocs/neureka/backend/main/operations/other/AssignLeft.html @@ -1,210 +1,286 @@ - + + - -AssignLeft (neureka 1.0.0 API) - - - - + +AssignLeft (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class AssignLeft

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.AssignLeft
    -
    +
    neureka.backend.main.operations.other
    +

    Class AssignLeft

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    +
    + +
    +
    -
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    +
    +
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      +
    • -
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      Constructor Details

      -
        -
      • -
        -

        AssignLeft

        -
        public AssignLeft()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            AssignLeft

            +
            public AssignLeft()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -220,25 +296,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/Cat.html b/docs/jdocs/neureka/backend/main/operations/other/Cat.html index 2a6c703a4..550550249 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/Cat.html +++ b/docs/jdocs/neureka/backend/main/operations/other/Cat.html @@ -1,194 +1,266 @@ - + + - -Cat (neureka 1.0.0 API) - - - - + +Cat (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class Cat

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.Cat
    +
    neureka.backend.main.operations.other
    +

    Class Cat

    -
    -
    -
    +
    + +
    +
    -
    - +
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    + - -
    -
      +
    +
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      +
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    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Cat

        -
        public Cat()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Cat

            +
            public Cat()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      execute

      -
      public Result execute(Function caller, - ExecutionCall<?> call)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          execute

          +
          public Result execute(Function caller,
+                      ExecutionCall<?> call)
          • -
        • -
          -

          calculate

          -
          public double calculate(double[] inputs, - int j, - int d, - Function[] src)
          -
          Description copied from interface: Operation
          +
        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -204,25 +276,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/DimFit.html b/docs/jdocs/neureka/backend/main/operations/other/DimFit.html index d724b8018..5b6dcbdc1 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/DimFit.html +++ b/docs/jdocs/neureka/backend/main/operations/other/DimFit.html @@ -1,183 +1,251 @@ - + + - -DimFit (neureka 1.0.0 API) - - - - + +DimFit (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class DimFit

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.DimFit
    +
    neureka.backend.main.operations.other
    +

    Class DimFit

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
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      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        DimFit

        -
        public DimFit()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            DimFit

            +
            public DimFit()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      calculate

      -
      public double calculate(double[] inputs, - int j, - int d, - Function[] src)
      -
      Description copied from interface: Operation
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -193,25 +261,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/DimTrim.html b/docs/jdocs/neureka/backend/main/operations/other/DimTrim.html index e14ca0c59..b92968053 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/DimTrim.html +++ b/docs/jdocs/neureka/backend/main/operations/other/DimTrim.html @@ -1,192 +1,264 @@ - + + - -DimTrim (neureka 1.0.0 API) - - - - + +DimTrim (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class DimTrim

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.DimTrim
    +
    neureka.backend.main.operations.other
    +

    Class DimTrim

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        DimTrim

        -
        public DimTrim()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            DimTrim

            +
            public DimTrim()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      endsFrom

      -
      public static int[] endsFrom(int[] shape)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          endsFrom

          +
          public static int[] endsFrom(int[] shape)
          • -
        • -
          -

          calculate

          -
          public double calculate(double[] inputs, - int j, - int d, - Function[] src)
          -
          Description copied from interface: Operation
          +
        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -202,25 +274,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/Max.html b/docs/jdocs/neureka/backend/main/operations/other/Max.html index b45de084b..4518f794f 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/Max.html +++ b/docs/jdocs/neureka/backend/main/operations/other/Max.html @@ -1,183 +1,251 @@ - + + - -Max (neureka 1.0.0 API) - - - - + +Max (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class Max

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.Max
    +
    neureka.backend.main.operations.other
    +

    Class Max

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Max

        -
        public Max()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Max

            +
            public Max()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      calculate

      -
      public double calculate(double[] inputs, - int j, - int d, - Function[] src)
      -
      Description copied from interface: Operation
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -193,25 +261,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/Min.html b/docs/jdocs/neureka/backend/main/operations/other/Min.html index 2a50477e3..6a54fc9db 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/Min.html +++ b/docs/jdocs/neureka/backend/main/operations/other/Min.html @@ -1,183 +1,251 @@ - + + - -Min (neureka 1.0.0 API) - - - - + +Min (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class Min

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.Min
    +
    neureka.backend.main.operations.other
    +

    Class Min

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Min

        -
        public Min()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Min

            +
            public Min()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      calculate

      -
      public double calculate(double[] inputs, - int j, - int d, - Function[] src)
      -
      Description copied from interface: Operation
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -193,25 +261,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/Permute.html b/docs/jdocs/neureka/backend/main/operations/other/Permute.html index fd70b205c..6cae79c73 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/Permute.html +++ b/docs/jdocs/neureka/backend/main/operations/other/Permute.html @@ -1,219 +1,299 @@ - + + - -Permute (neureka 1.0.0 API) - - - - + +Permute (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class Permute

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.Permute
    -
    +
    neureka.backend.main.operations.other
    +

    Class Permute

    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Permute

        -
        public Permute()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Permute

            +
            public Permute()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • -
  • -
    -

    calculate

    -
    public double calculate(double[] inputs, - int j, - int d, - Function[] src)
    -
    Description copied from interface: Operation
    + + + + +
      +
    • +

      calculate

      +
      public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
      +
      Description copied from interface: Operation
      This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
      + Function instances...
      ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

      This is also the reason why the last parameter of this method is a list of Function objects : @@ -229,25 +309,84 @@

      calculate

      Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - An array of scalar input variables.
      j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
      d - The index of the variable of which a derivative ought to be calculated.
      src - The child nodes of the Function node to which this very OperationType belongs.
      -
      Returns:
      +
      Returns:
      The result of the calculation.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/Randomization.html b/docs/jdocs/neureka/backend/main/operations/other/Randomization.html index fa1fc0031..0323d2b39 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/Randomization.html +++ b/docs/jdocs/neureka/backend/main/operations/other/Randomization.html @@ -1,190 +1,258 @@ - + + - -Randomization (neureka 1.0.0 API) - - - - + +Randomization (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class Randomization

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.Randomization
    +
    neureka.backend.main.operations.other
    +

    Class Randomization

    -
    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Operation
      +
      Operation
      -
      public class Randomization -extends AbstractOperation
      -
      This Operation takes an optional user seed, +
      +
      public class Randomization
+extends AbstractOperation
      +
      This Operation takes an optional user seed, the shape of its input tensor, and the indices of individual elements within said tensor to generate floats or doubles with a gaussian distribution where the mean is 0 and the standard deviation is 1. This operation is very fast because it generates numbers in parallel unlike - the JDKs random number generator class Random.
      -
    -
    -
      + the JDKs random number generator class Random.
    + + +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Randomization

        -
        public Randomization()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Randomization

            +
            public Randomization()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      calculate

      -
      public double calculate(double[] inputs, - int j, - int d, - Function[] src)
      -
      Description copied from interface: Operation
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -200,25 +268,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/ReLayout.html b/docs/jdocs/neureka/backend/main/operations/other/ReLayout.html index 25be98a3f..b8637a293 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/ReLayout.html +++ b/docs/jdocs/neureka/backend/main/operations/other/ReLayout.html @@ -1,187 +1,256 @@ - + + - -ReLayout (neureka 1.0.0 API) - - - - + +ReLayout (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class ReLayout

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.ReLayout
    +
    neureka.backend.main.operations.other
    +

    Class ReLayout

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        ReLayout

        -
        public ReLayout()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ReLayout

            +
            public ReLayout()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      calculate

      -
      public double calculate(double[] inputs, - int j, - int d, - Function[] src)
      -
      Description copied from interface: Operation
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -197,32 +266,94 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
      • -
    • -
      -

      toLayout

      -
      public static Tensor<?> toLayout(Tensor<?> t, - NDConfiguration.Layout target)
      -
      +
    + + + +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/Reshape.html b/docs/jdocs/neureka/backend/main/operations/other/Reshape.html index 56d02a95d..759472ca3 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/Reshape.html +++ b/docs/jdocs/neureka/backend/main/operations/other/Reshape.html @@ -1,183 +1,251 @@ - + + - -Reshape (neureka 1.0.0 API) - - - - + +Reshape (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class Reshape

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.Reshape
    +
    neureka.backend.main.operations.other
    +

    Class Reshape

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Reshape

        -
        public Reshape()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Reshape

            +
            public Reshape()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      calculate

      -
      public double calculate(double[] inputs, - int j, - int d, - Function[] src)
      -
      Description copied from interface: Operation
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -193,25 +261,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/Slice.html b/docs/jdocs/neureka/backend/main/operations/other/Slice.html index 471f1cf41..e0d22153b 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/Slice.html +++ b/docs/jdocs/neureka/backend/main/operations/other/Slice.html @@ -1,183 +1,251 @@ - + + - -Slice (neureka 1.0.0 API) - - - - + +Slice (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class Slice

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.Slice
    +
    neureka.backend.main.operations.other
    +

    Class Slice

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Slice

        -
        public Slice()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Slice

            +
            public Slice()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      calculate

      -
      public double calculate(double[] inputs, - int j, - int d, - Function[] src)
      -
      Description copied from interface: Operation
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -193,25 +261,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/Sum.html b/docs/jdocs/neureka/backend/main/operations/other/Sum.html index 06d17d1f0..bd5c913e8 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/Sum.html +++ b/docs/jdocs/neureka/backend/main/operations/other/Sum.html @@ -1,183 +1,251 @@ - + + - -Sum (neureka 1.0.0 API) - - - - + +Sum (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other
    -

    Class Sum

    -
    -
    java.lang.Object -
    neureka.backend.api.template.operations.AbstractOperation -
    neureka.backend.main.operations.other.Sum
    +
    neureka.backend.main.operations.other
    +

    Class Sum

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Sum

        -
        public Sum()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Sum

            +
            public Sum()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      calculate

      -
      public double calculate(double[] inputs, - int j, - int d, - Function[] src)
      -
      Description copied from interface: Operation
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          calculate

          +
          public double calculate(double[] inputs,
+                        int j,
+                        int d,
+                        Function[] src)
          +
          Description copied from interface: Operation
          This method mainly ought to serve as a reference- and fallback- implementation for tensor backends and also as the backend for handling the calculation of scalar inputs passed to a given abstract syntax tree of - Function instances...
          + Function instances...
          ( (almost) every Function instance contains an OperationType reference to which it passes scalar executions... )

          This is also the reason why the last parameter of this method is a list of Function objects : @@ -193,25 +261,84 @@

          calculate

          Depending on this variable and also the nature of the operation, the execution calls to the child nodes of this node change considerably!

          -
          -
          Parameters:
          +
          +
          Parameters:
          inputs - An array of scalar input variables.
          j - The index variable for indexed execution on the input array. (-1 if no indexing should occur)
          d - The index of the variable of which a derivative ought to be calculated.
          src - The child nodes of the Function node to which this very OperationType belongs.
          -
          Returns:
          +
          Returns:
          The result of the calculation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/internal/CPUReduce.Type.html b/docs/jdocs/neureka/backend/main/operations/other/internal/CPUReduce.Type.html index dc3bd50bb..0d23fd114 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/internal/CPUReduce.Type.html +++ b/docs/jdocs/neureka/backend/main/operations/other/internal/CPUReduce.Type.html @@ -1,221 +1,342 @@ - + + - -CPUReduce.Type (neureka 1.0.0 API) - - - - + +CPUReduce.Type (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other.internal
    -

    Enum Class CPUReduce.Type

    -
    -
    java.lang.Object -
    java.lang.Enum<CPUReduce.Type> -
    neureka.backend.main.operations.other.internal.CPUReduce.Type
    +
    neureka.backend.main.operations.other.internal
    +

    Enum CPUReduce.Type

    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • java.lang.Enum<CPUReduce.Type>
        • +
      • +
          +
        • neureka.backend.main.operations.other.internal.CPUReduce.Type
          • +
        +
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from class java.lang.Enum

      -Enum.EnumDesc<E extends Enum<E>>
      -
      +
      +
      public static enum CPUReduce.Type
+extends java.lang.Enum<CPUReduce.Type>
      • - -
    • -
      -

      Enum Constant Summary

      -
      Enum Constants
      -
      -
      Enum Constant
      -
      Description
      -
      MAX
      -
       
      -
      MIN
      -
       
      +
    - +
    + - -
    -
      +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static CPUReduce.Type[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static CPUReduce.Type[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (CPUReduce.Type c : CPUReduce.Type.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static CPUReduce.Type valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static CPUReduce.Type valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/internal/CPUReduce.html b/docs/jdocs/neureka/backend/main/operations/other/internal/CPUReduce.html index 61276f677..d301fd2ef 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/internal/CPUReduce.html +++ b/docs/jdocs/neureka/backend/main/operations/other/internal/CPUReduce.html @@ -1,201 +1,315 @@ - + + - -CPUReduce (neureka 1.0.0 API) - - - - + +CPUReduce (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other.internal
    -

    Class CPUReduce

    +
    neureka.backend.main.operations.other.internal
    +

    Class CPUReduce

    -
    java.lang.Object -
    neureka.backend.main.operations.other.internal.CPUReduce
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.other.internal.CPUReduce
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      ImplementationFor<CPU>
      +
      ImplementationFor<CPU>
      -
      public class CPUReduce -extends Object -implements ImplementationFor<CPU>
      +
      +
      public class CPUReduce
+extends java.lang.Object
+implements ImplementationFor<CPU>
      An implementation of the min and max algorithm running on the CPU. This algorithm splits the provided input tensor into chucks which are then reduced to local min and max values. This happens iteratively until only a single value is left. Each workload also returns the index of the found min/max value, which is important for backpropagation...
      -
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Class
      -
      Description
      -
      static enum 
      -
      CPUReduce.Type
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      run

      -
      public Tensor<Integer> run(ExecutionCall<CPU> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<java.lang.Integer> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/internal/CPUSum.html b/docs/jdocs/neureka/backend/main/operations/other/internal/CPUSum.html index 6b7ce3d41..85ec3b7ee 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/internal/CPUSum.html +++ b/docs/jdocs/neureka/backend/main/operations/other/internal/CPUSum.html @@ -1,184 +1,294 @@ - + + - -CPUSum (neureka 1.0.0 API) - - - - + +CPUSum (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.main.operations.other.internal
    -

    Class CPUSum

    +
    neureka.backend.main.operations.other.internal
    +

    Class CPUSum

    -
    java.lang.Object -
    neureka.backend.main.operations.other.internal.CPUSum
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.main.operations.other.internal.CPUSum
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      ImplementationFor<CPU>
      +
      ImplementationFor<CPU>
      -
      public class CPUSum -extends Object -implements ImplementationFor<CPU>
      +
      +
      public class CPUSum
+extends java.lang.Object
+implements ImplementationFor<CPU>
      An implementation of the sum and may algorithm running on the CPU. This algorithm splits the provided input tensor into chucks which are then reduced to local sum values. This happens iteratively until only a single value is left.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      CPUSum()
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        CPUSum() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      Tensor<?>
      -
      run(ExecutionCall<CPU> call)
      -
      + -
      -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CPUSum

        -
        public CPUSum()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CPUSum

            +
            public CPUSum()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      run

      -
      public Tensor<?> run(ExecutionCall<CPU> call)
      -
      Description copied from interface: ImplementationFor
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          run

          +
          public Tensor<?> run(ExecutionCall<CPU> call)
          +
          Description copied from interface: ImplementationFor
          This method is the entrypoint for a concrete implementation of the algorithm to which instances of this interface belong and the device on which this is implemented. One has to keep in mind that the implementation details with respect to the target device are specific to the methods of the "TargetDevice" type on which this call should run...
          -
          -
          Specified by:
          -
          run in interface ImplementationFor<CPU>
          -
          Parameters:
          +
          +
          Specified by:
          +
          run in interface ImplementationFor<CPU>
          +
          Parameters:
          call - The call which ought to be executed on this implementation.
          -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/internal/package-frame.html b/docs/jdocs/neureka/backend/main/operations/other/internal/package-frame.html new file mode 100644 index 000000000..e66228c7f --- /dev/null +++ b/docs/jdocs/neureka/backend/main/operations/other/internal/package-frame.html @@ -0,0 +1,24 @@ + + + + + +neureka.backend.main.operations.other.internal (neureka 1.0.1 API) + + + + +

    neureka.backend.main.operations.other.internal

    +
    +

    Classes

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/operations/other/internal/package-summary.html b/docs/jdocs/neureka/backend/main/operations/other/internal/package-summary.html index 5ed9e5720..187c12bd6 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/internal/package-summary.html +++ b/docs/jdocs/neureka/backend/main/operations/other/internal/package-summary.html @@ -1,111 +1,162 @@ - + + - -neureka.backend.main.operations.other.internal (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.other.internal (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.operations.other.internal

    -
    -
    -
    package neureka.backend.main.operations.other.internal
    -
    -
      -
    • - -
      • -
    • -
      -
      -
      -
      -
      Class
      -
      Description
      -
      CPUReduce
      -
      +
      +
        +
      • + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        CPUReduce
        An implementation of the min and max algorithm running on the CPU.
        - -
        CPUReduce.Type
        -
         
        -
        CPUSum
        -
        +
        CPUSum
        An implementation of the sum and may algorithm running on the CPU.
        - - - - +
        +
        • +
      • + + + + + + + + + + + + +
        Enum Summary 
        EnumDescription
        CPUReduce.Type 
      -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/internal/package-tree.html b/docs/jdocs/neureka/backend/main/operations/other/internal/package-tree.html index 45c6ced0e..c8cd13171 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/internal/package-tree.html +++ b/docs/jdocs/neureka/backend/main/operations/other/internal/package-tree.html @@ -1,86 +1,147 @@ - + + - -neureka.backend.main.operations.other.internal Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.other.internal Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.operations.other.internal

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    -

    Enum Class Hierarchy

    +

    Enum Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/package-frame.html b/docs/jdocs/neureka/backend/main/operations/other/package-frame.html new file mode 100644 index 000000000..20fc3e72d --- /dev/null +++ b/docs/jdocs/neureka/backend/main/operations/other/package-frame.html @@ -0,0 +1,30 @@ + + + + + +neureka.backend.main.operations.other (neureka 1.0.1 API) + + + + +

    neureka.backend.main.operations.other

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/operations/other/package-summary.html b/docs/jdocs/neureka/backend/main/operations/other/package-summary.html index f79101d0f..623077a77 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/package-summary.html +++ b/docs/jdocs/neureka/backend/main/operations/other/package-summary.html @@ -1,156 +1,202 @@ - + + - -neureka.backend.main.operations.other (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.other (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.operations.other

    -
    -
    -
    package neureka.backend.main.operations.other
    -
    -
    Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
    -
    -
    -
    -
    + + + +

    Package neureka.backend.main.operations.other Description

    +
    Everything in this package should be considered library-private! + DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! + Code inside this package or any sub-packages might change frequently...
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
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    + diff --git a/docs/jdocs/neureka/backend/main/operations/other/package-tree.html b/docs/jdocs/neureka/backend/main/operations/other/package-tree.html index 88a21d127..24ebb14be 100644 --- a/docs/jdocs/neureka/backend/main/operations/other/package-tree.html +++ b/docs/jdocs/neureka/backend/main/operations/other/package-tree.html @@ -1,86 +1,149 @@ - + + - -neureka.backend.main.operations.other Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations.other Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.operations.other

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
        -
      • neureka.backend.api.template.operations.AbstractOperation (implements neureka.backend.api.Operation) +
      • neureka.backend.api.template.operations.AbstractOperation (implements neureka.backend.api.Operation)
          -
        • neureka.backend.main.operations.other.AssignLeft
          • -
        • neureka.backend.main.operations.other.Cat
          • -
        • neureka.backend.main.operations.other.DimFit
          • -
        • neureka.backend.main.operations.other.DimTrim
          • -
        • neureka.backend.main.operations.other.Max
          • -
        • neureka.backend.main.operations.other.Min
          • -
        • neureka.backend.main.operations.other.Permute
          • -
        • neureka.backend.main.operations.other.Randomization
          • -
        • neureka.backend.main.operations.other.ReLayout
          • -
        • neureka.backend.main.operations.other.Reshape
          • -
        • neureka.backend.main.operations.other.Slice
          • -
        • neureka.backend.main.operations.other.Sum
          • +
        • neureka.backend.main.operations.other.AssignLeft
          • +
        • neureka.backend.main.operations.other.Cat
          • +
        • neureka.backend.main.operations.other.DimFit
          • +
        • neureka.backend.main.operations.other.DimTrim
          • +
        • neureka.backend.main.operations.other.Max
          • +
        • neureka.backend.main.operations.other.Min
          • +
        • neureka.backend.main.operations.other.Permute
          • +
        • neureka.backend.main.operations.other.Randomization
          • +
        • neureka.backend.main.operations.other.ReLayout
          • +
        • neureka.backend.main.operations.other.Reshape
          • +
        • neureka.backend.main.operations.other.Slice
          • +
        • neureka.backend.main.operations.other.Sum
    -
    -
    + +
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    Skip navigation links
    + + + + +
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    + diff --git a/docs/jdocs/neureka/backend/main/operations/package-frame.html b/docs/jdocs/neureka/backend/main/operations/package-frame.html new file mode 100644 index 000000000..f670b7e1d --- /dev/null +++ b/docs/jdocs/neureka/backend/main/operations/package-frame.html @@ -0,0 +1,20 @@ + + + + + +neureka.backend.main.operations (neureka 1.0.1 API) + + + + +

    neureka.backend.main.operations

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/main/operations/package-summary.html b/docs/jdocs/neureka/backend/main/operations/package-summary.html index 81e4b9d9a..2a2dc3259 100644 --- a/docs/jdocs/neureka/backend/main/operations/package-summary.html +++ b/docs/jdocs/neureka/backend/main/operations/package-summary.html @@ -1,131 +1,159 @@ - + + - -neureka.backend.main.operations (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.main.operations

    -
    -
    -
    package neureka.backend.main.operations
    -
    -
    Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
    -
    -
    -
      -
    • -
      • -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      ConvUtil
      -
       
      -
      ElemWiseUtil
      -
      -
      Methods inside this utility class execute only some ExecutionCall arguments - in groups if their total number exceeds the arity of an operation.
      -
      -
      -
      -
      • + +
      + + +
      Skip navigation links
      + + + + -
    -
    -
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/main/operations/package-tree.html b/docs/jdocs/neureka/backend/main/operations/package-tree.html index 3cda5cdd8..213c9117e 100644 --- a/docs/jdocs/neureka/backend/main/operations/package-tree.html +++ b/docs/jdocs/neureka/backend/main/operations/package-tree.html @@ -1,72 +1,135 @@ - + + - -neureka.backend.main.operations Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.main.operations Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.main.operations

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/ocl/CLBackend.html b/docs/jdocs/neureka/backend/ocl/CLBackend.html index 0de02bab9..e148536b4 100644 --- a/docs/jdocs/neureka/backend/ocl/CLBackend.html +++ b/docs/jdocs/neureka/backend/ocl/CLBackend.html @@ -1,337 +1,489 @@ - + + - -CLBackend (neureka 1.0.0 API) - - - - + +CLBackend (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.ocl
    -

    Class CLBackend

    -
    -
    java.lang.Object -
    neureka.backend.ocl.CLBackend
    +
    neureka.backend.ocl
    +

    Class CLBackend

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.backend.ocl.CLBackend
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      BackendExtension, Component<Extensions>
      +
      BackendExtension, Component<Extensions>
      -
      public final class CLBackend -extends Object -implements BackendExtension
      -
      This is an OpenCL context component for any given BackendContext which +
      +
      public final class CLBackend
+extends java.lang.Object
+implements BackendExtension
      +
      This is an OpenCL context component for any given BackendContext which extends a given backend context instance for additional functionality, which in this case is the OpenCL backend storing platform and device information. - BackendContexts are thread local states - used for managing Operation, Function - as well as Component implementation instances like this one. + BackendContexts are thread local states + used for managing Operation, Function + as well as Component implementation instances like this one. A given state might not be compatible with the concepts introduced in other contexts which is why it makes sense to have separate "worlds" with potential different operations... - The component system of the BackendContext exist so that a given context + The component system of the BackendContext exist so that a given context can be extended for more functionality - and also to attach relevant states like for example in this case the CLBackend + and also to attach relevant states like for example in this case the CLBackend instance will directly or indirectly reference kernels, memory objects and other concepts exposed by OpenCL...
      -
    -
    -
      + +
    +
    +
    + - + -
  • -
    -

    Method Details

    - + + + +
    • -
  • -
    -

    getSettings

    -
    public CLSettings getSettings()
    -
    -
    Returns:
    + + + + +
      +
    • +

      getSettings

      +
      public CLSettings getSettings()
      +
      +
      Returns:
      A container for OpenCL specific settings.
      -
    • -
  • -
    -

    update

    -
    public boolean update(Component.OwnerChangeRequest<Extensions> changeRequest)
    -
    Updating the CLContext will cause the list of existing OpenCLPlatform instances to be - cleared and refilled with completely new OpenCLPlatform instances. - This will in effect also cause the recreation of any OpenCLDevice instances - as part of these OpenCLPlatforms. + + + + +
      +
    • +

      update

      +
      public boolean update(Component.OwnerChangeRequest<Extensions> changeRequest)
      +
      Updating the CLContext will cause the list of existing OpenCLPlatform instances to be + cleared and refilled with completely new OpenCLPlatform instances. + This will in effect also cause the recreation of any OpenCLDevice instances + as part of these OpenCLPlatforms. This will subsequently cause the recompilation of many OpenCL kernels.
      -
      -
      Specified by:
      -
      update in interface Component<Extensions>
      -
      Parameters:
      -
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      -
      Returns:
      +
      +
      Specified by:
      +
      update in interface Component<Extensions>
      +
      Parameters:
      +
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      +
      Returns:
      The truth value determining if the state change should be aborted or not.
      -
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • -
  • -
    -

    find

    -
    public BackendExtension.DeviceOption find(String searchKey)
    -
    Description copied from interface: BackendExtension
    -
    The BackendContext does not handle Device instances directly. - Instead, the task of instantiating and exposing Device implementations - should be carried by BackendExtension implementations. + + + + +
      +
    • +

      find

      +
      public BackendExtension.DeviceOption find(java.lang.String searchKey)
      +
      Description copied from interface: BackendExtension
      +
      The BackendContext does not handle Device instances directly. + Instead, the task of instantiating and exposing Device implementations + should be carried by BackendExtension implementations. One extension might be implementing CUDA operations, - therefore, the extension should also deal with some sort of CUDADevice implementation.
      -
      -
      Specified by:
      -
      find in interface BackendExtension
      -
      Parameters:
      -
      searchKey - The search key used to find a suitable Device implementation in this extension.
      -
      Returns:
      -
      A suitable BackendExtension.DeviceOption or null if nothing was found.
      + therefore, the extension should also deal with some sort of CUDADevice implementation.
    +
    +
    Specified by:
    +
    find in interface BackendExtension
    +
    Parameters:
    +
    searchKey - The search key used to find a suitable Device implementation in this extension.
    +
    Returns:
    +
    A suitable BackendExtension.DeviceOption or null if nothing was found.
    -
    • -
  • -
    -

    reset

    -
    public void reset()
    -
    Description copied from interface: BackendExtension
    -
    This will indirectly be called through the Neureka.reset() method, + + + + +
    • -
  • -
    -

    dispose

    -
    public void dispose()
    + + + + +
      +
    • +

      dispose

      +
      public void dispose()
      This method will free all the resources occupied by this context, meaning that all platforms and their devices will be disposed. Their kernels will be removed and their tensors restored.
      -
      -
      Specified by:
      -
      dispose in interface BackendExtension
      +
      +
      Specified by:
      +
      dispose in interface BackendExtension
      -
    • -
  • -
    -

    getLoader

    -
    public BackendLoader getLoader()
    -
    -
    Specified by:
    -
    getLoader in interface BackendExtension
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/ocl/CLSettings.html b/docs/jdocs/neureka/backend/ocl/CLSettings.html index 70fb7503f..eecd736c8 100644 --- a/docs/jdocs/neureka/backend/ocl/CLSettings.html +++ b/docs/jdocs/neureka/backend/ocl/CLSettings.html @@ -1,177 +1,295 @@ - + + - -CLSettings (neureka 1.0.0 API) - - - - + +CLSettings (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.backend.ocl
    -

    Class CLSettings

    -
    -
    java.lang.Object -
    neureka.backend.ocl.CLSettings
    +
    neureka.backend.ocl
    +

    Class CLSettings

    -
    -
    -
    public class CLSettings -extends Object
    -
    OpenCL related settings for the CLBackend extension.
    -
    -
    -
      - +
      +
        +
      • java.lang.Object
      • -
        -

        Constructor Summary

        -
        Constructors
        -
        -
        Constructor
        -
        Description
        -
        CLSettings()
        -
         
        +
          +
        • neureka.backend.ocl.CLSettings
          • +
        +
        • +
      +
      +
        +
      • +
        +
        +
        public class CLSettings
+extends java.lang.Object
        +
        OpenCL related settings for the CLBackend extension.
        +
        • +
      -
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CLSettings

        -
        public CLSettings()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLSettings

            +
            public CLSettings()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      isAutoConvertToFloat

      -
      public boolean isAutoConvertToFloat()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          isAutoConvertToFloat

          +
          public boolean isAutoConvertToFloat()
          • -
        • -
          -

          setAutoConvertToFloat

          -
          public CLSettings setAutoConvertToFloat(boolean autoConvertToFloat)
          -
          +
        + + + +
          +
        • +

          setAutoConvertToFloat

          +
          public CLSettings setAutoConvertToFloat(boolean autoConvertToFloat)
          • -
        • -
          -

          reset

          -
          public void reset()
          -
          +
        + + + +
          +
        • +

          reset

          +
          public void reset()
          +
          • +
      -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/ocl/package-frame.html b/docs/jdocs/neureka/backend/ocl/package-frame.html new file mode 100644 index 000000000..e913a749f --- /dev/null +++ b/docs/jdocs/neureka/backend/ocl/package-frame.html @@ -0,0 +1,20 @@ + + + + + +neureka.backend.ocl (neureka 1.0.1 API) + + + + +

    neureka.backend.ocl

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/backend/ocl/package-summary.html b/docs/jdocs/neureka/backend/ocl/package-summary.html index 4001ddecc..5271ab73a 100644 --- a/docs/jdocs/neureka/backend/ocl/package-summary.html +++ b/docs/jdocs/neureka/backend/ocl/package-summary.html @@ -1,90 +1,149 @@ - + + - -neureka.backend.ocl (neureka 1.0.0 API) - - - - + +neureka.backend.ocl (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.backend.ocl

    +

    Package neureka.backend.ocl

    -
    -
    package neureka.backend.ocl
    -
    -
      -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      CLBackend
      -
      -
      This is an OpenCL context component for any given BackendContext which +
      +
        +
      • + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        CLBackend +
        This is an OpenCL context component for any given BackendContext which extends a given backend context instance for additional functionality, which in this case is the OpenCL backend storing platform and device information.
        - -
        CLSettings
        -
        -
        OpenCL related settings for the CLBackend extension.
        -
        - - +
        CLSettings +
        OpenCL related settings for the CLBackend extension.
        +
      -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/backend/ocl/package-tree.html b/docs/jdocs/neureka/backend/ocl/package-tree.html index f70a83c94..bb31a90f9 100644 --- a/docs/jdocs/neureka/backend/ocl/package-tree.html +++ b/docs/jdocs/neureka/backend/ocl/package-tree.html @@ -1,72 +1,135 @@ - + + - -neureka.backend.ocl Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.backend.ocl Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.backend.ocl

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/common/composition/AbstractComponentOwner.html b/docs/jdocs/neureka/common/composition/AbstractComponentOwner.html index 758882496..2d469b310 100644 --- a/docs/jdocs/neureka/common/composition/AbstractComponentOwner.html +++ b/docs/jdocs/neureka/common/composition/AbstractComponentOwner.html @@ -1,380 +1,473 @@ - + + - -AbstractComponentOwner (neureka 1.0.0 API) - - - - + +AbstractComponentOwner (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.composition
    -

    Class AbstractComponentOwner<C>

    -
    -
    java.lang.Object -
    neureka.common.composition.AbstractComponentOwner<C>
    +
    neureka.common.composition
    +

    Class AbstractComponentOwner<C>

    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.common.composition.AbstractComponentOwner<C>
        • +
      +
      • +
    +
    +
    -
    -
    - +
    +
      +
    • + + -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      protected void
      -
      _deleteComponents()
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods Concrete Methods 
        Modifier and TypeMethod and Description
        protected void_deleteComponents()
        This method deletes the array of components of this component owner by nulling the array variable field.
        - -
        protected abstract <T extends Component<C>>
        T
        -
        _removeOrReject(T newComponent)
        -
        +
        protected abstract <T extends Component<C>>
        T        		_removeOrReject(T newComponent)
        An implementation of this method checks if the passed component should be removed from the component collection of this class or its removal should be "rejected".
        - -
        protected <T> void
        -
        _set(Component<T> anyComponent)
        -
         
        -
        protected abstract <T extends Component<C>>
        T
        -
        _setOrReject(T newComponent)
        -
        +
        protected <T> void_set(Component<T> anyComponent) 
        protected abstract <T extends Component<C>>
        T        		_setOrReject(T newComponent)
        This abstract method ought to be implemented further down the inheritance hierarchy where it's responsibility makes more sense, namely : An implementation of this method checks if the passed component should be added or "rejected" to the component collection of this class.
        - -
        protected C
        -
        _this()
        -
         
        -
        protected void
        -
        _transferFrom(AbstractComponentOwner<C> other)
        -
        +
        protected C_this() 
        protected void_transferFrom(AbstractComponentOwner<C> other)
        A component owner might need to exchange components.
        - -
        <T extends Component>
        Optional<T>
        -
        find(Class<T> componentClass)
        -
        +
        <T extends Component>
        java.util.Optional<T>        		find(java.lang.Class<T> componentClass)
        This method finds a component of the given type/class - and returns it as an Optional which may or may not be empty.
        - -
        <T extends Component<?>>
        T
        -
        get(Class<T> componentClass)
        -
        + and returns it as an Optional which may or may not be empty.
        +
        <T extends Component<?>>
        T        		get(java.lang.Class<T> componentClass)
        This method tries to find a component inside the internal component array whose class matches the one provided.
        - -
        <T extends Component<?>>
        List<T>
        -
        getAll(Class<T> componentClass)
        -
        +
        <T extends Component<?>>
        java.util.List<T>        		getAll(java.lang.Class<T> componentClass)
        This method tries to find all components inside the internal component array whose classes are sub types of the one provided.
        - -
        <T extends Component<C>>
        boolean
        -
        has(Class<T> componentClass)
        -
        -
        This method checks if a component identified by the passed Class +
        <T extends Component<C>>
        boolean        		has(java.lang.Class<T> componentClass) +
        This method checks if a component identified by the passed Class instance is present inside the stored component collection.
        - -
        <T extends Component<C>>
        C
        -
        remove(Class<T> componentClass)
        -
        +
        <T extends Component<C>>
        C        		remove(java.lang.Class<T> componentClass)
        This method removes a component identified by the passed Class instance if found in the stored component collection.
        - -
        <T extends Component<C>>
        C
        -
        set(T newComponent)
        -
        +
        <T extends Component<C>>
        C        		set(T newComponent)
        This methods stores the passed component inside the component collection of this class...
        - - - - -
        -

        Methods inherited from class java.lang.Object

        -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
        - +
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
      -
      -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        AbstractComponentOwner

        -
        public AbstractComponentOwner()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            AbstractComponentOwner

            +
            public AbstractComponentOwner()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      _this

      -
      protected C _this()
      -
      +
        +
      • + + +

        Method Detail

        + + + + + + + +
          +
        • +

          _transferFrom

          +
          protected void _transferFrom(AbstractComponentOwner<C> other)
          A component owner might need to exchange components.
          Meaning that the components of another owner will be transferred and adopted by the current one. During this process the transferred components will be notified of their new owner. This is important because some components might reference their owners...

          - This change happens for example in the Tensor class when tensors are being instantiated by + This change happens for example in the Tensor class when tensors are being instantiated by certain constructors which require the injection of the contents of another tensor into a new one.
          -
          -
          Parameters:
          +
          +
          Parameters:
          other - The other owner which will be stripped of its components which are then incorporated into this owner.
          -
    • -
  • -
    -

    _deleteComponents

    -
    protected void _deleteComponents()
    + + + + +
      +
    • +

      _deleteComponents

      +
      protected void _deleteComponents()
      This method deletes the array of components of this component owner by nulling the array variable field.
      -
    • -
  • -
    -

    get

    -
    public <T extends Component<?>> T get(Class<T> componentClass)
    + + + + +
      +
    • +

      get

      +
      public <T extends Component<?>> T get(java.lang.Class<T> componentClass)
      This method tries to find a component inside the internal component array whose class matches the one provided. If no such component could be found then the return value will simply be null.
      -
      -
      Specified by:
      -
      get in interface ComponentOwner<C>
      -
      Type Parameters:
      +
      +
      Specified by:
      +
      get in interface ComponentOwner<C>
      +
      Type Parameters:
      T - The type parameter defining the component class.
      -
      Parameters:
      +
      Parameters:
      componentClass - The type/class of the component which shall be found and returned.
      -
      Returns:
      +
      Returns:
      The correct component or null if nothing has been found.
      -
    • -
  • -
    -

    getAll

    -
    public <T extends Component<?>> List<T> getAll(Class<T> componentClass)
    + + + + +
      +
    • +

      getAll

      +
      public <T extends Component<?>> java.util.List<T> getAll(java.lang.Class<T> componentClass)
      This method tries to find all components inside the internal component array whose classes are sub types of the one provided. If no such components could be found then the return value will simply be an empty list.
      -
      -
      Specified by:
      -
      getAll in interface ComponentOwner<C>
      -
      Type Parameters:
      +
      +
      Specified by:
      +
      getAll in interface ComponentOwner<C>
      +
      Type Parameters:
      T - The type parameter defining the component class.
      -
      Parameters:
      +
      Parameters:
      componentClass - The type/class of the components which shall be found and returned as list.
      -
      Returns:
      +
      Returns:
      The correct component or null if nothing has been found.
      -
    • -
  • -
    -

    remove

    -
    public <T extends Component<C>> C remove(Class<T> componentClass)
    + + + + +
      +
    • +

      remove

      +
      public <T extends Component<C>> C remove(java.lang.Class<T> componentClass)
      This method removes a component identified by the passed Class instance if found in the stored component collection.
      -
      -
      Specified by:
      -
      remove in interface ComponentOwner<C>
      -
      Type Parameters:
      +
      +
      Specified by:
      +
      remove in interface ComponentOwner<C>
      +
      Type Parameters:
      T - The type parameter of the component which will be removed by this method.
      -
      Parameters:
      +
      Parameters:
      componentClass - The type/class of a component which will be removed by this method.
      -
      Returns:
      +
      Returns:
      This very class.
      -
    • -
  • -
    -

    has

    -
    public <T extends Component<C>> boolean has(Class<T> componentClass)
    -
    This method checks if a component identified by the passed Class + + + + +
      +
    • +

      has

      +
      public <T extends Component<C>> boolean has(java.lang.Class<T> componentClass)
      +
      This method checks if a component identified by the passed Class instance is present inside the stored component collection.
      -
      -
      Specified by:
      -
      has in interface ComponentOwner<C>
      -
      Type Parameters:
      +
      +
      Specified by:
      +
      has in interface ComponentOwner<C>
      +
      Type Parameters:
      T - The type of the component to be checked.
      -
      Parameters:
      +
      Parameters:
      componentClass - The class/type of component that might exist in components.
      -
      Returns:
      +
      Returns:
      True if the component of the given type/class has been found.
      -
    • -
  • -
    -

    set

    -
    public <T extends Component<C>> C set(T newComponent)
    + + + + + + +
      +
    • +

      set

      +
      public <T extends Component<C>> C set(T newComponent)
      This methods stores the passed component inside the component collection of this class... However, it only adds the provided component if it is not "rejected" by an abstract method, namely : "_addOrReject" ! Rejection means that this method simply returns null.
      -
      -
      Specified by:
      -
      set in interface ComponentOwner<C>
      -
      Type Parameters:
      +
      +
      Specified by:
      +
      set in interface ComponentOwner<C>
      +
      Type Parameters:
      T - The type of the component to be set.
      -
      Parameters:
      +
      Parameters:
      newComponent - The new component which should be added to the components list.
      -
      Returns:
      +
      Returns:
      This very class.
      -
    • -
  • -
    -

    _set

    -
    protected <T> void _set(Component<T> anyComponent)
    -
    + + + + +
      +
    • +

      _set

      +
      protected <T> void _set(Component<T> anyComponent)
      • -
    • -
      -

      _setOrReject

      -
      protected abstract <T extends Component<C>> T _setOrReject(T newComponent)
      +
    + + + + + +
      +
    • +

      _setOrReject

      +
      protected abstract <T extends Component<C>> T _setOrReject(T newComponent)
      This abstract method ought to be implemented further down the inheritance hierarchy where it's responsibility makes more sense, namely : @@ -383,59 +476,126 @@

      _setOrReject

      of this class. Rejection in this case simply means that it returns null instead of the passed component.
      -
      -
      Parameters:
      +
      +
      Parameters:
      newComponent - The component which should be added to the components list.
      -
      Returns:
      +
      Returns:
      The same component or null if it has been rejected.
      -
      • -
    • -
      -

      _removeOrReject

      -
      protected abstract <T extends Component<C>> T _removeOrReject(T newComponent)
      +
    + + + + + +
      +
    • +

      _removeOrReject

      +
      protected abstract <T extends Component<C>> T _removeOrReject(T newComponent)
      An implementation of this method checks if the passed component should be removed from the component collection of this class or its removal should be "rejected". Rejection in this case simply means that it returns null instead of the passed component.
      -
      -
      Parameters:
      +
      +
      Parameters:
      newComponent - The component which should be removed from the components list.
      -
      Returns:
      +
      Returns:
      The same component or null if its removal has been rejected.
      -
      • -
    • -
      -

      find

      -
      public <T extends Component> Optional<T> find(Class<T> componentClass)
      +
    + + + +
      +
    • +

      find

      +
      public <T extends Component> java.util.Optional<T> find(java.lang.Class<T> componentClass)
      This method finds a component of the given type/class - and returns it as an Optional which may or may not be empty.
      -
      -
      Specified by:
      -
      find in interface ComponentOwner<C>
      -
      Type Parameters:
      + and returns it as an Optional which may or may not be empty.
    • +
    +
    Specified by:
    +
    find in interface ComponentOwner<C>
    +
    Type Parameters:
    T - The type parameter of the component which should be found. - This type must be a subtype of Component.
    -
    Parameters:
    + This type must be a subtype of Component. +
    Parameters:
    componentClass - The type/class of the component which should be found. - This class must be a subtype of Component.
    -
    Returns:
    -
    An Optional which may or may not be empty.
    + This class must be a subtype of Component. +
    Returns:
    +
    An Optional which may or may not be empty.
    - - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/common/composition/Component.IsBeing.html b/docs/jdocs/neureka/common/composition/Component.IsBeing.html index 09153d715..4f8752b31 100644 --- a/docs/jdocs/neureka/common/composition/Component.IsBeing.html +++ b/docs/jdocs/neureka/common/composition/Component.IsBeing.html @@ -1,239 +1,368 @@ - + + - -Component.IsBeing (neureka 1.0.0 API) - - - - + +Component.IsBeing (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.composition
    -

    Enum Class Component.IsBeing

    -
    -
    java.lang.Object -
    java.lang.Enum<Component.IsBeing> -
    neureka.common.composition.Component.IsBeing
    -
    +
    neureka.common.composition
    +

    Enum Component.IsBeing

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • java.lang.Enum<Component.IsBeing>
        • +
      • +
          +
        • neureka.common.composition.Component.IsBeing
          • +
        +
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from class java.lang.Enum

      -Enum.EnumDesc<E extends Enum<E>>
      -
      + of the owner of a given Component instance.
    - -
  • -
    -

    Enum Constant Summary

    -
    Enum Constants
    -
    -
    Enum Constant
    -
    Description
    -
    ADDED
    -
     
    -
    REMOVED
    -
     
    -
    REPLACED
    -
     
    -
    UPDATED
    -
     
    +
    -
    +
    +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static Component.IsBeing[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static Component.IsBeing[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (Component.IsBeing c : Component.IsBeing.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static Component.IsBeing valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static Component.IsBeing valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/common/composition/Component.OwnerChangeRequest.html b/docs/jdocs/neureka/common/composition/Component.OwnerChangeRequest.html index 8ce645770..63602ed19 100644 --- a/docs/jdocs/neureka/common/composition/Component.OwnerChangeRequest.html +++ b/docs/jdocs/neureka/common/composition/Component.OwnerChangeRequest.html @@ -1,196 +1,302 @@ - + + - -Component.OwnerChangeRequest (neureka 1.0.0 API) - - - - + +Component.OwnerChangeRequest (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.composition
    -

    Interface Component.OwnerChangeRequest<O>

    +
    neureka.common.composition
    +

    Interface Component.OwnerChangeRequest<O>

    -
    -
    -
    Type Parameters:
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      O - The type parameter representing the concrete type of the component owner.
      -
      +
      Enclosing interface:
      -
      Component<O>
      +
      Component<O>
      -
      public static interface Component.OwnerChangeRequest<O>
      -
      Component.OwnerChangeRequest implementation instances will be passed to - the Component.update(OwnerChangeRequest) method which inform a +
      +
      public static interface Component.OwnerChangeRequest<O>
      +
      Component.OwnerChangeRequest implementation instances will be passed to + the Component.update(OwnerChangeRequest) method which inform a given component about a state change related to said component. They are used by component owners to communicate and - negotiate update events to their components using the Component.IsBeing enum and + negotiate update events to their components using the Component.IsBeing enum and some useful methods providing both a context for a component and the ability for the component to trigger the state change itself.
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      boolean
      -
      executeChange()
      -
      -
      This method will trigger the actual state change identified by the Component.IsBeing - constant returned by the type() method.
      +
      • +
    -
    O
    -
    getNewOwner()
    -
     
    -
    O
    -
    getOldOwner()
    -
     
    -
    default Component.IsBeing
    -
    type()
    -
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        getOldOwner

        -
        O getOldOwner()
        -
        -
        Returns:
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            getOldOwner

            +
            O getOldOwner()
            +
            +
            Returns:
            The previous owner type instance or null if the component is being added to the owner.
            -
        • -
      • -
        -

        getNewOwner

        -
        O getNewOwner()
        -
        -
        Returns:
        +
      + + + +
        +
      • +

        getNewOwner

        +
        O getNewOwner()
        +
        +
        Returns:
        The new owner type instance.
        -
      • -
    • -
      -

      executeChange

      -
      boolean executeChange()
      -
      This method will trigger the actual state change identified by the Component.IsBeing - constant returned by the type() method. +
    + + + +
      +
    • +

      executeChange

      +
      boolean executeChange()
      +
      This method will trigger the actual state change identified by the Component.IsBeing + constant returned by the type() method. It exists so that a component can decide when the change should occur. - If the change type is set to Component.IsBeing.ADDED for example then this would + If the change type is set to Component.IsBeing.ADDED for example then this would mean that after calling this method, the current component will now be a component of the current component owner.
      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if the state change was successfully executed.
      -
      • -
    • -
      -

      type

      -
      default Component.IsBeing type()
      +
    + + + +
    +
    +
    Returns:
    The type of change that is about to happen to the component receiving this.
    - - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/common/composition/Component.html b/docs/jdocs/neureka/common/composition/Component.html index 2e1029b65..a84599ee9 100644 --- a/docs/jdocs/neureka/common/composition/Component.html +++ b/docs/jdocs/neureka/common/composition/Component.html @@ -1,158 +1,198 @@ - + + - -Component (neureka 1.0.0 API) - - - - + +Component (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.composition
    -

    Interface Component<O>

    +
    neureka.common.composition
    +

    Interface Component<O>

    -
    -
    -
    Type Parameters:
    +
    +
    +
    -
    -
      +
      +
      public interface Component<O>
      +
      This interface alongside the AbstractComponentOwner class define a simple component system. + The component type defined by this interface is used to create components for the Tensor class + as well as the BackendContext class which both directly or indirectly + extend the AbstractComponentOwner class. + The type parameter of this interface represents the "owner" of the Component.
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        update

        -
        default boolean update(Component.OwnerChangeRequest<O> changeRequest)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            update

            +
            default boolean update(Component.OwnerChangeRequest<O> changeRequest)
            Components are not the slaves of their owners. If the owner registers any state changes related to a given component, then said component will be informed by the owner about the change as well as receive @@ -162,26 +202,85 @@

            update

            is being added to, or removed from, its current owner. If components hold references to their owners then this method gives them the ability to update said reference when a new owner takes over the components of an old one. - The Component.OwnerChangeRequest implementation instance passed to this method - informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). + The Component.OwnerChangeRequest implementation instance passed to this method + informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). If this method returns false then this means that this component rejects the proposed update. The component owner will then abort the proposed change.
            -
            -
            Parameters:
            -
            changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
            -
            Returns:
            +
            +
            Parameters:
            +
            changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
            +
            Returns:
            The truth value determining if the state change should be aborted or not.
            -
      -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/common/composition/ComponentOwner.html b/docs/jdocs/neureka/common/composition/ComponentOwner.html index efedca0d6..d4d5a87ce 100644 --- a/docs/jdocs/neureka/common/composition/ComponentOwner.html +++ b/docs/jdocs/neureka/common/composition/ComponentOwner.html @@ -1,253 +1,369 @@ - + + - -ComponentOwner (neureka 1.0.0 API) - - - - + +ComponentOwner (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.composition
    -

    Interface ComponentOwner<C>

    +
    neureka.common.composition
    +

    Interface ComponentOwner<C>

    -
    -
    -
    Type Parameters:
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      C - The concrete type of the component implementing this interface.
      -
      +
      All Known Subinterfaces:
      -
      Tensor<V>
      +
      Tensor<V>
      -
      +
      All Known Implementing Classes:
      -
      AbstractComponentOwner, Args, Extensions
      +
      AbstractComponentOwner, Args, Extensions
      -
      public interface ComponentOwner<C>
      +
      +
      public interface ComponentOwner<C>
      A component owner is a thing holding components which can be accessed by their type class. This interface is used to create extensible APIs through flexible composition.
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      <T extends Component>
      Optional<T>
      -
      find(Class<T> componentClass)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        <T extends Component>
        java.util.Optional<T>        		find(java.lang.Class<T> componentClass)
        This method finds a component of the given type/class - and returns it as an Optional which may or may not be empty.
        - -
        <T extends Component<?>>
        T
        -
        get(Class<T> componentClass)
        -
        + and returns it as an Optional which may or may not be empty.
        +
        <T extends Component<?>>
        T        		get(java.lang.Class<T> componentClass)
        Use this to get the component of the specified component type class.
        - -
        <T extends Component<?>>
        List<T>
        -
        getAll(Class<T> componentClass)
        -
        +
        <T extends Component<?>>
        java.util.List<T>        		getAll(java.lang.Class<T> componentClass)
        Use this to get all components of the specified component type class.
        - -
        <T extends Component<C>>
        boolean
        -
        has(Class<T> componentClass)
        -
        +
        <T extends Component<C>>
        boolean        		has(java.lang.Class<T> componentClass)
        Use this to check if a component of the specified component type class is present.
        - -
        <T extends Component<C>>
        C
        -
        remove(Class<T> componentClass)
        -
        +
        <T extends Component<C>>
        C        		remove(java.lang.Class<T> componentClass)
        Use this to remove a component of the specified component type class.
        - -
        <T extends Component<C>>
        C
        -
        set(T newComponent)
        -
        +
        <T extends Component<C>>
        C        		set(T newComponent)
        Use this to set a component.
        - - - - - +
      -
      -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        get

        -
        <T extends Component<?>> T get(Class<T> componentClass)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            get

            +
            <T extends Component<?>> T get(java.lang.Class<T> componentClass)
            Use this to get the component of the specified component type class.
            -
            -
            Type Parameters:
            +
            +
            Type Parameters:
            T - The type of the component to be retrieved.
            -
            Parameters:
            +
            Parameters:
            componentClass - The class of the component to be retrieved.
            -
            Returns:
            +
            Returns:
            The component of the specified type class.
            -
        • -
      • -
        -

        getAll

        -
        <T extends Component<?>> List<T> getAll(Class<T> componentClass)
        +
      + + + +
        +
      • +

        getAll

        +
        <T extends Component<?>> java.util.List<T> getAll(java.lang.Class<T> componentClass)
        Use this to get all components of the specified component type class.
        -
        -
        Type Parameters:
        +
        +
        Type Parameters:
        T - The type of the components to be retrieved.
        -
        Parameters:
        +
        Parameters:
        componentClass - The class of the components to be retrieved.
        -
        Returns:
        +
        Returns:
        A list of all components of the specified type class.
        -
      • -
    • -
      -

      remove

      -
      <T extends Component<C>> C remove(Class<T> componentClass)
      +
    + + + +
      +
    • +

      remove

      +
      <T extends Component<C>> C remove(java.lang.Class<T> componentClass)
      Use this to remove a component of the specified component type class.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type of the component to be removed.
      -
      Parameters:
      +
      Parameters:
      componentClass - The class of the component to be removed.
      -
      Returns:
      +
      Returns:
      This component owner instance (to allow for method chaining if so desired).
      -
      • -
    • -
      -

      has

      -
      <T extends Component<C>> boolean has(Class<T> componentClass)
      +
    + + + +
      +
    • +

      has

      +
      <T extends Component<C>> boolean has(java.lang.Class<T> componentClass)
      Use this to check if a component of the specified component type class is present.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type of the component to be checked.
      -
      Parameters:
      +
      Parameters:
      componentClass - The class of the component to be checked.
      -
      Returns:
      +
      Returns:
      True if a component of the specified type class is present, false otherwise.
      -
      • -
    • -
      -

      set

      -
      <T extends Component<C>> C set(T newComponent)
      +
    + + + + + +
      +
    • +

      set

      +
      <T extends Component<C>> C set(T newComponent)
      Use this to set a component.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type of the component to be set.
      -
      Parameters:
      +
      Parameters:
      newComponent - The new component to be set.
      -
      Returns:
      +
      Returns:
      This component owner instance (to allow for method chaining if so desired).
      -
      • -
    • -
      -

      find

      -
      <T extends Component> Optional<T> find(Class<T> componentClass)
      +
    + + + +
      +
    • +

      find

      +
      <T extends Component> java.util.Optional<T> find(java.lang.Class<T> componentClass)
      This method finds a component of the given type/class - and returns it as an Optional which may or may not be empty.
      -
      -
      Type Parameters:
      + and returns it as an Optional which may or may not be empty.
    +
    +
    Type Parameters:
    T - The type parameter of the component which should be found. - This type must be a subtype of Component.
    -
    Parameters:
    + This type must be a subtype of Component. +
    Parameters:
    componentClass - The type/class of the component which should be found. - This class must be a subtype of Component.
    -
    Returns:
    -
    An Optional which may or may not be empty.
    + This class must be a subtype of Component. +
    Returns:
    +
    An Optional which may or may not be empty.
    - - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/common/composition/package-frame.html b/docs/jdocs/neureka/common/composition/package-frame.html new file mode 100644 index 000000000..f9fdc6858 --- /dev/null +++ b/docs/jdocs/neureka/common/composition/package-frame.html @@ -0,0 +1,29 @@ + + + + + +neureka.common.composition (neureka 1.0.1 API) + + + + +

    neureka.common.composition

    +
    +

    Interfaces

    + +

    Classes

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/common/composition/package-summary.html b/docs/jdocs/neureka/common/composition/package-summary.html index b7c91b4eb..21f97c17b 100644 --- a/docs/jdocs/neureka/common/composition/package-summary.html +++ b/docs/jdocs/neureka/common/composition/package-summary.html @@ -1,111 +1,192 @@ - + + - -neureka.common.composition (neureka 1.0.0 API) - - - - + +neureka.common.composition (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.common.composition

    -
    -
    -
    package neureka.common.composition
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/common/composition/package-tree.html b/docs/jdocs/neureka/common/composition/package-tree.html index 98e157347..94d436efd 100644 --- a/docs/jdocs/neureka/common/composition/package-tree.html +++ b/docs/jdocs/neureka/common/composition/package-tree.html @@ -1,93 +1,152 @@ - + + - -neureka.common.composition Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.common.composition Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.common.composition

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -

    Interface Hierarchy

    -
    -
    -

    Enum Class Hierarchy

    +

    Enum Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/common/utility/Cache.LazyEntry.html b/docs/jdocs/neureka/common/utility/Cache.LazyEntry.html index db0fb400b..46363ab25 100644 --- a/docs/jdocs/neureka/common/utility/Cache.LazyEntry.html +++ b/docs/jdocs/neureka/common/utility/Cache.LazyEntry.html @@ -1,197 +1,317 @@ - + + - -Cache.LazyEntry (neureka 1.0.0 API) - - - - + +Cache.LazyEntry (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.utility
    -

    Class Cache.LazyEntry<K,V>

    -
    -
    java.lang.Object -
    neureka.common.utility.Cache.LazyEntry<K,V>
    +
    neureka.common.utility
    +

    Class Cache.LazyEntry<K,V>

    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.common.utility.Cache.LazyEntry<K,V>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      K - The key type parameter.
      V - The value type parameter.
      -
      +
      Enclosing class:
      -
      Cache<O>
      +
      Cache<O>
      -
      public static class Cache.LazyEntry<K,V> -extends Object
      +
      +
      public static class Cache.LazyEntry<K,V>
+extends java.lang.Object
      Lazy cache entries are entries whose values will be calculated only when the entry is being stored in the cache.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      LazyEntry(K directory, - Function<K,V> valueSupplier)
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        LazyEntry

        -
        public LazyEntry(K directory, - Function<K,V> valueSupplier)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + + + +
            +
          • +

            LazyEntry

            +
            public LazyEntry(K directory,
+                 java.util.function.Function<K,V> valueSupplier)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getValue

      -
      public V getValue()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getValue

          +
          public V getValue()
          • -
        • -
          -

          equals

          -
          public boolean equals(Object o)
          -
          -
          Overrides:
          -
          equals in class Object
          +
        + + + +
          +
        • +

          equals

          +
          public boolean equals(java.lang.Object o)
          +
          +
          Overrides:
          +
          equals in class java.lang.Object
          -
    • -
  • -
    -

    hashCode

    -
    public int hashCode()
    -
    -
    Overrides:
    -
    hashCode in class Object
    + + + + +
      +
    • +

      hashCode

      +
      public int hashCode()
      +
      +
      Overrides:
      +
      hashCode in class java.lang.Object
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/common/utility/Cache.html b/docs/jdocs/neureka/common/utility/Cache.html index 5bf346d8a..5e7fc78c2 100644 --- a/docs/jdocs/neureka/common/utility/Cache.html +++ b/docs/jdocs/neureka/common/utility/Cache.html @@ -1,207 +1,333 @@ - + + - -Cache (neureka 1.0.0 API) - - - - + +Cache (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.utility
    -

    Class Cache<O>

    -
    -
    java.lang.Object -
    neureka.common.utility.Cache<O>
    +
    neureka.common.utility
    +

    Class Cache<O>

    -
    -
    -
    Type Parameters:
    -
    O - The type that should be cached, this may be an NDConfiguration or int[] array.
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.common.utility.Cache<O>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      +
      O - The type that should be cached, this may be an NDConfiguration or int[] array.
      -
      public final class Cache<O> -extends Object
      +
      +
      public final class Cache<O>
+extends java.lang.Object
      This is a simple, fixed size cache for immutable objects which are shared throughout the library runtime... This is an internal class which should not be used outside Neurekas internals.
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Class
      -
      Description
      -
      static class 
      -
      Cache.LazyEntry<K,V>
      -
      +
        +
      • + + +

        Nested Class Summary

        + + + + + + + + + + +
        Nested Classes 
        Modifier and TypeClass and Description
        static class Cache.LazyEntry<K,V>
        Lazy cache entries are entries whose values will be calculated only when the entry is being stored in the cache.
        - - - +
        • +
      -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      Cache(int size)
      -
       
      -
      -
      +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        Cache(int size) 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      boolean
      -
      has(O o)
      -
       
      -
      <T extends O>
      T
      -
      process(T newObject)
      -
       
      -
      int
      -
      size()
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        booleanhas(O o) 
        <T extends O>
        T        		process(T newObject) 
        intsize() 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
      -
      • -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Cache

        -
        public Cache(int size)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Cache

            +
            public Cache(int size)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      process

      -
      public <T extends O> T process(T newObject)
      -
      -
      Parameters:
      +
        +
      • + + +

        Method Detail

        + + + + + +
          +
        • +

          process

          +
          public <T extends O> T process(T newObject)
          +
          +
          Parameters:
          newObject - The object which may or may not be cached.
          -
          Returns:
          +
          Returns:
          Either the provided object or the object found inside the cache...
          -
      • -
    • -
      -

      has

      -
      public boolean has(O o)
      -
      +
    + + + + + +
      +
    • +

      has

      +
      public boolean has(O o)
      • -
    • -
      -

      size

      -
      public int size()
      -
      +
    + + + +
      +
    • +

      size

      +
      public int size()
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/common/utility/DataConverter.ForTensor.html b/docs/jdocs/neureka/common/utility/DataConverter.ForTensor.html index af983e6d9..069e85421 100644 --- a/docs/jdocs/neureka/common/utility/DataConverter.ForTensor.html +++ b/docs/jdocs/neureka/common/utility/DataConverter.ForTensor.html @@ -1,220 +1,354 @@ - + + - -DataConverter.ForTensor (neureka 1.0.0 API) - - - - + +DataConverter.ForTensor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.utility
    -

    Class DataConverter.ForTensor

    +
    neureka.common.utility
    +

    Class DataConverter.ForTensor

    -
    java.lang.Object -
    neureka.common.utility.DataConverter.ForTensor
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.common.utility.DataConverter.ForTensor
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      DataConverter
      +
      DataConverter
      -
      public static class DataConverter.ForTensor -extends Object
      +
      +
      public static class DataConverter.ForTensor
+extends java.lang.Object
      This is a stateful and parallelized converter for converting the internal data array of a tensor to another data array based on a provided lambda. The converter will consider tensors with more complex access pattern like for example those of slices.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      ForTensor(Tensor<?> t)
      -
       
      +
      • +
    - +
    + - -
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      +
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    +
      +
    • -
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      Constructor Details

      -
        -
      • -
        -

        ForTensor

        -
        public ForTensor(Tensor<?> t)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            ForTensor

            +
            public ForTensor(Tensor<?> t)
          -
      • +
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  • -
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    Method Details

    -
      -
    • -
      -

      toFloatArray

      -
      public float[] toFloatArray(Function<Integer,Number> source)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          toFloatArray

          +
          public float[] toFloatArray(java.util.function.Function<java.lang.Integer,java.lang.Number> source)
          • -
        • -
          -

          toByteArray

          -
          public byte[] toByteArray(Function<Integer,Number> source)
          -
          +
        + + + +
          +
        • +

          toByteArray

          +
          public byte[] toByteArray(java.util.function.Function<java.lang.Integer,java.lang.Number> source)
          • -
        • -
          -

          toLongArray

          -
          public long[] toLongArray(Function<Integer,Number> source)
          -
          +
        + + + +
          +
        • +

          toLongArray

          +
          public long[] toLongArray(java.util.function.Function<java.lang.Integer,java.lang.Number> source)
          • -
        • -
          -

          toIntArray

          -
          public int[] toIntArray(Function<Integer,Number> source)
          -
          +
        + + + +
          +
        • +

          toIntArray

          +
          public int[] toIntArray(java.util.function.Function<java.lang.Integer,java.lang.Number> source)
          • -
        • -
          -

          toDoubleArray

          -
          public double[] toDoubleArray(Function<Integer,Number> source)
          -
          +
        + + + +
          +
        • +

          toDoubleArray

          +
          public double[] toDoubleArray(java.util.function.Function<java.lang.Integer,java.lang.Number> source)
          • -
        • -
          -

          toShortArray

          -
          public short[] toShortArray(Function<Integer,Number> source)
          -
          +
        + + + +
          +
        • +

          toShortArray

          +
          public short[] toShortArray(java.util.function.Function<java.lang.Integer,java.lang.Number> source)
          • -
        • -
          -

          toObjectArray

          -
          public Object[] toObjectArray(Function<Integer,Object> source)
          -
          +
        + + + +
          +
        • +

          toObjectArray

          +
          public java.lang.Object[] toObjectArray(java.util.function.Function<java.lang.Integer,java.lang.Object> source)
          +
          • +
      -
    • - +
    +
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/common/utility/DataConverter.Utility.html b/docs/jdocs/neureka/common/utility/DataConverter.Utility.html index ae53effdb..ff3d3d913 100644 --- a/docs/jdocs/neureka/common/utility/DataConverter.Utility.html +++ b/docs/jdocs/neureka/common/utility/DataConverter.Utility.html @@ -1,670 +1,992 @@ - + + - -DataConverter.Utility (neureka 1.0.0 API) - - - - + +DataConverter.Utility (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.utility
    -

    Class DataConverter.Utility

    -
    -
    java.lang.Object -
    neureka.common.utility.DataConverter.Utility
    +
    neureka.common.utility
    +

    Class DataConverter.Utility

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.common.utility.DataConverter.Utility
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      DataConverter
      +
      DataConverter
      -
      public static class DataConverter.Utility -extends Object
      +
      +
      public static class DataConverter.Utility
+extends java.lang.Object
      This is a static utility class containing the actual conversion logic which is usually referenced by the Converter lambdas via method signatures... Other than that it also provides the ability to create seeded arrays of data.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      Utility()
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Utility

        -
        public Utility()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Utility

            +
            public Utility()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      objFloatsToPrimFloats

      -
      public static float[] objFloatsToPrimFloats(Float[] objects)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          objFloatsToPrimFloats

          +
          public static float[] objFloatsToPrimFloats(java.lang.Float[] objects)
          • -
        • -
          -

          objDoublesToPrimDoubles

          -
          public static double[] objDoublesToPrimDoubles(Double[] objects)
          -
          +
        + + + +
          +
        • +

          objDoublesToPrimDoubles

          +
          public static double[] objDoublesToPrimDoubles(java.lang.Double[] objects)
          • -
        • -
          -

          objIntsToPrimInts

          -
          public static int[] objIntsToPrimInts(Integer[] objects)
          -
          +
        + + + +
          +
        • +

          objIntsToPrimInts

          +
          public static int[] objIntsToPrimInts(java.lang.Integer[] objects)
          • -
        • -
          -

          objLongsToPrimLongs

          -
          public static long[] objLongsToPrimLongs(Long[] objects)
          -
          +
        + + + +
          +
        • +

          objLongsToPrimLongs

          +
          public static long[] objLongsToPrimLongs(java.lang.Long[] objects)
          • -
        • -
          -

          objShortsToPrimShorts

          -
          public static short[] objShortsToPrimShorts(Short[] objects)
          -
          +
        + + + +
          +
        • +

          objShortsToPrimShorts

          +
          public static short[] objShortsToPrimShorts(java.lang.Short[] objects)
          • -
        • -
          -

          objBytesToPrimBytes

          -
          public static byte[] objBytesToPrimBytes(Byte[] objects)
          -
          +
        + + + +
          +
        • +

          objBytesToPrimBytes

          +
          public static byte[] objBytesToPrimBytes(java.lang.Byte[] objects)
          • -
        • -
          -

          objBooleansToPrimBooleans

          -
          public static boolean[] objBooleansToPrimBooleans(Boolean[] objects)
          -
          +
        + + + +
          +
        • +

          objBooleansToPrimBooleans

          +
          public static boolean[] objBooleansToPrimBooleans(java.lang.Boolean[] objects)
          • -
        • -
          -

          objCharsToPrimChars

          -
          public static char[] objCharsToPrimChars(Character[] objects)
          -
          +
        + + + +
          +
        • +

          objCharsToPrimChars

          +
          public static char[] objCharsToPrimChars(java.lang.Character[] objects)
          • -
        • -
          -

          byteToShort

          -
          public static short[] byteToShort(byte[] data)
          -
          +
        + + + +
          +
        • +

          byteToShort

          +
          public static short[] byteToShort(byte[] data)
          • -
        • -
          -

          byteToBigInteger

          -
          public static BigInteger[] byteToBigInteger(byte[] data)
          -
          +
        + + + +
          +
        • +

          byteToBigInteger

          +
          public static java.math.BigInteger[] byteToBigInteger(byte[] data)
          • -
        • -
          -

          doubleToFloat

          -
          public static float[] doubleToFloat(double[] data)
          -
          +
        + + + +
          +
        • +

          doubleToFloat

          +
          public static float[] doubleToFloat(double[] data)
          • -
        • -
          -

          doubleToByte

          -
          public static byte[] doubleToByte(double[] data)
          -
          +
        + + + +
          +
        • +

          doubleToByte

          +
          public static byte[] doubleToByte(double[] data)
          • -
        • -
          -

          doubleToShort

          -
          public static short[] doubleToShort(double[] data)
          -
          +
        + + + +
          +
        • +

          doubleToShort

          +
          public static short[] doubleToShort(double[] data)
          • -
        • -
          -

          doubleToLong

          -
          public static long[] doubleToLong(double[] data)
          -
          +
        + + + +
          +
        • +

          doubleToLong

          +
          public static long[] doubleToLong(double[] data)
          • -
        • -
          -

          doubleToBool

          -
          public static boolean[] doubleToBool(double[] data)
          -
          +
        + + + +
          +
        • +

          doubleToBool

          +
          public static boolean[] doubleToBool(double[] data)
          • -
        • -
          -

          boolToDouble

          -
          public static double[] boolToDouble(boolean[] data)
          -
          +
        + + + +
          +
        • +

          boolToDouble

          +
          public static double[] boolToDouble(boolean[] data)
          • -
        • -
          -

          boolToFloat

          -
          public static float[] boolToFloat(boolean[] data)
          -
          +
        + + + +
          +
        • +

          boolToFloat

          +
          public static float[] boolToFloat(boolean[] data)
          • -
        • -
          -

          floatToDouble

          -
          public static double[] floatToDouble(float[] data)
          -
          +
        + + + +
          +
        • +

          floatToDouble

          +
          public static double[] floatToDouble(float[] data)
          • -
        • -
          -

          floatToByte

          -
          public static byte[] floatToByte(float[] data)
          -
          +
        + + + +
          +
        • +

          floatToByte

          +
          public static byte[] floatToByte(float[] data)
          • -
        • -
          -

          floatToShort

          -
          public static short[] floatToShort(float[] data)
          -
          +
        + + + +
          +
        • +

          floatToShort

          +
          public static short[] floatToShort(float[] data)
          • -
        • -
          -

          floatToLong

          -
          public static long[] floatToLong(float[] data)
          -
          +
        + + + +
          +
        • +

          floatToLong

          +
          public static long[] floatToLong(float[] data)
          • -
        • -
          -

          shortToDouble

          -
          public static double[] shortToDouble(short[] data)
          -
          +
        + + + +
          +
        • +

          shortToDouble

          +
          public static double[] shortToDouble(short[] data)
          • -
        • -
          -

          byteToDouble

          -
          public static double[] byteToDouble(byte[] data)
          -
          +
        + + + +
          +
        • +

          byteToDouble

          +
          public static double[] byteToDouble(byte[] data)
          • -
        • -
          -

          byteToFloat

          -
          public static float[] byteToFloat(byte[] data)
          -
          +
        + + + +
          +
        • +

          byteToFloat

          +
          public static float[] byteToFloat(byte[] data)
          • -
        • -
          -

          shortToFloat

          -
          public static float[] shortToFloat(short[] data)
          -
          +
        + + + +
          +
        • +

          shortToFloat

          +
          public static float[] shortToFloat(short[] data)
          • -
        • -
          -

          byteToInt

          -
          public static int[] byteToInt(byte[] data)
          -
          +
        + + + +
          +
        • +

          byteToInt

          +
          public static int[] byteToInt(byte[] data)
          • -
        • -
          -

          shortToInt

          -
          public static int[] shortToInt(short[] data)
          -
          +
        + + + +
          +
        • +

          shortToInt

          +
          public static int[] shortToInt(short[] data)
          • -
        • -
          -

          shortToByte

          -
          public static byte[] shortToByte(short[] data)
          -
          +
        + + + +
          +
        • +

          shortToByte

          +
          public static byte[] shortToByte(short[] data)
          • -
        • -
          -

          byteToLong

          -
          public static long[] byteToLong(byte[] data)
          -
          +
        + + + +
          +
        • +

          byteToLong

          +
          public static long[] byteToLong(byte[] data)
          • -
        • -
          -

          shortToLong

          -
          public static long[] shortToLong(short[] data)
          -
          +
        + + + +
          +
        • +

          shortToLong

          +
          public static long[] shortToLong(short[] data)
          • -
        • -
          -

          shortToBigInteger

          -
          public static BigInteger[] shortToBigInteger(short[] data)
          -
          +
        + + + +
          +
        • +

          shortToBigInteger

          +
          public static java.math.BigInteger[] shortToBigInteger(short[] data)
          • -
        • -
          -

          intToFloat

          -
          public static float[] intToFloat(int[] data)
          -
          +
        + + + +
          +
        • +

          intToFloat

          +
          public static float[] intToFloat(int[] data)
          • -
        • -
          -

          floatToInt

          -
          public static int[] floatToInt(float[] data)
          -
          +
        + + + +
          +
        • +

          floatToInt

          +
          public static int[] floatToInt(float[] data)
          • -
        • -
          -

          floatToBigInteger

          -
          public static BigInteger[] floatToBigInteger(float[] data)
          -
          +
        + + + +
          +
        • +

          floatToBigInteger

          +
          public static java.math.BigInteger[] floatToBigInteger(float[] data)
          • -
        • -
          -

          doubleToInt

          -
          public static int[] doubleToInt(double[] data)
          -
          +
        + + + +
          +
        • +

          doubleToInt

          +
          public static int[] doubleToInt(double[] data)
          • -
        • -
          -

          doubleToBigInteger

          -
          public static BigInteger[] doubleToBigInteger(double[] data)
          -
          +
        + + + +
          +
        • +

          doubleToBigInteger

          +
          public static java.math.BigInteger[] doubleToBigInteger(double[] data)
          • -
        • -
          -

          intToDouble

          -
          public static double[] intToDouble(int[] data)
          -
          +
        + + + +
          +
        • +

          intToDouble

          +
          public static double[] intToDouble(int[] data)
          • -
        • -
          -

          intToLong

          -
          public static long[] intToLong(int[] data)
          -
          +
        + + + +
          +
        • +

          intToLong

          +
          public static long[] intToLong(int[] data)
          • -
        • -
          -

          intToShort

          -
          public static short[] intToShort(int[] data)
          -
          +
        + + + +
          +
        • +

          intToShort

          +
          public static short[] intToShort(int[] data)
          • -
        • -
          -

          intToByte

          -
          public static byte[] intToByte(int[] data)
          -
          +
        + + + +
          +
        • +

          intToByte

          +
          public static byte[] intToByte(int[] data)
          • -
        • -
          -

          intToBigInteger

          -
          public static BigInteger[] intToBigInteger(int[] data)
          -
          +
        + + + +
          +
        • +

          intToBigInteger

          +
          public static java.math.BigInteger[] intToBigInteger(int[] data)
          • -
        • -
          -

          longToByte

          -
          public static byte[] longToByte(long[] data)
          -
          +
        + + + +
          +
        • +

          longToByte

          +
          public static byte[] longToByte(long[] data)
          • -
        • -
          -

          longToShort

          -
          public static short[] longToShort(long[] data)
          -
          +
        + + + +
          +
        • +

          longToShort

          +
          public static short[] longToShort(long[] data)
          • -
        • -
          -

          longToInt

          -
          public static int[] longToInt(long[] data)
          -
          +
        + + + +
          +
        • +

          longToInt

          +
          public static int[] longToInt(long[] data)
          • -
        • -
          -

          longToFloat

          -
          public static float[] longToFloat(long[] data)
          -
          +
        + + + +
          +
        • +

          longToFloat

          +
          public static float[] longToFloat(long[] data)
          • -
        • -
          -

          longToDouble

          -
          public static double[] longToDouble(long[] data)
          -
          +
        + + + +
          +
        • +

          longToDouble

          +
          public static double[] longToDouble(long[] data)
          • -
        • -
          -

          longToBigInteger

          -
          public static BigInteger[] longToBigInteger(long[] data)
          -
          +
        + + + +
          +
        • +

          longToBigInteger

          +
          public static java.math.BigInteger[] longToBigInteger(long[] data)
          • -
        • -
          -

          objectsToDoubles

          -
          public static double[] objectsToDoubles(Object[] objects, - int targetSize)
          -
          +
        + + + +
          +
        • +

          objectsToDoubles

          +
          public static double[] objectsToDoubles(java.lang.Object[] objects,
+                                        int targetSize)
          • -
        • -
          -

          objectsToFloats

          -
          public static float[] objectsToFloats(Object[] objects, - int targetSize)
          -
          +
        + + + +
          +
        • +

          objectsToFloats

          +
          public static float[] objectsToFloats(java.lang.Object[] objects,
+                                      int targetSize)
          • -
        • -
          -

          objectsToShorts

          -
          public static short[] objectsToShorts(Object[] objects, - int targetSize)
          -
          +
        + + + +
          +
        • +

          objectsToShorts

          +
          public static short[] objectsToShorts(java.lang.Object[] objects,
+                                      int targetSize)
          • -
        • -
          -

          objectsToBytes

          -
          public static byte[] objectsToBytes(Object[] objects, - int targetSize)
          -
          +
        + + + +
          +
        • +

          objectsToBytes

          +
          public static byte[] objectsToBytes(java.lang.Object[] objects,
+                                    int targetSize)
          • -
        • -
          -

          objectsToLongs

          -
          public static long[] objectsToLongs(Object[] objects, - int targetSize)
          -
          +
        + + + +
          +
        • +

          objectsToLongs

          +
          public static long[] objectsToLongs(java.lang.Object[] objects,
+                                    int targetSize)
          • -
        • -
          -

          objectsToInts

          -
          public static int[] objectsToInts(Object[] objects, - int targetSize)
          -
          +
        + + + +
          +
        • +

          objectsToInts

          +
          public static int[] objectsToInts(java.lang.Object[] objects,
+                                  int targetSize)
          • -
        • -
          -

          intStream

          -
          public static IntStream intStream(int parallelThreshold, - int workload)
          -
          Use this to create a range based IntStream +
        + + + +
          +
        • +

          intStream

          +
          public static java.util.stream.IntStream intStream(int parallelThreshold,
+                                                   int workload)
          +
          Use this to create a range based IntStream which is only parallel if the provided threshold smaller than the provided workload size.
          -
          -
          Parameters:
          +
          +
          Parameters:
          parallelThreshold - If the workload is larger than the threshold then the returned stream will be parallel.
          workload - The number of integers processed by the returned stream.
          -
          Returns:
          -
          A sequential or parallel IntStream.
          +
          Returns:
          +
          A sequential or parallel IntStream.
          -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/common/utility/DataConverter.html b/docs/jdocs/neureka/common/utility/DataConverter.html index 0d956c620..66a32b7f6 100644 --- a/docs/jdocs/neureka/common/utility/DataConverter.html +++ b/docs/jdocs/neureka/common/utility/DataConverter.html @@ -1,194 +1,306 @@ - + + - -DataConverter (neureka 1.0.0 API) - - - - + +DataConverter (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.utility
    -

    Class DataConverter

    -
    -
    java.lang.Object -
    neureka.common.utility.DataConverter
    +
    neureka.common.utility
    +

    Class DataConverter

    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.common.utility.DataConverter
        • +
      +
      • +
    +
    +
      +
    • -
      public final class DataConverter -extends Object
      +
      +
      public final class DataConverter
+extends java.lang.Object
      This class is a singleton. Its sole job is to simply take in any kind ob object and convert it into another object of a provided Class type... - In essence the DataConverter is merely a utility class. - It also contains a nested static class named DataConverter.Utility which + In essence the DataConverter is merely a utility class. + It also contains a nested static class named DataConverter.Utility which provides useful methods to handle primitive data types and arrays of said types.
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Class
      -
      Description
      -
      static class 
      -
      DataConverter.ForTensor
      -
      +
        +
      • + + +

        Nested Class Summary

        + + + + + + + + + + + + + + +
        Nested Classes 
        Modifier and TypeClass and Description
        static class DataConverter.ForTensor
        This is a stateful and parallelized converter for converting the internal data array of a tensor to another data array based on a provided lambda.
        - -
        static class 
        -
        DataConverter.Utility
        -
        +
        static class DataConverter.Utility
        This is a static utility class containing the actual conversion logic which is usually referenced by the Converter lambdas via method signatures...
        - - - +
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      <T> T
      -
      convert(Object from, - Class<T> to)
      -
      +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        get

        -
        public static DataConverter get()
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            get

            +
            public static DataConverter get()
            This method returns the singleton.
            -
            -
            Returns:
            +
            +
            Returns:
            The singleton instance of this class.
            -
        • -
      • -
        -

        convert

        -
        public <T> T convert(Object from, - Class<T> to)
        +
      + + + +
        +
      • +

        convert

        +
        public <T> T convert(java.lang.Object from,
+                     java.lang.Class<T> to)
        This method embodies the purpose of this class. It receives objects for type conversion and queries the request through the nested "_converters" Map instance.
        -
        -
        Type Parameters:
        +
        +
        Type Parameters:
        T - The type parameter of the "to" Class.
        -
        Parameters:
        +
        Parameters:
        from - The object which ought to be converted.
        to - The target type for the provided object.
        -
        Returns:
        +
        Returns:
        The target object created by a Converter lambda.
        -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/common/utility/ListReader.Result.html b/docs/jdocs/neureka/common/utility/ListReader.Result.html index ffdbe9ab5..279794b12 100644 --- a/docs/jdocs/neureka/common/utility/ListReader.Result.html +++ b/docs/jdocs/neureka/common/utility/ListReader.Result.html @@ -1,153 +1,264 @@ - + + - -ListReader.Result (neureka 1.0.0 API) - - - - + +ListReader.Result (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.utility
    -

    Class ListReader.Result

    +
    neureka.common.utility
    +

    Class ListReader.Result

    -
    java.lang.Object -
    neureka.common.utility.ListReader.Result
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.common.utility.ListReader.Result
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      List<Object>
      -
      getData()
      -
       
      -
      List<Integer>
      -
      getShape()
      -
       
      -
      Class<?>
      -
      getType()
      -
       
      -
      -
      +
      +
      public static class ListReader.Result
+extends java.lang.Object
      +
      • +
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    - +
    +
      +
    • + +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        java.util.List<java.lang.Object>getData() 
        java.util.List<java.lang.Integer>getShape() 
        java.lang.Class<?>getType() 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
      - -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        getType

        -
        public Class<?> getType()
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            getType

            +
            public java.lang.Class<?> getType()
            • -
          • -
            -

            getShape

            -
            public List<Integer> getShape()
            -
            +
          + + + +
            +
          • +

            getShape

            +
            public java.util.List<java.lang.Integer> getShape()
            • -
          • -
            -

            getData

            -
            public List<Object> getData()
            -
            +
          + + + +
            +
          • +

            getData

            +
            public java.util.List<java.lang.Object> getData()
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/common/utility/ListReader.html b/docs/jdocs/neureka/common/utility/ListReader.html index 275ff5990..6bb593f9e 100644 --- a/docs/jdocs/neureka/common/utility/ListReader.html +++ b/docs/jdocs/neureka/common/utility/ListReader.html @@ -1,165 +1,272 @@ - + + - -ListReader (neureka 1.0.0 API) - - - - + +ListReader (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.utility
    -

    Class ListReader

    +
    neureka.common.utility
    +

    Class ListReader

    -
    java.lang.Object -
    neureka.common.utility.ListReader
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.common.utility.ListReader
        • +
      +
      • +
    +
    +
      +
    • -
      public final class ListReader -extends Object
      +
      +
      public final class ListReader
+extends java.lang.Object
      This is a simple utility class which traverses nested data structures and converts them into information which can be used to instantiate a tensor, namely: A flat data array, a shape array and a type class.
      -
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Class
      -
      Description
      -
      static class 
      -
      ListReader.Result
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        read

        -
        public static ListReader.Result read(List<Object> data, - Function<Object,Object> valueFilter)
        -
        Reads the provided data and turns it into a ListReader.Result object, +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            read

            +
            public static ListReader.Result read(java.util.List<java.lang.Object> data,
+                                     java.util.function.Function<java.lang.Object,java.lang.Object> valueFilter)
            +
            Reads the provided data and turns it into a ListReader.Result object, containing a flattened list of the data alongside its shape and data type.
            -
            -
            Parameters:
            +
            +
            Parameters:
            data - A list of data elements or nested lists with an arbitrary degree of nesting.
            valueFilter - A filter for the elements in the provided data list.
            -
            Returns:
            +
            Returns:
            The result object containing data, data type and shape information.
            -
      -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/common/utility/LogUtil.html b/docs/jdocs/neureka/common/utility/LogUtil.html index 430a21ef6..80fec3791 100644 --- a/docs/jdocs/neureka/common/utility/LogUtil.html +++ b/docs/jdocs/neureka/common/utility/LogUtil.html @@ -1,185 +1,301 @@ - + + - -LogUtil (neureka 1.0.0 API) - - - - + +LogUtil (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.utility
    -

    Class LogUtil

    +
    neureka.common.utility
    +

    Class LogUtil

    -
    java.lang.Object -
    neureka.common.utility.LogUtil
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.common.utility.LogUtil
        • +
      +
      • +
    +
    +
      +
    • -
      public final class LogUtil -extends Object
      +
      +
      public final class LogUtil
+extends java.lang.Object
      A utility class for message formatting.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      LogUtil()
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        LogUtil() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static String
      -
      format(String withPlaceholders, - Object... toBePutAtPlaceholders)
      -
       
      -
      static <T> void
      -
      nullArgCheck(T var, - String thing, - Class<?> type, - String... notes)
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static java.lang.Stringformat(java.lang.String withPlaceholders, + java.lang.Object... toBePutAtPlaceholders) 
        static <T> voidnullArgCheck(T var, + java.lang.String thing, + java.lang.Class<?> type, + java.lang.String... notes) 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        LogUtil

        -
        public LogUtil()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            LogUtil

            +
            public LogUtil()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      format

      -
      public static String format(String withPlaceholders, - Object... toBePutAtPlaceholders)
      -
      -
      Parameters:
      -
      withPlaceholders - The String which may or may not contain placeholder in the for of "{}".
      -
      toBePutAtPlaceholders - Arbitrary Objects which will be turned into - Strings instead of the placeholder brackets.
      -
      Returns:
      -
      A String containing the actual String representations of th Objects +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          format

          +
          public static java.lang.String format(java.lang.String withPlaceholders,
+                                      java.lang.Object... toBePutAtPlaceholders)
          +
          +
          Parameters:
          +
          withPlaceholders - The String which may or may not contain placeholder in the for of "{}".
          +
          toBePutAtPlaceholders - Arbitrary Objects which will be turned into + Strings instead of the placeholder brackets.
          +
          Returns:
          +
          A String containing the actual String representations of th Objects instead of the placeholder brackets within the first argument.
          -
      • -
    • -
      -

      nullArgCheck

      -
      public static <T> void nullArgCheck(T var, - String thing, - Class<?> type, - String... notes)
      -
      +
    + + + + + +
      +
    • +

      nullArgCheck

      +
      public static <T> void nullArgCheck(T var,
+                                    java.lang.String thing,
+                                    java.lang.Class<?> type,
+                                    java.lang.String... notes)
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/common/utility/SettingsLoader.html b/docs/jdocs/neureka/common/utility/SettingsLoader.html index e7c7fbc70..9f29ce48f 100644 --- a/docs/jdocs/neureka/common/utility/SettingsLoader.html +++ b/docs/jdocs/neureka/common/utility/SettingsLoader.html @@ -1,166 +1,277 @@ - + + - -SettingsLoader (neureka 1.0.0 API) - - - - + +SettingsLoader (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.common.utility
    -

    Class SettingsLoader

    +
    neureka.common.utility
    +

    Class SettingsLoader

    -
    java.lang.Object -
    neureka.common.utility.SettingsLoader
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.common.utility.SettingsLoader
        • +
      +
      • +
    +
    +
      +
    • -
      public final class SettingsLoader -extends Object
      -
      This class is a helper class for Neureka instances (Thread local singletons). +
      +
      public final class SettingsLoader
+extends java.lang.Object
      +
      This class is a helper class for Neureka instances (Thread local singletons). It loads the settings property file and interprets its contents - which are then translated to the Neureka.Settings.
      -
    -
    -
    + + +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        loadProperties

        -
        public static void loadProperties(Neureka instance)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            loadProperties

            +
            public static void loadProperties(Neureka instance)
            • -
          • -
            -

            tryGroovyClosureOn

            -
            public static Object tryGroovyClosureOn(Object closure, - Object delegate)
            +
          + + + +
            +
          • +

            tryGroovyClosureOn

            +
            public static java.lang.Object tryGroovyClosureOn(java.lang.Object closure,
+                                                  java.lang.Object delegate)
            This method makes it possible to configure the library via a Groovy DSL!
            -
            -
            Parameters:
            +
            +
            Parameters:
            closure - A Groovy closure which should be called with the provided delegate.
            delegate - The delegate for the provided closure (Can be a settings object).
            -
            Returns:
            +
            Returns:
            The result returned by provided closure.
            -
      • -
    • -
      -

      tryGroovyScriptsOn

      -
      public static void tryGroovyScriptsOn(Neureka instance, - Consumer<String> scriptConsumer)
      -
      +
    + + + +
      +
    • +

      tryGroovyScriptsOn

      +
      public static void tryGroovyScriptsOn(Neureka instance,
+                                      java.util.function.Consumer<java.lang.String> scriptConsumer)
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/common/utility/package-frame.html b/docs/jdocs/neureka/common/utility/package-frame.html new file mode 100644 index 000000000..5a195ac37 --- /dev/null +++ b/docs/jdocs/neureka/common/utility/package-frame.html @@ -0,0 +1,27 @@ + + + + + +neureka.common.utility (neureka 1.0.1 API) + + + + +

    neureka.common.utility

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/common/utility/package-summary.html b/docs/jdocs/neureka/common/utility/package-summary.html index e7298c520..bd8e2d8f8 100644 --- a/docs/jdocs/neureka/common/utility/package-summary.html +++ b/docs/jdocs/neureka/common/utility/package-summary.html @@ -1,120 +1,193 @@ - + + - -neureka.common.utility (neureka 1.0.0 API) - - - - + +neureka.common.utility (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.common.utility

    -
    -
    -
    package neureka.common.utility
    -
    -
      -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      Cache<O>
      -
      +

      Package neureka.common.utility

      +
      +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        Cache<O>
        This is a simple, fixed size cache for immutable objects which are shared throughout the library runtime...
        - -
        Cache.LazyEntry<K,V>
        -
        +
        Cache.LazyEntry<K,V>
        Lazy cache entries are entries whose values will be calculated only when the entry is being stored in the cache.
        - -
        DataConverter
        -
        +
        DataConverter
        This class is a singleton.
        - -
        DataConverter.ForTensor
        -
        +
        DataConverter.ForTensor
        This is a stateful and parallelized converter for converting the internal data array of a tensor to another data array based on a provided lambda.
        - -
        DataConverter.Utility
        -
        +
        DataConverter.Utility
        This is a static utility class containing the actual conversion logic which is usually referenced by the Converter lambdas via method signatures...
        - -
        ListReader
        -
        +
        ListReader
        This is a simple utility class which traverses nested data structures and converts them into information which can be used to instantiate a tensor, namely: A flat data array, a shape array and a type class.
        - -
        ListReader.Result
        -
         
        -
        LogUtil
        -
        +
        ListReader.Result 
        LogUtil
        A utility class for message formatting.
        - -
        SettingsLoader
        -
        -
        This class is a helper class for Neureka instances (Thread local singletons).
        -
        - - +
        SettingsLoader +
        This class is a helper class for Neureka instances (Thread local singletons).
        +
      -
    -
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/common/utility/package-tree.html b/docs/jdocs/neureka/common/utility/package-tree.html index d8c6e0a37..ee2799270 100644 --- a/docs/jdocs/neureka/common/utility/package-tree.html +++ b/docs/jdocs/neureka/common/utility/package-tree.html @@ -1,79 +1,142 @@ - + + - -neureka.common.utility Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.common.utility Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.common.utility

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/AbstractBaseDevice.html b/docs/jdocs/neureka/devices/AbstractBaseDevice.html index 002d551b7..013f2838e 100644 --- a/docs/jdocs/neureka/devices/AbstractBaseDevice.html +++ b/docs/jdocs/neureka/devices/AbstractBaseDevice.html @@ -1,318 +1,477 @@ - + + - -AbstractBaseDevice (neureka 1.0.0 API) - - - - + +AbstractBaseDevice (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Class AbstractBaseDevice<V>

    -
    -
    java.lang.Object -
    neureka.devices.AbstractBaseDevice<V>
    +
    neureka.devices
    +

    Class AbstractBaseDevice<V>

    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.AbstractBaseDevice<V>
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public abstract class AbstractBaseDevice<V>
+extends java.lang.Object
+implements Device<V>
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Field Details

      -
        -
      • -
        -

        _numberOfTensors

        -
        protected int _numberOfTensors
        -
        +
          +
        • + + +

          Field Detail

          + + + +
            +
          • +

            _numberOfTensors

            +
            protected int _numberOfTensors
            • -
          • -
            -

            _numberOfDataObjects

            -
            protected int _numberOfDataObjects
            -
            +
          + + + +
            +
          • +

            _numberOfDataObjects

            +
            protected int _numberOfDataObjects
          -
      • +
    -
  • -
    -

    Constructor Details

    -
      -
    • -
      -

      AbstractBaseDevice

      -
      public AbstractBaseDevice()
      -
      +
        +
      • + + +

        Constructor Detail

        + + + +
          +
        • +

          AbstractBaseDevice

          +
          public AbstractBaseDevice()
        -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      numberOfStored

      -
      public int numberOfStored()
      -
      -
      Specified by:
      -
      numberOfStored in interface Storage<V>
      -
      Returns:
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          numberOfStored

          +
          public int numberOfStored()
          +
          +
          Specified by:
          +
          numberOfStored in interface Storage<V>
          +
          Returns:
          The number of nd-array stored on this.
          -
      • -
    • -
      -

      numberOfDataObjects

      -
      public int numberOfDataObjects()
      -
      Description copied from interface: Device
      -
      Note that this is not necessarily equal to Storage.numberOfStored(), because - multiple tensors may share a single Data object.
      -
      -
      Specified by:
      -
      numberOfDataObjects in interface Device<V>
      -
      Returns:
      -
      The number of Data objects stored on this Device.
      +
    + + + +
      +
    • +

      numberOfDataObjects

      +
      public int numberOfDataObjects()
      +
      Description copied from interface: Device
      +
      Note that this is not necessarily equal to Storage.numberOfStored(), because + multiple tensors may share a single Data object.
      +
      +
      Specified by:
      +
      numberOfDataObjects in interface Device<V>
      +
      Returns:
      +
      The number of Data objects stored on this Device.
      -
    • -
  • -
    -

    isEmpty

    -
    public boolean isEmpty()
    + + + + +
      +
    • +

      isEmpty

      +
      public boolean isEmpty()
      A device is empty if there are no tensors stored on it.
      -
      -
      Specified by:
      -
      isEmpty in interface Storage<V>
      -
      Returns:
      +
      +
      Specified by:
      +
      isEmpty in interface Storage<V>
      +
      Returns:
      The truth value determining if there are no tensors stored on this device.
      -
    • -
  • -
    -

    contains

    -
    public final boolean contains(Tensor<V> o)
    -
    -
    Specified by:
    -
    contains in interface Storage<V>
    -
    Parameters:
    + + + + +
      +
    • +

      contains

      +
      public final boolean contains(Tensor<V> o)
      +
      +
      Specified by:
      +
      contains in interface Storage<V>
      +
      Parameters:
      o - The tensor which may or may not be stored on this.
      -
      Returns:
      +
      Returns:
      The truth value determining if the provided tensor is stored on this.
      -
    • -
  • -
    -

    has

    -
    public <T extends V> boolean has(Tensor<T> tensor)
    + + + + +
      +
    • +

      has

      +
      public <T extends V> boolean has(Tensor<T> tensor)
      This method checks if the passed tensor - is stored on this Device instance. + is stored on this Device instance. "Stored" means that the data of the tensor was created by this device. This data is referenced inside the tensor...
      -
      -
      Specified by:
      -
      has in interface Device<V>
      -
      Type Parameters:
      -
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      -
      Parameters:
      +
      +
      Specified by:
      +
      has in interface Device<V>
      +
      Type Parameters:
      +
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      +
      Parameters:
      tensor - The tensor in question.
      -
      Returns:
      +
      Returns:
      The truth value of the fact that the provided tensor is on this device.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/AbstractDevice.html b/docs/jdocs/neureka/devices/AbstractDevice.html index 0c578e7ba..d70dd31d6 100644 --- a/docs/jdocs/neureka/devices/AbstractDevice.html +++ b/docs/jdocs/neureka/devices/AbstractDevice.html @@ -1,478 +1,684 @@ - + + - -AbstractDevice (neureka 1.0.0 API) - - - - + +AbstractDevice (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Class AbstractDevice<V>

    +
    neureka.devices
    +

    Class AbstractDevice<V>

    -
    java.lang.Object -
    neureka.devices.AbstractBaseDevice<V> -
    neureka.devices.AbstractDevice<V>
    -
    -
    -
    -
    -
    Type Parameters:
    +
    + +
    +
    -
    -
      + applicable to most concrete implementations of the Device interface.
    + + +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Field Details

      -
        -
      • -
        -

        _log

        -
        protected org.slf4j.Logger _log
        -
        +
          +
        • + + +

          Field Detail

          + + + +
            +
          • +

            _log

            +
            protected org.slf4j.Logger _log
          -
      • +
    -
  • -
    -

    Constructor Details

    -
      -
    • -
      -

      AbstractDevice

      -
      protected AbstractDevice()
      -
      +
        +
      • + + +

        Constructor Detail

        + + + +
          +
        • +

          AbstractDevice

          +
          protected AbstractDevice()
        -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      _approveExecutionOf

      -
      protected abstract boolean _approveExecutionOf(Tensor<?>[] tensors, - int d, - Operation type)
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          _approveExecutionOf

          +
          protected abstract boolean _approveExecutionOf(Tensor<?>[] tensors,
+                                               int d,
+                                               Operation type)
          This method is the internal approval routine called by its public counterpart and implemented by classes extending this very abstract class. - It may or may not be called by an Algorithm - in order to allow a Device to checked if the provided arguments are suitable for execution.
          -
          -
          Parameters:
          + It may or may not be called by an Algorithm + in order to allow a Device to checked if the provided arguments are suitable for execution.
    • +
    +
    Parameters:
    tensors - An array of input tensors.
    d - The index of the input which ought to be derived.
    type - The type of operation.
    -
    Returns:
    +
    Returns:
    The truth value determining if the provided arguments can be executed.
    - -
  • -
    -

    update

    -
    public boolean update(Component.OwnerChangeRequest<Tensor<V>> changeRequest)
    -
    A Device is a component of a tensor. This method is used to inform the device + + + + +
      +
    • +

      update

      +
      public boolean update(Component.OwnerChangeRequest<Tensor<V>> changeRequest)
      +
      A Device is a component of a tensor. This method is used to inform the device that the device is being added, removed or replaced (from the tensor).
      -
      -
      Parameters:
      -
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      -
      Returns:
      +
      +
      Parameters:
      +
      changeRequest - An OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      +
      Returns:
      The truth value determining if the change should be executed.
      -
    • -
  • -
    -

    _cleaning

    -
    protected void _cleaning(Object o, - Runnable action)
    -
    + + + + +
      +
    • +

      _cleaning

      +
      protected void _cleaning(java.lang.Object o,
+                         java.lang.Runnable action)
      • -
    • -
      -

      approve

      -
      public Device<V> approve(ExecutionCall<? extends Device<?>> call)
      -
      This method plays an important role in approving a provided ExecutionCall. +
    + + + +
      +
    • +

      approve

      +
      public Device<V> approve(ExecutionCall<? extends Device<?>> call)
      +
      This method plays an important role in approving a provided ExecutionCall. When implementing custom operations or such for the backend of this library, then one may use - this in order to check if the provided call is suitable for this Device.
      -
      -
      Parameters:
      + this in order to check if the provided call is suitable for this Device.
    • +
    +
    Parameters:
    call - The execution call object containing tensor arguments and settings for the device to approve.
    -
    Returns:
    +
    Returns:
    This very device instance in order to enable method chaining.
    - -
  • -
    -

    store

    -
    public <T extends V> Storage<V> store(Tensor<T> tensor)
    + + + + +
      +
    • +

      store

      +
      public <T extends V> Storage<V> store(Tensor<T> tensor)
      Implementations of this method ought to store the data of the tensor in whatever formant suites the underlying implementation and or final type. Classes like "OpenCLDevice" or "FileDevice" for example are tensor storages.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - A valid data type of the tensor which should be stored on this device.
      -
      Parameters:
      +
      Parameters:
      tensor - The tensor whose data ought to be stored.
      -
      Returns:
      -
      This Storage instance, to allow for method chaining.
      +
      Returns:
      +
      This Storage instance, to allow for method chaining.
      -
    • -
  • -
    -

    access

    -
    public <T extends V> Device.Access<T> access(Tensor<T> tensor)
    + + + + +
      +
    • +

      access

      +
      public <T extends V> Device.Access<T> access(Tensor<T> tensor)
      This method exposes the tensor access API for reading from or writing to a tensor stored on this device. It may return null if this device does not support accessing stored tensors.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter of the tensor for which the access API should be returned.
      -
      Parameters:
      +
      Parameters:
      tensor - The tensor whose data ought to be accessed.
      -
      Returns:
      +
      Returns:
      The tensor access API for reading from or writing to a tensor stored on this device.
      -
    • -
  • -
    -

    _swap

    -
    protected abstract <T extends V> void _swap(Tensor<T> former, - Tensor<T> replacement)
    + + + + +
      +
    • +

      _swap

      +
      protected abstract <T extends V> void _swap(Tensor<T> former,
+                                            Tensor<T> replacement)
      This method is used internally mostly and should not be used in most cases.

      -
      -
      Type Parameters:
      -
      T - The type parameter for the value type of the tensors, which must be supported by this Device.
      -
      Parameters:
      +
      +
      Type Parameters:
      +
      T - The type parameter for the value type of the tensors, which must be supported by this Device.
      +
      Parameters:
      former - The tensor whose associated data (on the device) ought to be assigned to the other tensor.
      replacement - The tensor which ought to receive the data of the former tensor internally.
      -
    • -
  • -
    -

    _sizeOccupiedBy

    -
    protected abstract <T extends V> int _sizeOccupiedBy(Tensor<T> tensor)
    -
    + + + + +
      +
    • +

      _sizeOccupiedBy

      +
      protected abstract <T extends V> int _sizeOccupiedBy(Tensor<T> tensor)
      • -
    • -
      -

      _readAll

      -
      protected abstract <T extends V> Object _readAll(Tensor<T> tensor, - boolean clone)
      -
      +
    + + + +
      +
    • +

      _readAll

      +
      protected abstract <T extends V> java.lang.Object _readAll(Tensor<T> tensor,
+                                                           boolean clone)
      • -
    • -
      -

      _readItem

      -
      protected abstract <T extends V> T _readItem(Tensor<T> tensor, - int index)
      -
      +
    + + + +
      +
    • +

      _readItem

      +
      protected abstract <T extends V> T _readItem(Tensor<T> tensor,
+                                             int index)
      • -
    • -
      -

      _readArray

      -
      protected abstract <T extends V, -A> A _readArray(Tensor<T> tensor, - Class<A> arrayType, - int start, - int size)
      -
      +
    + + + +
      +
    • +

      _readArray

      +
      protected abstract <T extends V,A> A _readArray(Tensor<T> tensor,
+                                                java.lang.Class<A> arrayType,
+                                                int start,
+                                                int size)
      • -
    • -
      -

      _writeItem

      -
      protected abstract <T extends V> void _writeItem(Tensor<T> tensor, - T item, - int start, - int size)
      -
      +
    + + + + + +
      +
    • +

      _writeItem

      +
      protected abstract <T extends V> void _writeItem(Tensor<T> tensor,
+                                                 T item,
+                                                 int start,
+                                                 int size)
      • -
    • -
      -

      _writeArray

      -
      protected abstract <T extends V> void _writeArray(Tensor<T> tensor, - Object array, - int offset, - int start, - int size)
      -
      +
    + + + +
      +
    • +

      _writeArray

      +
      protected abstract <T extends V> void _writeArray(Tensor<T> tensor,
+                                                  java.lang.Object array,
+                                                  int offset,
+                                                  int start,
+                                                  int size)
      • -
    • -
      -

      _actualize

      -
      protected abstract Data<V> _actualize(Tensor<?> tensor)
      -
      +
    + + + +
      +
    • +

      _actualize

      +
      protected abstract Data<V> _actualize(Tensor<?> tensor)
      • -
    • -
      -

      _virtualize

      -
      protected abstract Data<V> _virtualize(Tensor<?> tensor)
      -
      +
    + + + +
      +
    • +

      _virtualize

      +
      protected abstract Data<V> _virtualize(Tensor<?> tensor)
      • -
    • -
      -

      _dataTypeOf

      -
      protected abstract DataType<?> _dataTypeOf(Object rawData)
      -
      +
    + + + +
      +
    • +

      _dataTypeOf

      +
      protected abstract DataType<?> _dataTypeOf(java.lang.Object rawData)
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/AbstractDeviceData.html b/docs/jdocs/neureka/devices/AbstractDeviceData.html index d377d9d7c..7fd51baba 100644 --- a/docs/jdocs/neureka/devices/AbstractDeviceData.html +++ b/docs/jdocs/neureka/devices/AbstractDeviceData.html @@ -1,324 +1,477 @@ - + + - -AbstractDeviceData (neureka 1.0.0 API) - - - - + +AbstractDeviceData (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Class AbstractDeviceData<T>

    -
    -
    java.lang.Object -
    neureka.devices.AbstractDeviceData<T>
    +
    neureka.devices
    +

    Class AbstractDeviceData<T>

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.AbstractDeviceData<T>
        • +
      +
      • +
    +
    +
    -
    -
    - +
    +
    +
    + -
  • -
    -

    Constructor Details

    -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      owner

      -
      public final Device<T> owner()
      -
      -
      Specified by:
      -
      owner in interface Data<T>
      -
      Returns:
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          owner

          +
          public final Device<T> owner()
          +
          +
          Specified by:
          +
          owner in interface Data<T>
          +
          Returns:
          The owner of this data array wrapper (the device which allocated the memory).
          -
      • -
    • -
      -

      getOrNull

      -
      public final Object getOrNull()
      -
      Description copied from interface: Data
      +
    + + + +
      +
    • +

      getOrNull

      +
      public final java.lang.Object getOrNull()
      +
      Description copied from interface: Data
      This returns the underlying raw data object of a nd-array or tensor of a backend specific type (e.g. OpenCL memory object or JVM array). - Contrary to the Nda.getItems() ()} method, this will + Contrary to the Nda.getItems() ()} method, this will return an unbiased view on the raw data of this tensor. Be careful using this, as it exposes mutable state!
      -
      -
      Specified by:
      -
      getOrNull in interface Data<T>
      -
      Returns:
      +
      +
      Specified by:
      +
      getOrNull in interface Data<T>
      +
      Returns:
      The raw data object underlying a nd-array/tensor, or null if the data is not present.
      -
    • -
  • -
    -

    dataType

    -
    public final DataType<T> dataType()
    -
    -
    Specified by:
    -
    dataType in interface Data<T>
    -
    Returns:
    + + + + +
      +
    • +

      dataType

      +
      public final DataType<T> dataType()
      +
      +
      Specified by:
      +
      dataType in interface Data<T>
      +
      Returns:
      The data type of the raw data array.
      -
    • -
  • -
    -

    incrementUsageCount

    -
    public final void incrementUsageCount()
    -
    -
    Specified by:
    -
    incrementUsageCount in interface DeviceData<T>
    + + + + +
    • -
  • -
    -

    decrementUsageCount

    -
    public final void decrementUsageCount()
    -
    -
    Specified by:
    -
    decrementUsageCount in interface DeviceData<T>
    + + + + +
    • -
  • -
    -

    usages

    -
    public final int usages()
    -
    Description copied from interface: Data
    + + + + +
      +
    • +

      usages

      +
      public final int usages()
      +
      Description copied from interface: Data
      This method returns the number of times this data object is currently in use by a nd-array, meaning that the number of usages is also the number of nd-arrays which are currently referencing this data object.
      The reason why this can be greater than one is because of the existence of sliced, transposed and reshaped nd-arrays which all share the same data object as their parent nd-array.
      -
      -
      Specified by:
      -
      usages in interface Data<T>
      -
      Returns:
      +
      +
      Specified by:
      +
      usages in interface Data<T>
      +
      Returns:
      The number of times this data object is currently in use by a nd-array.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/Device.Access.html b/docs/jdocs/neureka/devices/Device.Access.html index 01b8006ee..21ae4b671 100644 --- a/docs/jdocs/neureka/devices/Device.Access.html +++ b/docs/jdocs/neureka/devices/Device.Access.html @@ -1,297 +1,429 @@ - + + - -Device.Access (neureka 1.0.0 API) - - - - + +Device.Access (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Interface Device.Access<V>

    +
    neureka.devices
    +

    Interface Device.Access<V>

    -
    -
    -
    Type Parameters:
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The type parameter of the tensor accessed by an instance of this.
      -
      +
      Enclosing interface:
      -
      Device<V>
      +
      Device<V>
      -
      public static interface Device.Access<V>
      +
      +
      public static interface Device.Access<V>
      Implementations of this represent the access to tensors stored on a device in order to read from or write to said tensor.
      Warning: This API exposes the true underlying data of a tensor which does not take into account slice, permute or step information...
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      <T> Data<T>
      -
      actualize()
      -
       
      -
      void
      -
      cleanup(Runnable action)
      -
      -
      Use this to perform some custom memory cleanup for when the accessed Tensor gets garbage collected.
      +
      • +
    -
    int
    -
    getDataSize()
    -
     
    -
    Object
    -
    readAll(boolean clone)
    -
    +
    +
      +
    • + +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethod and Description
        <T> Data<T>actualize() 
        voidcleanup(java.lang.Runnable action) +
        Use this to perform some custom memory cleanup for when the accessed Tensor gets garbage collected.
        +
        intgetDataSize() 
        java.lang.ObjectreadAll(boolean clone)
        Use this to read the full data array of the accessed tensor.
        - -
        <A> A
        -
        readArray(Class<A> arrayType, - int start, - int size)
        -
        +
        <A> AreadArray(java.lang.Class<A> arrayType, + int start, + int size)
        Use this to read an array of items from the accessed tensor by specifying a start position of the chunk of data that should be read.
        - -
        V
        -
        readAt(int index)
        -
        +
        VreadAt(int index)
        Find a particular tensor item by providing its location.
        - -
        <T> Data<T>
        -
        virtualize()
        -
         
        -
        Device.Writer
        -
        write(V item)
        -
        +
        <T> Data<T>virtualize() 
        Device.Writerwrite(V item)
        Use this to write a single scalar item into the accessed tensor at one or more positions within the tensor.
        - -
        default void
        -
        writeFrom(Object array)
        -
        +
        default voidwriteFrom(java.lang.Object array)
        Use this method to write data to the provided tensor, given that the tensor is already stored on this device!

        - -
        Device.Writer
        -
        writeFrom(Object array, - int offset)
        -
        +
        Device.WriterwriteFrom(java.lang.Object array, + int offset)
        Use this to write data from an array into the accessed tensor.
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        write

        -
        Device.Writer write(V item)
        +
          +
        • + + +

          Method Detail

          + + + + + +
            +
          • +

            write

            +
            Device.Writer write(V item)
            Use this to write a single scalar item into the accessed tensor at one or more positions within the tensor.
            -
            -
            Parameters:
            +
            +
            Parameters:
            item - The item which should be written to the tensor.
            -
            Returns:
            -
            A Device.Writer implementation which expects the type of writing to be specified.
            +
            Returns:
            +
            A Device.Writer implementation which expects the type of writing to be specified.
            -
        • -
      • -
        -

        writeFrom

        -
        Device.Writer writeFrom(Object array, - int offset)
        +
      + + + +
        +
      • +

        writeFrom

        +
        Device.Writer writeFrom(java.lang.Object array,
+                        int offset)
        Use this to write data from an array into the accessed tensor.
        -
        -
        Parameters:
        +
        +
        Parameters:
        array - The data array whose data should be britten from.
        offset - The start index offset within the provided data array.
        -
        Returns:
        -
        A Device.Writer implementation which expects the type of writing to be specified.
        +
        Returns:
        +
        A Device.Writer implementation which expects the type of writing to be specified.
        -
      • -
    • -
      -

      writeFrom

      -
      default void writeFrom(Object array)
      +
    + + + +
      +
    • +

      writeFrom

      +
      default void writeFrom(java.lang.Object array)
      Use this method to write data to the provided tensor, given that the tensor is already stored on this device!

      -
      -
      Parameters:
      +
      +
      Parameters:
      array - The data inn the form of a primitive array.
      -
      • -
    • -
      -

      readAt

      -
      V readAt(int index)
      +
    + + + +
      +
    • +

      readAt

      +
      V readAt(int index)
      Find a particular tensor item by providing its location.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The index at which a tensor item should be read and returned.
      -
      Returns:
      +
      Returns:
      The tensor item found at the provided location.
      -
      • -
    • -
      -

      readArray

      -
      <A> A readArray(Class<A> arrayType, - int start, - int size)
      +
    + + + +
      +
    • +

      readArray

      +
      <A> A readArray(java.lang.Class<A> arrayType,
+                int start,
+                int size)
      Use this to read an array of items from the accessed tensor by specifying a start position of the chunk of data that should be read.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      A - The array type parameter specified by the provided class.
      -
      Parameters:
      +
      Parameters:
      arrayType - The type of (primitive) array which should be read.
      start - The start position of the read cursor.
      size - The number of items which should be read from the tensor.
      -
      Returns:
      +
      Returns:
      An instance of the provided array type class.
      -
      • -
    • -
      -

      readAll

      -
      Object readAll(boolean clone)
      +
    + + + +
      +
    • +

      readAll

      +
      java.lang.Object readAll(boolean clone)
      Use this to read the full data array of the accessed tensor.
      -
      -
      Parameters:
      +
      +
      Parameters:
      clone - The truth value determining if the tensor should be copied or not.
      -
      Returns:
      +
      Returns:
      The full data array of the tensor accessed by this API.
      -
      • -
    • -
      -

      getDataSize

      -
      int getDataSize()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      getDataSize

      +
      int getDataSize()
      +
      +
      Returns:
      The size of the underlying data array of the accessed tensor.
      -
      • -
    • -
      -

      cleanup

      -
      void cleanup(Runnable action)
      -
      Use this to perform some custom memory cleanup for when the accessed Tensor gets garbage collected.

      -
      -
      Parameters:
      -
      action - The Runnable action which ought to be performed when the tensor gets garbage collected.
      +
    + + + +
      +
    • +

      cleanup

      +
      void cleanup(java.lang.Runnable action)
      +
      Use this to perform some custom memory cleanup for when the accessed Tensor gets garbage collected.

      +
      +
      Parameters:
      +
      action - The Runnable action which ought to be performed when the tensor gets garbage collected.
      -
      • -
    • -
      -

      actualize

      -
      <T> Data<T> actualize()
      -
      +
    + + + +
      +
    • +

      actualize

      +
      <T> Data<T> actualize()
      • -
    • -
      -

      virtualize

      -
      <T> Data<T> virtualize()
      -
      +
    + + + +
      +
    • +

      virtualize

      +
      <T> Data<T> virtualize()
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
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      • +
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      • +
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      • +
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      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/Device.In.html b/docs/jdocs/neureka/devices/Device.In.html index bd3ac3169..222e39031 100644 --- a/docs/jdocs/neureka/devices/Device.In.html +++ b/docs/jdocs/neureka/devices/Device.In.html @@ -1,139 +1,233 @@ - + + - -Device.In (neureka 1.0.0 API) - - - - + +Device.In (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Interface Device.In

    +
    neureka.devices
    +

    Interface Device.In

    -
    -
    +
    +
    +
      +
    • +
      Enclosing interface:
      -
      Device<V>
      +
      Device<V>
      -
      public static interface Device.In
      +
      +
      public static interface Device.In
      The second part of the method chain of the fluent API for executing - tensors on this Device temporarily.
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      <R> R
      -
      in(Supplier<R> lambda)
      -
       
      -
      -
      + tensors on this Device temporarily.
      +
      • +
    - +
    +
      +
    • + +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        in

        -
        <R> R in(Supplier<R> lambda)
        -
        -
        Type Parameters:
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            in

            +
            <R> R in(java.util.function.Supplier<R> lambda)
            +
            +
            Type Parameters:
            R - The return type parameter of the lambda which is expected to be passed to - the context runner Device.In returned by this method.
            -
            Parameters:
            -
            lambda - The lambda during which the previously provided tensors should be stored on this Device.
            -
            Returns:
            + the context runner Device.In returned by this method. +
            Parameters:
            +
            lambda - The lambda during which the previously provided tensors should be stored on this Device.
            +
            Returns:
            The return value, which may be anything.
            -
      -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
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      • +
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      • +
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      • +
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      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/Device.Writer.html b/docs/jdocs/neureka/devices/Device.Writer.html index fb65214cc..a5740b8a9 100644 --- a/docs/jdocs/neureka/devices/Device.Writer.html +++ b/docs/jdocs/neureka/devices/Device.Writer.html @@ -1,177 +1,279 @@ - + + - -Device.Writer (neureka 1.0.0 API) - - - - + +Device.Writer (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Interface Device.Writer

    +
    neureka.devices
    +

    Interface Device.Writer

    -
    -
    +
    +
    +
      +
    • +
      Enclosing interface:
      -
      Device<V>
      +
      Device<V>
      -
      public static interface Device.Writer
      +
      +
      public static interface Device.Writer
      Instances of this complete a request for writing to an accessed tensor stored on a device. One may write at a particular position in a tensor, a range of positions or write to every possible value.
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      default void
      -
      at(int index)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethod and Description
        default voidat(int index)
        Writes whatever kind of data was previously specified, to the tensors' data at the position targeted by the provided index.
        - -
        void
        -
        fully()
        -
        +
        voidfully()
        A convenience method for specifying that the entire data array of the accessed tensor should be written to.
        - -
        void
        -
        intoRange(int start, - int limit)
        -
        +
        voidintoRange(int start, + int limit)
        Writes whatever kind of data was previously specified, to the tensors' data into the range targeted by the provided start and limit.
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        at

        -
        default void at(int index)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            at

            +
            default void at(int index)
            Writes whatever kind of data was previously specified, to the tensors' data at the position targeted by the provided index.
            -
            -
            Parameters:
            +
            +
            Parameters:
            index - The position at which data should be written to.
            -
        • -
      • -
        -

        intoRange

        -
        void intoRange(int start, - int limit)
        +
      + + + +
        +
      • +

        intoRange

        +
        void intoRange(int start,
+               int limit)
        Writes whatever kind of data was previously specified, to the tensors' data into the range targeted by the provided start and limit.
        -
        -
        Parameters:
        +
        +
        Parameters:
        start - The first position of the writing cursor in the accessed tensor.
        limit - The exclusive limit of the range which should be written to.
        -
      • -
    • -
      -

      fully

      -
      void fully()
      +
    + + + +
      +
    • +

      fully

      +
      void fully()
      A convenience method for specifying that the entire data array of the accessed tensor should be written to. - This is equivalent to calling intoRange(int, int) with the arguments + This is equivalent to calling intoRange(int, int) with the arguments 0 and tensor.size().
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
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      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/Device.html b/docs/jdocs/neureka/devices/Device.html index d8f1db96b..d5b156ce0 100644 --- a/docs/jdocs/neureka/devices/Device.html +++ b/docs/jdocs/neureka/devices/Device.html @@ -1,539 +1,717 @@ - + + - -Device (neureka 1.0.0 API) - - - - + +Device (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Interface Device<V>

    +
    neureka.devices
    +

    Interface Device<V>

    -
    -
    -
    Type Parameters:
    -
    V - The super type of all values that can be stored on a Device implementation...
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      +
      V - The super type of all values that can be stored on a Device implementation...
      -
      +
      All Superinterfaces:
      -
      Component<Tensor<V>>, Storage<V>
      +
      Component<Tensor<V>>, Storage<V>
      -
      +
      All Known Implementing Classes:
      -
      AbstractBaseDevice, AbstractDevice, CPU, FileDevice, OpenCLDevice
      +
      AbstractBaseDevice, AbstractDevice, CPU, FileDevice, OpenCLDevice
      -
      public interface Device<V> -extends Component<Tensor<V>>, Storage<V>
      +
      +
      public interface Device<V>
+extends Component<Tensor<V>>, Storage<V>
      Implementations of this represent computational devices for storing tensors (instances of the Tensor<V> class), which may also expose a useful API for executing operations on tensors (used in backend operations). Such instances are also components of tensors, which is why this interface extends the Component<Tensor<V>> interface.

      - Because devices store tensors, this interface extends the "Storage" interface + Because devices store tensors, this interface extends the "Storage" interface which defines the API for storing them. A tensor stored on a device holds a reference to that device, as well as the device itself which may also know about the tensors it holds. A tensor stored on a device will have its "isOutsourced" property set to true!
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Interface
      -
      Description
      -
      static interface 
      -
      Device.Access<V>
      -
      +
        +
      • + + +

        Nested Class Summary

        + + + + + + + + + + + + + + + + + + +
        Nested Classes 
        Modifier and TypeInterface and Description
        static interface Device.Access<V>
        Implementations of this represent the access to tensors stored on a device in order to read from or write to said tensor.
        - -
        static interface 
        -
        Device.In
        -
        +
        static interface Device.In
        The second part of the method chain of the fluent API for executing - tensors on this Device temporarily.
        - -
        static interface 
        -
        Device.Writer
        -
        + tensors on this Device temporarily.
        +
        static interface Device.Writer
        Instances of this complete a request for writing to an accessed tensor stored on a device.
        - - -
        -

        Nested classes/interfaces inherited from interface neureka.common.composition.Component

        -Component.IsBeing, Component.OwnerChangeRequest<O>
        - +
        +
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      <T extends V>
      Device.Access<T>
      -
      access(Tensor<T> tensor)
      -
      + -
      -
      -
        + +
      +
      • +
      +
        +
      • -
      • -
        -

        Method Details

        -
          -
        • -
          -

          find

          -
          static Optional<Device<Object>> find(String... searchKeys)
          -
          This method returns Device instances matching +
            +
          • + + +

            Method Detail

            + + + +
              +
            • +

              find

              +
              static java.util.Optional<Device<java.lang.Object>> find(java.lang.String... searchKeys)
              +
              This method returns Device instances matching the given search parameter.
              -
              -
              Parameters:
              -
              searchKeys - The search parameter and name of the requested Device instance.
              -
              Returns:
              -
              The found Device instance or simply the CPU instance by default.
              +
              +
              Parameters:
              +
              searchKeys - The search parameter and name of the requested Device instance.
              +
              Returns:
              +
              The found Device instance or simply the CPU instance by default.
              -
          • -
        • -
          -

          find

          -
          static <T, -D extends Device<T>> Optional<D> find(Class<D> deviceType, - String... searchKeys)
          -
          This method returns Device instances matching +
        + + + +
          +
        • +

          find

          +
          static <T,D extends Device<T>> java.util.Optional<D> find(java.lang.Class<D> deviceType,
+                                                          java.lang.String... searchKeys)
          +
          This method returns Device instances matching the given search parameters.
          -
          -
          Type Parameters:
          +
          +
          Type Parameters:
          T - The value super types of the tensors stored on the requested device.
          D - The device type parameter.
          -
          Parameters:
          +
          Parameters:
          deviceType - The device type class which should be found.
          -
          searchKeys - The search parameter and name of the requested Device instance.
          -
          Returns:
          -
          The found Device instance or simply the CPU instance by default.
          +
          searchKeys - The search parameter and name of the requested Device instance.
          +
          Returns:
          +
          The found Device instance or simply the CPU instance by default.
          -
        • -
      • -
        -

        any

        -
        static Device<Object> any(String... searchKeys)
        -
        This method returns Device instances matching +
      + + + +
        +
      • +

        any

        +
        static Device<java.lang.Object> any(java.lang.String... searchKeys)
        +
        This method returns Device instances matching the given search parameter. - If the provided keys do not match anything then this method will simply return a CPU instance.
        -
        -
        Parameters:
        -
        searchKeys - The search parameter and name of the requested Device instance.
        -
        Returns:
        -
        The found Device instance or simply the CPU instance by default.
        + If the provided keys do not match anything then this method will simply return a CPU instance.
      +
      +
      Parameters:
      +
      searchKeys - The search parameter and name of the requested Device instance.
      +
      Returns:
      +
      The found Device instance or simply the CPU instance by default.
      -
      • -
    • -
      -

      get

      -
      static Device<Object> get(String... searchKeys)
      -
      This method returns Device instances matching +
    + + + +
      +
    • +

      get

      +
      static Device<java.lang.Object> get(java.lang.String... searchKeys)
      +
      This method returns Device instances matching the given search parameter. If the provided keys do not match anything then this method may return null.
      -
      -
      Parameters:
      -
      searchKeys - The search parameter and name of the requested Device instance.
      -
      Returns:
      -
      The found Device instance or simply null by default.
      +
      +
      Parameters:
      +
      searchKeys - The search parameter and name of the requested Device instance.
      +
      Returns:
      +
      The found Device instance or simply null by default.
      -
      • -
    • -
      -

      get

      -
      static <T, -D extends Device<T>> D get(Class<D> deviceType, - String... searchKeys)
      -
      This method returns Device instances matching +
    + + + +
      +
    • +

      get

      +
      static <T,D extends Device<T>> D get(java.lang.Class<D> deviceType,
+                                     java.lang.String... searchKeys)
      +
      This method returns Device instances matching the given search parameters.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The value super types of the tensors stored on the requested device.
      D - The device type parameter.
      -
      Parameters:
      +
      Parameters:
      deviceType - The device type class which should be found.
      -
      searchKeys - The search parameter and name of the requested Device instance.
      -
      Returns:
      -
      The found Device instance or null if nothing was found which matches the provided search hints well enough.
      +
      searchKeys - The search parameter and name of the requested Device instance.
      +
      Returns:
      +
      The found Device instance or null if nothing was found which matches the provided search hints well enough.
      -
      • -
    • -
      -

      numberOfDataObjects

      -
      int numberOfDataObjects()
      -
      Note that this is not necessarily equal to Storage.numberOfStored(), because - multiple tensors may share a single Data object.
      -
      -
      Returns:
      -
      The number of Data objects stored on this Device.
      +
    + + + +
      +
    • +

      numberOfDataObjects

      +
      int numberOfDataObjects()
      +
      Note that this is not necessarily equal to Storage.numberOfStored(), because + multiple tensors may share a single Data object.
      +
      +
      Returns:
      +
      The number of Data objects stored on this Device.
      -
      • -
    • -
      -

      dispose

      -
      void dispose()
      +
    + + + +
      +
    • +

      dispose

      +
      void dispose()
      This method signals the device to get ready for garbage collection. A given device may have resources which ought to be freed when it is no longer used. One may also choose to do resource freeing manually.
      -
      • -
    • -
      -

      has

      -
      <T extends V> boolean has(Tensor<T> tensor)
      -
      Use this to check if a tensor is stored on this Device!

      -
      -
      Type Parameters:
      -
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      -
      Parameters:
      -
      tensor - The tensor which may or may not be stored on this Device.
      -
      Returns:
      -
      The truth value determining if the provided tensor is stored on this Device.
      +
    + + + +
      +
    • +

      has

      +
      <T extends V> boolean has(Tensor<T> tensor)
      +
      Use this to check if a tensor is stored on this Device!

      +
      +
      Type Parameters:
      +
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      +
      Parameters:
      +
      tensor - The tensor which may or may not be stored on this Device.
      +
      Returns:
      +
      The truth value determining if the provided tensor is stored on this Device.
      -
      • -
    • -
      -

      free

      -
      <T extends V> Device<V> free(Tensor<T> tensor)
      -
      Use this to remove the provided tensor from this Device!

      -
      -
      Type Parameters:
      -
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      -
      Parameters:
      -
      tensor - The tensor which ought to be removed from this Device.
      -
      Returns:
      +
    + + + +
      +
    • +

      free

      +
      <T extends V> Device<V> free(Tensor<T> tensor)
      +
      Use this to remove the provided tensor from this Device!

      +
      +
      Type Parameters:
      +
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      +
      Parameters:
      +
      tensor - The tensor which ought to be removed from this Device.
      +
      Returns:
      This very instance to allow for method chaining.
      -
      • -
    • -
      -

      access

      -
      <T extends V> Device.Access<T> access(Tensor<T> tensor)
      +
    + + + +
      +
    • +

      access

      +
      <T extends V> Device.Access<T> access(Tensor<T> tensor)
      This method exposes the tensor access API for reading from or writing to a tensor stored on this device. It may return null if this device does not support accessing stored tensors.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter of the tensor for which the access API should be returned.
      -
      Parameters:
      +
      Parameters:
      tensor - The tensor whose data ought to be accessed.
      -
      Returns:
      +
      Returns:
      The tensor access API for reading from or writing to a tensor stored on this device.
      -
      • -
    • -
      -

      approve

      -
      Device<V> approve(ExecutionCall<? extends Device<?>> call)
      -
      This method is used internally to give Device implementations the opportunity - to perform some exception handling before the ExecutionCall will be dispatched. +
    + + + +
      +
    • +

      approve

      +
      Device<V> approve(ExecutionCall<? extends Device<?>> call)
      +
      This method is used internally to give Device implementations the opportunity + to perform some exception handling before the ExecutionCall will be dispatched. Use this for debugging when doing custom backend operations.
      -
      -
      Parameters:
      -
      call - The ExecutionCall which should be approved by this Device before execution.
      -
      Returns:
      +
      +
      Parameters:
      +
      call - The ExecutionCall which should be approved by this Device before execution.
      +
      Returns:
      This very instance to allow for method chaining.
      -
      • -
    • -
      -

      allocate

      -
      <T extends V> Data<T> allocate(DataType<T> dataType, - NDConfiguration ndc)
      -
      +
    + + + + + + + +
      +
    • +

      allocate

      +
      default <T extends V> Data<T> allocate(DataType<T> dataType,
+                                       int size)
      • -
    • -
      -

      allocateFromOne

      -
      <T extends V> Data<T> allocateFromOne(DataType<T> dataType, - NDConfiguration ndc, - T initialValue)
      -
      +
    + + + + + + + + + +
      +
    • +

      allocateFromAll

      +
      <T extends V> Data<T> allocateFromAll(DataType<T> dataType,
+                                      NDConfiguration ndc,
+                                      java.lang.Object jvmData)
      • -
    • -
      -

      optimizedOperationOf

      -
      Operation optimizedOperationOf(Function function, - String name)
      -
      This method tries to allow this device to produce an optimized Operation +
    + + + +
      +
    • +

      optimizedOperationOf

      +
      Operation optimizedOperationOf(Function function,
+                               java.lang.String name)
      +
      This method tries to allow this device to produce an optimized Operation based on the provided function. This is especially useful in an OpenCL context which can compile the function into native GPU kernels at runtime.
      -
      -
      Parameters:
      +
      +
      Parameters:
      function - The function which should be turned into an optimized operation.
      name - The name of the returned operation.
      -
      Returns:
      +
      Returns:
      An optimized operation based on the provided function, or null if optimization is not possible.
      -
      • -
    • -
      -

      optimizedFunctionOf

      -
      default Function optimizedFunctionOf(Function function, - String name)
      -
      This method tries to allow this device to produce an optimized Function +
    + + + +
      +
    • +

      optimizedFunctionOf

      +
      default Function optimizedFunctionOf(Function function,
+                                     java.lang.String name)
      +
      This method tries to allow this device to produce an optimized Function based on the provided function. This is especially useful in an OpenCL context which can compile the function into native GPU kernels at runtime.
      -
      -
      Parameters:
      +
      +
      Parameters:
      function - The function which should be used to design a new optimized function.
      name - The name of the optimized operation underlying the returned function.
      -
      Returns:
      +
      Returns:
      An instance of the optimized function.
      -
      • -
    • -
      -

      borrow

      -
      default Device.In borrow(Tensor<V> first, - Tensor<V>... rest)
      +
    + + + +
      +
    • +

      borrow

      +
      default Device.In borrow(Tensor<V> first,
+                         Tensor<V>... rest)
      This is a very simple fluent API for temporarily storing a number - of tensors on this Device, executing a provided lambda action, + of tensors on this Device, executing a provided lambda action, and then migrating all the tensors back to their original devices.

      -
      -
      Parameters:
      +
      +
      Parameters:
      first - The first tensor among all passed tensors which ought to be - stored temporarily on this Device.
      + stored temporarily on this Device.
      rest - Any number of other tensors passed to this method to be - stored temporarily on this Device.
      -
      Returns:
      + stored temporarily on this Device. +
      Returns:
      A simple lambda runner which will migrate the tensors passed to this method to - this very Device, execute the provided lambda, and then migrate all the + this very Device, execute the provided lambda, and then migrate all the tensors back to their original devices!
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/DeviceCleaner.html b/docs/jdocs/neureka/devices/DeviceCleaner.html index 444c68d0a..8c7bec67d 100644 --- a/docs/jdocs/neureka/devices/DeviceCleaner.html +++ b/docs/jdocs/neureka/devices/DeviceCleaner.html @@ -1,164 +1,269 @@ - + + - -DeviceCleaner (neureka 1.0.0 API) - - - - + +DeviceCleaner (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Interface DeviceCleaner

    +
    neureka.devices
    +

    Interface DeviceCleaner

    -
    +
    +
    +
      +
    • -
      public interface DeviceCleaner
      -
    -
    -
      - -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      static final DeviceCleaner
      -
      INSTANCE
      -
       
      +
      +
      public interface DeviceCleaner
      +
      • +
    - +
    + - -
    -
      +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getNewInstance

      -
      static DeviceCleaner getNewInstance()
      -
      +
        +
      • + + +

        Method Detail

        + + + + + + + +
          +
        • +

          register

          +
          void register(java.lang.Object o,
+              java.lang.Runnable action)
          • -
        • -
          -

          register

          -
          void register(Object o, - Runnable action)
          -
          +
      -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/DeviceData.html b/docs/jdocs/neureka/devices/DeviceData.html index 55184b9f8..2b407692d 100644 --- a/docs/jdocs/neureka/devices/DeviceData.html +++ b/docs/jdocs/neureka/devices/DeviceData.html @@ -1,151 +1,253 @@ - + + - -DeviceData (neureka 1.0.0 API) - - - - + +DeviceData (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Interface DeviceData<V>

    +
    neureka.devices
    +

    Interface DeviceData<V>

    -
    -
    -
    Type Parameters:
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The data type of the data.
      -
      +
      All Superinterfaces:
      -
      Data<V>
      +
      Data<V>
      -
      +
      All Known Implementing Classes:
      -
      AbstractDeviceData
      +
      AbstractDeviceData
      -
      public interface DeviceData<V> -extends Data<V>
      -
      A sub-interface of the Data interface providing +
      +
      public interface DeviceData<V>
+extends Data<V>
      +
      A sub-interface of the Data interface providing more device specific methods.
      -
    -
    -
    -
    -

    Methods inherited from interface neureka.Data

    -as, dataType, get, getOrNull, owner, usages
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        incrementUsageCount

        -
        void incrementUsageCount()
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            incrementUsageCount

            +
            void incrementUsageCount()
            • -
          • -
            -

            decrementUsageCount

            -
            void decrementUsageCount()
            -
            +
          + + + +
            +
          • +

            decrementUsageCount

            +
            void decrementUsageCount()
          -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/ReferenceCounter.ChangeEvent.html b/docs/jdocs/neureka/devices/ReferenceCounter.ChangeEvent.html index e16011359..b38ab95ae 100644 --- a/docs/jdocs/neureka/devices/ReferenceCounter.ChangeEvent.html +++ b/docs/jdocs/neureka/devices/ReferenceCounter.ChangeEvent.html @@ -1,184 +1,302 @@ - + + - -ReferenceCounter.ChangeEvent (neureka 1.0.0 API) - - - - + +ReferenceCounter.ChangeEvent (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Class ReferenceCounter.ChangeEvent

    -
    -
    java.lang.Object -
    neureka.devices.ReferenceCounter.ChangeEvent
    +
    neureka.devices
    +

    Class ReferenceCounter.ChangeEvent

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.ReferenceCounter.ChangeEvent
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      ChangeEvent(ReferenceCounter.ChangeType changeType, - int change, - int count)
      -
       
      +
      +
      public static class ReferenceCounter.ChangeEvent
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    + -
  • -
    -

    Method Details

    -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/ReferenceCounter.ChangeType.html b/docs/jdocs/neureka/devices/ReferenceCounter.ChangeType.html index 8e2adb239..5f1b06289 100644 --- a/docs/jdocs/neureka/devices/ReferenceCounter.ChangeType.html +++ b/docs/jdocs/neureka/devices/ReferenceCounter.ChangeType.html @@ -1,229 +1,354 @@ - + + - -ReferenceCounter.ChangeType (neureka 1.0.0 API) - - - - + +ReferenceCounter.ChangeType (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Enum Class ReferenceCounter.ChangeType

    -
    -
    java.lang.Object -
    java.lang.Enum<ReferenceCounter.ChangeType> -
    neureka.devices.ReferenceCounter.ChangeType
    -
    +
    neureka.devices
    +

    Enum ReferenceCounter.ChangeType

    -
    -
    +
    + +
    +
    -
    -
    - +
    + - -
    -
      +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static ReferenceCounter.ChangeType[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static ReferenceCounter.ChangeType[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (ReferenceCounter.ChangeType c : ReferenceCounter.ChangeType.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static ReferenceCounter.ChangeType valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static ReferenceCounter.ChangeType valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/ReferenceCounter.html b/docs/jdocs/neureka/devices/ReferenceCounter.html index 21b0ada8b..0b7eae27d 100644 --- a/docs/jdocs/neureka/devices/ReferenceCounter.html +++ b/docs/jdocs/neureka/devices/ReferenceCounter.html @@ -1,203 +1,330 @@ - + + - -ReferenceCounter (neureka 1.0.0 API) - - - - + +ReferenceCounter (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Class ReferenceCounter

    -
    -
    java.lang.Object -
    neureka.devices.ReferenceCounter
    +
    neureka.devices
    +

    Class ReferenceCounter

    -
    -
    -
    public final class ReferenceCounter -extends Object
    -
    -
    -
      - +
      + +
      +
        +
      • +
        +
        +
        public final class ReferenceCounter
+extends java.lang.Object
        +
        • +
      -
    +
    + - -
    -
      +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      increment

      -
      public void increment()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          increment

          +
          public void increment()
          • -
        • -
          -

          decrement

          -
          public void decrement()
          -
          +
        + + + +
          +
        • +

          decrement

          +
          public void decrement()
          • -
        • -
          -

          fullDelete

          -
          public void fullDelete()
          -
          +
        + + + +
          +
        • +

          fullDelete

          +
          public void fullDelete()
          • -
        • -
          -

          count

          -
          public int count()
          -
          +
        + + + +
          +
        • +

          count

          +
          public int count()
          +
          • +
      -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/Storage.html b/docs/jdocs/neureka/devices/Storage.html index 3390df8f0..348c7c181 100644 --- a/docs/jdocs/neureka/devices/Storage.html +++ b/docs/jdocs/neureka/devices/Storage.html @@ -1,211 +1,321 @@ - + + - -Storage (neureka 1.0.0 API) - - - - + +Storage (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices
    -

    Interface Storage<V>

    +
    neureka.devices
    +

    Interface Storage<V>

    -
    -
    +
    +
    +
      +
    • +
      All Known Subinterfaces:
      -
      Device<V>, FileHandle<FinalType,ValType>
      +
      Device<V>, FileHandle<FinalType,ValType>
      -
      +
      All Known Implementing Classes:
      -
      AbstractBaseDevice, AbstractDevice, CPU, CSVHandle, FileDevice, IDXHandle, OpenCLDevice
      +
      AbstractBaseDevice, AbstractDevice, CPU, CSVHandle, FileDevice, IDXHandle, OpenCLDevice
      -
      public interface Storage<V>
      +
      +
      public interface Storage<V>
      This is an abstract interface which simply describes "a thing that stores tensors". Therefore, the expected method signatures defining this abstract entity boil down to a "store" and a "restore" method. Classes like "OpenCLDevice" or "FileDevice" implement this interface indirectly (via the Device interface) because they are in essence also just entities that store tensors! - Besides the "Device" interface this interface is also extended by the FileHandle interface + Besides the "Device" interface this interface is also extended by the FileHandle interface which is an internal component of the FileDevice architecture...
      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        store

        -
        <T extends V> Storage<V> store(Tensor<T> tensor)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            store

            +
            <T extends V> Storage<V> store(Tensor<T> tensor)
            Implementations of this method ought to store the data of the tensor in whatever formant suites the underlying implementation and or final type. Classes like "OpenCLDevice" or "FileDevice" for example are tensor storages.
            -
            -
            Type Parameters:
            +
            +
            Type Parameters:
            T - A valid data type of the tensor which should be stored on this device.
            -
            Parameters:
            +
            Parameters:
            tensor - The tensor whose data ought to be stored.
            -
            Returns:
            -
            This Storage instance, to allow for method chaining.
            +
            Returns:
            +
            This Storage instance, to allow for method chaining.
            -
        • -
      • -
        -

        restore

        -
        Storage<V> restore(Tensor<V> tensor)
        -
        -
        Parameters:
        +
      + + + +
        +
      • +

        restore

        +
        Storage<V> restore(Tensor<V> tensor)
        +
        +
        Parameters:
        tensor - The tensor whose data ought to be restored (loaded to RAM/CPU device).
        -
        Returns:
        -
        This Storage instance, to allow for method chaining.
        +
        Returns:
        +
        This Storage instance, to allow for method chaining.
        -
      • -
    • -
      -

      numberOfStored

      -
      int numberOfStored()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfStored

      +
      int numberOfStored()
      +
      +
      Returns:
      The number of nd-array stored on this.
      -
      • -
    • -
      -

      isEmpty

      -
      boolean isEmpty()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      isEmpty

      +
      boolean isEmpty()
      +
      +
      Returns:
      The truth value determining if there are no tensors stored on this or false if there are tensors stored.
      -
      • -
    • -
      -

      contains

      -
      boolean contains(Tensor<V> o)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      contains

      +
      boolean contains(Tensor<V> o)
      +
      +
      Parameters:
      o - The tensor which may or may not be stored on this.
      -
      Returns:
      +
      Returns:
      The truth value determining if the provided tensor is stored on this.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/file/CSVHandle.html b/docs/jdocs/neureka/devices/file/CSVHandle.html index 458925de3..c42cd962e 100644 --- a/docs/jdocs/neureka/devices/file/CSVHandle.html +++ b/docs/jdocs/neureka/devices/file/CSVHandle.html @@ -1,580 +1,805 @@ - + + - -CSVHandle (neureka 1.0.0 API) - - - - + +CSVHandle (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.file
    -

    Class CSVHandle

    -
    -
    java.lang.Object -
    neureka.devices.file.CSVHandle
    +
    neureka.devices.file
    +

    Class CSVHandle

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.file.CSVHandle
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      FileHandle<CSVHandle,String>, Storage<String>
      +
      FileHandle<CSVHandle,java.lang.String>, Storage<java.lang.String>
      -
      public final class CSVHandle -extends Object
      +
      +
      public final class CSVHandle
+extends java.lang.Object
      This class is one of many extensions of the AbstractFileHandle which - is therefore ultimately an implementation of the FileHandle interface. - Like other FileHandle implementations this class represents a file + is therefore ultimately an implementation of the FileHandle interface. + Like other FileHandle implementations this class represents a file of a given type, in this case it represents a CSV file.
      -
    -
    -
      - -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      protected final String
      -
      _fileName
      -
       
      -
      protected static org.slf4j.Logger
      -
      _LOG
      -
       
      -
      protected int
      -
      _size
      -
       
      +
      • +
    -
    -

    Fields inherited from interface neureka.devices.file.FileHandle

    -FACTORY
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Field Details

      -
        -
      • -
        -

        _LOG

        -
        protected static org.slf4j.Logger _LOG
        -
        +
          +
        • + + +

          Field Detail

          + + + +
            +
          • +

            _LOG

            +
            protected static org.slf4j.Logger _LOG
            • -
          • -
            -

            _fileName

            -
            protected final String _fileName
            -
            +
          + + + +
            +
          • +

            _fileName

            +
            protected final java.lang.String _fileName
            • -
          • -
            -

            _size

            -
            protected int _size
            -
            +
          + + + +
            +
          • +

            _size

            +
            protected int _size
          -
      • +
    -
  • -
    -

    Constructor Details

    -
      -
    • -
      -

      CSVHandle

      -
      public CSVHandle(String fileName, - Map<String,Object> settings)
      -
      +
        +
      • + + +

        Constructor Detail

        + + + +
          +
        • +

          CSVHandle

          +
          public CSVHandle(java.lang.String fileName,
+                 java.util.Map<java.lang.String,java.lang.Object> settings)
        -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      store

      -
      public <T extends String> Storage<String> store(Tensor<T> tensor)
      -
      Description copied from interface: Storage
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          store

          +
          public <T extends java.lang.String> Storage<java.lang.String> store(Tensor<T> tensor)
          +
          Description copied from interface: Storage
          Implementations of this method ought to store the data of the tensor in whatever formant suites the underlying implementation and or final type. Classes like "OpenCLDevice" or "FileDevice" for example are tensor storages.
          -
          -
          Type Parameters:
          +
          +
          Type Parameters:
          T - A valid data type of the tensor which should be stored on this device.
          -
          Parameters:
          +
          Parameters:
          tensor - The tensor whose data ought to be stored.
          -
          Returns:
          -
          This Storage instance, to allow for method chaining.
          +
          Returns:
          +
          This Storage instance, to allow for method chaining.
          -
      • -
    • -
      -

      _loadData

      -
      protected Object _loadData()
      -
      +
    + + + +
      +
    • +

      _loadData

      +
      protected java.lang.Object _loadData()
      • -
    • -
      -

      load

      -
      public Tensor<String> load() - throws IOException
      -
      Description copied from interface: FileHandle
      +
    + + + +
      +
    • +

      load

      +
      public Tensor<java.lang.String> load()
+                              throws java.io.IOException
      +
      Description copied from interface: FileHandle
      An implementation of this method ought to create a new tensor instance containing the data which - is stored in the file whose access this FileHandle manages.
      -
      -
      Returns:
      + is stored in the file whose access this FileHandle manages.
    • +
    +
    Returns:
    A new tensor filled with the data from the targeted file.
    -
    Throws:
    -
    IOException - If loading goes wrong an exception is being thrown.
    +
    Throws:
    +
    java.io.IOException - If loading goes wrong an exception is being thrown.
    - -
  • -
    -

    getValueSize

    -
    public int getValueSize()
    -
    Description copied from interface: FileHandle
    + + + + +
      +
    • +

      getValueSize

      +
      public int getValueSize()
      +
      Description copied from interface: FileHandle
      This method return the size of the value which is stored - in the tensor of the file which is managed by this FileHandle. + in the tensor of the file which is managed by this FileHandle. The size however does not represent the byte size of the data. This means that the returned size is dependent on the data type of the underlying data of the file...
      -
      -
      Returns:
      +
      +
      Returns:
      The size of the value of the underlying tensor body.
      -
    • -
  • -
    -

    getDataSize

    -
    public int getDataSize()
    -
    Description copied from interface: FileHandle
    + + + + +
      +
    • +

      getDataSize

      +
      public int getDataSize()
      +
      Description copied from interface: FileHandle
      This method returns the byte size of the data which is stored - in the tensor of the file which is managed by this FileHandle. + in the tensor of the file which is managed by this FileHandle. The underlying datatype of the data within the file does not matter.
      -
      -
      Returns:
      +
      +
      Returns:
      The byte size of the data of the underlying tensor body.
      -
    • -
  • -
    -

    getTotalSize

    -
    public int getTotalSize()
    -
    Description copied from interface: FileHandle
    + + + + +
      +
    • +

      getTotalSize

      +
      public int getTotalSize()
      +
      Description copied from interface: FileHandle
      This method returns the number of bytes which are used to store the tensor in the file whose access is being managed by an implementation - of th FileHandle interface. + of th FileHandle interface. Metadata stored inside the file will also be included in this returned size.
      -
      -
      Returns:
      +
      +
      Returns:
      The byte size of all the bytes used to represent the tensor in the file.
      -
    • -
  • -
    -

    getDataType

    -
    public DataType<?> getDataType()
    -
    -
    Returns:
    -
    The data type of the tensor stored in the file which is managed by a FileHandle.
    + + + + +
      +
    • +

      getDataType

      +
      public DataType<?> getDataType()
      +
      +
      Returns:
      +
      The data type of the tensor stored in the file which is managed by a FileHandle.
      -
    • -
  • -
    -

    getShape

    -
    public Shape getShape()
    -
    -
    Returns:
    -
    The shape of the tensor stored in the file which is managed by a FileHandle.
    + + + + +
      +
    • +

      getShape

      +
      public Shape getShape()
      +
      +
      Returns:
      +
      The shape of the tensor stored in the file which is managed by a FileHandle.
      -
    • -
  • -
    -

    getDelimiter

    -
    public String getDelimiter()
    -
    + + + + +
      +
    • +

      getDelimiter

      +
      public java.lang.String getDelimiter()
      • -
    • -
      -

      isFirstRowIsLabels

      -
      public boolean isFirstRowIsLabels()
      -
      +
    + + + +
      +
    • +

      isFirstRowIsLabels

      +
      public boolean isFirstRowIsLabels()
      • -
    • -
      -

      getColLabels

      -
      public String[] getColLabels()
      -
      +
    + + + +
      +
    • +

      getColLabels

      +
      public java.lang.String[] getColLabels()
      • -
    • -
      -

      isFirstColIsIndex

      -
      public boolean isFirstColIsIndex()
      -
      +
    + + + +
      +
    • +

      isFirstColIsIndex

      +
      public boolean isFirstColIsIndex()
      • -
    • -
      -

      getRowLabels

      -
      public String[] getRowLabels()
      -
      +
    + + + +
      +
    • +

      getRowLabels

      +
      public java.lang.String[] getRowLabels()
      • -
    • -
      -

      getNumberOfRows

      -
      public Integer getNumberOfRows()
      -
      +
    + + + +
      +
    • +

      getNumberOfRows

      +
      public java.lang.Integer getNumberOfRows()
      • -
    • -
      -

      getNumberOfColumns

      -
      public Integer getNumberOfColumns()
      -
      +
    + + + +
      +
    • +

      getNumberOfColumns

      +
      public java.lang.Integer getNumberOfColumns()
      • -
    • -
      -

      numberOfStored

      -
      public int numberOfStored()
      -
      -
      Specified by:
      -
      numberOfStored in interface Storage<C>
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfStored

      +
      public int numberOfStored()
      +
      +
      Specified by:
      +
      numberOfStored in interface Storage<V>
      +
      Returns:
      The number of nd-array stored on this.
      -
      • -
    • -
      -

      isEmpty

      -
      public boolean isEmpty()
      -
      -
      Specified by:
      -
      isEmpty in interface Storage<C>
      -
      Returns:
      +
    + + + +
      +
    • +

      isEmpty

      +
      public boolean isEmpty()
      +
      +
      Specified by:
      +
      isEmpty in interface Storage<V>
      +
      Returns:
      The truth value determining if there are no tensors stored on this or false if there are tensors stored.
      -
      • -
    • -
      -

      contains

      -
      public boolean contains(Tensor<String> o)
      -
      -
      Specified by:
      -
      contains in interface Storage<C>
      -
      Parameters:
      +
    + + + +
      +
    • +

      contains

      +
      public boolean contains(Tensor<V> o)
      +
      +
      Specified by:
      +
      contains in interface Storage<V>
      +
      Parameters:
      o - The tensor which may or may not be stored on this.
      -
      Returns:
      +
      Returns:
      The truth value determining if the provided tensor is stored on this.
      -
      • -
    • -
      -

      _loadFile

      -
      protected File _loadFile()
      -
      +
    + + + + + + + +
      +
    • +

      _loadFileInputStream

      +
      protected java.io.FileInputStream _loadFileInputStream()
+                                                throws java.io.IOException
      +
      +
      Throws:
      +
      java.io.IOException
      -
      • -
    • -
      -

      free

      -
      public CSVHandle free()
      -
      Description copied from interface: FileHandle
      +
    + + + +
      +
    • +

      free

      +
      public C free()
      +
      Description copied from interface: FileHandle
      An implementation of this method ought to "free" up the memory used to store a tensor. Therefore, the method is expected to delete the underlying file - whose access this very FileHandle implementation manages. + whose access this very FileHandle implementation manages. The method also returns an instance of the final implementation of this class, meaning it adheres to the factory pattern.
      -
      -
      Specified by:
      -
      free in interface FileHandle<C,V>
      -
      Returns:
      +
      +
      Specified by:
      +
      free in interface FileHandle<C,V>
      +
      Returns:
      A reference of this very object in order to enable method chaining.
      -
      • -
    • -
      -

      getLocation

      -
      public String getLocation()
      -
      -
      Specified by:
      -
      getLocation in interface FileHandle<C,V>
      -
      Returns:
      +
    + + + +
      +
    • +

      getLocation

      +
      public java.lang.String getLocation()
      +
      +
      Specified by:
      +
      getLocation in interface FileHandle<C,V>
      +
      Returns:
      The full path as well as name of the file which stores a tensor.
      -
      • -
    • -
      -

      getFileName

      -
      public String getFileName()
      -
      -
      Specified by:
      -
      getFileName in interface FileHandle<C,V>
      -
      Returns:
      +
    + + + +
      +
    • +

      getFileName

      +
      public java.lang.String getFileName()
      +
      +
      Specified by:
      +
      getFileName in interface FileHandle<C,V>
      +
      Returns:
      The name of the file which stores a tensor.
      -
      • -
    • -
      -

      restore

      -
      public Storage<String> restore(Tensor<String> tensor)
      -
      -
      Specified by:
      -
      restore in interface Storage<C>
      -
      Parameters:
      +
    + + + +
      +
    • +

      restore

      +
      public Storage<V> restore(Tensor<V> tensor)
      +
      +
      Specified by:
      +
      restore in interface Storage<V>
      +
      Parameters:
      tensor - The tensor whose data ought to be restored (loaded to RAM/CPU device).
      -
      Returns:
      -
      This Storage instance, to allow for method chaining.
      +
      Returns:
      +
      This Storage instance, to allow for method chaining.
      -
      • -
    • -
      -

      extension

      -
      public final String extension()
      -
      Description copied from interface: FileHandle
      +
    + + + +
      +
    • +

      extension

      +
      public final java.lang.String extension()
      +
      Description copied from interface: FileHandle
      The file ending which comes after the '.' character...
      -
      -
      Specified by:
      -
      extension in interface FileHandle<C,V>
      -
      Returns:
      +
      +
      Specified by:
      +
      extension in interface FileHandle<C,V>
      +
      Returns:
      The file ending which implies the encoding of the data in the file.
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/file/FileDevice.html b/docs/jdocs/neureka/devices/file/FileDevice.html index 0f7517d98..a60c25c06 100644 --- a/docs/jdocs/neureka/devices/file/FileDevice.html +++ b/docs/jdocs/neureka/devices/file/FileDevice.html @@ -1,510 +1,655 @@ - + + - -FileDevice (neureka 1.0.0 API) - - - - + +FileDevice (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.file
    -

    Class FileDevice

    +
    neureka.devices.file
    +

    Class FileDevice

    -
    java.lang.Object -
    neureka.devices.AbstractBaseDevice<Object> -
    neureka.devices.file.FileDevice
    -
    -
    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Component<Tensor<Object>>, Device<Object>, Storage<Object>
      +
      Component<Tensor<java.lang.Object>>, Device<java.lang.Object>, Storage<java.lang.Object>
      -
      public final class FileDevice -extends AbstractBaseDevice<Object>
      -
      The FileDevice is a Device implementation +
      +
      public final class FileDevice
+extends AbstractBaseDevice<java.lang.Object>
      +
      The FileDevice is a Device implementation responsible for reading tensors from and or writing them to a given directory.

      - The abstraction provided by the "Device" interface + The abstraction provided by the "Device" interface does not necessitate that concrete implementations represent accelerator hardware.
      Generally speaking a device is a thing that stores tensors and optionally - also expose the Device.Access API for + also expose the Device.Access API for data access as well as an API useful for implementing operations... But, an implementation might also represent a simple storage device like your local SSD ord HDD, or in this case, a directory...

      The directory which ought to be governed by an instance of this - class has to be passed to the at(String) factory method (as relative path), + class has to be passed to the at(String) factory method (as relative path), after which the files within this directory will be read, making potential tensors accessible. Tensors on a file device however are not loaded onto memory entirely, instead a mere file handle for each "file tensor" is being instantiated. Therefore, tensors that are stored on this device are not fit for computation. - The restore(Tensor) method has to be called in order to load the provided + The restore(Tensor) method has to be called in order to load the provided tensor back into RAM.

      - A FileDevice can load PNG, JPG and IDX files. By default, tensors will + A FileDevice can load PNG, JPG and IDX files. By default, tensors will be stored as IDX files if not explicitly specified otherwise.

      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        at

        -
        public static FileDevice at(String path)
        -
        -
        Parameters:
        -
        path - The directory path for which the responsible FileDevice instance ought to be returned.
        -
        Returns:
        -
        A FileDevice instance representing the provided directory path and all compatible files within it.
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            at

            +
            public static FileDevice at(java.lang.String path)
            +
            +
            Parameters:
            +
            path - The directory path for which the responsible FileDevice instance ought to be returned.
            +
            Returns:
            +
            A FileDevice instance representing the provided directory path and all compatible files within it.
            -
        • -
      • -
        -

        load

        -
        public <V> Optional<Tensor<V>> load(String filename) - throws IOException
        -
        -
        Throws:
        -
        IOException
        +
      + + + +
        +
      • +

        load

        +
        public <V> java.util.Optional<Tensor<V>> load(java.lang.String filename)
+                                       throws java.io.IOException
        +
        +
        Throws:
        +
        java.io.IOException
        -
      • -
    • -
      -

      load

      -
      public <V> Optional<Tensor<V>> load(String filename, - Map<String,Object> conf) - throws IOException
      -
      -
      Throws:
      -
      IOException
      +
    + + + +
      +
    • +

      load

      +
      public <V> java.util.Optional<Tensor<V>> load(java.lang.String filename,
+                                              java.util.Map<java.lang.String,java.lang.Object> conf)
+                                       throws java.io.IOException
      +
      +
      Throws:
      +
      java.io.IOException
      -
      • -
    • -
      -

      fileHandleOf

      -
      public FileHandle<?,?> fileHandleOf(Tensor<?> tensor)
      -
      +
    + + + +
      +
    • +

      fileHandleOf

      +
      public FileHandle<?,?> fileHandleOf(Tensor<?> tensor)
      • -
    • -
      -

      dispose

      -
      public void dispose()
      -
      Description copied from interface: Device
      +
    + + + +
      +
    • +

      dispose

      +
      public void dispose()
      +
      Description copied from interface: Device
      This method signals the device to get ready for garbage collection. A given device may have resources which ought to be freed when it is no longer used. One may also choose to do resource freeing manually.
      -
      • -
    • -
      -

      restore

      -
      public Device<Object> restore(Tensor<Object> tensor)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      restore

      +
      public Device<java.lang.Object> restore(Tensor<java.lang.Object> tensor)
      +
      +
      Parameters:
      tensor - The tensor whose data ought to be restored (loaded to RAM/CPU device).
      -
      Returns:
      -
      This Storage instance, to allow for method chaining.
      +
      Returns:
      +
      This Storage instance, to allow for method chaining.
      -
      • -
    • -
      -

      store

      -
      public <T> Device<Object> store(Tensor<T> tensor)
      +
    + + + +
      +
    • +

      store

      +
      public <T> Device<java.lang.Object> store(Tensor<T> tensor)
      Implementations of this method ought to store the data of the tensor in whatever formant suites the underlying implementation and or final type. Classes like "OpenCLDevice" or "FileDevice" for example are tensor storages.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - A valid data type of the tensor which should be stored on this device.
      -
      Parameters:
      +
      Parameters:
      tensor - The tensor whose data ought to be stored.
      -
      Returns:
      -
      This Storage instance, to allow for method chaining.
      +
      Returns:
      +
      This Storage instance, to allow for method chaining.
      -
      • -
    • -
      -

      store

      -
      public <T> FileDevice store(Tensor<T> tensor, - String filename)
      +
    + + + +
      +
    • +

      store

      +
      public <T> FileDevice store(Tensor<T> tensor,
+                            java.lang.String filename)
      Stores the given tensor in the file system with the given filename.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type of the tensor.
      -
      Parameters:
      +
      Parameters:
      tensor - The tensor to store
      filename - The filename of the file containing the tensor.
      -
      Returns:
      +
      Returns:
      The file device itself.
      -
      • -
    • -
      -

      store

      -
      public <T> FileDevice store(Tensor<T> tensor, - String filename, - Map<String,Object> configurations)
      +
    + + + +
      +
    • +

      store

      +
      public <T> FileDevice store(Tensor<T> tensor,
+                            java.lang.String filename,
+                            java.util.Map<java.lang.String,java.lang.Object> configurations)
      Stores the given tensor in the file system with the given filename.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type of the tensor.
      -
      Parameters:
      +
      Parameters:
      tensor - The tensor to store
      filename - The filename of the file containing the tensor.
      configurations - The configurations to use when storing the tensor.
      -
      Returns:
      +
      Returns:
      The file device itself.
      -
      • -
    • -
      -

      has

      -
      public <T> boolean has(Tensor<T> tensor)
      -
      Description copied from class: AbstractBaseDevice
      +
    + + + +
      +
    • +

      has

      +
      public <T> boolean has(Tensor<T> tensor)
      +
      Description copied from class: AbstractBaseDevice
      This method checks if the passed tensor - is stored on this Device instance. + is stored on this Device instance. "Stored" means that the data of the tensor was created by this device. This data is referenced inside the tensor...
      -
      -
      Specified by:
      -
      has in interface Device<Object>
      -
      Overrides:
      -
      has in class AbstractBaseDevice<Object>
      -
      Type Parameters:
      -
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      -
      Parameters:
      +
      +
      Specified by:
      +
      has in interface Device<java.lang.Object>
      +
      Overrides:
      +
      has in class AbstractBaseDevice<java.lang.Object>
      +
      Type Parameters:
      +
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      +
      Parameters:
      tensor - The tensor in question.
      -
      Returns:
      +
      Returns:
      The truth value of the fact that the provided tensor is on this device.
      -
      • -
    • -
      -

      free

      -
      public <T> Device<Object> free(Tensor<T> tensor)
      -
      Description copied from interface: Device
      -
      Use this to remove the provided tensor from this Device!

      -
      -
      Type Parameters:
      -
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      -
      Parameters:
      -
      tensor - The tensor which ought to be removed from this Device.
      -
      Returns:
      +
    + + + +
      +
    • +

      free

      +
      public <T> Device<java.lang.Object> free(Tensor<T> tensor)
      +
      Description copied from interface: Device
      +
      Use this to remove the provided tensor from this Device!

      +
      +
      Type Parameters:
      +
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      +
      Parameters:
      +
      tensor - The tensor which ought to be removed from this Device.
      +
      Returns:
      This very instance to allow for method chaining.
      -
      • -
    • -
      -

      access

      -
      public <T> Device.Access<T> access(Tensor<T> tensor)
      -
      Description copied from interface: Device
      +
    + + + +
      +
    • +

      access

      +
      public <T> Device.Access<T> access(Tensor<T> tensor)
      +
      Description copied from interface: Device
      This method exposes the tensor access API for reading from or writing to a tensor stored on this device. It may return null if this device does not support accessing stored tensors.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter of the tensor for which the access API should be returned.
      -
      Parameters:
      +
      Parameters:
      tensor - The tensor whose data ought to be accessed.
      -
      Returns:
      +
      Returns:
      The tensor access API for reading from or writing to a tensor stored on this device.
      -
      • -
    • -
      -

      approve

      -
      public Device<Object> approve(ExecutionCall<? extends Device<?>> call)
      -
      Description copied from interface: Device
      -
      This method is used internally to give Device implementations the opportunity - to perform some exception handling before the ExecutionCall will be dispatched. +
    + + + +
      +
    • +

      approve

      +
      public Device<java.lang.Object> approve(ExecutionCall<? extends Device<?>> call)
      +
      Description copied from interface: Device
      +
      This method is used internally to give Device implementations the opportunity + to perform some exception handling before the ExecutionCall will be dispatched. Use this for debugging when doing custom backend operations.
      -
      -
      Parameters:
      -
      call - The ExecutionCall which should be approved by this Device before execution.
      -
      Returns:
      +
      +
      Parameters:
      +
      call - The ExecutionCall which should be approved by this Device before execution.
      +
      Returns:
      This very instance to allow for method chaining.
      -
      • -
    • -
      -

      allocate

      -
      public <V> Data<V> allocate(DataType<V> dataType, - NDConfiguration ndc)
      -
      +
    + + + + + + + + + + + + + +
      +
    • +

      allocateFromAll

      +
      public <T> Data<T> allocateFromAll(DataType<T> dataType,
+                                   NDConfiguration ndc,
+                                   java.lang.Object jvmData)
      • -
    • -
      -

      optimizedOperationOf

      -
      public Operation optimizedOperationOf(Function function, - String name)
      -
      Description copied from interface: Device
      -
      This method tries to allow this device to produce an optimized Operation +
    + + + +
      +
    • +

      optimizedOperationOf

      +
      public Operation optimizedOperationOf(Function function,
+                                      java.lang.String name)
      +
      Description copied from interface: Device
      +
      This method tries to allow this device to produce an optimized Operation based on the provided function. This is especially useful in an OpenCL context which can compile the function into native GPU kernels at runtime.
      -
      -
      Parameters:
      +
      +
      Parameters:
      function - The function which should be turned into an optimized operation.
      name - The name of the returned operation.
      -
      Returns:
      +
      Returns:
      An optimized operation based on the provided function, or null if optimization is not possible.
      -
      • -
    • -
      -

      update

      -
      public boolean update(Component.OwnerChangeRequest<Tensor<Object>> changeRequest)
      -
      Description copied from interface: Component
      +
    + + + +
      +
    • +

      update

      +
      public boolean update(Component.OwnerChangeRequest<Tensor<java.lang.Object>> changeRequest)
      +
      Description copied from interface: Component
      Components are not the slaves of their owners. If the owner registers any state changes related to a given component, then said component will be informed by the owner about the change as well as receive @@ -514,54 +659,125 @@

      update

      is being added to, or removed from, its current owner. If components hold references to their owners then this method gives them the ability to update said reference when a new owner takes over the components of an old one. - The Component.OwnerChangeRequest implementation instance passed to this method - informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). + The Component.OwnerChangeRequest implementation instance passed to this method + informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). If this method returns false then this means that this component rejects the proposed update. The component owner will then abort the proposed change.
      -
      -
      Parameters:
      -
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      -
      Returns:
      +
      +
      Parameters:
      +
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      +
      Returns:
      The truth value determining if the state change should be aborted or not.
      -
      • -
    • -
      -

      toString

      -
      public String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
      • -
    • -
      -

      getDirectory

      -
      public String getDirectory()
      -
      +
    + + + +
      +
    • +

      getDirectory

      +
      public java.lang.String getDirectory()
      • -
    • -
      -

      getLoadable

      -
      public List<String> getLoadable()
      -
      +
    + + + +
      +
    • +

      getLoadable

      +
      public java.util.List<java.lang.String> getLoadable()
      • -
    • -
      -

      getLoaded

      -
      public List<String> getLoaded()
      -
      +
    + + + +
      +
    • +

      getLoaded

      +
      public java.util.List<java.lang.String> getLoaded()
      +
      • +
    - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/file/FileHandle.html b/docs/jdocs/neureka/devices/file/FileHandle.html index 9d8ecb0a2..062ff2808 100644 --- a/docs/jdocs/neureka/devices/file/FileHandle.html +++ b/docs/jdocs/neureka/devices/file/FileHandle.html @@ -1,331 +1,472 @@ - + + - -FileHandle (neureka 1.0.0 API) - - - - + +FileHandle (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.file
    -

    Interface FileHandle<FinalType,ValType>

    +
    neureka.devices.file
    +

    Interface FileHandle<FinalType,ValType>

    -
    -
    +
    +
    +
    -
    -
      - -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      static final neureka.devices.file.HandleFactory
      -
      FACTORY
      -
       
      +
      +
      public interface FileHandle<FinalType,ValType>
+extends Storage<ValType>
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Field Details

      -
        -
      • -
        -

        FACTORY

        -
        static final neureka.devices.file.HandleFactory FACTORY
        -
        +
          +
        • + + +

          Field Detail

          + + + +
            +
          • +

            FACTORY

            +
            static final neureka.devices.file.HandleFactory FACTORY
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      load

      -
      Tensor<ValType> load() - throws IOException
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          load

          +
          Tensor<ValType> load()
+              throws java.io.IOException
          An implementation of this method ought to create a new tensor instance containing the data which - is stored in the file whose access this FileHandle manages.
          -
          -
          Returns:
          + is stored in the file whose access this FileHandle manages.
    • +
    +
    Returns:
    A new tensor filled with the data from the targeted file.
    -
    Throws:
    -
    IOException - If loading goes wrong an exception is being thrown.
    +
    Throws:
    +
    java.io.IOException - If loading goes wrong an exception is being thrown.
    - -
  • -
    -

    free

    -
    FinalType free() - throws IOException
    + + + + +
      +
    • +

      free

      +
      FinalType free()
+        throws java.io.IOException
      An implementation of this method ought to "free" up the memory used to store a tensor. Therefore, the method is expected to delete the underlying file - whose access this very FileHandle implementation manages. + whose access this very FileHandle implementation manages. The method also returns an instance of the final implementation of this class, meaning it adheres to the factory pattern.
      -
      -
      Returns:
      +
      +
      Returns:
      A reference of this very object in order to enable method chaining.
      -
      Throws:
      -
      IOException - Freeing / deleting resources might result in io exceptions.
      +
      Throws:
      +
      java.io.IOException - Freeing / deleting resources might result in io exceptions.
      -
    • -
  • -
    -

    getValueSize

    -
    int getValueSize()
    + + + + +
      +
    • +

      getValueSize

      +
      int getValueSize()
      This method return the size of the value which is stored - in the tensor of the file which is managed by this FileHandle. + in the tensor of the file which is managed by this FileHandle. The size however does not represent the byte size of the data. This means that the returned size is dependent on the data type of the underlying data of the file...
      -
      -
      Returns:
      +
      +
      Returns:
      The size of the value of the underlying tensor body.
      -
    • -
  • -
    -

    getDataSize

    -
    int getDataSize()
    + + + + +
      +
    • +

      getDataSize

      +
      int getDataSize()
      This method returns the byte size of the data which is stored - in the tensor of the file which is managed by this FileHandle. + in the tensor of the file which is managed by this FileHandle. The underlying datatype of the data within the file does not matter.
      -
      -
      Returns:
      +
      +
      Returns:
      The byte size of the data of the underlying tensor body.
      -
    • -
  • -
    -

    getTotalSize

    -
    int getTotalSize()
    + + + + +
      +
    • +

      getTotalSize

      +
      int getTotalSize()
      This method returns the number of bytes which are used to store the tensor in the file whose access is being managed by an implementation - of th FileHandle interface. + of th FileHandle interface. Metadata stored inside the file will also be included in this returned size.
      -
      -
      Returns:
      +
      +
      Returns:
      The byte size of all the bytes used to represent the tensor in the file.
      -
    • -
  • -
    -

    getLocation

    -
    String getLocation()
    -
    -
    Returns:
    + + + + +
      +
    • +

      getLocation

      +
      java.lang.String getLocation()
      +
      +
      Returns:
      The full path as well as name of the file which stores a tensor.
      -
    • -
  • -
    -

    getFileName

    -
    String getFileName()
    -
    -
    Returns:
    + + + + +
      +
    • +

      getFileName

      +
      java.lang.String getFileName()
      +
      +
      Returns:
      The name of the file which stores a tensor.
      -
    • -
  • -
    -

    getDataType

    -
    DataType<?> getDataType()
    -
    -
    Returns:
    -
    The data type of the tensor stored in the file which is managed by a FileHandle.
    + + + + +
      +
    • +

      getDataType

      +
      DataType<?> getDataType()
      +
      +
      Returns:
      +
      The data type of the tensor stored in the file which is managed by a FileHandle.
      -
    • -
  • -
    -

    getShape

    -
    Shape getShape()
    -
    -
    Returns:
    -
    The shape of the tensor stored in the file which is managed by a FileHandle.
    + + + + +
      +
    • +

      getShape

      +
      Shape getShape()
      +
      +
      Returns:
      +
      The shape of the tensor stored in the file which is managed by a FileHandle.
      -
    • -
  • -
    -

    extension

    -
    String extension()
    + + + + +
      +
    • +

      extension

      +
      java.lang.String extension()
      The file ending which comes after the '.' character...
      -
      -
      Returns:
      +
      +
      Returns:
      The file ending which implies the encoding of the data in the file.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/file/IDXHandle.html b/docs/jdocs/neureka/devices/file/IDXHandle.html index cab9712a2..bae26cff0 100644 --- a/docs/jdocs/neureka/devices/file/IDXHandle.html +++ b/docs/jdocs/neureka/devices/file/IDXHandle.html @@ -1,530 +1,731 @@ - + + - -IDXHandle (neureka 1.0.0 API) - - - - + +IDXHandle (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.file
    -

    Class IDXHandle

    +
    neureka.devices.file
    +

    Class IDXHandle

    -
    java.lang.Object -
    neureka.devices.file.IDXHandle
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.file.IDXHandle
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      FileHandle<IDXHandle,Number>, Storage<Number>
      +
      FileHandle<IDXHandle,java.lang.Number>, Storage<java.lang.Number>
      -
      public final class IDXHandle -extends Object
      +
      +
      public final class IDXHandle
+extends java.lang.Object
      This class is one of many extensions of the AbstractFileHandle which - is therefore ultimately an implementation of the FileHandle interface. - Like other FileHandle implementations this class represents a file + is therefore ultimately an implementation of the FileHandle interface. + Like other FileHandle implementations this class represents a file of a given type, in this case it represents a IDX file.
      -
    -
    -
      - -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      protected final String
      -
      _fileName
      -
       
      -
      protected static org.slf4j.Logger
      -
      _LOG
      -
       
      -
      protected int
      -
      _size
      -
       
      +
      • +
    -
    -

    Fields inherited from interface neureka.devices.file.FileHandle

    -FACTORY
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Field Details

      -
        -
      • -
        -

        _LOG

        -
        protected static org.slf4j.Logger _LOG
        -
        +
          +
        • + + +

          Field Detail

          + + + +
            +
          • +

            _LOG

            +
            protected static org.slf4j.Logger _LOG
            • -
          • -
            -

            _fileName

            -
            protected final String _fileName
            -
            +
          + + + +
            +
          • +

            _fileName

            +
            protected final java.lang.String _fileName
            • -
          • -
            -

            _size

            -
            protected int _size
            -
            +
          + + + +
            +
          • +

            _size

            +
            protected int _size
          -
      • +
    -
  • -
    -

    Constructor Details

    -
      -
    • -
      -

      IDXHandle

      -
      public IDXHandle(String fileName)
      -
      +
        +
      • + + +

        Constructor Detail

        + + + +
          +
        • +

          IDXHandle

          +
          public IDXHandle(java.lang.String fileName)
          • -
        • -
          -

          IDXHandle

          -
          public IDXHandle(Tensor<Number> t, - String filename)
          -
          +
        + + + +
          +
        • +

          IDXHandle

          +
          public IDXHandle(Tensor<java.lang.Number> t,
+                 java.lang.String filename)
        -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      store

      -
      public <T extends Number> IDXHandle store(Tensor<T> tensor)
      -
      Description copied from interface: Storage
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          store

          +
          public <T extends java.lang.Number> IDXHandle store(Tensor<T> tensor)
          +
          Description copied from interface: Storage
          Implementations of this method ought to store the data of the tensor in whatever formant suites the underlying implementation and or final type. Classes like "OpenCLDevice" or "FileDevice" for example are tensor storages.
          -
          -
          Type Parameters:
          +
          +
          Type Parameters:
          T - A valid data type of the tensor which should be stored on this device.
          -
          Parameters:
          +
          Parameters:
          tensor - The tensor whose data ought to be stored.
          -
          Returns:
          -
          This Storage instance, to allow for method chaining.
          +
          Returns:
          +
          This Storage instance, to allow for method chaining.
          -
      • -
    • -
      -

      _loadData

      -
      protected Object _loadData() - throws IOException
      -
      -
      Throws:
      -
      IOException
      +
    + + + +
      +
    • +

      _loadData

      +
      protected java.lang.Object _loadData()
+                              throws java.io.IOException
      +
      +
      Throws:
      +
      java.io.IOException
      -
    • -
  • -
    -

    load

    -
    public Tensor<Number> load() - throws IOException
    -
    Description copied from interface: FileHandle
    + + + + +
      +
    • +

      load

      +
      public Tensor<java.lang.Number> load()
+                              throws java.io.IOException
      +
      Description copied from interface: FileHandle
      An implementation of this method ought to create a new tensor instance containing the data which - is stored in the file whose access this FileHandle manages.
      -
      -
      Returns:
      + is stored in the file whose access this FileHandle manages.
    • +
    +
    Returns:
    A new tensor filled with the data from the targeted file.
    -
    Throws:
    -
    IOException - If loading goes wrong an exception is being thrown.
    +
    Throws:
    +
    java.io.IOException - If loading goes wrong an exception is being thrown.
    - -
  • -
    -

    getDataSize

    -
    public int getDataSize()
    -
    Description copied from interface: FileHandle
    + + + + +
      +
    • +

      getDataSize

      +
      public int getDataSize()
      +
      Description copied from interface: FileHandle
      This method returns the byte size of the data which is stored - in the tensor of the file which is managed by this FileHandle. + in the tensor of the file which is managed by this FileHandle. The underlying datatype of the data within the file does not matter.
      -
      -
      Returns:
      +
      +
      Returns:
      The byte size of the data of the underlying tensor body.
      -
    • -
  • -
    -

    getTotalSize

    -
    public int getTotalSize()
    -
    Description copied from interface: FileHandle
    + + + + +
      +
    • +

      getTotalSize

      +
      public int getTotalSize()
      +
      Description copied from interface: FileHandle
      This method returns the number of bytes which are used to store the tensor in the file whose access is being managed by an implementation - of th FileHandle interface. + of th FileHandle interface. Metadata stored inside the file will also be included in this returned size.
      -
      -
      Returns:
      +
      +
      Returns:
      The byte size of all the bytes used to represent the tensor in the file.
      -
    • -
  • -
    -

    getDataType

    -
    public DataType<?> getDataType()
    -
    -
    Returns:
    -
    The data type of the tensor stored in the file which is managed by a FileHandle.
    + + + + +
      +
    • +

      getDataType

      +
      public DataType<?> getDataType()
      +
      +
      Returns:
      +
      The data type of the tensor stored in the file which is managed by a FileHandle.
      -
    • -
  • -
    -

    getValueSize

    -
    public int getValueSize()
    -
    Description copied from interface: FileHandle
    + + + + +
      +
    • +

      getValueSize

      +
      public int getValueSize()
      +
      Description copied from interface: FileHandle
      This method return the size of the value which is stored - in the tensor of the file which is managed by this FileHandle. + in the tensor of the file which is managed by this FileHandle. The size however does not represent the byte size of the data. This means that the returned size is dependent on the data type of the underlying data of the file...
      -
      -
      Returns:
      +
      +
      Returns:
      The size of the value of the underlying tensor body.
      -
    • -
  • -
    -

    getShape

    -
    public Shape getShape()
    -
    -
    Returns:
    -
    The shape of the tensor stored in the file which is managed by a FileHandle.
    + + + + +
      +
    • +

      getShape

      +
      public Shape getShape()
      +
      +
      Returns:
      +
      The shape of the tensor stored in the file which is managed by a FileHandle.
      -
    • -
  • -
    -

    numberOfStored

    -
    public int numberOfStored()
    -
    -
    Specified by:
    -
    numberOfStored in interface Storage<C>
    -
    Returns:
    + + + + +
      +
    • +

      numberOfStored

      +
      public int numberOfStored()
      +
      +
      Specified by:
      +
      numberOfStored in interface Storage<V>
      +
      Returns:
      The number of nd-array stored on this.
      -
    • -
  • -
    -

    isEmpty

    -
    public boolean isEmpty()
    -
    -
    Specified by:
    -
    isEmpty in interface Storage<C>
    -
    Returns:
    + + + + +
      +
    • +

      isEmpty

      +
      public boolean isEmpty()
      +
      +
      Specified by:
      +
      isEmpty in interface Storage<V>
      +
      Returns:
      The truth value determining if there are no tensors stored on this or false if there are tensors stored.
      -
    • -
  • -
    -

    contains

    -
    public boolean contains(Tensor<Number> o)
    -
    -
    Specified by:
    -
    contains in interface Storage<C>
    -
    Parameters:
    + + + + +
      +
    • +

      contains

      +
      public boolean contains(Tensor<V> o)
      +
      +
      Specified by:
      +
      contains in interface Storage<V>
      +
      Parameters:
      o - The tensor which may or may not be stored on this.
      -
      Returns:
      +
      Returns:
      The truth value determining if the provided tensor is stored on this.
      -
    • -
  • -
    -

    _loadFile

    -
    protected File _loadFile()
    -
    + + + + + + + + +
      +
    • +

      _loadFileInputStream

      +
      protected java.io.FileInputStream _loadFileInputStream()
+                                                throws java.io.IOException
      +
      +
      Throws:
      +
      java.io.IOException
      -
      • -
    • -
      -

      free

      -
      public IDXHandle free()
      -
      Description copied from interface: FileHandle
      +
    + + + +
      +
    • +

      free

      +
      public C free()
      +
      Description copied from interface: FileHandle
      An implementation of this method ought to "free" up the memory used to store a tensor. Therefore, the method is expected to delete the underlying file - whose access this very FileHandle implementation manages. + whose access this very FileHandle implementation manages. The method also returns an instance of the final implementation of this class, meaning it adheres to the factory pattern.
      -
      -
      Specified by:
      -
      free in interface FileHandle<C,V>
      -
      Returns:
      +
      +
      Specified by:
      +
      free in interface FileHandle<C,V>
      +
      Returns:
      A reference of this very object in order to enable method chaining.
      -
      • -
    • -
      -

      getLocation

      -
      public String getLocation()
      -
      -
      Specified by:
      -
      getLocation in interface FileHandle<C,V>
      -
      Returns:
      +
    + + + +
      +
    • +

      getLocation

      +
      public java.lang.String getLocation()
      +
      +
      Specified by:
      +
      getLocation in interface FileHandle<C,V>
      +
      Returns:
      The full path as well as name of the file which stores a tensor.
      -
      • -
    • -
      -

      getFileName

      -
      public String getFileName()
      -
      -
      Specified by:
      -
      getFileName in interface FileHandle<C,V>
      -
      Returns:
      +
    + + + +
      +
    • +

      getFileName

      +
      public java.lang.String getFileName()
      +
      +
      Specified by:
      +
      getFileName in interface FileHandle<C,V>
      +
      Returns:
      The name of the file which stores a tensor.
      -
      • -
    • -
      -

      restore

      -
      public Storage<Number> restore(Tensor<Number> tensor)
      -
      -
      Specified by:
      -
      restore in interface Storage<C>
      -
      Parameters:
      +
    + + + +
      +
    • +

      restore

      +
      public Storage<V> restore(Tensor<V> tensor)
      +
      +
      Specified by:
      +
      restore in interface Storage<V>
      +
      Parameters:
      tensor - The tensor whose data ought to be restored (loaded to RAM/CPU device).
      -
      Returns:
      -
      This Storage instance, to allow for method chaining.
      +
      Returns:
      +
      This Storage instance, to allow for method chaining.
      -
      • -
    • -
      -

      extension

      -
      public final String extension()
      -
      Description copied from interface: FileHandle
      +
    + + + +
      +
    • +

      extension

      +
      public final java.lang.String extension()
      +
      Description copied from interface: FileHandle
      The file ending which comes after the '.' character...
      -
      -
      Specified by:
      -
      extension in interface FileHandle<C,V>
      -
      Returns:
      +
      +
      Specified by:
      +
      extension in interface FileHandle<C,V>
      +
      Returns:
      The file ending which implies the encoding of the data in the file.
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/file/package-frame.html b/docs/jdocs/neureka/devices/file/package-frame.html new file mode 100644 index 000000000..0e484ba7f --- /dev/null +++ b/docs/jdocs/neureka/devices/file/package-frame.html @@ -0,0 +1,25 @@ + + + + + +neureka.devices.file (neureka 1.0.1 API) + + + + +

    neureka.devices.file

    +
    +

    Interfaces

    + +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/devices/file/package-summary.html b/docs/jdocs/neureka/devices/file/package-summary.html index 0275cdf80..67a14a44d 100644 --- a/docs/jdocs/neureka/devices/file/package-summary.html +++ b/docs/jdocs/neureka/devices/file/package-summary.html @@ -1,118 +1,171 @@ - + + - -neureka.devices.file (neureka 1.0.0 API) - - - - + +neureka.devices.file (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.devices.file

    -
    -
    -
    package neureka.devices.file
    -
    -
      -
    • - +
      +
        +
      • + + + + + + + + + + + + +
        Interface Summary 
        InterfaceDescription
        FileHandle<FinalType,ValType> 
        • -
      • -
        -
        -
        -
        -
        Class
        -
        Description
        -
        CSVHandle
        -
        +
      • + + + + + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        CSVHandle
        This class is one of many extensions of the AbstractFileHandle which - is therefore ultimately an implementation of the FileHandle interface.
        - -
        FileDevice
        -
        -
        The FileDevice is a Device implementation + is therefore ultimately an implementation of the FileHandle interface.
        +
        FileDevice +
        The FileDevice is a Device implementation responsible for reading tensors from and or writing them to a given directory.
        - -
        FileHandle<FinalType,ValType>
        -
         
        -
        IDXHandle
        -
        +
        IDXHandle
        This class is one of many extensions of the AbstractFileHandle which - is therefore ultimately an implementation of the FileHandle interface.
        - - - - + is therefore ultimately an implementation of the FileHandle interface. +
      -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/file/package-tree.html b/docs/jdocs/neureka/devices/file/package-tree.html index c37fdcf17..5dde2e17c 100644 --- a/docs/jdocs/neureka/devices/file/package-tree.html +++ b/docs/jdocs/neureka/devices/file/package-tree.html @@ -1,87 +1,148 @@ - + + - -neureka.devices.file Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.devices.file Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.devices.file

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -

    Interface Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/CPU.IndexedWorkload.html b/docs/jdocs/neureka/devices/host/CPU.IndexedWorkload.html index 3278b980e..fa48bd55c 100644 --- a/docs/jdocs/neureka/devices/host/CPU.IndexedWorkload.html +++ b/docs/jdocs/neureka/devices/host/CPU.IndexedWorkload.html @@ -1,133 +1,227 @@ - + + - -CPU.IndexedWorkload (neureka 1.0.0 API) - - - - + +CPU.IndexedWorkload (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host
    -

    Interface CPU.IndexedWorkload

    +
    neureka.devices.host
    +

    Interface CPU.IndexedWorkload

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      CPU
      +
      CPU
      -
      +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public static interface CPU.IndexedWorkload
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      void
      -
      execute(int i)
      -
       
      -
      -
      +
      +
      @FunctionalInterface
+public static interface CPU.IndexedWorkload
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        execute

        -
        void execute(int i)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            execute

            +
            void execute(int i)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/CPU.JVMExecutor.html b/docs/jdocs/neureka/devices/host/CPU.JVMExecutor.html index f8ea047ed..404328363 100644 --- a/docs/jdocs/neureka/devices/host/CPU.JVMExecutor.html +++ b/docs/jdocs/neureka/devices/host/CPU.JVMExecutor.html @@ -1,299 +1,433 @@ - + + - -CPU.JVMExecutor (neureka 1.0.0 API) - - - - + +CPU.JVMExecutor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host
    -

    Class CPU.JVMExecutor

    -
    -
    java.lang.Object -
    neureka.devices.host.CPU.JVMExecutor
    +
    neureka.devices.host
    +

    Class CPU.JVMExecutor

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.host.CPU.JVMExecutor
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      CPU
      +
      CPU
      -
      public static class CPU.JVMExecutor -extends Object
      -
      The CPU.JVMExecutor offers a similar functionality as the parallel stream API, - however it differs in that the CPU.JVMExecutor is processing CPU.RangeWorkload lambdas +
      +
      public static class CPU.JVMExecutor
+extends java.lang.Object
      +
      The CPU.JVMExecutor offers a similar functionality as the parallel stream API, + however it differs in that the CPU.JVMExecutor is processing CPU.RangeWorkload lambdas instead of simply exposing a single index or concrete elements for a given workload size. - This means that a CPU.RangeWorkload lambda will be called with the work range of a single worker thread + This means that a CPU.RangeWorkload lambda will be called with the work range of a single worker thread processing its current workload. This range is dependent on the number of available threads as well as the size of the workload. If the workload is very small, then the current main thread will process the entire workload range - whereas the underlying ThreadPoolExecutor will not be used to avoid unnecessary overhead.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      JVMExecutor()
      -
       
      + whereas the underlying ThreadPoolExecutor will not be used to avoid unnecessary overhead.
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        JVMExecutor

        -
        public JVMExecutor()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            JVMExecutor

            +
            public JVMExecutor()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getActiveThreadCount

      -
      public int getActiveThreadCount()
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getActiveThreadCount

          +
          public int getActiveThreadCount()
          Returns the approximate number of threads that are actively executing tasks.
          -
          -
          Returns:
          +
          +
          Returns:
          the number of threads
          -
      • -
    • -
      -

      getCorePoolSize

      -
      public int getCorePoolSize()
      +
    + + + +
      +
    • +

      getCorePoolSize

      +
      public int getCorePoolSize()
      Returns the core number of threads.
      -
      -
      Returns:
      +
      +
      Returns:
      the core number of threads
      -
    • -
  • -
    -

    getCompletedTaskCount

    -
    public long getCompletedTaskCount()
    + + + + +
      +
    • +

      getCompletedTaskCount

      +
      public long getCompletedTaskCount()
      Returns the approximate total number of tasks that have completed execution. Because the states of tasks and threads may change dynamically during computation, the returned value is only an approximation, but one that does not ever decrease across successive calls.
      -
      -
      Returns:
      +
      +
      Returns:
      the number of tasks
      -
    • -
  • -
    -

    threaded

    -
    public void threaded(int workloadSize, - CPU.RangeWorkload workload)
    + + + + +
      +
    • +

      threaded

      +
      public void threaded(int workloadSize,
+                     CPU.RangeWorkload workload)
      This method slices the provided workload size into multiple ranges which can be executed in parallel.
      -
      -
      Parameters:
      +
      +
      Parameters:
      workloadSize - The total workload size which ought to be split into multiple ranges.
      workload - The range lambda which ought to be executed across multiple threads.
      -
    • -
  • -
    -

    threaded

    -
    public void threaded(int numberOfWorkloads, - CPU.IndexedWorkload workload)
    + + + + +
      +
    • +

      threaded

      +
      public void threaded(int numberOfWorkloads,
+                     CPU.IndexedWorkload workload)
      Executes the provided workload lambda across multiple threads where the provided worker lambda will receive the index/id of the current worker.
      -
      -
      Parameters:
      +
      +
      Parameters:
      numberOfWorkloads - The total number of workloads to be executed.
      workload - The workload lambda to be executed.
      -
    • -
  • -
    -

    sequential

    -
    public void sequential(int workloadSize, - CPU.RangeWorkload workload)
    -
    This method will simply execute the provided CPU.RangeWorkload lambda sequentially + + + + +
      +
    • +

      sequential

      +
      public void sequential(int workloadSize,
+                       CPU.RangeWorkload workload)
      +
      This method will simply execute the provided CPU.RangeWorkload lambda sequentially with 0 as the start index and workloadSize as the exclusive range.

      -
      -
      Parameters:
      -
      workloadSize - The workload size which will be passed to the provided CPU.RangeWorkload as second argument.
      -
      workload - The CPU.RangeWorkload which will be executed sequentially.
      +
      +
      Parameters:
      +
      workloadSize - The workload size which will be passed to the provided CPU.RangeWorkload as second argument.
      +
      workload - The CPU.RangeWorkload which will be executed sequentially.
      -
    • -
  • -
    -

    threaded

    -
    public void threaded(int first, - int limit, - CPU.RangeWorkload rangeWorkload)
    + + + + +
      +
    • +

      threaded

      +
      public void threaded(int first,
+                     int limit,
+                     CPU.RangeWorkload rangeWorkload)
      Takes the provided range and divides it into multithreaded workloads.
      -
      -
      Parameters:
      +
      +
      Parameters:
      first - The start index of the threaded workload range.
      limit - The limit for the workload range, which is exclusive.
      rangeWorkload - A workload lambda which will be called by different threads with different sub-ranges.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/CPU.RangeWorkload.html b/docs/jdocs/neureka/devices/host/CPU.RangeWorkload.html index 38739629d..db0cf0ce0 100644 --- a/docs/jdocs/neureka/devices/host/CPU.RangeWorkload.html +++ b/docs/jdocs/neureka/devices/host/CPU.RangeWorkload.html @@ -1,138 +1,232 @@ - + + - -CPU.RangeWorkload (neureka 1.0.0 API) - - - - + +CPU.RangeWorkload (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host
    -

    Interface CPU.RangeWorkload

    +
    neureka.devices.host
    +

    Interface CPU.RangeWorkload

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      CPU
      +
      CPU
      -
      +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public static interface CPU.RangeWorkload
      +
      +
      @FunctionalInterface
+public static interface CPU.RangeWorkload
      A simple functional interface for executing a range whose implementations will either be executed sequentially or they are being dispatched to a thread-pool, given that the provided workload is large enough.
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      void
      -
      execute(int start, - int end)
      -
       
      -
      -
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        execute

        -
        void execute(int start, - int end)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            execute

            +
            void execute(int start,
+             int end)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/CPU.html b/docs/jdocs/neureka/devices/host/CPU.html index 3c2d9c1b2..6f767035a 100644 --- a/docs/jdocs/neureka/devices/host/CPU.html +++ b/docs/jdocs/neureka/devices/host/CPU.html @@ -1,702 +1,950 @@ - + + - -CPU (neureka 1.0.0 API) - - - - + +CPU (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host
    -

    Class CPU

    -
    -
    java.lang.Object -
    neureka.devices.AbstractBaseDevice<V> -
    neureka.devices.AbstractDevice<Object> -
    neureka.devices.host.CPU
    -
    +
    neureka.devices.host
    +

    Class CPU

    -
    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Component<Tensor<Object>>, Device<Object>, Storage<Object>
      +
      Component<Tensor<java.lang.Object>>, Device<java.lang.Object>, Storage<java.lang.Object>
      -
      public class CPU -extends AbstractDevice<Object>
      -
      The CPU class, one of many implementations of the Device interface, +
      +
      public class CPU
+extends AbstractDevice<java.lang.Object>
      +
      The CPU class, one of many implementations of the Device interface, is simply supposed to be an API for dispatching threaded workloads onto the CPU as well as reading from or writing to tensors it stores. Contrary to other types of devices, the CPU will represent a tensors' data by default, simply because the tensors will be stored in RAM (JVM heap) by default if no device was specified. - This means that they are implicitly "stored" on the CPU device. - The class is also a singleton instead of being part of a BackendExtension.
      -
    -
    -
      + This means that they are implicitly "stored" on the CPU device. + The class is also a singleton instead of being part of a BackendExtension.
    + + +
    +
    +
    -
    String
    -
    toString()
    -
     
    -
    boolean
    -
    update(Component.OwnerChangeRequest<Tensor<Object>> changeRequest)
    -
    -
    This method is part of the component system built into the Tensor class.
    -
    -
    -
    -
    -
    -

    Methods inherited from class neureka.devices.AbstractDevice

    -_cleaning, access, approve
    -
    -

    Methods inherited from class neureka.devices.AbstractBaseDevice

    -contains, has, isEmpty, numberOfDataObjects, numberOfStored
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.devices.Device

    -allocate, borrow, optimizedFunctionOf
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Field Details

        -
          -
        • -
          -

          PARALLELIZATION_THRESHOLD

          -
          public static final int PARALLELIZATION_THRESHOLD
          -
          -
          See Also:
          -
          - -
          +
            +
          • + + +

            Field Detail

            + + + +
              +
            • +

              PARALLELIZATION_THRESHOLD

              +
              public static final int PARALLELIZATION_THRESHOLD
              +
              +
              See Also:
              +
              Constant Field Values
              -
          • -
        • -
          -

          THREAD_PREFIX

          -
          public static final String THREAD_PREFIX
          -
          -
          See Also:
          -
          - -
          +
        + + + +
          +
        • +

          THREAD_PREFIX

          +
          public static final java.lang.String THREAD_PREFIX
          +
          +
          See Also:
          +
          Constant Field Values
          -
      -
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      get

      -
      public static CPU get()
      -
      Use this method to access the singleton instance of this CPU class, - which is a Device type and default location for freshly instantiated Tensor instances. - Tensor instances located on the CPU device will reside in regular RAM +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          get

          +
          public static CPU get()
          +
          Use this method to access the singleton instance of this CPU class, + which is a Device type and default location for freshly instantiated Tensor instances. + Tensor instances located on the CPU device will reside in regular RAM causing operations to run on the JVM and thereby the CPU.
          -
          -
          Returns:
          -
          The singleton instance of this CPU class.
          +
          +
          Returns:
          +
          The singleton instance of this CPU class.
          -
      • -
    • -
      -

      getExecutor

      -
      public CPU.JVMExecutor getExecutor()
      -
      The CPU.JVMExecutor offers a similar functionality as the parallel stream API, - however it differs in that the CPU.JVMExecutor is processing CPU.RangeWorkload lambdas +
    + + + +
      +
    • +

      getExecutor

      +
      public CPU.JVMExecutor getExecutor()
      +
      The CPU.JVMExecutor offers a similar functionality as the parallel stream API, + however it differs in that the CPU.JVMExecutor is processing CPU.RangeWorkload lambdas instead of simply exposing a single index or concrete elements for a given workload size.
      -
      -
      Returns:
      +
      +
      Returns:
      A parallel range based execution API running on the JVM.
      -
    • -
  • -
    -

    _approveExecutionOf

    -
    protected boolean _approveExecutionOf(Tensor<?>[] tensors, - int d, - Operation operation)
    -
    Description copied from class: AbstractDevice
    + + + + +
      +
    • +

      _approveExecutionOf

      +
      protected boolean _approveExecutionOf(Tensor<?>[] tensors,
+                                      int d,
+                                      Operation operation)
      +
      Description copied from class: AbstractDevice
      This method is the internal approval routine called by its public counterpart and implemented by classes extending this very abstract class. - It may or may not be called by an Algorithm - in order to allow a Device to checked if the provided arguments are suitable for execution.
      -
      -
      Specified by:
      -
      _approveExecutionOf in class AbstractDevice<Object>
      -
      Parameters:
      + It may or may not be called by an Algorithm + in order to allow a Device to checked if the provided arguments are suitable for execution.
    • +
    +
    Specified by:
    +
    _approveExecutionOf in class AbstractDevice<java.lang.Object>
    +
    Parameters:
    tensors - An array of input tensors.
    d - The index of the input which ought to be derived.
    operation - The type of operation.
    -
    Returns:
    +
    Returns:
    The truth value determining if the provided arguments can be executed.
    - -
  • -
    -

    dispose

    -
    public void dispose()
    + + + + +
      +
    • +

      dispose

      +
      public void dispose()
      This method will shut down the internal thread-pool used by this class to execute JVM/CPU based operations in parallel.
      -
    • -
  • -
    -

    restore

    -
    public CPU restore(Tensor<Object> tensor)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      restore

      +
      public CPU restore(Tensor<java.lang.Object> tensor)
      +
      +
      Parameters:
      tensor - The tensor whose data ought to be restored (loaded to RAM/CPU device).
      -
      Returns:
      -
      This Storage instance, to allow for method chaining.
      +
      Returns:
      +
      This Storage instance, to allow for method chaining.
      -
    • -
  • -
    -

    store

    -
    public <T> CPU store(Tensor<T> tensor)
    -
    Description copied from class: AbstractDevice
    + + + + +
      +
    • +

      store

      +
      public <T> CPU store(Tensor<T> tensor)
      +
      Description copied from class: AbstractDevice
      Implementations of this method ought to store the data of the tensor in whatever formant suites the underlying implementation and or final type. Classes like "OpenCLDevice" or "FileDevice" for example are tensor storages.
      -
      -
      Specified by:
      -
      store in interface Storage<Object>
      -
      Overrides:
      -
      store in class AbstractDevice<Object>
      -
      Type Parameters:
      +
      +
      Specified by:
      +
      store in interface Storage<java.lang.Object>
      +
      Overrides:
      +
      store in class AbstractDevice<java.lang.Object>
      +
      Type Parameters:
      T - A valid data type of the tensor which should be stored on this device.
      -
      Parameters:
      +
      Parameters:
      tensor - The tensor whose data ought to be stored.
      -
      Returns:
      -
      This Storage instance, to allow for method chaining.
      +
      Returns:
      +
      This Storage instance, to allow for method chaining.
      -
    • -
  • -
    -

    _sizeOccupiedBy

    -
    protected final <T> int _sizeOccupiedBy(Tensor<T> tensor)
    -
    -
    Specified by:
    -
    _sizeOccupiedBy in class AbstractDevice<Object>
    + + + + +
    • -
  • -
    -

    _readAll

    -
    protected final <T> Object _readAll(Tensor<T> tensor, - boolean clone)
    -
    -
    Specified by:
    -
    _readAll in class AbstractDevice<Object>
    + + + + +
      +
    • +

      _readAll

      +
      protected final <T> java.lang.Object _readAll(Tensor<T> tensor,
+                                              boolean clone)
      +
      +
      Specified by:
      +
      _readAll in class AbstractDevice<java.lang.Object>
      -
    • -
  • -
    -

    _readItem

    -
    protected final <T> T _readItem(Tensor<T> tensor, - int index)
    -
    -
    Specified by:
    -
    _readItem in class AbstractDevice<Object>
    + + + + +
      +
    • +

      _readItem

      +
      protected final <T> T _readItem(Tensor<T> tensor,
+                                int index)
      +
      +
      Specified by:
      +
      _readItem in class AbstractDevice<java.lang.Object>
      -
    • -
  • -
    -

    _readArray

    -
    protected final <T, -A> A _readArray(Tensor<T> tensor, - Class<A> arrayType, - int start, - int size)
    -
    -
    Specified by:
    -
    _readArray in class AbstractDevice<Object>
    + + + + +
      +
    • +

      _readArray

      +
      protected final <T,A> A _readArray(Tensor<T> tensor,
+                                   java.lang.Class<A> arrayType,
+                                   int start,
+                                   int size)
      +
      +
      Specified by:
      +
      _readArray in class AbstractDevice<java.lang.Object>
      -
    • -
  • -
    -

    _writeItem

    -
    protected final <T> void _writeItem(Tensor<T> tensor, - T item, - int start, - int size)
    -
    -
    Specified by:
    -
    _writeItem in class AbstractDevice<Object>
    + + + + + + +
      +
    • +

      _writeItem

      +
      protected final <T> void _writeItem(Tensor<T> tensor,
+                                    T item,
+                                    int start,
+                                    int size)
      +
      +
      Specified by:
      +
      _writeItem in class AbstractDevice<java.lang.Object>
      -
    • -
  • -
    -

    _writeArray

    -
    protected final <T> void _writeArray(Tensor<T> tensor, - Object array, - int offset, - int start, - int size)
    -
    -
    Specified by:
    -
    _writeArray in class AbstractDevice<Object>
    + + + + +
      +
    • +

      _writeArray

      +
      protected final <T> void _writeArray(Tensor<T> tensor,
+                                     java.lang.Object array,
+                                     int offset,
+                                     int start,
+                                     int size)
      +
      +
      Specified by:
      +
      _writeArray in class AbstractDevice<java.lang.Object>
      -
    • -
  • -
    -

    allocateFromOne

    -
    public <T> Data<T> allocateFromOne(DataType<T> dataType, - NDConfiguration ndc, - T initialValue)
    -
    + + + + + + + + + + +
      +
    • +

      allocateFromAll

      +
      public <T> Data<T> allocateFromAll(DataType<T> dataType,
+                                   NDConfiguration ndc,
+                                   java.lang.Object jvmData)
      • -
    • -
      -

      allocate

      -
      public final <T> Data<T> allocate(Class<T> type, - Object data)
      -
      +
    + + + +
      +
    • +

      allocate

      +
      public final <T> Data<T> allocate(java.lang.Class<T> type,
+                                  java.lang.Object data)
      • -
    • -
      -

      allocate

      -
      public final <T> Data<T> allocate(Class<T> type, - int size, - Object source)
      -
      +
    + + + +
      +
    • +

      allocate

      +
      public final <T> Data<T> allocate(java.lang.Class<T> type,
+                                  int size,
+                                  java.lang.Object source)
      • -
    • -
      -

      _actualize

      -
      protected final Data<Object> _actualize(Tensor<?> tensor)
      -
      -
      Specified by:
      -
      _actualize in class AbstractDevice<Object>
      +
    + + + + + + + + + + + +
      +
    • +

      _dataTypeOf

      +
      protected final DataType<?> _dataTypeOf(java.lang.Object rawData)
      +
      +
      Specified by:
      +
      _dataTypeOf in class AbstractDevice<java.lang.Object>
      -
      • -
    • -
      -

      free

      -
      public <T> CPU free(Tensor<T> tensor)
      -
      Description copied from interface: Device
      -
      Use this to remove the provided tensor from this Device!

      -
      -
      Type Parameters:
      -
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      -
      Parameters:
      -
      tensor - The tensor which ought to be removed from this Device.
      -
      Returns:
      +
    + + + +
      +
    • +

      free

      +
      public <T> CPU free(Tensor<T> tensor)
      +
      Description copied from interface: Device
      +
      Use this to remove the provided tensor from this Device!

      +
      +
      Type Parameters:
      +
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      +
      Parameters:
      +
      tensor - The tensor which ought to be removed from this Device.
      +
      Returns:
      This very instance to allow for method chaining.
      -
      • -
    • -
      -

      _swap

      -
      protected <T> void _swap(Tensor<T> former, - Tensor<T> replacement)
      -
      Description copied from class: AbstractDevice
      +
    + + + +
      +
    • +

      _swap

      +
      protected <T> void _swap(Tensor<T> former,
+                         Tensor<T> replacement)
      +
      Description copied from class: AbstractDevice
      This method is used internally mostly and should not be used in most cases.

      -
      -
      Specified by:
      -
      _swap in class AbstractDevice<Object>
      -
      Type Parameters:
      -
      T - The type parameter for the value type of the tensors, which must be supported by this Device.
      -
      Parameters:
      +
      +
      Specified by:
      +
      _swap in class AbstractDevice<java.lang.Object>
      +
      Type Parameters:
      +
      T - The type parameter for the value type of the tensors, which must be supported by this Device.
      +
      Parameters:
      former - The tensor whose associated data (on the device) ought to be assigned to the other tensor.
      replacement - The tensor which ought to receive the data of the former tensor internally.
      -
      • -
    • -
      -

      allocate

      -
      public <T> Data<T> allocate(DataType<T> dataType, - NDConfiguration ndc)
      -
      +
    + + + + + + + +
      +
    • +

      optimizedOperationOf

      +
      public Operation optimizedOperationOf(Function function,
+                                      java.lang.String name)
      +
      Description copied from interface: Device
      +
      This method tries to allow this device to produce an optimized Operation based on the provided function. This is especially useful in an OpenCL context which can compile the function into native GPU kernels at runtime.
      -
      -
      Parameters:
      +
      +
      Parameters:
      function - The function which should be turned into an optimized operation.
      name - The name of the returned operation.
      -
      Returns:
      +
      Returns:
      An optimized operation based on the provided function, or null if optimization is not possible.
      -
      • -
    • -
      -

      update

      -
      public boolean update(Component.OwnerChangeRequest<Tensor<Object>> changeRequest)
      -
      This method is part of the component system built into the Tensor class. +
    + + + +
      +
    • +

      update

      +
      public boolean update(Component.OwnerChangeRequest<Tensor<java.lang.Object>> changeRequest)
      +
      This method is part of the component system built into the Tensor class. Do not use this as part of anything but said component system.
      -
      -
      Specified by:
      -
      update in interface Component<Tensor<Object>>
      -
      Overrides:
      -
      update in class AbstractDevice<Object>
      -
      Parameters:
      +
      +
      Specified by:
      +
      update in interface Component<Tensor<java.lang.Object>>
      +
      Overrides:
      +
      update in class AbstractDevice<java.lang.Object>
      +
      Parameters:
      changeRequest - An API which describes the type of update and a method for executing said update.
      -
      Returns:
      -
      The truth value determining if this Device ought to be added to a tensor (Here always false!).
      +
      Returns:
      +
      The truth value determining if this Device ought to be added to a tensor (Here always false!).
      -
      • -
    • -
      -

      getCoreCount

      -
      public int getCoreCount()
      +
    + + + +
      +
    • +

      getCoreCount

      +
      public int getCoreCount()
      Returns the number of CPU cores available to the Java virtual machine. This value may change during a particular invocation of the virtual machine. Applications that are sensitive to the number of available processors should therefore occasionally poll this property and adjust their resource usage appropriately.
      -
      -
      Returns:
      +
      +
      Returns:
      The maximum number of CPU cores available to the JVM. This number is never smaller than one!
      -
      • -
    • -
      -

      toString

      -
      public String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/concurrent/Parallelism.html b/docs/jdocs/neureka/devices/host/concurrent/Parallelism.html index 31e396c6e..a67a037da 100644 --- a/docs/jdocs/neureka/devices/host/concurrent/Parallelism.html +++ b/docs/jdocs/neureka/devices/host/concurrent/Parallelism.html @@ -1,295 +1,440 @@ - + + - -Parallelism (neureka 1.0.0 API) - - - - + +Parallelism (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host.concurrent
    -

    Enum Class Parallelism

    -
    -
    java.lang.Object -
    java.lang.Enum<Parallelism> -
    neureka.devices.host.concurrent.Parallelism
    -
    +
    neureka.devices.host.concurrent
    +

    Enum Parallelism

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • java.lang.Enum<Parallelism>
        • +
      • +
          +
        • neureka.devices.host.concurrent.Parallelism
          • +
        +
        • +
      +
      • +
    +
    +
    -
    -
    + +
    +
    +
      +
    • -
    • -
      -

      Enum Constant Summary

      -
      Enum Constants
      -
      -
      Enum Constant
      -
      Description
      -
      CORES
      -
      +
        +
      • + + +

        Enum Constant Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Enum Constants 
        Enum Constant and Description
        CORES
        The number of CPU cores
        - -
        EIGHT
        -
        +
        EIGHT
        8
        - -
        FOUR
        -
        +
        FOUR
        4
        - -
        ONE
        -
        +
        ONE
        1
        - -
        THREADS
        -
        +
        THREADS
        The total number of threads (incl.
        - -
        TWO
        -
        +
        TWO
        2
        - -
        UNITS
        -
        +
        UNITS
        The number of top level (L2 or L3) cache units
        - - - +
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      int
      -
      getAsInt()
      -
       
      -
      static Parallelism
      -
      valueOf(String name)
      -
      -
      Returns the enum constant of this class with the specified name.
      -
      -
      static Parallelism[]
      -
      values()
      -
      -
      Returns an array containing the constants of this enum class, in + -
      -
      -
        + +
      +
      • +
      +
        +
      • -
      • -
        -

        Enum Constant Details

        -
          -
        • -
          -

          THREADS

          -
          public static final Parallelism THREADS
          +
            +
          • + + +

            Enum Constant Detail

            + + + +
              +
            • +

              THREADS

              +
              public static final Parallelism THREADS
              The total number of threads (incl. hyper-threads)
              -
          • -
        • -
          -

          CORES

          -
          public static final Parallelism CORES
          +
        + + + +
          +
        • +

          CORES

          +
          public static final Parallelism CORES
          The number of CPU cores
          -
        • -
      • -
        -

        UNITS

        -
        public static final Parallelism UNITS
        +
      + + + +
        +
      • +

        UNITS

        +
        public static final Parallelism UNITS
        The number of top level (L2 or L3) cache units
        -
      • -
    • -
      -

      EIGHT

      -
      public static final Parallelism EIGHT
      +
    + + + + + + + + + + + + + + + + - + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static Parallelism[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static Parallelism[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (Parallelism c : Parallelism.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static Parallelism valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static Parallelism valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • -
  • -
    -

    getAsInt

    -
    public int getAsInt()
    -
    -
    Specified by:
    -
    getAsInt in interface IntSupplier
    + + + + +
      +
    • +

      getAsInt

      +
      public int getAsInt()
      +
      +
      Specified by:
      +
      getAsInt in interface java.util.function.IntSupplier
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/concurrent/WorkScheduler.Divider.html b/docs/jdocs/neureka/devices/host/concurrent/WorkScheduler.Divider.html index dd224dc85..ab7037bf1 100644 --- a/docs/jdocs/neureka/devices/host/concurrent/WorkScheduler.Divider.html +++ b/docs/jdocs/neureka/devices/host/concurrent/WorkScheduler.Divider.html @@ -1,209 +1,335 @@ - + + - -WorkScheduler.Divider (neureka 1.0.0 API) - - - - + +WorkScheduler.Divider (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host.concurrent
    -

    Class WorkScheduler.Divider

    -
    -
    java.lang.Object -
    neureka.devices.host.concurrent.WorkScheduler.Divider
    +
    neureka.devices.host.concurrent
    +

    Class WorkScheduler.Divider

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.host.concurrent.WorkScheduler.Divider
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      WorkScheduler
      +
      WorkScheduler
      -
      public static final class WorkScheduler.Divider -extends Object
      +
      +
      public static final class WorkScheduler.Divider
+extends java.lang.Object
      Divides workloads until they can be processed efficiently and then submits them to a thread pool for execution...
      This is a library internal class, do not depend on this.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      Divider(ExecutorService executor)
      -
       
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Divider

        -
        public Divider(ExecutorService executor)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Divider

            +
            public Divider(java.util.concurrent.ExecutorService executor)
          -
      • +
    -
  • -
    -

    Method Details

    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/concurrent/WorkScheduler.html b/docs/jdocs/neureka/devices/host/concurrent/WorkScheduler.html index ab786507b..cbd3955c2 100644 --- a/docs/jdocs/neureka/devices/host/concurrent/WorkScheduler.html +++ b/docs/jdocs/neureka/devices/host/concurrent/WorkScheduler.html @@ -1,205 +1,323 @@ - + + - -WorkScheduler (neureka 1.0.0 API) - - - - + +WorkScheduler (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host.concurrent
    -

    Class WorkScheduler

    -
    -
    java.lang.Object -
    neureka.devices.host.concurrent.WorkScheduler
    +
    neureka.devices.host.concurrent
    +

    Class WorkScheduler

    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.host.concurrent.WorkScheduler
        • +
      +
      • +
    +
    +
      +
    • -
      public abstract class WorkScheduler -extends Object
      +
      +
      public abstract class WorkScheduler
+extends java.lang.Object
      An API for registering workloads which will be divided into smaller workloads so that they can be executed efficiently by a thread pool...
      This is a library internal class, do not depend on this.
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Class
      -
      Description
      -
      static final class 
      -
      WorkScheduler.Divider
      -
      +
        +
      • + + +

        Nested Class Summary

        + + + + + + + + + + +
        Nested Classes 
        Modifier and TypeClass and Description
        static class WorkScheduler.Divider
        Divides workloads until they can be processed efficiently and then submits them to a thread pool for execution...
        - - - +
        • +
      -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      WorkScheduler()
      -
       
      -
      -
      +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        WorkScheduler() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      protected abstract void
      -
      _work(int first, - int limit)
      -
       
      -
      final void
      -
      invoke(ExecutorService executor, - int first, - int limit, - int threshold)
      -
      +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        WorkScheduler

        -
        public WorkScheduler()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            WorkScheduler

            +
            public WorkScheduler()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      invoke

      -
      public final void invoke(ExecutorService executor, - int first, - int limit, - int threshold)
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          invoke

          +
          public final void invoke(java.util.concurrent.ExecutorService executor,
+                         int first,
+                         int limit,
+                         int threshold)
          Synchronous execution - wait until it's finished.
          -
          -
          Parameters:
          +
          +
          Parameters:
          first - The first index, in a range, to include.
          limit - The first index NOT to include - last (excl.) index in a range.
          threshold - The work size threshold.
          -
      • -
    • -
      -

      _work

      -
      protected abstract void _work(int first, - int limit)
      -
      +
    + + + +
      +
    • +

      _work

      +
      protected abstract void _work(int first,
+                              int limit)
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/concurrent/package-frame.html b/docs/jdocs/neureka/devices/host/concurrent/package-frame.html new file mode 100644 index 000000000..11a6d7add --- /dev/null +++ b/docs/jdocs/neureka/devices/host/concurrent/package-frame.html @@ -0,0 +1,24 @@ + + + + + +neureka.devices.host.concurrent (neureka 1.0.1 API) + + + + +

    neureka.devices.host.concurrent

    +
    +

    Classes

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/devices/host/concurrent/package-summary.html b/docs/jdocs/neureka/devices/host/concurrent/package-summary.html index ae564c810..575321503 100644 --- a/docs/jdocs/neureka/devices/host/concurrent/package-summary.html +++ b/docs/jdocs/neureka/devices/host/concurrent/package-summary.html @@ -1,121 +1,178 @@ - + + - -neureka.devices.host.concurrent (neureka 1.0.0 API) - - - - + +neureka.devices.host.concurrent (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.devices.host.concurrent

    -
    -
    -
    package neureka.devices.host.concurrent
    -
    -
    Everything in this package should be considered library-private! - DO NOT USE CLASSES INSIDE THIS PACKAGE!
    -
    -
    -
      -
    • -
      • -
    • -
      -
      -
      -
      -
      Class
      -
      Description
      -
      Parallelism
      -
      -
      A set of standard levels of parallelism derived from the number of available cores and optionally capped by - reserving a specified amount of memory per thread.
      -
      -
      WorkScheduler
      -
      +

      See: Description

      +
      +
      +
        +
      • + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        WorkScheduler
        An API for registering workloads which will be divided into smaller workloads so that they can be executed efficiently by a thread pool...
        - -
        WorkScheduler.Divider
        -
        +
        WorkScheduler.Divider
        Divides workloads until they can be processed efficiently and then submits them to a thread pool for execution...
        - - - - +
        +
        • +
      • + + + + + + + + + + + + +
        Enum Summary 
        EnumDescription
        Parallelism +
        A set of standard levels of parallelism derived from the number of available cores and optionally capped by + reserving a specified amount of memory per thread.
        +
      -
    -
    + + + +

    Package neureka.devices.host.concurrent Description

    +
    Everything in this package should be considered library-private! + DO NOT USE CLASSES INSIDE THIS PACKAGE!
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/concurrent/package-tree.html b/docs/jdocs/neureka/devices/host/concurrent/package-tree.html index fee31d0e8..479fd5652 100644 --- a/docs/jdocs/neureka/devices/host/concurrent/package-tree.html +++ b/docs/jdocs/neureka/devices/host/concurrent/package-tree.html @@ -1,86 +1,147 @@ - + + - -neureka.devices.host.concurrent Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.devices.host.concurrent Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.devices.host.concurrent

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    -

    Enum Class Hierarchy

    +

    Enum Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
        -
      • java.lang.Enum<E> (implements java.lang.Comparable<T>, java.lang.constant.Constable, java.io.Serializable) +
      • java.lang.Enum<E> (implements java.lang.Comparable<T>, java.io.Serializable)
          -
        • neureka.devices.host.concurrent.Parallelism (implements java.util.function.IntSupplier)
          • +
        • neureka.devices.host.concurrent.Parallelism (implements java.util.function.IntSupplier)
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/machine/BasicMachine.html b/docs/jdocs/neureka/devices/host/machine/BasicMachine.html index e7a1df8c5..08524cb8c 100644 --- a/docs/jdocs/neureka/devices/host/machine/BasicMachine.html +++ b/docs/jdocs/neureka/devices/host/machine/BasicMachine.html @@ -1,236 +1,365 @@ - + + - -BasicMachine (neureka 1.0.0 API) - - - - + +BasicMachine (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host.machine
    -

    Class BasicMachine

    -
    -
    java.lang.Object -
    neureka.devices.host.machine.BasicMachine
    +
    neureka.devices.host.machine
    +

    Class BasicMachine

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.host.machine.BasicMachine
        • +
      +
      • +
    +
    +
      +
    • +
      Direct Known Subclasses:
      -
      CommonMachine
      +
      CommonMachine
      -
      public class BasicMachine -extends Object
      +
      +
      public class BasicMachine
+extends java.lang.Object
      How much memory, and how many threads share that memory. This is used to describe either total system resources (system RAM and total number of threads handled by the processors) or a cache (processor's L1, L2 or L3 cache).
      -
    -
    -
      - -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      final long
      -
      memory
      -
       
      -
      final int
      -
      threads
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Field Summary

        + + + + + + + + + + + + + + +
        Fields 
        Modifier and TypeField and Description
        longmemory 
        intthreads 
        • +
      -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      BasicMachine(long memory, - int threads)
      -
       
      -
      -
      +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        BasicMachine(long memory, + int threads) 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      boolean
      -
      equals(Object obj)
      -
       
      -
      int
      -
      hashCode()
      -
       
      -
      String
      -
      toString()
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, finalize, getClass, notify, notifyAll, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        booleanequals(java.lang.Object obj) 
        inthashCode() 
        java.lang.StringtoString() 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, finalize, getClass, notify, notifyAll, wait, wait, wait
          • +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Field Details

      -
        -
      • -
        -

        memory

        -
        public final long memory
        -
        +
          +
        • + + +

          Field Detail

          + + + +
            +
          • +

            memory

            +
            public final long memory
            • -
          • -
            -

            threads

            -
            public final int threads
            -
            +
          + + + +
            +
          • +

            threads

            +
            public final int threads
          -
      • +
    -
  • -
    -

    Constructor Details

    -
      -
    • -
      -

      BasicMachine

      -
      public BasicMachine(long memory, - int threads)
      -
      +
        +
      • + + +

        Constructor Detail

        + + + +
          +
        • +

          BasicMachine

          +
          public BasicMachine(long memory,
+                    int threads)
        -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      equals

      -
      public boolean equals(Object obj)
      -
      -
      Overrides:
      -
      equals in class Object
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          equals

          +
          public boolean equals(java.lang.Object obj)
          +
          +
          Overrides:
          +
          equals in class java.lang.Object
          -
      • -
    • -
      -

      hashCode

      -
      public int hashCode()
      -
      -
      Overrides:
      -
      hashCode in class Object
      +
    + + + +
      +
    • +

      hashCode

      +
      public int hashCode()
      +
      +
      Overrides:
      +
      hashCode in class java.lang.Object
      -
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/machine/CommonMachine.html b/docs/jdocs/neureka/devices/host/machine/CommonMachine.html index e2b8328d3..656a91608 100644 --- a/docs/jdocs/neureka/devices/host/machine/CommonMachine.html +++ b/docs/jdocs/neureka/devices/host/machine/CommonMachine.html @@ -1,298 +1,446 @@ - + + - -CommonMachine (neureka 1.0.0 API) - - - - + +CommonMachine (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host.machine
    -

    Class CommonMachine

    -
    -
    java.lang.Object -
    neureka.devices.host.machine.BasicMachine -
    neureka.devices.host.machine.CommonMachine
    +
    neureka.devices.host.machine
    +

    Class CommonMachine

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    +
      +
    • -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      final String
      -
      architecture
      -
       
      -
      final long
      -
      cache
      -
      +
        +
      • + + +

        Field Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Fields 
        Modifier and TypeField and Description
        java.lang.Stringarchitecture 
        longcache
        The size of one top level (L3 or L2) cache unit in bytes.
        - -
        final int
        -
        cores
        -
        +
        intcores
        The total number of processor cores.
        - -
        protected static final long
        -
        K
        -
         
        -
        final int
        -
        units
        -
        +
        protected static longK 
        intunits
        The number of top level (L3 or L2) cache units.
        - - -
        -

        Fields inherited from class neureka.devices.host.machine.BasicMachine

        -memory, threads
        - +
        +
        • +
      -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Modifier
      -
      Constructor
      -
      Description
      -
      protected
      -
      CommonMachine(String architecture, - BasicMachine[] levels)
      -
      +
        +
      • + + +

        Constructor Summary

        + + + + + + + + + + + + + + +
        Constructors 
        ModifierConstructor and Description
        protected CommonMachine(Hardware hardware, + java.lang.Runtime runtime) 
        protected CommonMachine(java.lang.String architecture, + BasicMachine[] levels)
        new MemoryThreads[] { SYSTEM, L3, L2, L1 } or new MemoryThreads[] { SYSTEM, L2, L1 } or in worst case new MemoryThreads[] { SYSTEM, L1 }
        - -
        protected
        -
        CommonMachine(Hardware hardware, - Runtime runtime)
        -
         
        - - +
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      boolean
      -
      equals(Object obj)
      -
       
      -
      int
      -
      hashCode()
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class neureka.devices.host.machine.BasicMachine

      -toString
      -
      -

      Methods inherited from class java.lang.Object

      -clone, finalize, getClass, notify, notifyAll, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + +
        All Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        booleanequals(java.lang.Object obj) 
        inthashCode() 
        + +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, finalize, getClass, notify, notifyAll, wait, wait, wait
          • +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Field Details

      - + + + +
        +
      • +

        architecture

        +
        public final java.lang.String architecture
        • -
      • -
        -

        cache

        -
        public final long cache
        +
      + + + +
        +
      • +

        cache

        +
        public final long cache
        The size of one top level (L3 or L2) cache unit in bytes.
        -
      • -
    • -
      -

      cores

      -
      public final int cores
      +
    + + + +
      +
    • +

      cores

      +
      public final int cores
      The total number of processor cores.
      -
      • -
    • -
      -

      units

      -
      public final int units
      +
    + + + +
      +
    • +

      units

      +
      public final int units
      The number of top level (L3 or L2) cache units. With L3 cache defined, this corresponds to the number of CPUs.
      -
    - + -
  • -
    -

    Constructor Details

    -
      -
    • -
      -

      CommonMachine

      -
      protected CommonMachine(Hardware hardware, - Runtime runtime)
      -
      +
        +
      • + + +

        Constructor Detail

        + + + +
          +
        • +

          CommonMachine

          +
          protected CommonMachine(Hardware hardware,
+                        java.lang.Runtime runtime)
          • -
        • -
          -

          CommonMachine

          -
          protected CommonMachine(String architecture, - BasicMachine[] levels)
          +
        + + + +
          +
        • +

          CommonMachine

          +
          protected CommonMachine(java.lang.String architecture,
+                        BasicMachine[] levels)
          new MemoryThreads[] { SYSTEM, L3, L2, L1 } or new MemoryThreads[] { SYSTEM, L2, L1 } or in worst case new MemoryThreads[] { SYSTEM, L1 }
          -
          -
          Parameters:
          +
          +
          Parameters:
          architecture - The CPU architecture (like x86 for example).
          levels - Cache levels.
          -
    • - + -
  • -
    -

    Method Details

    - + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/machine/ConcreteMachine.html b/docs/jdocs/neureka/devices/host/machine/ConcreteMachine.html index 70871ba6e..a812866fd 100644 --- a/docs/jdocs/neureka/devices/host/machine/ConcreteMachine.html +++ b/docs/jdocs/neureka/devices/host/machine/ConcreteMachine.html @@ -1,227 +1,371 @@ - + + - -ConcreteMachine (neureka 1.0.0 API) - - - - + +ConcreteMachine (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host.machine
    -

    Class ConcreteMachine

    -
    -
    java.lang.Object -
    neureka.devices.host.machine.BasicMachine -
    neureka.devices.host.machine.CommonMachine -
    neureka.devices.host.machine.ConcreteMachine
    -
    -
    +
    neureka.devices.host.machine
    +

    Class ConcreteMachine

    -
    -
    -
    public final class ConcreteMachine -extends CommonMachine
    -
    -
    -
    +
    +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getArchitecture

      -
      public static String getArchitecture()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getArchitecture

          +
          public static java.lang.String getArchitecture()
          • -
        • -
          -

          getMemory

          -
          public static long getMemory()
          -
          +
        + + + +
          +
        • +

          getMemory

          +
          public static long getMemory()
          • -
        • -
          -

          getThreads

          -
          public static int getThreads()
          -
          +
        + + + +
          +
        • +

          getThreads

          +
          public static int getThreads()
          • -
        • -
          -

          equals

          -
          public boolean equals(Object obj)
          -
          -
          Overrides:
          -
          equals in class CommonMachine
          +
        + + + +
          +
        • +

          equals

          +
          public boolean equals(java.lang.Object obj)
          +
          +
          Overrides:
          +
          equals in class CommonMachine
          -
    • -
  • -
    -

    hashCode

    -
    public int hashCode()
    -
    -
    Overrides:
    -
    hashCode in class CommonMachine
    + + + + +
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class BasicMachine
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class BasicMachine
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/machine/Hardware.html b/docs/jdocs/neureka/devices/host/machine/Hardware.html index 536a5a318..441dd68aa 100644 --- a/docs/jdocs/neureka/devices/host/machine/Hardware.html +++ b/docs/jdocs/neureka/devices/host/machine/Hardware.html @@ -1,98 +1,135 @@ - + + - -Hardware (neureka 1.0.0 API) - - - - + +Hardware (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.host.machine
    -

    Class Hardware

    -
    -
    java.lang.Object -
    neureka.devices.host.machine.BasicMachine -
    neureka.devices.host.machine.CommonMachine -
    neureka.devices.host.machine.Hardware
    -
    +
    neureka.devices.host.machine
    +

    Class Hardware

    -
    -
    -
    +
    + +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Comparable<Hardware>
      +
      java.lang.Comparable<Hardware>
      -
      public final class Hardware -extends CommonMachine -implements Comparable<Hardware>
      +
      +
      public final class Hardware
+extends CommonMachine
+implements java.lang.Comparable<Hardware>
      This models the cache levels and threads of a CPU using an array of where each entry represents a memory level.
      • The first element in the array should correspond to total system resources; the total amount of RAM and the total number of threads (Typically the same as what is returned by - Runtime.availableProcessors()).
        • + Runtime.availableProcessors()).
      • The last element in the array should describe the L1 cache. Typically Intel processors have 32k L1 cache and AMD 64k. 1 or maybe 2 threads use/share this cache.
      • Caches, all levels except L1, are described between the first and last elements in descending order (L3 @@ -105,247 +142,373 @@

        Class Hardware

      • Define new entries using this pattern: [SYSTEM, L3, L2, L1] or [SYSTEM, L2, L1] or [SYSTEM, L1]
      -
    -
    -
      + +
    +
    +
    + -
  • -
    -

    Constructor Details

    -
      -
    • -
      -

      Hardware

      -
      public Hardware(String architecture, - BasicMachine[] levels)
      +
        +
      • + + +

        Constructor Detail

        + + + +
          +
        • +

          Hardware

          +
          public Hardware(java.lang.String architecture,
+                BasicMachine[] levels)
          new BasicMachine[] { SYSTEM, L3, L2, L1 } or new BasicMachine[] { SYSTEM, L2, L1 } or in worst case new BasicMachine[] { SYSTEM, L1 }
          -
          -
          Parameters:
          +
          +
          Parameters:
          architecture - The architecture code.
          levels - The cache levels.
          -
    -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      makeSimple

      -
      public static Hardware makeSimple()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          makeSimple

          +
          public static Hardware makeSimple()
          • -
        • -
          -

          makeSimple

          -
          public static Hardware makeSimple(String systemArchitecture, - long systemMemory, - int systemThreads)
          -
          +
        + + + +
          +
        • +

          makeSimple

          +
          public static Hardware makeSimple(java.lang.String systemArchitecture,
+                                  long systemMemory,
+                                  int systemThreads)
          • -
        • -
          -

          compareTo

          -
          public int compareTo(Hardware other)
          -
          -
          Specified by:
          -
          compareTo in interface Comparable<Hardware>
          +
        + + + +
          +
        • +

          compareTo

          +
          public int compareTo(Hardware other)
          +
          +
          Specified by:
          +
          compareTo in interface java.lang.Comparable<Hardware>
          -
    • -
  • -
    -

    equals

    -
    public boolean equals(Object obj)
    -
    -
    Overrides:
    -
    equals in class CommonMachine
    + + + + +
      +
    • +

      equals

      +
      public boolean equals(java.lang.Object obj)
      +
      +
      Overrides:
      +
      equals in class CommonMachine
      -
    • -
  • -
    -

    hashCode

    -
    public int hashCode()
    -
    -
    Overrides:
    -
    hashCode in class CommonMachine
    + + + + +
    • -
  • -
    -

    isL2Specified

    -
    public boolean isL2Specified()
    -
    + + + + +
      +
    • +

      isL2Specified

      +
      public boolean isL2Specified()
      • -
    • -
      -

      isL3Specified

      -
      public boolean isL3Specified()
      -
      +
    + + + +
      +
    • +

      isL3Specified

      +
      public boolean isL3Specified()
      • -
    • -
      -

      toString

      -
      public String toString()
      -
      -
      Overrides:
      -
      toString in class BasicMachine
      +
    + + + + + + + +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/machine/package-frame.html b/docs/jdocs/neureka/devices/host/machine/package-frame.html new file mode 100644 index 000000000..8d55b337a --- /dev/null +++ b/docs/jdocs/neureka/devices/host/machine/package-frame.html @@ -0,0 +1,22 @@ + + + + + +neureka.devices.host.machine (neureka 1.0.1 API) + + + + +

    neureka.devices.host.machine

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/devices/host/machine/package-summary.html b/docs/jdocs/neureka/devices/host/machine/package-summary.html index 99ba3004b..2198f359d 100644 --- a/docs/jdocs/neureka/devices/host/machine/package-summary.html +++ b/docs/jdocs/neureka/devices/host/machine/package-summary.html @@ -1,115 +1,169 @@ - + + - -neureka.devices.host.machine (neureka 1.0.0 API) - - - - + +neureka.devices.host.machine (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.devices.host.machine

    -
    -
    -
    package neureka.devices.host.machine
    -
    -
    Everything in this package should be considered library-private! - DO NOT USE CLASSES INSIDE THIS PACKAGE!
    -
    -
    -
    -
    + + + +

    Package neureka.devices.host.machine Description

    +
    Everything in this package should be considered library-private! + DO NOT USE CLASSES INSIDE THIS PACKAGE!
    +
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/machine/package-tree.html b/docs/jdocs/neureka/devices/host/machine/package-tree.html index 181fb9353..4a77511d1 100644 --- a/docs/jdocs/neureka/devices/host/machine/package-tree.html +++ b/docs/jdocs/neureka/devices/host/machine/package-tree.html @@ -1,70 +1,90 @@ - + + - -neureka.devices.host.machine Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.devices.host.machine Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.devices.host.machine

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/package-frame.html b/docs/jdocs/neureka/devices/host/package-frame.html new file mode 100644 index 000000000..eeeb06c9d --- /dev/null +++ b/docs/jdocs/neureka/devices/host/package-frame.html @@ -0,0 +1,25 @@ + + + + + +neureka.devices.host (neureka 1.0.1 API) + + + + +

    neureka.devices.host

    +
    +

    Interfaces

    + +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/devices/host/package-summary.html b/docs/jdocs/neureka/devices/host/package-summary.html index 6a3b3bc73..833062d5a 100644 --- a/docs/jdocs/neureka/devices/host/package-summary.html +++ b/docs/jdocs/neureka/devices/host/package-summary.html @@ -1,131 +1,174 @@ - + + - -neureka.devices.host (neureka 1.0.0 API) - - - - + +neureka.devices.host (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.devices.host

    -
    -
    -
    package neureka.devices.host
    -
    -
    +
    +
      +
    • + + + + + + + + + + + + + + + + +
      Interface Summary 
      InterfaceDescription
      CPU.IndexedWorkload 
      CPU.RangeWorkload +
      A simple functional interface for executing a range whose implementations will + either be executed sequentially or they are being dispatched to + a thread-pool, given that the provided workload is large enough.
      +
      • -
    • -
      -
      -
      -
      -
      Class
      -
      Description
      -
      CPU
      -
      -
      The CPU class, one of many implementations of the Device interface, +
    • + + + + + + + + + + + + + + + + +
      Class Summary 
      ClassDescription
      CPU +
      The CPU class, one of many implementations of the Device interface, is simply supposed to be an API for dispatching threaded workloads onto the CPU as well as reading from or writing to tensors it stores.
      - -
      CPU.IndexedWorkload
      -
       
      -
      CPU.JVMExecutor
      -
      -
      The CPU.JVMExecutor offers a similar functionality as the parallel stream API, - however it differs in that the CPU.JVMExecutor is processing CPU.RangeWorkload lambdas +
      CPU.JVMExecutor +
      The CPU.JVMExecutor offers a similar functionality as the parallel stream API, + however it differs in that the CPU.JVMExecutor is processing CPU.RangeWorkload lambdas instead of simply exposing a single index or concrete elements for a given workload size.
      - -
      CPU.RangeWorkload
      -
      -
      A simple functional interface for executing a range whose implementations will - either be executed sequentially or they are being dispatched to - a thread-pool, given that the provided workload is large enough.
      -
      - - - +
    - -
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/host/package-tree.html b/docs/jdocs/neureka/devices/host/package-tree.html index f780b1506..8974988c5 100644 --- a/docs/jdocs/neureka/devices/host/package-tree.html +++ b/docs/jdocs/neureka/devices/host/package-tree.html @@ -1,87 +1,148 @@ - + + - -neureka.devices.host Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.devices.host Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.devices.host

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -

    Interface Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/KernelCache.html b/docs/jdocs/neureka/devices/opencl/KernelCache.html index 0740c706f..839d54b58 100644 --- a/docs/jdocs/neureka/devices/opencl/KernelCache.html +++ b/docs/jdocs/neureka/devices/opencl/KernelCache.html @@ -1,185 +1,303 @@ - + + - -KernelCache (neureka 1.0.0 API) - - - - + +KernelCache (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl
    -

    Class KernelCache

    -
    -
    java.lang.Object -
    neureka.devices.opencl.KernelCache
    +
    neureka.devices.opencl
    +

    Class KernelCache

    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.opencl.KernelCache
        • +
      +
      • +
    +
    +
      +
    • -
      public final class KernelCache -extends Object
      -
      A fixed sized cache for ad-hoc (just in time compiled) OpenCLDevice kernels. +
      +
      public final class KernelCache
+extends java.lang.Object
      +
      A fixed sized cache for ad-hoc (just in time compiled) OpenCLDevice kernels. This cache will mostly only be utilized when integrating with custom kernels - or when Operations are being optimized for - the OpenCLDevice.

      + or when Operations are being optimized for + the OpenCLDevice.

      Warning: This is an internal class, meaning it should not be used anywhere but within this library.
      This class or its public methods might change or get removed in future versions!
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      KernelCache()
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        KernelCache() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      neureka.devices.opencl.OpenCLDevice.cl_ad_hoc
      -
      get(String name)
      -
       
      -
      boolean
      -
      has(String name)
      -
       
      -
      void
      -
      put(String name, - neureka.devices.opencl.OpenCLDevice.cl_ad_hoc kernel)
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        neureka.devices.opencl.OpenCLDevice.cl_ad_hocget(java.lang.String name) 
        booleanhas(java.lang.String name) 
        voidput(java.lang.String name, + neureka.devices.opencl.OpenCLDevice.cl_ad_hoc kernel) 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        KernelCache

        -
        public KernelCache()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            KernelCache

            +
            public KernelCache()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      put

      -
      public void put(String name, - neureka.devices.opencl.OpenCLDevice.cl_ad_hoc kernel)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          put

          +
          public void put(java.lang.String name,
+                neureka.devices.opencl.OpenCLDevice.cl_ad_hoc kernel)
          • -
        • -
          -

          has

          -
          public boolean has(String name)
          -
          +
        + + + +
          +
        • +

          has

          +
          public boolean has(java.lang.String name)
          • -
        • -
          -

          get

          -
          public neureka.devices.opencl.OpenCLDevice.cl_ad_hoc get(String name)
          -
          +
        + + + +
          +
        • +

          get

          +
          public neureka.devices.opencl.OpenCLDevice.cl_ad_hoc get(java.lang.String name)
          +
          • +
      -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/KernelCaller.html b/docs/jdocs/neureka/devices/opencl/KernelCaller.html index 4e86894b5..9d87ff668 100644 --- a/docs/jdocs/neureka/devices/opencl/KernelCaller.html +++ b/docs/jdocs/neureka/devices/opencl/KernelCaller.html @@ -1,390 +1,513 @@ - + + - -KernelCaller (neureka 1.0.0 API) - - - - + +KernelCaller (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl
    -

    Class KernelCaller

    -
    -
    java.lang.Object -
    neureka.devices.opencl.KernelCaller
    +
    neureka.devices.opencl
    +

    Class KernelCaller

    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.opencl.KernelCaller
        • +
      +
      • +
    +
    +
      +
    • -
      public class KernelCaller -extends Object
      -
      Instances of this class are utility factories provided by OpenCLDevice instances. - When building new operations for tensors then this KernelCaller class is essential +
      +
      public class KernelCaller
+extends java.lang.Object
      +
      Instances of this class are utility factories provided by OpenCLDevice instances. + When building new operations for tensors then this KernelCaller class is essential for calling compiled kernels residing within the gpu.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      KernelCaller(org.jocl.cl_kernel kernel, - org.jocl.cl_command_queue queue)
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        KernelCaller

        -
        public KernelCaller(org.jocl.cl_kernel kernel, - org.jocl.cl_command_queue queue)
        -
        -
        Parameters:
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            KernelCaller

            +
            public KernelCaller(org.jocl.cl_kernel kernel,
+                    org.jocl.cl_command_queue queue)
            +
            +
            Parameters:
            kernel - The kernel which ought to be called.
            queue - The queue on which calls ought to be dispatched.
            -
      -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      passAllOf

      -
      public KernelCaller passAllOf(Tensor<Number> tensor)
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          passAllOf

          +
          public KernelCaller passAllOf(Tensor<java.lang.Number> tensor)
          This method passes 2 arguments to the kernel. One for the data of the tensor and one for the configuration data!
          -
          -
          Parameters:
          +
          +
          Parameters:
          tensor - The tensor whose data and configuration ought to be passed to the kernel.
          -
          Returns:
          +
          Returns:
          This very KernelCaller instance (factory pattern).
          -
      • -
    • -
      -

      passConfOf

      -
      public KernelCaller passConfOf(Tensor<Number> tensor)
      +
    + + + +
      +
    • +

      passConfOf

      +
      public KernelCaller passConfOf(Tensor<java.lang.Number> tensor)
      This method passes the ND-Configuration in the form of a flattened int array to the kernel. Kernels can use this information for more complex indexing mechanisms as one would expect them to be present in tensor which have been permuted or are simply slices of other tensors.
      -
      -
      Parameters:
      +
      +
      Parameters:
      tensor - The tensor whose ND configuration ought to be passed to the kernel.
      -
      Returns:
      +
      Returns:
      This very KernelCaller instance (factory pattern).
      -
    • -
  • -
    -

    pass

    -
    public <T extends Number> KernelCaller pass(Tensor<T> tensor)
    + + + + +
      +
    • +

      pass

      +
      public <T extends java.lang.Number> KernelCaller pass(Tensor<T> tensor)
      This method passes 1 argument to the kernel. Namely, the data of the tensor!
      -
      -
      Parameters:
      +
      +
      Parameters:
      tensor - The tensor whose data ought to be passed to the kernel.
      -
      Returns:
      +
      Returns:
      This very KernelCaller instance (factory pattern).
      -
    • -
  • -
    -

    pass

    -
    public KernelCaller pass(int value)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      pass

      +
      public KernelCaller pass(int value)
      +
      +
      Parameters:
      value - An int value which ought to be passed to the kernel.
      -
      Returns:
      +
      Returns:
      This very KernelCaller instance (factory pattern).
      -
    • -
  • -
    -

    pass

    -
    public KernelCaller pass(int... values)
    + + + + +
      +
    • +

      pass

      +
      public KernelCaller pass(int... values)
      Use this to pass an array of int values to the kernel.
      -
      -
      Parameters:
      +
      +
      Parameters:
      values - An array of int values which ought to be passed to the kernel.
      -
      Returns:
      +
      Returns:
      This very KernelCaller instance (factory pattern).
      -
    • -
  • -
    -

    pass

    -
    public KernelCaller pass(float... values)
    + + + + +
      +
    • +

      pass

      +
      public KernelCaller pass(float... values)
      Use this to pass an array of float values to the kernel.
      -
      -
      Parameters:
      +
      +
      Parameters:
      values - An array of float values which ought to be passed to the kernel.
      -
      Returns:
      +
      Returns:
      This very KernelCaller instance (factory pattern).
      -
    • -
  • -
    -

    pass

    -
    public KernelCaller pass(double... values)
    -
    + + + + + + + + + + + + + + + + +
      +
    • +

      pass

      +
      public KernelCaller pass(byte... values)
      • -
    • -
      -

      pass

      -
      public KernelCaller pass(float value)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      pass

      +
      public KernelCaller pass(float value)
      +
      +
      Parameters:
      value - A float value which ought to be passed to the kernel.
      -
      Returns:
      +
      Returns:
      This very KernelCaller instance (factory pattern).
      -
      • -
    • -
      -

      pass

      -
      public KernelCaller pass(double value)
      -
      +
    + + + + + + + + + + + + + + + + + + + +
      +
    • +

      pass

      +
      public KernelCaller pass(java.lang.Number value)
      • -
    • -
      -

      passLocalFloats

      -
      public KernelCaller passLocalFloats(long size)
      -
      +
    + + + +
      +
    • +

      passLocalFloats

      +
      public KernelCaller passLocalFloats(long size)
      • -
    • -
      -

      call

      -
      public void call(int globalWorkSize)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      call

      +
      public void call(int globalWorkSize)
      +
      +
      Parameters:
      globalWorkSize - The number of global threads which will be dispatched.
      -
      • -
    • -
      -

      call

      -
      public void call(long[] globalWorkSizes, - long[] localWorkSizes)
      +
    + + + +
      +
    • +

      call

      +
      public void call(long[] globalWorkSizes,
+                 long[] localWorkSizes)
      Use this to call the kernel with 2 long arrays defining how the kernel should be indexed and parallelized. The globalWorkSizes span an n-dimensional grid of global threads, whereas the localWorkSizes defines the dimensions of a grid of local work items (which are called "work groups"). @@ -399,21 +522,80 @@

      call

      This can usually be circumvented by padding the data to be a multiple of a more appropriate local work size or by introducing boundary checks in your kernel.

      -
      -
      Parameters:
      +
      +
      Parameters:
      globalWorkSizes - An arrays of long values which span a nd-grid of global threads.
      localWorkSizes - An arrays of long values which span a nd-grid of local threads (work groups).
      -
    -
    • - + + +
    +
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    + + +
    Skip navigation links
    + + + +
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    + diff --git a/docs/jdocs/neureka/devices/opencl/KernelCode.html b/docs/jdocs/neureka/devices/opencl/KernelCode.html index d2922cf69..f2b6a0ff6 100644 --- a/docs/jdocs/neureka/devices/opencl/KernelCode.html +++ b/docs/jdocs/neureka/devices/opencl/KernelCode.html @@ -1,216 +1,346 @@ - + + - -KernelCode (neureka 1.0.0 API) - - - - + +KernelCode (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl
    -

    Class KernelCode

    -
    -
    java.lang.Object -
    neureka.devices.opencl.KernelCode
    +
    neureka.devices.opencl
    +

    Class KernelCode

    -
    -
    -
    public final class KernelCode -extends Object
    -
    -
    -
      - +
      + +
      +
        +
      • +
        +
        +
        public final class KernelCode
+extends java.lang.Object
        +
        • +
      -
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        KernelCode

        -
        public KernelCode(String name, - String code)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            KernelCode

            +
            public KernelCode(java.lang.String name,
+                  java.lang.String code)
            • -
          • -
            -

            KernelCode

            -
            public KernelCode(String name, - String code, - DataType<?> dataType)
            -
            +
          + + + +
            +
          • +

            KernelCode

            +
            public KernelCode(java.lang.String name,
+                  java.lang.String code,
+                  DataType<?> dataType)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getName

      -
      public String getName()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getName

          +
          public java.lang.String getName()
          • -
        • -
          -

          getCode

          -
          public String getCode()
          -
          +
        + + + +
          +
        • +

          getCode

          +
          public java.lang.String getCode()
          • -
        • -
          -

          getDataType

          -
          public DataType<?> getDataType()
          -
          +
        + + + +
          +
        • +

          getDataType

          +
          public DataType<?> getDataType()
          • -
        • -
          -

          equals

          -
          public boolean equals(Object o)
          -
          -
          Overrides:
          -
          equals in class Object
          +
        + + + +
          +
        • +

          equals

          +
          public boolean equals(java.lang.Object o)
          +
          +
          Overrides:
          +
          equals in class java.lang.Object
          -
    • -
  • -
    -

    hashCode

    -
    public int hashCode()
    -
    -
    Overrides:
    -
    hashCode in class Object
    + + + + +
      +
    • +

      hashCode

      +
      public int hashCode()
      +
      +
      Overrides:
      +
      hashCode in class java.lang.Object
      -
    • - - + + +
    +
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    + + +
    Skip navigation links
    + + + + +
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    + diff --git a/docs/jdocs/neureka/devices/opencl/KernelSource.html b/docs/jdocs/neureka/devices/opencl/KernelSource.html index 971762829..fb53ad38d 100644 --- a/docs/jdocs/neureka/devices/opencl/KernelSource.html +++ b/docs/jdocs/neureka/devices/opencl/KernelSource.html @@ -1,138 +1,232 @@ - + + - -KernelSource (neureka 1.0.0 API) - - - - + +KernelSource (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl
    -

    Interface KernelSource

    +
    neureka.devices.opencl
    +

    Interface KernelSource

    -
    -
    +
    +
    +
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      KernelCode
      -
      getKernelFor(ExecutionCall<OpenCLDevice> call)
      -
       
      -
      -
      +
      +
      @FunctionalInterface
+public interface KernelSource
      +
      Provides kernel source code for a provided ExecutionCall.
      +
      • +
    - +
    + - -
    -
      +
    +
    + - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
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    +
      +
    • Summary: 
      • +
    • Nested | 
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      • +
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    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/OpenCLDevice.Query.html b/docs/jdocs/neureka/devices/opencl/OpenCLDevice.Query.html index f2d789daf..7a9916e5c 100644 --- a/docs/jdocs/neureka/devices/opencl/OpenCLDevice.Query.html +++ b/docs/jdocs/neureka/devices/opencl/OpenCLDevice.Query.html @@ -1,343 +1,485 @@ - + + - -OpenCLDevice.Query (neureka 1.0.0 API) - - - - + +OpenCLDevice.Query (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl
    -

    Class OpenCLDevice.Query

    -
    -
    java.lang.Object -
    neureka.devices.opencl.OpenCLDevice.Query
    +
    neureka.devices.opencl
    +

    Class OpenCLDevice.Query

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.opencl.OpenCLDevice.Query
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      Query()
      -
       
      +
      +
      public static class OpenCLDevice.Query
+extends java.lang.Object
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        Query() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static int
      -
      getInt(org.jocl.cl_device_id device, - int paramName)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static intgetInt(org.jocl.cl_device_id device, + int paramName)
        Returns the value of the device info parameter with the given name
        - -
        static int[]
        -
        getInts(org.jocl.cl_device_id device, - int paramName, - int numValues)
        -
        +
        static int[]getInts(org.jocl.cl_device_id device, + int paramName, + int numValues)
        Returns the values of the device info parameter with the given name
        - -
        static long
        -
        getLong(org.jocl.cl_device_id device, - int paramName)
        -
        +
        static longgetLong(org.jocl.cl_device_id device, + int paramName)
        Returns the value of the device info parameter with the given name
        - -
        static long[]
        -
        getLongs(int numValues, - ByteBuffer buffer, - long[] values)
        -
         
        -
        static long[]
        -
        getLongs(org.jocl.cl_device_id device, - int paramName, - int numValues)
        -
        +
        static long[]getLongs(org.jocl.cl_device_id device, + int paramName, + int numValues)
        Returns the values of the device info parameter with the given name
        - -
        static long
        -
        getSize(org.jocl.cl_device_id device, - int paramName)
        -
        +
        static long[]getLongs(int numValues, + java.nio.ByteBuffer buffer, + long[] values) 
        static longgetSize(org.jocl.cl_device_id device, + int paramName)
        Returns the value of the device info parameter with the given name
        - -
        static long[]
        -
        getSizes(org.jocl.cl_device_id device, - int paramName, - int numValues)
        -
        +
        static long[]getSizes(org.jocl.cl_device_id device, + int paramName, + int numValues)
        Returns the values of the device info parameter with the given name
        - -
        static String
        -
        getString(org.jocl.cl_device_id device, - int paramName)
        -
        +
        static java.lang.StringgetString(org.jocl.cl_device_id device, + int paramName)
        Returns the value of the device info parameter with the given name
        - -
        static String
        -
        getString(org.jocl.cl_platform_id platform, - int paramName)
        -
        +
        static java.lang.StringgetString(org.jocl.cl_platform_id platform, + int paramName)
        Returns the value of the platform info parameter with the given name
        - - - - -
        -

        Methods inherited from class java.lang.Object

        -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
        - +
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Query

        -
        public Query()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Query

            +
            public Query()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getInt

      -
      public static int getInt(org.jocl.cl_device_id device, - int paramName)
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getInt

          +
          public static int getInt(org.jocl.cl_device_id device,
+                         int paramName)
          Returns the value of the device info parameter with the given name
          -
          -
          Parameters:
          +
          +
          Parameters:
          device - The device
          paramName - The parameter name
          -
          Returns:
          +
          Returns:
          The value
          -
      • -
    • -
      -

      getInts

      -
      public static int[] getInts(org.jocl.cl_device_id device, - int paramName, - int numValues)
      +
    + + + +
      +
    • +

      getInts

      +
      public static int[] getInts(org.jocl.cl_device_id device,
+                            int paramName,
+                            int numValues)
      Returns the values of the device info parameter with the given name
      -
      -
      Parameters:
      +
      +
      Parameters:
      device - The device
      paramName - The parameter name
      numValues - The number of values
      -
      Returns:
      +
      Returns:
      The value
      -
    • -
  • -
    -

    getLong

    -
    public static long getLong(org.jocl.cl_device_id device, - int paramName)
    + + + + +
      +
    • +

      getLong

      +
      public static long getLong(org.jocl.cl_device_id device,
+                           int paramName)
      Returns the value of the device info parameter with the given name
      -
      -
      Parameters:
      +
      +
      Parameters:
      device - The device
      paramName - The parameter name
      -
      Returns:
      +
      Returns:
      The value
      -
    • -
  • -
    -

    getLongs

    -
    public static long[] getLongs(org.jocl.cl_device_id device, - int paramName, - int numValues)
    + + + + +
      +
    • +

      getLongs

      +
      public static long[] getLongs(org.jocl.cl_device_id device,
+                              int paramName,
+                              int numValues)
      Returns the values of the device info parameter with the given name
      -
      -
      Parameters:
      +
      +
      Parameters:
      device - The device
      paramName - The parameter name
      numValues - The number of values
      -
      Returns:
      +
      Returns:
      The value
      -
    • -
  • -
    -

    getString

    -
    public static String getString(org.jocl.cl_device_id device, - int paramName)
    + + + + +
      +
    • +

      getString

      +
      public static java.lang.String getString(org.jocl.cl_device_id device,
+                                         int paramName)
      Returns the value of the device info parameter with the given name
      -
      -
      Parameters:
      +
      +
      Parameters:
      device - The device
      paramName - The parameter name
      -
      Returns:
      +
      Returns:
      The value
      -
    • -
  • -
    -

    getString

    -
    public static String getString(org.jocl.cl_platform_id platform, - int paramName)
    + + + + +
      +
    • +

      getString

      +
      public static java.lang.String getString(org.jocl.cl_platform_id platform,
+                                         int paramName)
      Returns the value of the platform info parameter with the given name
      -
      -
      Parameters:
      +
      +
      Parameters:
      platform - The platform
      paramName - The parameter name
      -
      Returns:
      +
      Returns:
      The value
      -
    • -
  • -
    -

    getSize

    -
    public static long getSize(org.jocl.cl_device_id device, - int paramName)
    + + + + +
      +
    • +

      getSize

      +
      public static long getSize(org.jocl.cl_device_id device,
+                           int paramName)
      Returns the value of the device info parameter with the given name
      -
      -
      Parameters:
      +
      +
      Parameters:
      device - The device
      paramName - The parameter name
      -
      Returns:
      +
      Returns:
      The value64
      -
    • -
  • -
    -

    getSizes

    -
    public static long[] getSizes(org.jocl.cl_device_id device, - int paramName, - int numValues)
    + + + + +
      +
    • +

      getSizes

      +
      public static long[] getSizes(org.jocl.cl_device_id device,
+                              int paramName,
+                              int numValues)
      Returns the values of the device info parameter with the given name
      -
      -
      Parameters:
      +
      +
      Parameters:
      device - The device
      paramName - The parameter name
      numValues - The number of values
      -
      Returns:
      +
      Returns:
      The value64
      -
    • -
  • -
    -

    getLongs

    -
    public static long[] getLongs(int numValues, - ByteBuffer buffer, - long[] values)
    -
    + + + + +
      +
    • +

      getLongs

      +
      public static long[] getLongs(int numValues,
+                              java.nio.ByteBuffer buffer,
+                              long[] values)
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/OpenCLDevice.Type.html b/docs/jdocs/neureka/devices/opencl/OpenCLDevice.Type.html index df65b2f21..bc02f699e 100644 --- a/docs/jdocs/neureka/devices/opencl/OpenCLDevice.Type.html +++ b/docs/jdocs/neureka/devices/opencl/OpenCLDevice.Type.html @@ -1,261 +1,402 @@ - + + - -OpenCLDevice.Type (neureka 1.0.0 API) - - - - + +OpenCLDevice.Type (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl
    -

    Enum Class OpenCLDevice.Type

    -
    -
    java.lang.Object -
    java.lang.Enum<OpenCLDevice.Type> -
    neureka.devices.opencl.OpenCLDevice.Type
    +
    neureka.devices.opencl
    +

    Enum OpenCLDevice.Type

    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • java.lang.Enum<OpenCLDevice.Type>
        • +
      • +
          +
        • neureka.devices.opencl.OpenCLDevice.Type
          • +
        +
        • +
      +
      • +
    +
    +
    -
    -
    - +
    +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static OpenCLDevice.Type[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static OpenCLDevice.Type[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (OpenCLDevice.Type c : OpenCLDevice.Type.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static OpenCLDevice.Type valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static OpenCLDevice.Type valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/OpenCLDevice.html b/docs/jdocs/neureka/devices/opencl/OpenCLDevice.html index af3237432..e18a6a183 100644 --- a/docs/jdocs/neureka/devices/opencl/OpenCLDevice.html +++ b/docs/jdocs/neureka/devices/opencl/OpenCLDevice.html @@ -1,485 +1,653 @@ - + + - -OpenCLDevice (neureka 1.0.0 API) - - - - + +OpenCLDevice (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl
    -

    Class OpenCLDevice

    +
    neureka.devices.opencl
    +

    Class OpenCLDevice

    -
    java.lang.Object -
    neureka.devices.AbstractBaseDevice<V> -
    neureka.devices.AbstractDevice<Number> -
    neureka.devices.opencl.OpenCLDevice
    -
    -
    -
    -
    -
    +
    + +
    +
    -
    -
    -
    -

    Nested classes/interfaces inherited from interface neureka.common.composition.Component

    -Component.IsBeing, Component.OwnerChangeRequest<O>
    -
    -

    Nested classes/interfaces inherited from interface neureka.devices.Device

    -Device.Access<V>, Device.In, Device.Writer
    - +
    +
    -
    long
    -
    singleFPConfig()
    -
     
    -
    final String
    -
    toString()
    -
     
    -
    OpenCLDevice.Type
    -
    type()
    -
     
    -
    boolean
    -
    update(Component.OwnerChangeRequest<Tensor<Number>> changeRequest)
    -
    -
    A Device is a component of a tensor.
    -
    -
    String
    -
    vendor()
    -
     
    -
    String
    -
    version()
    -
     
    -
    -
    -
    -
    -

    Methods inherited from class neureka.devices.AbstractDevice

    -_cleaning, access, approve, store
    -
    -

    Methods inherited from class neureka.devices.AbstractBaseDevice

    -contains, has, isEmpty, numberOfDataObjects, numberOfStored
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.devices.Device

    -allocate, borrow, optimizedFunctionOf
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Method Details

        -
          -
        • -
          -

          toString

          -
          public final String toString()
          -
          -
          Overrides:
          -
          toString in class Object
          +
            +
          • + + +

            Method Detail

            + + + +
              +
            • +

              toString

              +
              public final java.lang.String toString()
              +
              +
              Overrides:
              +
              toString in class java.lang.Object
              -
          • -
        • -
          -

          getId

          -
          public final org.jocl.cl_device_id getId()
          -
          +
        + + + +
          +
        • +

          getId

          +
          public final org.jocl.cl_device_id getId()
          • -
        • -
          -

          getPlatform

          -
          public final OpenCLPlatform getPlatform()
          -
          +
        + + + +
          +
        • +

          getPlatform

          +
          public final OpenCLPlatform getPlatform()
          • -
        • -
          -

          hasAdHocKernel

          -
          public boolean hasAdHocKernel(String name)
          -
          -
          Parameters:
          +
        + + + +
          +
        • +

          hasAdHocKernel

          +
          public boolean hasAdHocKernel(java.lang.String name)
          +
          +
          Parameters:
          name - The name of the kernel whose presents should be checked.
          -
          Returns:
          +
          Returns:
          True if the kernel is present in the cache, false otherwise.
          -
        • -
      • -
        -

        getAdHocKernel

        -
        public KernelCaller getAdHocKernel(String name)
        -
        -
        Parameters:
        +
      + + + +
        +
      • +

        getAdHocKernel

        +
        public KernelCaller getAdHocKernel(java.lang.String name)
        +
        +
        Parameters:
        name - The name of the kernel which should be retrieved.
        -
        Returns:
        +
        Returns:
        The kernel with the given name if it is present in the cache, throws an exception otherwise.
        -
    -
  • -
    -

    findAdHocKernel

    -
    public Optional<KernelCaller> findAdHocKernel(String name)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      findAdHocKernel

      +
      public java.util.Optional<KernelCaller> findAdHocKernel(java.lang.String name)
      +
      +
      Parameters:
      name - The name of the kernel which should be retrieved.
      -
      Returns:
      -
      An Optional containing the kernel with the given name if it is present in the cache, an empty optional otherwise.
      +
      Returns:
      +
      An Optional containing the kernel with the given name if it is present in the cache, an empty optional otherwise.
      -
    • -
  • -
    -

    findOrCompileAdHocKernel

    -
    public KernelCaller findOrCompileAdHocKernel(String name, - Supplier<String> source)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      findOrCompileAdHocKernel

      +
      public KernelCaller findOrCompileAdHocKernel(java.lang.String name,
+                                             java.util.function.Supplier<java.lang.String> source)
      +
      +
      Parameters:
      name - The name of the kernel which should be retrieved.
      source - The source code of the kernel which should be compiled if it is not present in the cache.
      -
      Returns:
      +
      Returns:
      The kernel caller for the kernel of the requested name, either from cache, or compiled from the given source code if it was not present in the cache.
      -
    • -
  • -
    -

    compileAndGetAdHocKernel

    -
    public KernelCaller compileAndGetAdHocKernel(String name, - String source)
    -
    This method compiles and returns the KernelCaller for a so called "ad hoc" kernel. + + + + +
      +
    • +

      compileAndGetAdHocKernel

      +
      public KernelCaller compileAndGetAdHocKernel(java.lang.String name,
+                                             java.lang.String source)
      +
      This method compiles and returns the KernelCaller for a so called "ad hoc" kernel. Ad hoc is a Latin phrase meaning literally 'to this'. In English, it generally signifies a solution designed for a specific problem or task, non-generalizable, and not intended to be adapted to other purposes. @@ -487,20 +655,23 @@

      compileAndGetAdHocKernel

      unique kernels with a specific purpose created on the fly during runtime by operations. This might be useful for high performance operations on tensors with specific dimensions and or possibly other variables / properties which might be taken into account...
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - The name of the kernel which ought to be compiled.
      source - The source of the kernel which ought to be compiled.
      -
      Returns:
      -
      The KernelCaller for the compiled kernel.
      +
      Returns:
      +
      The KernelCaller for the compiled kernel.
      -
    • -
  • -
    -

    compileAdHocKernel

    -
    public OpenCLDevice compileAdHocKernel(String name, - String source)
    + + + + +
      +
    • +

      compileAdHocKernel

      +
      public OpenCLDevice compileAdHocKernel(java.lang.String name,
+                                       java.lang.String source)
      This method compiles so called "ad hoc" kernel. Ad hoc is a Latin phrase meaning literally 'to this'. In English, it generally signifies a solution designed for a specific problem or task, @@ -509,495 +680,722 @@

      compileAdHocKernel

      unique kernels with a specific purpose created on the fly during runtime by operations. This might be useful for high performance operations on tensors with specific dimensions and or possibly other variables / properties which might be taken into account...
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - The name of the kernel which ought to be compiled.
      source - The source of the kernel which ought to be compiled.
      -
      Returns:
      +
      Returns:
      This very instance in order to enable the factory pattern.
      -
    • -
  • -
    -

    optimizedOperationOf

    -
    public Operation optimizedOperationOf(Function function, - String name)
    -
    Description copied from interface: Device
    -
    This method tries to allow this device to produce an optimized Operation + + + + +
      +
    • +

      optimizedOperationOf

      +
      public Operation optimizedOperationOf(Function function,
+                                      java.lang.String name)
      +
      Description copied from interface: Device
      +
      This method tries to allow this device to produce an optimized Operation based on the provided function. This is especially useful in an OpenCL context which can compile the function into native GPU kernels at runtime.
      -
      -
      Parameters:
      +
      +
      Parameters:
      function - The function which should be turned into an optimized operation.
      name - The name of the returned operation.
      -
      Returns:
      +
      Returns:
      An optimized operation based on the provided function, or null if optimization is not possible.
      -
    • -
  • -
    -

    dispose

    -
    public void dispose()
    + + + + +
      +
    • +

      dispose

      +
      public void dispose()
      This method tells the to restore all tensors stored on it and release all resources.
      -
    • -
  • -
    -

    restore

    -
    public Device<Number> restore(Tensor<Number> tensor)
    + + + + +
      +
    • +

      restore

      +
      public Device<java.lang.Number> restore(Tensor<java.lang.Number> tensor)
      This method assumes that the passed tensor is stored on this device instance. If the tensor is stored on the device then the method loads the outsourced data of the tensor back into primitive JVM arrays and restores the tensor freshly in RAM.
      -
      -
      Parameters:
      +
      +
      Parameters:
      tensor - The tensor whose data ought to be restored (loaded to RAM).
      -
      Returns:
      +
      Returns:
      This device, which enables method chaining.
      -
    • -
  • -
    -

    clConfigOf

    -
    public neureka.devices.opencl.OpenCLDevice.cl_config clConfigOf(Tensor<?> t)
    -
    + + + + +
      +
    • +

      clConfigOf

      +
      public neureka.devices.opencl.OpenCLDevice.cl_config clConfigOf(Tensor<?> t)
      • -
    • -
      -

      clConfigOf

      -
      public neureka.devices.opencl.OpenCLDevice.cl_config clConfigOf(NDConfiguration ndc)
      -
      +
    + + + +
      +
    • +

      clConfigOf

      +
      public neureka.devices.opencl.OpenCLDevice.cl_config clConfigOf(NDConfiguration ndc)
      • -
    • -
      -

      free

      -
      public final <T extends Number> Device<Number> free(Tensor<T> tensor)
      -
      Description copied from interface: Device
      -
      Use this to remove the provided tensor from this Device!

      -
      -
      Type Parameters:
      -
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      -
      Parameters:
      -
      tensor - The tensor which ought to be removed from this Device.
      -
      Returns:
      +
    + + + +
      +
    • +

      free

      +
      public final <T extends java.lang.Number> Device<java.lang.Number> free(Tensor<T> tensor)
      +
      Description copied from interface: Device
      +
      Use this to remove the provided tensor from this Device!

      +
      +
      Type Parameters:
      +
      T - The type parameter for the value type of the tensor, which must be supported by this Device.
      +
      Parameters:
      +
      tensor - The tensor which ought to be removed from this Device.
      +
      Returns:
      This very instance to allow for method chaining.
      -
      • -
    • -
      -

      _readItem

      -
      protected final <T extends Number> T _readItem(Tensor<T> tensor, - int index)
      -
      -
      Specified by:
      -
      _readItem in class AbstractDevice<Number>
      +
    + + + +
      +
    • +

      _readItem

      +
      protected final <T extends java.lang.Number> T _readItem(Tensor<T> tensor,
+                                                         int index)
      +
      +
      Specified by:
      +
      _readItem in class AbstractDevice<java.lang.Number>
      -
      • -
    • -
      -

      _readArray

      -
      protected final <T extends Number, -A> A _readArray(Tensor<T> tensor, - Class<A> arrayType, - int start, - int size)
      -
      -
      Specified by:
      -
      _readArray in class AbstractDevice<Number>
      +
    + + + +
      +
    • +

      _readArray

      +
      protected final <T extends java.lang.Number,A> A _readArray(Tensor<T> tensor,
+                                                            java.lang.Class<A> arrayType,
+                                                            int start,
+                                                            int size)
      +
      +
      Specified by:
      +
      _readArray in class AbstractDevice<java.lang.Number>
      -
      • -
    • -
      -

      _writeItem

      -
      protected final <T extends Number> void _writeItem(Tensor<T> tensor, - T item, - int start, - int size)
      -
      -
      Specified by:
      -
      _writeItem in class AbstractDevice<Number>
      +
    + + + + + +
      +
    • +

      _writeItem

      +
      protected final <T extends java.lang.Number> void _writeItem(Tensor<T> tensor,
+                                                             T item,
+                                                             int start,
+                                                             int size)
      +
      +
      Specified by:
      +
      _writeItem in class AbstractDevice<java.lang.Number>
      -
      • -
    • -
      -

      _writeArray

      -
      protected final <T extends Number> void _writeArray(Tensor<T> tensor, - Object array, - int offset, - int start, - int size)
      -
      -
      Specified by:
      -
      _writeArray in class AbstractDevice<Number>
      +
    + + + +
      +
    • +

      _writeArray

      +
      protected final <T extends java.lang.Number> void _writeArray(Tensor<T> tensor,
+                                                              java.lang.Object array,
+                                                              int offset,
+                                                              int start,
+                                                              int size)
      +
      +
      Specified by:
      +
      _writeArray in class AbstractDevice<java.lang.Number>
      -
      • -
    • -
      -

      allocate

      -
      public <T extends Number> Data<T> allocate(DataType<T> dataType, - NDConfiguration ndc)
      -
      +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
    • +

      _swap

      +
      protected final <T extends java.lang.Number> void _swap(Tensor<T> former,
+                                                        Tensor<T> replacement)
      +
      Description copied from class: AbstractDevice
      This method is used internally mostly and should not be used in most cases.

      -
      -
      Specified by:
      -
      _swap in class AbstractDevice<Number>
      -
      Type Parameters:
      -
      T - The type parameter for the value type of the tensors, which must be supported by this Device.
      -
      Parameters:
      +
      +
      Specified by:
      +
      _swap in class AbstractDevice<java.lang.Number>
      +
      Type Parameters:
      +
      T - The type parameter for the value type of the tensors, which must be supported by this Device.
      +
      Parameters:
      former - The tensor whose associated data (on the device) ought to be assigned to the other tensor.
      replacement - The tensor which ought to receive the data of the former tensor internally.
      -
      • -
    • -
      -

      update

      -
      public boolean update(Component.OwnerChangeRequest<Tensor<Number>> changeRequest)
      -
      Description copied from class: AbstractDevice
      -
      A Device is a component of a tensor. This method is used to inform the device +
    + + + +
      +
    • +

      update

      +
      public boolean update(Component.OwnerChangeRequest<Tensor<java.lang.Number>> changeRequest)
      +
      Description copied from class: AbstractDevice
      +
      A Device is a component of a tensor. This method is used to inform the device that the device is being added, removed or replaced (from the tensor).
      -
      -
      Specified by:
      -
      update in interface Component<Tensor<Number>>
      -
      Overrides:
      -
      update in class AbstractDevice<Number>
      -
      Parameters:
      -
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      -
      Returns:
      +
      +
      Specified by:
      +
      update in interface Component<Tensor<java.lang.Number>>
      +
      Overrides:
      +
      update in class AbstractDevice<java.lang.Number>
      +
      Parameters:
      +
      changeRequest - An OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      +
      Returns:
      The truth value determining if the change should be executed.
      -
      • -
    • -
      -

      _sizeOccupiedBy

      -
      protected <T extends Number> int _sizeOccupiedBy(Tensor<T> tensor)
      -
      -
      Specified by:
      -
      _sizeOccupiedBy in class AbstractDevice<Number>
      +
    + + + + + + + + + + + + + + + +
      +
    • +

      getKernel

      +
      public KernelCaller getKernel(java.lang.String name)
      +
      +
      Parameters:
      +
      name - The name of the kernel for which a KernelCaller should be returned.
      +
      Returns:
      +
      A KernelCaller for calling the requested kernel.
      -
      • -
    • -
      -

      _approveExecutionOf

      -
      protected boolean _approveExecutionOf(Tensor<?>[] tensors, - int d, - Operation type)
      -
      Description copied from class: AbstractDevice
      +
    + + + +
      +
    • +

      _approveExecutionOf

      +
      protected boolean _approveExecutionOf(Tensor<?>[] tensors,
+                                      int d,
+                                      Operation type)
      +
      Description copied from class: AbstractDevice
      This method is the internal approval routine called by its public counterpart and implemented by classes extending this very abstract class. - It may or may not be called by an Algorithm - in order to allow a Device to checked if the provided arguments are suitable for execution.
      -
      -
      Specified by:
      -
      _approveExecutionOf in class AbstractDevice<Number>
      -
      Parameters:
      + It may or may not be called by an Algorithm + in order to allow a Device to checked if the provided arguments are suitable for execution.
    • +
    +
    Specified by:
    +
    _approveExecutionOf in class AbstractDevice<java.lang.Number>
    +
    Parameters:
    tensors - An array of input tensors.
    d - The index of the input which ought to be derived.
    type - The type of operation.
    -
    Returns:
    +
    Returns:
    The truth value determining if the provided arguments can be executed.
    - -
  • -
    -

    name

    -
    public String name()
    -
    + + + + +
      +
    • +

      name

      +
      public java.lang.String name()
      • -
    • -
      -

      vendor

      -
      public String vendor()
      -
      +
    + + + +
      +
    • +

      vendor

      +
      public java.lang.String vendor()
      • -
    • -
      -

      version

      -
      public String version()
      -
      +
    + + + +
      +
    • +

      version

      +
      public java.lang.String version()
      • -
    • -
      -

      type

      -
      public OpenCLDevice.Type type()
      -
      +
    + + + +
      +
    • +

      type

      +
      public OpenCLDevice.Type type()
      • -
    • -
      -

      maxComputeUnits

      -
      public int maxComputeUnits()
      -
      +
    + + + +
      +
    • +

      maxComputeUnits

      +
      public int maxComputeUnits()
      • -
    • -
      -

      maxWorkItemSimensions

      -
      public long maxWorkItemSimensions()
      -
      +
    + + + +
      +
    • +

      maxWorkItemSimensions

      +
      public long maxWorkItemSimensions()
      • -
    • -
      -

      maxWorkItemSizes

      -
      public long[] maxWorkItemSizes()
      -
      +
    + + + +
      +
    • +

      maxWorkItemSizes

      +
      public long[] maxWorkItemSizes()
      • -
    • -
      -

      maxWorkGroupSize

      -
      public long maxWorkGroupSize()
      -
      +
    + + + +
      +
    • +

      maxWorkGroupSize

      +
      public long maxWorkGroupSize()
      • -
    • -
      -

      maxClockFrequenzy

      -
      public long maxClockFrequenzy()
      -
      +
    + + + +
      +
    • +

      maxClockFrequenzy

      +
      public long maxClockFrequenzy()
      • -
    • -
      -

      maxAddressBits

      -
      public int maxAddressBits()
      -
      +
    + + + +
      +
    • +

      maxAddressBits

      +
      public int maxAddressBits()
      • -
    • -
      -

      maxMemAllocSize

      -
      public long maxMemAllocSize()
      -
      +
    + + + +
      +
    • +

      maxMemAllocSize

      +
      public long maxMemAllocSize()
      • -
    • -
      -

      globalMemSize

      -
      public long globalMemSize()
      -
      +
    + + + +
      +
    • +

      globalMemSize

      +
      public long globalMemSize()
      • -
    • -
      -

      errorCorrectionSupport

      -
      public int errorCorrectionSupport()
      -
      +
    + + + +
      +
    • +

      errorCorrectionSupport

      +
      public int errorCorrectionSupport()
      • -
    • -
      -

      localMemType

      -
      public int localMemType()
      -
      +
    + + + +
      +
    • +

      localMemType

      +
      public int localMemType()
      • -
    • -
      -

      localMemSize

      -
      public long localMemSize()
      -
      +
    + + + +
      +
    • +

      localMemSize

      +
      public long localMemSize()
      • -
    • -
      -

      maxConstantBufferSize

      -
      public long maxConstantBufferSize()
      -
      +
    + + + +
      +
    • +

      maxConstantBufferSize

      +
      public long maxConstantBufferSize()
      • -
    • -
      -

      maxConstantBufferSizeKB

      -
      public long maxConstantBufferSizeKB()
      -
      +
    + + + +
      +
    • +

      maxConstantBufferSizeKB

      +
      public long maxConstantBufferSizeKB()
      • -
    • -
      -

      imageSupport

      -
      public int imageSupport()
      -
      +
    + + + +
      +
    • +

      imageSupport

      +
      public int imageSupport()
      • -
    • -
      -

      maxReadImageArgs

      -
      public int maxReadImageArgs()
      -
      +
    + + + +
      +
    • +

      maxReadImageArgs

      +
      public int maxReadImageArgs()
      • -
    • -
      -

      maxWriteImageArgs

      -
      public int maxWriteImageArgs()
      -
      +
    + + + +
      +
    • +

      maxWriteImageArgs

      +
      public int maxWriteImageArgs()
      • -
    • -
      -

      singleFPConfig

      -
      public long singleFPConfig()
      -
      +
    + + + +
      +
    • +

      singleFPConfig

      +
      public long singleFPConfig()
      • -
    • -
      -

      image2DMaxWidth

      -
      public long image2DMaxWidth()
      -
      +
    + + + +
      +
    • +

      image2DMaxWidth

      +
      public long image2DMaxWidth()
      • -
    • -
      -

      image2DMaxHeight

      -
      public long image2DMaxHeight()
      -
      +
    + + + +
      +
    • +

      image2DMaxHeight

      +
      public long image2DMaxHeight()
      • -
    • -
      -

      image3DMaxWidth

      -
      public long image3DMaxWidth()
      -
      +
    + + + +
      +
    • +

      image3DMaxWidth

      +
      public long image3DMaxWidth()
      • -
    • -
      -

      image3DMaxHeight

      -
      public long image3DMaxHeight()
      -
      +
    + + + +
      +
    • +

      image3DMaxHeight

      +
      public long image3DMaxHeight()
      • -
    • -
      -

      image3DMaxDepth

      -
      public long image3DMaxDepth()
      -
      +
    + + + +
      +
    • +

      image3DMaxDepth

      +
      public long image3DMaxDepth()
      • -
    • -
      -

      prefVecWidthChar

      -
      public int prefVecWidthChar()
      -
      +
    + + + +
      +
    • +

      prefVecWidthChar

      +
      public int prefVecWidthChar()
      • -
    • -
      -

      prefVecWidthShort

      -
      public int prefVecWidthShort()
      -
      +
    + + + +
      +
    • +

      prefVecWidthShort

      +
      public int prefVecWidthShort()
      • -
    • -
      -

      prefVecWidthInt

      -
      public int prefVecWidthInt()
      -
      +
    + + + +
      +
    • +

      prefVecWidthInt

      +
      public int prefVecWidthInt()
      • -
    • -
      -

      prefVecWidthLong

      -
      public int prefVecWidthLong()
      -
      +
    + + + +
      +
    • +

      prefVecWidthLong

      +
      public int prefVecWidthLong()
      • -
    • -
      -

      prefVecWidthFloat

      -
      public int prefVecWidthFloat()
      -
      +
    + + + +
      +
    • +

      prefVecWidthFloat

      +
      public int prefVecWidthFloat()
      • -
    • -
      -

      prefVecWidthDouble

      -
      public int prefVecWidthDouble()
      -
      +
    + + + +
      +
    • +

      prefVecWidthDouble

      +
      public int prefVecWidthDouble()
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/OpenCLPlatform.html b/docs/jdocs/neureka/devices/opencl/OpenCLPlatform.html index 9968108d1..9798b587d 100644 --- a/docs/jdocs/neureka/devices/opencl/OpenCLPlatform.html +++ b/docs/jdocs/neureka/devices/opencl/OpenCLPlatform.html @@ -1,83 +1,114 @@ - + + - -OpenCLPlatform (neureka 1.0.0 API) - - - - + +OpenCLPlatform (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl
    -

    Class OpenCLPlatform

    +
    neureka.devices.opencl
    +

    Class OpenCLPlatform

    -
    java.lang.Object -
    neureka.devices.opencl.OpenCLPlatform
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.opencl.OpenCLPlatform
        • +
      +
      • +
    +
    +
      +
    • -
      public class OpenCLPlatform -extends Object
      +
      +
      public class OpenCLPlatform
+extends java.lang.Object
      This class models the OpenCL concept of platforms, which refer to device vendors / or vendor OpenCL runtime drivers. For example, in a system with 1 Intel CPU, 1 Nvidia GPUs and 2 AMD GPU, @@ -92,174 +123,289 @@

      Class OpenCLPlatform

      Then you have 2 Platforms (Intel and POCL), each with the same Intel CPU as device. - For every platform exposed by the OpenCL runtime (modelled by a CLBackend instance), - there will be a OpenCLPlatform instance.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      OpenCLPlatform(org.jocl.cl_platform_id pid)
      -
       
      + For every platform exposed by the OpenCL runtime (modelled by a CLBackend instance), + there will be a OpenCLPlatform instance.
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        OpenCLPlatform

        -
        public OpenCLPlatform(org.jocl.cl_platform_id pid)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            OpenCLPlatform

            +
            public OpenCLPlatform(org.jocl.cl_platform_id pid)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      recompile

      -
      public void recompile()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          recompile

          +
          public void recompile()
          • -
        • -
          -

          getDevices

          -
          public List<OpenCLDevice> getDevices()
          -
          +
        + + + +
          +
        • +

          getDevices

          +
          public java.util.List<OpenCLDevice> getDevices()
          • -
        • -
          -

          has

          -
          public boolean has(org.jocl.cl_device_id did)
          -
          -
          Parameters:
          +
        + + + +
          +
        • +

          has

          +
          public boolean has(org.jocl.cl_device_id did)
          +
          +
          Parameters:
          did - The cl_device_id representing an OpenCL supporting device.
          -
          Returns:
          +
          Returns:
          The truth value determining if this platform hosts the device represented by the provided id.
          -
    • -
  • -
    -

    get

    -
    public OpenCLDevice get(org.jocl.cl_device_id did)
    -
    + + + + +
      +
    • +

      get

      +
      public OpenCLDevice get(org.jocl.cl_device_id did)
      • -
    • -
      -

      getKernel

      -
      public org.jocl.cl_kernel getKernel(String kernelName)
      -
      +
    + + + +
      +
    • +

      getKernel

      +
      public org.jocl.cl_kernel getKernel(java.lang.String kernelName)
      • -
    • -
      -

      hasKernel

      -
      public boolean hasKernel(String kernelName)
      -
      +
    + + + +
      +
    • +

      hasKernel

      +
      public boolean hasKernel(java.lang.String kernelName)
      • -
    • -
      -

      getId

      -
      public final long getId()
      -
      +
    + + + +
      +
    • +

      getId

      +
      public final long getId()
      • -
    • -
      -

      getContext

      -
      public org.jocl.cl_context getContext()
      -
      +
    + + + +
      +
    • +

      getContext

      +
      public org.jocl.cl_context getContext()
      • -
    • -
      -

      dispose

      -
      public void dispose()
      -
      +
    + + + +
      +
    • +

      dispose

      +
      public void dispose()
      • -
    • -
      -

      toString

      -
      public String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/StaticKernelSource.html b/docs/jdocs/neureka/devices/opencl/StaticKernelSource.html index 1d9d8721e..221ca0f21 100644 --- a/docs/jdocs/neureka/devices/opencl/StaticKernelSource.html +++ b/docs/jdocs/neureka/devices/opencl/StaticKernelSource.html @@ -1,136 +1,234 @@ - + + - -StaticKernelSource (neureka 1.0.0 API) - - - - + +StaticKernelSource (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl
    -

    Interface StaticKernelSource

    +
    neureka.devices.opencl
    +

    Interface StaticKernelSource

    -
    -
    +
    +
    +
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      KernelCode[]
      -
      getKernelCode()
      -
       
      -
      -
      +
      +
      public interface StaticKernelSource
+extends KernelSource
      +
      • +
    -
    -

    Methods inherited from interface neureka.devices.opencl.KernelSource

    -getKernelFor
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/package-frame.html b/docs/jdocs/neureka/devices/opencl/package-frame.html new file mode 100644 index 000000000..602495fc1 --- /dev/null +++ b/docs/jdocs/neureka/devices/opencl/package-frame.html @@ -0,0 +1,33 @@ + + + + + +neureka.devices.opencl (neureka 1.0.1 API) + + + + +

    neureka.devices.opencl

    +
    +

    Interfaces

    + +

    Classes

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/devices/opencl/package-summary.html b/docs/jdocs/neureka/devices/opencl/package-summary.html index 274b0ba70..4b42425b3 100644 --- a/docs/jdocs/neureka/devices/opencl/package-summary.html +++ b/docs/jdocs/neureka/devices/opencl/package-summary.html @@ -1,133 +1,205 @@ - + + - -neureka.devices.opencl (neureka 1.0.0 API) - - - - + +neureka.devices.opencl (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.devices.opencl

    -
    -
    -
    package neureka.devices.opencl
    -
    -
    +
    + - -
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/package-tree.html b/docs/jdocs/neureka/devices/opencl/package-tree.html index 4f247fd50..6bb9b9c25 100644 --- a/docs/jdocs/neureka/devices/opencl/package-tree.html +++ b/docs/jdocs/neureka/devices/opencl/package-tree.html @@ -1,108 +1,167 @@ - + + - -neureka.devices.opencl Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.devices.opencl Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.devices.opencl

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -

    Interface Hierarchy

    -
    -
    -

    Enum Class Hierarchy

    +

    Enum Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/utility/CLFunctionCompiler.html b/docs/jdocs/neureka/devices/opencl/utility/CLFunctionCompiler.html index ea3740a59..c508a427f 100644 --- a/docs/jdocs/neureka/devices/opencl/utility/CLFunctionCompiler.html +++ b/docs/jdocs/neureka/devices/opencl/utility/CLFunctionCompiler.html @@ -1,164 +1,274 @@ - + + - -CLFunctionCompiler (neureka 1.0.0 API) - - - - + +CLFunctionCompiler (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl.utility
    -

    Class CLFunctionCompiler

    +
    neureka.devices.opencl.utility
    +

    Class CLFunctionCompiler

    -
    java.lang.Object -
    neureka.devices.opencl.utility.CLFunctionCompiler
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.opencl.utility.CLFunctionCompiler
        • +
      +
      • +
    +
    +
      +
    • -
      public final class CLFunctionCompiler -extends Object
      -
      Turns a Function into OpenCL kernel code to make +
      +
      public final class CLFunctionCompiler
+extends java.lang.Object
      +
      Turns a Function into OpenCL kernel code to make optimized just in time compilation possible.
      -
    -
    -
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        CLFunctionCompiler

        -
        public CLFunctionCompiler(OpenCLDevice device, - Function toBeOptimized, - String functionName)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            CLFunctionCompiler

            +
            public CLFunctionCompiler(OpenCLDevice device,
+                          Function toBeOptimized,
+                          java.lang.String functionName)
          -
      • +
    -
  • -
    -

    Method Details

    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/utility/DeviceQuery.html b/docs/jdocs/neureka/devices/opencl/utility/DeviceQuery.html index a73be3ba2..fa2bd1625 100644 --- a/docs/jdocs/neureka/devices/opencl/utility/DeviceQuery.html +++ b/docs/jdocs/neureka/devices/opencl/utility/DeviceQuery.html @@ -1,140 +1,243 @@ - + + - -DeviceQuery (neureka 1.0.0 API) - - - - + +DeviceQuery (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl.utility
    -

    Class DeviceQuery

    +
    neureka.devices.opencl.utility
    +

    Class DeviceQuery

    -
    java.lang.Object -
    neureka.devices.opencl.utility.DeviceQuery
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.devices.opencl.utility.DeviceQuery
        • +
      +
      • +
    +
    +
      +
    • -
      public final class DeviceQuery -extends Object
      +
      +
      public final class DeviceQuery
+extends java.lang.Object
      A program that queries and prints information about all available devices.
      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        query

        -
        public static String query()
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            query

            +
            public static java.lang.String query()
            The entry point of this program
            -
            -
            Returns:
            +
            +
            Returns:
            A String containing a detailed summary of all OpenCL related...
            -
      -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/utility/Messages.Tips.html b/docs/jdocs/neureka/devices/opencl/utility/Messages.Tips.html index 6c7dbe7ea..72f828a46 100644 --- a/docs/jdocs/neureka/devices/opencl/utility/Messages.Tips.html +++ b/docs/jdocs/neureka/devices/opencl/utility/Messages.Tips.html @@ -1,284 +1,428 @@ - + + - -Messages.Tips (neureka 1.0.0 API) - - - - + +Messages.Tips (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl.utility
    -

    Enum Class Messages.Tips

    -
    -
    java.lang.Object -
    java.lang.Enum<Messages.Tips> -
    neureka.devices.opencl.utility.Messages.Tips
    +
    neureka.devices.opencl.utility
    +

    Enum Messages.Tips

    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • java.lang.Enum<Messages.Tips>
        • +
      • +
          +
        • neureka.devices.opencl.utility.Messages.Tips
          • +
        +
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from class java.lang.Enum

      -Enum.EnumDesc<E extends Enum<E>>
      -
      +
      +
      public static enum Messages.Tips
+extends java.lang.Enum<Messages.Tips>
      • - -
    • -
      -

      Enum Constant Summary

      -
      Enum Constants
      -
      -
      Enum Constant
      -
      Description
      -
      FEDORA
      -
       
      -
      UBUNTU
      -
       
      -
      UNKNOWN
      -
       
      -
      WINDOWS
      -
       
      +
    - +
    +
    +
    + -
  • -
    -

    Field Details

    -
      -
    • -
      -

      HOW_TO_INSTALL_OPENCL

      -
      public final String HOW_TO_INSTALL_OPENCL
      -
      +
        +
      • + + +

        Field Detail

        + + + +
          +
        • +

          HOW_TO_INSTALL_OPENCL

          +
          public final java.lang.String HOW_TO_INSTALL_OPENCL
          • -
        • -
          -

          HOW_TO_INSTALL_OPENCL_DRIVERS

          -
          public final String HOW_TO_INSTALL_OPENCL_DRIVERS
          -
          +
        + + + +
          +
        • +

          HOW_TO_INSTALL_OPENCL_DRIVERS

          +
          public final java.lang.String HOW_TO_INSTALL_OPENCL_DRIVERS
        -
    • + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static Messages.Tips[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static Messages.Tips[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (Messages.Tips c : Messages.Tips.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static Messages.Tips valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static Messages.Tips valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • -
  • -
    -

    bootstrapTip

    -
    public String bootstrapTip()
    -
    + + + + +
      +
    • +

      bootstrapTip

      +
      public java.lang.String bootstrapTip()
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/utility/Messages.html b/docs/jdocs/neureka/devices/opencl/utility/Messages.html index 8c5f0a13f..23e95e41f 100644 --- a/docs/jdocs/neureka/devices/opencl/utility/Messages.html +++ b/docs/jdocs/neureka/devices/opencl/utility/Messages.html @@ -1,164 +1,279 @@ - + + - -Messages (neureka 1.0.0 API) - - - - + +Messages (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.devices.opencl.utility
    -

    Class Messages

    +
    neureka.devices.opencl.utility
    +

    Class Messages

    -
    java.lang.Object -
    neureka.devices.opencl.utility.Messages
    -
    -
    -
    -
    public final class Messages -extends Object
    -
    -
    -
      - +
      +
        +
      • java.lang.Object
      • -
        -

        Nested Class Summary

        -
        Nested Classes
        -
        -
        Modifier and Type
        -
        Class
        -
        Description
        -
        static enum 
        -
        Messages.Tips
        -
         
        +
          +
        • neureka.devices.opencl.utility.Messages
          • +
        +
        • +
      +
      +
        +
      • +
        +
        +
        public final class Messages
+extends java.lang.Object
        +
        • +
      -
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        clContextCreationFailed

        -
        public static String clContextCreationFailed()
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            clContextCreationFailed

            +
            public static java.lang.String clContextCreationFailed()
            • -
          • -
            -

            clContextCouldNotFindAnyDevices

            -
            public static String clContextCouldNotFindAnyDevices()
            -
            +
          + + + +
            +
          • +

            clContextCouldNotFindAnyDevices

            +
            public static java.lang.String clContextCouldNotFindAnyDevices()
            • -
          • -
            -

            findTip

            -
            public static Messages.Tips findTip()
            -
            +
          + + + + -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/utility/package-frame.html b/docs/jdocs/neureka/devices/opencl/utility/package-frame.html new file mode 100644 index 000000000..1d2b9f216 --- /dev/null +++ b/docs/jdocs/neureka/devices/opencl/utility/package-frame.html @@ -0,0 +1,25 @@ + + + + + +neureka.devices.opencl.utility (neureka 1.0.1 API) + + + + +

    neureka.devices.opencl.utility

    +
    +

    Classes

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/devices/opencl/utility/package-summary.html b/docs/jdocs/neureka/devices/opencl/utility/package-summary.html index dcb0c66d2..f01e93c23 100644 --- a/docs/jdocs/neureka/devices/opencl/utility/package-summary.html +++ b/docs/jdocs/neureka/devices/opencl/utility/package-summary.html @@ -1,111 +1,168 @@ - + + - -neureka.devices.opencl.utility (neureka 1.0.0 API) - - - - + +neureka.devices.opencl.utility (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.devices.opencl.utility

    -
    -
    -
    package neureka.devices.opencl.utility
    -
    -
      -
    • - -
      • -
    • -
      -
      -
      -
      -
      Class
      -
      Description
      -
      CLFunctionCompiler
      -
      -
      Turns a Function into OpenCL kernel code to make +
      + -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/opencl/utility/package-tree.html b/docs/jdocs/neureka/devices/opencl/utility/package-tree.html index 76c7320e9..b913b8e38 100644 --- a/docs/jdocs/neureka/devices/opencl/utility/package-tree.html +++ b/docs/jdocs/neureka/devices/opencl/utility/package-tree.html @@ -1,87 +1,148 @@ - + + - -neureka.devices.opencl.utility Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.devices.opencl.utility Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.devices.opencl.utility

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    -

    Enum Class Hierarchy

    +

    Enum Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/package-frame.html b/docs/jdocs/neureka/devices/package-frame.html new file mode 100644 index 000000000..a8ee1dc2f --- /dev/null +++ b/docs/jdocs/neureka/devices/package-frame.html @@ -0,0 +1,37 @@ + + + + + +neureka.devices (neureka 1.0.1 API) + + + + +

    neureka.devices

    +
    +

    Interfaces

    + +

    Classes

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/devices/package-summary.html b/docs/jdocs/neureka/devices/package-summary.html index 1ae3f49e1..9311d12d3 100644 --- a/docs/jdocs/neureka/devices/package-summary.html +++ b/docs/jdocs/neureka/devices/package-summary.html @@ -1,150 +1,230 @@ - + + - -neureka.devices (neureka 1.0.0 API) - - - - + +neureka.devices (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.devices

    -
    -
    -
    package neureka.devices
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/devices/package-tree.html b/docs/jdocs/neureka/devices/package-tree.html index d52de7790..9cc77afbe 100644 --- a/docs/jdocs/neureka/devices/package-tree.html +++ b/docs/jdocs/neureka/devices/package-tree.html @@ -1,116 +1,175 @@ - + + - -neureka.devices Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.devices Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.devices

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -

    Interface Hierarchy

    -
    -
    -

    Enum Class Hierarchy

    +

    Enum Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/DataType.html b/docs/jdocs/neureka/dtype/DataType.html index 4339f6026..7bcb39ea3 100644 --- a/docs/jdocs/neureka/dtype/DataType.html +++ b/docs/jdocs/neureka/dtype/DataType.html @@ -1,240 +1,369 @@ - + + - -DataType (neureka 1.0.0 API) - - - - + +DataType (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype
    -

    Class DataType<T>

    +
    neureka.dtype
    +

    Class DataType<T>

    -
    java.lang.Object -
    neureka.dtype.DataType<T>
    -
    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.DataType<T>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      T - The type parameter of the type class whose instances ought to be represented.
      -
      public final class DataType<T> -extends Object
      +
      +
      public final class DataType<T>
+extends java.lang.Object
      This class is a Multiton implementation for wrapping and representing type classes. - Every DataType instance uniquely wraps a Class instance which will always differ - from instances wrapped by other DataType instances. + Every DataType instance uniquely wraps a Class instance which will always differ + from instances wrapped by other DataType instances. This is because the Multiton implementation utilizes a hash map where classes are the keys and their corresponding values are DataType instances.
      -
    -
    -
    -
    -

    Methods inherited from class java.lang.Object

    -clone, finalize, getClass, notify, notifyAll, wait, wait, wait
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        public static <T> DataType<T> of(Class<T> typeClass)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            of

            +
            public static <T> DataType<T> of(java.lang.Class<T> typeClass)
            • -
          • -
            -

            getTypeClassInstance

            -
            public <T extends NumericType<?, -?, -?, -?>> T getTypeClassInstance(Class<T> type)
            -
            -
            Returns:
            +
          + + + +
            +
          • +

            getTypeClassInstance

            +
            public <T extends NumericType<?,?,?,?>> T getTypeClassInstance(java.lang.Class<T> type)
            +
            +
            Returns:
            An instance of the type class if possible.
            -
      • -
    • -
      -

      typeClassImplements

      -
      public boolean typeClassImplements(Class<?> interfaceClass)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      typeClassImplements

      +
      public boolean typeClassImplements(java.lang.Class<?> interfaceClass)
      +
      +
      Parameters:
      interfaceClass - The type class which ought to be checked for compatibility.
      -
      Returns:
      +
      Returns:
      True if the provided type is a sub-type of the type represented by this instance.
      -
      • -
    • -
      -

      dataArrayType

      -
      public Class<?> dataArrayType()
      -
      +
    + + + +
      +
    • +

      dataArrayType

      +
      public java.lang.Class<?> dataArrayType()
      • -
    • -
      -

      equals

      -
      public boolean equals(Object o)
      -
      -
      Overrides:
      -
      equals in class Object
      +
    + + + +
      +
    • +

      equals

      +
      public boolean equals(java.lang.Object o)
      +
      +
      Overrides:
      +
      equals in class java.lang.Object
      -
      • -
    • -
      -

      hashCode

      -
      public int hashCode()
      -
      -
      Overrides:
      -
      hashCode in class Object
      +
    + + + +
      +
    • +

      hashCode

      +
      public int hashCode()
      +
      +
      Overrides:
      +
      hashCode in class java.lang.Object
      -
      • -
    • -
      -

      toString

      -
      public String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
      • -
    • -
      -

      getRepresentativeType

      -
      public Class<?> getRepresentativeType()
      -
      +
    + + + +
      +
    • +

      getRepresentativeType

      +
      public java.lang.Class<?> getRepresentativeType()
      • -
    • -
      -

      getItemTypeClass

      -
      public Class<T> getItemTypeClass()
      -
      +
    + + + +
      +
    • +

      getItemTypeClass

      +
      public java.lang.Class<T> getItemTypeClass()
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/NumericType.html b/docs/jdocs/neureka/dtype/NumericType.html index 6ec4f8f27..1755aef91 100644 --- a/docs/jdocs/neureka/dtype/NumericType.html +++ b/docs/jdocs/neureka/dtype/NumericType.html @@ -1,90 +1,116 @@ - + + - -NumericType (neureka 1.0.0 API) - - - - + +NumericType (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype
    -

    Interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>

    +
    neureka.dtype
    +

    Interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>

    -
    -
    -
    Type Parameters:
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      TargetType - The target type is the targeted JVM data-type which can represent the holder type.
      TargetArrayType - The target array type is the targeted JVM array data-type which can represent the holder array type.
      HolderType - The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint).
      HolderArrayType - The holder array type is the JVM array type which can hold the data but not necessarily represent it (int[] cant represent uint[]).
      -
      +
      All Known Implementing Classes:
      -
      F32, F64, I16, I32, I64, I8, UI16, UI32, UI64, UI8
      +
      F32, F64, I16, I32, I64, I8, UI16, UI32, UI64, UI8
      -
      public interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      +
      public interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      This interface enables "Polymorphic" utility by defining common functionalities used for handling various numeric types. Implementations of this interface are utility classes which represent the numeric data types @@ -92,410 +118,554 @@

      Interface NumericType<TargetT v

      Instances of concrete sub-types do not embody data types themselves, they simply provide standardized methods which handle the type represented by the class.

      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        signed

        -
        boolean signed()
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            signed

            +
            boolean signed()
            This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
            -
            -
            Returns:
            +
            +
            Returns:
            The truth value which defines if the represented data-type is signed.
            -
        • -
      • -
        -

        numberOfBytes

        -
        int numberOfBytes()
        -
        -
        Returns:
        +
      + + + +
        +
      • +

        numberOfBytes

        +
        int numberOfBytes()
        +
        +
        Returns:
        The number of bytes which it takes to represent the data-type.
        -
      • -
    • -
      -

      targetType

      -
      Class<TargetType> targetType()
      +
    + + + +
      +
    • +

      targetType

      +
      java.lang.Class<TargetType> targetType()
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
      • -
    • -
      -

      targetArrayType

      -
      Class<TargetArrayType> targetArrayType()
      +
    + + + +
      +
    • +

      targetArrayType

      +
      java.lang.Class<TargetArrayType> targetArrayType()
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
      • -
    • -
      -

      holderType

      -
      Class<HolderType> holderType()
      +
    + + + +
      +
    • +

      holderType

      +
      java.lang.Class<HolderType> holderType()
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
      • -
    • -
      -

      holderArrayType

      -
      Class<HolderArrayType> holderArrayType()
      +
    + + + +
      +
    • +

      holderArrayType

      +
      java.lang.Class<HolderArrayType> holderArrayType()
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
      • -
    • -
      -

      getNumericTypeTarget

      -
      Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
    + + + +
      +
    • +

      getNumericTypeTarget

      +
      java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Returns:
      +
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
      • -
    • -
      -

      foreignHolderBytesToTarget

      -
      TargetType foreignHolderBytesToTarget(byte[] bytes)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      TargetType foreignHolderBytesToTarget(byte[] bytes)
      +
      +
      Parameters:
      bytes - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
      • -
    • -
      -

      toTarget

      -
      TargetType toTarget(HolderType original)
      -
      -
      Parameters:
      +
    + + + + + +
      +
    • +

      toTarget

      +
      TargetType toTarget(HolderType original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
      • -
    • -
      -

      targetToForeignHolderBytes

      -
      byte[] targetToForeignHolderBytes(TargetType number)
      -
      -
      Parameters:
      +
    + + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      byte[] targetToForeignHolderBytes(TargetType number)
      +
      +
      Parameters:
      number - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
      • -
    • -
      -

      readAndConvertForeignDataFrom

      -
      TargetArrayType readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
      +
    + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      TargetArrayType readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                              int size)
+                                       throws java.io.IOException
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
      • -
    • -
      -

      readAndConvertForeignDataFrom

      -
      <T> TargetArrayType readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
      +
    + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      <T> TargetArrayType readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                                  int size)
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
      • -
    • -
      -

      readForeignDataFrom

      -
      HolderArrayType readForeignDataFrom(DataInput stream, - int size) - throws IOException
      +
    + + + +
      +
    • +

      readForeignDataFrom

      +
      HolderArrayType readForeignDataFrom(java.io.DataInput stream,
+                                    int size)
+                             throws java.io.IOException
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
      • -
    • -
      -

      readForeignDataFrom

      -
      <T> HolderArrayType readForeignDataFrom(Iterator<T> iterator, - int size)
      +
    + + + +
      +
    • +

      readForeignDataFrom

      +
      <T> HolderArrayType readForeignDataFrom(java.util.Iterator<T> iterator,
+                                        int size)
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
      • -
    • -
      -

      writeDataTo

      -
      void writeDataTo(DataOutput stream, - Iterator<TargetType> iterator) - throws IOException
      +
    + + + +
      +
    • +

      writeDataTo

      +
      void writeDataTo(java.io.DataOutput stream,
+                 java.util.Iterator<TargetType> iterator)
+          throws java.io.IOException
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
      • -
    • -
      -

      convertToHolder

      -
      HolderType convertToHolder(Object from)
      +
    + + + +
      +
    • +

      convertToHolder

      +
      HolderType convertToHolder(java.lang.Object from)
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
      • -
    • -
      -

      convertToHolderArray

      -
      HolderArrayType convertToHolderArray(Object from)
      +
    + + + +
      +
    • +

      convertToHolderArray

      +
      HolderArrayType convertToHolderArray(java.lang.Object from)
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
      • -
    • -
      -

      convertToTarget

      -
      TargetType convertToTarget(Object from)
      +
    + + + +
      +
    • +

      convertToTarget

      +
      TargetType convertToTarget(java.lang.Object from)
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
      • -
    • -
      -

      convertToTargetArray

      -
      TargetArrayType convertToTargetArray(Object from)
      +
    + + + +
      +
    • +

      convertToTargetArray

      +
      TargetArrayType convertToTargetArray(java.lang.Object from)
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/F32.html b/docs/jdocs/neureka/dtype/custom/F32.html index e9c75249b..ddfafc57e 100644 --- a/docs/jdocs/neureka/dtype/custom/F32.html +++ b/docs/jdocs/neureka/dtype/custom/F32.html @@ -1,540 +1,722 @@ - + + - -F32 (neureka 1.0.0 API) - - - - + +F32 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype.custom
    -

    Class F32

    +
    neureka.dtype.custom
    +

    Class F32

    -
    java.lang.Object -
    neureka.dtype.custom.F32
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.custom.F32
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      F32()
      -
       
      +
      +
      public final class F32
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        F32

        -
        public F32()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            F32

            +
            public F32()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      signed

      -
      public boolean signed()
      -
      Description copied from interface: NumericType
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          signed

          +
          public boolean signed()
          +
          Description copied from interface: NumericType
          This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
          -
          -
          Returns:
          +
          +
          Returns:
          The truth value which defines if the represented data-type is signed.
          -
      • -
    • -
      -

      numberOfBytes

      -
      public int numberOfBytes()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfBytes

      +
      public int numberOfBytes()
      +
      +
      Returns:
      The number of bytes which it takes to represent the data-type.
      -
    • -
  • -
    -

    targetType

    -
    public Class<Float> targetType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetType

      +
      public java.lang.Class<java.lang.Float> targetType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
    • -
  • -
    -

    targetArrayType

    -
    public Class<float[]> targetArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetArrayType

      +
      public java.lang.Class<float[]> targetArrayType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
    • -
  • -
    -

    holderType

    -
    public Class<Float> holderType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderType

      +
      public java.lang.Class<java.lang.Float> holderType()
      +
      Description copied from interface: NumericType
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
    • -
  • -
    -

    holderArrayType

    -
    public Class<float[]> holderArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderArrayType

      +
      public java.lang.Class<float[]> holderArrayType()
      +
      Description copied from interface: NumericType
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
    • -
  • -
    -

    foreignHolderBytesToTarget

    -
    public Float foreignHolderBytesToTarget(byte[] bytes)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      public java.lang.Float foreignHolderBytesToTarget(byte[] bytes)
      +
      +
      Parameters:
      bytes - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
    • -
  • -
    -

    toTarget

    -
    public Float toTarget(Float original)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      toTarget

      +
      public java.lang.Float toTarget(java.lang.Float original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
    • -
  • -
    -

    targetToForeignHolderBytes

    -
    public byte[] targetToForeignHolderBytes(Float number)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      public byte[] targetToForeignHolderBytes(java.lang.Float number)
      +
      +
      Parameters:
      number - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public float[] readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public float[] readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                             int size)
+                                      throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public <T> float[] readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public <T> float[] readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                                 int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public float[] readForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public float[] readForeignDataFrom(java.io.DataInput stream,
+                                   int size)
+                            throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public <T> float[] readForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public <T> float[] readForeignDataFrom(java.util.Iterator<T> iterator,
+                                       int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
    • -
  • -
    -

    convertToHolder

    -
    public Float convertToHolder(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolder

      +
      public java.lang.Float convertToHolder(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToHolderArray

    -
    public float[] convertToHolderArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolderArray

      +
      public float[] convertToHolderArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTarget

    -
    public Float convertToTarget(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTarget

      +
      public java.lang.Float convertToTarget(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTargetArray

    -
    public float[] convertToTargetArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTargetArray

      +
      public float[] convertToTargetArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    getNumericTypeTarget

    -
    public Class<NumericType<Float,float[],Float,float[]>> getNumericTypeTarget()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      getNumericTypeTarget

      +
      public java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
      Description copied from interface: NumericType
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Specified by:
      -
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Returns:
      +
      +
      Specified by:
      +
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
    • -
  • -
    -

    writeDataTo

    -
    public void writeDataTo(DataOutput stream, - Iterator<Float> iterator) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      writeDataTo

      +
      public void writeDataTo(java.io.DataOutput stream,
+                        java.util.Iterator<TargetType> iterator)
+                 throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Specified by:
      -
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Parameters:
      +
      +
      Specified by:
      +
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/F64.html b/docs/jdocs/neureka/dtype/custom/F64.html index e8d90c2ba..ae8bcb6ea 100644 --- a/docs/jdocs/neureka/dtype/custom/F64.html +++ b/docs/jdocs/neureka/dtype/custom/F64.html @@ -1,540 +1,722 @@ - + + - -F64 (neureka 1.0.0 API) - - - - + +F64 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype.custom
    -

    Class F64

    +
    neureka.dtype.custom
    +

    Class F64

    -
    java.lang.Object -
    neureka.dtype.custom.F64
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.custom.F64
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      F64()
      -
       
      +
      +
      public final class F64
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        F64

        -
        public F64()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            F64

            +
            public F64()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      signed

      -
      public boolean signed()
      -
      Description copied from interface: NumericType
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          signed

          +
          public boolean signed()
          +
          Description copied from interface: NumericType
          This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
          -
          -
          Returns:
          +
          +
          Returns:
          The truth value which defines if the represented data-type is signed.
          -
      • -
    • -
      -

      numberOfBytes

      -
      public int numberOfBytes()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfBytes

      +
      public int numberOfBytes()
      +
      +
      Returns:
      The number of bytes which it takes to represent the data-type.
      -
    • -
  • -
    -

    targetType

    -
    public Class<Double> targetType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetType

      +
      public java.lang.Class<java.lang.Double> targetType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
    • -
  • -
    -

    targetArrayType

    -
    public Class<double[]> targetArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetArrayType

      +
      public java.lang.Class<double[]> targetArrayType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
    • -
  • -
    -

    holderType

    -
    public Class<Double> holderType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderType

      +
      public java.lang.Class<java.lang.Double> holderType()
      +
      Description copied from interface: NumericType
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
    • -
  • -
    -

    holderArrayType

    -
    public Class<double[]> holderArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderArrayType

      +
      public java.lang.Class<double[]> holderArrayType()
      +
      Description copied from interface: NumericType
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
    • -
  • -
    -

    foreignHolderBytesToTarget

    -
    public Double foreignHolderBytesToTarget(byte[] bytes)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      public java.lang.Double foreignHolderBytesToTarget(byte[] bytes)
      +
      +
      Parameters:
      bytes - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
    • -
  • -
    -

    toTarget

    -
    public Double toTarget(Double original)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      toTarget

      +
      public java.lang.Double toTarget(java.lang.Double original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
    • -
  • -
    -

    targetToForeignHolderBytes

    -
    public byte[] targetToForeignHolderBytes(Double number)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      public byte[] targetToForeignHolderBytes(java.lang.Double number)
      +
      +
      Parameters:
      number - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public double[] readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public double[] readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                              int size)
+                                       throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public <T> double[] readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public <T> double[] readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                                  int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public double[] readForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public double[] readForeignDataFrom(java.io.DataInput stream,
+                                    int size)
+                             throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public <T> double[] readForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public <T> double[] readForeignDataFrom(java.util.Iterator<T> iterator,
+                                        int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
    • -
  • -
    -

    convertToHolder

    -
    public Double convertToHolder(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolder

      +
      public java.lang.Double convertToHolder(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToHolderArray

    -
    public double[] convertToHolderArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolderArray

      +
      public double[] convertToHolderArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTarget

    -
    public Double convertToTarget(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTarget

      +
      public java.lang.Double convertToTarget(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTargetArray

    -
    public double[] convertToTargetArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTargetArray

      +
      public double[] convertToTargetArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    getNumericTypeTarget

    -
    public Class<NumericType<Double,double[],Double,double[]>> getNumericTypeTarget()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      getNumericTypeTarget

      +
      public java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
      Description copied from interface: NumericType
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Specified by:
      -
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Returns:
      +
      +
      Specified by:
      +
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
    • -
  • -
    -

    writeDataTo

    -
    public void writeDataTo(DataOutput stream, - Iterator<Double> iterator) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      writeDataTo

      +
      public void writeDataTo(java.io.DataOutput stream,
+                        java.util.Iterator<TargetType> iterator)
+                 throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Specified by:
      -
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Parameters:
      +
      +
      Specified by:
      +
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/I16.html b/docs/jdocs/neureka/dtype/custom/I16.html index 506d88fd5..3362e7290 100644 --- a/docs/jdocs/neureka/dtype/custom/I16.html +++ b/docs/jdocs/neureka/dtype/custom/I16.html @@ -1,540 +1,722 @@ - + + - -I16 (neureka 1.0.0 API) - - - - + +I16 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype.custom
    -

    Class I16

    +
    neureka.dtype.custom
    +

    Class I16

    -
    java.lang.Object -
    neureka.dtype.custom.I16
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.custom.I16
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      I16()
      -
       
      +
      +
      public final class I16
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        I16

        -
        public I16()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            I16

            +
            public I16()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      signed

      -
      public boolean signed()
      -
      Description copied from interface: NumericType
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          signed

          +
          public boolean signed()
          +
          Description copied from interface: NumericType
          This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
          -
          -
          Returns:
          +
          +
          Returns:
          The truth value which defines if the represented data-type is signed.
          -
      • -
    • -
      -

      numberOfBytes

      -
      public int numberOfBytes()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfBytes

      +
      public int numberOfBytes()
      +
      +
      Returns:
      The number of bytes which it takes to represent the data-type.
      -
    • -
  • -
    -

    targetType

    -
    public Class<Short> targetType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetType

      +
      public java.lang.Class<java.lang.Short> targetType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
    • -
  • -
    -

    targetArrayType

    -
    public Class<short[]> targetArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetArrayType

      +
      public java.lang.Class<short[]> targetArrayType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
    • -
  • -
    -

    holderType

    -
    public Class<Short> holderType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderType

      +
      public java.lang.Class<java.lang.Short> holderType()
      +
      Description copied from interface: NumericType
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
    • -
  • -
    -

    holderArrayType

    -
    public Class<short[]> holderArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderArrayType

      +
      public java.lang.Class<short[]> holderArrayType()
      +
      Description copied from interface: NumericType
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
    • -
  • -
    -

    foreignHolderBytesToTarget

    -
    public Short foreignHolderBytesToTarget(byte[] bytes)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      public java.lang.Short foreignHolderBytesToTarget(byte[] bytes)
      +
      +
      Parameters:
      bytes - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
    • -
  • -
    -

    toTarget

    -
    public Short toTarget(Short original)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      toTarget

      +
      public java.lang.Short toTarget(java.lang.Short original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
    • -
  • -
    -

    targetToForeignHolderBytes

    -
    public byte[] targetToForeignHolderBytes(Short number)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      public byte[] targetToForeignHolderBytes(java.lang.Short number)
      +
      +
      Parameters:
      number - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public short[] readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public short[] readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                             int size)
+                                      throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public <T> short[] readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public <T> short[] readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                                 int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public short[] readForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public short[] readForeignDataFrom(java.io.DataInput stream,
+                                   int size)
+                            throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public <T> short[] readForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public <T> short[] readForeignDataFrom(java.util.Iterator<T> iterator,
+                                       int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
    • -
  • -
    -

    convertToHolder

    -
    public Short convertToHolder(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolder

      +
      public java.lang.Short convertToHolder(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToHolderArray

    -
    public short[] convertToHolderArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolderArray

      +
      public short[] convertToHolderArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTarget

    -
    public Short convertToTarget(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTarget

      +
      public java.lang.Short convertToTarget(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTargetArray

    -
    public short[] convertToTargetArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTargetArray

      +
      public short[] convertToTargetArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    getNumericTypeTarget

    -
    public Class<NumericType<Short,short[],Short,short[]>> getNumericTypeTarget()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      getNumericTypeTarget

      +
      public java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
      Description copied from interface: NumericType
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Specified by:
      -
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Returns:
      +
      +
      Specified by:
      +
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
    • -
  • -
    -

    writeDataTo

    -
    public void writeDataTo(DataOutput stream, - Iterator<Short> iterator) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      writeDataTo

      +
      public void writeDataTo(java.io.DataOutput stream,
+                        java.util.Iterator<TargetType> iterator)
+                 throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Specified by:
      -
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Parameters:
      +
      +
      Specified by:
      +
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/I32.html b/docs/jdocs/neureka/dtype/custom/I32.html index 05d09491c..f3049c269 100644 --- a/docs/jdocs/neureka/dtype/custom/I32.html +++ b/docs/jdocs/neureka/dtype/custom/I32.html @@ -1,540 +1,722 @@ - + + - -I32 (neureka 1.0.0 API) - - - - + +I32 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype.custom
    -

    Class I32

    +
    neureka.dtype.custom
    +

    Class I32

    -
    java.lang.Object -
    neureka.dtype.custom.I32
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.custom.I32
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      I32()
      -
       
      +
      +
      public final class I32
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        I32

        -
        public I32()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            I32

            +
            public I32()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      signed

      -
      public boolean signed()
      -
      Description copied from interface: NumericType
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          signed

          +
          public boolean signed()
          +
          Description copied from interface: NumericType
          This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
          -
          -
          Returns:
          +
          +
          Returns:
          The truth value which defines if the represented data-type is signed.
          -
      • -
    • -
      -

      numberOfBytes

      -
      public int numberOfBytes()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfBytes

      +
      public int numberOfBytes()
      +
      +
      Returns:
      The number of bytes which it takes to represent the data-type.
      -
    • -
  • -
    -

    targetType

    -
    public Class<Integer> targetType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetType

      +
      public java.lang.Class<java.lang.Integer> targetType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
    • -
  • -
    -

    targetArrayType

    -
    public Class<int[]> targetArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetArrayType

      +
      public java.lang.Class<int[]> targetArrayType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
    • -
  • -
    -

    holderType

    -
    public Class<Integer> holderType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderType

      +
      public java.lang.Class<java.lang.Integer> holderType()
      +
      Description copied from interface: NumericType
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
    • -
  • -
    -

    holderArrayType

    -
    public Class<int[]> holderArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderArrayType

      +
      public java.lang.Class<int[]> holderArrayType()
      +
      Description copied from interface: NumericType
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
    • -
  • -
    -

    foreignHolderBytesToTarget

    -
    public Integer foreignHolderBytesToTarget(byte[] bytes)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      public java.lang.Integer foreignHolderBytesToTarget(byte[] bytes)
      +
      +
      Parameters:
      bytes - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
    • -
  • -
    -

    toTarget

    -
    public Integer toTarget(Integer original)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      toTarget

      +
      public java.lang.Integer toTarget(java.lang.Integer original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
    • -
  • -
    -

    targetToForeignHolderBytes

    -
    public byte[] targetToForeignHolderBytes(Integer number)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      public byte[] targetToForeignHolderBytes(java.lang.Integer number)
      +
      +
      Parameters:
      number - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public int[] readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public int[] readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                           int size)
+                                    throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public <T> int[] readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public <T> int[] readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                               int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public int[] readForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public int[] readForeignDataFrom(java.io.DataInput stream,
+                                 int size)
+                          throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public <T> int[] readForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public <T> int[] readForeignDataFrom(java.util.Iterator<T> iterator,
+                                     int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
    • -
  • -
    -

    convertToHolder

    -
    public Integer convertToHolder(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolder

      +
      public java.lang.Integer convertToHolder(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToHolderArray

    -
    public int[] convertToHolderArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolderArray

      +
      public int[] convertToHolderArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTarget

    -
    public Integer convertToTarget(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTarget

      +
      public java.lang.Integer convertToTarget(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTargetArray

    -
    public int[] convertToTargetArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTargetArray

      +
      public int[] convertToTargetArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    getNumericTypeTarget

    -
    public Class<NumericType<Integer,int[],Integer,int[]>> getNumericTypeTarget()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      getNumericTypeTarget

      +
      public java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
      Description copied from interface: NumericType
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Specified by:
      -
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Returns:
      +
      +
      Specified by:
      +
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
    • -
  • -
    -

    writeDataTo

    -
    public void writeDataTo(DataOutput stream, - Iterator<Integer> iterator) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      writeDataTo

      +
      public void writeDataTo(java.io.DataOutput stream,
+                        java.util.Iterator<TargetType> iterator)
+                 throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Specified by:
      -
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Parameters:
      +
      +
      Specified by:
      +
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/I64.html b/docs/jdocs/neureka/dtype/custom/I64.html index 55068a8b2..c84a942f1 100644 --- a/docs/jdocs/neureka/dtype/custom/I64.html +++ b/docs/jdocs/neureka/dtype/custom/I64.html @@ -1,540 +1,722 @@ - + + - -I64 (neureka 1.0.0 API) - - - - + +I64 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype.custom
    -

    Class I64

    +
    neureka.dtype.custom
    +

    Class I64

    -
    java.lang.Object -
    neureka.dtype.custom.I64
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.custom.I64
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      I64()
      -
       
      +
      +
      public final class I64
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        I64

        -
        public I64()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            I64

            +
            public I64()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      signed

      -
      public boolean signed()
      -
      Description copied from interface: NumericType
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          signed

          +
          public boolean signed()
          +
          Description copied from interface: NumericType
          This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
          -
          -
          Returns:
          +
          +
          Returns:
          The truth value which defines if the represented data-type is signed.
          -
      • -
    • -
      -

      numberOfBytes

      -
      public int numberOfBytes()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfBytes

      +
      public int numberOfBytes()
      +
      +
      Returns:
      The number of bytes which it takes to represent the data-type.
      -
    • -
  • -
    -

    targetType

    -
    public Class<Long> targetType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetType

      +
      public java.lang.Class<java.lang.Long> targetType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
    • -
  • -
    -

    targetArrayType

    -
    public Class<long[]> targetArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetArrayType

      +
      public java.lang.Class<long[]> targetArrayType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
    • -
  • -
    -

    holderType

    -
    public Class<Long> holderType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderType

      +
      public java.lang.Class<java.lang.Long> holderType()
      +
      Description copied from interface: NumericType
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
    • -
  • -
    -

    holderArrayType

    -
    public Class<long[]> holderArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderArrayType

      +
      public java.lang.Class<long[]> holderArrayType()
      +
      Description copied from interface: NumericType
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
    • -
  • -
    -

    foreignHolderBytesToTarget

    -
    public Long foreignHolderBytesToTarget(byte[] bytes)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      public java.lang.Long foreignHolderBytesToTarget(byte[] bytes)
      +
      +
      Parameters:
      bytes - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
    • -
  • -
    -

    toTarget

    -
    public Long toTarget(Long original)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      toTarget

      +
      public java.lang.Long toTarget(java.lang.Long original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
    • -
  • -
    -

    targetToForeignHolderBytes

    -
    public byte[] targetToForeignHolderBytes(Long number)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      public byte[] targetToForeignHolderBytes(java.lang.Long number)
      +
      +
      Parameters:
      number - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public long[] readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public long[] readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                            int size)
+                                     throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public <T> long[] readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public <T> long[] readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                                int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public long[] readForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public long[] readForeignDataFrom(java.io.DataInput stream,
+                                  int size)
+                           throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public <T> long[] readForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public <T> long[] readForeignDataFrom(java.util.Iterator<T> iterator,
+                                      int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
    • -
  • -
    -

    convertToHolder

    -
    public Long convertToHolder(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolder

      +
      public java.lang.Long convertToHolder(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToHolderArray

    -
    public long[] convertToHolderArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolderArray

      +
      public long[] convertToHolderArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTarget

    -
    public Long convertToTarget(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTarget

      +
      public java.lang.Long convertToTarget(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTargetArray

    -
    public long[] convertToTargetArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTargetArray

      +
      public long[] convertToTargetArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    getNumericTypeTarget

    -
    public Class<NumericType<Long,long[],Long,long[]>> getNumericTypeTarget()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      getNumericTypeTarget

      +
      public java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
      Description copied from interface: NumericType
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Specified by:
      -
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Returns:
      +
      +
      Specified by:
      +
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
    • -
  • -
    -

    writeDataTo

    -
    public void writeDataTo(DataOutput stream, - Iterator<Long> iterator) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      writeDataTo

      +
      public void writeDataTo(java.io.DataOutput stream,
+                        java.util.Iterator<TargetType> iterator)
+                 throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Specified by:
      -
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Parameters:
      +
      +
      Specified by:
      +
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/I8.html b/docs/jdocs/neureka/dtype/custom/I8.html index 6f0d6ce0d..974403b28 100644 --- a/docs/jdocs/neureka/dtype/custom/I8.html +++ b/docs/jdocs/neureka/dtype/custom/I8.html @@ -1,542 +1,724 @@ - + + - -I8 (neureka 1.0.0 API) - - - - + +I8 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype.custom
    -

    Class I8

    +
    neureka.dtype.custom
    +

    Class I8

    -
    java.lang.Object -
    neureka.dtype.custom.I8
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.custom.I8
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      NumericType<Byte,byte[],Byte,byte[]>
      +
      NumericType<java.lang.Byte,byte[],java.lang.Byte,byte[]>
      -
      public final class I8 -extends Object
      +
      +
      public final class I8
+extends java.lang.Object
      The following abstract class implements some basic logic which is applicable across all final concrete classes extending this abstract one.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      I8()
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        I8

        -
        public I8()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            I8

            +
            public I8()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      signed

      -
      public boolean signed()
      -
      Description copied from interface: NumericType
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          signed

          +
          public boolean signed()
          +
          Description copied from interface: NumericType
          This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
          -
          -
          Returns:
          +
          +
          Returns:
          The truth value which defines if the represented data-type is signed.
          -
      • -
    • -
      -

      numberOfBytes

      -
      public int numberOfBytes()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfBytes

      +
      public int numberOfBytes()
      +
      +
      Returns:
      The number of bytes which it takes to represent the data-type.
      -
    • -
  • -
    -

    targetType

    -
    public Class<Byte> targetType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetType

      +
      public java.lang.Class<java.lang.Byte> targetType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
    • -
  • -
    -

    targetArrayType

    -
    public Class<byte[]> targetArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetArrayType

      +
      public java.lang.Class<byte[]> targetArrayType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
    • -
  • -
    -

    holderType

    -
    public Class<Byte> holderType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderType

      +
      public java.lang.Class<java.lang.Byte> holderType()
      +
      Description copied from interface: NumericType
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
    • -
  • -
    -

    holderArrayType

    -
    public Class<byte[]> holderArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderArrayType

      +
      public java.lang.Class<byte[]> holderArrayType()
      +
      Description copied from interface: NumericType
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
    • -
  • -
    -

    foreignHolderBytesToTarget

    -
    public Byte foreignHolderBytesToTarget(byte[] bytes)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      public java.lang.Byte foreignHolderBytesToTarget(byte[] bytes)
      +
      +
      Parameters:
      bytes - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
    • -
  • -
    -

    toTarget

    -
    public Byte toTarget(Byte original)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      toTarget

      +
      public java.lang.Byte toTarget(java.lang.Byte original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
    • -
  • -
    -

    targetToForeignHolderBytes

    -
    public byte[] targetToForeignHolderBytes(Byte number)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      public byte[] targetToForeignHolderBytes(java.lang.Byte number)
      +
      +
      Parameters:
      number - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public byte[] readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public byte[] readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                            int size)
+                                     throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public <T> byte[] readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public <T> byte[] readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                                int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public byte[] readForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public byte[] readForeignDataFrom(java.io.DataInput stream,
+                                  int size)
+                           throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public <T> byte[] readForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public <T> byte[] readForeignDataFrom(java.util.Iterator<T> iterator,
+                                      int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
    • -
  • -
    -

    convertToHolder

    -
    public Byte convertToHolder(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolder

      +
      public java.lang.Byte convertToHolder(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToHolderArray

    -
    public byte[] convertToHolderArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolderArray

      +
      public byte[] convertToHolderArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTarget

    -
    public Byte convertToTarget(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTarget

      +
      public java.lang.Byte convertToTarget(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTargetArray

    -
    public byte[] convertToTargetArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTargetArray

      +
      public byte[] convertToTargetArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    getNumericTypeTarget

    -
    public Class<NumericType<Byte,byte[],Byte,byte[]>> getNumericTypeTarget()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      getNumericTypeTarget

      +
      public java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
      Description copied from interface: NumericType
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Specified by:
      -
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Returns:
      +
      +
      Specified by:
      +
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
    • -
  • -
    -

    writeDataTo

    -
    public void writeDataTo(DataOutput stream, - Iterator<Byte> iterator) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      writeDataTo

      +
      public void writeDataTo(java.io.DataOutput stream,
+                        java.util.Iterator<TargetType> iterator)
+                 throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Specified by:
      -
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Parameters:
      +
      +
      Specified by:
      +
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/UI16.html b/docs/jdocs/neureka/dtype/custom/UI16.html index 1da34f8b1..69e83637d 100644 --- a/docs/jdocs/neureka/dtype/custom/UI16.html +++ b/docs/jdocs/neureka/dtype/custom/UI16.html @@ -1,540 +1,722 @@ - + + - -UI16 (neureka 1.0.0 API) - - - - + +UI16 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype.custom
    -

    Class UI16

    +
    neureka.dtype.custom
    +

    Class UI16

    -
    java.lang.Object -
    neureka.dtype.custom.UI16
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.custom.UI16
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      UI16()
      -
       
      +
      +
      public final class UI16
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        UI16

        -
        public UI16()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            UI16

            +
            public UI16()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      signed

      -
      public boolean signed()
      -
      Description copied from interface: NumericType
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          signed

          +
          public boolean signed()
          +
          Description copied from interface: NumericType
          This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
          -
          -
          Returns:
          +
          +
          Returns:
          The truth value which defines if the represented data-type is signed.
          -
      • -
    • -
      -

      numberOfBytes

      -
      public int numberOfBytes()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfBytes

      +
      public int numberOfBytes()
      +
      +
      Returns:
      The number of bytes which it takes to represent the data-type.
      -
    • -
  • -
    -

    targetType

    -
    public Class<Integer> targetType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetType

      +
      public java.lang.Class<java.lang.Integer> targetType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
    • -
  • -
    -

    targetArrayType

    -
    public Class<int[]> targetArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetArrayType

      +
      public java.lang.Class<int[]> targetArrayType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
    • -
  • -
    -

    holderType

    -
    public Class<Short> holderType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderType

      +
      public java.lang.Class<java.lang.Short> holderType()
      +
      Description copied from interface: NumericType
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
    • -
  • -
    -

    holderArrayType

    -
    public Class<short[]> holderArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderArrayType

      +
      public java.lang.Class<short[]> holderArrayType()
      +
      Description copied from interface: NumericType
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
    • -
  • -
    -

    foreignHolderBytesToTarget

    -
    public Integer foreignHolderBytesToTarget(byte[] b)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      public java.lang.Integer foreignHolderBytesToTarget(byte[] b)
      +
      +
      Parameters:
      b - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
    • -
  • -
    -

    toTarget

    -
    public Integer toTarget(Short original)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      toTarget

      +
      public java.lang.Integer toTarget(java.lang.Short original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
    • -
  • -
    -

    targetToForeignHolderBytes

    -
    public byte[] targetToForeignHolderBytes(Integer number)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      public byte[] targetToForeignHolderBytes(java.lang.Integer number)
      +
      +
      Parameters:
      number - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public int[] readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public int[] readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                           int size)
+                                    throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public <T> int[] readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public <T> int[] readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                               int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public short[] readForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public short[] readForeignDataFrom(java.io.DataInput stream,
+                                   int size)
+                            throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public <T> short[] readForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public <T> short[] readForeignDataFrom(java.util.Iterator<T> iterator,
+                                       int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
    • -
  • -
    -

    convertToHolder

    -
    public Short convertToHolder(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolder

      +
      public java.lang.Short convertToHolder(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToHolderArray

    -
    public short[] convertToHolderArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolderArray

      +
      public short[] convertToHolderArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTarget

    -
    public Integer convertToTarget(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTarget

      +
      public java.lang.Integer convertToTarget(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTargetArray

    -
    public int[] convertToTargetArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTargetArray

      +
      public int[] convertToTargetArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    getNumericTypeTarget

    -
    public Class<NumericType<Integer,int[],Integer,int[]>> getNumericTypeTarget()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      getNumericTypeTarget

      +
      public java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
      Description copied from interface: NumericType
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Specified by:
      -
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Returns:
      +
      +
      Specified by:
      +
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
    • -
  • -
    -

    writeDataTo

    -
    public void writeDataTo(DataOutput stream, - Iterator<Integer> iterator) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      writeDataTo

      +
      public void writeDataTo(java.io.DataOutput stream,
+                        java.util.Iterator<TargetType> iterator)
+                 throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Specified by:
      -
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Parameters:
      +
      +
      Specified by:
      +
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/UI32.html b/docs/jdocs/neureka/dtype/custom/UI32.html index 90194d8c3..068108a8e 100644 --- a/docs/jdocs/neureka/dtype/custom/UI32.html +++ b/docs/jdocs/neureka/dtype/custom/UI32.html @@ -1,540 +1,722 @@ - + + - -UI32 (neureka 1.0.0 API) - - - - + +UI32 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype.custom
    -

    Class UI32

    +
    neureka.dtype.custom
    +

    Class UI32

    -
    java.lang.Object -
    neureka.dtype.custom.UI32
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.custom.UI32
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      UI32()
      -
       
      +
      +
      public final class UI32
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        UI32

        -
        public UI32()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            UI32

            +
            public UI32()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      signed

      -
      public boolean signed()
      -
      Description copied from interface: NumericType
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          signed

          +
          public boolean signed()
          +
          Description copied from interface: NumericType
          This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
          -
          -
          Returns:
          +
          +
          Returns:
          The truth value which defines if the represented data-type is signed.
          -
      • -
    • -
      -

      numberOfBytes

      -
      public int numberOfBytes()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfBytes

      +
      public int numberOfBytes()
      +
      +
      Returns:
      The number of bytes which it takes to represent the data-type.
      -
    • -
  • -
    -

    targetType

    -
    public Class<Long> targetType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetType

      +
      public java.lang.Class<java.lang.Long> targetType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
    • -
  • -
    -

    targetArrayType

    -
    public Class<long[]> targetArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetArrayType

      +
      public java.lang.Class<long[]> targetArrayType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
    • -
  • -
    -

    holderType

    -
    public Class<Integer> holderType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderType

      +
      public java.lang.Class<java.lang.Integer> holderType()
      +
      Description copied from interface: NumericType
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
    • -
  • -
    -

    holderArrayType

    -
    public Class<int[]> holderArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderArrayType

      +
      public java.lang.Class<int[]> holderArrayType()
      +
      Description copied from interface: NumericType
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
    • -
  • -
    -

    foreignHolderBytesToTarget

    -
    public Long foreignHolderBytesToTarget(byte[] bytes)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      public java.lang.Long foreignHolderBytesToTarget(byte[] bytes)
      +
      +
      Parameters:
      bytes - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
    • -
  • -
    -

    toTarget

    -
    public Long toTarget(Integer original)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      toTarget

      +
      public java.lang.Long toTarget(java.lang.Integer original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
    • -
  • -
    -

    targetToForeignHolderBytes

    -
    public byte[] targetToForeignHolderBytes(Long number)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      public byte[] targetToForeignHolderBytes(java.lang.Long number)
      +
      +
      Parameters:
      number - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public long[] readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public long[] readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                            int size)
+                                     throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public <T> long[] readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public <T> long[] readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                                int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public int[] readForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public int[] readForeignDataFrom(java.io.DataInput stream,
+                                 int size)
+                          throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public <T> int[] readForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public <T> int[] readForeignDataFrom(java.util.Iterator<T> iterator,
+                                     int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
    • -
  • -
    -

    convertToHolder

    -
    public Integer convertToHolder(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolder

      +
      public java.lang.Integer convertToHolder(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToHolderArray

    -
    public int[] convertToHolderArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolderArray

      +
      public int[] convertToHolderArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTarget

    -
    public Long convertToTarget(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTarget

      +
      public java.lang.Long convertToTarget(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTargetArray

    -
    public long[] convertToTargetArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTargetArray

      +
      public long[] convertToTargetArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    getNumericTypeTarget

    -
    public Class<NumericType<Long,long[],Long,long[]>> getNumericTypeTarget()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      getNumericTypeTarget

      +
      public java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
      Description copied from interface: NumericType
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Specified by:
      -
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Returns:
      +
      +
      Specified by:
      +
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
    • -
  • -
    -

    writeDataTo

    -
    public void writeDataTo(DataOutput stream, - Iterator<Long> iterator) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      writeDataTo

      +
      public void writeDataTo(java.io.DataOutput stream,
+                        java.util.Iterator<TargetType> iterator)
+                 throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Specified by:
      -
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Parameters:
      +
      +
      Specified by:
      +
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/UI64.html b/docs/jdocs/neureka/dtype/custom/UI64.html index 0fb734e46..f05ac6631 100644 --- a/docs/jdocs/neureka/dtype/custom/UI64.html +++ b/docs/jdocs/neureka/dtype/custom/UI64.html @@ -1,540 +1,722 @@ - + + - -UI64 (neureka 1.0.0 API) - - - - + +UI64 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype.custom
    -

    Class UI64

    +
    neureka.dtype.custom
    +

    Class UI64

    -
    java.lang.Object -
    neureka.dtype.custom.UI64
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.custom.UI64
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      UI64()
      -
       
      +
      +
      public final class UI64
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        UI64

        -
        public UI64()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            UI64

            +
            public UI64()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      signed

      -
      public boolean signed()
      -
      Description copied from interface: NumericType
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          signed

          +
          public boolean signed()
          +
          Description copied from interface: NumericType
          This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
          -
          -
          Returns:
          +
          +
          Returns:
          The truth value which defines if the represented data-type is signed.
          -
      • -
    • -
      -

      numberOfBytes

      -
      public int numberOfBytes()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfBytes

      +
      public int numberOfBytes()
      +
      +
      Returns:
      The number of bytes which it takes to represent the data-type.
      -
    • -
  • -
    -

    targetType

    -
    public Class<BigInteger> targetType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetType

      +
      public java.lang.Class<java.math.BigInteger> targetType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
    • -
  • -
    -

    targetArrayType

    -
    public Class<BigInteger[]> targetArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetArrayType

      +
      public java.lang.Class<java.math.BigInteger[]> targetArrayType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
    • -
  • -
    -

    holderType

    -
    public Class<Long> holderType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderType

      +
      public java.lang.Class<java.lang.Long> holderType()
      +
      Description copied from interface: NumericType
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
    • -
  • -
    -

    holderArrayType

    -
    public Class<long[]> holderArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderArrayType

      +
      public java.lang.Class<long[]> holderArrayType()
      +
      Description copied from interface: NumericType
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
    • -
  • -
    -

    foreignHolderBytesToTarget

    -
    public BigInteger foreignHolderBytesToTarget(byte[] bytes)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      public java.math.BigInteger foreignHolderBytesToTarget(byte[] bytes)
      +
      +
      Parameters:
      bytes - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
    • -
  • -
    -

    toTarget

    -
    public BigInteger toTarget(Long original)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      toTarget

      +
      public java.math.BigInteger toTarget(java.lang.Long original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
    • -
  • -
    -

    targetToForeignHolderBytes

    -
    public byte[] targetToForeignHolderBytes(BigInteger b)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      public byte[] targetToForeignHolderBytes(java.math.BigInteger b)
      +
      +
      Parameters:
      b - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public BigInteger[] readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public java.math.BigInteger[] readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                                            int size)
+                                                     throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public <T> BigInteger[] readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public <T> java.math.BigInteger[] readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                                                int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public long[] readForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public long[] readForeignDataFrom(java.io.DataInput stream,
+                                  int size)
+                           throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public <T> long[] readForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public <T> long[] readForeignDataFrom(java.util.Iterator<T> iterator,
+                                      int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
    • -
  • -
    -

    convertToHolder

    -
    public Long convertToHolder(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolder

      +
      public java.lang.Long convertToHolder(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToHolderArray

    -
    public long[] convertToHolderArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolderArray

      +
      public long[] convertToHolderArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTarget

    -
    public BigInteger convertToTarget(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTarget

      +
      public java.math.BigInteger convertToTarget(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTargetArray

    -
    public BigInteger[] convertToTargetArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTargetArray

      +
      public java.math.BigInteger[] convertToTargetArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    getNumericTypeTarget

    -
    public Class<NumericType<BigInteger,BigInteger[],BigInteger,BigInteger[]>> getNumericTypeTarget()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      getNumericTypeTarget

      +
      public java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
      Description copied from interface: NumericType
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Specified by:
      -
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Returns:
      +
      +
      Specified by:
      +
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
    • -
  • -
    -

    writeDataTo

    -
    public void writeDataTo(DataOutput stream, - Iterator<BigInteger> iterator) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      writeDataTo

      +
      public void writeDataTo(java.io.DataOutput stream,
+                        java.util.Iterator<TargetType> iterator)
+                 throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Specified by:
      -
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Parameters:
      +
      +
      Specified by:
      +
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/UI8.html b/docs/jdocs/neureka/dtype/custom/UI8.html index 2a338b441..cea607c24 100644 --- a/docs/jdocs/neureka/dtype/custom/UI8.html +++ b/docs/jdocs/neureka/dtype/custom/UI8.html @@ -1,540 +1,722 @@ - + + - -UI8 (neureka 1.0.0 API) - - - - + +UI8 (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.dtype.custom
    -

    Class UI8

    +
    neureka.dtype.custom
    +

    Class UI8

    -
    java.lang.Object -
    neureka.dtype.custom.UI8
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.dtype.custom.UI8
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      UI8()
      -
       
      +
      +
      public final class UI8
+extends java.lang.Object
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        UI8

        -
        public UI8()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            UI8

            +
            public UI8()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      signed

      -
      public boolean signed()
      -
      Description copied from interface: NumericType
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          signed

          +
          public boolean signed()
          +
          Description copied from interface: NumericType
          This boolean value tells if the data-type represented by concrete instances of implementations of this interface is signed!
          -
          -
          Returns:
          +
          +
          Returns:
          The truth value which defines if the represented data-type is signed.
          -
      • -
    • -
      -

      numberOfBytes

      -
      public int numberOfBytes()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      numberOfBytes

      +
      public int numberOfBytes()
      +
      +
      Returns:
      The number of bytes which it takes to represent the data-type.
      -
    • -
  • -
    -

    targetType

    -
    public Class<Short> targetType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetType

      +
      public java.lang.Class<java.lang.Short> targetType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type class which can represent the holder type.
      -
    • -
  • -
    -

    targetArrayType

    -
    public Class<short[]> targetArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      targetArrayType

      +
      public java.lang.Class<short[]> targetArrayType()
      +
      Description copied from interface: NumericType
      The target type is the targeted JVM data-type which can represent the holder type. This method returns the class object of an array of this target type.
      -
      -
      Returns:
      +
      +
      Returns:
      The targeted JVM data-type array class which can represent the holder array type.
      -
    • -
  • -
    -

    holderType

    -
    public Class<Byte> holderType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderType

      +
      public java.lang.Class<java.lang.Byte> holderType()
      +
      Description copied from interface: NumericType
      The holder type is the JVM type which can hold the data but not necessarily represent it (int cant represent uint). This method returns the class object of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder type class which can hold the data type but not necessarily represent it (int cant represent uint).
      -
    • -
  • -
    -

    holderArrayType

    -
    public Class<byte[]> holderArrayType()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      holderArrayType

      +
      public java.lang.Class<byte[]> holderArrayType()
      +
      Description copied from interface: NumericType
      The holder array type is the JVM type which can hold the data but not necessarily represent it (int[] cant represent uint[]). This method returns the array class object of an array of this holder type.
      -
      -
      Returns:
      +
      +
      Returns:
      The holder array type class which can hold the data type array but not necessarily represent it (int[] cant represent uint[]).
      -
    • -
  • -
    -

    foreignHolderBytesToTarget

    -
    public Short foreignHolderBytesToTarget(byte[] bytes)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      foreignHolderBytesToTarget

      +
      public java.lang.Short foreignHolderBytesToTarget(byte[] bytes)
      +
      +
      Parameters:
      bytes - The raw bytes of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of a target type built based on the provided holder bytes.
      -
    • -
  • -
    -

    toTarget

    -
    public Short toTarget(Byte original)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      toTarget

      +
      public java.lang.Short toTarget(java.lang.Byte original)
      +
      +
      Parameters:
      original - An instance of a holder type which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type converted from the provided holder type.
      -
    • -
  • -
    -

    targetToForeignHolderBytes

    -
    public byte[] targetToForeignHolderBytes(Short number)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      targetToForeignHolderBytes

      +
      public byte[] targetToForeignHolderBytes(java.lang.Short number)
      +
      +
      Parameters:
      number - An instance of a target type which ought to be converted to holder bytes.
      -
      Returns:
      +
      Returns:
      Holder bytes converted from an instance of the target type.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public short[] readAndConvertForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public short[] readAndConvertForeignDataFrom(java.io.DataInput stream,
+                                             int size)
+                                      throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readAndConvertForeignDataFrom

    -
    public <T> short[] readAndConvertForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readAndConvertForeignDataFrom

      +
      public <T> short[] readAndConvertForeignDataFrom(java.util.Iterator<T> iterator,
+                                                 int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then ought to convert these to an array of target types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of target types converted from the holder type elements read from the iterator.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public byte[] readForeignDataFrom(DataInput stream, - int size) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public byte[] readForeignDataFrom(java.io.DataInput stream,
+                                  int size)
+                           throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method expects the provided stream to spit out bytes which can be read as target type elements. It then ought to convert these to an array of holder types of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      stream - A DataInput stream whose data ought to be read as target type elements.
      size - The number of elements which ought to be read.
      -
      Returns:
      +
      Returns:
      An array of holder types converted from the target type elements read from the stream.
      -
      Throws:
      -
      IOException - If reading from the stream was not successful.
      +
      Throws:
      +
      java.io.IOException - If reading from the stream was not successful.
      -
    • -
  • -
    -

    readForeignDataFrom

    -
    public <T> byte[] readForeignDataFrom(Iterator<T> iterator, - int size)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      readForeignDataFrom

      +
      public <T> byte[] readForeignDataFrom(java.util.Iterator<T> iterator,
+                                      int size)
      +
      Description copied from interface: NumericType
      This method expects the provided iterator to return elements which can be read as holder type elements. It then will write them into an array of holder types of the specified size.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The generic type parameter for the iterator.
      -
      Parameters:
      +
      Parameters:
      iterator - An iterator whose elements ought to be understood as holder type elements.
      size - The number of elements which ought to be read and then written into an array.
      -
      Returns:
      +
      Returns:
      An array of holder types populated by holder elements read from the iterator.
      -
    • -
  • -
    -

    convertToHolder

    -
    public Byte convertToHolder(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolder

      +
      public java.lang.Byte convertToHolder(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder type.
      -
      Returns:
      +
      Returns:
      An instance of the holder type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToHolderArray

    -
    public byte[] convertToHolderArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToHolderArray

      +
      public byte[] convertToHolderArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the HolderType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the holder array type.
      -
      Returns:
      +
      Returns:
      An instance of the holder array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTarget

    -
    public Short convertToTarget(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTarget

      +
      public java.lang.Short convertToTarget(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target type.
      -
      Returns:
      +
      Returns:
      An instance of the target type based on the conversion of the provided object.
      -
    • -
  • -
    -

    convertToTargetArray

    -
    public short[] convertToTargetArray(Object from)
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      convertToTargetArray

      +
      public short[] convertToTargetArray(java.lang.Object from)
      +
      Description copied from interface: NumericType
      This method is a generic converter from any object to an instance of the TargetArrayType parameter specified by an implementation of this interface.
      -
      -
      Parameters:
      +
      +
      Parameters:
      from - The object which ought to be converted to an instance of the target array type.
      -
      Returns:
      +
      Returns:
      An instance of the target array type based on the conversion of the provided object.
      -
    • -
  • -
    -

    getNumericTypeTarget

    -
    public Class<NumericType<Short,short[],Short,short[]>> getNumericTypeTarget()
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      getNumericTypeTarget

      +
      public java.lang.Class<NumericType<TargetType,TargetArrayType,TargetType,TargetArrayType>> getNumericTypeTarget()
      +
      Description copied from interface: NumericType
      This method returns the NumericType representation of the target type of this class. An example would be the UI32 (32 bit integer) which can be represented by the NumericType implementation I64 (64 bit integer). If this NumericType representation can represent itself, meaning its target type is also the holder type like for instance I32, then this method will simply return its own class!
      -
      -
      Specified by:
      -
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Returns:
      +
      +
      Specified by:
      +
      getNumericTypeTarget in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Returns:
      The NumericType representation of the target type class which can represent the data type of this class.
      -
    • -
  • -
    -

    writeDataTo

    -
    public void writeDataTo(DataOutput stream, - Iterator<Short> iterator) - throws IOException
    -
    Description copied from interface: NumericType
    + + + + +
      +
    • +

      writeDataTo

      +
      public void writeDataTo(java.io.DataOutput stream,
+                        java.util.Iterator<TargetType> iterator)
+                 throws java.io.IOException
      +
      Description copied from interface: NumericType
      This method writes all the target type elements returned by the provided iterator and write them into the provided "DataOutput" stream as bytes.
      -
      -
      Specified by:
      -
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      Parameters:
      +
      +
      Specified by:
      +
      writeDataTo in interface NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      +
      Parameters:
      stream - The output stream which ought to be feed with bytes of the elements provided by the iterator.
      iterator - The iterator whose returned elements ought to be translated into bytes for the output stream.
      -
      Throws:
      -
      IOException - An IOException forcing the caller to handle in case the conversion fails.
      +
      Throws:
      +
      java.io.IOException - An IOException forcing the caller to handle in case the conversion fails.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/package-frame.html b/docs/jdocs/neureka/dtype/custom/package-frame.html new file mode 100644 index 000000000..483df3172 --- /dev/null +++ b/docs/jdocs/neureka/dtype/custom/package-frame.html @@ -0,0 +1,28 @@ + + + + + +neureka.dtype.custom (neureka 1.0.1 API) + + + + +

    neureka.dtype.custom

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/dtype/custom/package-summary.html b/docs/jdocs/neureka/dtype/custom/package-summary.html index c704f0364..c31019249 100644 --- a/docs/jdocs/neureka/dtype/custom/package-summary.html +++ b/docs/jdocs/neureka/dtype/custom/package-summary.html @@ -1,119 +1,191 @@ - + + - -neureka.dtype.custom (neureka 1.0.0 API) - - - - + +neureka.dtype.custom (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.dtype.custom

    -
    -
    -
    package neureka.dtype.custom
    -
    +

    Package neureka.dtype.custom

    +
    Everything in this package should be considered library-private! DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! Code inside this package or any sub-packages might change frequently...
    -
    -
    -
      -
    • - -
      • -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      F32
      -
       
      -
      F64
      -
       
      -
      I16
      -
       
      -
      I32
      -
       
      -
      I64
      -
       
      -
      I8
      -
      +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        F32 
        F64 
        I16 
        I32 
        I64 
        I8
        The following abstract class implements some basic logic which is applicable across all final concrete classes extending this abstract one.
        - -
        UI16
        -
         
        -
        UI32
        -
         
        -
        UI64
        -
         
        -
        UI8
        -
         
        - - +
        UI16 
        UI32 
        UI64 
        UI8 
      -
    -
    + + + +

    Package neureka.dtype.custom Description

    +
    Everything in this package should be considered library-private! + DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! + Code inside this package or any sub-packages might change frequently...
    +
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/dtype/custom/package-tree.html b/docs/jdocs/neureka/dtype/custom/package-tree.html index 5378e3a74..6722c84e7 100644 --- a/docs/jdocs/neureka/dtype/custom/package-tree.html +++ b/docs/jdocs/neureka/dtype/custom/package-tree.html @@ -1,80 +1,143 @@ - + + - -neureka.dtype.custom Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.dtype.custom Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.dtype.custom

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
        -
      • neureka.dtype.custom.F32
        • -
      • neureka.dtype.custom.F64
        • -
      • neureka.dtype.custom.I16
        • -
      • neureka.dtype.custom.I32
        • -
      • neureka.dtype.custom.I64
        • -
      • neureka.dtype.custom.I8
        • -
      • neureka.dtype.custom.UI16
        • -
      • neureka.dtype.custom.UI32
        • -
      • neureka.dtype.custom.UI64
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        • +
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    neureka.dtype

    +
    +

    Interfaces

    + +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/dtype/package-summary.html b/docs/jdocs/neureka/dtype/package-summary.html index 1194d0137..84c8f1ef4 100644 --- a/docs/jdocs/neureka/dtype/package-summary.html +++ b/docs/jdocs/neureka/dtype/package-summary.html @@ -1,112 +1,159 @@ - + + - -neureka.dtype (neureka 1.0.0 API) - - - - + +neureka.dtype (neureka 1.0.1 API) - - - - - - + + -
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    Package neureka.dtype

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    package neureka.dtype
    -
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    • - -
      • -
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      -
      Class
      -
      Description
      -
      DataType<T>
      -
      -
      This class is a Multiton implementation for wrapping and representing type classes.
      -
      -
      NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      -
      +
      +
        +
      • + + + + + + + + + + + + +
        Interface Summary 
        InterfaceDescription
        NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
        This interface enables "Polymorphic" utility by defining common functionalities used for handling various numeric types.
        - - - - +
        +
        • +
      • + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        DataType<T> +
        This class is a Multiton implementation for wrapping and representing type classes.
        +
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    Hierarchy For Package neureka.dtype

    -Package Hierarchies: +Package Hierarchies:
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    Class Hierarchy

    -
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    Interface Hierarchy

      -
    • neureka.dtype.NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
      • +
    • neureka.dtype.NumericType<TargetType,TargetArrayType,HolderType,HolderArrayType>
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    + diff --git a/docs/jdocs/neureka/fluent/building/NdaBuilder.html b/docs/jdocs/neureka/fluent/building/NdaBuilder.html index ac731351d..3342d1e63 100644 --- a/docs/jdocs/neureka/fluent/building/NdaBuilder.html +++ b/docs/jdocs/neureka/fluent/building/NdaBuilder.html @@ -1,93 +1,124 @@ - + + - -NdaBuilder (neureka 1.0.0 API) - - - - + +NdaBuilder (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.building
    -

    Class NdaBuilder<V>

    -
    -
    java.lang.Object -
    neureka.fluent.building.NdaBuilder<V>
    +
    neureka.fluent.building
    +

    Class NdaBuilder<V>

    -
    -
    -
    Type Parameters:
    -
    V - The type of the values which ought to be represented by the Tensor built by this NdaBuilder.
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.fluent.building.NdaBuilder<V>
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      NdaBuilder(Class<V> typeClass)
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        NdaBuilder

        -
        public NdaBuilder(Class<V> typeClass)
        -
        -
        Parameters:
        -
        typeClass - The type of the values which ought to be represented by the Tensor built by this NdaBuilder.
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            NdaBuilder

            +
            public NdaBuilder(java.lang.Class<V> typeClass)
            +
            +
            Parameters:
            +
            typeClass - The type of the values which ought to be represented by the Tensor built by this NdaBuilder.
            -
      -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      andFill

      -
      @SafeVarargs -public final Tensor<V> andFill(V... values)
      -
      Description copied from interface: IterByOrIterFromOrAllTensor
      +
        +
      • + + +

        Method Detail

        + + + + + +
          +
        • +

          andFill

          +
          @SafeVarargs
+public final Tensor<V> andFill(V... values)
          +
          Description copied from interface: IterByOrIterFromOrAllTensor
          Provide an array of values which will be used to fill - the Tensor instance returned by this last fluent builder method. + the Tensor instance returned by this last fluent builder method. If the configured tensor is larger than the number of provided elements, then they will simply be read multiple times until the result has been sufficiently populated.
          -
          -
          Specified by:
          -
          andFill in interface IterByOrIterFromOrAll<V>
          -
          Specified by:
          -
          andFill in interface IterByOrIterFromOrAllTensor<V>
          -
          Parameters:
          -
          values - The values which will recurrently populate the returned Tensor with values until it is filled.
          -
          Returns:
          -
          A new Tensor instance populated by the array of values supplied to this method.
          +
          +
          Specified by:
          +
          andFill in interface IterByOrIterFromOrAll<V>
          +
          Specified by:
          +
          andFill in interface IterByOrIterFromOrAllTensor<V>
          +
          Parameters:
          +
          values - The values which will recurrently populate the returned Tensor with values until it is filled.
          +
          Returns:
          +
          A new Tensor instance populated by the array of values supplied to this method.
          -
      • -
    • -
      -

      andWhere

      -
      public Tensor<V> andWhere(Filler<V> filler)
      -
      This method receives an Filler lambda which will be - used to populate the Tensor instance produced by this API with values.
      -
      -
      Specified by:
      -
      andWhere in interface IterByOrIterFromOrAll<V>
      -
      Specified by:
      -
      andWhere in interface IterByOrIterFromOrAllTensor<V>
      -
      Parameters:
      -
      filler - The Filler which ought to populate the returned Tensor.
      -
      Returns:
      -
      A new Tensor instance populated by the lambda supplied to this method.
      +
    + + + +
    • -
  • -
    -

    andFillFrom

    -
    public ToForTensor<V> andFillFrom(V index)
    -
    Description copied from interface: IterByOrIterFromOrAllTensor
    + + + + + + +
      +
    • +

      andFillFrom

      +
      public ToForTensor<V> andFillFrom(V index)
      +
      Description copied from interface: IterByOrIterFromOrAllTensor
      This part of the builder API allows for specifying a range which starts from the provided value and will end at the value specified in the next builder step returned by this method. If the number in the created range is not sufficiently large enough to - fully populate the final Tensor instance built by this API, then the resulting + fully populate the final Tensor instance built by this API, then the resulting range will fill the underlying data array of the tensor recurrently.
      -
      -
      Specified by:
      -
      andFillFrom in interface IterByOrIterFromOrAll<V>
      -
      Specified by:
      -
      andFillFrom in interface IterByOrIterFromOrAllTensor<V>
      -
      Parameters:
      -
      index - The start of the range which ought to supply the Tensor instance built by this API.
      -
      Returns:
      +
      +
      Specified by:
      +
      andFillFrom in interface IterByOrIterFromOrAll<V>
      +
      Specified by:
      +
      andFillFrom in interface IterByOrIterFromOrAllTensor<V>
      +
      Parameters:
      +
      index - The start of the range which ought to supply the Tensor instance built by this API.
      +
      Returns:
      The next step in the builder method chain which expects to receive the end point of the range.
      -
    • -
  • -
    -

    all

    -
    public Tensor<V> all(V value)
    -
    Description copied from interface: IterByOrIterFromOrAllTensor
    -
    This method creates and return a Tensor instance which + + + + + + +
    • -
  • -
    -

    andSeed

    -
    public Tensor<V> andSeed(Object seed)
    -
    Description copied from interface: IterByOrIterFromOrAllTensor
    -
    This method creates and return a Tensor instance which + + + + +
    • -
  • -
    -

    withShape

    -
    public IterByOrIterFromOrAllTensor<V> withShape(int... shape)
    -
    Description copied from interface: WithShapeOrScalarOrVectorTensor
    + + + + +
    • -
  • -
    -

    vector

    -
    public Tensor<V> vector(Object[] values)
    -
    Description copied from interface: WithShapeOrScalarOrVectorTensor
    -
    This method creates and returns a vector Tensor instance + + + + +
    • -
  • -
    -

    scalar

    -
    public Tensor<V> scalar(V value)
    -
    Description copied from interface: WithShapeOrScalarOrVectorTensor
    -
    This method created and return a scalar Tensor instance + + + + + + +
    • -
  • -
    -

    to

    -
    public StepForTensor<V> to(V index)
    -
    Description copied from interface: ToForTensor
    + + + + + + +
    • +
    +
    Specified by:
    +
    to in interface To<V>
    +
    Specified by:
    +
    to in interface ToForTensor<V>
    +
    Parameters:
    +
    index - The end point of the range previously specified in IterByOrIterFromOrAll.andFillFrom(Object).
    +
    Returns:
    +
    The last step in the call transition graph of the fluent builder API for building range based Tensor instances.
    - -
  • -
    -

    step

    -
    public Tensor<V> step(double size)
    -
    Description copied from interface: StepForTensor
    + + + + +
    • +
    +
    Specified by:
    +
    step in interface Step<V>
    +
    Specified by:
    +
    step in interface StepForTensor<V>
    +
    Parameters:
    size - The size of the step within the range defined by this fluent builder API.
    -
    Returns:
    -
    A new Tensor instance whose contents are filled based on the provided range.
    +
    Returns:
    +
    A new Tensor instance whose contents are filled based on the provided range.
    - -
  • -
    -

    on

    -
    public WithShapeOrScalarOrVectorTensor<V> on(Device<V> device)
    -
    Description copied from interface: WithShapeOrScalarOrVectorOnDevice
    + + + + +
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    + diff --git a/docs/jdocs/neureka/fluent/building/package-frame.html b/docs/jdocs/neureka/fluent/building/package-frame.html new file mode 100644 index 000000000..bc62be33d --- /dev/null +++ b/docs/jdocs/neureka/fluent/building/package-frame.html @@ -0,0 +1,19 @@ + + + + + +neureka.fluent.building (neureka 1.0.1 API) + + + + +

    neureka.fluent.building

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    Classes

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    + + diff --git a/docs/jdocs/neureka/fluent/building/package-summary.html b/docs/jdocs/neureka/fluent/building/package-summary.html index 428e2742c..881d4896e 100644 --- a/docs/jdocs/neureka/fluent/building/package-summary.html +++ b/docs/jdocs/neureka/fluent/building/package-summary.html @@ -1,95 +1,141 @@ - + + - -neureka.fluent.building (neureka 1.0.0 API) - - - - + +neureka.fluent.building (neureka 1.0.1 API) - - - - - - + + -
    - -
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    Package neureka.fluent.building

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    package neureka.fluent.building
    -
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    +
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    +
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    + diff --git a/docs/jdocs/neureka/fluent/building/package-tree.html b/docs/jdocs/neureka/fluent/building/package-tree.html index 0e23265a3..00d68f65f 100644 --- a/docs/jdocs/neureka/fluent/building/package-tree.html +++ b/docs/jdocs/neureka/fluent/building/package-tree.html @@ -1,71 +1,134 @@ - + + - -neureka.fluent.building Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.fluent.building Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
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    Hierarchy For Package neureka.fluent.building

    -Package Hierarchies: +Package Hierarchies:
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    Class Hierarchy

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    + diff --git a/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAll.html b/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAll.html index 744714ad0..8652f6eb9 100644 --- a/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAll.html +++ b/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAll.html @@ -1,261 +1,381 @@ - + + - -IterByOrIterFromOrAll (neureka 1.0.0 API) - - - - + +IterByOrIterFromOrAll (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface IterByOrIterFromOrAll<V>

    +
    neureka.fluent.building.states
    +

    Interface IterByOrIterFromOrAll<V>

    -
    -
    +
    +
    +
    -
    -
      +
      +
      public interface IterByOrIterFromOrAll<V>
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      Nda<V>
      -
      all(V value)
      -
      -
      This method creates and return a Tensor instance which +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        Nda<V>all(V value) +
        This method creates and return a Tensor instance which will be homogeneously filled by the the provided value irrespective of the previously defined shape.
        - -
        Nda<V>
        -
        andFill(List<V> values)
        -
        +
        Nda<V>andFill(java.util.List<V> values)
        Provide a list of values which will be used to fill - the Tensor instance returned by this last fluent builder method.
        - -
        Nda<V>
        -
        andFill(V... values)
        -
        + the Tensor instance returned by this last fluent builder method.
        +
        Nda<V>andFill(V... values)
        Provide an array of values which will be used to fill - the Tensor instance returned by this last fluent builder method.
        - -
        To<V>
        -
        andFillFrom(V index)
        -
        + the Tensor instance returned by this last fluent builder method.
        +
        To<V>andFillFrom(V index)
        This part of the builder API allows for specifying a range which starts from the provided value and will end at the value specified in the next builder step returned by this method.
        - -
        Nda<V>
        -
        andSeed(Object seed)
        -
        -
        This method creates and return a Tensor instance which +
        Nda<V>andSeed(java.lang.Object seed) +
        This method creates and return a Tensor instance which will be filled based on the provided seed object.
        - -
        Nda<V>
        -
        andWhere(Filler<V> filler)
        -
        -
        Pass a lambda to this method which will be used to populate the Tensor +
        Nda<V>andWhere(Filler<V> filler) +
        Pass a lambda to this method which will be used to populate the Tensor built by this fluent builder API based on the indices of the tensor.
        - - - - - +
      -
      -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        andFill

        -
        Nda<V> andFill(V... values)
        +
          +
        • + + +

          Method Detail

          + + + + + +
            +
          • +

            andFill

            +
            Nda<V> andFill(V... values)
            Provide an array of values which will be used to fill - the Tensor instance returned by this last fluent builder method. + the Tensor instance returned by this last fluent builder method. If the configured tensor is larger than the number of provided elements, then they will simply be read multiple times until the result has been sufficiently populated.
            -
            -
            Parameters:
            -
            values - The values which will be used to populate the Tensor instance returned by this method.
            -
            Returns:
            +
            +
            Parameters:
            +
            values - The values which will be used to populate the Tensor instance returned by this method.
            +
            Returns:
            The final result of the fluent tensor builder API having a tensor filled with custom values.
            -
        • -
      • -
        -

        andFill

        -
        Nda<V> andFill(List<V> values)
        +
      + + + +
        +
      • +

        andFill

        +
        Nda<V> andFill(java.util.List<V> values)
        Provide a list of values which will be used to fill - the Tensor instance returned by this last fluent builder method. + the Tensor instance returned by this last fluent builder method. If the configured tensor is larger than the number of provided elements, then they will simply be read multiple times until the result has been sufficiently populated.
        -
        -
        Parameters:
        -
        values - The values which will be used to populate the Tensor instance returned by this method.
        -
        Returns:
        +
        +
        Parameters:
        +
        values - The values which will be used to populate the Tensor instance returned by this method.
        +
        Returns:
        The final result of the fluent tensor builder API having a tensor filled with custom values.
        -
      • -
    • -
      -

      andWhere

      -
      Nda<V> andWhere(Filler<V> filler)
      -
      Pass a lambda to this method which will be used to populate the Tensor +
    + + + +
      +
    • +

      andWhere

      +
      Nda<V> andWhere(Filler<V> filler)
      +
      Pass a lambda to this method which will be used to populate the Tensor built by this fluent builder API based on the indices of the tensor. - The lambda will receive the absolute index ranging from 0 to NDimensional.size() + The lambda will receive the absolute index ranging from 0 to NDimensional.size() as well as an array of shape based nd-indices which can be used to - initialize the underlying data of the Tensor more selectively.
      -
      -
      Parameters:
      + initialize the underlying data of the Tensor more selectively.
    +
    +
    Parameters:
    filler - A data element provider lambda mapping the indices to custom values.
    -
    Returns:
    -
    The resulting Tensor instance populated by the provided Filler.
    +
    Returns:
    +
    The resulting Tensor instance populated by the provided Filler.
    - -
  • -
    -

    andFillFrom

    -
    To<V> andFillFrom(V index)
    + + + + + + +
      +
    • +

      andFillFrom

      +
      To<V> andFillFrom(V index)
      This part of the builder API allows for specifying a range which starts from the provided value and will end at the value specified in the next builder step returned by this method. If the number in the created range is not sufficiently large enough to - fully populate the final Tensor instance built by this API, then the resulting + fully populate the final Tensor instance built by this API, then the resulting range will fill the underlying data array of the tensor recurrently.
      -
      -
      Parameters:
      -
      index - The start of the range which ought to supply the Tensor instance built by this API.
      -
      Returns:
      +
      +
      Parameters:
      +
      index - The start of the range which ought to supply the Tensor instance built by this API.
      +
      Returns:
      The next step in the builder method chain which expects to receive the end point of the range.
      -
    • -
  • -
    -

    all

    -
    Nda<V> all(V value)
    -
    This method creates and return a Tensor instance which + + + + + + +
      +
    • +

      all

      +
      Nda<V> all(V value)
      +
      This method creates and return a Tensor instance which will be homogeneously filled by the the provided value irrespective of the previously defined shape.
      -
      -
      Parameters:
      -
      value - The value which ought to populate the entire Tensor.
      -
      Returns:
      -
      The homogeneously populated Tensor instance.
      +
      +
      Parameters:
      +
      value - The value which ought to populate the entire Tensor.
      +
      Returns:
      +
      The homogeneously populated Tensor instance.
      -
    • -
  • -
    -

    andSeed

    -
    Nda<V> andSeed(Object seed)
    -
    This method creates and return a Tensor instance which + + + + +
      +
    • +

      andSeed

      +
      Nda<V> andSeed(java.lang.Object seed)
      +
      This method creates and return a Tensor instance which will be filled based on the provided seed object. The seed can be any object whose hash will serve as a basis for supplying the tensor with random data...
      -
      -
      Parameters:
      -
      seed - The seed based on which the value for populating the entire Tensor will be generated.
      -
      Returns:
      -
      The pseudo randomly populated Tensor instance.
      +
      +
      Parameters:
      +
      seed - The seed based on which the value for populating the entire Tensor will be generated.
      +
      Returns:
      +
      The pseudo randomly populated Tensor instance.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
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      • +
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      • +
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    • Detail: 
      • +
    • Field | 
      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAllTensor.html b/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAllTensor.html index 82bfeff33..1e0513bd5 100644 --- a/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAllTensor.html +++ b/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAllTensor.html @@ -1,274 +1,394 @@ - + + - -IterByOrIterFromOrAllTensor (neureka 1.0.0 API) - - - - + +IterByOrIterFromOrAllTensor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface IterByOrIterFromOrAllTensor<V>

    +
    neureka.fluent.building.states
    +

    Interface IterByOrIterFromOrAllTensor<V>

    -
    -
    +
    +
    +
    -
    - +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      Tensor<V>
      -
      all(V value)
      -
      -
      This method creates and return a Tensor instance which +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethod and Description
        Tensor<V>all(V value) +
        This method creates and return a Tensor instance which will be homogeneously filled by the the provided value irrespective of the previously defined shape.
        - -
        default Tensor<V>
        -
        andFill(List<V> values)
        -
        +
        default Tensor<V>andFill(java.util.List<V> values)
        Provide a list of values which will be used to fill - the Tensor instance returned by this last fluent builder method.
        - -
        Tensor<V>
        -
        andFill(V... values)
        -
        + the Tensor instance returned by this last fluent builder method.
        +
        Tensor<V>andFill(V... values)
        Provide an array of values which will be used to fill - the Tensor instance returned by this last fluent builder method.
        - -
        ToForTensor<V>
        -
        andFillFrom(V index)
        -
        + the Tensor instance returned by this last fluent builder method.
        +
        ToForTensor<V>andFillFrom(V index)
        This part of the builder API allows for specifying a range which starts from the provided value and will end at the value specified in the next builder step returned by this method.
        - -
        Tensor<V>
        -
        andSeed(Object seed)
        -
        -
        This method creates and return a Tensor instance which +
        Tensor<V>andSeed(java.lang.Object seed) +
        This method creates and return a Tensor instance which will be filled based on the provided seed object.
        - -
        Tensor<V>
        -
        andWhere(Filler<V> filler)
        -
        -
        Pass a lambda to this method which will be used to populate the Tensor +
        Tensor<V>andWhere(Filler<V> filler) +
        Pass a lambda to this method which will be used to populate the Tensor built by this fluent builder API based on the indices of the tensor.
        - - - - - +
      -
      -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        andFill

        -
        Tensor<V> andFill(V... values)
        +
          +
        • + + +

          Method Detail

          + + + + + +
            +
          • +

            andFill

            +
            Tensor<V> andFill(V... values)
            Provide an array of values which will be used to fill - the Tensor instance returned by this last fluent builder method. + the Tensor instance returned by this last fluent builder method. If the configured tensor is larger than the number of provided elements, then they will simply be read multiple times until the result has been sufficiently populated.
            -
            -
            Specified by:
            -
            andFill in interface IterByOrIterFromOrAll<V>
            -
            Parameters:
            -
            values - The values which will be used to populate the Tensor instance returned by this method.
            -
            Returns:
            +
            +
            Specified by:
            +
            andFill in interface IterByOrIterFromOrAll<V>
            +
            Parameters:
            +
            values - The values which will be used to populate the Tensor instance returned by this method.
            +
            Returns:
            The final result of the fluent tensor builder API having a tensor filled with custom values.
            -
        • -
      • -
        -

        andFill

        -
        default Tensor<V> andFill(List<V> values)
        +
      + + + +
        +
      • +

        andFill

        +
        default Tensor<V> andFill(java.util.List<V> values)
        Provide a list of values which will be used to fill - the Tensor instance returned by this last fluent builder method. + the Tensor instance returned by this last fluent builder method. If the configured tensor is larger than the number of provided elements, then they will simply be read multiple times until the result has been sufficiently populated.
        -
        -
        Specified by:
        -
        andFill in interface IterByOrIterFromOrAll<V>
        -
        Parameters:
        -
        values - The values which will be used to populate the Tensor instance returned by this method.
        -
        Returns:
        +
        +
        Specified by:
        +
        andFill in interface IterByOrIterFromOrAll<V>
        +
        Parameters:
        +
        values - The values which will be used to populate the Tensor instance returned by this method.
        +
        Returns:
        The final result of the fluent tensor builder API having a tensor filled with custom values.
        -
      • -
    • -
      -

      andWhere

      -
      Tensor<V> andWhere(Filler<V> filler)
      -
      Pass a lambda to this method which will be used to populate the Tensor +
    + + + +
      +
    • +

      andWhere

      +
      Tensor<V> andWhere(Filler<V> filler)
      +
      Pass a lambda to this method which will be used to populate the Tensor built by this fluent builder API based on the indices of the tensor. - The lambda will receive the absolute index ranging from 0 to NDimensional.size() + The lambda will receive the absolute index ranging from 0 to NDimensional.size() as well as an array of shape based nd-indices which can be used to - initialize the underlying data of the Tensor more selectively.
      -
      -
      Specified by:
      -
      andWhere in interface IterByOrIterFromOrAll<V>
      -
      Parameters:
      + initialize the underlying data of the Tensor more selectively.
    +
    +
    Specified by:
    +
    andWhere in interface IterByOrIterFromOrAll<V>
    +
    Parameters:
    filler - A data element provider lambda mapping the indices to custom values.
    -
    Returns:
    -
    The resulting Tensor instance populated by the provided Filler.
    +
    Returns:
    +
    The resulting Tensor instance populated by the provided Filler.
    - -
  • -
    -

    andFillFrom

    -
    ToForTensor<V> andFillFrom(V index)
    + + + + + + +
      +
    • +

      andFillFrom

      +
      ToForTensor<V> andFillFrom(V index)
      This part of the builder API allows for specifying a range which starts from the provided value and will end at the value specified in the next builder step returned by this method. If the number in the created range is not sufficiently large enough to - fully populate the final Tensor instance built by this API, then the resulting + fully populate the final Tensor instance built by this API, then the resulting range will fill the underlying data array of the tensor recurrently.
      -
      -
      Specified by:
      -
      andFillFrom in interface IterByOrIterFromOrAll<V>
      -
      Parameters:
      -
      index - The start of the range which ought to supply the Tensor instance built by this API.
      -
      Returns:
      +
      +
      Specified by:
      +
      andFillFrom in interface IterByOrIterFromOrAll<V>
      +
      Parameters:
      +
      index - The start of the range which ought to supply the Tensor instance built by this API.
      +
      Returns:
      The next step in the builder method chain which expects to receive the end point of the range.
      -
    • -
  • -
    -

    all

    -
    Tensor<V> all(V value)
    -
    This method creates and return a Tensor instance which + + + + + + +
      +
    • +

      all

      +
      Tensor<V> all(V value)
      +
      This method creates and return a Tensor instance which will be homogeneously filled by the the provided value irrespective of the previously defined shape.
      -
      -
      Specified by:
      -
      all in interface IterByOrIterFromOrAll<V>
      -
      Parameters:
      -
      value - The value which ought to populate the entire Tensor.
      -
      Returns:
      -
      The homogeneously populated Tensor instance.
      +
      +
      Specified by:
      +
      all in interface IterByOrIterFromOrAll<V>
      +
      Parameters:
      +
      value - The value which ought to populate the entire Tensor.
      +
      Returns:
      +
      The homogeneously populated Tensor instance.
      -
    • -
  • -
    -

    andSeed

    -
    Tensor<V> andSeed(Object seed)
    -
    This method creates and return a Tensor instance which + + + + +
      +
    • +

      andSeed

      +
      Tensor<V> andSeed(java.lang.Object seed)
      +
      This method creates and return a Tensor instance which will be filled based on the provided seed object. The seed can be any object whose hash will serve as a basis for supplying the tensor with random data...
      -
      -
      Specified by:
      -
      andSeed in interface IterByOrIterFromOrAll<V>
      -
      Parameters:
      -
      seed - The seed based on which the value for populating the entire Tensor will be generated.
      -
      Returns:
      -
      The pseudo randomly populated Tensor instance.
      +
      +
      Specified by:
      +
      andSeed in interface IterByOrIterFromOrAll<V>
      +
      Parameters:
      +
      seed - The seed based on which the value for populating the entire Tensor will be generated.
      +
      Returns:
      +
      The pseudo randomly populated Tensor instance.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAllTsr.html b/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAllTsr.html deleted file mode 100644 index 45c7c76a4..000000000 --- a/docs/jdocs/neureka/fluent/building/states/IterByOrIterFromOrAllTsr.html +++ /dev/null @@ -1,428 +0,0 @@ - - - - - -IterByOrIterFromOrAllTsr (neureka 0.21.0 API) - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface IterByOrIterFromOrAllTsr<V>

    -
    -
    -
    - -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Summary

        - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        All Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethodDescription
        Tsr<V>all​(V value) -
        This method creates and return a Tsr instance which - will be homogeneously filled by the the provided value irrespective - of the previously defined shape.
        -
        default Tsr<V>andFill​(java.util.List<V> values) -
        Provide a list of values which will be used to fill - the Tsr instance returned by this last fluent builder method.
        -
        Tsr<V>andFill​(V... values) -
        Provide an array of values which will be used to fill - the Tsr instance returned by this last fluent builder method.
        -
        ToForTsr<V>andFillFrom​(V index) -
        This part of the builder API allows for specifying a range which starts from the - provided value and will end at the value specified in the next - builder step returned by this method.
        -
        Tsr<V>andSeed​(java.lang.Object seed) -
        This method creates and return a Tsr instance which - will be filled based on the provided seed object.
        -
        Tsr<V>andWhere​(Filler<V> filler) -
        Pass a lambda to this method which will be used to populate the Tsr - built by this fluent builder API based on the indices of the tensor.
        -
        -
        • -
      -
      -
      • -
    -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Detail

        - - - - - -
          -
        • -

          andFill

          -
          Tsr<V> andFill​(V... values)
          -
          Provide an array of values which will be used to fill - the Tsr instance returned by this last fluent builder method. - If the configured tensor is larger than the number of provided - elements, then they will simply be read multiple times until - the result has been sufficiently populated.
          -
          -
          Specified by:
          -
          andFill in interface IterByOrIterFromOrAll<V>
          -
          Parameters:
          -
          values - The values which will be used to populate the Tsr instance returned by this method.
          -
          Returns:
          -
          The final result of the fluent tensor builder API having a tensor filled with custom values.
          -
          -
          • -
        - - - -
          -
        • -

          andFill

          -
          default Tsr<V> andFill​(java.util.List<V> values)
          -
          Provide a list of values which will be used to fill - the Tsr instance returned by this last fluent builder method. - If the configured tensor is larger than the number of provided - elements, then they will simply be read multiple times until - the result has been sufficiently populated.
          -
          -
          Specified by:
          -
          andFill in interface IterByOrIterFromOrAll<V>
          -
          Parameters:
          -
          values - The values which will be used to populate the Tsr instance returned by this method.
          -
          Returns:
          -
          The final result of the fluent tensor builder API having a tensor filled with custom values.
          -
          -
          • -
        - - - -
          -
        • -

          andWhere

          -
          Tsr<V> andWhere​(Filler<V> filler)
          -
          Pass a lambda to this method which will be used to populate the Tsr - built by this fluent builder API based on the indices of the tensor. - The lambda will receive the absolute index ranging from 0 to NDimensional.size() - as well as an array of shape based nd-indices which can be used to - initialize the underlying data of the Tsr more selectively.
          -
          -
          Specified by:
          -
          andWhere in interface IterByOrIterFromOrAll<V>
          -
          Parameters:
          -
          filler - A data element provider lambda mapping the indices to custom values.
          -
          Returns:
          -
          The resulting Tsr instance populated by the provided Filler.
          -
          -
          • -
        - - - - - -
          -
        • -

          andFillFrom

          -
          ToForTsr<V> andFillFrom​(V index)
          -
          This part of the builder API allows for specifying a range which starts from the - provided value and will end at the value specified in the next - builder step returned by this method. - If the number in the created range is not sufficiently large enough to - fully populate the final Tsr instance built by this API, then the resulting - range will fill the underlying data array of the tensor recurrently.
          -
          -
          Specified by:
          -
          andFillFrom in interface IterByOrIterFromOrAll<V>
          -
          Parameters:
          -
          index - The start of the range which ought to supply the Tsr instance built by this API.
          -
          Returns:
          -
          The next step in the builder method chain which expects to receive the end point of the range.
          -
          -
          • -
        - - - - - -
          -
        • -

          all

          -
          Tsr<V> all​(V value)
          -
          This method creates and return a Tsr instance which - will be homogeneously filled by the the provided value irrespective - of the previously defined shape.
          -
          -
          Specified by:
          -
          all in interface IterByOrIterFromOrAll<V>
          -
          Parameters:
          -
          value - The value which ought to populate the entire Tsr.
          -
          Returns:
          -
          The homogeneously populated Tsr instance.
          -
          -
          • -
        - - - -
          -
        • -

          andSeed

          -
          Tsr<V> andSeed​(java.lang.Object seed)
          -
          This method creates and return a Tsr instance which - will be filled based on the provided seed object. - The seed can be any object whose hash will serve as a basis for - supplying the tensor with random data...
          -
          -
          Specified by:
          -
          andSeed in interface IterByOrIterFromOrAll<V>
          -
          Parameters:
          -
          seed - The seed based on which the value for populating the entire Tsr will be generated.
          -
          Returns:
          -
          The pseudo randomly populated Tsr instance.
          -
          -
          • -
        -
        • -
      -
      -
      • -
    -
    -
    -
    - -
    - -
    - - diff --git a/docs/jdocs/neureka/fluent/building/states/Step.html b/docs/jdocs/neureka/fluent/building/states/Step.html index e10c21ee6..525e1efcf 100644 --- a/docs/jdocs/neureka/fluent/building/states/Step.html +++ b/docs/jdocs/neureka/fluent/building/states/Step.html @@ -1,159 +1,253 @@ - + + - -Step (neureka 1.0.0 API) - - - - + +Step (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface Step<V>

    +
    neureka.fluent.building.states
    +

    Interface Step<V>

    -
    -
    -
    Type Parameters:
    -
    V - The type of the values wrapped by the Tensor which is about to be instantiated.
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      +
      V - The type of the values wrapped by the Tensor which is about to be instantiated.
      -
      +
      All Known Subinterfaces:
      -
      StepForTensor<V>
      +
      StepForTensor<V>
      -
      +
      All Known Implementing Classes:
      -
      NdaBuilder
      +
      NdaBuilder
      -
      public interface Step<V>
      +
      +
      public interface Step<V>
      This interface defines the last step in the call transition graph of the fluent builder API when - building a Tensor instance populated based on the values within a defined range. + building a Tensor instance populated based on the values within a defined range. This method embodies the last part of this range definition which consists of the chained - methods IterByOrIterFromOrAll.andFillFrom(Object), To.to(Object) - and lastly the method defined in this interface, namely: step(double).
      -
    -
    -
    + + +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      Nda<V>
      -
      step(double size)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        Nda<V>step(double size)
        This is the last step in the call transition graph of the fluent builder API when - building a Tensor instance populated based on the values within a defined range.
        - - - - - + building a Tensor instance populated based on the values within a defined range. +
        +
        • +
    - -
    -
      +
    +
    +
    +
    +
    Parameters:
    size - The size of the step within the range defined by this fluent builder API.
    -
    Returns:
    -
    A new Tensor instance whose contents are filled based on the provided range.
    +
    Returns:
    +
    A new Tensor instance whose contents are filled based on the provided range.
    - - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/building/states/StepForTensor.html b/docs/jdocs/neureka/fluent/building/states/StepForTensor.html index c3c9a4cc8..78ee326e9 100644 --- a/docs/jdocs/neureka/fluent/building/states/StepForTensor.html +++ b/docs/jdocs/neureka/fluent/building/states/StepForTensor.html @@ -1,153 +1,247 @@ - + + - -StepForTensor (neureka 1.0.0 API) - - - - + +StepForTensor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface StepForTensor<V>

    +
    neureka.fluent.building.states
    +

    Interface StepForTensor<V>

    -
    -
    +
    +
    +
    -
    -
      +
      +
      public interface StepForTensor<V>
+extends Step<V>
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      Tensor<V>
      -
      step(double size)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        Tensor<V>step(double size)
        This is the last step in the call transition graph of the fluent builder API when - building a Tensor instance populated based on the values within a defined range.
        - - - - - + building a Tensor instance populated based on the values within a defined range. +
        +
        • +
    - -
    -
      +
    +
    +
    +
    +
    Specified by:
    +
    step in interface Step<V>
    +
    Parameters:
    size - The size of the step within the range defined by this fluent builder API.
    -
    Returns:
    -
    A new Tensor instance whose contents are filled based on the provided range.
    +
    Returns:
    +
    A new Tensor instance whose contents are filled based on the provided range.
    - - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/building/states/StepForTsr.html b/docs/jdocs/neureka/fluent/building/states/StepForTsr.html deleted file mode 100644 index 626b71b7e..000000000 --- a/docs/jdocs/neureka/fluent/building/states/StepForTsr.html +++ /dev/null @@ -1,276 +0,0 @@ - - - - - -StepForTsr (neureka 0.21.0 API) - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface StepForTsr<V>

    -
    -
    -
    -
      -
    • -
      -
      All Superinterfaces:
      -
      Step<V>
      -
      -
      -
      All Known Implementing Classes:
      -
      NdaBuilder
      -
      -
      -
      public interface StepForTsr<V>
-extends Step<V>
      -
      • -
    -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Summary

        - - - - - - - - - - - - -
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethodDescription
        Tsr<V>step​(double size) -
        This is the last step in the call transition graph of the fluent builder API when - building a Tsr instance populated based on the values within a defined range.
        -
        -
        • -
      -
      -
      • -
    -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Detail

        - - - -
          -
        • -

          step

          -
          Tsr<V> step​(double size)
          -
          This is the last step in the call transition graph of the fluent builder API when - building a Tsr instance populated based on the values within a defined range. - This method embodies the last part of this range definition which consists of the chained - methods IterByOrIterFromOrAll.andFillFrom(Object), To.to(Object) - and lastly this very method Step.step(double). - This method allows one to set the step size used to space the entries within - the previously defined span between what has - been passed to IterByOrIterFromOrAll.andFillFrom(Object) - and what has been passed to To.to(Object)...
          -
          -
          Specified by:
          -
          step in interface Step<V>
          -
          Parameters:
          -
          size - The size of the step within the range defined by this fluent builder API.
          -
          Returns:
          -
          A new Tsr instance whose contents are filled based on the provided range.
          -
          -
          • -
        -
        • -
      -
      -
      • -
    -
    -
    -
    - -
    - -
    - - diff --git a/docs/jdocs/neureka/fluent/building/states/To.html b/docs/jdocs/neureka/fluent/building/states/To.html index 1383b824e..a58617eed 100644 --- a/docs/jdocs/neureka/fluent/building/states/To.html +++ b/docs/jdocs/neureka/fluent/building/states/To.html @@ -1,156 +1,252 @@ - + + - -To (neureka 1.0.0 API) - - - - + +To (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface To<V>

    +
    neureka.fluent.building.states
    +

    Interface To<V>

    -
    -
    -
    Type Parameters:
    +
    +
    +
    -
    -
    + + +
    +
    + - -
    -
      +
    +
    +
    +
    +
    Parameters:
    +
    index - The end point of the range previously specified in IterByOrIterFromOrAll.andFillFrom(Object).
    +
    Returns:
    +
    The last step in the call transition graph of the fluent builder API for building range based Tensor instances.
    - - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
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      +
    • Summary: 
      • +
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      • +
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      • +
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    + diff --git a/docs/jdocs/neureka/fluent/building/states/ToForTensor.html b/docs/jdocs/neureka/fluent/building/states/ToForTensor.html index 40dd34a9d..97f65ca15 100644 --- a/docs/jdocs/neureka/fluent/building/states/ToForTensor.html +++ b/docs/jdocs/neureka/fluent/building/states/ToForTensor.html @@ -1,150 +1,246 @@ - + + - -ToForTensor (neureka 1.0.0 API) - - - - + +ToForTensor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface ToForTensor<V>

    +
    neureka.fluent.building.states
    +

    Interface ToForTensor<V>

    -
    -
    +
    +
    +
      +
    • +
      All Superinterfaces:
      -
      To<V>
      +
      To<V>
      -
      +
      All Known Implementing Classes:
      -
      NdaBuilder
      +
      NdaBuilder
      -
      public interface ToForTensor<V> -extends To<V>
      -
    -
    -
      +
      +
      public interface ToForTensor<V>
+extends To<V>
      + +
    +
    +
    + - -
    -
      +
    +
    +
    +
    +
    Specified by:
    +
    to in interface To<V>
    +
    Parameters:
    +
    index - The end point of the range previously specified in IterByOrIterFromOrAll.andFillFrom(Object).
    +
    Returns:
    +
    The last step in the call transition graph of the fluent builder API for building range based Tensor instances.
    - - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
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      • +
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      • +
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    + diff --git a/docs/jdocs/neureka/fluent/building/states/ToForTsr.html b/docs/jdocs/neureka/fluent/building/states/ToForTsr.html deleted file mode 100644 index 959b33457..000000000 --- a/docs/jdocs/neureka/fluent/building/states/ToForTsr.html +++ /dev/null @@ -1,275 +0,0 @@ - - - - - -ToForTsr (neureka 0.21.0 API) - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface ToForTsr<V>

    -
    -
    -
    -
      -
    • -
      -
      All Superinterfaces:
      -
      To<V>
      -
      -
      -
      All Known Implementing Classes:
      -
      NdaBuilder
      -
      -
      -
      public interface ToForTsr<V>
-extends To<V>
      -
      • -
    -
    -
    - -
    -
    - -
    -
    -
    - -
    - -
    - - diff --git a/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVector.html b/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVector.html index 7257f9635..bb15a2c7e 100644 --- a/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVector.html +++ b/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVector.html @@ -1,245 +1,363 @@ - + + - -WithShapeOrScalarOrVector (neureka 1.0.0 API) - - - - + +WithShapeOrScalarOrVector (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface WithShapeOrScalarOrVector<V>

    +
    neureka.fluent.building.states
    +

    Interface WithShapeOrScalarOrVector<V>

    -
    -
    +
    +
    +
    -
    -
      +
      +
      public interface WithShapeOrScalarOrVector<V>
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      Nda<V>
      -
      scalar(V value)
      -
      -
      This method created and return a scalar Tensor instance +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        Nda<V>scalar(V value) +
        This method created and return a scalar Tensor instance which wraps the provided value.
        - -
        Nda<V>
        -
        vector(Iterable<V> values)
        -
        -
        This method creates and returns a vector Tensor instance +
        Nda<V>vector(java.lang.Iterable<V> values) +
        This method creates and returns a vector Tensor instance which wraps the provided values.
        - -
        Nda<V>
        -
        vector(List<V> values)
        -
        -
        This method creates and returns a vector Tensor instance +
        Nda<V>vector(java.util.List<V> values) +
        This method creates and returns a vector Tensor instance which wraps the provided values.
        - -
        Nda<V>
        -
        vector(V... values)
        -
        -
        This method creates and returns a vector Tensor instance +
        Nda<V>vector(V... values) +
        This method creates and returns a vector Tensor instance which wraps the provided values.
        - -
        IterByOrIterFromOrAll<V>
        -
        withShape(int... shape)
        -
        +
        IterByOrIterFromOrAll<V>withShape(int... shape)
        Define a tensor shape by passing an array of int values to this method, - which represent the shape of the Tensor that should be built.
        - -
        <N extends Number>
        IterByOrIterFromOrAll<V>
        -
        withShape(List<N> shape)
        -
        + which represent the shape of the Tensor that should be built.
        +
        <N extends java.lang.Number>
        IterByOrIterFromOrAll<V>        		withShape(java.util.List<N> shape)
        Define a tensor shape by passing a list of numbers to this method, - which represent the shape of the Tensor that should be built.
        - - - - - + which represent the shape of the Tensor that should be built. +
      -
      -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        withShape

        -
        IterByOrIterFromOrAll<V> withShape(int... shape)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            withShape

            +
            IterByOrIterFromOrAll<V> withShape(int... shape)
            Define a tensor shape by passing an array of int values to this method, - which represent the shape of the Tensor that should be built. + which represent the shape of the Tensor that should be built. This should be called immediately after having specified the type of the tensor.
            -
            -
            Parameters:
            -
            shape - The shape array of the Tensor that should be built.
            -
            Returns:
            +
            +
            Parameters:
            +
            shape - The shape array of the Tensor that should be built.
            +
            Returns:
            The next step in the call transition graph of this fluent builder API.
            -
        • -
      • -
        -

        withShape

        -
        <N extends Number> IterByOrIterFromOrAll<V> withShape(List<N> shape)
        +
      + + + +
        +
      • +

        withShape

        +
        <N extends java.lang.Number> IterByOrIterFromOrAll<V> withShape(java.util.List<N> shape)
        Define a tensor shape by passing a list of numbers to this method, - which represent the shape of the Tensor that should be built. + which represent the shape of the Tensor that should be built. This should be called immediately after having specified the type of the tensor.
        -
        -
        Parameters:
        -
        shape - The shape list of the Tensor that should be built.
        -
        Returns:
        +
        +
        Parameters:
        +
        shape - The shape list of the Tensor that should be built.
        +
        Returns:
        The next step in the call transition graph of this fluent builder API.
        -
      • -
    • -
      -

      vector

      -
      Nda<V> vector(V... values)
      -
      This method creates and returns a vector Tensor instance +
    + + + + + +
      +
    • +

      vector

      +
      Nda<V> vector(V... values)
      +
      This method creates and returns a vector Tensor instance which wraps the provided values.
      -
      -
      Parameters:
      -
      values - The values which ought to be wrapped by a new vector Tensor instance.
      -
      Returns:
      -
      A vector Tensor instance wrapping the provided values.
      +
      +
      Parameters:
      +
      values - The values which ought to be wrapped by a new vector Tensor instance.
      +
      Returns:
      +
      A vector Tensor instance wrapping the provided values.
      -
      • -
    • -
      -

      vector

      -
      Nda<V> vector(List<V> values)
      -
      This method creates and returns a vector Tensor instance +
    + + + +
      +
    • +

      vector

      +
      Nda<V> vector(java.util.List<V> values)
      +
      This method creates and returns a vector Tensor instance which wraps the provided values.
      -
      -
      Parameters:
      +
      +
      Parameters:
      values - The list of values which ought to be turned into a vector.
      -
      Returns:
      +
      Returns:
      A vector representing the provided values.
      -
      • -
    • -
      -

      vector

      -
      Nda<V> vector(Iterable<V> values)
      -
      This method creates and returns a vector Tensor instance +
    + + + +
      +
    • +

      vector

      +
      Nda<V> vector(java.lang.Iterable<V> values)
      +
      This method creates and returns a vector Tensor instance which wraps the provided values.
      -
      -
      Parameters:
      +
      +
      Parameters:
      values - The list of values which ought to be turned into a vector.
      -
      Returns:
      +
      Returns:
      A vector representing the provided values.
      -
      • -
    • -
      -

      scalar

      -
      Nda<V> scalar(V value)
      -
      This method created and return a scalar Tensor instance +
    + + + + + +
      +
    • +

      scalar

      +
      Nda<V> scalar(V value)
      +
      This method created and return a scalar Tensor instance which wraps the provided value.
      -
      -
      Parameters:
      -
      value - The value which ought to be wrapped by a new scalar Tensor instance.
      -
      Returns:
      -
      A scala Tensor instance wrapping the provided value.
      +
      +
      Parameters:
      +
      value - The value which ought to be wrapped by a new scalar Tensor instance.
      +
      Returns:
      +
      A scala Tensor instance wrapping the provided value.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
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    +
      +
    • Summary: 
      • +
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      • +
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      +
    • Detail: 
      • +
    • Field | 
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorOnDevice.html b/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorOnDevice.html index 59af01ac5..a636b1b03 100644 --- a/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorOnDevice.html +++ b/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorOnDevice.html @@ -1,145 +1,243 @@ - + + - -WithShapeOrScalarOrVectorOnDevice (neureka 1.0.0 API) - - - - + +WithShapeOrScalarOrVectorOnDevice (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface WithShapeOrScalarOrVectorOnDevice<V>

    +
    neureka.fluent.building.states
    +

    Interface WithShapeOrScalarOrVectorOnDevice<V>

    -
    -
    +
    +
    +
    -
    - +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        on

        -
        WithShapeOrScalarOrVectorTensor<V> on(Device<V> device)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            on

            +
            WithShapeOrScalarOrVectorTensor<V> on(Device<? super V> device)
            Use this to specify the type onto which the tensor should be stored.
            -
            -
            Parameters:
            -
            device - The Device which should host the tensor built by this builder.
            -
            Returns:
            +
            +
            Parameters:
            +
            device - The Device which should host the tensor built by this builder.
            +
            Returns:
            The next fluent builder API step, which requires the definition of the shape of the tensor.
            -
      -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
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      • +
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    • Detail: 
      • +
    • Field | 
      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorTensor.html b/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorTensor.html index 014aafc24..a15aedcc1 100644 --- a/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorTensor.html +++ b/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorTensor.html @@ -1,263 +1,380 @@ - + + - -WithShapeOrScalarOrVectorTensor (neureka 1.0.0 API) - - - - + +WithShapeOrScalarOrVectorTensor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface WithShapeOrScalarOrVectorTensor<V>

    +
    neureka.fluent.building.states
    +

    Interface WithShapeOrScalarOrVectorTensor<V>

    -
    -
    +
    +
    +
    -
    - +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      - + + + +
        +
      • +

        withShape

        +
        default <N extends java.lang.Number> IterByOrIterFromOrAllTensor<V> withShape(java.util.List<N> shape)
        Define a tensor shape by passing a list of numbers to this method, - which represent the shape of the Tensor that should be built. + which represent the shape of the Tensor that should be built. This should be called immediately after having specified the type of the tensor.
        -
        -
        Specified by:
        -
        withShape in interface WithShapeOrScalarOrVector<V>
        -
        Parameters:
        -
        shape - The shape list of the Tensor that should be built.
        -
        Returns:
        +
        +
        Specified by:
        +
        withShape in interface WithShapeOrScalarOrVector<V>
        +
        Parameters:
        +
        shape - The shape list of the Tensor that should be built.
        +
        Returns:
        The next step in the call transition graph of this fluent builder API.
        -
      • -
    • -
      -

      vector

      -
      Tensor<V> vector(V... values)
      -
      This method creates and returns a vector Tensor instance +
    + + + + + +
      +
    • +

      vector

      +
      Tensor<V> vector(V... values)
      +
      This method creates and returns a vector Tensor instance which wraps the provided values.
      -
      -
      Specified by:
      -
      vector in interface WithShapeOrScalarOrVector<V>
      -
      Parameters:
      -
      values - The values which ought to be wrapped by a new vector Tensor instance.
      -
      Returns:
      -
      A vector Tensor instance wrapping the provided values.
      +
      +
      Specified by:
      +
      vector in interface WithShapeOrScalarOrVector<V>
      +
      Parameters:
      +
      values - The values which ought to be wrapped by a new vector Tensor instance.
      +
      Returns:
      +
      A vector Tensor instance wrapping the provided values.
      -
      • -
    • -
      -

      vector

      -
      default Tensor<V> vector(List<V> values)
      -
      This method creates and returns a vector Tensor instance +
    + + + +
      +
    • +

      vector

      +
      default Tensor<V> vector(java.util.List<V> values)
      +
      This method creates and returns a vector Tensor instance which wraps the provided values.
      -
      -
      Specified by:
      -
      vector in interface WithShapeOrScalarOrVector<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      vector in interface WithShapeOrScalarOrVector<V>
      +
      Parameters:
      values - The list of values which ought to be turned into a vector.
      -
      Returns:
      +
      Returns:
      A vector representing the provided values.
      -
      • -
    • -
      -

      vector

      -
      default Tensor<V> vector(Iterable<V> values)
      -
      This method creates and returns a vector Tensor instance +
    + + + +
      +
    • +

      vector

      +
      default Tensor<V> vector(java.lang.Iterable<V> values)
      +
      This method creates and returns a vector Tensor instance which wraps the provided values.
      -
      -
      Specified by:
      -
      vector in interface WithShapeOrScalarOrVector<V>
      -
      Parameters:
      +
      +
      Specified by:
      +
      vector in interface WithShapeOrScalarOrVector<V>
      +
      Parameters:
      values - The list of values which ought to be turned into a vector.
      -
      Returns:
      +
      Returns:
      A vector representing the provided values.
      -
      • -
    • -
      -

      scalar

      -
      Tensor<V> scalar(V value)
      -
      This method created and return a scalar Tensor instance +
    + + + + + +
      +
    • +

      scalar

      +
      Tensor<V> scalar(V value)
      +
      This method created and return a scalar Tensor instance which wraps the provided value.
      -
      -
      Specified by:
      -
      scalar in interface WithShapeOrScalarOrVector<V>
      -
      Parameters:
      -
      value - The value which ought to be wrapped by a new scalar Tensor instance.
      -
      Returns:
      -
      A scala Tensor instance wrapping the provided value.
      +
      +
      Specified by:
      +
      scalar in interface WithShapeOrScalarOrVector<V>
      +
      Parameters:
      +
      value - The value which ought to be wrapped by a new scalar Tensor instance.
      +
      Returns:
      +
      A scala Tensor instance wrapping the provided value.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
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      • +
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    + diff --git a/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorTsr.html b/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorTsr.html deleted file mode 100644 index efe220807..000000000 --- a/docs/jdocs/neureka/fluent/building/states/WithShapeOrScalarOrVectorTsr.html +++ /dev/null @@ -1,414 +0,0 @@ - - - - - -WithShapeOrScalarOrVectorTsr (neureka 0.21.0 API) - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    Package neureka.fluent.building.states
    -

    Interface WithShapeOrScalarOrVectorTsr<V>

    -
    -
    -
    - -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Summary

        - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        All Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethodDescription
        Tsr<V>scalar​(V value) -
        This method created and return a scalar Tsr instance - which wraps the provided value.
        -
        default Tsr<V>vector​(java.lang.Iterable<V> values) -
        This method creates and returns a vector Tsr instance - which wraps the provided values.
        -
        default Tsr<V>vector​(java.util.List<V> values) -
        This method creates and returns a vector Tsr instance - which wraps the provided values.
        -
        Tsr<V>vector​(V... values) -
        This method creates and returns a vector Tsr instance - which wraps the provided values.
        -
        IterByOrIterFromOrAllTsr<V>withShape​(int... shape) -
        Define a tensor shape by passing an array of int values to this method, - which represent the shape of the Tsr that should be built.
        -
        default <N extends java.lang.Number>
        IterByOrIterFromOrAllTsr<V>        		withShape​(java.util.List<N> shape) -
        Define a tensor shape by passing a list of numbers to this method, - which represent the shape of the Tsr that should be built.
        -
        -
        • -
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    • - -
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        -
      • - - -

        Method Detail

        - - - -
          -
        • -

          withShape

          -
          IterByOrIterFromOrAllTsr<V> withShape​(int... shape)
          -
          Define a tensor shape by passing an array of int values to this method, - which represent the shape of the Tsr that should be built. - This should be called immediately after having specified the type of the tensor.
          -
          -
          Specified by:
          -
          withShape in interface WithShapeOrScalarOrVector<V>
          -
          Parameters:
          -
          shape - The shape array of the Tsr that should be built.
          -
          Returns:
          -
          The next step in the call transition graph of this fluent builder API.
          -
          -
          • -
        - - - -
          -
        • -

          withShape

          -
          default <N extends java.lang.Number> IterByOrIterFromOrAllTsr<V> withShape​(java.util.List<N> shape)
          -
          Define a tensor shape by passing a list of numbers to this method, - which represent the shape of the Tsr that should be built. - This should be called immediately after having specified the type of the tensor.
          -
          -
          Specified by:
          -
          withShape in interface WithShapeOrScalarOrVector<V>
          -
          Parameters:
          -
          shape - The shape list of the Tsr that should be built.
          -
          Returns:
          -
          The next step in the call transition graph of this fluent builder API.
          -
          -
          • -
        - - - - - -
          -
        • -

          vector

          -
          Tsr<V> vector​(V... values)
          -
          This method creates and returns a vector Tsr instance - which wraps the provided values.
          -
          -
          Specified by:
          -
          vector in interface WithShapeOrScalarOrVector<V>
          -
          Parameters:
          -
          values - The values which ought to be wrapped by a new vector Tsr instance.
          -
          Returns:
          -
          A vector Tsr instance wrapping the provided values.
          -
          -
          • -
        - - - -
          -
        • -

          vector

          -
          default Tsr<V> vector​(java.util.List<V> values)
          -
          This method creates and returns a vector Tsr instance - which wraps the provided values.
          -
          -
          Specified by:
          -
          vector in interface WithShapeOrScalarOrVector<V>
          -
          Parameters:
          -
          values - The list of values which ought to be turned into a vector.
          -
          Returns:
          -
          A vector representing the provided values.
          -
          -
          • -
        - - - -
          -
        • -

          vector

          -
          default Tsr<V> vector​(java.lang.Iterable<V> values)
          -
          This method creates and returns a vector Tsr instance - which wraps the provided values.
          -
          -
          Specified by:
          -
          vector in interface WithShapeOrScalarOrVector<V>
          -
          Parameters:
          -
          values - The list of values which ought to be turned into a vector.
          -
          Returns:
          -
          A vector representing the provided values.
          -
          -
          • -
        - - - - - -
          -
        • -

          scalar

          -
          Tsr<V> scalar​(V value)
          -
          This method created and return a scalar Tsr instance - which wraps the provided value.
          -
          -
          Specified by:
          -
          scalar in interface WithShapeOrScalarOrVector<V>
          -
          Parameters:
          -
          value - The value which ought to be wrapped by a new scalar Tsr instance.
          -
          Returns:
          -
          A scala Tsr instance wrapping the provided value.
          -
          -
          • -
        -
        • -
      -
      -
      • -
    -
    -
    -
    - -
    - -
    - - diff --git a/docs/jdocs/neureka/fluent/building/states/package-frame.html b/docs/jdocs/neureka/fluent/building/states/package-frame.html new file mode 100644 index 000000000..acdbb92e0 --- /dev/null +++ b/docs/jdocs/neureka/fluent/building/states/package-frame.html @@ -0,0 +1,27 @@ + + + + + +neureka.fluent.building.states (neureka 1.0.1 API) + + + + +

    neureka.fluent.building.states

    +
    +

    Interfaces

    + +
    + + diff --git a/docs/jdocs/neureka/fluent/building/states/package-summary.html b/docs/jdocs/neureka/fluent/building/states/package-summary.html index 14dee598e..253959636 100644 --- a/docs/jdocs/neureka/fluent/building/states/package-summary.html +++ b/docs/jdocs/neureka/fluent/building/states/package-summary.html @@ -1,116 +1,178 @@ - + + - -neureka.fluent.building.states (neureka 1.0.0 API) - - - - + +neureka.fluent.building.states (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.fluent.building.states

    +

    Package neureka.fluent.building.states

    -
    -
    package neureka.fluent.building.states
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/building/states/package-tree.html b/docs/jdocs/neureka/fluent/building/states/package-tree.html index c37fa94ed..b3d9e217e 100644 --- a/docs/jdocs/neureka/fluent/building/states/package-tree.html +++ b/docs/jdocs/neureka/fluent/building/states/package-tree.html @@ -1,90 +1,153 @@ - + + - -neureka.fluent.building.states Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.fluent.building.states Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.fluent.building.states

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Interface Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/AxisSliceBuilder.html b/docs/jdocs/neureka/fluent/slicing/AxisSliceBuilder.html index 3e09ae806..cf2cd3e03 100644 --- a/docs/jdocs/neureka/fluent/slicing/AxisSliceBuilder.html +++ b/docs/jdocs/neureka/fluent/slicing/AxisSliceBuilder.html @@ -1,342 +1,477 @@ - + + - -AxisSliceBuilder (neureka 1.0.0 API) - - - - + +AxisSliceBuilder (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing
    -

    Class AxisSliceBuilder<V>

    -
    -
    java.lang.Object -
    neureka.fluent.slicing.AxisSliceBuilder<V>
    +
    neureka.fluent.slicing
    +

    Class AxisSliceBuilder<V>

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.fluent.slicing.AxisSliceBuilder<V>
        • +
      +
      • +
    +
    +
    -
    - +
    +
    +
    +
    + + + + + + + + + + + + +
      +
    • +

      all

      +
      public AxisOrGetTensor<V> all()
      +
      Description copied from interface: FromOrAtTensor
      This is a convenience method replacing "from(0).to(axisSize-1)", meaning that it simply slices the whole current axis from the original tensor.
      -
      -
      Specified by:
      -
      all in interface FromOrAt<V>
      -
      Specified by:
      -
      all in interface FromOrAtTensor<V>
      -
      Returns:
      +
      +
      Specified by:
      +
      all in interface FromOrAt<V>
      +
      Specified by:
      +
      all in interface FromOrAtTensor<V>
      +
      Returns:
      The next step in the slicing API which allows one to slice another axis or simply perform the actual slicing and get the tensor.
      -
      • -
    • -
      -

      axis

      -
      public FromOrAtTensor<V> axis(int axis)
      -
      This method returns an instance of the AxisSliceBuilder targeted by the provided index.
      -
      -
      Specified by:
      -
      axis in interface AxisOrGet<V>
      -
      Specified by:
      -
      axis in interface AxisOrGetTensor<V>
      -
      Parameters:
      +
    + + + + + + + + + + + +
      +
    • +

      detached

      +
      public Tensor<V> detached()
      +
      Description copied from interface: StepsOrAxisOrGetTensor
      This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration... - Contrary to the AxisOrGet.get() method, this method returns a slice which + Contrary to the AxisOrGet.get() method, this method returns a slice which is not part of the computation graph of the original tensor (meaning no autograd).
      -
      -
      Specified by:
      -
      detached in interface AxisOrGet<V>
      -
      Specified by:
      -
      detached in interface AxisOrGetTensor<V>
      -
      Specified by:
      -
      detached in interface StepsOrAxisOrGetTensor<V>
      -
      Returns:
      -
      A new Tensor instance which is a slice of the original tensor without autograd.
      +
      +
      Specified by:
      +
      detached in interface AxisOrGet<V>
      +
      Specified by:
      +
      detached in interface AxisOrGetTensor<V>
      +
      Specified by:
      +
      detached in interface StepsOrAxisOrGetTensor<V>
      +
      Returns:
      +
      A new Tensor instance which is a slice of the original tensor without autograd.
      -
      • -
    • -
      -

      resolve

      -
      public void resolve()
      -
      +
    + + + +
      +
    • +

      resolve

      +
      public void resolve()
      +
      • +
    - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/SliceBuilder.html b/docs/jdocs/neureka/fluent/slicing/SliceBuilder.html index 7815ee103..1e532ef25 100644 --- a/docs/jdocs/neureka/fluent/slicing/SliceBuilder.html +++ b/docs/jdocs/neureka/fluent/slicing/SliceBuilder.html @@ -1,252 +1,370 @@ - + + - -SliceBuilder (neureka 1.0.0 API) - - - - + +SliceBuilder (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing
    -

    Class SliceBuilder<V>

    -
    -
    java.lang.Object -
    neureka.fluent.slicing.SliceBuilder<V>
    +
    neureka.fluent.slicing
    +

    Class SliceBuilder<V>

    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.fluent.slicing.SliceBuilder<V>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The type of the value(s) held by the tensor which ought to be sliced with the help of this builder.
      -
      +
      All Implemented Interfaces:
      -
      AxisOrGet<V>, AxisOrGetTensor<V>
      +
      AxisOrGet<V>, AxisOrGetTensor<V>
      -
      public class SliceBuilder<V> -extends Object -implements AxisOrGetTensor<V>
      +
      +
      public class SliceBuilder<V>
+extends java.lang.Object
+implements AxisOrGetTensor<V>
      This class is the heart of the slice builder API, collecting range configurations by exposing an API consisting of multiple interfaces which form a call state transition graph. - Instances of this class do not perform the actual slicing of a Tensor instance themselves, + Instances of this class do not perform the actual slicing of a Tensor instance themselves, however instead they merely serve as collectors of slice configuration data. - The API exposed by the SliceBuilder uses method chaining as well as a set of implemented interfaces + The API exposed by the SliceBuilder uses method chaining as well as a set of implemented interfaces which reference themselves in the form of the return types defined by the method signatures of said interfaces. A user of the API can only call methods exposed by the current "view" of the builder, namely a interface. This ensures a controlled order of calls to the API...
      -
    -
    -
      + +
    +
    +
    + + + + +
      +
    • +

      detached

      +
      public Tensor<V> detached()
      +
      Description copied from interface: AxisOrGetTensor
      This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration... - Contrary to the AxisOrGet.get() method, this method returns a slice which + Contrary to the AxisOrGet.get() method, this method returns a slice which is not part of the computation graph of the original tensor (meaning no autograd).
      -
      -
      Specified by:
      -
      detached in interface AxisOrGet<V>
      -
      Specified by:
      -
      detached in interface AxisOrGetTensor<V>
      -
      Returns:
      -
      A new Tensor instance which is a slice of the original tensor without autograd.
      +
      +
      Specified by:
      +
      detached in interface AxisOrGet<V>
      +
      Specified by:
      +
      detached in interface AxisOrGetTensor<V>
      +
      Returns:
      +
      A new Tensor instance which is a slice of the original tensor without autograd.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/SmartSlicer.html b/docs/jdocs/neureka/fluent/slicing/SmartSlicer.html index 207ab9c16..be044847f 100644 --- a/docs/jdocs/neureka/fluent/slicing/SmartSlicer.html +++ b/docs/jdocs/neureka/fluent/slicing/SmartSlicer.html @@ -1,162 +1,272 @@ - + + - -SmartSlicer (neureka 1.0.0 API) - - - - + +SmartSlicer (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing
    -

    Class SmartSlicer

    +
    neureka.fluent.slicing
    +

    Class SmartSlicer

    -
    java.lang.Object -
    neureka.fluent.slicing.SmartSlicer
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.fluent.slicing.SmartSlicer
        • +
      +
      • +
    +
    +
      +
    • -
      public class SmartSlicer -extends Object
      +
      +
      public class SmartSlicer
+extends java.lang.Object
      This class is responsible for receiving any input and trying to interpret it so that a slice can be formed.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      SmartSlicer()
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        SmartSlicer

        -
        public SmartSlicer()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            SmartSlicer

            +
            public SmartSlicer()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      slice

      -
      public static <ValType> Tensor<ValType> slice(Object[] ranges, - Tensor<ValType> source)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          slice

          +
          public static <ValType> Tensor<ValType> slice(java.lang.Object[] ranges,
+                                              Tensor<ValType> source)
        -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/package-frame.html b/docs/jdocs/neureka/fluent/slicing/package-frame.html new file mode 100644 index 000000000..2169502a4 --- /dev/null +++ b/docs/jdocs/neureka/fluent/slicing/package-frame.html @@ -0,0 +1,21 @@ + + + + + +neureka.fluent.slicing (neureka 1.0.1 API) + + + + +

    neureka.fluent.slicing

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/fluent/slicing/package-summary.html b/docs/jdocs/neureka/fluent/slicing/package-summary.html index 10050adf3..1fe09cce6 100644 --- a/docs/jdocs/neureka/fluent/slicing/package-summary.html +++ b/docs/jdocs/neureka/fluent/slicing/package-summary.html @@ -1,103 +1,153 @@ - + + - -neureka.fluent.slicing (neureka 1.0.0 API) - - - - + +neureka.fluent.slicing (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.fluent.slicing

    +

    Package neureka.fluent.slicing

    -
    -
    package neureka.fluent.slicing
    -
    -
      -
    • - -
      • -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      AxisSliceBuilder<V>
      -
       
      -
      SliceBuilder<V>
      -
      +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        AxisSliceBuilder<V> 
        SliceBuilder<V>
        This class is the heart of the slice builder API, collecting range configurations by exposing an API consisting of multiple interfaces which form a call state transition graph.
        - -
        SmartSlicer
        -
        +
        SmartSlicer
        This class is responsible for receiving any input and trying to interpret it so that a slice can be formed.
        - - - +
      -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/package-tree.html b/docs/jdocs/neureka/fluent/slicing/package-tree.html index 5540d96e9..4ae61fbd5 100644 --- a/docs/jdocs/neureka/fluent/slicing/package-tree.html +++ b/docs/jdocs/neureka/fluent/slicing/package-tree.html @@ -1,73 +1,136 @@ - + + - -neureka.fluent.slicing Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.fluent.slicing Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.fluent.slicing

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/states/AxisOrGet.html b/docs/jdocs/neureka/fluent/slicing/states/AxisOrGet.html index c697a5136..d9acf1f63 100644 --- a/docs/jdocs/neureka/fluent/slicing/states/AxisOrGet.html +++ b/docs/jdocs/neureka/fluent/slicing/states/AxisOrGet.html @@ -1,194 +1,296 @@ - + + - -AxisOrGet (neureka 1.0.0 API) - - - - + +AxisOrGet (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface AxisOrGet<V>

    +
    neureka.fluent.slicing.states
    +

    Interface AxisOrGet<V>

    -
    -
    -
    Type Parameters:
    -
    V - The type parameter for items of the Tensor which ought to be sliced.
    +
    +
    +
    -
    -
      + triggers the slicing and returns the resulting Tensor instance... or a call to + the FromOrAt interface which is the starting point for slicing individual axis of a tensor...
    + + +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      FromOrAt<V>
      -
      axis(int axis)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        FromOrAt<V>axis(int axis)
        Slicing a tensor ultimately means slicing one or more of its axes! This method allows one to specify which axis should be sliced next.
        - -
        Nda<V>
        -
        detached()
        -
        +
        Nda<V>detached()
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration...
        - -
        Nda<V>
        -
        get()
        -
        +
        Nda<V>get()
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration...
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        axis

        -
        FromOrAt<V> axis(int axis)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            axis

            +
            FromOrAt<V> axis(int axis)
            Slicing a tensor ultimately means slicing one or more of its axes! This method allows one to specify which axis should be sliced next.
            -
            -
            Parameters:
            +
            +
            Parameters:
            axis - The axis which ought to be sliced next.
            -
            Returns:
            +
            Returns:
            The fluent axis slicing API.
            -
        • -
      • -
        -

        get

        -
        Nda<V> get()
        +
      + + + +
        +
      • +

        get

        +
        Nda<V> get()
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration...
        -
        -
        Returns:
        -
        A new Tensor instance which is a slice of the original tensor.
        +
        +
        Returns:
        +
        A new Tensor instance which is a slice of the original tensor.
        -
      • -
    • -
      -

      detached

      -
      Nda<V> detached()
      +
    + + + +
      +
    • +

      detached

      +
      Nda<V> detached()
      This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration... - Contrary to the get() method, this method returns a slice which + Contrary to the get() method, this method returns a slice which is not part of the computation graph of the original tensor (meaning no autograd).
      -
      -
      Returns:
      -
      A new Tensor instance which is a slice of the original tensor without autograd.
      +
      +
      Returns:
      +
      A new Tensor instance which is a slice of the original tensor without autograd.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/states/AxisOrGetTensor.html b/docs/jdocs/neureka/fluent/slicing/states/AxisOrGetTensor.html index 8415c2a1a..d1ab63881 100644 --- a/docs/jdocs/neureka/fluent/slicing/states/AxisOrGetTensor.html +++ b/docs/jdocs/neureka/fluent/slicing/states/AxisOrGetTensor.html @@ -1,196 +1,298 @@ - + + - -AxisOrGetTensor (neureka 1.0.0 API) - - - - + +AxisOrGetTensor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface AxisOrGetTensor<V>

    +
    neureka.fluent.slicing.states
    +

    Interface AxisOrGetTensor<V>

    -
    -
    +
    +
    +
    -
    -
      +
      +
      public interface AxisOrGetTensor<V>
+extends AxisOrGet<V>
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      FromOrAtTensor<V>
      -
      axis(int axis)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        FromOrAtTensor<V>axis(int axis)
        Slicing a tensor ultimately means slicing one or more of its axes! This method allows one to specify which axis should be sliced next.
        - -
        Tensor<V>
        -
        detached()
        -
        +
        Tensor<V>detached()
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration...
        - -
        Tensor<V>
        -
        get()
        -
        +
        Tensor<V>get()
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration...
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        axis

        -
        FromOrAtTensor<V> axis(int axis)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            axis

            +
            FromOrAtTensor<V> axis(int axis)
            Slicing a tensor ultimately means slicing one or more of its axes! This method allows one to specify which axis should be sliced next.
            -
            -
            Specified by:
            -
            axis in interface AxisOrGet<V>
            -
            Parameters:
            +
            +
            Specified by:
            +
            axis in interface AxisOrGet<V>
            +
            Parameters:
            axis - The axis which ought to be sliced next.
            -
            Returns:
            +
            Returns:
            The fluent axis slicing API.
            -
        • -
      • -
        -

        get

        -
        Tensor<V> get()
        +
      + + + +
        +
      • +

        get

        +
        Tensor<V> get()
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration...
        -
        -
        Specified by:
        -
        get in interface AxisOrGet<V>
        -
        Returns:
        -
        A new Tensor instance which is a slice of the original tensor.
        +
        +
        Specified by:
        +
        get in interface AxisOrGet<V>
        +
        Returns:
        +
        A new Tensor instance which is a slice of the original tensor.
        -
      • -
    • -
      -

      detached

      -
      Tensor<V> detached()
      +
    + + + +
      +
    • +

      detached

      +
      Tensor<V> detached()
      This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration... - Contrary to the AxisOrGet.get() method, this method returns a slice which + Contrary to the AxisOrGet.get() method, this method returns a slice which is not part of the computation graph of the original tensor (meaning no autograd).
      -
      -
      Specified by:
      -
      detached in interface AxisOrGet<V>
      -
      Returns:
      -
      A new Tensor instance which is a slice of the original tensor without autograd.
      +
      +
      Specified by:
      +
      detached in interface AxisOrGet<V>
      +
      Returns:
      +
      A new Tensor instance which is a slice of the original tensor without autograd.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/states/AxisOrGetTsr.html b/docs/jdocs/neureka/fluent/slicing/states/AxisOrGetTsr.html deleted file mode 100644 index c881c0d55..000000000 --- a/docs/jdocs/neureka/fluent/slicing/states/AxisOrGetTsr.html +++ /dev/null @@ -1,329 +0,0 @@ - - - - - -AxisOrGetTsr (neureka 0.21.0 API) - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface AxisOrGetTsr<V>

    -
    -
    -
    - -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Summary

        - - - - - - - - - - - - - - - - - - - - - - -
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethodDescription
        FromOrAtTsr<V>axis​(int axis) -
        Slicing a tensor ultimately means slicing one or more of its axes! - This method allows one to specify which axis should be sliced next.
        -
        Tsr<V>detached() -
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tsr instance based on the previously - specified slice configuration...
        -
        Tsr<V>get() -
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tsr instance based on the previously - specified slice configuration...
        -
        -
        • -
      -
      -
      • -
    -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Detail

        - - - -
          -
        • -

          axis

          -
          FromOrAtTsr<V> axis​(int axis)
          -
          Slicing a tensor ultimately means slicing one or more of its axes! - This method allows one to specify which axis should be sliced next.
          -
          -
          Specified by:
          -
          axis in interface AxisOrGet<V>
          -
          Parameters:
          -
          axis - The axis which ought to be sliced next.
          -
          Returns:
          -
          The fluent axis slicing API.
          -
          -
          • -
        - - - -
          -
        • -

          get

          -
          Tsr<V> get()
          -
          This method concludes the slicing API by performing the actual slicing and - returning the resulting Tsr instance based on the previously - specified slice configuration...
          -
          -
          Specified by:
          -
          get in interface AxisOrGet<V>
          -
          Returns:
          -
          A new Tsr instance which is a slice of the original tensor.
          -
          -
          • -
        - - - -
          -
        • -

          detached

          -
          Tsr<V> detached()
          -
          This method concludes the slicing API by performing the actual slicing and - returning the resulting Tsr instance based on the previously - specified slice configuration... - Contrary to the AxisOrGet.get() method, this method returns a slice which - is not part of the computation graph of the original tensor (meaning no autograd).
          -
          -
          Specified by:
          -
          detached in interface AxisOrGet<V>
          -
          Returns:
          -
          A new Tsr instance which is a slice of the original tensor without autograd.
          -
          -
          • -
        -
        • -
      -
      -
      • -
    -
    -
    -
    - -
    - -
    - - diff --git a/docs/jdocs/neureka/fluent/slicing/states/FromOrAt.html b/docs/jdocs/neureka/fluent/slicing/states/FromOrAt.html index 0ea84812c..b8d53aa09 100644 --- a/docs/jdocs/neureka/fluent/slicing/states/FromOrAt.html +++ b/docs/jdocs/neureka/fluent/slicing/states/FromOrAt.html @@ -1,195 +1,297 @@ - + + - -FromOrAt (neureka 1.0.0 API) - - - - + +FromOrAt (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface FromOrAt<V>

    +
    neureka.fluent.slicing.states
    +

    Interface FromOrAt<V>

    -
    -
    -
    Type Parameters:
    -
    V - The type parameter for items of the Tensor which ought to be sliced.
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      +
      V - The type parameter for items of the Tensor which ought to be sliced.
      -
      +
      All Known Subinterfaces:
      -
      FromOrAtTensor<V>
      +
      FromOrAtTensor<V>
      -
      +
      All Known Implementing Classes:
      -
      AxisSliceBuilder
      +
      AxisSliceBuilder
      -
      public interface FromOrAt<V>
      +
      +
      public interface FromOrAt<V>
      This is the starting point for defining the slice range of a specified axis within the call transition graph exposed by the slice builder API. - This interface defines 2 transition paths, namely a route to the To interface + This interface defines 2 transition paths, namely a route to the To interface of the call transition graph, which expects a range to be defined by - calling the methods from(int) and To.to(int), - or a call to the "at" method, which is a shortcut for calling from(int) and To.to(int) + calling the methods from(int) and To.to(int), + or a call to the "at" method, which is a shortcut for calling from(int) and To.to(int) with the same arguments.
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      AxisOrGet<V>
      -
      all()
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        AxisOrGet<V>all()
        This is a convenience method replacing "from(0).to(axisSize-1)", meaning that it simply slices the whole current axis from the original tensor.
        - -
        AxisOrGet<V>
        -
        at(int index)
        -
        +
        AxisOrGet<V>at(int index)
        This is a convenience method replacing "from(i).to(i)", meaning that it simply slices a single axis from the original tensor at the specified index.
        - -
        To<V>
        -
        from(int index)
        -
        +
        To<V>from(int index)
        This is the starting point for defining the slice range of a specified axis within the method chain/graph exposed by the slice builder API.
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        from

        -
        To<V> from(int index)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            from

            +
            To<V> from(int index)
            This is the starting point for defining the slice range of a specified axis within the method chain/graph exposed by the slice builder API. It receives the index at which the slice range should start.
            -
            -
            Parameters:
            +
            +
            Parameters:
            index - A valid index in the current axis from which the slice should start.
            -
            Returns:
            +
            Returns:
            The next step in the slicing API which expects one to specify the end of the slice range.
            -
        • -
      • -
        -

        at

        -
        AxisOrGet<V> at(int index)
        +
      + + + +
        +
      • +

        at

        +
        AxisOrGet<V> at(int index)
        This is a convenience method replacing "from(i).to(i)", meaning that it simply slices a single axis from the original tensor at the specified index.
        -
        -
        Parameters:
        +
        +
        Parameters:
        index - The index which ought to be sliced.
        -
        Returns:
        +
        Returns:
        The next step in the slicing API which allows one to slice another axis or simply perform the actual slicing and get the tensor.
        -
      • -
    • -
      -

      all

      -
      AxisOrGet<V> all()
      +
    + + + +
      +
    • +

      all

      +
      AxisOrGet<V> all()
      This is a convenience method replacing "from(0).to(axisSize-1)", meaning that it simply slices the whole current axis from the original tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      The next step in the slicing API which allows one to slice another axis or simply perform the actual slicing and get the tensor.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/states/FromOrAtTensor.html b/docs/jdocs/neureka/fluent/slicing/states/FromOrAtTensor.html index 16eaa669e..0b7bc0416 100644 --- a/docs/jdocs/neureka/fluent/slicing/states/FromOrAtTensor.html +++ b/docs/jdocs/neureka/fluent/slicing/states/FromOrAtTensor.html @@ -1,191 +1,293 @@ - + + - -FromOrAtTensor (neureka 1.0.0 API) - - - - + +FromOrAtTensor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface FromOrAtTensor<V>

    +
    neureka.fluent.slicing.states
    +

    Interface FromOrAtTensor<V>

    -
    -
    +
    +
    +
    -
    -
      +
      +
      public interface FromOrAtTensor<V>
+extends FromOrAt<V>
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      AxisOrGetTensor<V>
      -
      all()
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        AxisOrGetTensor<V>all()
        This is a convenience method replacing "from(0).to(axisSize-1)", meaning that it simply slices the whole current axis from the original tensor.
        - -
        AxisOrGetTensor<V>
        -
        at(int index)
        -
        +
        AxisOrGetTensor<V>at(int index)
        This is a convenience method replacing "from(i).to(i)", meaning that it simply slices a single axis from the original tensor at the specified index.
        - -
        ToForTensor<V>
        -
        from(int index)
        -
        +
        ToForTensor<V>from(int index)
        This is the starting point for defining the slice range of a specified axis within the method chain/graph exposed by the slice builder API.
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        from

        -
        ToForTensor<V> from(int index)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            from

            +
            ToForTensor<V> from(int index)
            This is the starting point for defining the slice range of a specified axis within the method chain/graph exposed by the slice builder API. It receives the index at which the slice range should start.
            -
            -
            Specified by:
            -
            from in interface FromOrAt<V>
            -
            Parameters:
            +
            +
            Specified by:
            +
            from in interface FromOrAt<V>
            +
            Parameters:
            index - A valid index in the current axis from which the slice should start.
            -
            Returns:
            +
            Returns:
            The next step in the slicing API which expects one to specify the end of the slice range.
            -
        • -
      • -
        -

        at

        -
        AxisOrGetTensor<V> at(int index)
        +
      + + + +
        +
      • +

        at

        +
        AxisOrGetTensor<V> at(int index)
        This is a convenience method replacing "from(i).to(i)", meaning that it simply slices a single axis from the original tensor at the specified index.
        -
        -
        Specified by:
        -
        at in interface FromOrAt<V>
        -
        Parameters:
        +
        +
        Specified by:
        +
        at in interface FromOrAt<V>
        +
        Parameters:
        index - The index which ought to be sliced.
        -
        Returns:
        +
        Returns:
        The next step in the slicing API which allows one to slice another axis or simply perform the actual slicing and get the tensor.
        -
      • -
    • -
      -

      all

      -
      AxisOrGetTensor<V> all()
      +
    + + + +
      +
    • +

      all

      +
      AxisOrGetTensor<V> all()
      This is a convenience method replacing "from(0).to(axisSize-1)", meaning that it simply slices the whole current axis from the original tensor.
      -
      -
      Specified by:
      -
      all in interface FromOrAt<V>
      -
      Returns:
      +
      +
      Specified by:
      +
      all in interface FromOrAt<V>
      +
      Returns:
      The next step in the slicing API which allows one to slice another axis or simply perform the actual slicing and get the tensor.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/states/FromOrAtTsr.html b/docs/jdocs/neureka/fluent/slicing/states/FromOrAtTsr.html deleted file mode 100644 index 3b0bf8eab..000000000 --- a/docs/jdocs/neureka/fluent/slicing/states/FromOrAtTsr.html +++ /dev/null @@ -1,324 +0,0 @@ - - - - - -FromOrAtTsr (neureka 0.21.0 API) - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface FromOrAtTsr<V>

    -
    -
    -
    - -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Summary

        - - - - - - - - - - - - - - - - - - - - - - -
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethodDescription
        AxisOrGetTsr<V>all() -
        This is a convenience method replacing "from(0).to(axisSize-1)", meaning that - it simply slices the whole current axis from the original tensor.
        -
        AxisOrGetTsr<V>at​(int index) -
        This is a convenience method replacing "from(i).to(i)", meaning that - it simply slices a single axis from the original tensor at the specified index.
        -
        ToForTsr<V>from​(int index) -
        This is the starting point for defining the slice range of a specified axis within - the method chain/graph exposed by the slice builder API.
        -
        -
        • -
      -
      -
      • -
    -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Detail

        - - - -
          -
        • -

          from

          -
          ToForTsr<V> from​(int index)
          -
          This is the starting point for defining the slice range of a specified axis within - the method chain/graph exposed by the slice builder API. - It receives the index at which the slice range should start.
          -
          -
          Specified by:
          -
          from in interface FromOrAt<V>
          -
          Parameters:
          -
          index - A valid index in the current axis from which the slice should start.
          -
          Returns:
          -
          The next step in the slicing API which expects one to specify the end of the slice range.
          -
          -
          • -
        - - - -
          -
        • -

          at

          -
          AxisOrGetTsr<V> at​(int index)
          -
          This is a convenience method replacing "from(i).to(i)", meaning that - it simply slices a single axis from the original tensor at the specified index.
          -
          -
          Specified by:
          -
          at in interface FromOrAt<V>
          -
          Parameters:
          -
          index - The index which ought to be sliced.
          -
          Returns:
          -
          The next step in the slicing API which allows one to slice another axis or simply - perform the actual slicing and get the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          all

          -
          AxisOrGetTsr<V> all()
          -
          This is a convenience method replacing "from(0).to(axisSize-1)", meaning that - it simply slices the whole current axis from the original tensor.
          -
          -
          Specified by:
          -
          all in interface FromOrAt<V>
          -
          Returns:
          -
          The next step in the slicing API which allows one to slice another axis or simply - perform the actual slicing and get the tensor.
          -
          -
          • -
        -
        • -
      -
      -
      • -
    -
    -
    -
    - -
    - -
    - - diff --git a/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGet.html b/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGet.html index df878c38a..a58ba1f66 100644 --- a/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGet.html +++ b/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGet.html @@ -1,162 +1,260 @@ - + + - -StepsOrAxisOrGet (neureka 1.0.0 API) - - - - + +StepsOrAxisOrGet (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface StepsOrAxisOrGet<V>

    +
    neureka.fluent.slicing.states
    +

    Interface StepsOrAxisOrGet<V>

    -
    -
    -
    Type Parameters:
    -
    V - The type parameter for items of the Tensor which ought to be sliced.
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      +
      V - The type parameter for items of the Tensor which ought to be sliced.
      -
      +
      All Superinterfaces:
      -
      AxisOrGet<V>
      +
      AxisOrGet<V>
      -
      +
      All Known Subinterfaces:
      -
      StepsOrAxisOrGetTensor<V>
      +
      StepsOrAxisOrGetTensor<V>
      -
      +
      All Known Implementing Classes:
      -
      AxisSliceBuilder
      +
      AxisSliceBuilder
      -
      public interface StepsOrAxisOrGet<V> -extends AxisOrGet<V>
      -
      This interface extends the AxisOrGet interface which provides the option to either continue +
      +
      public interface StepsOrAxisOrGet<V>
+extends AxisOrGet<V>
      +
      This interface extends the AxisOrGet interface which provides the option to either continue slicing another axis or simply trigger the creation and return of a slice instance based on the already provided slice configuration. The method signature introduced in this interface provides the possibility to set a step size - for the previously defined range (FromOrAt.from(int) and To.to(int)). + for the previously defined range (FromOrAt.from(int) and To.to(int)). This step size will be used to create spread steps within the sliced axis.
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      AxisOrGet<V>
      -
      step(int size)
      -
      +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        step

        -
        AxisOrGet<V> step(int size)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            step

            +
            AxisOrGet<V> step(int size)
            This method allows one to specify a step size within the slice range previously specified for the currently sliced axis.
            -
            -
            Parameters:
            -
            size - The step size of the iterator slicing the underlying Tensor shape.
            -
            Returns:
            +
            +
            Parameters:
            +
            size - The step size of the iterator slicing the underlying Tensor shape.
            +
            Returns:
            The next step in the slicing API which allows one to slice another axis or simply perform the actual slicing and get the tensor.
            -
      -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGetTensor.html b/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGetTensor.html index 5faa8e8fa..779edefb5 100644 --- a/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGetTensor.html +++ b/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGetTensor.html @@ -1,200 +1,306 @@ - + + - -StepsOrAxisOrGetTensor (neureka 1.0.0 API) - - - - + +StepsOrAxisOrGetTensor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface StepsOrAxisOrGetTensor<V>

    +
    neureka.fluent.slicing.states
    +

    Interface StepsOrAxisOrGetTensor<V>

    -
    -
    +
    +
    +
    -
    - +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      Tensor<V>
      -
      detached()
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        Tensor<V>detached()
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration...
        - -
        Tensor<V>
        -
        get()
        -
        +
        Tensor<V>get()
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration...
        - -
        AxisOrGetTensor<V>
        -
        step(int size)
        -
        +
        AxisOrGetTensor<V>step(int size)
        This method allows one to specify a step size within the slice range previously specified for the currently sliced axis.
        - - - - -
        -

        Methods inherited from interface neureka.fluent.slicing.states.AxisOrGetTensor

        -axis
        - +
        +
      -
      -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        step

        -
        AxisOrGetTensor<V> step(int size)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            step

            +
            AxisOrGetTensor<V> step(int size)
            This method allows one to specify a step size within the slice range previously specified for the currently sliced axis.
            -
            -
            Specified by:
            -
            step in interface StepsOrAxisOrGet<V>
            -
            Parameters:
            -
            size - The step size of the iterator slicing the underlying Tensor shape.
            -
            Returns:
            +
            +
            Specified by:
            +
            step in interface StepsOrAxisOrGet<V>
            +
            Parameters:
            +
            size - The step size of the iterator slicing the underlying Tensor shape.
            +
            Returns:
            The next step in the slicing API which allows one to slice another axis or simply perform the actual slicing and get the tensor.
            -
        • -
      • -
        -

        get

        -
        Tensor<V> get()
        +
      + + + +
        +
      • +

        get

        +
        Tensor<V> get()
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration...
        -
        -
        Specified by:
        -
        get in interface AxisOrGet<V>
        -
        Specified by:
        -
        get in interface AxisOrGetTensor<V>
        -
        Returns:
        -
        A new Tensor instance which is a slice of the original tensor.
        +
        +
        Specified by:
        +
        get in interface AxisOrGet<V>
        +
        Specified by:
        +
        get in interface AxisOrGetTensor<V>
        +
        Returns:
        +
        A new Tensor instance which is a slice of the original tensor.
        -
      • -
    • -
      -

      detached

      -
      Tensor<V> detached()
      +
    + + + +
      +
    • +

      detached

      +
      Tensor<V> detached()
      This method concludes the slicing API by performing the actual slicing and - returning the resulting Tensor instance based on the previously + returning the resulting Tensor instance based on the previously specified slice configuration... - Contrary to the AxisOrGet.get() method, this method returns a slice which + Contrary to the AxisOrGet.get() method, this method returns a slice which is not part of the computation graph of the original tensor (meaning no autograd).
      -
      -
      Specified by:
      -
      detached in interface AxisOrGet<V>
      -
      Specified by:
      -
      detached in interface AxisOrGetTensor<V>
      -
      Returns:
      -
      A new Tensor instance which is a slice of the original tensor without autograd.
      +
      +
      Specified by:
      +
      detached in interface AxisOrGet<V>
      +
      Specified by:
      +
      detached in interface AxisOrGetTensor<V>
      +
      Returns:
      +
      A new Tensor instance which is a slice of the original tensor without autograd.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGetTsr.html b/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGetTsr.html deleted file mode 100644 index 267cb2883..000000000 --- a/docs/jdocs/neureka/fluent/slicing/states/StepsOrAxisOrGetTsr.html +++ /dev/null @@ -1,337 +0,0 @@ - - - - - -StepsOrAxisOrGetTsr (neureka 0.21.0 API) - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface StepsOrAxisOrGetTsr<V>

    -
    -
    -
    - -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Summary

        - - - - - - - - - - - - - - - - - - - - - - -
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethodDescription
        Tsr<V>detached() -
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tsr instance based on the previously - specified slice configuration...
        -
        Tsr<V>get() -
        This method concludes the slicing API by performing the actual slicing and - returning the resulting Tsr instance based on the previously - specified slice configuration...
        -
        AxisOrGetTsr<V>step​(int size) -
        This method allows one to specify a step size within the slice range - previously specified for the currently sliced axis.
        -
        - -
        • -
      -
      -
      • -
    -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Detail

        - - - -
          -
        • -

          step

          -
          AxisOrGetTsr<V> step​(int size)
          -
          This method allows one to specify a step size within the slice range - previously specified for the currently sliced axis.
          -
          -
          Specified by:
          -
          step in interface StepsOrAxisOrGet<V>
          -
          Parameters:
          -
          size - The step size of the iterator slicing the underlying Tsr shape.
          -
          Returns:
          -
          The next step in the slicing API which allows one to slice another axis or simply - perform the actual slicing and get the tensor.
          -
          -
          • -
        - - - -
          -
        • -

          get

          -
          Tsr<V> get()
          -
          This method concludes the slicing API by performing the actual slicing and - returning the resulting Tsr instance based on the previously - specified slice configuration...
          -
          -
          Specified by:
          -
          get in interface AxisOrGet<V>
          -
          Specified by:
          -
          get in interface AxisOrGetTsr<V>
          -
          Returns:
          -
          A new Tsr instance which is a slice of the original tensor.
          -
          -
          • -
        - - - -
          -
        • -

          detached

          -
          Tsr<V> detached()
          -
          This method concludes the slicing API by performing the actual slicing and - returning the resulting Tsr instance based on the previously - specified slice configuration... - Contrary to the AxisOrGet.get() method, this method returns a slice which - is not part of the computation graph of the original tensor (meaning no autograd).
          -
          -
          Specified by:
          -
          detached in interface AxisOrGet<V>
          -
          Specified by:
          -
          detached in interface AxisOrGetTsr<V>
          -
          Returns:
          -
          A new Tsr instance which is a slice of the original tensor without autograd.
          -
          -
          • -
        -
        • -
      -
      -
      • -
    -
    -
    -
    - -
    - -
    - - diff --git a/docs/jdocs/neureka/fluent/slicing/states/To.html b/docs/jdocs/neureka/fluent/slicing/states/To.html index 21e60abcc..8d7c9598c 100644 --- a/docs/jdocs/neureka/fluent/slicing/states/To.html +++ b/docs/jdocs/neureka/fluent/slicing/states/To.html @@ -1,155 +1,249 @@ - + + - -To (neureka 1.0.0 API) - - - - + +To (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface To<V>

    +
    neureka.fluent.slicing.states
    +

    Interface To<V>

    -
    -
    -
    Type Parameters:
    -
    V - The type parameter for items of the Tensor which ought to be sliced.
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      +
      V - The type parameter for items of the Tensor which ought to be sliced.
      -
      +
      All Known Subinterfaces:
      -
      ToForTensor<V>
      +
      ToForTensor<V>
      -
      +
      All Known Implementing Classes:
      -
      AxisSliceBuilder
      +
      AxisSliceBuilder
      -
      public interface To<V>
      +
      +
      public interface To<V>
      This is the second part for defining the slice range of a specified axis within the call transition graph exposed by the slice builder API. - This interface defines only 1 transition path, namely a route to the StepsOrAxisOrGet interface + This interface defines only 1 transition path, namely a route to the StepsOrAxisOrGet interface which, as the name suggests, offers 3 further call transition paths.
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      StepsOrAxisOrGet<V>
      -
      to(int index)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        StepsOrAxisOrGet<V>to(int index)
        This is the second part for defining the slice range of a specified axis within the call transition graph exposed by the slice fluent builder API.
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        to

        -
        StepsOrAxisOrGet<V> to(int index)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            to

            +
            StepsOrAxisOrGet<V> to(int index)
            This is the second part for defining the slice range of a specified axis within the call transition graph exposed by the slice fluent builder API. This method is the only transition path possible for this interface. - It is leads to the StepsOrAxisOrGet interface + It is leads to the StepsOrAxisOrGet interface which, as the name suggests, offers 3 further call transition paths. This method simply expects the completion of a specified slice range for the current axis.
            -
            -
            Parameters:
            +
            +
            Parameters:
            index - The position where the range should end.
            -
            Returns:
            +
            Returns:
            The next step in the call transition graph of this fluent slice builder API.
            -
      -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/states/ToForTensor.html b/docs/jdocs/neureka/fluent/slicing/states/ToForTensor.html index 46be721a9..7900e802f 100644 --- a/docs/jdocs/neureka/fluent/slicing/states/ToForTensor.html +++ b/docs/jdocs/neureka/fluent/slicing/states/ToForTensor.html @@ -1,150 +1,244 @@ - + + - -ToForTensor (neureka 1.0.0 API) - - - - + +ToForTensor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface ToForTensor<V>

    +
    neureka.fluent.slicing.states
    +

    Interface ToForTensor<V>

    -
    -
    +
    +
    +
    -
    -
      +
      +
      public interface ToForTensor<V>
+extends To<V>
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      StepsOrAxisOrGetTensor<V>
      -
      to(int index)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethod and Description
        StepsOrAxisOrGetTensor<V>to(int index)
        This is the second part for defining the slice range of a specified axis within the call transition graph exposed by the slice fluent builder API.
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        to

        -
        StepsOrAxisOrGetTensor<V> to(int index)
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            to

            +
            StepsOrAxisOrGetTensor<V> to(int index)
            This is the second part for defining the slice range of a specified axis within the call transition graph exposed by the slice fluent builder API. This method is the only transition path possible for this interface. - It is leads to the StepsOrAxisOrGet interface + It is leads to the StepsOrAxisOrGet interface which, as the name suggests, offers 3 further call transition paths. This method simply expects the completion of a specified slice range for the current axis.
            -
            -
            Specified by:
            -
            to in interface To<V>
            -
            Parameters:
            +
            +
            Specified by:
            +
            to in interface To<V>
            +
            Parameters:
            index - The position where the range should end.
            -
            Returns:
            +
            Returns:
            The next step in the call transition graph of this fluent slice builder API.
            -
      -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/states/ToForTsr.html b/docs/jdocs/neureka/fluent/slicing/states/ToForTsr.html deleted file mode 100644 index fc2171b28..000000000 --- a/docs/jdocs/neureka/fluent/slicing/states/ToForTsr.html +++ /dev/null @@ -1,273 +0,0 @@ - - - - - -ToForTsr (neureka 0.21.0 API) - - - - - - - - - - - - - -
    - -
    - -
    -
    -
    Package neureka.fluent.slicing.states
    -

    Interface ToForTsr<V>

    -
    -
    -
    -
      -
    • -
      -
      All Superinterfaces:
      -
      To<V>
      -
      -
      -
      All Known Implementing Classes:
      -
      AxisSliceBuilder
      -
      -
      -
      public interface ToForTsr<V>
-extends To<V>
      -
      • -
    -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Summary

        - - - - - - - - - - - - -
        All Methods Instance Methods Abstract Methods 
        Modifier and TypeMethodDescription
        StepsOrAxisOrGetTsr<V>to​(int index) -
        This is the second part for defining the slice range of a specified axis within - the call transition graph exposed by the slice fluent builder API.
        -
        -
        • -
      -
      -
      • -
    -
    -
    -
      -
    • - -
      -
        -
      • - - -

        Method Detail

        - - - -
          -
        • -

          to

          -
          StepsOrAxisOrGetTsr<V> to​(int index)
          -
          This is the second part for defining the slice range of a specified axis within - the call transition graph exposed by the slice fluent builder API. - This method is the only transition path possible for this interface. - It is leads to the StepsOrAxisOrGet interface - which, as the name suggests, offers 3 further call transition paths. - This method simply expects the completion of a specified slice range for the current axis.
          -
          -
          Specified by:
          -
          to in interface To<V>
          -
          Parameters:
          -
          index - The position where the range should end.
          -
          Returns:
          -
          The next step in the call transition graph of this fluent slice builder API.
          -
          -
          • -
        -
        • -
      -
      -
      • -
    -
    -
    -
    - -
    - -
    - - diff --git a/docs/jdocs/neureka/fluent/slicing/states/package-frame.html b/docs/jdocs/neureka/fluent/slicing/states/package-frame.html new file mode 100644 index 000000000..632649ca3 --- /dev/null +++ b/docs/jdocs/neureka/fluent/slicing/states/package-frame.html @@ -0,0 +1,26 @@ + + + + + +neureka.fluent.slicing.states (neureka 1.0.1 API) + + + + +

    neureka.fluent.slicing.states

    +
    +

    Interfaces

    + +
    + + diff --git a/docs/jdocs/neureka/fluent/slicing/states/package-summary.html b/docs/jdocs/neureka/fluent/slicing/states/package-summary.html index fe2ed600e..942d2c0cc 100644 --- a/docs/jdocs/neureka/fluent/slicing/states/package-summary.html +++ b/docs/jdocs/neureka/fluent/slicing/states/package-summary.html @@ -1,119 +1,179 @@ - + + - -neureka.fluent.slicing.states (neureka 1.0.0 API) - - - - + +neureka.fluent.slicing.states (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.fluent.slicing.states

    -
    -
    -
    package neureka.fluent.slicing.states
    -
    -
      -
    • - -
      • -
    • -
      -
      Interfaces
      -
      -
      Class
      -
      Description
      -
      AxisOrGet<V>
      -
      +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Interface Summary 
        InterfaceDescription
        AxisOrGet<V>
        This is the starting point of the call transition graph exposed by the slice builder API.
        - -
        AxisOrGetTensor<V>
        -
         
        -
        FromOrAt<V>
        -
        +
        AxisOrGetTensor<V> 
        FromOrAt<V>
        This is the starting point for defining the slice range of a specified axis within the call transition graph exposed by the slice builder API.
        - -
        FromOrAtTensor<V>
        -
         
        -
        StepsOrAxisOrGet<V>
        -
        -
        This interface extends the AxisOrGet interface which provides the option to either continue +
        FromOrAtTensor<V> 
        StepsOrAxisOrGet<V> +
        This interface extends the AxisOrGet interface which provides the option to either continue slicing another axis or simply trigger the creation and return of a slice instance based on the already provided slice configuration.
        - -
        StepsOrAxisOrGetTensor<V>
        -
         
        -
        To<V>
        -
        +
        StepsOrAxisOrGetTensor<V> 
        To<V>
        This is the second part for defining the slice range of a specified axis within the call transition graph exposed by the slice builder API.
        - -
        ToForTensor<V>
        -
         
        - - +
        ToForTensor<V> 
      -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/fluent/slicing/states/package-tree.html b/docs/jdocs/neureka/fluent/slicing/states/package-tree.html index ff709238a..318ea113b 100644 --- a/docs/jdocs/neureka/fluent/slicing/states/package-tree.html +++ b/docs/jdocs/neureka/fluent/slicing/states/package-tree.html @@ -1,90 +1,153 @@ - + + - -neureka.fluent.slicing.states Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.fluent.slicing.states Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.fluent.slicing.states

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Interface Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/NDFrame.html b/docs/jdocs/neureka/framing/NDFrame.html index 8530f6c42..156eb3fc9 100644 --- a/docs/jdocs/neureka/framing/NDFrame.html +++ b/docs/jdocs/neureka/framing/NDFrame.html @@ -1,92 +1,123 @@ - + + - -NDFrame (neureka 1.0.0 API) - - - - + +NDFrame (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.framing
    -

    Class NDFrame<V>

    -
    -
    java.lang.Object -
    neureka.framing.NDFrame<V>
    +
    neureka.framing
    +

    Class NDFrame<V>

    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.framing.NDFrame<V>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The type parameter of the value type of the tensor type to whom this component should belong.
      -
      +
      All Implemented Interfaces:
      -
      Component<Tensor<V>>
      +
      Component<Tensor<V>>
      -
      public final class NDFrame<V> -extends Object -implements Component<Tensor<V>>
      +
      +
      public final class NDFrame<V>
+extends java.lang.Object
+implements Component<Tensor<V>>
      Instances of this class are components of tensors, which store aliases for the indices of the tensor. These indices aliases can be anything that has an identity, meaning any plain old object.
      There are two layers of aliasing/labeling provided by this class: @@ -99,202 +130,266 @@

      Class NDFrame<V>

    Lets for example imagine a tensor of rank 2 with the shape (3, 4), then the axis could for example be labeled - with a tuple of two String instances like: ("a","b").
    + with a tuple of two String instances like: ("a","b").
    Labeling the indices of the axis for this example requires 2 arrays whose length matches the axis sizes.
    The following mapping would be able to label both the axis and their indices:

    "a" : ["first", "second", "third"],
    "b" : ["one", "two", "three", "four"]

    -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        NDFrame

        -
        public NDFrame(List<List<Object>> labels, - Tensor<V> host, - String mainLabel)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            NDFrame

            +
            public NDFrame(java.util.List<java.util.List<java.lang.Object>> labels,
+               Tensor<V> host,
+               java.lang.String mainLabel)
            • -
          • -
            -

            NDFrame

            -
            public NDFrame(Tensor<V> host, - String tensorName)
            -
            +
          + + + + + + + +
            +
          • +

            NDFrame

            +
            public NDFrame(java.util.Map<java.lang.Object,java.util.List<java.lang.Object>> labels,
+               Tensor<V> host,
+               java.lang.String ndaMainLabel)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      withLabel

      -
      public NDFrame<V> withLabel(String newLabel)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          withLabel

          +
          public NDFrame<V> withLabel(java.lang.String newLabel)
          • -
        • -
          -

          withAxesLabels

          -
          public NDFrame<V> withAxesLabels(List<List<Object>> labels)
          -
          +
        + + + +
          +
        • +

          withAxesLabels

          +
          public NDFrame<V> withAxesLabels(java.util.List<java.util.List<java.lang.Object>> labels)
          • -
        • -
          -

          get

          -
          public int[] get(List<Object> keys)
          -
          +
        + + + +
          +
        • +

          get

          +
          public int[] get(java.util.List<java.lang.Object> keys)
          • -
        • -
          -

          get

          -
          public int[] get(Object... keys)
          -
          +
        + + + +
          +
        • +

          get

          +
          public int[] get(java.lang.Object... keys)
          • -
        • -
          -

          hasLabelsForAxis

          -
          public boolean hasLabelsForAxis(Object axisAlias)
          -
          +
        + + + +
          +
        • +

          hasLabelsForAxis

          +
          public boolean hasLabelsForAxis(java.lang.Object axisAlias)
          • -
        • -
          -

          atAxis

          -
          public AxisFrame<Integer,V> atAxis(Object axisAlias)
          -
          A NDFrame exposes aliases for axes as well as aliases for individual positions within an axis. +
        + + + +
          +
        • +

          atAxis

          +
          public AxisFrame<java.lang.Integer,V> atAxis(java.lang.Object axisAlias)
          +
          A NDFrame exposes aliases for axes as well as aliases for individual positions within an axis. This method returns a view on a axis which is targeted by an axis alias as key. - This view is an instance of the AxisFrame class which provides useful methods + This view is an instance of the AxisFrame class which provides useful methods for getting or setting alias objects for individual positions for the given axis. This is useful when for example replacing certain aliases or simply taking a look at them.
          -
          -
          Parameters:
          -
          axisAlias - The axis alias object which targets an AxisFrame of NDFrame.
          -
          Returns:
          -
          A view of the targeted axis in the for of anAxisFrame which provides getters and setters for aliases.
          +
          +
          Parameters:
          +
          axisAlias - The axis alias object which targets an AxisFrame of NDFrame.
          +
          Returns:
          +
          A view of the targeted axis in the for of anAxisFrame which provides getters and setters for aliases.
          -
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • -
  • -
    -

    update

    -
    public boolean update(Component.OwnerChangeRequest<Tensor<V>> changeRequest)
    -
    Description copied from interface: Component
    + + + + +
      +
    • +

      update

      +
      public boolean update(Component.OwnerChangeRequest<Tensor<V>> changeRequest)
      +
      Description copied from interface: Component
      Components are not the slaves of their owners. If the owner registers any state changes related to a given component, then said component will be informed by the owner about the change as well as receive @@ -304,40 +399,105 @@

      update

      is being added to, or removed from, its current owner. If components hold references to their owners then this method gives them the ability to update said reference when a new owner takes over the components of an old one. - The Component.OwnerChangeRequest implementation instance passed to this method - informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). + The Component.OwnerChangeRequest implementation instance passed to this method + informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). If this method returns false then this means that this component rejects the proposed update. The component owner will then abort the proposed change.
      -
      -
      Specified by:
      -
      update in interface Component<V>
      -
      Parameters:
      -
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      -
      Returns:
      +
      +
      Specified by:
      +
      update in interface Component<Tensor<V>>
      +
      Parameters:
      +
      changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
      +
      Returns:
      The truth value determining if the state change should be aborted or not.
      -
    • -
  • -
    -

    getState

    -
    public Map<Object,List<Object>> getState()
    -
    + + + + +
      +
    • +

      getState

      +
      public java.util.Map<java.lang.Object,java.util.List<java.lang.Object>> getState()
      • -
    • -
      -

      getLabel

      -
      public String getLabel()
      -
      +
    + + + +
      +
    • +

      getLabel

      +
      public java.lang.String getLabel()
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/Relation.html b/docs/jdocs/neureka/framing/Relation.html index bf3d69331..8f4e0b16f 100644 --- a/docs/jdocs/neureka/framing/Relation.html +++ b/docs/jdocs/neureka/framing/Relation.html @@ -1,214 +1,283 @@ - + + - -Relation (neureka 1.0.0 API) - - - - + +Relation (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.framing
    -

    Class Relation<V>

    -
    -
    java.lang.Object -
    neureka.framing.Relation<V>
    +
    neureka.framing
    +

    Class Relation<V>

    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.framing.Relation<V>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The data type class of the elements of the tensor to which this Relation belongs to.
      -
      +
      All Implemented Interfaces:
      -
      Component<Tensor<V>>
      +
      Component<Tensor<V>>
      -
      public class Relation<V> -extends Object -implements Component<Tensor<V>>
      +
      +
      public class Relation<V>
+extends java.lang.Object
+implements Component<Tensor<V>>
      This class is an important tensor component responsible for managing the relationships between slices and the tensors from which they have been derived. - In case a tensor is a slice then it will have a Relation instance as + In case a tensor is a slice then it will have a Relation instance as component which will reference the parent tensor strongly (so that its data will not be lost). However, in case a tensor is the "parent" of a slice tensor then it will - contain a Relation instance which references the slices weakly (so that they can be garbage collected).
      + contain a Relation instance which references the slices weakly (so that they can be garbage collected).

      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        newParentToChildren

        -
        public static <T> Relation<T> newParentToChildren()
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            newParentToChildren

            +
            public static <T> Relation<T> newParentToChildren()
            • -
          • -
            -

            newChildToParent

            -
            public static <T> Relation<T> newChildToParent(Tensor<T> parent)
            -
            +
          + + + + + + + +
            +
          • +

            update

            +
            public boolean update(Component.OwnerChangeRequest<Tensor<V>> changeRequest)
            +
            Description copied from interface: Component
            Components are not the slaves of their owners. If the owner registers any state changes related to a given component, then said component will be informed by the owner about the change as well as receive @@ -218,31 +287,37 @@

            update

            is being added to, or removed from, its current owner. If components hold references to their owners then this method gives them the ability to update said reference when a new owner takes over the components of an old one. - The Component.OwnerChangeRequest implementation instance passed to this method - informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). + The Component.OwnerChangeRequest implementation instance passed to this method + informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). If this method returns false then this means that this component rejects the proposed update. The component owner will then abort the proposed change.
            -
            -
            Specified by:
            -
            update in interface Component<V>
            -
            Parameters:
            -
            changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
            -
            Returns:
            +
            +
            Specified by:
            +
            update in interface Component<Tensor<V>>
            +
            Parameters:
            +
            changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
            +
            Returns:
            The truth value determining if the state change should be aborted or not.
            -
      • -
    • -
      -

      addChild

      -
      public Relation<V> addChild(Tensor<V> child)
      -
      +
    + + + +
      +
    • +

      addChild

      +
      public Relation<V> addChild(Tensor<V> child)
      • -
    • -
      -

      addPermuteRelationFor

      -
      public void addPermuteRelationFor(Tensor<V> child, - int[] permuteOrder)
      +
    + + + +
      +
    • +

      addPermuteRelationFor

      +
      public void addPermuteRelationFor(Tensor<V> child,
+                                  int[] permuteOrder)
      When creating permuted versions of slices then there must be a translation between the shape configuration between this new slice and the original parent tensor from which both slices @@ -256,17 +331,20 @@

      addPermuteRelationFor

      to a slice, which is also the "child" of the tensor to which this Relation component belongs!

      -
      -
      Parameters:
      +
      +
      Parameters:
      child - The child (slice) tensor which has a shape whose dimensions are in a different order.
      permuteOrder - The int array defining the axis order (dimension index mapping).
      -
      • -
    • -
      -

      getPermuteRelationFor

      -
      public int[] getPermuteRelationFor(Tensor<V> child)
      +
    + + + +
      +
    • +

      getPermuteRelationFor

      +
      public int[] getPermuteRelationFor(Tensor<V> child)
      When creating permuted versions of slices then there must be a translation between the shape configuration between this new slice and the original parent tensor from which both slices @@ -280,81 +358,164 @@

      getPermuteRelationFor

      from the order of the parent tensor (which is the component owner of this Relation) and the passed slice (which is a weakly referenced child tensor...).

      -
      -
      Parameters:
      +
      +
      Parameters:
      child - The child (slice) tensor which has a shape whose dimensions are in a different order.
      -
      Returns:
      +
      Returns:
      The int array defining the reshaping (dimension index mapping).
      -
      • -
    • -
      -

      getChildren

      -
      public List<Tensor<?>> getChildren()
      -
      +
    + + + +
      +
    • +

      getChildren

      +
      public java.util.List<Tensor<?>> getChildren()
      • -
    • -
      -

      findRootTensor

      -
      public Optional<Tensor<V>> findRootTensor()
      +
    + + + +
      +
    • +

      findRootTensor

      +
      public java.util.Optional<Tensor<V>> findRootTensor()
      This method tries to find the root data ancestor of this tensor. If this tensor is not a slice of another tensor, then it can not have data parents and therefore also not a root tensor, in which case the method will return null!
      -
      -
      Returns:
      +
      +
      Returns:
      The root data parent which actually owns the data of the sliced data or null if the tensor is not a slice.
      -
      • -
    • -
      -

      hasParent

      -
      public boolean hasParent()
      -
      +
    + + + +
      +
    • +

      hasParent

      +
      public boolean hasParent()
      • -
    • -
      -

      hasChildren

      -
      public boolean hasChildren()
      -
      +
    + + + +
      +
    • +

      hasChildren

      +
      public boolean hasChildren()
      • -
    • -
      -

      childCount

      -
      public int childCount()
      -
      +
    + + + +
      +
    • +

      childCount

      +
      public int childCount()
      • -
    • -
      -

      removeChild

      -
      public void removeChild(Tensor<V> child)
      -
      +
    + + + +
      +
    • +

      removeChild

      +
      public void removeChild(Tensor<V> child)
      • -
    • -
      -

      toString

      -
      public String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
      • -
    • -
      -

      getParent

      -
      public Optional<Tensor<V>> getParent()
      -
      +
    + + + +
      +
    • +

      getParent

      +
      public java.util.Optional<Tensor<V>> getParent()
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
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    + diff --git a/docs/jdocs/neureka/framing/fluent/At.html b/docs/jdocs/neureka/framing/fluent/At.html index e4517a9d4..1c1a553ff 100644 --- a/docs/jdocs/neureka/framing/fluent/At.html +++ b/docs/jdocs/neureka/framing/fluent/At.html @@ -1,129 +1,225 @@ - + + - -At (neureka 1.0.0 API) - - - - + +At (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.framing.fluent
    -

    Interface At<K,R>

    +
    neureka.framing.fluent
    +

    Interface At<K,R>

    -
    -
    -
    Type Parameters:
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      K - The key type which will be provided by the user of this method.
      R - The return type which will be provided by an implementation of this method.
      -
      public interface At<K,R>
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      R
      -
      at(K key)
      -
       
      -
      -
      +
      +
      public interface At<K,R>
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        at

        -
        R at(K key)
        -
        + -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
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      • +
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      • +
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      +
    • Detail: 
      • +
    • Field | 
      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/fluent/AxisFrame.Builder.html b/docs/jdocs/neureka/framing/fluent/AxisFrame.Builder.html index b98ab2f21..6eb6aab54 100644 --- a/docs/jdocs/neureka/framing/fluent/AxisFrame.Builder.html +++ b/docs/jdocs/neureka/framing/fluent/AxisFrame.Builder.html @@ -1,180 +1,303 @@ - + + - -AxisFrame.Builder (neureka 1.0.0 API) - - - - + +AxisFrame.Builder (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.framing.fluent
    -

    Class AxisFrame.Builder<SetType,GetType,ValueType>

    -
    -
    java.lang.Object -
    neureka.framing.fluent.AxisFrame.Builder<SetType,GetType,ValueType>
    +
    neureka.framing.fluent
    +

    Class AxisFrame.Builder<SetType,GetType,ValueType>

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.framing.fluent.AxisFrame.Builder<SetType,GetType,ValueType>
        • +
      +
      • +
    +
    +
    -
    -
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    - +
    + - -
    -
      +
    +
    + - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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    + + + +
    + +
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    +
      +
    • Summary: 
      • +
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      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/fluent/AxisFrame.Set.html b/docs/jdocs/neureka/framing/fluent/AxisFrame.Set.html index d88c18d28..350bf4ccd 100644 --- a/docs/jdocs/neureka/framing/fluent/AxisFrame.Set.html +++ b/docs/jdocs/neureka/framing/fluent/AxisFrame.Set.html @@ -1,128 +1,222 @@ - + + - -AxisFrame.Set (neureka 1.0.0 API) - - - - + +AxisFrame.Set (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.framing.fluent
    -

    Interface AxisFrame.Set<V>

    +
    neureka.framing.fluent
    +

    Interface AxisFrame.Set<V>

    -
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    +
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    -
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      - -
    • -
      -

      Method Summary

      -
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      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      NDFrame<V>
      -
      setIndex(int value)
      -
       
      -
      -
      +
      +
      public static interface AxisFrame.Set<V>
      +
      • +
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    + - -
    -
      +
    +
    +
      +
    • -
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      -

      Method Details

      -
        -
      • -
        -

        setIndex

        -
        NDFrame<V> setIndex(int value)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            setIndex

            +
            NDFrame<V> setIndex(int value)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
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    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
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    • Constr | 
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    • Method
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      +
    • Detail: 
      • +
    • Field | 
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    • Constr | 
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    • Method
      • +
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    + diff --git a/docs/jdocs/neureka/framing/fluent/AxisFrame.html b/docs/jdocs/neureka/framing/fluent/AxisFrame.html index 5b31ff2aa..8353fb7c3 100644 --- a/docs/jdocs/neureka/framing/fluent/AxisFrame.html +++ b/docs/jdocs/neureka/framing/fluent/AxisFrame.html @@ -1,205 +1,328 @@ - + + - -AxisFrame (neureka 1.0.0 API) - - - - + +AxisFrame (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.framing.fluent
    -

    Class AxisFrame<G,V>

    -
    -
    java.lang.Object -
    neureka.framing.fluent.AxisFrame<G,V>
    +
    neureka.framing.fluent
    +

    Class AxisFrame<G,V>

    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.framing.fluent.AxisFrame<G,V>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      G - The get type which represents the aliases for this axis.
      -
      V - The value type which is the value type of the Tensor with this AxisFrame.
      +
      V - The value type which is the value type of the Tensor with this AxisFrame.
      -
      public final class AxisFrame<G,V> -extends Object
      -
      This class represents the labeled axis of an NDFrame.
      -
    -
    -
      - -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Class
      -
      Description
      -
      static class 
      -
      AxisFrame.Builder<SetType,GetType,ValueType>
      -
       
      -
      static interface 
      -
      AxisFrame.Set<V>
      -
       
      +
      +
      public final class AxisFrame<G,V>
+extends java.lang.Object
      +
      This class represents the labeled axis of an NDFrame.
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        builder

        -
        public static <SetType, -GetType, -ValueType> -AxisFrame.Builder<SetType,GetType,ValueType> builder()
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            builder

            +
            public static <SetType,GetType,ValueType> AxisFrame.Builder<SetType,GetType,ValueType> builder()
            • -
          • -
            -

            getIndexAtAlias

            -
            public G getIndexAtAlias(Object aliasKey)
            -
            +
          + + + +
            +
          • +

            getIndexAtAlias

            +
            public G getIndexAtAlias(java.lang.Object aliasKey)
            • -
          • -
            -

            atIndexAlias

            -
            public AxisFrame.Set<V> atIndexAlias(Object aliasKey)
            -
            +
          + + + + + + + +
            +
          • +

            replace

            +
            public With<java.lang.Object,NDFrame<V>> replace(java.lang.Object indexAlias)
            • -
          • -
            -

            getAllAliases

            -
            public List<Object> getAllAliases()
            -
            +
          + + + +
            +
          • +

            getAllAliases

            +
            public java.util.List<java.lang.Object> getAllAliases()
            • -
          • -
            -

            getAllAliasesForIndex

            -
            public List<Object> getAllAliasesForIndex(int index)
            -
            +
          + + + +
            +
          • +

            getAllAliasesForIndex

            +
            public java.util.List<java.lang.Object> getAllAliasesForIndex(int index)
          -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
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      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/fluent/Get.html b/docs/jdocs/neureka/framing/fluent/Get.html index 3511506e2..813f9dd5a 100644 --- a/docs/jdocs/neureka/framing/fluent/Get.html +++ b/docs/jdocs/neureka/framing/fluent/Get.html @@ -1,124 +1,218 @@ - + + - -Get (neureka 1.0.0 API) - - - - + +Get (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.framing.fluent
    -

    Interface Get<ValueType>

    +
    neureka.framing.fluent
    +

    Interface Get<ValueType>

    -
    +
    +
    +
      +
    • -
      public interface Get<ValueType>
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      ValueType
      -
      get()
      -
       
      -
      -
      +
      +
      public interface Get<ValueType>
      +
      • +
    - +
    + - -
    -
      +
    +
    + - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
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      +
    • Summary: 
      • +
    • Nested | 
      • +
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    • Constr | 
      • +
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      • +
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      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/fluent/Replace.html b/docs/jdocs/neureka/framing/fluent/Replace.html index 365b8d704..fc59bbaf6 100644 --- a/docs/jdocs/neureka/framing/fluent/Replace.html +++ b/docs/jdocs/neureka/framing/fluent/Replace.html @@ -1,124 +1,220 @@ - + + - -Replace (neureka 1.0.0 API) - - - - + +Replace (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.framing.fluent
    -

    Interface Replace<ValueType,ReplacementType,ReturnType>

    +
    neureka.framing.fluent
    +

    Interface Replace<ValueType,ReplacementType,ReturnType>

    -
    +
    +
    +
      +
    • -
      public interface Replace<ValueType,ReplacementType,ReturnType>
      -
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      - -
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      Method Summary

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      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      With<ReplacementType,ReturnType>
      -
      replace(ValueType value)
      -
       
      -
      -
      +
      +
      public interface Replace<ValueType,ReplacementType,ReturnType>
      +
      • +
    - +
    + - -
    -
      +
    +
    + - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/fluent/Set.html b/docs/jdocs/neureka/framing/fluent/Set.html index 6b8ed0da6..1aaaa061f 100644 --- a/docs/jdocs/neureka/framing/fluent/Set.html +++ b/docs/jdocs/neureka/framing/fluent/Set.html @@ -1,124 +1,218 @@ - + + - -Set (neureka 1.0.0 API) - - - - + +Set (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.framing.fluent
    -

    Interface Set<V>

    +
    neureka.framing.fluent
    +

    Interface Set<V>

    -
    +
    +
    +
      +
    • -
      public interface Set<V>
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      NDFrame<V>
      -
      set(int value)
      -
       
      -
      -
      +
      +
      public interface Set<V>
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/fluent/With.html b/docs/jdocs/neureka/framing/fluent/With.html index 2b41747b7..cc07c027b 100644 --- a/docs/jdocs/neureka/framing/fluent/With.html +++ b/docs/jdocs/neureka/framing/fluent/With.html @@ -1,124 +1,220 @@ - + + - -With (neureka 1.0.0 API) - - - - + +With (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.framing.fluent
    -

    Interface With<ValueType,TargetType>

    +
    neureka.framing.fluent
    +

    Interface With<ValueType,TargetType>

    -
    +
    +
    +
      +
    • -
      public interface With<ValueType,TargetType>
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      TargetType
      -
      with(ValueType value)
      -
       
      -
      -
      +
      +
      public interface With<ValueType,TargetType>
      +
      • +
    - +
    + - -
    -
      +
    +
    + - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/fluent/package-frame.html b/docs/jdocs/neureka/framing/fluent/package-frame.html new file mode 100644 index 000000000..afaddb33c --- /dev/null +++ b/docs/jdocs/neureka/framing/fluent/package-frame.html @@ -0,0 +1,29 @@ + + + + + +neureka.framing.fluent (neureka 1.0.1 API) + + + + +

    neureka.framing.fluent

    +
    +

    Interfaces

    + +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/framing/fluent/package-summary.html b/docs/jdocs/neureka/framing/fluent/package-summary.html index cdd1db91b..97bf4504c 100644 --- a/docs/jdocs/neureka/framing/fluent/package-summary.html +++ b/docs/jdocs/neureka/framing/fluent/package-summary.html @@ -1,115 +1,180 @@ - + + - -neureka.framing.fluent (neureka 1.0.0 API) - - - - + +neureka.framing.fluent (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.framing.fluent

    -
    -
    -
    package neureka.framing.fluent
    -
    -
      -
    • - +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Interface Summary 
        InterfaceDescription
        At<K,R> 
        AxisFrame.Set<V> 
        Get<ValueType> 
        Replace<ValueType,ReplacementType,ReturnType> 
        Set<V> 
        With<ValueType,TargetType> 
        • -
      • -
        -
        -
        -
        -
        Class
        -
        Description
        -
        At<K,R>
        -
         
        -
        AxisFrame<G,V>
        -
        -
        This class represents the labeled axis of an NDFrame.
        -
        -
        AxisFrame.Builder<SetType,GetType,ValueType>
        -
         
        -
        AxisFrame.Set<V>
        -
         
        -
        Get<ValueType>
        -
         
        -
        Replace<ValueType,ReplacementType,ReturnType>
        -
         
        -
        Set<V>
        -
         
        -
        With<ValueType,TargetType>
        -
         
        -
        -
        -
        +
      • + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        AxisFrame<G,V> +
        This class represents the labeled axis of an NDFrame.
        +
        AxisFrame.Builder<SetType,GetType,ValueType> 
      -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/fluent/package-tree.html b/docs/jdocs/neureka/framing/fluent/package-tree.html index e243468f5..72e9236dd 100644 --- a/docs/jdocs/neureka/framing/fluent/package-tree.html +++ b/docs/jdocs/neureka/framing/fluent/package-tree.html @@ -1,83 +1,144 @@ - + + - -neureka.framing.fluent Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.framing.fluent Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.framing.fluent

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -

    Interface Hierarchy

      -
    • neureka.framing.fluent.At<K,R>
      • -
    • neureka.framing.fluent.AxisFrame.Set<V>
      • -
    • neureka.framing.fluent.Get<ValueType>
      • -
    • neureka.framing.fluent.Replace<ValueType,ReplacementType,ReturnType>
      • -
    • neureka.framing.fluent.Set<V>
      • -
    • neureka.framing.fluent.With<ValueType,TargetType>
      • +
    • neureka.framing.fluent.At<K,R>
      • +
    • neureka.framing.fluent.AxisFrame.Set<V>
      • +
    • neureka.framing.fluent.Get<ValueType>
      • +
    • neureka.framing.fluent.Replace<ValueType,ReplacementType,ReturnType>
      • +
    • neureka.framing.fluent.Set<V>
      • +
    • neureka.framing.fluent.With<ValueType,TargetType>
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/package-frame.html b/docs/jdocs/neureka/framing/package-frame.html new file mode 100644 index 000000000..0d3f48438 --- /dev/null +++ b/docs/jdocs/neureka/framing/package-frame.html @@ -0,0 +1,20 @@ + + + + + +neureka.framing (neureka 1.0.1 API) + + + + +

    neureka.framing

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/framing/package-summary.html b/docs/jdocs/neureka/framing/package-summary.html index 609551ca4..0cdba5961 100644 --- a/docs/jdocs/neureka/framing/package-summary.html +++ b/docs/jdocs/neureka/framing/package-summary.html @@ -1,103 +1,149 @@ - + + - -neureka.framing (neureka 1.0.0 API) - - - - + +neureka.framing (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.framing

    +

    Package neureka.framing

    -
    -
    package neureka.framing
    -
    -
      -
    • - -
      • -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      NDFrame<V>
      -
      +
      +
        +
      • + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        NDFrame<V>
        Instances of this class are components of tensors, which store aliases for the indices of the tensor.
        - -
        Relation<V>
        -
        +
        Relation<V>
        This class is an important tensor component responsible for managing the relationships between slices and the tensors from which they have been derived.
        - - - +
      -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/framing/package-tree.html b/docs/jdocs/neureka/framing/package-tree.html index 1a71d0385..2a790affe 100644 --- a/docs/jdocs/neureka/framing/package-tree.html +++ b/docs/jdocs/neureka/framing/package-tree.html @@ -1,72 +1,135 @@ - + + - -neureka.framing Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.framing Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.framing

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/Function.Callable.html b/docs/jdocs/neureka/math/Function.Callable.html index 019f2baad..6a9b6e21c 100644 --- a/docs/jdocs/neureka/math/Function.Callable.html +++ b/docs/jdocs/neureka/math/Function.Callable.html @@ -1,179 +1,281 @@ - + + - -Function.Callable (neureka 1.0.0 API) - - - - + +Function.Callable (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math
    -

    Interface Function.Callable

    +
    neureka.math
    +

    Interface Function.Callable

    -
    -
    +
    +
    +
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      default <T> Tensor<T>
      -
      call(Tensor<T>... inputs)
      -
       
      -
      Tensor<?>
      -
      execute(Tensor<?>... inputs)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + +
        All Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethod and Description
        default <T> Tensor<T>call(Tensor<T>... inputs) 
        Tensor<?>execute(Tensor<?>... inputs)
        Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
        - -
        default <T> Tensor<T>
        -
        invoke(Tensor<T>... inputs)
        -
        -
        This method is functionally identically to call(Tensor[]), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
        -
        - - - - +
        default <T> Tensor<T>invoke(Tensor<T>... inputs) +
        This method is functionally identically to call(Tensor[]), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
        +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        invoke

        -
        default <T> Tensor<T> invoke(Tensor<T>... inputs)
        -
        This method is functionally identically to call(Tensor[]), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
        -
        -
        Type Parameters:
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            invoke

            +
            default <T> Tensor<T> invoke(Tensor<T>... inputs)
            +
            This method is functionally identically to call(Tensor[]), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
            +
            +
            Type Parameters:
            T - The type parameter of the tensors passed to and returned by this function.
            -
            Parameters:
            -
            inputs - The tensors which should be sent through the owner function of this Function.Callable.
            -
            Returns:
            +
            Parameters:
            +
            inputs - The tensors which should be sent through the owner function of this Function.Callable.
            +
            Returns:
            The resulting tensor produced by this function.
            -
        • -
      • -
        -

        call

        -
        default <T> Tensor<T> call(Tensor<T>... inputs)
        -
        -
        Type Parameters:
        +
      + + + +
        +
      • +

        call

        +
        default <T> Tensor<T> call(Tensor<T>... inputs)
        +
        +
        Type Parameters:
        T - The type parameter of the tensors passed to and returned by this function.
        -
        Parameters:
        -
        inputs - The tensors which should be sent through the owner function of this Function.Callable.
        -
        Returns:
        +
        Parameters:
        +
        inputs - The tensors which should be sent through the owner function of this Function.Callable.
        +
        Returns:
        The resulting tensor produced by this function.
        -
      • -
    • -
      -

      execute

      -
      Tensor<?> execute(Tensor<?>... inputs)
      +
    + + + +
      +
    • +

      execute

      +
      Tensor<?> execute(Tensor<?>... inputs)
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - The tensors which should be sent through this function.
      -
      Returns:
      +
      Returns:
      The result from the execution of the provided tensors.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/Function.html b/docs/jdocs/neureka/math/Function.html index 3df8f96a1..8324b42cd 100644 --- a/docs/jdocs/neureka/math/Function.html +++ b/docs/jdocs/neureka/math/Function.html @@ -1,87 +1,113 @@ - + + - -Function (neureka 1.0.0 API) - - - - + +Function (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math
    -

    Interface Function

    +
    neureka.math
    +

    Interface Function

    -
    -
    +
    +
    +
      +
    • +
      All Known Implementing Classes:
      -
      FunctionConstant, FunctionInput, FunctionNode, FunctionVariable
      +
      FunctionConstant, FunctionInput, FunctionNode, FunctionVariable
      -
      public interface Function
      -
      Besides the Tensor class, which is the core class of Neureka, this interface and its implementations +
      +
      public interface Function
      +
      Besides the Tensor class, which is the core class of Neureka, this interface and its implementations represents the second most important feature of this library. - Instances of Function implementations form an abstract syntax tree which is being built - from a provided expression String containing function syntax. + Instances of Function implementations form an abstract syntax tree which is being built + from a provided expression String containing function syntax.

      Just like functions in the mathematical sense, implementations of this interface receive a fixed number of inputs. @@ -89,958 +115,1196 @@

      Interface Function

      recognized by 'I[j]', 'Ij' or 'ij', where j is the input index. Functions accept arrays as their inputs, which is why variables must be targeted in such a way.
      -
    -
    -
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Interface
      -
      Description
      -
      static interface 
      -
      Function.Callable
      -
      -
      An API for calling a Function after having specified - a set of Arg instances through the with(Args) +
        +
      • + + +

        Nested Class Summary

        + + + + + + + + + + +
        Nested Classes 
        Modifier and TypeInterface and Description
        static interface Function.Callable +
        An API for calling a Function after having specified + a set of Arg instances through the with(Args) method.
        - - - +
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      default double
      -
      call(double input)
      -
      -
      Invokes this Function with the provided scalar as a single input and returns the scalar result.
      -
      -
      default double
      -
      call(double... inputs)
      -
      -
      Invokes this Function with the provided array of inputs.
      -
      -
      double
      -
      call(double[] inputs, - int j)
      -
      -
      Invokes this Function with the provided array of inputs ad an index for input dependent indexing.
      -
      -
      default <T> Tensor<T>
      -
      call(List<Tensor<T>> inputs)
      -
       
      -
      default <T, -D extends Device<T>>
      Tensor<T>
      -
      call(Call.Builder<T,D> call)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethod and Description
        default <T> Tensor<T>call(Args arguments, + Tensor<T>... tensors) +
        Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
        +
        default <T,D extends Device<T>>
        Tensor<T>        		call(Call.Builder<T,D> call)
        Use this for more control over the execution, which is especially useful when interfacing with more complex types of operations, requiring more context information.
        - -
        default <T, -D extends Device<T>>
        Tensor<T>
        -
        call(Call<D> call)
        -
        +
        default <T,D extends Device<T>>
        Tensor<T>        		call(Call<D> call)
        Use this for more control over the execution, which is very helpful when interfacing with more complex types of operations, requiring more context information.
        - -
        default <T> Tensor<T>
        -
        call(Args arguments, - Tensor<T>... tensors)
        -
        -
        Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
        -
        -
        default <T> Tensor<T>
        -
        call(Tensor<T> input)
        -
         
        -
        default <T> Tensor<T>
        -
        call(Tensor<T>... inputs)
        -
         
        -
        default <T> Tensor<T>
        -
        call(Tensor<T>[] inputs, - int j)
        -
         
        -
        boolean
        -
        dependsOn(int index)
        -
        +
        default doublecall(double... inputs) +
        Invokes this Function with the provided array of inputs.
        +
        default doublecall(double input) +
        Invokes this Function with the provided scalar as a single input and returns the scalar result.
        +
        doublecall(double[] inputs, + int j) +
        Invokes this Function with the provided array of inputs ad an index for input dependent indexing.
        +
        default <T> Tensor<T>call(java.util.List<Tensor<T>> inputs) 
        default <T> Tensor<T>call(Tensor<T>... inputs) 
        default <T> Tensor<T>call(Tensor<T> input) 
        default <T> Tensor<T>call(Tensor<T>[] inputs, + int j) 
        booleandependsOn(int index)
        Use this to determine if this function directly or indirectly references an input with the provided index.
        - -
        double
        -
        derive(double[] inputs, - int index)
        -
        -
        Calculates the derivative of a particular input with respect to the output of this Function +
        doublederive(double[] inputs, + int index) +
        Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs and an index targeting the input to be derived.
        - -
        double
        -
        derive(double[] inputs, - int index, - int j)
        -
        -
        Calculates the derivative of a particular input with respect to the output of this Function +
        doublederive(double[] inputs, + int index, + int j) +
        Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs, an index targeting the input to be derived and an index for input dependent indexing.
        - -
        default <T> Tensor<T>
        -
        derive(List<Tensor<T>> inputs, - int index)
        -
         
        -
        default <T> Tensor<T>
        -
        derive(List<Tensor<T>> inputs, - int index, - int j)
        -
         
        -
        default <T> Tensor<T>
        -
        derive(Tensor<T>[] inputs, - int index)
        -
         
        -
        default <T> Tensor<T>
        -
        derive(Tensor<T>[] inputs, - int index, - int j)
        -
         
        -
        default Tensor<?>
        -
        execute(Call<?> call)
        -
        -
        Warning: Tensors returned by this method are eligible for deletion when consumed by other functions. - Use this to pass more context information for execution of input tensors.
        -
        -
        Tensor<?>
        -
        execute(Args arguments, - Tensor<?>... inputs)
        -
        +
        default <T> Tensor<T>derive(java.util.List<Tensor<T>> inputs, + int index) 
        default <T> Tensor<T>derive(java.util.List<Tensor<T>> inputs, + int index, + int j) 
        default <T> Tensor<T>derive(Tensor<T>[] inputs, + int index) 
        default <T> Tensor<T>derive(Tensor<T>[] inputs, + int index, + int j) 
        Tensor<?>execute(Args arguments, + Tensor<?>... inputs)
        Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
        - Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
        - -
        default Tensor<?>
        -
        execute(Tensor<?>... inputs)
        -
        + Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
        +
        default Tensor<?>execute(Call<?> call) +
        Warning: Tensors returned by this method are eligible for deletion when consumed by other functions. + Use this to pass more context information for execution of input tensors.
        +
        default Tensor<?>execute(Tensor<?>... inputs)
        Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
        - -
        default Tensor<?>
        -
        execute(Tensor<?>[] inputs, - int j)
        -
        +
        default Tensor<?>execute(Tensor<?>[] inputs, + int j)
        Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
        - -
        default Tensor<?>
        -
        executeDerive(Tensor<?>[] inputs, - int index)
        -
        +
        default Tensor<?>executeDerive(Tensor<?>[] inputs, + int index)
        Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
        - -
        default Tensor<?>
        -
        executeDerive(Tensor<?>[] inputs, - int index, - int j)
        -
        +
        default Tensor<?>executeDerive(Tensor<?>[] inputs, + int index, + int j)
        Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
        - -
        default List<Function>
        -
        getAllFunctions()
        -
         
        -
        Function
        -
        getDerivative(int index)
        -
        -
        This method builds a new Function which is the derivative of this Function with respect to the provided input index.
        -
        -
        Operation
        -
        getOperation()
        -
         
        -
        List<Function>
        -
        getSubFunctions()
        -
         
        -
        default double
        -
        invoke(double input)
        -
        -
        Invokes this Function with the provided scalar as a single input and returns the scalar result.
        -
        -
        default double
        -
        invoke(double... inputs)
        -
        -
        Invokes this Function with the provided array of inputs.
        -
        -
        default double
        -
        invoke(double[] inputs, - int j)
        -
        -
        Invokes this Function with the provided array of inputs ad an index for input dependent indexing.
        -
        -
        default <T> Tensor<T>
        -
        invoke(List<Tensor<T>> input)
        -
        -
        This method is functionally identically to call(List), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
        -
        -
        default <T, -D extends Device<T>>
        Tensor<T>
        -
        invoke(Call.Builder<T,D> call)
        -
        +
        default java.util.List<Function>getAllFunctions() 
        FunctiongetDerivative(int index) +
        This method builds a new Function which is the derivative of this Function with respect to the provided input index.
        +
        OperationgetOperation() 
        java.util.List<Function>getSubFunctions() 
        default <T> Tensor<T>invoke(Args arguments, + Tensor<T>... inputs) +
        Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
        +
        default <T,D extends Device<T>>
        Tensor<T>        		invoke(Call.Builder<T,D> call)
        Use this to pass more context information for execution of input tensors.
        - -
        default <T> Tensor<T>
        -
        invoke(Args arguments, - Tensor<T>... inputs)
        -
        -
        Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
        -
        -
        default <T> Tensor<T>
        -
        invoke(Tensor<T> input)
        -
        -
        This method is functionally identically to call(Tensor), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
        -
        -
        default <T> Tensor<T>
        -
        invoke(Tensor<T>... inputs)
        -
        -
        This method is functionally identically to call(Tensor[]), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
        -
        -
        default <T> Tensor<T>
        -
        invoke(Tensor<T>[] inputs, - int j)
        -
        -
        This method is functionally identically to call(Tensor[], int), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
        -
        -
        boolean
        -
        isDoingAD()
        -
        -
        Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions - whose isFlat() flag is set to false!
        -
        -
        boolean
        -
        isFlat()
        -
         
        -
        default int
        -
        numberOfArgs()
        -
         
        -
        static Function
        -
        of(String expression)
        -
        -
        This static factory method will return Function instances - based on a provided mathematical String expression describing the function +
        default doubleinvoke(double... inputs) +
        Invokes this Function with the provided array of inputs.
        +
        default doubleinvoke(double input) +
        Invokes this Function with the provided scalar as a single input and returns the scalar result.
        +
        default doubleinvoke(double[] inputs, + int j) +
        Invokes this Function with the provided array of inputs ad an index for input dependent indexing.
        +
        default <T> Tensor<T>invoke(java.util.List<Tensor<T>> input) +
        This method is functionally identically to call(List), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
        +
        default <T> Tensor<T>invoke(Tensor<T>... inputs) +
        This method is functionally identically to call(Tensor[]), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
        +
        default <T> Tensor<T>invoke(Tensor<T> input) +
        This method is functionally identically to call(Tensor), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
        +
        default <T> Tensor<T>invoke(Tensor<T>[] inputs, + int j) +
        This method is functionally identically to call(Tensor[], int), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
        +
        booleanisDoingAD() +
        Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions + whose isFlat() flag is set to false!
        +
        booleanisFlat() 
        default intnumberOfArgs() 
        static Functionof(java.lang.String expression) +
        This static factory method will return Function instances + based on a provided mathematical String expression describing the function using 'I[0]', 'I[1]', 'I[2]'...
        - -
        static Function
        -
        of(String expression, - boolean doAD)
        -
        -
        This static factory method will return Function instances - based on a provided mathematical String expression describing the function +
        static Functionof(java.lang.String expression, + boolean doAD) +
        This static factory method will return Function instances + based on a provided mathematical String expression describing the function using 'I[0]', 'I[1]', 'I[2]'...
        - -
        String
        -
        toString()
        -
        +
        java.lang.StringtoString()
        Turns this function into a string representation which can be used to reconstruct this function or combine it with other function strings to parse entirely new functions...
        - -
        default Function.Callable
        -
        with(Arg<?>... arguments)
        -
        -
        Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
        -
        -
        default Function.Callable
        -
        with(Args arguments)
        -
        -
        Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
        -
        - - - - +
        default Function.Callablewith(Arg<?>... arguments) +
        Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
        +
        default Function.Callablewith(Args arguments) +
        Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
        +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        static Function of(String expression)
        -
        This static factory method will return Function instances - based on a provided mathematical String expression describing the function +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            of

            +
            static Function of(java.lang.String expression)
            +
            This static factory method will return Function instances + based on a provided mathematical String expression describing the function using 'I[0]', 'I[1]', 'I[2]'... as input variables or 'I[j]' to enable input dependent indexing like for example "sum( I[j] / 2 )". - The Function instances returned by this method will - by default perform autograd if any involved input Tensor requires gradients (see Tensor.rqsGradient()). - If one wishes to disable this behavior one might consider the use of the of(String, boolean) + The Function instances returned by this method will + by default perform autograd if any involved input Tensor requires gradients (see Tensor.rqsGradient()). + If one wishes to disable this behavior one might consider the use of the of(String, boolean) factory method.
            -
            -
            Parameters:
            +
            +
            Parameters:
            expression - The right part of a function equation where inputs are denoted by 'I[0]', 'I[1]', 'I[2]'...
            -
            Returns:
            -
            A Function instance created based on the provided String, ready to receive inputs and execute on them.
            +
            Returns:
            +
            A Function instance created based on the provided String, ready to receive inputs and execute on them.
            -
        • -
      • -
        -

        of

        -
        static Function of(String expression, - boolean doAD)
        -
        This static factory method will return Function instances - based on a provided mathematical String expression describing the function +
      + + + +
        +
      • +

        of

        +
        static Function of(java.lang.String expression,
+                   boolean doAD)
        +
        This static factory method will return Function instances + based on a provided mathematical String expression describing the function using 'I[0]', 'I[1]', 'I[2]'... as input variables or 'I[j]' to enable input dependent indexing - like for example "sum( I[j] / 2 )" as well as a flag determining if the resulting Function + like for example "sum( I[j] / 2 )" as well as a flag determining if the resulting Function ought to be able to perform autograd or not.
        -
        -
        Parameters:
        +
        +
        Parameters:
        expression - The right part of a function equation where inputs are denoted by 'I[0]', 'I[1]', 'I[2]'...
        -
        doAD - A flag determining if the produced Function should be able to perform autograd (aka. auto-differentiation)
        -
        Returns:
        -
        A Function instance created based on the provided String, ready to receive inputs and execute on them.
        +
        doAD - A flag determining if the produced Function should be able to perform autograd (aka. auto-differentiation)
        +
        Returns:
        +
        A Function instance created based on the provided String, ready to receive inputs and execute on them.
        -
      • -
    • -
      -

      isDoingAD

      -
      boolean isDoingAD()
      -
      Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions - whose isFlat() flag is set to false!
      -
      -
      Returns:
      -
      The truth value determining if this Function can perform autograd/auto-differentiation on the input tensors it receives.
      +
    + + + +
      +
    • +

      isDoingAD

      +
      boolean isDoingAD()
      +
      Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions + whose isFlat() flag is set to false!
      +
      +
      Returns:
      +
      The truth value determining if this Function can perform autograd/auto-differentiation on the input tensors it receives.
      -
      • -
    • -
      -

      isFlat

      -
      boolean isFlat()
      -
      -
      Returns:
      -
      The truth value determining if the sub-functions of this Function do not themselves reference Functions.
      +
    + + + +
      +
    • +

      isFlat

      +
      boolean isFlat()
      +
      +
      Returns:
      +
      The truth value determining if the sub-functions of this Function do not themselves reference Functions.
      -
      • -
    • -
      -

      getOperation

      -
      Operation getOperation()
      -
      -
      Returns:
      -
      The Operation implementation instance responsible for executing any inputs received by this Function or null if this isFlat().
      +
    + + + +
      +
    • +

      getOperation

      +
      Operation getOperation()
      +
      +
      Returns:
      +
      The Operation implementation instance responsible for executing any inputs received by this Function or null if this isFlat().
      -
      • -
    • -
      -

      dependsOn

      -
      boolean dependsOn(int index)
      +
    + + + +
      +
    • +

      dependsOn

      +
      boolean dependsOn(int index)
      Use this to determine if this function directly or indirectly references an input with the provided index.
      -
      -
      Parameters:
      -
      index - The index which ought to match the input index of a potentially referenced FunctionInput.
      -
      Returns:
      -
      The truth value determining if this Function (or any sub-functions) reference a FunctionInput with the provided index.
      +
      +
      Parameters:
      +
      index - The index which ought to match the input index of a potentially referenced FunctionInput.
      +
      Returns:
      +
      The truth value determining if this Function (or any sub-functions) reference a FunctionInput with the provided index.
      -
      • -
    • -
      -

      getDerivative

      -
      Function getDerivative(int index)
      -
      This method builds a new Function which is the derivative of this Function with respect to the provided input index.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      getDerivative

      +
      Function getDerivative(int index)
      +
      This method builds a new Function which is the derivative of this Function with respect to the provided input index.
      +
      +
      Parameters:
      index - The index of the input which ought to serve as the variable which ought to be derived.
      -
      Returns:
      -
      The derivative of this Function.
      +
      Returns:
      +
      The derivative of this Function.
      -
      • -
    • -
      -

      getSubFunctions

      -
      List<Function> getSubFunctions()
      -
      -
      Returns:
      -
      The referenced child Function nodes of this Function AST node.
      +
    + + + +
      +
    • +

      getSubFunctions

      +
      java.util.List<Function> getSubFunctions()
      +
      +
      Returns:
      +
      The referenced child Function nodes of this Function AST node.
      -
      • -
    • -
      -

      getAllFunctions

      -
      default List<Function> getAllFunctions()
      -
      -
      Returns:
      -
      A list of all Function nodes within the abstract syntax tree defined by this.
      +
    + + + +
      +
    • +

      getAllFunctions

      +
      default java.util.List<Function> getAllFunctions()
      +
      +
      Returns:
      +
      A list of all Function nodes within the abstract syntax tree defined by this.
      -
      • -
    • -
      -

      numberOfArgs

      -
      default int numberOfArgs()
      -
      -
      Returns:
      -
      The number of inputs that this Function AST depends on.
      +
    + + + +
      +
    • +

      numberOfArgs

      +
      default int numberOfArgs()
      +
      +
      Returns:
      +
      The number of inputs that this Function AST depends on.
      -
      • -
    • -
      -

      call

      -
      default double call(double input)
      -
      Invokes this Function with the provided scalar as a single input and returns the scalar result. - This method is functionally equivalent to invoke(double) or - calling call(double...) or invoke(double...) with a single element array.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      call

      +
      default double call(double input)
      +
      Invokes this Function with the provided scalar as a single input and returns the scalar result. + This method is functionally equivalent to invoke(double) or + calling call(double...) or invoke(double...) with a single element array.
      +
      +
      Parameters:
      input - The scalar input, a single double value.
      -
      Returns:
      +
      Returns:
      The scalar result, a single double value.
      -
      • -
    • -
      -

      invoke

      -
      default double invoke(double input)
      -
      Invokes this Function with the provided scalar as a single input and returns the scalar result. - This method is functionally equivalent to call(double) or - calling invoke(double...) or call(double...) with a single element array.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      invoke

      +
      default double invoke(double input)
      +
      Invokes this Function with the provided scalar as a single input and returns the scalar result. + This method is functionally equivalent to call(double) or + calling invoke(double...) or call(double...) with a single element array.
      +
      +
      Parameters:
      input - The scalar input, a single double value.
      -
      Returns:
      +
      Returns:
      The scalar result, a single double value.
      -
      • -
    • -
      -

      call

      -
      double call(double[] inputs, - int j)
      -
      Invokes this Function with the provided array of inputs ad an index for input dependent indexing. - This method is functionally equivalent to invoke(double[], int).
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      call

      +
      double call(double[] inputs,
+            int j)
      +
      Invokes this Function with the provided array of inputs ad an index for input dependent indexing. + This method is functionally equivalent to invoke(double[], int).
      +
      +
      Parameters:
      inputs - The array of inputs.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar result, a single double value.
      -
      • -
    • -
      -

      invoke

      -
      default double invoke(double[] inputs, - int j)
      -
      Invokes this Function with the provided array of inputs ad an index for input dependent indexing. - This method is functionally equivalent to call(double[], int).
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      invoke

      +
      default double invoke(double[] inputs,
+                      int j)
      +
      Invokes this Function with the provided array of inputs ad an index for input dependent indexing. + This method is functionally equivalent to call(double[], int).
      +
      +
      Parameters:
      inputs - The array of inputs.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar result, a single double value.
      -
      • -
    • -
      -

      call

      -
      default double call(double... inputs)
      -
      Invokes this Function with the provided array of inputs. - This method is functionally equivalent to invoke(double[]).
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      call

      +
      default double call(double... inputs)
      +
      Invokes this Function with the provided array of inputs. + This method is functionally equivalent to invoke(double[]).
      +
      +
      Parameters:
      inputs - A double array of inputs.
      -
      Returns:
      +
      Returns:
      The scalar result, a single double value.
      -
      • -
    • -
      -

      invoke

      -
      default double invoke(double... inputs)
      -
      Invokes this Function with the provided array of inputs. - This method is functionally equivalent to call(double[]).
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      invoke

      +
      default double invoke(double... inputs)
      +
      Invokes this Function with the provided array of inputs. + This method is functionally equivalent to call(double[]).
      +
      +
      Parameters:
      inputs - The double array of inputs.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
      • -
    • -
      -

      derive

      -
      double derive(double[] inputs, - int index, - int j)
      -
      Calculates the derivative of a particular input with respect to the output of this Function +
    + + + +
      +
    • +

      derive

      +
      double derive(double[] inputs,
+              int index,
+              int j)
      +
      Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs, an index targeting the input to be derived and an index for input dependent indexing.
      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - The double array of inputs.
      index - The index of the input to be derived.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
      • -
    • -
      -

      derive

      -
      double derive(double[] inputs, - int index)
      -
      Calculates the derivative of a particular input with respect to the output of this Function +
    + + + +
      +
    • +

      derive

      +
      double derive(double[] inputs,
+              int index)
      +
      Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs and an index targeting the input to be derived.
      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - The double array of inputs.
      index - The index of the input to be derived.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
      • -
    • -
      -

      call

      -
      default <T, -D extends Device<T>> Tensor<T> call(Call.Builder<T,D> call)
      +
    + + + +
      +
    • +

      call

      +
      default <T,D extends Device<T>> Tensor<T> call(Call.Builder<T,D> call)
      Use this for more control over the execution, which is especially useful when interfacing with more complex types of operations, requiring more context information.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter of the tensors wrapped by the provided call.
      D - The type parameter of the device targeted by the provided call.
      -
      Parameters:
      +
      Parameters:
      call - A wrapper for input tensors, a target device and additional meta-arguments.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function executing the provided call.
      -
      • -
    • -
      -

      call

      -
      default <T, -D extends Device<T>> Tensor<T> call(Call<D> call)
      +
    + + + +
      +
    • +

      call

      +
      default <T,D extends Device<T>> Tensor<T> call(Call<D> call)
      Use this for more control over the execution, which is very helpful when interfacing with more complex types of operations, requiring more context information.
      -
      -
      Type Parameters:
      +
      +
      Type Parameters:
      T - The type parameter of the tensors wrapped by the provided call.
      D - The type parameter of the device targeted by the provided call.
      -
      Parameters:
      +
      Parameters:
      call - A wrapper for input tensors, a target device and additional meta-arguments.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function executing the provided call.
      -
      • -
    • -
      -

      invoke

      -
      default <T, -D extends Device<T>> Tensor<T> invoke(Call.Builder<T,D> call)
      +
    + + + +
      +
    • +

      invoke

      +
      default <T,D extends Device<T>> Tensor<T> invoke(Call.Builder<T,D> call)
      Use this to pass more context information for execution of input tensors. This is important when interfacing with more complex types of operations, requiring more fine-grained control over the execution. - This method is functionally identically to call(Call.Builder), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
      -
      -
      Type Parameters:
      + This method is functionally identically to call(Call.Builder), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
    +
    +
    Type Parameters:
    T - The type parameter of the tensors wrapped by the provided call.
    D - The type parameter of the device targeted by the provided call.
    -
    Parameters:
    +
    Parameters:
    call - A wrapper for input tensors, a target device and additional meta-arguments.
    -
    Returns:
    +
    Returns:
    The resulting tensor produced by this function executing the provided call.
    - -
  • -
    -

    execute

    -
    default Tensor<?> execute(Call<?> call)
    + + + + +
      +
    • +

      execute

      +
      default Tensor<?> execute(Call<?> call)
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions. Use this to pass more context information for execution of input tensors. This is important when interfacing with more complex types of operations, requiring more fine-grained control over the execution.
      -
    • -
  • -
    -

    with

    -
    default Function.Callable with(Arg<?>... arguments)
    -
    Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
    -
    -
    Parameters:
    + + + + +
      +
    • +

      with

      +
      default Function.Callable with(Arg<?>... arguments)
      +
      Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
      +
      +
      Parameters:
      arguments - A set of arguments you want to supply to this function for further control over the execution.
      -
      Returns:
      -
      A simple API for passing the Tensor arguments and calling this Function.
      +
      Returns:
      +
      A simple API for passing the Tensor arguments and calling this Function.
      -
    • -
  • -
    -

    with

    -
    default Function.Callable with(Args arguments)
    -
    Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
    -
    -
    Parameters:
    + + + + +
      +
    • +

      with

      +
      default Function.Callable with(Args arguments)
      +
      Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
      +
      +
      Parameters:
      arguments - A set of arguments you want to supply to this function for further control over the execution.
      -
      Returns:
      -
      A simple API for passing the Tensor arguments and calling this Function.
      +
      Returns:
      +
      A simple API for passing the Tensor arguments and calling this Function.
      -
    • -
  • -
    -

    call

    -
    default <T> Tensor<T> call(Args arguments, - Tensor<T>... tensors)
    -
    Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      call

      +
      default <T> Tensor<T> call(Args arguments,
+                           Tensor<T>... tensors)
      +
      Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      arguments - A set of arguments you want to supply to this function for further control over the execution.
      tensors - The tensors which should be sent through this function.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    invoke

    -
    default <T> Tensor<T> invoke(Args arguments, - Tensor<T>... inputs)
    -
    Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      invoke

      +
      default <T> Tensor<T> invoke(Args arguments,
+                             Tensor<T>... inputs)
      +
      Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      arguments - A set of arguments you want to supply to this function for further control over the execution.
      inputs - The tensors which should be sent through this function.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    execute

    -
    Tensor<?> execute(Args arguments, - Tensor<?>... inputs)
    + + + + +
      +
    • +

      execute

      +
      Tensor<?> execute(Args arguments,
+                  Tensor<?>... inputs)
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
      - Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
      -
      -
      Parameters:
      + Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
    • +
    +
    Parameters:
    arguments - A set of arguments you want to supply to this function for further control over the execution.
    inputs - The tensors which should be sent through this function.
    -
    Returns:
    +
    Returns:
    The resulting tensor produced by this function.
    - -
  • -
    -

    execute

    -
    default Tensor<?> execute(Tensor<?>... inputs)
    + + + + +
      +
    • +

      execute

      +
      default Tensor<?> execute(Tensor<?>... inputs)
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - The tensors which should be sent through this function.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    execute

    -
    default Tensor<?> execute(Tensor<?>[] inputs, - int j)
    + + + + +
      +
    • +

      execute

      +
      default Tensor<?> execute(Tensor<?>[] inputs,
+                          int j)
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
      -
      -
      Parameters:
      +
      +
      Parameters:
      inputs - The tensors which should be sent through this function.
      j - The input index used by indexer operations to target a particular input.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    executeDerive

    -
    default Tensor<?> executeDerive(Tensor<?>[] inputs, - int index, - int j)
    + + + + +
      +
    • +

      executeDerive

      +
      default Tensor<?> executeDerive(Tensor<?>[] inputs,
+                                int index,
+                                int j)
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
      -
    • -
  • -
    -

    executeDerive

    -
    default Tensor<?> executeDerive(Tensor<?>[] inputs, - int index)
    + + + + +
      +
    • +

      executeDerive

      +
      default Tensor<?> executeDerive(Tensor<?>[] inputs,
+                                int index)
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
      -
    • -
  • -
    -

    call

    -
    default <T> Tensor<T> call(Tensor<T> input)
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      call

      +
      default <T> Tensor<T> call(Tensor<T> input)
      +
      +
      Type Parameters:
      T - The type parameter of the tensor passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      input - The tensor which should be sent through this function.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    invoke

    -
    default <T> Tensor<T> invoke(Tensor<T> input)
    -
    This method is functionally identically to call(Tensor), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      invoke

      +
      default <T> Tensor<T> invoke(Tensor<T> input)
      +
      This method is functionally identically to call(Tensor), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
      +
      +
      Type Parameters:
      T - The type parameter of the tensor passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      input - The tensor which should be sent through this function.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    call

    -
    default <T> Tensor<T> call(List<Tensor<T>> inputs)
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      call

      +
      default <T> Tensor<T> call(java.util.List<Tensor<T>> inputs)
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      inputs - The list tensors which should be sent through this function.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    invoke

    -
    default <T> Tensor<T> invoke(List<Tensor<T>> input)
    -
    This method is functionally identically to call(List), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      invoke

      +
      default <T> Tensor<T> invoke(java.util.List<Tensor<T>> input)
      +
      This method is functionally identically to call(List), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
      +
      +
      Type Parameters:
      T - The type parameter of the tensor passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      input - The tensor which should be sent through this function.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    call

    -
    default <T> Tensor<T> call(Tensor<T>[] inputs, - int j)
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      call

      +
      default <T> Tensor<T> call(Tensor<T>[] inputs,
+                           int j)
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      inputs - The tensors which should be sent through this function.
      j - The input index used by indexer operations to target a particular input.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    invoke

    -
    default <T> Tensor<T> invoke(Tensor<T>[] inputs, - int j)
    -
    This method is functionally identically to call(Tensor[], int), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      invoke

      +
      default <T> Tensor<T> invoke(Tensor<T>[] inputs,
+                             int j)
      +
      This method is functionally identically to call(Tensor[], int), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      inputs - The tensors which should be sent through this function.
      j - The input index used by indexer operations to target a particular input.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    call

    -
    default <T> Tensor<T> call(Tensor<T>... inputs)
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      call

      +
      default <T> Tensor<T> call(Tensor<T>... inputs)
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      inputs - The tensors which should be sent through this function.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    invoke

    -
    default <T> Tensor<T> invoke(Tensor<T>... inputs)
    -
    This method is functionally identically to call(Tensor[]), however it is best used - in Kotlin, where one can omit the function name entirely and call this Function directly!
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      invoke

      +
      default <T> Tensor<T> invoke(Tensor<T>... inputs)
      +
      This method is functionally identically to call(Tensor[]), however it is best used + in Kotlin, where one can omit the function name entirely and call this Function directly!
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      inputs - The tensors which should be sent through this function.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    derive

    -
    default <T> Tensor<T> derive(Tensor<T>[] inputs, - int index, - int j)
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      derive

      +
      default <T> Tensor<T> derive(Tensor<T>[] inputs,
+                             int index,
+                             int j)
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      inputs - The tensors which should be sent through this function.
      index - The index of the input tensor which should be derived.
      j - The input index used by indexer operations to target a particular input.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    derive

    -
    default <T> Tensor<T> derive(Tensor<T>[] inputs, - int index)
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      derive

      +
      default <T> Tensor<T> derive(Tensor<T>[] inputs,
+                             int index)
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      inputs - The tensors which should be sent through this function.
      index - The index of the input tensor which should be derived.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    derive

    -
    default <T> Tensor<T> derive(List<Tensor<T>> inputs, - int index, - int j)
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      derive

      +
      default <T> Tensor<T> derive(java.util.List<Tensor<T>> inputs,
+                             int index,
+                             int j)
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      inputs - The list of tensors which should be sent through this function.
      index - The index of the input tensor which should be derived.
      j - The input index used by indexer operations to target a particular input.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    derive

    -
    default <T> Tensor<T> derive(List<Tensor<T>> inputs, - int index)
    -
    -
    Type Parameters:
    + + + + +
      +
    • +

      derive

      +
      default <T> Tensor<T> derive(java.util.List<Tensor<T>> inputs,
+                             int index)
      +
      +
      Type Parameters:
      T - The type parameter of the tensors passed to and returned by this function.
      -
      Parameters:
      +
      Parameters:
      inputs - The list of tensors which should be sent through this function.
      index - The index of the input tensor which should be derived.
      -
      Returns:
      +
      Returns:
      The resulting tensor produced by this function.
      -
    • -
  • -
    -

    toString

    -
    String toString()
    + + + + +
      +
    • +

      toString

      +
      java.lang.String toString()
      Turns this function into a string representation which can be used to reconstruct this function or combine it with other function strings to parse entirely new functions...
      -
      -
      Overrides:
      -
      toString in class Object
      -
      Returns:
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      +
      Returns:
      The string representation of this function.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/FunctionCache.html b/docs/jdocs/neureka/math/FunctionCache.html index 1961dd636..770a2df74 100644 --- a/docs/jdocs/neureka/math/FunctionCache.html +++ b/docs/jdocs/neureka/math/FunctionCache.html @@ -1,197 +1,319 @@ - + + - -FunctionCache (neureka 1.0.0 API) - - - - + +FunctionCache (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math
    -

    Class FunctionCache

    -
    -
    java.lang.Object -
    neureka.math.FunctionCache
    +
    neureka.math
    +

    Class FunctionCache

    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.math.FunctionCache
        • +
      +
      • +
    +
    +
      +
    • -
      public final class FunctionCache -extends Object
      -
      This class is part of a given BackendContext instance - responsible for caching Function references based on - their String representation generated by Object.toString() +
      +
      public final class FunctionCache
+extends java.lang.Object
      +
      This class is part of a given BackendContext instance + responsible for caching Function references based on + their String representation generated by Object.toString() as well as caching of results for active functions.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      FunctionCache()
      -
       
      +
      • +
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        FunctionCache

        -
        public FunctionCache()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            FunctionCache

            +
            public FunctionCache()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      put

      -
      public void put(Function function)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          put

          +
          public void put(Function function)
          • -
        • -
          -

          get

          -
          public Function get(String expression, - boolean doesAD)
          -
          +
        + + + +
          +
        • +

          get

          +
          public Function get(java.lang.String expression,
+                    boolean doesAD)
          • -
        • -
          -

          has

          -
          public boolean has(String expression, - boolean doesAD)
          -
          +
        + + + +
          +
        • +

          has

          +
          public boolean has(java.lang.String expression,
+                   boolean doesAD)
          • -
        • -
          -

          toString

          -
          public String toString()
          -
          -
          Overrides:
          -
          toString in class Object
          +
        + + + +
          +
        • +

          toString

          +
          public java.lang.String toString()
          +
          +
          Overrides:
          +
          toString in class java.lang.Object
          -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/Functions.html b/docs/jdocs/neureka/math/Functions.html index cc236af45..e818185e1 100644 --- a/docs/jdocs/neureka/math/Functions.html +++ b/docs/jdocs/neureka/math/Functions.html @@ -1,1252 +1,1752 @@ - + + - -Functions (neureka 1.0.0 API) - - - - + +Functions (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math
    -

    Class Functions

    +
    neureka.math
    +

    Class Functions

    -
    java.lang.Object -
    neureka.math.Functions
    -
    -
    -
    -
    public class Functions -extends Object
    -
    -
    -
      - +
      +
        +
      • java.lang.Object
      • -
        -

        Constructor Summary

        -
        Constructors
        -
        -
        Constructor
        -
        Description
        -
        Functions(boolean doingAD)
        -
         
        +
          +
        • neureka.math.Functions
          • +
        +
        • +
      +
      +
        +
      • +
        +
        +
        public class Functions
+extends java.lang.Object
        +
        • +
      -
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Functions

        -
        public Functions(boolean doingAD)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Functions

            +
            public Functions(boolean doingAD)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      getReshape

      -
      public final Function getReshape()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          getReshape

          +
          public final Function getReshape()
          • -
        • -
          -

          reshape

          -
          public final Function reshape()
          -
          +
        + + + +
          +
        • +

          reshape

          +
          public final Function reshape()
          • -
        • -
          -

          getRelayout

          -
          public final Function getRelayout()
          -
          +
        + + + +
          +
        • +

          getRelayout

          +
          public final Function getRelayout()
          • -
        • -
          -

          relayout

          -
          public final Function relayout()
          -
          +
        + + + +
          +
        • +

          relayout

          +
          public final Function relayout()
          • -
        • -
          -

          getPermute

          -
          public final Function getPermute()
          -
          +
        + + + +
          +
        • +

          getPermute

          +
          public final Function getPermute()
          • -
        • -
          -

          permute

          -
          public final Function permute()
          -
          +
        + + + +
          +
        • +

          permute

          +
          public final Function permute()
          • -
        • -
          -

          getDimTrim

          -
          public final Function getDimTrim()
          -
          +
        + + + +
          +
        • +

          getDimTrim

          +
          public final Function getDimTrim()
          • -
        • -
          -

          dimTrim

          -
          public final Function dimTrim()
          -
          +
        + + + +
          +
        • +

          dimTrim

          +
          public final Function dimTrim()
          • -
        • -
          -

          getIdy

          -
          public final Function getIdy()
          -
          +
        + + + +
          +
        • +

          getIdy

          +
          public final Function getIdy()
          • -
        • -
          -

          idy

          -
          public final Function idy()
          -
          +
        + + + +
          +
        • +

          idy

          +
          public final Function idy()
          • -
        • -
          -

          getConv

          -
          public final Function getConv()
          -
          -
          Returns:
          -
          A Function which represents the standard convolution operation without 0 padding.
          +
        + + + +
          +
        • +

          getConv

          +
          public final Function getConv()
          +
          +
          Returns:
          +
          A Function which represents the standard convolution operation without 0 padding.
          -
    • -
  • -
    -

    conv

    -
    public final Function conv()
    -
    -
    Returns:
    -
    A Function which represents the standard convolution operation without 0 padding.
    + + + + +
      +
    • +

      conv

      +
      public final Function conv()
      +
      +
      Returns:
      +
      A Function which represents the standard convolution operation without 0 padding.
      -
    • -
  • -
    -

    getPlus

    -
    public final Function getPlus()
    -
    + + + + +
      +
    • +

      getPlus

      +
      public final Function getPlus()
      • -
    • -
      -

      plus

      -
      public final Function plus()
      -
      +
    + + + +
      +
    • +

      plus

      +
      public final Function plus()
      • -
    • -
      -

      getPlusAssign

      -
      public final Function getPlusAssign()
      -
      +
    + + + +
      +
    • +

      getPlusAssign

      +
      public final Function getPlusAssign()
      • -
    • -
      -

      plusAssign

      -
      public final Function plusAssign()
      -
      +
    + + + +
      +
    • +

      plusAssign

      +
      public final Function plusAssign()
      • -
    • -
      -

      getMinus

      -
      public final Function getMinus()
      -
      +
    + + + +
      +
    • +

      getMinus

      +
      public final Function getMinus()
      • -
    • -
      -

      minus

      -
      public final Function minus()
      -
      +
    + + + +
      +
    • +

      minus

      +
      public final Function minus()
      • -
    • -
      -

      getMinusAssign

      -
      public final Function getMinusAssign()
      -
      +
    + + + +
      +
    • +

      getMinusAssign

      +
      public final Function getMinusAssign()
      • -
    • -
      -

      minusAssign

      -
      public final Function minusAssign()
      -
      +
    + + + +
      +
    • +

      minusAssign

      +
      public final Function minusAssign()
      • -
    • -
      -

      getDiv

      -
      public final Function getDiv()
      -
      +
    + + + +
      +
    • +

      getDiv

      +
      public final Function getDiv()
      • -
    • -
      -

      div

      -
      public final Function div()
      -
      +
    + + + +
      +
    • +

      div

      +
      public final Function div()
      • -
    • -
      -

      getDivAssign

      -
      public final Function getDivAssign()
      -
      +
    + + + +
      +
    • +

      getDivAssign

      +
      public final Function getDivAssign()
      • -
    • -
      -

      divAssign

      -
      public final Function divAssign()
      -
      +
    + + + +
      +
    • +

      divAssign

      +
      public final Function divAssign()
      • -
    • -
      -

      getPow

      -
      public final Function getPow()
      -
      +
    + + + +
      +
    • +

      getPow

      +
      public final Function getPow()
      • -
    • -
      -

      pow

      -
      public final Function pow()
      -
      +
    + + + +
      +
    • +

      pow

      +
      public final Function pow()
      • -
    • -
      -

      getPowAssign

      -
      public final Function getPowAssign()
      -
      +
    + + + +
      +
    • +

      getPowAssign

      +
      public final Function getPowAssign()
      • -
    • -
      -

      powAssign

      -
      public final Function powAssign()
      -
      +
    + + + +
      +
    • +

      powAssign

      +
      public final Function powAssign()
      • -
    • -
      -

      getMul

      -
      public final Function getMul()
      -
      +
    + + + +
      +
    • +

      getMul

      +
      public final Function getMul()
      • -
    • -
      -

      mul

      -
      public final Function mul()
      -
      +
    + + + +
      +
    • +

      mul

      +
      public final Function mul()
      • -
    • -
      -

      getMulAssign

      -
      public final Function getMulAssign()
      -
      +
    + + + +
      +
    • +

      getMulAssign

      +
      public final Function getMulAssign()
      • -
    • -
      -

      mulAssign

      -
      public final Function mulAssign()
      -
      +
    + + + +
      +
    • +

      mulAssign

      +
      public final Function mulAssign()
      • -
    • -
      -

      getAdd

      -
      public final Function getAdd()
      -
      +
    + + + +
      +
    • +

      getAdd

      +
      public final Function getAdd()
      • -
    • -
      -

      add

      -
      public final Function add()
      -
      +
    + + + +
      +
    • +

      add

      +
      public final Function add()
      • -
    • -
      -

      getAddAssign

      -
      public final Function getAddAssign()
      -
      +
    + + + +
      +
    • +

      getAddAssign

      +
      public final Function getAddAssign()
      • -
    • -
      -

      addAssign

      -
      public final Function addAssign()
      -
      +
    + + + +
      +
    • +

      addAssign

      +
      public final Function addAssign()
      • -
    • -
      -

      getMod

      -
      public final Function getMod()
      -
      +
    + + + +
      +
    • +

      getMod

      +
      public final Function getMod()
      • -
    • -
      -

      mod

      -
      public final Function mod()
      -
      +
    + + + +
      +
    • +

      mod

      +
      public final Function mod()
      • -
    • -
      -

      getModAssign

      -
      public final Function getModAssign()
      -
      +
    + + + +
      +
    • +

      getModAssign

      +
      public final Function getModAssign()
      • -
    • -
      -

      modAssign

      -
      public final Function modAssign()
      -
      +
    + + + +
      +
    • +

      modAssign

      +
      public final Function modAssign()
      • -
    • -
      -

      getNeg

      -
      public final Function getNeg()
      -
      +
    + + + +
      +
    • +

      getNeg

      +
      public final Function getNeg()
      • -
    • -
      -

      neg

      -
      public final Function neg()
      -
      +
    + + + +
      +
    • +

      neg

      +
      public final Function neg()
      • -
    • -
      -

      getMatMul

      -
      public final Function getMatMul()
      -
      +
    + + + +
      +
    • +

      getMatMul

      +
      public final Function getMatMul()
      • -
    • -
      -

      matMul

      -
      public final Function matMul()
      -
      +
    + + + +
      +
    • +

      matMul

      +
      public final Function matMul()
      • -
    • -
      -

      getDot

      -
      public final Function getDot()
      -
      +
    + + + +
      +
    • +

      getDot

      +
      public final Function getDot()
      • -
    • -
      -

      dot

      -
      public final Function dot()
      -
      +
    + + + +
      +
    • +

      dot

      +
      public final Function dot()
      • -
    • -
      -

      getTranspose2D

      -
      public final Function getTranspose2D()
      -
      +
    + + + +
      +
    • +

      getTranspose2D

      +
      public final Function getTranspose2D()
      • -
    • -
      -

      transpose2D

      -
      public final Function transpose2D()
      -
      +
    + + + +
      +
    • +

      transpose2D

      +
      public final Function transpose2D()
      • -
    • -
      -

      getRandom

      -
      public final Function getRandom()
      -
      +
    + + + +
      +
    • +

      getRandom

      +
      public final Function getRandom()
      • -
    • -
      -

      random

      -
      public final Function random()
      -
      +
    + + + +
      +
    • +

      random

      +
      public final Function random()
      • -
    • -
      -

      getTanh

      -
      public final Function getTanh()
      -
      -
      Returns:
      -
      A tanh Function based on: 2 / ( 1 + Math.exp( -x * 2 ) ) - 1.
      +
    + + + +
      +
    • +

      getTanh

      +
      public final Function getTanh()
      +
      +
      Returns:
      +
      A tanh Function based on: 2 / ( 1 + Math.exp( -x * 2 ) ) - 1.
      -
      • -
    • -
      -

      tanh

      -
      public final Function tanh()
      -
      -
      Returns:
      -
      A tanh Function based on: 2 / ( 1 + Math.exp( -x * 2 ) ) - 1.
      +
    + + + +
      +
    • +

      tanh

      +
      public final Function tanh()
      +
      +
      Returns:
      +
      A tanh Function based on: 2 / ( 1 + Math.exp( -x * 2 ) ) - 1.
      -
      • -
    • -
      -

      getFastTanh

      -
      public final Function getFastTanh()
      -
      -
      Returns:
      -
      A fast quasi tanh Function based on: x * FastFun.invSqrt( 1 + x * x ).
      +
    + + + +
      +
    • +

      getFastTanh

      +
      public final Function getFastTanh()
      +
      +
      Returns:
      +
      A fast quasi tanh Function based on: x * FastFun.invSqrt( 1 + x * x ).
      -
      • -
    • -
      -

      fastTanh

      -
      public final Function fastTanh()
      -
      -
      Returns:
      -
      A fast quasi tanh Function based on: x * FastFun.invSqrt( 1 + x * x ).
      +
    + + + +
      +
    • +

      fastTanh

      +
      public final Function fastTanh()
      +
      +
      Returns:
      +
      A fast quasi tanh Function based on: x * FastFun.invSqrt( 1 + x * x ).
      -
      • -
    • -
      -

      getSoftsign

      -
      public final Function getSoftsign()
      +
    + + + +
      +
    • +

      getSoftsign

      +
      public final Function getSoftsign()
      The softsign function, defined as x / ( 1 + Math.abs( x ) ), is a computationally cheap 0 centered activation function - which rescales the inputs between -1 and 1, very much like the Tanh function. + which rescales the inputs between -1 and 1, very much like the Tanh function. The softsign function converges polynomially and is computationally cheaper than the tanh function which converges exponentially.
      -
      -
      Returns:
      -
      A very fast quasi tanh Function based on: x / ( 1 + Math.abs( x ) ).
      +
      +
      Returns:
      +
      A very fast quasi tanh Function based on: x / ( 1 + Math.abs( x ) ).
      -
      • -
    • -
      -

      softsign

      -
      public final Function softsign()
      +
    + + + +
      +
    • +

      softsign

      +
      public final Function softsign()
      The softsign function, defined as x / ( 1 + Math.abs( x ) ), is a computationally cheap 0 centered activation function - which rescales the inputs between -1 and 1, very much like the Tanh function. + which rescales the inputs between -1 and 1, very much like the Tanh function. The softsign function converges polynomially and is computationally cheaper than the tanh function which converges exponentially.
      -
      -
      Returns:
      -
      A very fast quasi tanh Function based on: x / ( 1 + Math.abs( x ) ).
      +
      +
      Returns:
      +
      A very fast quasi tanh Function based on: x / ( 1 + Math.abs( x ) ).
      -
      • -
    • -
      -

      getSigmoid

      -
      public final Function getSigmoid()
      -
      -
      Returns:
      -
      A sigmoid Function based on: 1 / ( 1 + Math.exp( -x ) ).
      +
    + + + +
      +
    • +

      getSigmoid

      +
      public final Function getSigmoid()
      +
      +
      Returns:
      +
      A sigmoid Function based on: 1 / ( 1 + Math.exp( -x ) ).
      -
      • -
    • -
      -

      sigmoid

      -
      public final Function sigmoid()
      -
      -
      Returns:
      -
      A sigmoid Function based on: 1 / ( 1 + Math.exp( -x ) ).
      +
    + + + +
      +
    • +

      sigmoid

      +
      public final Function sigmoid()
      +
      +
      Returns:
      +
      A sigmoid Function based on: 1 / ( 1 + Math.exp( -x ) ).
      -
      • -
    • -
      -

      getGaus

      -
      public final Function getGaus()
      -
      -
      Returns:
      -
      A gaussian Function based on: Math.exp( -( x * x ) ).
      +
    + + + +
      +
    • +

      getGaus

      +
      public final Function getGaus()
      +
      +
      Returns:
      +
      A gaussian Function based on: Math.exp( -( x * x ) ).
      -
      • -
    • -
      -

      gaus

      -
      public final Function gaus()
      -
      -
      Returns:
      -
      A gaussian Function based on: Math.exp( -( x * x ) ).
      +
    + + + +
      +
    • +

      gaus

      +
      public final Function gaus()
      +
      +
      Returns:
      +
      A gaussian Function based on: Math.exp( -( x * x ) ).
      -
      • -
    • -
      -

      getFastGaus

      -
      public final Function getFastGaus()
      -
      -
      Returns:
      -
      A quasi gaussian Function based on: 1 / ( 1 + x * x ).
      +
    + + + +
      +
    • +

      getFastGaus

      +
      public final Function getFastGaus()
      +
      +
      Returns:
      +
      A quasi gaussian Function based on: 1 / ( 1 + x * x ).
      -
      • -
    • -
      -

      fastGaus

      -
      public final Function fastGaus()
      -
      -
      Returns:
      -
      A quasi gaussian Function based on: 1 / ( 1 + x * x ).
      +
    + + + +
      +
    • +

      fastGaus

      +
      public final Function fastGaus()
      +
      +
      Returns:
      +
      A quasi gaussian Function based on: 1 / ( 1 + x * x ).
      -
      • -
    • -
      -

      getLn

      -
      public final Function getLn()
      -
      -
      Returns:
      -
      A natural log Function based on: Math.log( x ).
      +
    + + + +
      +
    • +

      getLn

      +
      public final Function getLn()
      +
      +
      Returns:
      +
      A natural log Function based on: Math.log( x ).
      -
      • -
    • -
      -

      ln

      -
      public final Function ln()
      -
      -
      Returns:
      -
      A natural log Function based on: Math.log( x ).
      +
    + + + +
      +
    • +

      ln

      +
      public final Function ln()
      +
      +
      Returns:
      +
      A natural log Function based on: Math.log( x ).
      -
      • -
    • -
      -

      getQuad

      -
      public final Function getQuad()
      -
      -
      Returns:
      -
      A quadratic Function based on: x * x.
      +
    + + + +
      +
    • +

      getQuad

      +
      public final Function getQuad()
      +
      +
      Returns:
      +
      A quadratic Function based on: x * x.
      -
      • -
    • -
      -

      quad

      -
      public final Function quad()
      -
      -
      Returns:
      -
      A quadratic Function based on: x * x.
      +
    + + + +
      +
    • +

      quad

      +
      public final Function quad()
      +
      +
      Returns:
      +
      A quadratic Function based on: x * x.
      -
      • -
    • -
      -

      getRelu

      -
      public final Function getRelu()
      -
      -
      Returns:
      -
      A rectified linear unit Function based on: ( x >= 0 ? x : x * .01 ).
      +
    + + + +
      +
    • +

      getRelu

      +
      public final Function getRelu()
      +
      +
      Returns:
      +
      A rectified linear unit Function based on: ( x >= 0 ? x : x * .01 ).
      -
      • -
    • -
      -

      relu

      -
      public final Function relu()
      -
      -
      Returns:
      -
      A rectified linear unit Function based on: ( x >= 0 ? x : x * .01 ).
      +
    + + + +
      +
    • +

      relu

      +
      public final Function relu()
      +
      +
      Returns:
      +
      A rectified linear unit Function based on: ( x >= 0 ? x : x * .01 ).
      -
      • -
    • -
      -

      getAbs

      -
      public final Function getAbs()
      -
      -
      Returns:
      -
      An absolute Function based on: Math.abs(x).
      +
    + + + +
      +
    • +

      getAbs

      +
      public final Function getAbs()
      +
      +
      Returns:
      +
      An absolute Function based on: Math.abs(x).
      -
      • -
    • -
      -

      abs

      -
      public final Function abs()
      -
      -
      Returns:
      -
      An absolute Function based on: Math.abs(x).
      +
    + + + +
      +
    • +

      abs

      +
      public final Function abs()
      +
      +
      Returns:
      +
      An absolute Function based on: Math.abs(x).
      -
      • -
    • -
      -

      getSin

      -
      public final Function getSin()
      -
      -
      Returns:
      -
      A sine Function based on: Math.sin(x).
      +
    + + + +
      +
    • +

      getSin

      +
      public final Function getSin()
      +
      +
      Returns:
      +
      A sine Function based on: Math.sin(x).
      -
      • -
    • -
      -

      sin

      -
      public final Function sin()
      -
      -
      Returns:
      -
      A sine Function based on: Math.sin(x).
      +
    + + + +
      +
    • +

      sin

      +
      public final Function sin()
      +
      +
      Returns:
      +
      A sine Function based on: Math.sin(x).
      -
      • -
    • -
      -

      getCos

      -
      public final Function getCos()
      -
      -
      Returns:
      -
      A cosine Function based on: Math.cos(x).
      +
    + + + +
      +
    • +

      getCos

      +
      public final Function getCos()
      +
      +
      Returns:
      +
      A cosine Function based on: Math.cos(x).
      -
      • -
    • -
      -

      cos

      -
      public final Function cos()
      -
      -
      Returns:
      -
      A cosine Function based on: Math.cos(x).
      +
    + + + +
      +
    • +

      cos

      +
      public final Function cos()
      +
      +
      Returns:
      +
      A cosine Function based on: Math.cos(x).
      -
      • -
    • -
      -

      getSoftplus

      -
      public final Function getSoftplus()
      +
    + + + +
      +
    • +

      getSoftplus

      +
      public final Function getSoftplus()
      SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.
      -
      -
      Returns:
      -
      A softplus Function based on: Math.log( 1 + Math.exp( x ) ).
      +
      +
      Returns:
      +
      A softplus Function based on: Math.log( 1 + Math.exp( x ) ).
      -
      • -
    • -
      -

      softplus

      -
      public final Function softplus()
      +
    + + + +
      +
    • +

      softplus

      +
      public final Function softplus()
      SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive.
      -
      -
      Returns:
      -
      A softplus Function based on: Math.log( 1 + Math.exp( x ) ).
      +
      +
      Returns:
      +
      A softplus Function based on: Math.log( 1 + Math.exp( x ) ).
      -
      • -
    • -
      -

      getSilu

      -
      public final Function getSilu()
      +
    + + + +
      +
    • +

      getSilu

      +
      public final Function getSilu()
      The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x). It is a smooth, non-monotonic function that consistently matches or outperforms ReLU on deep networks, it is unbounded above and bounded below.
      -
      -
      Returns:
      -
      A SiLu Function (also known as swish) based on: x / ( 1 + Math.exp( -x ) ).
      +
      +
      Returns:
      +
      A SiLu Function (also known as swish) based on: x / ( 1 + Math.exp( -x ) ).
      -
      • -
    • -
      -

      silu

      -
      public final Function silu()
      +
    + + + +
      +
    • +

      silu

      +
      public final Function silu()
      The SiLu activation function, also known as the swish function, is defined as x * sigmoid(x). It is a smooth, non-monotonic function that consistently matches or outperforms ReLU on deep networks, it is unbounded above and bounded below.
      -
      -
      Returns:
      -
      A SiLu Function (also known as swish) based on: x / ( 1 + Math.exp( -x ) ).
      +
      +
      Returns:
      +
      A SiLu Function (also known as swish) based on: x / ( 1 + Math.exp( -x ) ).
      -
      • -
    • -
      -

      getGelu

      -
      public final Function getGelu()
      -
      -
      Returns:
      -
      A GeLU Function based on: x / ( 1 + Math.exp( -x * 1.702 ) ).
      +
    + + + +
      +
    • +

      getGelu

      +
      public final Function getGelu()
      +
      +
      Returns:
      +
      A GeLU Function based on: x / ( 1 + Math.exp( -x * 1.702 ) ).
      -
      • -
    • -
      -

      gelu

      -
      public final Function gelu()
      -
      -
      Returns:
      -
      A GeLU Function based on: x / ( 1 + Math.exp( -x * 1.702 ) ).
      +
    + + + +
      +
    • +

      gelu

      +
      public final Function gelu()
      +
      +
      Returns:
      +
      A GeLU Function based on: x / ( 1 + Math.exp( -x * 1.702 ) ).
      -
      • -
    • -
      -

      getSelu

      -
      public final Function getSelu()
      +
    + + + +
      +
    • +

      getSelu

      +
      public final Function getSelu()
      The Scaled Exponential Linear Unit, or SELU, is an activation functions that induce self-normalizing properties. The SELU activation function is implemented as: - - if ( x > 0 ) return SCALE * x; + if ( x > 0 ) return SCALE * x; else if ( x <= 0 ) return SCALE * ALPHA * (Math.exp(x) - 1); else return Float.NaN;
      ...where ALPHA == 1.6733 and SCALE == 1.0507.
      -
      -
      Returns:
      -
      A SeLU Function.
      +
      +
      Returns:
      +
      A SeLU Function.
      -
      • -
    • -
      -

      selu

      -
      public final Function selu()
      +
    + + + +
      +
    • +

      selu

      +
      public final Function selu()
      The Scaled Exponential Linear Unit, or SELU, is an activation functions that induce self-normalizing properties. The SELU activation function is implemented as: - - if ( x > 0 ) return SCALE * x; + if ( x > 0 ) return SCALE * x; else if ( x <= 0 ) return SCALE * ALPHA * Math.exp(x); else return Double.NaN;
      ...where ALPHA == 1.6733 and SCALE == 1.0507.
      -
      -
      Returns:
      -
      A SeLU Function.
      +
      +
      Returns:
      +
      A SeLU Function.
      -
      • -
    • -
      -

      getMin

      -
      public final Function getMin()
      -
      +
    + + + +
      +
    • +

      getMin

      +
      public final Function getMin()
      • -
    • -
      -

      min

      -
      public final Function min()
      -
      +
    + + + +
      +
    • +

      min

      +
      public final Function min()
      • -
    • -
      -

      getMax

      -
      public final Function getMax()
      -
      +
    + + + +
      +
    • +

      getMax

      +
      public final Function getMax()
      • -
    • -
      -

      max

      -
      public final Function max()
      -
      +
    + + + +
      +
    • +

      max

      +
      public final Function max()
      • -
    • -
      -

      getSum

      -
      public final Function getSum()
      -
      +
    + + + +
      +
    • +

      getSum

      +
      public final Function getSum()
      • -
    • -
      -

      sum

      -
      public final Function sum()
      -
      +
    + + + +
      +
    • +

      sum

      +
      public final Function sum()
      • -
    • -
      -

      getExp

      -
      public final Function getExp()
      -
      +
    + + + +
      +
    • +

      getExp

      +
      public final Function getExp()
      • -
    • -
      -

      exp

      -
      public final Function exp()
      -
      +
    + + + +
      +
    • +

      exp

      +
      public final Function exp()
      • -
    • -
      -

      getLog10

      -
      public final Function getLog10()
      -
      +
    + + + +
      +
    • +

      getLog10

      +
      public final Function getLog10()
      • -
    • -
      -

      log10

      -
      public final Function log10()
      -
      +
    + + + +
      +
    • +

      log10

      +
      public final Function log10()
      • -
    • -
      -

      getSqrt

      -
      public final Function getSqrt()
      -
      +
    + + + +
      +
    • +

      getSqrt

      +
      public final Function getSqrt()
      • -
    • -
      -

      sqrt

      -
      public final Function sqrt()
      -
      +
    + + + +
      +
    • +

      sqrt

      +
      public final Function sqrt()
      • -
    • -
      -

      getCbrt

      -
      public final Function getCbrt()
      -
      +
    + + + +
      +
    • +

      getCbrt

      +
      public final Function getCbrt()
      • -
    • -
      -

      cbrt

      -
      public final Function cbrt()
      -
      +
    + + + +
      +
    • +

      cbrt

      +
      public final Function cbrt()
      • -
    • -
      -

      getConcat

      -
      public final Function getConcat()
      -
      -
      Returns:
      -
      The "concat" Function that concatenates two Tensors alongside a specific axis.
      +
    + + + +
      +
    • +

      getConcat

      +
      public final Function getConcat()
      +
      +
      Returns:
      +
      The "concat" Function that concatenates two Tensors alongside a specific axis.
      -
      • -
    • -
      -

      concat

      -
      public final Function concat()
      -
      -
      Returns:
      -
      The "concat" Function that concatenates two Tensors alongside a specific axis.
      +
    + + + +
      +
    • +

      concat

      +
      public final Function concat()
      +
      +
      Returns:
      +
      The "concat" Function that concatenates two Tensors alongside a specific axis.
      -
      • -
    • -
      -

      toString

      -
      public final String toString()
      -
      -
      Overrides:
      -
      toString in class Object
      +
    + + + +
      +
    • +

      toString

      +
      public final java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.Axis.html b/docs/jdocs/neureka/math/args/Arg.Axis.html index 1f50be05f..d62035c83 100644 --- a/docs/jdocs/neureka/math/args/Arg.Axis.html +++ b/docs/jdocs/neureka/math/args/Arg.Axis.html @@ -1,156 +1,276 @@ - + + - -Arg.Axis (neureka 1.0.0 API) - - - - + +Arg.Axis (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.Axis

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<Integer> -
    neureka.math.args.Arg.Axis
    +
    neureka.math.args
    +

    Class Arg.Axis

    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • + +
      • +
    +
    +
    -
    -
      +
      +
      public static class Arg.Axis
+extends Arg<java.lang.Integer>
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        public static Arg.Axis of(int index)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            of

            +
            public static Arg.Axis of(int index)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.DerivIdx.html b/docs/jdocs/neureka/math/args/Arg.DerivIdx.html index 1a87f0830..4d059bc7f 100644 --- a/docs/jdocs/neureka/math/args/Arg.DerivIdx.html +++ b/docs/jdocs/neureka/math/args/Arg.DerivIdx.html @@ -1,93 +1,127 @@ - + + - -Arg.DerivIdx (neureka 1.0.0 API) - - - - + +Arg.DerivIdx (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.DerivIdx

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<Integer> -
    neureka.math.args.Arg.DerivIdx
    +
    neureka.math.args
    +

    Class Arg.DerivIdx

    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • + +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Component<Args>
      +
      Component<Args>
      -
      +
      Enclosing class:
      -
      Arg<T>
      +
      Arg<T>
      -
      public static class Arg.DerivIdx -extends Arg<Integer>
      +
      +
      public static class Arg.DerivIdx
+extends Arg<java.lang.Integer>
      This is an import argument whose role might not be clear at first : An operation can have multiple inputs, however @@ -96,69 +130,155 @@

      Class Arg.DerivIdx

      So the "derivative index" targets said input. This property is -1 when no derivative should be calculated, however 0... when targeting an input to calculate the derivative of.
      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.Derivative.html b/docs/jdocs/neureka/math/args/Arg.Derivative.html index 81909c170..c101eadcc 100644 --- a/docs/jdocs/neureka/math/args/Arg.Derivative.html +++ b/docs/jdocs/neureka/math/args/Arg.Derivative.html @@ -1,156 +1,276 @@ - + + - -Arg.Derivative (neureka 1.0.0 API) - - - - + +Arg.Derivative (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.Derivative<V>

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<Tensor<V>> -
    neureka.math.args.Arg.Derivative<V>
    +
    neureka.math.args
    +

    Class Arg.Derivative<V>

    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public static class Arg.Derivative<V>
+extends Arg<Tensor<V>>
      + +
    +
    +
    +
    +
    + - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
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      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.Ends.html b/docs/jdocs/neureka/math/args/Arg.Ends.html index 420290490..4fdaad6e5 100644 --- a/docs/jdocs/neureka/math/args/Arg.Ends.html +++ b/docs/jdocs/neureka/math/args/Arg.Ends.html @@ -1,156 +1,276 @@ - + + - -Arg.Ends (neureka 1.0.0 API) - - - - + +Arg.Ends (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.Ends

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<int[]> -
    neureka.math.args.Arg.Ends
    +
    neureka.math.args
    +

    Class Arg.Ends

    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public static class Arg.Ends
+extends Arg<int[]>
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        public static Arg.Ends of(int[] arg)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            of

            +
            public static Arg.Ends of(int[] arg)
            +
            • +
        -
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    + diff --git a/docs/jdocs/neureka/math/args/Arg.Indices.html b/docs/jdocs/neureka/math/args/Arg.Indices.html index 2a0054c86..bdd57ec6e 100644 --- a/docs/jdocs/neureka/math/args/Arg.Indices.html +++ b/docs/jdocs/neureka/math/args/Arg.Indices.html @@ -1,156 +1,276 @@ - + + - -Arg.Indices (neureka 1.0.0 API) - - - - + +Arg.Indices (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.Indices

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<int[]> -
    neureka.math.args.Arg.Indices
    +
    neureka.math.args
    +

    Class Arg.Indices

    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public static class Arg.Indices
+extends Arg<int[]>
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
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      • +
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    + diff --git a/docs/jdocs/neureka/math/args/Arg.Layout.html b/docs/jdocs/neureka/math/args/Arg.Layout.html index 0f42cb7d8..d5499f7be 100644 --- a/docs/jdocs/neureka/math/args/Arg.Layout.html +++ b/docs/jdocs/neureka/math/args/Arg.Layout.html @@ -1,156 +1,276 @@ - + + - -Arg.Layout (neureka 1.0.0 API) - - - - + +Arg.Layout (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.Layout

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<NDConfiguration.Layout> -
    neureka.math.args.Arg.Layout
    +
    neureka.math.args
    +

    Class Arg.Layout

    -
    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    + - +
    +
    - + +
    + + +
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      • +
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    + diff --git a/docs/jdocs/neureka/math/args/Arg.MinRank.html b/docs/jdocs/neureka/math/args/Arg.MinRank.html index 6d4901b16..7c4eba87e 100644 --- a/docs/jdocs/neureka/math/args/Arg.MinRank.html +++ b/docs/jdocs/neureka/math/args/Arg.MinRank.html @@ -1,156 +1,276 @@ - + + - -Arg.MinRank (neureka 1.0.0 API) - - - - + +Arg.MinRank (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.MinRank

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<Integer> -
    neureka.math.args.Arg.MinRank
    +
    neureka.math.args
    +

    Class Arg.MinRank

    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • + +
      • +
    +
    +
    -
    -
      +
      +
      public static class Arg.MinRank
+extends Arg<java.lang.Integer>
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
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      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.Offset.html b/docs/jdocs/neureka/math/args/Arg.Offset.html index 972b4336d..a02310579 100644 --- a/docs/jdocs/neureka/math/args/Arg.Offset.html +++ b/docs/jdocs/neureka/math/args/Arg.Offset.html @@ -1,156 +1,276 @@ - + + - -Arg.Offset (neureka 1.0.0 API) - - - - + +Arg.Offset (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.Offset

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<int[]> -
    neureka.math.args.Arg.Offset
    +
    neureka.math.args
    +

    Class Arg.Offset

    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public static class Arg.Offset
+extends Arg<int[]>
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        public static Arg.Offset of(int... arg)
        -
        + -
    - +
    +
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    + + +
    Skip navigation links
    + + + +
    +
    + + + +
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      +
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      • +
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      • +
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      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.Seed.html b/docs/jdocs/neureka/math/args/Arg.Seed.html index 632cdd7f4..6e7439f03 100644 --- a/docs/jdocs/neureka/math/args/Arg.Seed.html +++ b/docs/jdocs/neureka/math/args/Arg.Seed.html @@ -1,165 +1,289 @@ - + + - -Arg.Seed (neureka 1.0.0 API) - - - - + +Arg.Seed (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.Seed

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<Long> -
    neureka.math.args.Arg.Seed
    +
    neureka.math.args
    +

    Class Arg.Seed

    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public static class Arg.Seed
+extends Arg<java.lang.Long>
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        public static Arg.Seed of(String arg)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            of

            +
            public static Arg.Seed of(java.lang.String arg)
            • -
          • -
            -

            of

            -
            public static Arg.Seed of(long arg)
            -
            +
          + + + +
            +
          • +

            of

            +
            public static Arg.Seed of(long arg)
            +
            • +
        -
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    +
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    + + +
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    + + + +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.Shape.html b/docs/jdocs/neureka/math/args/Arg.Shape.html index 81daffba1..240f8f18a 100644 --- a/docs/jdocs/neureka/math/args/Arg.Shape.html +++ b/docs/jdocs/neureka/math/args/Arg.Shape.html @@ -1,156 +1,276 @@ - + + - -Arg.Shape (neureka 1.0.0 API) - - - - + +Arg.Shape (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.Shape

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<int[]> -
    neureka.math.args.Arg.Shape
    +
    neureka.math.args
    +

    Class Arg.Shape

    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public static class Arg.Shape
+extends Arg<int[]>
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        public static Arg.Shape of(int... arg)
        -
        + -
    - +
    +
    - + +
    + + +
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    + + + +
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      • +
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      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.Stride.html b/docs/jdocs/neureka/math/args/Arg.Stride.html index 963b78aab..29f1097cf 100644 --- a/docs/jdocs/neureka/math/args/Arg.Stride.html +++ b/docs/jdocs/neureka/math/args/Arg.Stride.html @@ -1,156 +1,276 @@ - + + - -Arg.Stride (neureka 1.0.0 API) - - - - + +Arg.Stride (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.Stride

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<int[]> -
    neureka.math.args.Arg.Stride
    +
    neureka.math.args
    +

    Class Arg.Stride

    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public static class Arg.Stride
+extends Arg<int[]>
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        public static Arg.Stride of(int... arg)
        -
        + -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
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    +
      +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.TargetDevice.html b/docs/jdocs/neureka/math/args/Arg.TargetDevice.html index 6ae34381d..a5ea562a4 100644 --- a/docs/jdocs/neureka/math/args/Arg.TargetDevice.html +++ b/docs/jdocs/neureka/math/args/Arg.TargetDevice.html @@ -1,156 +1,276 @@ - + + - -Arg.TargetDevice (neureka 1.0.0 API) - - - - + +Arg.TargetDevice (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.TargetDevice

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<Device<?>> -
    neureka.math.args.Arg.TargetDevice
    +
    neureka.math.args
    +

    Class Arg.TargetDevice

    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public static class Arg.TargetDevice
+extends Arg<Device<?>>
      + +
    +
    +
    +
    +
    + - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
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      • +
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      • +
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      • +
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      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.VarIdx.html b/docs/jdocs/neureka/math/args/Arg.VarIdx.html index 74c8a498a..be7aabaa0 100644 --- a/docs/jdocs/neureka/math/args/Arg.VarIdx.html +++ b/docs/jdocs/neureka/math/args/Arg.VarIdx.html @@ -1,160 +1,280 @@ - + + - -Arg.VarIdx (neureka 1.0.0 API) - - - - + +Arg.VarIdx (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg.VarIdx

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<Integer> -
    neureka.math.args.Arg.VarIdx
    +
    neureka.math.args
    +

    Class Arg.VarIdx

    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • + +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Component<Args>
      +
      Component<Args>
      -
      +
      Enclosing class:
      -
      Arg<T>
      +
      Arg<T>
      -
      public static class Arg.VarIdx -extends Arg<Integer>
      +
      +
      public static class Arg.VarIdx
+extends Arg<java.lang.Integer>
      The following argument is relevant for a particular type of operation, namely: an "indexer".
      An indexer automatically applies an operation on all inputs for a given function. The (indexer) function will execute the sub functions (of the AST) for every input index. If a particular index is not targeted however this variable will simply default to -1.
      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
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      • +
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      • +
    +
      +
    • Detail: 
      • +
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      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Arg.html b/docs/jdocs/neureka/math/args/Arg.html index 8320848aa..127bdbd43 100644 --- a/docs/jdocs/neureka/math/args/Arg.html +++ b/docs/jdocs/neureka/math/args/Arg.html @@ -1,243 +1,321 @@ - + + - -Arg (neureka 1.0.0 API) - - - - + +Arg (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Arg<T>

    -
    -
    java.lang.Object -
    neureka.math.args.Arg<T>
    +
    neureka.math.args
    +

    Class Arg<T>

    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.math.args.Arg<T>
        • +
      +
      • +
    +
    +
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Arg

        -
        public Arg(T arg)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + + + +
            +
          • +

            Arg

            +
            public Arg(T arg)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      get

      -
      public T get()
      -
      +
        +
      • + + +

        Method Detail

        + + + + + + + +
          +
        • +

          update

          +
          public boolean update(Component.OwnerChangeRequest<Args> changeRequest)
          +
          Description copied from interface: Component
          Components are not the slaves of their owners. If the owner registers any state changes related to a given component, then said component will be informed by the owner about the change as well as receive @@ -247,38 +325,100 @@

          update

          is being added to, or removed from, its current owner. If components hold references to their owners then this method gives them the ability to update said reference when a new owner takes over the components of an old one. - The Component.OwnerChangeRequest implementation instance passed to this method - informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). + The Component.OwnerChangeRequest implementation instance passed to this method + informs this component about the current state change and its type (Component.OwnerChangeRequest.type()). If this method returns false then this means that this component rejects the proposed update. The component owner will then abort the proposed change.
          -
          -
          Specified by:
          -
          update in interface Component<T>
          -
          Parameters:
          -
          changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
          -
          Returns:
          +
          +
          Specified by:
          +
          update in interface Component<Args>
          +
          Parameters:
          +
          changeRequest - An Component.OwnerChangeRequest implementation instance used to communicate the type of change, context information and the ability to execute the change directly.
          +
          Returns:
          The truth value determining if the state change should be aborted or not.
          -
    • -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/Args.html b/docs/jdocs/neureka/math/args/Args.html index ea7dbdd2c..5e803fa04 100644 --- a/docs/jdocs/neureka/math/args/Args.html +++ b/docs/jdocs/neureka/math/args/Args.html @@ -1,205 +1,276 @@ - + + - -Args (neureka 1.0.0 API) - - - - + +Args (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.args
    -

    Class Args

    -
    -
    java.lang.Object -
    neureka.common.composition.AbstractComponentOwner<Args> -
    neureka.math.args.Args
    -
    +
    neureka.math.args
    +

    Class Args

    -
    -
    +
    + +
    +
    -
    -
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Args

        -
        public Args()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Args

            +
            public Args()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      of

      -
      public static Args of(Arg<?>... arguments)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          of

          +
          public static Args of(Arg<?>... arguments)
          • -
        • -
          -

          valOf

          -
          public <V, -T extends Arg<V>> V valOf(Class<T> argumentClass)
          -
          +
        + + + +
          +
        • +

          valOf

          +
          public <V,T extends Arg<V>> V valOf(java.lang.Class<T> argumentClass)
          • -
        • -
          -

          valOfOr

          -
          public <V, -T extends Arg<V>> V valOfOr(Class<T> argumentClass, - V fallback)
          -
          +
        + + + + + +
          +
        • +

          valOfOr

          +
          public <V,T extends Arg<V>> V valOfOr(java.lang.Class<T> argumentClass,
+                                      V fallback)
          • -
        • -
          -

          _setOrReject

          -
          protected <T extends Component<Args>> T _setOrReject(T newComponent)
          -
          Description copied from class: AbstractComponentOwner
          +
        + + + + + +
          +
        • +

          _setOrReject

          +
          protected <T extends Component<Args>> T _setOrReject(T newComponent)
          +
          Description copied from class: AbstractComponentOwner
          This abstract method ought to be implemented further down the inheritance hierarchy where it's responsibility makes more sense, namely : @@ -208,44 +279,108 @@

          _setOrReject

          of this class. Rejection in this case simply means that it returns null instead of the passed component.
          -
          -
          Specified by:
          -
          _setOrReject in class AbstractComponentOwner<Args>
          -
          Parameters:
          +
          +
          Specified by:
          +
          _setOrReject in class AbstractComponentOwner<Args>
          +
          Parameters:
          newComponent - The component which should be added to the components list.
          -
          Returns:
          +
          Returns:
          The same component or null if it has been rejected.
          -
    • -
  • -
    -

    _removeOrReject

    -
    protected <T extends Component<Args>> T _removeOrReject(T newComponent)
    -
    Description copied from class: AbstractComponentOwner
    + + + + + + +
      +
    • +

      _removeOrReject

      +
      protected <T extends Component<Args>> T _removeOrReject(T newComponent)
      +
      Description copied from class: AbstractComponentOwner
      An implementation of this method checks if the passed component should be removed from the component collection of this class or its removal should be "rejected". Rejection in this case simply means that it returns null instead of the passed component.
      -
      -
      Specified by:
      -
      _removeOrReject in class AbstractComponentOwner<Args>
      -
      Parameters:
      +
      +
      Specified by:
      +
      _removeOrReject in class AbstractComponentOwner<Args>
      +
      Parameters:
      newComponent - The component which should be removed from the components list.
      -
      Returns:
      +
      Returns:
      The same component or null if its removal has been rejected.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/package-frame.html b/docs/jdocs/neureka/math/args/package-frame.html new file mode 100644 index 000000000..f9ff2d945 --- /dev/null +++ b/docs/jdocs/neureka/math/args/package-frame.html @@ -0,0 +1,33 @@ + + + + + +neureka.math.args (neureka 1.0.1 API) + + + + +

    neureka.math.args

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/math/args/package-summary.html b/docs/jdocs/neureka/math/args/package-summary.html index edacca943..6f80b7837 100644 --- a/docs/jdocs/neureka/math/args/package-summary.html +++ b/docs/jdocs/neureka/math/args/package-summary.html @@ -1,143 +1,205 @@ - + + - -neureka.math.args (neureka 1.0.0 API) - - - - + +neureka.math.args (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.math.args

    -
    -
    -
    package neureka.math.args
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/math/args/package-tree.html b/docs/jdocs/neureka/math/args/package-tree.html index d799a8f2f..fa47a874b 100644 --- a/docs/jdocs/neureka/math/args/package-tree.html +++ b/docs/jdocs/neureka/math/args/package-tree.html @@ -1,92 +1,155 @@ - + + - -neureka.math.args Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.math.args Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.math.args

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/implementations/FunctionConstant.html b/docs/jdocs/neureka/math/implementations/FunctionConstant.html index 33692557b..6b634001c 100644 --- a/docs/jdocs/neureka/math/implementations/FunctionConstant.html +++ b/docs/jdocs/neureka/math/implementations/FunctionConstant.html @@ -1,428 +1,592 @@ - + + - -FunctionConstant (neureka 1.0.0 API) - - - - + +FunctionConstant (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.implementations
    -

    Class FunctionConstant

    -
    -
    java.lang.Object -
    neureka.math.implementations.FunctionConstant
    +
    neureka.math.implementations
    +

    Class FunctionConstant

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.math.implementations.FunctionConstant
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Function
      +
      Function
      -
      public final class FunctionConstant -extends Object -implements Function
      -
      Instances of this implementation of the Function interface +
      +
      public final class FunctionConstant
+extends java.lang.Object
+implements Function
      +
      Instances of this implementation of the Function interface are leave nodes within the abstract syntax tree of a function, representing constant numeric values to a function. When parsing an expression into a function then these constants are recognized by a series of digit characters optionally separated by '.' to represent decimal digits.
      So for example, when creating a function by calling the following factory method...

      - Function.of(java.lang.String)( "I[1] + (4 * I[0]) / 2.1" )
      + Function.of(java.lang.String)( "I[1] + (4 * I[0]) / 2.1" )

      ...then the substrings "4" and "2.1" will be parsed into instances of this class!
      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        FunctionConstant

        -
        public FunctionConstant(String expression)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            FunctionConstant

            +
            public FunctionConstant(java.lang.String expression)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      value

      -
      public double value()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          value

          +
          public double value()
          • -
        • -
          -

          isFlat

          -
          public boolean isFlat()
          -
          -
          Specified by:
          -
          isFlat in interface Function
          -
          Returns:
          -
          The truth value determining if the sub-functions of this Function do not themselves reference Functions.
          +
        + + + +
          +
        • +

          isFlat

          +
          public boolean isFlat()
          +
          +
          Specified by:
          +
          isFlat in interface Function
          +
          Returns:
          +
          The truth value determining if the sub-functions of this Function do not themselves reference Functions.
          -
    • -
  • -
    -

    isDoingAD

    -
    public boolean isDoingAD()
    -
    Description copied from interface: Function
    -
    Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions - whose Function.isFlat() flag is set to false!
    -
    -
    Specified by:
    -
    isDoingAD in interface Function
    -
    Returns:
    -
    The truth value determining if this Function can perform autograd/auto-differentiation on the input tensors it receives.
    + + + + +
      +
    • +

      isDoingAD

      +
      public boolean isDoingAD()
      +
      Description copied from interface: Function
      +
      Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions + whose Function.isFlat() flag is set to false!
      +
      +
      Specified by:
      +
      isDoingAD in interface Function
      +
      Returns:
      +
      The truth value determining if this Function can perform autograd/auto-differentiation on the input tensors it receives.
      -
    • -
  • -
    -

    getOperation

    -
    public AbstractOperation getOperation()
    -
    -
    Specified by:
    -
    getOperation in interface Function
    -
    Returns:
    -
    The Operation implementation instance responsible for executing any inputs received by this Function or null if this Function.isFlat().
    + + + + +
    • -
  • -
    -

    dependsOn

    -
    public boolean dependsOn(int index)
    -
    Description copied from interface: Function
    + + + + +
      +
    • +

      dependsOn

      +
      public boolean dependsOn(int index)
      +
      Description copied from interface: Function
      Use this to determine if this function directly or indirectly references an input with the provided index.
      -
      -
      Specified by:
      -
      dependsOn in interface Function
      -
      Parameters:
      -
      index - The index which ought to match the input index of a potentially referenced FunctionInput.
      -
      Returns:
      -
      The truth value determining if this Function (or any sub-functions) reference a FunctionInput with the provided index.
      +
      +
      Specified by:
      +
      dependsOn in interface Function
      +
      Parameters:
      +
      index - The index which ought to match the input index of a potentially referenced FunctionInput.
      +
      Returns:
      +
      The truth value determining if this Function (or any sub-functions) reference a FunctionInput with the provided index.
      -
    • -
  • -
    -

    getDerivative

    -
    public Function getDerivative(int index)
    -
    Description copied from interface: Function
    -
    This method builds a new Function which is the derivative of this Function with respect to the provided input index.
    -
    -
    Specified by:
    -
    getDerivative in interface Function
    -
    Parameters:
    + + + + +
      +
    • +

      getDerivative

      +
      public Function getDerivative(int index)
      +
      Description copied from interface: Function
      +
      This method builds a new Function which is the derivative of this Function with respect to the provided input index.
      +
      +
      Specified by:
      +
      getDerivative in interface Function
      +
      Parameters:
      index - The index of the input which ought to serve as the variable which ought to be derived.
      -
      Returns:
      -
      The derivative of this Function.
      +
      Returns:
      +
      The derivative of this Function.
      -
    • -
  • -
    -

    getSubFunctions

    -
    public List<Function> getSubFunctions()
    -
    -
    Specified by:
    -
    getSubFunctions in interface Function
    -
    Returns:
    -
    The referenced child Function nodes of this Function AST node.
    + + + + +
    • -
  • -
    -

    call

    -
    public double call(double[] inputs, - int j)
    -
    Description copied from interface: Function
    -
    Invokes this Function with the provided array of inputs ad an index for input dependent indexing. - This method is functionally equivalent to Function.invoke(double[], int).
    -
    -
    Specified by:
    -
    call in interface Function
    -
    Parameters:
    + + + + +
      +
    • +

      call

      +
      public double call(double[] inputs,
+                   int j)
      +
      Description copied from interface: Function
      +
      Invokes this Function with the provided array of inputs ad an index for input dependent indexing. + This method is functionally equivalent to Function.invoke(double[], int).
      +
      +
      Specified by:
      +
      call in interface Function
      +
      Parameters:
      inputs - The array of inputs.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar result, a single double value.
      -
    • -
  • -
    -

    derive

    -
    public double derive(double[] inputs, - int index)
    -
    Description copied from interface: Function
    -
    Calculates the derivative of a particular input with respect to the output of this Function + + + + +
      +
    • +

      derive

      +
      public double derive(double[] inputs,
+                     int index)
      +
      Description copied from interface: Function
      +
      Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs and an index targeting the input to be derived.
      -
      -
      Specified by:
      -
      derive in interface Function
      -
      Parameters:
      +
      +
      Specified by:
      +
      derive in interface Function
      +
      Parameters:
      inputs - The double array of inputs.
      index - The index of the input to be derived.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
    • -
  • -
    -

    derive

    -
    public double derive(double[] inputs, - int index, - int j)
    -
    Description copied from interface: Function
    -
    Calculates the derivative of a particular input with respect to the output of this Function + + + + +
      +
    • +

      derive

      +
      public double derive(double[] inputs,
+                     int index,
+                     int j)
      +
      Description copied from interface: Function
      +
      Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs, an index targeting the input to be derived and an index for input dependent indexing.
      -
      -
      Specified by:
      -
      derive in interface Function
      -
      Parameters:
      +
      +
      Specified by:
      +
      derive in interface Function
      +
      Parameters:
      inputs - The double array of inputs.
      index - The index of the input to be derived.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
    • -
  • -
    -

    execute

    -
    public Tensor<?> execute(Args arguments, - Tensor<?>... inputs)
    -
    Description copied from interface: Function
    + + + + +
      +
    • +

      execute

      +
      public Tensor<?> execute(Args arguments,
+                         Tensor<?>... inputs)
      +
      Description copied from interface: Function
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
      - Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
      -
      -
      Specified by:
      -
      execute in interface Function
      -
      Parameters:
      + Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
    • +
    +
    Specified by:
    +
    execute in interface Function
    +
    Parameters:
    arguments - A set of arguments you want to supply to this function for further control over the execution.
    inputs - The tensors which should be sent through this function.
    -
    Returns:
    +
    Returns:
    The resulting tensor produced by this function.
    - -
  • -
    -

    toString

    -
    public String toString()
    -
    Description copied from interface: Function
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      Description copied from interface: Function
      Turns this function into a string representation which can be used to reconstruct this function or combine it with other function strings to parse entirely new functions...
      -
      -
      Specified by:
      -
      toString in interface Function
      -
      Overrides:
      -
      toString in class Object
      -
      Returns:
      +
      +
      Specified by:
      +
      toString in interface Function
      +
      Overrides:
      +
      toString in class java.lang.Object
      +
      Returns:
      The string representation of this function.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/implementations/FunctionInput.html b/docs/jdocs/neureka/math/implementations/FunctionInput.html index 44d6bacc8..d5ed6a7e8 100644 --- a/docs/jdocs/neureka/math/implementations/FunctionInput.html +++ b/docs/jdocs/neureka/math/implementations/FunctionInput.html @@ -1,442 +1,613 @@ - + + - -FunctionInput (neureka 1.0.0 API) - - - - + +FunctionInput (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.implementations
    -

    Class FunctionInput

    -
    -
    java.lang.Object -
    neureka.math.implementations.FunctionInput
    +
    neureka.math.implementations
    +

    Class FunctionInput

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.math.implementations.FunctionInput
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Function
      +
      Function
      -
      public class FunctionInput -extends Object -implements Function
      -
      Instances of this implementation of the Function interface +
      +
      public class FunctionInput
+extends java.lang.Object
+implements Function
      +
      Instances of this implementation of the Function interface are leave nodes within the abstract syntax tree of a function, representing inputs to a function. When parsing an expression into a function then these inputs are recognized by the character 'i' or 'I', followed by a whole number starting at zero (optionally wrapped by '[' and ']'), which is the index - of the argument within the list/array of arguments passed to a concrete Function instance.
      + of the argument within the list/array of arguments passed to a concrete Function instance.
      So for example, when creating a function by calling the following factory method...

      - Function.of(java.lang.String)( "I[1] + (4 * I[0]) / 2" )
      + Function.of(java.lang.String)( "I[1] + (4 * I[0]) / 2" )

      ...then the substrings "I[1]" and "I[0]" will be parsed into instances of this class!
      When calling this function by passing two arguments, let's say (first, second) then - the FunctionInput "I[0]" will pick the first argument, whereas "I[1]" + the FunctionInput "I[0]" will pick the first argument, whereas "I[1]" will pick the second argument when evaluating the array of arguments.
      -
    -
    -
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        public static Function of(String equation, - boolean doAD)
        -
        Description copied from interface: Function
        -
        This static factory method will return Function instances - based on a provided mathematical String expression describing the function +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            of

            +
            public static Function of(java.lang.String equation,
+                          boolean doAD)
            +
            Description copied from interface: Function
            +
            This static factory method will return Function instances + based on a provided mathematical String expression describing the function using 'I[0]', 'I[1]', 'I[2]'... as input variables or 'I[j]' to enable input dependent indexing - like for example "sum( I[j] / 2 )" as well as a flag determining if the resulting Function + like for example "sum( I[j] / 2 )" as well as a flag determining if the resulting Function ought to be able to perform autograd or not.
            -
            -
            Parameters:
            +
            +
            Specified by:
            +
            of in interface Function
            +
            Parameters:
            equation - The right part of a function equation where inputs are denoted by 'I[0]', 'I[1]', 'I[2]'...
            -
            doAD - A flag determining if the produced Function should be able to perform autograd (aka. auto-differentiation)
            -
            Returns:
            -
            A Function instance created based on the provided String, ready to receive inputs and execute on them.
            +
            doAD - A flag determining if the produced Function should be able to perform autograd (aka. auto-differentiation)
            +
            Returns:
            +
            A Function instance created based on the provided String, ready to receive inputs and execute on them.
            -
        • -
      • -
        -

        index

        -
        public int index()
        -
        +
      + + + +
        +
      • +

        index

        +
        public int index()
        • -
      • -
        -

        providesGradient

        -
        public boolean providesGradient()
        -
        +
      + + + +
        +
      • +

        providesGradient

        +
        public boolean providesGradient()
        +
        +
        Returns:
        +
        The answer of the question : "does this variable/input node return a gradient?"
        +
        • -
      • -
        -

        isFlat

        -
        public boolean isFlat()
        -
        -
        Specified by:
        -
        isFlat in interface Function
        -
        Returns:
        -
        The truth value determining if the sub-functions of this Function do not themselves reference Functions.
        +
      + + + +
        +
      • +

        isFlat

        +
        public boolean isFlat()
        +
        +
        Specified by:
        +
        isFlat in interface Function
        +
        Returns:
        +
        The truth value determining if the sub-functions of this Function do not themselves reference Functions.
        -
      • -
    • -
      -

      isDoingAD

      -
      public boolean isDoingAD()
      -
      Description copied from interface: Function
      -
      Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions - whose Function.isFlat() flag is set to false!
      -
      -
      Specified by:
      -
      isDoingAD in interface Function
      -
      Returns:
      -
      The truth value determining if this Function can perform autograd/auto-differentiation on the input tensors it receives.
      +
    + + + +
      +
    • +

      isDoingAD

      +
      public boolean isDoingAD()
      +
      Description copied from interface: Function
      +
      Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions + whose Function.isFlat() flag is set to false!
      +
      +
      Specified by:
      +
      isDoingAD in interface Function
      +
      Returns:
      +
      The truth value determining if this Function can perform autograd/auto-differentiation on the input tensors it receives.
      -
      • -
    • -
      -

      getOperation

      -
      public AbstractOperation getOperation()
      -
      -
      Specified by:
      -
      getOperation in interface Function
      -
      Returns:
      -
      The Operation implementation instance responsible for executing any inputs received by this Function or null if this Function.isFlat().
      +
    + + + + + + + +
      +
    • +

      dependsOn

      +
      public boolean dependsOn(int index)
      +
      Description copied from interface: Function
      Use this to determine if this function directly or indirectly references an input with the provided index.
      -
      -
      Specified by:
      -
      dependsOn in interface Function
      -
      Parameters:
      -
      index - The index which ought to match the input index of a potentially referenced FunctionInput.
      -
      Returns:
      -
      The truth value determining if this Function (or any sub-functions) reference a FunctionInput with the provided index.
      +
      +
      Specified by:
      +
      dependsOn in interface Function
      +
      Parameters:
      +
      index - The index which ought to match the input index of a potentially referenced FunctionInput.
      +
      Returns:
      +
      The truth value determining if this Function (or any sub-functions) reference a FunctionInput with the provided index.
      -
      • -
    • -
      -

      getDerivative

      -
      public Function getDerivative(int index)
      -
      Description copied from interface: Function
      -
      This method builds a new Function which is the derivative of this Function with respect to the provided input index.
      -
      -
      Specified by:
      -
      getDerivative in interface Function
      -
      Parameters:
      +
    + + + +
      +
    • +

      getDerivative

      +
      public Function getDerivative(int index)
      +
      Description copied from interface: Function
      +
      This method builds a new Function which is the derivative of this Function with respect to the provided input index.
      +
      +
      Specified by:
      +
      getDerivative in interface Function
      +
      Parameters:
      index - The index of the input which ought to serve as the variable which ought to be derived.
      -
      Returns:
      -
      The derivative of this Function.
      +
      Returns:
      +
      The derivative of this Function.
      -
      • -
    • -
      -

      getSubFunctions

      -
      public List<Function> getSubFunctions()
      -
      -
      Specified by:
      -
      getSubFunctions in interface Function
      -
      Returns:
      -
      The referenced child Function nodes of this Function AST node.
      +
    + + + +
      +
    • +

      getSubFunctions

      +
      public java.util.List<Function> getSubFunctions()
      +
      +
      Specified by:
      +
      getSubFunctions in interface Function
      +
      Returns:
      +
      The referenced child Function nodes of this Function AST node.
      -
      • -
    • -
      -

      call

      -
      public double call(double[] inputs, - int j)
      -
      Description copied from interface: Function
      -
      Invokes this Function with the provided array of inputs ad an index for input dependent indexing. - This method is functionally equivalent to Function.invoke(double[], int).
      -
      -
      Specified by:
      -
      call in interface Function
      -
      Parameters:
      +
    + + + +
      +
    • +

      call

      +
      public double call(double[] inputs,
+                   int j)
      +
      Description copied from interface: Function
      +
      Invokes this Function with the provided array of inputs ad an index for input dependent indexing. + This method is functionally equivalent to Function.invoke(double[], int).
      +
      +
      Specified by:
      +
      call in interface Function
      +
      Parameters:
      inputs - The array of inputs.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar result, a single double value.
      -
      • -
    • -
      -

      derive

      -
      public double derive(double[] inputs, - int index)
      -
      Description copied from interface: Function
      -
      Calculates the derivative of a particular input with respect to the output of this Function +
    + + + +
      +
    • +

      derive

      +
      public double derive(double[] inputs,
+                     int index)
      +
      Description copied from interface: Function
      +
      Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs and an index targeting the input to be derived.
      -
      -
      Specified by:
      -
      derive in interface Function
      -
      Parameters:
      +
      +
      Specified by:
      +
      derive in interface Function
      +
      Parameters:
      inputs - The double array of inputs.
      index - The index of the input to be derived.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
      • -
    • -
      -

      derive

      -
      public double derive(double[] inputs, - int index, - int j)
      -
      Description copied from interface: Function
      -
      Calculates the derivative of a particular input with respect to the output of this Function +
    + + + +
      +
    • +

      derive

      +
      public double derive(double[] inputs,
+                     int index,
+                     int j)
      +
      Description copied from interface: Function
      +
      Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs, an index targeting the input to be derived and an index for input dependent indexing.
      -
      -
      Specified by:
      -
      derive in interface Function
      -
      Parameters:
      +
      +
      Specified by:
      +
      derive in interface Function
      +
      Parameters:
      inputs - The double array of inputs.
      index - The index of the input to be derived.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
      • -
    • -
      -

      execute

      -
      public Tensor<?> execute(Args arguments, - Tensor<?>... inputs)
      -
      Description copied from interface: Function
      +
    + + + +
      +
    • +

      execute

      +
      public Tensor<?> execute(Args arguments,
+                         Tensor<?>... inputs)
      +
      Description copied from interface: Function
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
      - Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
      -
      -
      Specified by:
      -
      execute in interface Function
      -
      Parameters:
      + Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
    +
    +
    Specified by:
    +
    execute in interface Function
    +
    Parameters:
    arguments - A set of arguments you want to supply to this function for further control over the execution.
    inputs - The tensors which should be sent through this function.
    -
    Returns:
    +
    Returns:
    The resulting tensor produced by this function.
    - -
  • -
    -

    toString

    -
    public String toString()
    -
    Description copied from interface: Function
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      Description copied from interface: Function
      Turns this function into a string representation which can be used to reconstruct this function or combine it with other function strings to parse entirely new functions...
      -
      -
      Specified by:
      -
      toString in interface Function
      -
      Overrides:
      -
      toString in class Object
      -
      Returns:
      +
      +
      Specified by:
      +
      toString in interface Function
      +
      Overrides:
      +
      toString in class java.lang.Object
      +
      Returns:
      The string representation of this function.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/math/implementations/FunctionNode.html b/docs/jdocs/neureka/math/implementations/FunctionNode.html index 36ded1d0a..cbf26af8e 100644 --- a/docs/jdocs/neureka/math/implementations/FunctionNode.html +++ b/docs/jdocs/neureka/math/implementations/FunctionNode.html @@ -1,422 +1,582 @@ - + + - -FunctionNode (neureka 1.0.0 API) - - - - + +FunctionNode (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.implementations
    -

    Class FunctionNode

    -
    -
    java.lang.Object -
    neureka.math.implementations.FunctionNode
    +
    neureka.math.implementations
    +

    Class FunctionNode

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.math.implementations.FunctionNode
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Function
      +
      Function
      -
      public final class FunctionNode -extends Object -implements Function
      -
      The most common type of Function which references other Functions to +
      +
      public final class FunctionNode
+extends java.lang.Object
+implements Function
      +
      The most common type of Function which references other Functions to form an abstract syntax tree.
      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        FunctionNode

        -
        public FunctionNode(Operation type, - List<Function> sources, - boolean doAD)
        -
        -
        Parameters:
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            FunctionNode

            +
            public FunctionNode(Operation type,
+                    java.util.List<Function> sources,
+                    boolean doAD)
            +
            +
            Parameters:
            type - The operation which ought to be represented.
            sources - The child function nodes of this node.
            doAD - A flag determining if this function should perform autograd.
            -
      -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      toString

      -
      public String toString()
      -
      Description copied from interface: Function
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          toString

          +
          public java.lang.String toString()
          +
          Description copied from interface: Function
          Turns this function into a string representation which can be used to reconstruct this function or combine it with other function strings to parse entirely new functions...
          -
          -
          Specified by:
          -
          toString in interface Function
          -
          Overrides:
          -
          toString in class Object
          -
          Returns:
          +
          +
          Specified by:
          +
          toString in interface Function
          +
          Overrides:
          +
          toString in class java.lang.Object
          +
          Returns:
          The string representation of this function.
          -
      • -
    • -
      -

      dependsOn

      -
      public boolean dependsOn(int index)
      -
      Description copied from interface: Function
      +
    + + + +
      +
    • +

      dependsOn

      +
      public boolean dependsOn(int index)
      +
      Description copied from interface: Function
      Use this to determine if this function directly or indirectly references an input with the provided index.
      -
      -
      Specified by:
      -
      dependsOn in interface Function
      -
      Parameters:
      -
      index - The index which ought to match the input index of a potentially referenced FunctionInput.
      -
      Returns:
      -
      The truth value determining if this Function (or any sub-functions) reference a FunctionInput with the provided index.
      +
      +
      Specified by:
      +
      dependsOn in interface Function
      +
      Parameters:
      +
      index - The index which ought to match the input index of a potentially referenced FunctionInput.
      +
      Returns:
      +
      The truth value determining if this Function (or any sub-functions) reference a FunctionInput with the provided index.
      -
    • -
  • -
    -

    getDerivative

    -
    public Function getDerivative(int index)
    -
    Description copied from interface: Function
    -
    This method builds a new Function which is the derivative of this Function with respect to the provided input index.
    -
    -
    Specified by:
    -
    getDerivative in interface Function
    -
    Parameters:
    + + + + +
      +
    • +

      getDerivative

      +
      public Function getDerivative(int index)
      +
      Description copied from interface: Function
      +
      This method builds a new Function which is the derivative of this Function with respect to the provided input index.
      +
      +
      Specified by:
      +
      getDerivative in interface Function
      +
      Parameters:
      index - The index of the input which ought to serve as the variable which ought to be derived.
      -
      Returns:
      -
      The derivative of this Function.
      +
      Returns:
      +
      The derivative of this Function.
      -
    • -
  • -
    -

    getSubFunctions

    -
    public List<Function> getSubFunctions()
    -
    -
    Specified by:
    -
    getSubFunctions in interface Function
    -
    Returns:
    -
    The referenced child Function nodes of this Function AST node.
    + + + + +
    • -
  • -
    -

    execute

    -
    public Tensor<?> execute(Args arguments, - Tensor<?>... inputs)
    -
    Description copied from interface: Function
    + + + + +
      +
    • +

      execute

      +
      public Tensor<?> execute(Args arguments,
+                         Tensor<?>... inputs)
      +
      Description copied from interface: Function
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
      - Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
      -
      -
      Specified by:
      -
      execute in interface Function
      -
      Parameters:
      + Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
    • +
    +
    Specified by:
    +
    execute in interface Function
    +
    Parameters:
    arguments - A set of arguments you want to supply to this function for further control over the execution.
    inputs - The tensors which should be sent through this function.
    -
    Returns:
    +
    Returns:
    The resulting tensor produced by this function.
    - -
  • -
    -

    call

    -
    public double call(double[] inputs, - int j)
    -
    Description copied from interface: Function
    -
    Invokes this Function with the provided array of inputs ad an index for input dependent indexing. - This method is functionally equivalent to Function.invoke(double[], int).
    -
    -
    Specified by:
    -
    call in interface Function
    -
    Parameters:
    + + + + +
      +
    • +

      call

      +
      public double call(double[] inputs,
+                   int j)
      +
      Description copied from interface: Function
      +
      Invokes this Function with the provided array of inputs ad an index for input dependent indexing. + This method is functionally equivalent to Function.invoke(double[], int).
      +
      +
      Specified by:
      +
      call in interface Function
      +
      Parameters:
      inputs - The array of inputs.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar result, a single double value.
      -
    • -
  • -
    -

    derive

    -
    public double derive(double[] inputs, - int d, - int j)
    -
    Description copied from interface: Function
    -
    Calculates the derivative of a particular input with respect to the output of this Function + + + + +
      +
    • +

      derive

      +
      public double derive(double[] inputs,
+                     int d,
+                     int j)
      +
      Description copied from interface: Function
      +
      Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs, an index targeting the input to be derived and an index for input dependent indexing.
      -
      -
      Specified by:
      -
      derive in interface Function
      -
      Parameters:
      +
      +
      Specified by:
      +
      derive in interface Function
      +
      Parameters:
      inputs - The double array of inputs.
      d - The index of the input to be derived.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
    • -
  • -
    -

    derive

    -
    public double derive(double[] inputs, - int d)
    -
    Description copied from interface: Function
    -
    Calculates the derivative of a particular input with respect to the output of this Function + + + + +
      +
    • +

      derive

      +
      public double derive(double[] inputs,
+                     int d)
      +
      Description copied from interface: Function
      +
      Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs and an index targeting the input to be derived.
      -
      -
      Specified by:
      -
      derive in interface Function
      -
      Parameters:
      +
      +
      Specified by:
      +
      derive in interface Function
      +
      Parameters:
      inputs - The double array of inputs.
      d - The index of the input to be derived.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
    • -
  • -
    -

    getOperation

    -
    public Operation getOperation()
    -
    -
    Specified by:
    -
    getOperation in interface Function
    -
    Returns:
    -
    The Operation implementation instance responsible for executing any inputs received by this Function or null if this Function.isFlat().
    + + + + +
    • -
  • -
    -

    isFlat

    -
    public boolean isFlat()
    -
    -
    Specified by:
    -
    isFlat in interface Function
    -
    Returns:
    -
    The truth value determining if the sub-functions of this Function do not themselves reference Functions.
    + + + + +
      +
    • +

      isFlat

      +
      public boolean isFlat()
      +
      +
      Specified by:
      +
      isFlat in interface Function
      +
      Returns:
      +
      The truth value determining if the sub-functions of this Function do not themselves reference Functions.
      -
    • -
  • -
    -

    isDoingAD

    -
    public boolean isDoingAD()
    -
    Description copied from interface: Function
    -
    Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions - whose Function.isFlat() flag is set to false!
    -
    -
    Specified by:
    -
    isDoingAD in interface Function
    -
    Returns:
    -
    The truth value determining if this Function can perform autograd/auto-differentiation on the input tensors it receives.
    + + + + +
      +
    • +

      isDoingAD

      +
      public boolean isDoingAD()
      +
      Description copied from interface: Function
      +
      Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions + whose Function.isFlat() flag is set to false!
      +
      +
      Specified by:
      +
      isDoingAD in interface Function
      +
      Returns:
      +
      The truth value determining if this Function can perform autograd/auto-differentiation on the input tensors it receives.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/implementations/FunctionVariable.html b/docs/jdocs/neureka/math/implementations/FunctionVariable.html index 21ae76f58..2172e06c5 100644 --- a/docs/jdocs/neureka/math/implementations/FunctionVariable.html +++ b/docs/jdocs/neureka/math/implementations/FunctionVariable.html @@ -1,430 +1,598 @@ - + + - -FunctionVariable (neureka 1.0.0 API) - - - - + +FunctionVariable (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.implementations
    -

    Class FunctionVariable

    -
    -
    java.lang.Object -
    neureka.math.implementations.FunctionVariable
    +
    neureka.math.implementations
    +

    Class FunctionVariable

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.math.implementations.FunctionVariable
        • +
      +
      • +
    +
    +
      +
    • +
      All Implemented Interfaces:
      -
      Function
      +
      Function
      -
      public final class FunctionVariable -extends Object -implements Function
      -
      Instances of this implementation of the Function interface +
      +
      public final class FunctionVariable
+extends java.lang.Object
+implements Function
      +
      Instances of this implementation of the Function interface are leave nodes within the abstract syntax tree of a function, representing indexed inputs to a function. When parsing an expression into a function then these inputs are recognized by the character 'i' or 'I', followed by the character 'j' or 'J' (optionally wrapped by '[' and ']'), which is a placeholder for the index - of the argument within the list/array of arguments passed to a concrete Function instance.
      + of the argument within the list/array of arguments passed to a concrete Function instance.
      So for example, when creating a function by calling the following factory method...

      - Function.of(java.lang.String)( "3 * sum( (I[j] + 4) * I[0] )" )
      + Function.of(java.lang.String)( "3 * sum( (I[j] + 4) * I[0] )" )

      ...then the substrings "I[j]" will be parsed into instances of this class!
      The substring "I[0]" on the other hand will not be parsed into an instance of this class!
      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        FunctionVariable

        -
        public FunctionVariable(String equation)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            FunctionVariable

            +
            public FunctionVariable(java.lang.String equation)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      providesGradient

      -
      public boolean providesGradient()
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          providesGradient

          +
          public boolean providesGradient()
          +
          +
          Returns:
          +
          The answer of the question : "does this variable/input node return a gradient?"
          +
          • -
        • -
          -

          isFlat

          -
          public boolean isFlat()
          -
          -
          Specified by:
          -
          isFlat in interface Function
          -
          Returns:
          -
          The truth value determining if the sub-functions of this Function do not themselves reference Functions.
          +
        + + + +
          +
        • +

          isFlat

          +
          public boolean isFlat()
          +
          +
          Specified by:
          +
          isFlat in interface Function
          +
          Returns:
          +
          The truth value determining if the sub-functions of this Function do not themselves reference Functions.
          -
    • -
  • -
    -

    isDoingAD

    -
    public boolean isDoingAD()
    -
    Description copied from interface: Function
    -
    Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions - whose Function.isFlat() flag is set to false!
    -
    -
    Specified by:
    -
    isDoingAD in interface Function
    -
    Returns:
    -
    The truth value determining if this Function can perform autograd/auto-differentiation on the input tensors it receives.
    + + + + +
      +
    • +

      isDoingAD

      +
      public boolean isDoingAD()
      +
      Description copied from interface: Function
      +
      Only branch Functions can do autograd / 'Auto-Differentiation', meaning functions + whose Function.isFlat() flag is set to false!
      +
      +
      Specified by:
      +
      isDoingAD in interface Function
      +
      Returns:
      +
      The truth value determining if this Function can perform autograd/auto-differentiation on the input tensors it receives.
      -
    • -
  • -
    -

    getOperation

    -
    public AbstractOperation getOperation()
    -
    -
    Specified by:
    -
    getOperation in interface Function
    -
    Returns:
    -
    The Operation implementation instance responsible for executing any inputs received by this Function or null if this Function.isFlat().
    + + + + +
    • -
  • -
    -

    dependsOn

    -
    public boolean dependsOn(int index)
    -
    Description copied from interface: Function
    + + + + +
      +
    • +

      dependsOn

      +
      public boolean dependsOn(int index)
      +
      Description copied from interface: Function
      Use this to determine if this function directly or indirectly references an input with the provided index.
      -
      -
      Specified by:
      -
      dependsOn in interface Function
      -
      Parameters:
      -
      index - The index which ought to match the input index of a potentially referenced FunctionInput.
      -
      Returns:
      -
      The truth value determining if this Function (or any sub-functions) reference a FunctionInput with the provided index.
      +
      +
      Specified by:
      +
      dependsOn in interface Function
      +
      Parameters:
      +
      index - The index which ought to match the input index of a potentially referenced FunctionInput.
      +
      Returns:
      +
      The truth value determining if this Function (or any sub-functions) reference a FunctionInput with the provided index.
      -
    • -
  • -
    -

    getDerivative

    -
    public Function getDerivative(int index)
    -
    Description copied from interface: Function
    -
    This method builds a new Function which is the derivative of this Function with respect to the provided input index.
    -
    -
    Specified by:
    -
    getDerivative in interface Function
    -
    Parameters:
    + + + + +
      +
    • +

      getDerivative

      +
      public Function getDerivative(int index)
      +
      Description copied from interface: Function
      +
      This method builds a new Function which is the derivative of this Function with respect to the provided input index.
      +
      +
      Specified by:
      +
      getDerivative in interface Function
      +
      Parameters:
      index - The index of the input which ought to serve as the variable which ought to be derived.
      -
      Returns:
      -
      The derivative of this Function.
      +
      Returns:
      +
      The derivative of this Function.
      -
    • -
  • -
    -

    getSubFunctions

    -
    public List<Function> getSubFunctions()
    -
    -
    Specified by:
    -
    getSubFunctions in interface Function
    -
    Returns:
    -
    The referenced child Function nodes of this Function AST node.
    + + + + +
    • -
  • -
    -

    call

    -
    public double call(double[] inputs, - int j)
    -
    Description copied from interface: Function
    -
    Invokes this Function with the provided array of inputs ad an index for input dependent indexing. - This method is functionally equivalent to Function.invoke(double[], int).
    -
    -
    Specified by:
    -
    call in interface Function
    -
    Parameters:
    + + + + +
      +
    • +

      call

      +
      public double call(double[] inputs,
+                   int j)
      +
      Description copied from interface: Function
      +
      Invokes this Function with the provided array of inputs ad an index for input dependent indexing. + This method is functionally equivalent to Function.invoke(double[], int).
      +
      +
      Specified by:
      +
      call in interface Function
      +
      Parameters:
      inputs - The array of inputs.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar result, a single double value.
      -
    • -
  • -
    -

    derive

    -
    public double derive(double[] inputs, - int index)
    -
    Description copied from interface: Function
    -
    Calculates the derivative of a particular input with respect to the output of this Function + + + + +
      +
    • +

      derive

      +
      public double derive(double[] inputs,
+                     int index)
      +
      Description copied from interface: Function
      +
      Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs and an index targeting the input to be derived.
      -
      -
      Specified by:
      -
      derive in interface Function
      -
      Parameters:
      +
      +
      Specified by:
      +
      derive in interface Function
      +
      Parameters:
      inputs - The double array of inputs.
      index - The index of the input to be derived.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
    • -
  • -
    -

    derive

    -
    public double derive(double[] inputs, - int index, - int j)
    -
    Description copied from interface: Function
    -
    Calculates the derivative of a particular input with respect to the output of this Function + + + + +
      +
    • +

      derive

      +
      public double derive(double[] inputs,
+                     int index,
+                     int j)
      +
      Description copied from interface: Function
      +
      Calculates the derivative of a particular input with respect to the output of this Function based on the provided array of inputs, an index targeting the input to be derived and an index for input dependent indexing.
      -
      -
      Specified by:
      -
      derive in interface Function
      -
      Parameters:
      +
      +
      Specified by:
      +
      derive in interface Function
      +
      Parameters:
      inputs - The double array of inputs.
      index - The index of the input to be derived.
      j - The index for input dependent indexing.
      -
      Returns:
      +
      Returns:
      The scalar double result, a single double value.
      -
    • -
  • -
    -

    execute

    -
    public Tensor<?> execute(Args arguments, - Tensor<?>... inputs)
    -
    Description copied from interface: Function
    + + + + +
      +
    • +

      execute

      +
      public Tensor<?> execute(Args arguments,
+                         Tensor<?>... inputs)
      +
      Description copied from interface: Function
      Warning: Tensors returned by this method are eligible for deletion when consumed by other functions.
      - Use this to call this Function alongside with some additional meta-arguments - which will be passed to the underlying Operation(s).
      -
      -
      Specified by:
      -
      execute in interface Function
      -
      Parameters:
      + Use this to call this Function alongside with some additional meta-arguments + which will be passed to the underlying Operation(s).
    • +
    +
    Specified by:
    +
    execute in interface Function
    +
    Parameters:
    arguments - A set of arguments you want to supply to this function for further control over the execution.
    inputs - The tensors which should be sent through this function.
    -
    Returns:
    +
    Returns:
    The resulting tensor produced by this function.
    - -
  • -
    -

    toString

    -
    public String toString()
    -
    Description copied from interface: Function
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      Description copied from interface: Function
      Turns this function into a string representation which can be used to reconstruct this function or combine it with other function strings to parse entirely new functions...
      -
      -
      Specified by:
      -
      toString in interface Function
      -
      Overrides:
      -
      toString in class Object
      -
      Returns:
      +
      +
      Specified by:
      +
      toString in interface Function
      +
      Overrides:
      +
      toString in class java.lang.Object
      +
      Returns:
      The string representation of this function.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/implementations/package-frame.html b/docs/jdocs/neureka/math/implementations/package-frame.html new file mode 100644 index 000000000..f51c64fea --- /dev/null +++ b/docs/jdocs/neureka/math/implementations/package-frame.html @@ -0,0 +1,22 @@ + + + + + +neureka.math.implementations (neureka 1.0.1 API) + + + + +

    neureka.math.implementations

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/math/implementations/package-summary.html b/docs/jdocs/neureka/math/implementations/package-summary.html index c29d6d81b..6cc916d23 100644 --- a/docs/jdocs/neureka/math/implementations/package-summary.html +++ b/docs/jdocs/neureka/math/implementations/package-summary.html @@ -1,124 +1,176 @@ - + + - -neureka.math.implementations (neureka 1.0.0 API) - - - - + +neureka.math.implementations (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.math.implementations

    -
    -
    -
    package neureka.math.implementations
    -
    +

    Package neureka.math.implementations

    +
    Everything in this package should be considered library-private! DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! Code inside this package or any sub-packages might change frequently...
    -
    -
    -
      -
    • - -
      • -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      FunctionConstant
      -
      -
      Instances of this implementation of the Function interface +

      See: Description

      +
      +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        FunctionConstant +
        Instances of this implementation of the Function interface are leave nodes within the abstract syntax tree of a function, representing constant numeric values to a function.
        - -
        FunctionInput
        -
        -
        Instances of this implementation of the Function interface +
        FunctionInput +
        Instances of this implementation of the Function interface are leave nodes within the abstract syntax tree of a function, representing inputs to a function.
        - -
        FunctionNode
        -
        -
        The most common type of Function which references other Functions to +
        FunctionNode +
        The most common type of Function which references other Functions to form an abstract syntax tree.
        - -
        FunctionVariable
        -
        -
        Instances of this implementation of the Function interface +
        FunctionVariable +
        Instances of this implementation of the Function interface are leave nodes within the abstract syntax tree of a function, representing indexed inputs to a function.
        - - - +
      -
    -
    + + + +

    Package neureka.math.implementations Description

    +
    Everything in this package should be considered library-private! + DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! + Code inside this package or any sub-packages might change frequently...
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/math/implementations/package-tree.html b/docs/jdocs/neureka/math/implementations/package-tree.html index b657f9fa8..c194c2e72 100644 --- a/docs/jdocs/neureka/math/implementations/package-tree.html +++ b/docs/jdocs/neureka/math/implementations/package-tree.html @@ -1,74 +1,137 @@ - + + - -neureka.math.implementations Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.math.implementations Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.math.implementations

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/package-frame.html b/docs/jdocs/neureka/math/package-frame.html new file mode 100644 index 000000000..50f7067b4 --- /dev/null +++ b/docs/jdocs/neureka/math/package-frame.html @@ -0,0 +1,25 @@ + + + + + +neureka.math (neureka 1.0.1 API) + + + + +

    neureka.math

    +
    +

    Interfaces

    + +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/math/package-summary.html b/docs/jdocs/neureka/math/package-summary.html index 51ead3b0e..98aed8602 100644 --- a/docs/jdocs/neureka/math/package-summary.html +++ b/docs/jdocs/neureka/math/package-summary.html @@ -1,131 +1,174 @@ - + + - -neureka.math (neureka 1.0.0 API) - - - - + +neureka.math (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.math

    -
    -
    -
    package neureka.math
    -
    -
      -
    • - -
      • -
    • -
      -
      -
      -
      -
      Class
      -
      Description
      -
      Function
      -
      -
      Besides the Tensor class, which is the core class of Neureka, this interface and its implementations +

      Package neureka.math

      +
      +
      + -
    -
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/math/package-tree.html b/docs/jdocs/neureka/math/package-tree.html index 1d0ea1676..fbce5f61f 100644 --- a/docs/jdocs/neureka/math/package-tree.html +++ b/docs/jdocs/neureka/math/package-tree.html @@ -1,79 +1,140 @@ - + + - -neureka.math Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.math Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.math

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -

    Interface Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/parsing/FunctionParser.html b/docs/jdocs/neureka/math/parsing/FunctionParser.html index aed578716..40eae5123 100644 --- a/docs/jdocs/neureka/math/parsing/FunctionParser.html +++ b/docs/jdocs/neureka/math/parsing/FunctionParser.html @@ -1,197 +1,311 @@ - + + - -FunctionParser (neureka 1.0.0 API) - - - - + +FunctionParser (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.parsing
    -

    Class FunctionParser

    +
    neureka.math.parsing
    +

    Class FunctionParser

    -
    java.lang.Object -
    neureka.math.parsing.FunctionParser
    -
    -
    -
    -
    public class FunctionParser -extends Object
    -
    The FunctionParser takes a BackendContext instance based on which - it builds Function implementation instances, usually by parsing Strings. - The information needed for parsing is being provided by the Operations within the formerly - mentioned BackendContext...
    -
    -
    -
      - +
      +
        +
      • java.lang.Object
      • -
        -

        Constructor Summary

        -
        Constructors
        -
        -
        Constructor
        -
        Description
        -
        FunctionParser(BackendContext context)
        -
         
        +
          +
        • neureka.math.parsing.FunctionParser
          • +
        +
        • +
      +
      +
        +
      • +
        +
        +
        public class FunctionParser
+extends java.lang.Object
        +
        The FunctionParser takes a BackendContext instance based on which + it builds Function implementation instances, usually by parsing Strings. + The information needed for parsing is being provided by the Operations within the formerly + mentioned BackendContext...
        +
        • +
      -
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        FunctionParser

        -
        public FunctionParser(BackendContext context)
        -
        -
        Parameters:
        -
        context - The BackendContext which will be used as a basis to parse new Function - implementation instance from provided String expressions.
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            FunctionParser

            +
            public FunctionParser(BackendContext context)
            +
            +
            Parameters:
            +
            context - The BackendContext which will be used as a basis to parse new Function + implementation instance from provided String expressions.
            -
      -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      parse

      -
      public Function parse(Operation operation, - int numberOfArgs, - boolean doAD)
      -
      -
      Parameters:
      -
      operation - The Operation based on which the Function ought to be created.
      -
      numberOfArgs - The number of arguments the produced Function ought to have.
      -
      doAD - The flag determining if the Function built by this method should perform autograd or not.
      -
      Returns:
      -
      A Function implementation instance which satisfied the supplied parameters.
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          parse

          +
          public Function parse(Operation operation,
+                      int numberOfArgs,
+                      boolean doAD)
          +
          +
          Parameters:
          +
          operation - The Operation based on which the Function ought to be created.
          +
          numberOfArgs - The number of arguments the produced Function ought to have.
          +
          doAD - The flag determining if the Function built by this method should perform autograd or not.
          +
          Returns:
          +
          A Function implementation instance which satisfied the supplied parameters.
          -
      • -
    • -
      -

      parse

      -
      public Function parse(String expression, - boolean doAD)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      parse

      +
      public Function parse(java.lang.String expression,
+                      boolean doAD)
      +
      +
      Parameters:
      expression - contains the function as String provided by the user
      doAD - is used to turn autograd on or off for this function
      -
      Returns:
      +
      Returns:
      the function which has been built from the expression
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/math/parsing/ParseUtil.html b/docs/jdocs/neureka/math/parsing/ParseUtil.html index 4a91366c0..8aa362015 100644 --- a/docs/jdocs/neureka/math/parsing/ParseUtil.html +++ b/docs/jdocs/neureka/math/parsing/ParseUtil.html @@ -1,246 +1,385 @@ - + + - -ParseUtil (neureka 1.0.0 API) - - - - + +ParseUtil (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.math.parsing
    -

    Class ParseUtil

    +
    neureka.math.parsing
    +

    Class ParseUtil

    -
    java.lang.Object -
    neureka.math.parsing.ParseUtil
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.math.parsing.ParseUtil
        • +
      +
      • +
    +
    +
      +
    • -
      public final class ParseUtil -extends Object
      +
      +
      public final class ParseUtil
+extends java.lang.Object
      Utility for parsing function expressions.
      -
    -
    -
    -
    -

    Methods inherited from class java.lang.Object

    -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
    - +
    +
      +
    • + +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static java.lang.StringassumptionBasedOn(java.lang.String expression) +
        This method tries to find the next best operation String the user might have meant.
        +
        static java.lang.StringcleanedHeadAndTail(java.lang.String exp) 
        static java.lang.StringfindComponentIn(java.lang.String exp, + int index) 
        static java.util.List<java.lang.String>findParametersIn(java.lang.String exp, + int index) 
        static java.lang.StringgroupBy(java.lang.String operation, + java.lang.String currentChain, + java.lang.String currentComponent, + java.lang.String currentOperation) 
        static booleanisAnOperation(java.lang.String operationName) 
        static intnumberOfOperationsWithin(java.util.List<java.lang.String> operations) 
        static java.lang.StringparsedOperation(java.lang.String exp, + int index) 
        static doublesimilarity(java.lang.String s1, + java.lang.String s2) +
        This method estimates the similarity between 2 provided String instances.
        +
        static java.lang.StringunpackAndCorrect(java.lang.String exp) 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
      - -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        numberOfOperationsWithin

        -
        public static int numberOfOperationsWithin(List<String> operations)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            numberOfOperationsWithin

            +
            public static int numberOfOperationsWithin(java.util.List<java.lang.String> operations)
            • -
          • -
            -

            parsedOperation

            -
            public static String parsedOperation(String exp, - int index)
            -
            +
          + + + +
            +
          • +

            parsedOperation

            +
            public static java.lang.String parsedOperation(java.lang.String exp,
+                                               int index)
            • -
          • -
            -

            findComponentIn

            -
            public static String findComponentIn(String exp, - int index)
            -
            +
          + + + +
            +
          • +

            findComponentIn

            +
            public static java.lang.String findComponentIn(java.lang.String exp,
+                                               int index)
            • -
          • -
            -

            findParametersIn

            -
            public static List<String> findParametersIn(String exp, - int index)
            -
            +
          + + + +
            +
          • +

            findParametersIn

            +
            public static java.util.List<java.lang.String> findParametersIn(java.lang.String exp,
+                                                                int index)
            • -
          • -
            -

            isAnOperation

            -
            public static boolean isAnOperation(String operationName)
            -
            +
          + + + +
            +
          • +

            isAnOperation

            +
            public static boolean isAnOperation(java.lang.String operationName)
            • -
          • -
            -

            groupBy

            -
            public static String groupBy(String operation, - String currentChain, - String currentComponent, - String currentOperation)
            -
            +
          + + + +
            +
          • +

            groupBy

            +
            public static java.lang.String groupBy(java.lang.String operation,
+                                       java.lang.String currentChain,
+                                       java.lang.String currentComponent,
+                                       java.lang.String currentOperation)
            • -
          • -
            -

            cleanedHeadAndTail

            -
            public static String cleanedHeadAndTail(String exp)
            -
            +
          + + + +
            +
          • +

            cleanedHeadAndTail

            +
            public static java.lang.String cleanedHeadAndTail(java.lang.String exp)
            • -
          • -
            -

            unpackAndCorrect

            -
            public static String unpackAndCorrect(String exp)
            -
            +
          + + + +
            +
          • +

            unpackAndCorrect

            +
            public static java.lang.String unpackAndCorrect(java.lang.String exp)
            • -
          • -
            -

            assumptionBasedOn

            -
            public static String assumptionBasedOn(String expression)
            -
            This method tries to find the next best operation String the user might have meant.
            -
            -
            Parameters:
            +
          + + + +
            +
          • +

            assumptionBasedOn

            +
            public static java.lang.String assumptionBasedOn(java.lang.String expression)
            +
            This method tries to find the next best operation String the user might have meant.
            +
            +
            Parameters:
            expression - The expression which should be interpreted as something similar.
            -
            Returns:
            +
            Returns:
            Something similar or null if the expression is not similar enough.
            -
      • -
    • -
      -

      similarity

      -
      public static double similarity(String s1, - String s2)
      -
      This method estimates the similarity between 2 provided String instances.
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      similarity

      +
      public static double similarity(java.lang.String s1,
+                                java.lang.String s2)
      +
      This method estimates the similarity between 2 provided String instances.
      +
      +
      Parameters:
      s1 - The first string which should be compared to the second string.
      s2 - The second string which should be compared to the first string.
      -
      Returns:
      +
      Returns:
      A similarity score between 0 and 1 where 1 would be 100% similar (equal).
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
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    + diff --git a/docs/jdocs/neureka/math/parsing/package-frame.html b/docs/jdocs/neureka/math/parsing/package-frame.html new file mode 100644 index 000000000..edd6283f6 --- /dev/null +++ b/docs/jdocs/neureka/math/parsing/package-frame.html @@ -0,0 +1,20 @@ + + + + + +neureka.math.parsing (neureka 1.0.1 API) + + + + +

    neureka.math.parsing

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/math/parsing/package-summary.html b/docs/jdocs/neureka/math/parsing/package-summary.html index 1592de7e9..2fcb32b6c 100644 --- a/docs/jdocs/neureka/math/parsing/package-summary.html +++ b/docs/jdocs/neureka/math/parsing/package-summary.html @@ -1,113 +1,161 @@ - + + - -neureka.math.parsing (neureka 1.0.0 API) - - - - + +neureka.math.parsing (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.math.parsing

    -
    -
    -
    package neureka.math.parsing
    -
    -
    Everything in this package should be considered library-private! - DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! - Code inside this package or any sub-packages might change frequently...
    -
    -
    -
    -
    + + + +

    Package neureka.math.parsing Description

    +
    Everything in this package should be considered library-private! + DO NOT DEPEND ON CLASSES INSIDE THIS PACKAGE! + Code inside this package or any sub-packages might change frequently...
    +
    + +
    + + +
    Skip navigation links
    + + + +
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    + + + +
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    + diff --git a/docs/jdocs/neureka/math/parsing/package-tree.html b/docs/jdocs/neureka/math/parsing/package-tree.html index 9e74e54f5..85527ad29 100644 --- a/docs/jdocs/neureka/math/parsing/package-tree.html +++ b/docs/jdocs/neureka/math/parsing/package-tree.html @@ -1,72 +1,135 @@ - + + - -neureka.math.parsing Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.math.parsing Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.math.parsing

    -Package Hierarchies: +Package Hierarchies:
    -
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    Class Hierarchy

    -
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    + +
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    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/ndim/Filler.html b/docs/jdocs/neureka/ndim/Filler.html index ca1a6e048..30131e096 100644 --- a/docs/jdocs/neureka/ndim/Filler.html +++ b/docs/jdocs/neureka/ndim/Filler.html @@ -1,137 +1,231 @@ - + + - -Filler (neureka 1.0.0 API) - - - - + +Filler (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim
    -

    Interface Filler<T>

    +
    neureka.ndim
    +

    Interface Filler<T>

    -
    -
    -
    Type Parameters:
    +
    +
    +
      +
    • +
      +
      Type Parameters:
      T - The type parameter determining the type of the supplied values.
      -
      +
      Functional Interface:
      This is a functional interface and can therefore be used as the assignment target for a lambda expression or method reference.
      -
      @FunctionalInterface -public interface Filler<T>
      +
      +
      @FunctionalInterface
+public interface Filler<T>
      Implementations of this ought to map the index of a tensor entry to a value which should be placed at that entry position.
      -
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      T
      -
      init(int i, - int[] index)
      -
       
      -
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      Method Details

      -
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      • -
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        init

        -
        T init(int i, - int[] index)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            init

            +
            T init(int i,
+       int[] index)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
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    • Detail: 
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    + + +
    + diff --git a/docs/jdocs/neureka/ndim/NDConstructor.html b/docs/jdocs/neureka/ndim/NDConstructor.html index f6321f4e8..fbda35dec 100644 --- a/docs/jdocs/neureka/ndim/NDConstructor.html +++ b/docs/jdocs/neureka/ndim/NDConstructor.html @@ -1,186 +1,304 @@ - + + - -NDConstructor (neureka 1.0.0 API) - - - - + +NDConstructor (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim
    -

    Interface NDConstructor

    +
    neureka.ndim
    +

    Interface NDConstructor

    -
    +
    +
    +
      +
    • -
      public interface NDConstructor
      -
    -
    -
    - +
    + - -
    -
      +
    +
    + - +
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    Skip navigation links
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    • Detail: 
      • +
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/NDUtil.html b/docs/jdocs/neureka/ndim/NDUtil.html index 49b981e7b..fcef5e7aa 100644 --- a/docs/jdocs/neureka/ndim/NDUtil.html +++ b/docs/jdocs/neureka/ndim/NDUtil.html @@ -1,159 +1,269 @@ - + + - -NDUtil (neureka 1.0.0 API) - - - - + +NDUtil (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim
    -

    Class NDUtil

    +
    neureka.ndim
    +

    Class NDUtil

    -
    java.lang.Object -
    neureka.ndim.NDUtil
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.ndim.NDUtil
        • +
      +
      • +
    +
    +
      +
    • -
      public class NDUtil -extends Object
      +
      +
      public class NDUtil
+extends java.lang.Object
      Static utility methods for the NDArray.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      NDUtil()
      -
       
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        NDUtil

        -
        public NDUtil()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            NDUtil

            +
            public NDUtil()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      shapeString

      -
      public static String shapeString(int[] conf)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          shapeString

          +
          public static java.lang.String shapeString(int[] conf)
        -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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    +
      +
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      • +
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/ndim/NDimensional.html b/docs/jdocs/neureka/ndim/NDimensional.html index 67cafe077..17c40787b 100644 --- a/docs/jdocs/neureka/ndim/NDimensional.html +++ b/docs/jdocs/neureka/ndim/NDimensional.html @@ -1,296 +1,381 @@ - + + - -NDimensional (neureka 1.0.0 API) - - - - + +NDimensional (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim
    -

    Interface NDimensional

    +
    neureka.ndim
    +

    Interface NDimensional

    -
    -
    +
    +
    +
      +
    • +
      All Known Subinterfaces:
      -
      Nda<V>, Tensor<V>
      +
      Nda<V>, Tensor<V>
      -
      public interface NDimensional
      +
      +
      public interface NDimensional
      This interface defines the most essential methods of the nd-array/tensor API, which describe them with respect to their dimensionality.
      - How many dimensions does a tensor/nd-array have? (rank())
      - How many elements does a tensor/nd-array have? (size())
      - What are the sizes of the individual dimensions? (shape(int i))
      + How many dimensions does a tensor/nd-array have? (rank())
      + How many elements does a tensor/nd-array have? (size())
      + What are the sizes of the individual dimensions? (shape(int i))
      ...
      -
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
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      +
    • -
    • -
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      Method Details

      -
        -
      • -
        -

        rank

        -
        default int rank()
        -
        -
        Returns:
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            rank

            +
            default int rank()
            +
            +
            Returns:
            The number of dimensions of this tensor / nd-array.
            -
        • -
      • -
        -

        getRank

        -
        default int getRank()
        -
        -
        Returns:
        +
      + + + +
        +
      • +

        getRank

        +
        default int getRank()
        +
        +
        Returns:
        The number of dimensions of this tensor / nd-array.
        -
      • -
    • -
      -

      shape

      -
      default Shape shape()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      shape

      +
      default Shape shape()
      +
      +
      Returns:
      A list of the dimensions of this tensor / array.
      -
      • -
    • -
      -

      getShape

      -
      default Shape getShape()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      getShape

      +
      default Shape getShape()
      +
      +
      Returns:
      A list of the dimensions of this tensor / array.
      -
      • -
    • -
      -

      indicesMap

      -
      default List<Integer> indicesMap()
      -
      +
    + + + +
      +
    • +

      indicesMap

      +
      default java.util.List<java.lang.Integer> indicesMap()
      • -
    • -
      -

      strides

      -
      default List<Integer> strides()
      -
      +
    + + + +
      +
    • +

      strides

      +
      default java.util.List<java.lang.Integer> strides()
      • -
    • -
      -

      spread

      -
      default List<Integer> spread()
      -
      +
    + + + +
      +
    • +

      spread

      +
      default java.util.List<java.lang.Integer> spread()
      • -
    • -
      -

      offset

      -
      default List<Integer> offset()
      +
    + + + +
      +
    • +

      offset

      +
      default java.util.List<java.lang.Integer> offset()
      The offset is the position of a slice within the n-dimensional data array of its parent array. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent array.
      -
      • -
    • -
      -

      getNDConf

      -
      NDConfiguration getNDConf()
      -
      -
      Returns:
      -
      The NDConfiguration implementation instance of this Tensor storing dimensionality information.
      +
    + + + +
      +
    • +

      getNDConf

      +
      NDConfiguration getNDConf()
      +
      +
      Returns:
      +
      The NDConfiguration implementation instance of this Tensor storing dimensionality information.
      -
      • -
    • -
      -

      shape

      -
      default int shape(int i)
      +
    + + + +
      +
    • +

      shape

      +
      default int shape(int i)
      This method receives an axis index and return the size of the targeted axis / dimension. It enables readable access to the shape of this tensor.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the shape dimension size which should be returned.
      -
      Returns:
      +
      Returns:
      The dimension size targeted by the provided dimension index.
      -
      • -
    • -
      -

      size

      -
      default int size()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      size

      +
      default int size()
      +
      +
      Returns:
      The number of elements stored inside the nd-array.
      -
      • -
    • -
      -

      getSize

      -
      default int getSize()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      getSize

      +
      default int getSize()
      +
      +
      Returns:
      The number of elements stored inside the nd-array.
      -
      • -
    • -
      -

      indexOfIndex

      -
      default int indexOfIndex(int index)
      +
    + + + +
      +
    • +

      indexOfIndex

      +
      default int indexOfIndex(int index)
      This is a convenience method identical to ndArray.getNDConf().indexOfIndex(i). Use this to calculate the true index for an element in the data array (data array index) based on a provided "user index", or "user array index". @@ -298,55 +383,120 @@

      indexOfIndex

      like for example if the nd-array is a slice of another larger nd-array, or if it is in fact a permuted version of another nd-array. The basis for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
      • -
    • -
      -

      indicesOfIndex

      -
      default int[] indicesOfIndex(int index)
      +
    + + + +
      +
    • +

      indicesOfIndex

      +
      default int[] indicesOfIndex(int index)
      This is a convenience method identical to ndArray.getNDConf().IndicesOfIndex(i). Use this to calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
      • -
    • -
      -

      indexOfIndices

      -
      default int indexOfIndices(int[] indices)
      +
    + + + +
      +
    • +

      indexOfIndices

      +
      default int indexOfIndices(int[] indices)
      This is a convenience method identical to ndArray.getNDConf().indexOfIndices(indices). Use this to calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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    + diff --git a/docs/jdocs/neureka/ndim/config/AbstractNDC.html b/docs/jdocs/neureka/ndim/config/AbstractNDC.html index 83b4eecc2..2a06afec1 100644 --- a/docs/jdocs/neureka/ndim/config/AbstractNDC.html +++ b/docs/jdocs/neureka/ndim/config/AbstractNDC.html @@ -1,300 +1,450 @@ - + + - -AbstractNDC (neureka 1.0.0 API) - - - - + +AbstractNDC (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config
    -

    Class AbstractNDC

    +
    neureka.ndim.config
    +

    Class AbstractNDC

    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.ndim.config.AbstractNDC
        • +
      +
      • +
    +
    +
    -
    -
      + In these cases tensors can simply share their NDConfiguration instances for memory efficiency.
    + + +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        AbstractNDC

        -
        public AbstractNDC()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            AbstractNDC

            +
            public AbstractNDC()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      _cacheArray

      -
      protected static int[] _cacheArray(int[] data)
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          _cacheArray

          +
          protected static int[] _cacheArray(int[] data)
          This method receives an int array and returns an int array which can either be the one provided or an array found in the global int array cache residing inside this class. Integer array based configurations are not very large, that is why their state can uniquely be encoded in long keys.
          -
          -
          Parameters:
          +
          +
          Parameters:
          data - The integer array which ought to be cached.
          -
          Returns:
          +
          Returns:
          The provided array or an already present array found in the int array cache.
          -
      • -
    • -
      -

      _cached

      -
      protected static <T extends NDConfiguration> T _cached(T ndc)
      -
      +
    + + + + + + + + + +
      +
    • +

      toString

      +
      public final java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • -
  • -
    -

    _simpleReshape

    -
    protected static NDConfiguration _simpleReshape(int[] newForm, - NDConfiguration ndc)
    -
    + + + + + + + + +
    • +
    +
    Specified by:
    +
    newReshaped in interface NDConfiguration
    +
    Parameters:
    newForm - An array of indices which define how the axis ought to be rearranged.
    -
    Returns:
    -
    A new NDConfiguration which carries the needed information for the permuted view.
    +
    Returns:
    +
    A new NDConfiguration which carries the needed information for the permuted view.
    - -
  • -
    -

    hashCode

    -
    public int hashCode()
    -
    -
    Specified by:
    -
    hashCode in interface NDConfiguration
    -
    Overrides:
    -
    hashCode in class Object
    + + + + +
      +
    • +

      hashCode

      +
      public int hashCode()
      +
      +
      Specified by:
      +
      hashCode in interface NDConfiguration
      +
      Overrides:
      +
      hashCode in class java.lang.Object
      -
    • -
  • -
    -

    equals

    -
    public final boolean equals(Object other)
    -
    -
    Overrides:
    -
    equals in class Object
    + + + + +
      +
    • +

      equals

      +
      public final boolean equals(java.lang.Object other)
      +
      +
      Overrides:
      +
      equals in class java.lang.Object
      -
    • -
  • -
    -

    equals

    -
    public final boolean equals(NDConfiguration ndc)
    -
    -
    Specified by:
    -
    equals in interface NDConfiguration
    + + + + +
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/NDConfiguration.IndexToIndexFunction.html b/docs/jdocs/neureka/ndim/config/NDConfiguration.IndexToIndexFunction.html index 919202fc6..25eb40ef6 100644 --- a/docs/jdocs/neureka/ndim/config/NDConfiguration.IndexToIndexFunction.html +++ b/docs/jdocs/neureka/ndim/config/NDConfiguration.IndexToIndexFunction.html @@ -1,131 +1,225 @@ - + + - -NDConfiguration.IndexToIndexFunction (neureka 1.0.0 API) - - - - + +NDConfiguration.IndexToIndexFunction (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config
    -

    Interface NDConfiguration.IndexToIndexFunction

    +
    neureka.ndim.config
    +

    Interface NDConfiguration.IndexToIndexFunction

    -
    -
    +
    +
    +
    -
    -
      - -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      int
      -
      map(int i)
      -
       
      -
      -
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        map

        -
        int map(int i)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            map

            +
            int map(int i)
            +
            • +
        -
    - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
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      +
    • Summary: 
      • +
    • Nested | 
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    • Detail: 
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    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/NDConfiguration.Layout.html b/docs/jdocs/neureka/ndim/config/NDConfiguration.Layout.html index 39f717098..2f0123653 100644 --- a/docs/jdocs/neureka/ndim/config/NDConfiguration.Layout.html +++ b/docs/jdocs/neureka/ndim/config/NDConfiguration.Layout.html @@ -1,93 +1,127 @@ - + + - -NDConfiguration.Layout (neureka 1.0.0 API) - - - - + +NDConfiguration.Layout (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config
    -

    Enum Class NDConfiguration.Layout

    -
    -
    java.lang.Object -
    java.lang.Enum<NDConfiguration.Layout> -
    neureka.ndim.config.NDConfiguration.Layout
    +
    neureka.ndim.config
    +

    Enum NDConfiguration.Layout

    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • + +
      • +
    +
    +
    -
    -
    - +
    +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static NDConfiguration.Layout[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static NDConfiguration.Layout[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (NDConfiguration.Layout c : NDConfiguration.Layout.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static NDConfiguration.Layout valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static NDConfiguration.Layout valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • -
  • -
    -

    isCompatible

    -
    public boolean isCompatible(NDConfiguration.Layout other)
    -
    + + + + +
      +
    • +

      isCompatible

      +
      public boolean isCompatible(NDConfiguration.Layout other)
      • -
    • -
      -

      newStridesFor

      -
      public int[] newStridesFor(int[] shape)
      -
      +
    + + + +
      +
    • +

      newStridesFor

      +
      public int[] newStridesFor(int[] shape)
      • -
    • -
      -

      rearrange

      -
      public int[] rearrange(int[] tln, - int[] shape, - int[] newForm)
      -
      +
    + + + +
      +
    • +

      rearrange

      +
      public int[] rearrange(int[] tln,
+                       int[] shape,
+                       int[] newForm)
      +
      • +
    • - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/NDConfiguration.Utility.html b/docs/jdocs/neureka/ndim/config/NDConfiguration.Utility.html index 8771f42b0..3f1236f65 100644 --- a/docs/jdocs/neureka/ndim/config/NDConfiguration.Utility.html +++ b/docs/jdocs/neureka/ndim/config/NDConfiguration.Utility.html @@ -1,198 +1,320 @@ - + + - -NDConfiguration.Utility (neureka 1.0.0 API) - - - - + +NDConfiguration.Utility (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config
    -

    Class NDConfiguration.Utility

    -
    -
    java.lang.Object -
    neureka.ndim.config.NDConfiguration.Utility
    +
    neureka.ndim.config
    +

    Class NDConfiguration.Utility

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.ndim.config.NDConfiguration.Utility
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing interface:
      -
      NDConfiguration
      +
      NDConfiguration
      -
      public static class NDConfiguration.Utility -extends Object
      +
      +
      public static class NDConfiguration.Utility
+extends java.lang.Object
      This utility class provides static methods which are helpful for nd-configuration related operations like reshaping, incrementing or decrementing index arrays...
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      Utility()
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        Utility() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static void
      -
      decrement(int[] indices, - int[] shape)
      -
       
      -
      static void
      -
      increment(int[] indices, - int[] shape)
      -
       
      -
      static int[]
      -
      rearrange(int[] array, - int[] pointers)
      -
       
      -
      static int
      -
      sizeOfShape(int[] shape)
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static voiddecrement(int[] indices, + int[] shape) 
        static voidincrement(int[] indices, + int[] shape) 
        static int[]rearrange(int[] array, + int[] pointers) 
        static intsizeOfShape(int[] shape) 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
      - -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Utility

        -
        public Utility()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Utility

            +
            public Utility()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      rearrange

      -
      public static int[] rearrange(int[] array, - int[] pointers)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          rearrange

          +
          public static int[] rearrange(int[] array,
+                              int[] pointers)
          • -
        • -
          -

          increment

          -
          public static void increment(int[] indices, - int[] shape)
          -
          +
        + + + +
          +
        • +

          increment

          +
          public static void increment(int[] indices,
+                             int[] shape)
          • -
        • -
          -

          decrement

          -
          public static void decrement(int[] indices, - int[] shape)
          -
          +
        + + + +
          +
        • +

          decrement

          +
          public static void decrement(int[] indices,
+                             int[] shape)
          • -
        • -
          -

          sizeOfShape

          -
          public static int sizeOfShape(int[] shape)
          -
          +
        + + + +
          +
        • +

          sizeOfShape

          +
          public static int sizeOfShape(int[] shape)
        -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/NDConfiguration.html b/docs/jdocs/neureka/ndim/config/NDConfiguration.html index 0d55a733e..ff95f925f 100644 --- a/docs/jdocs/neureka/ndim/config/NDConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/NDConfiguration.html @@ -1,304 +1,391 @@ - + + - -NDConfiguration (neureka 1.0.0 API) - - - - + +NDConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config
    -

    Interface NDConfiguration

    -
    -
    -
    +
    neureka.ndim.config
    +

    Interface NDConfiguration

    +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      Nested Classes
      -
      -
      Modifier and Type
      -
      Interface
      -
      Description
      -
      static interface 
      -
      NDConfiguration.IndexToIndexFunction
      -
      -
      Implementations of this are produced and returned by the getIndexToIndexAccessPattern() + -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      default int[]
      -
      asInlineArray()
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Abstract Methods Default Methods 
        Modifier and TypeMethod and Description
        default int[]asInlineArray()
        This method returns an array of flattened arrays which define this nd-configuration in a compact manner.
        - -
        boolean
        -
        equals(NDConfiguration ndc)
        -
         
        -
        default NDConfiguration.IndexToIndexFunction
        -
        getIndexToIndexAccessPattern()
        -
         
        -
        default NDConfiguration.Layout
        -
        getLayout()
        -
        +
        booleanequals(NDConfiguration ndc) 
        default NDConfiguration.IndexToIndexFunctiongetIndexToIndexAccessPattern() 
        default NDConfiguration.LayoutgetLayout()
        The layout of most tensors is either row major or column major.
        - -
        default List<NDTrait>
        -
        getTraits()
        -
         
        -
        default boolean
        -
        has(NDTrait trait)
        -
         
        -
        int
        -
        hashCode()
        -
         
        -
        int
        -
        indexOfIndex(int index)
        -
        +
        default java.util.List<NDTrait>getTraits() 
        default booleanhas(NDTrait trait) 
        inthashCode() 
        intindexOfIndex(int index)
        Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
        - -
        int
        -
        indexOfIndices(int[] indices)
        -
        +
        intindexOfIndices(int[] indices)
        The following method calculates the true index for an element in the data array based on a provided index array.
        - -
        int[]
        -
        indicesMap()
        -
        +
        int[]indicesMap()
        If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the indicesMap() + within a tensor based on a scalar index x then the indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
        - -
        int
        -
        indicesMap(int i)
        -
        + for every axis of the tensor represented by this NDConfiguration.
        +
        intindicesMap(int i)
        This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
        - -
        int[]
        -
        indicesOfIndex(int index)
        -
        +
        int[]indicesOfIndex(int index)
        The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
        - -
        default boolean
        -
        isCompact()
        -
        -
        NDConfiguration instance where this flag is true +
        default booleanisCompact() +
        NDConfiguration instance where this flag is true will most likely not be slices because they have no offset (all 0) and a compact spread / step array (all 1).
        - -
        default boolean
        -
        isSimple()
        -
        +
        default booleanisSimple()
        The boolean returned by this method simply reports if this configuration is the most basic form of configuration possible for the given shape represented by this instance.
        - -
        default boolean
        -
        isVirtual()
        -
         
        -
        NDConfiguration
        -
        newReshaped(int[] newForm)
        -
        -
        This method enables reshaping for NDConfiguration implementation instances.
        -
        -
        static NDConfiguration
        -
        of(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
        -
         
        -
        int[]
        -
        offset()
        -
        +
        default booleanisVirtual() 
        NDConfigurationnewReshaped(int[] newForm) +
        This method enables reshaping for NDConfiguration implementation instances.
        +
        static NDConfigurationnone() 
        static NDConfigurationof(int[] shape, + int[] strides, + int[] indicesMap, + int[] spread, + int[] offset) 
        int[]offset()
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        int
        -
        offset(int i)
        -
        +
        intoffset(int i)
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        int
        -
        rank()
        -
        +
        intrank()
        This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
        - -
        int[]
        -
        shape()
        -
        + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
        +
        int[]shape()
        This method returns an array of axis sizes.
        - -
        int
        -
        shape(int i)
        -
        +
        intshape(int i)
        This method receives an axis index and return the size of the axis.
        - -
        default int
        -
        size()
        -
         
        -
        int[]
        -
        spread()
        -
        +
        default intsize() 
        int[]spread()
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        int
        -
        spread(int i)
        -
        +
        intspread(int i)
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        int[]
        -
        strides()
        -
        +
        int[]strides()
        The array returned by this method is used to translate an array of axis indices to a single ata array index.
        - -
        int
        -
        strides(int i)
        -
        +
        intstrides(int i)
        This method receives an axis index and returns the translation value for the targeted axis.
        - - - - - +
        +
        • +
    - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        of

        -
        static NDConfiguration of(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
        -
        -
        • -
      • -
        -

        getLayout

        -
        default NDConfiguration.Layout getLayout()
        +
          +
        • + + +

          Method Detail

          + + + + + + + +
            +
          • +

            of

            +
            static NDConfiguration of(int[] shape,
+                          int[] strides,
+                          int[] indicesMap,
+                          int[] spread,
+                          int[] offset)
            +
            • +
          + + + +
            +
          • +

            getLayout

            +
            default NDConfiguration.Layout getLayout()
            The layout of most tensors is either row major or column major. Row major means that row elements are right next to one another in the underlying data array of a tensor. @@ -306,192 +393,237 @@

            getLayout

            A tensor can also be symmetric, meaning it supports both column major and row major (scalar tensors have this property). Other than that there are also tensors which are unspecific, meaning they are not row major or column major. This is the case for tensors which are slices of other tensors or tensors which have been permuted.
            -
            -
            Returns:
            +
            +
            Returns:
            The layout of the underlying data array of a tensor.
            -
        -
        • -
      • -
        -

        getTraits

        -
        default List<NDTrait> getTraits()
        -
        -
        • -
      • -
        -

        has

        -
        default boolean has(NDTrait trait)
        -
        -
        • -
      • -
        -

        rank

        -
        int rank()
        +
        • +
      + + + +
        +
      • +

        getTraits

        +
        default java.util.List<NDTrait> getTraits()
        +
        • +
      + + + +
        +
      • +

        has

        +
        default boolean has(NDTrait trait)
        +
        • +
      + + + +
        +
      • +

        rank

        +
        int rank()
        This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
        -
        -
        Returns:
        + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    +
    +
    Returns:
    The number of axis of an nd-array.
    - - -
  • -
    -

    size

    -
    default int size()
    -
    -
    • -
  • -
    -

    shape

    -
    int[] shape()
    +
    • + + + + +
      +
    • +

      size

      +
      default int size()
      +
      • +
    + + + +
      +
    • +

      shape

      +
      int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
      • -
    • -
      -

      shape

      -
      int shape(int i)
      +
    + + + +
      +
    • +

      shape

      +
      int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
      • -
    • -
      -

      indicesMap

      -
      int[] indicesMap()
      +
    + + + +
      +
    • +

      indicesMap

      +
      int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the indicesMap() + within a tensor based on a scalar index x then the indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the indicesMap() + within a tensor based on a scalar index x then the indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    int[] spread()
    + + + + +
      +
    • +

      spread

      +
      int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    int[] offset()
    + + + + +
      +
    • +

      offset

      +
      int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -500,154 +632,243 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    asInlineArray

    -
    default int[] asInlineArray()
    + + + + +
      +
    • +

      asInlineArray

      +
      default int[] asInlineArray()
      This method returns an array of flattened arrays which define this nd-configuration in a compact manner. The array consists of the following arrays joined in the following order: [ shape | translation | indicesMap | offsets | spreads ]
      -
      -
      Returns:
      +
      +
      Returns:
      An array of flattened arrays which define this nd-configuration in a compact manner.
      -
    -
    • -
  • -
    -

    hashCode

    -
    int hashCode()
    -
    -
    Overrides:
    -
    hashCode in class Object
    +
    • + + + + +
      +
    • +

      hashCode

      +
      int hashCode()
      +
      +
      Overrides:
      +
      hashCode in class java.lang.Object
      - -
      • -
    • -
      -

      equals

      -
      boolean equals(NDConfiguration ndc)
      -
      -
      • -
    • -
      -

      newReshaped

      -
      NDConfiguration newReshaped(int[] newForm)
      -
      This method enables reshaping for NDConfiguration implementation instances. - Because NDConfigurations are in essence things which define +
      • +
    + + + + + + + +
      +
    • +

      newReshaped

      +
      NDConfiguration newReshaped(int[] newForm)
      +
      This method enables reshaping for NDConfiguration implementation instances. + Because NDConfigurations are in essence things which define the access relationship from shape indices to the actual underlying data, - the creation of permuted NDConfiguration is up to a specific implementation.
      -
      -
      Parameters:
      + the creation of permuted NDConfiguration is up to a specific implementation.
    +
    +
    Parameters:
    newForm - An array of indices which define how the axis ought to be rearranged.
    -
    Returns:
    -
    A new NDConfiguration which carries the needed information for the permuted view.
    +
    Returns:
    +
    A new NDConfiguration which carries the needed information for the permuted view.
    - -
  • -
    -

    isSimple

    -
    default boolean isSimple()
    + + + + +
      +
    • +

      isSimple

      +
      default boolean isSimple()
      The boolean returned by this method simply reports if this configuration is the most basic form of configuration possible for the given shape represented by this instance. This type of configuration is the typical for freshly created tensors which are neither slices nor permuted variants of an original tensor... - Therefore, such "simple tensors" do not need a fancy NDIterator + Therefore, such "simple tensors" do not need a fancy NDIterator in order to perform operations on them. One can simply iterate over their underlying data array. - (This does not mean that the tensor owning this NDConfiguration is not a slice!)
      -
      -
      Returns:
      + (This does not mean that the tensor owning this NDConfiguration is not a slice!)
    • +
    +
    Returns:
    The truth value determining if this configuration is not modeling more complex indices like permuted views or slices...
    - -
  • -
    -

    isCompact

    -
    default boolean isCompact()
    -
    NDConfiguration instance where this flag is true + + + + +
      +
    • +

      isCompact

      +
      default boolean isCompact()
      +
      NDConfiguration instance where this flag is true will most likely not be slices because they have no offset (all 0) and a compact spread / step array (all 1).
      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining if this configuration has no offset and spread/steps larger than 1.
      -
    -
    • -
  • -
    -

    isVirtual

    -
    default boolean isVirtual()
    -
    -
    Returns:
    -
    The truth value determining if this NDConfiguration - represents virtual tensors (see Tensor.isVirtual()).
    +
    • + + + + + + + + +
      +
    • +

      getIndexToIndexAccessPattern

      +
      default NDConfiguration.IndexToIndexFunction getIndexToIndexAccessPattern()
      +
      +
      Returns:
      A function which can map tensor indices to the indices of its data array.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
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    • Detail: 
      • +
    • Field | 
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      • +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/NDTrait.html b/docs/jdocs/neureka/ndim/config/NDTrait.html index bddea8623..a524468bf 100644 --- a/docs/jdocs/neureka/ndim/config/NDTrait.html +++ b/docs/jdocs/neureka/ndim/config/NDTrait.html @@ -1,249 +1,386 @@ - + + - -NDTrait (neureka 1.0.0 API) - - - - + +NDTrait (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config
    -

    Enum Class NDTrait

    -
    -
    java.lang.Object -
    java.lang.Enum<NDTrait> -
    neureka.ndim.config.NDTrait
    -
    +
    neureka.ndim.config
    +

    Enum NDTrait

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • java.lang.Enum<NDTrait>
        • +
      • +
          +
        • neureka.ndim.config.NDTrait
          • +
        +
        • +
      +
      • +
    +
    +
    -
    -
    - +
    +
      +
    • + + -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static NDTrait
      -
      valueOf(String name)
      -
      -
      Returns the enum constant of this class with the specified name.
      -
      -
      static NDTrait[]
      -
      values()
      -
      -
      Returns an array containing the constants of this enum class, in +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static NDTraitvalueOf(java.lang.String name) +
        Returns the enum constant of this type with the specified name.
        +
        static NDTrait[]values() +
        Returns an array containing the constants of this enum type, in the order they are declared.
        +
        +
          +
        • + + +

          Methods inherited from class java.lang.Enum

          +clone, compareTo, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf
          • +
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +getClass, notify, notifyAll, wait, wait, wait
          • +
        +
        • +
      +
      • +
    -
    -
    -
    -
    -

    Methods inherited from class java.lang.Enum

    -clone, compareTo, describeConstable, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf
    -
    -

    Methods inherited from class java.lang.Object

    -getClass, notify, notifyAll, wait, wait, wait
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Enum Constant Details

        -
          -
        • -
          -

          COMPACT

          -
          public static final NDTrait COMPACT
          -
          +
            +
          • + + +

            Enum Constant Detail

            + + + +
              +
            • +

              COMPACT

              +
              public static final NDTrait COMPACT
              • -
            • -
              -

              SIMPLE

              -
              public static final NDTrait SIMPLE
              -
              +
            + + + +
              +
            • +

              SIMPLE

              +
              public static final NDTrait SIMPLE
              • -
            • -
              -

              ROW_MAJOR

              -
              public static final NDTrait ROW_MAJOR
              -
              +
            + + + +
              +
            • +

              ROW_MAJOR

              +
              public static final NDTrait ROW_MAJOR
              • -
            • -
              -

              COL_MAJOR

              -
              public static final NDTrait COL_MAJOR
              -
              +
            + + + +
              +
            • +

              COL_MAJOR

              +
              public static final NDTrait COL_MAJOR
              • -
            • -
              -

              CONTINUOUS_MATRIX

              -
              public static final NDTrait CONTINUOUS_MATRIX
              -
              +
            + + + +
              +
            • +

              CONTINUOUS_MATRIX

              +
              public static final NDTrait CONTINUOUS_MATRIX
              • -
            • -
              -

              OFFSET_MATRIX

              -
              public static final NDTrait OFFSET_MATRIX
              -
              +
            + + + +
              +
            • +

              OFFSET_MATRIX

              +
              public static final NDTrait OFFSET_MATRIX
            -
        • +
      -
    • -
      -

      Method Details

      -
        -
      • -
        -

        values

        -
        public static NDTrait[] values()
        -
        Returns an array containing the constants of this enum class, in -the order they are declared.
        -
        -
        Returns:
        -
        an array containing the constants of this enum class, in the order they are declared
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            values

            +
            public static NDTrait[] values()
            +
            Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
            +for (NDTrait c : NDTrait.values())
+    System.out.println(c);
+
            +
            +
            Returns:
            +
            an array containing the constants of this enum type, in the order they are declared
            -
        • -
      • -
        -

        valueOf

        -
        public static NDTrait valueOf(String name)
        -
        Returns the enum constant of this class with the specified name. +
      + + + +
        +
      • +

        valueOf

        +
        public static NDTrait valueOf(java.lang.String name)
        +
        Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
        -
        -
        Parameters:
        +
        +
        Parameters:
        name - the name of the enum constant to be returned.
        -
        Returns:
        +
        Returns:
        the enum constant with the specified name
        -
        Throws:
        -
        IllegalArgumentException - if this enum class has no constant with the specified name
        -
        NullPointerException - if the argument is null
        +
        Throws:
        +
        java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
        +
        java.lang.NullPointerException - if the argument is null
        -
    -
    - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/package-frame.html b/docs/jdocs/neureka/ndim/config/package-frame.html new file mode 100644 index 000000000..4662390f7 --- /dev/null +++ b/docs/jdocs/neureka/ndim/config/package-frame.html @@ -0,0 +1,30 @@ + + + + + +neureka.ndim.config (neureka 1.0.1 API) + + + + +

    neureka.ndim.config

    +
    +

    Interfaces

    + +

    Classes

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/config/package-summary.html b/docs/jdocs/neureka/ndim/config/package-summary.html index 18e35c0fd..a521bcf76 100644 --- a/docs/jdocs/neureka/ndim/config/package-summary.html +++ b/docs/jdocs/neureka/ndim/config/package-summary.html @@ -1,130 +1,198 @@ - + + - -neureka.ndim.config (neureka 1.0.0 API) - - - - + +neureka.ndim.config (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.config

    -
    -
    -
    package neureka.ndim.config
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/package-tree.html b/docs/jdocs/neureka/ndim/config/package-tree.html index fd4bb5447..8fea3f3f3 100644 --- a/docs/jdocs/neureka/ndim/config/package-tree.html +++ b/docs/jdocs/neureka/ndim/config/package-tree.html @@ -1,94 +1,153 @@ - + + - -neureka.ndim.config Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.config Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.config

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -

    Interface Hierarchy

    -
    -
    -

    Enum Class Hierarchy

    +

    Enum Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/D1C.html b/docs/jdocs/neureka/ndim/config/types/D1C.html index 11ea3859f..4f573b500 100644 --- a/docs/jdocs/neureka/ndim/config/types/D1C.html +++ b/docs/jdocs/neureka/ndim/config/types/D1C.html @@ -1,185 +1,312 @@ - + + - -D1C (neureka 1.0.0 API) - - - - + +D1C (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types
    -

    Class D1C

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D1C
    +
    neureka.ndim.config.types
    +

    Class D1C

    -
    -
    -
    +
    + +
    +
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        D1C

        -
        public D1C()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            D1C

            +
            public D1C()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      indexOfIndices

      -
      public abstract int indexOfIndices(int d1)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          indexOfIndices

          +
          public abstract int indexOfIndices(int d1)
          +
          • +
      -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/D2C.html b/docs/jdocs/neureka/ndim/config/types/D2C.html index 55113374d..77b5f9271 100644 --- a/docs/jdocs/neureka/ndim/config/types/D2C.html +++ b/docs/jdocs/neureka/ndim/config/types/D2C.html @@ -1,187 +1,314 @@ - + + - -D2C (neureka 1.0.0 API) - - - - + +D2C (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types
    -

    Class D2C

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D2C
    +
    neureka.ndim.config.types
    +

    Class D2C

    -
    -
    -
    +
    + +
    +
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        D2C

        -
        public D2C()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            D2C

            +
            public D2C()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      indexOfIndices

      -
      public abstract int indexOfIndices(int d1, - int d2)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          indexOfIndices

          +
          public abstract int indexOfIndices(int d1,
+                                   int d2)
          +
          • +
      -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/D3C.html b/docs/jdocs/neureka/ndim/config/types/D3C.html index bdaa592e7..d287f8349 100644 --- a/docs/jdocs/neureka/ndim/config/types/D3C.html +++ b/docs/jdocs/neureka/ndim/config/types/D3C.html @@ -1,189 +1,316 @@ - + + - -D3C (neureka 1.0.0 API) - - - - + +D3C (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types
    -

    Class D3C

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D3C
    +
    neureka.ndim.config.types
    +

    Class D3C

    -
    -
    -
    +
    + +
    +
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        D3C

        -
        public D3C()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            D3C

            +
            public D3C()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      indexOfIndices

      -
      public abstract int indexOfIndices(int d1, - int d2, - int d3)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          indexOfIndices

          +
          public abstract int indexOfIndices(int d1,
+                                   int d2,
+                                   int d3)
          +
          • +
      -
    • - +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
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    + + + +
    +
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      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/package-frame.html b/docs/jdocs/neureka/ndim/config/types/package-frame.html new file mode 100644 index 000000000..dd973bd4d --- /dev/null +++ b/docs/jdocs/neureka/ndim/config/types/package-frame.html @@ -0,0 +1,21 @@ + + + + + +neureka.ndim.config.types (neureka 1.0.1 API) + + + + +

    neureka.ndim.config.types

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/config/types/package-summary.html b/docs/jdocs/neureka/ndim/config/types/package-summary.html index a0715d6b2..ddd573ddf 100644 --- a/docs/jdocs/neureka/ndim/config/types/package-summary.html +++ b/docs/jdocs/neureka/ndim/config/types/package-summary.html @@ -1,114 +1,156 @@ - + + - -neureka.ndim.config.types (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.config.types

    +

    Package neureka.ndim.config.types

    -
    -
    package neureka.ndim.config.types
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/package-tree.html b/docs/jdocs/neureka/ndim/config/types/package-tree.html index 091d1d4f2..67a02ac5a 100644 --- a/docs/jdocs/neureka/ndim/config/types/package-tree.html +++ b/docs/jdocs/neureka/ndim/config/types/package-tree.html @@ -1,77 +1,140 @@ - + + - -neureka.ndim.config.types Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.config.types

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

      -
    • java.lang.Object +
    • java.lang.Object
        -
      • neureka.ndim.config.AbstractNDC (implements neureka.ndim.config.NDConfiguration) +
      • neureka.ndim.config.AbstractNDC (implements neureka.ndim.config.NDConfiguration)
          -
        • neureka.ndim.config.types.D1C
          • -
        • neureka.ndim.config.types.D2C
          • -
        • neureka.ndim.config.types.D3C
          • +
        • neureka.ndim.config.types.D1C
          • +
        • neureka.ndim.config.types.D2C
          • +
        • neureka.ndim.config.types.D3C
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/permuted/Permuted1DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/permuted/Permuted1DConfiguration.html index be9f4596a..66a85693c 100644 --- a/docs/jdocs/neureka/ndim/config/types/permuted/Permuted1DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/permuted/Permuted1DConfiguration.html @@ -1,467 +1,596 @@ - + + - -Permuted1DConfiguration (neureka 1.0.0 API) - - - - + +Permuted1DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.permuted
    -

    Class Permuted1DConfiguration

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D1C -
    neureka.ndim.config.types.permuted.Permuted1DConfiguration
    +
    neureka.ndim.config.types.permuted
    +

    Class Permuted1DConfiguration

    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class Permuted1DConfiguration
+extends D1C
      + +
    +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static Permuted1DConfiguration construct(int[] shape, - int[] strides, - int[] indicesMap)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static Permuted1DConfiguration construct(int[] shape,
+                                                int[] strides,
+                                                int[] indicesMap)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -470,63 +599,131 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int d1)
    -
    -
    Specified by:
    -
    indexOfIndices in class D1C
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int d1)
      +
      +
      Specified by:
      +
      indexOfIndices in class D1C
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/permuted/Permuted2DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/permuted/Permuted2DConfiguration.html index dd1d7be61..7cbaebd6b 100644 --- a/docs/jdocs/neureka/ndim/config/types/permuted/Permuted2DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/permuted/Permuted2DConfiguration.html @@ -1,481 +1,614 @@ - + + - -Permuted2DConfiguration (neureka 1.0.0 API) - - - - + +Permuted2DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.permuted
    -

    Class Permuted2DConfiguration

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D2C -
    neureka.ndim.config.types.permuted.Permuted2DConfiguration
    +
    neureka.ndim.config.types.permuted
    +

    Class Permuted2DConfiguration

    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class Permuted2DConfiguration
+extends D2C
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from interface neureka.ndim.config.NDConfiguration

      -NDConfiguration.IndexToIndexFunction, NDConfiguration.Layout, NDConfiguration.Utility
      -
      + -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      protected final int
      -
      _shape1
      -
      +
        +
      • + + +

        Field Summary

        + + + + + + + + + + + + + + +
        Fields 
        Modifier and TypeField and Description
        protected int_shape1
        The shape of the NDArray.
        - -
        protected final int
        -
        _shape2
        -
         
        - - +
        protected int_shape2 
        • +
      -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Modifier
      -
      Constructor
      -
      Description
      -
      protected
      -
      Permuted2DConfiguration(int[] shape, - int[] strides, - int[] indicesMap)
      -
       
      -
      -
      +
        +
      • + + +

        Constructor Summary

        + + + + + + + + + + +
        Constructors 
        ModifierConstructor and Description
        protected Permuted2DConfiguration(int[] shape, + int[] strides, + int[] indicesMap) 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static Permuted2DConfiguration
      -
      construct(int[] shape, - int[] strides, - int[] indicesMap)
      -
       
      -
      final int
      -
      indexOfIndex(int index)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static Permuted2DConfigurationconstruct(int[] shape, + int[] strides, + int[] indicesMap) 
        intindexOfIndex(int index)
        Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
        - -
        final int
        -
        indexOfIndices(int[] indices)
        -
        +
        intindexOfIndices(int[] indices)
        The following method calculates the true index for an element in the data array based on a provided index array.
        - -
        final int
        -
        indexOfIndices(int d1, - int d2)
        -
        -
        final int[]
        -
        indicesMap()
        -
        +
        intindexOfIndices(int d1, + int d2)
        int[]indicesMap()
        If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
        - -
        final int
        -
        indicesMap(int i)
        -
        + for every axis of the tensor represented by this NDConfiguration.
        +
        intindicesMap(int i)
        This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
        - -
        final int[]
        -
        indicesOfIndex(int index)
        -
        +
        int[]indicesOfIndex(int index)
        The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
        - -
        final int[]
        -
        offset()
        -
        +
        int[]offset()
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        offset(int i)
        -
        +
        intoffset(int i)
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        rank()
        -
        +
        intrank()
        This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
        - -
        final int[]
        -
        shape()
        -
        + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
        +
        int[]shape()
        This method returns an array of axis sizes.
        - -
        final int
        -
        shape(int i)
        -
        +
        intshape(int i)
        This method receives an axis index and return the size of the axis.
        - -
        final int[]
        -
        spread()
        -
        +
        int[]spread()
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        spread(int i)
        -
        +
        intspread(int i)
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int[]
        -
        strides()
        -
        +
        int[]strides()
        The array returned by this method is used to translate an array of axis indices to a single ata array index.
        - -
        final int
        -
        strides(int i)
        -
        +
        intstrides(int i)
        This method receives an axis index and returns the translation value for the targeted axis.
        +
        + +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, finalize, getClass, notify, notifyAll, wait, wait, wait
          • +
        + +
        • +
      +
      • +
    -
    -
    -
    -
    -

    Methods inherited from class neureka.ndim.config.AbstractNDC

    -_cacheArray, _cached, _simpleReshape, equals, equals, hashCode, newReshaped, toString
    -
    -

    Methods inherited from class java.lang.Object

    -clone, finalize, getClass, notify, notifyAll, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.ndim.config.NDConfiguration

    -asInlineArray, getIndexToIndexAccessPattern, getLayout, getTraits, has, isCompact, isSimple, isVirtual, size
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Field Details

        -
          -
        • -
          -

          _shape1

          -
          protected final int _shape1
          +
            +
          • + + +

            Field Detail

            + + + +
              +
            • +

              _shape1

              +
              protected final int _shape1
              The shape of the NDArray.
              -
          • -
        • -
          -

          _shape2

          -
          protected final int _shape2
          -
          +
        + + + +
          +
        • +

          _shape2

          +
          protected final int _shape2
        -
        • +
      -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Permuted2DConfiguration

        -
        protected Permuted2DConfiguration(int[] shape, - int[] strides, - int[] indicesMap)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Permuted2DConfiguration

            +
            protected Permuted2DConfiguration(int[] shape,
+                                  int[] strides,
+                                  int[] indicesMap)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static Permuted2DConfiguration construct(int[] shape, - int[] strides, - int[] indicesMap)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static Permuted2DConfiguration construct(int[] shape,
+                                                int[] strides,
+                                                int[] indicesMap)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -484,64 +617,132 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int d1, - int d2)
    -
    -
    Specified by:
    -
    indexOfIndices in class D2C
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int d1,
+                                int d2)
      +
      +
      Specified by:
      +
      indexOfIndices in class D2C
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/permuted/Permuted3DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/permuted/Permuted3DConfiguration.html index 0d21edf4b..2ee528f41 100644 --- a/docs/jdocs/neureka/ndim/config/types/permuted/Permuted3DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/permuted/Permuted3DConfiguration.html @@ -1,491 +1,628 @@ - + + - -Permuted3DConfiguration (neureka 1.0.0 API) - - - - + +Permuted3DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.permuted
    -

    Class Permuted3DConfiguration

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D3C -
    neureka.ndim.config.types.permuted.Permuted3DConfiguration
    +
    neureka.ndim.config.types.permuted
    +

    Class Permuted3DConfiguration

    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class Permuted3DConfiguration
+extends D3C
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from interface neureka.ndim.config.NDConfiguration

      -NDConfiguration.IndexToIndexFunction, NDConfiguration.Layout, NDConfiguration.Utility
      -
      + -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      protected final int
      -
      _shape1
      -
      +
        +
      • + + +

        Field Summary

        + + + + + + + + + + + + + + + + + + +
        Fields 
        Modifier and TypeField and Description
        protected int_shape1
        The shape of the NDArray.
        - -
        protected final int
        -
        _shape2
        -
         
        -
        protected final int
        -
        _shape3
        -
         
        - - +
        protected int_shape2 
        protected int_shape3 
        • +
      -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Modifier
      -
      Constructor
      -
      Description
      -
      protected
      -
      Permuted3DConfiguration(int[] shape, - int[] strides, - int[] indicesMap)
      -
       
      -
      -
      +
        +
      • + + +

        Constructor Summary

        + + + + + + + + + + +
        Constructors 
        ModifierConstructor and Description
        protected Permuted3DConfiguration(int[] shape, + int[] strides, + int[] indicesMap) 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static Permuted3DConfiguration
      -
      construct(int[] shape, - int[] strides, - int[] indicesMap)
      -
       
      -
      final int
      -
      indexOfIndex(int index)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static Permuted3DConfigurationconstruct(int[] shape, + int[] strides, + int[] indicesMap) 
        intindexOfIndex(int index)
        Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
        - -
        final int
        -
        indexOfIndices(int[] indices)
        -
        +
        intindexOfIndices(int[] indices)
        The following method calculates the true index for an element in the data array based on a provided index array.
        - -
        final int
        -
        indexOfIndices(int d1, - int d2, - int d3)
        -
        -
        final int[]
        -
        indicesMap()
        -
        +
        intindexOfIndices(int d1, + int d2, + int d3)
        int[]indicesMap()
        If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
        - -
        final int
        -
        indicesMap(int i)
        -
        + for every axis of the tensor represented by this NDConfiguration.
        +
        intindicesMap(int i)
        This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
        - -
        final int[]
        -
        indicesOfIndex(int index)
        -
        +
        int[]indicesOfIndex(int index)
        The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
        - -
        final int[]
        -
        offset()
        -
        +
        int[]offset()
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        offset(int i)
        -
        +
        intoffset(int i)
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        rank()
        -
        +
        intrank()
        This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
        - -
        final int[]
        -
        shape()
        -
        + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
        +
        int[]shape()
        This method returns an array of axis sizes.
        - -
        final int
        -
        shape(int i)
        -
        +
        intshape(int i)
        This method receives an axis index and return the size of the axis.
        - -
        final int[]
        -
        spread()
        -
        +
        int[]spread()
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        spread(int i)
        -
        +
        intspread(int i)
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int[]
        -
        strides()
        -
        +
        int[]strides()
        The array returned by this method is used to translate an array of axis indices to a single ata array index.
        - -
        final int
        -
        strides(int i)
        -
        +
        intstrides(int i)
        This method receives an axis index and returns the translation value for the targeted axis.
        +
        + +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, finalize, getClass, notify, notifyAll, wait, wait, wait
          • +
        + +
        • +
      +
      • +
    -
    -
    -
    -
    -

    Methods inherited from class neureka.ndim.config.AbstractNDC

    -_cacheArray, _cached, _simpleReshape, equals, equals, hashCode, newReshaped, toString
    -
    -

    Methods inherited from class java.lang.Object

    -clone, finalize, getClass, notify, notifyAll, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.ndim.config.NDConfiguration

    -asInlineArray, getIndexToIndexAccessPattern, getLayout, getTraits, has, isCompact, isSimple, isVirtual, size
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Field Details

        -
          -
        • -
          -

          _shape1

          -
          protected final int _shape1
          +
            +
          • + + +

            Field Detail

            + + + +
              +
            • +

              _shape1

              +
              protected final int _shape1
              The shape of the NDArray.
              -
          • -
        • -
          -

          _shape2

          -
          protected final int _shape2
          -
          +
        + + + +
          +
        • +

          _shape2

          +
          protected final int _shape2
          • -
        • -
          -

          _shape3

          -
          protected final int _shape3
          -
          +
        + + + +
          +
        • +

          _shape3

          +
          protected final int _shape3
        -
        • +
      -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Permuted3DConfiguration

        -
        protected Permuted3DConfiguration(int[] shape, - int[] strides, - int[] indicesMap)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Permuted3DConfiguration

            +
            protected Permuted3DConfiguration(int[] shape,
+                                  int[] strides,
+                                  int[] indicesMap)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static Permuted3DConfiguration construct(int[] shape, - int[] strides, - int[] indicesMap)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static Permuted3DConfiguration construct(int[] shape,
+                                                int[] strides,
+                                                int[] indicesMap)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -494,65 +631,133 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int d1, - int d2, - int d3)
    -
    -
    Specified by:
    -
    indexOfIndices in class D3C
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int d1,
+                                int d2,
+                                int d3)
      +
      +
      Specified by:
      +
      indexOfIndices in class D3C
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/permuted/PermutedNDConfiguration.html b/docs/jdocs/neureka/ndim/config/types/permuted/PermutedNDConfiguration.html index 6b08f445a..01950fa15 100644 --- a/docs/jdocs/neureka/ndim/config/types/permuted/PermutedNDConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/permuted/PermutedNDConfiguration.html @@ -1,430 +1,548 @@ - + + - -PermutedNDConfiguration (neureka 1.0.0 API) - - - - + +PermutedNDConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.permuted
    -

    Class PermutedNDConfiguration

    +
    neureka.ndim.config.types.permuted
    +

    Class PermutedNDConfiguration

    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.permuted.PermutedNDConfiguration
    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class PermutedNDConfiguration
+extends AbstractNDC
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        PermutedNDConfiguration

        -
        protected PermutedNDConfiguration(int[] shape, - int[] strides, - int[] indicesMap)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            PermutedNDConfiguration

            +
            protected PermutedNDConfiguration(int[] shape,
+                                  int[] strides,
+                                  int[] indicesMap)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static PermutedNDConfiguration construct(int[] shape, - int[] strides, - int[] indicesMap)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static PermutedNDConfiguration construct(int[] shape,
+                                                int[] strides,
+                                                int[] indicesMap)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -433,53 +551,118 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/permuted/package-frame.html b/docs/jdocs/neureka/ndim/config/types/permuted/package-frame.html new file mode 100644 index 000000000..bdbdea368 --- /dev/null +++ b/docs/jdocs/neureka/ndim/config/types/permuted/package-frame.html @@ -0,0 +1,22 @@ + + + + + +neureka.ndim.config.types.permuted (neureka 1.0.1 API) + + + + +

    neureka.ndim.config.types.permuted

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/config/types/permuted/package-summary.html b/docs/jdocs/neureka/ndim/config/types/permuted/package-summary.html index d19ba1498..930ea9d39 100644 --- a/docs/jdocs/neureka/ndim/config/types/permuted/package-summary.html +++ b/docs/jdocs/neureka/ndim/config/types/permuted/package-summary.html @@ -1,105 +1,151 @@ - + + - -neureka.ndim.config.types.permuted (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types.permuted (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.config.types.permuted

    -
    -
    -
    package neureka.ndim.config.types.permuted
    -
    -
    -
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/permuted/package-tree.html b/docs/jdocs/neureka/ndim/config/types/permuted/package-tree.html index 2008bdf92..81dc773bd 100644 --- a/docs/jdocs/neureka/ndim/config/types/permuted/package-tree.html +++ b/docs/jdocs/neureka/ndim/config/types/permuted/package-tree.html @@ -1,90 +1,153 @@ - + + - -neureka.ndim.config.types.permuted Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types.permuted Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.config.types.permuted

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/simple/Simple0DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/simple/Simple0DConfiguration.html index 550111a22..dba3af994 100644 --- a/docs/jdocs/neureka/ndim/config/types/simple/Simple0DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/simple/Simple0DConfiguration.html @@ -1,393 +1,504 @@ - + + - -Simple0DConfiguration (neureka 1.0.0 API) - - - - + +Simple0DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.simple
    -

    Class Simple0DConfiguration

    +
    neureka.ndim.config.types.simple
    +

    Class Simple0DConfiguration

    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.simple.Simple0DConfiguration
    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public final class Simple0DConfiguration
+extends AbstractNDC
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        construct

        -
        public static Simple0DConfiguration construct()
        -
        +
          +
        • + + +

          Method Detail

          + + + + + + + +
            +
          • +

            rank

            +
            public int rank()
            This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
            -
            -
            Returns:
            + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    public int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -396,53 +507,118 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/simple/Simple1DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/simple/Simple1DConfiguration.html index b78e1f224..2cfa94595 100644 --- a/docs/jdocs/neureka/ndim/config/types/simple/Simple1DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/simple/Simple1DConfiguration.html @@ -1,467 +1,596 @@ - + + - -Simple1DConfiguration (neureka 1.0.0 API) - - - - + +Simple1DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.simple
    -

    Class Simple1DConfiguration

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D1C -
    neureka.ndim.config.types.simple.Simple1DConfiguration
    +
    neureka.ndim.config.types.simple
    +

    Class Simple1DConfiguration

    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class Simple1DConfiguration
+extends D1C
      + +
    +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static Simple1DConfiguration construct(int[] shape, - int[] strides)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static Simple1DConfiguration construct(int[] shape,
+                                              int[] strides)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -470,63 +599,131 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int d1)
    -
    -
    Specified by:
    -
    indexOfIndices in class D1C
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int d1)
      +
      +
      Specified by:
      +
      indexOfIndices in class D1C
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/simple/Simple2DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/simple/Simple2DConfiguration.html index a5b64ea0f..b3c8eb4e7 100644 --- a/docs/jdocs/neureka/ndim/config/types/simple/Simple2DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/simple/Simple2DConfiguration.html @@ -1,477 +1,610 @@ - + + - -Simple2DConfiguration (neureka 1.0.0 API) - - - - + +Simple2DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.simple
    -

    Class Simple2DConfiguration

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D2C -
    neureka.ndim.config.types.simple.Simple2DConfiguration
    +
    neureka.ndim.config.types.simple
    +

    Class Simple2DConfiguration

    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class Simple2DConfiguration
+extends D2C
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from interface neureka.ndim.config.NDConfiguration

      -NDConfiguration.IndexToIndexFunction, NDConfiguration.Layout, NDConfiguration.Utility
      -
      + -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      protected final int
      -
      _shape1
      -
      +
        +
      • + + +

        Field Summary

        + + + + + + + + + + + + + + +
        Fields 
        Modifier and TypeField and Description
        protected int_shape1
        The shape of the NDArray.
        - -
        protected final int
        -
        _shape2
        -
         
        - - +
        protected int_shape2 
        • +
      -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Modifier
      -
      Constructor
      -
      Description
      -
      protected
      -
      Simple2DConfiguration(int[] shape, - int[] strides)
      -
       
      -
      -
      +
        +
      • + + +

        Constructor Summary

        + + + + + + + + + + +
        Constructors 
        ModifierConstructor and Description
        protected Simple2DConfiguration(int[] shape, + int[] strides) 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static Simple2DConfiguration
      -
      construct(int[] shape, - int[] strides)
      -
       
      -
      final int
      -
      indexOfIndex(int index)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static Simple2DConfigurationconstruct(int[] shape, + int[] strides) 
        intindexOfIndex(int index)
        Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
        - -
        final int
        -
        indexOfIndices(int[] indices)
        -
        +
        intindexOfIndices(int[] indices)
        The following method calculates the true index for an element in the data array based on a provided index array.
        - -
        final int
        -
        indexOfIndices(int d1, - int d2)
        -
        -
        final int[]
        -
        indicesMap()
        -
        +
        intindexOfIndices(int d1, + int d2)
        int[]indicesMap()
        If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
        - -
        final int
        -
        indicesMap(int i)
        -
        + for every axis of the tensor represented by this NDConfiguration.
        +
        intindicesMap(int i)
        This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
        - -
        final int[]
        -
        indicesOfIndex(int index)
        -
        +
        int[]indicesOfIndex(int index)
        The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
        - -
        final int[]
        -
        offset()
        -
        +
        int[]offset()
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        offset(int i)
        -
        +
        intoffset(int i)
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        rank()
        -
        +
        intrank()
        This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
        - -
        final int[]
        -
        shape()
        -
        + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
        +
        int[]shape()
        This method returns an array of axis sizes.
        - -
        final int
        -
        shape(int i)
        -
        +
        intshape(int i)
        This method receives an axis index and return the size of the axis.
        - -
        final int[]
        -
        spread()
        -
        +
        int[]spread()
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        spread(int i)
        -
        +
        intspread(int i)
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int[]
        -
        strides()
        -
        +
        int[]strides()
        The array returned by this method is used to translate an array of axis indices to a single ata array index.
        - -
        final int
        -
        strides(int i)
        -
        +
        intstrides(int i)
        This method receives an axis index and returns the translation value for the targeted axis.
        +
        + +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, finalize, getClass, notify, notifyAll, wait, wait, wait
          • +
        + +
        • +
      +
      • +
    -
    -
    -
    -
    -

    Methods inherited from class neureka.ndim.config.AbstractNDC

    -_cacheArray, _cached, _simpleReshape, equals, equals, hashCode, newReshaped, toString
    -
    -

    Methods inherited from class java.lang.Object

    -clone, finalize, getClass, notify, notifyAll, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.ndim.config.NDConfiguration

    -asInlineArray, getIndexToIndexAccessPattern, getLayout, getTraits, has, isCompact, isSimple, isVirtual, size
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Field Details

        -
          -
        • -
          -

          _shape1

          -
          protected final int _shape1
          +
            +
          • + + +

            Field Detail

            + + + +
              +
            • +

              _shape1

              +
              protected final int _shape1
              The shape of the NDArray.
              -
          • -
        • -
          -

          _shape2

          -
          protected final int _shape2
          -
          +
        + + + +
          +
        • +

          _shape2

          +
          protected final int _shape2
        -
        • +
      -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Simple2DConfiguration

        -
        protected Simple2DConfiguration(int[] shape, - int[] strides)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Simple2DConfiguration

            +
            protected Simple2DConfiguration(int[] shape,
+                                int[] strides)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static Simple2DConfiguration construct(int[] shape, - int[] strides)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static Simple2DConfiguration construct(int[] shape,
+                                              int[] strides)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -480,64 +613,132 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int d1, - int d2)
    -
    -
    Specified by:
    -
    indexOfIndices in class D2C
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int d1,
+                                int d2)
      +
      +
      Specified by:
      +
      indexOfIndices in class D2C
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/simple/Simple3DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/simple/Simple3DConfiguration.html index 0abe5f906..df78d37fb 100644 --- a/docs/jdocs/neureka/ndim/config/types/simple/Simple3DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/simple/Simple3DConfiguration.html @@ -1,487 +1,624 @@ - + + - -Simple3DConfiguration (neureka 1.0.0 API) - - - - + +Simple3DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.simple
    -

    Class Simple3DConfiguration

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D3C -
    neureka.ndim.config.types.simple.Simple3DConfiguration
    +
    neureka.ndim.config.types.simple
    +

    Class Simple3DConfiguration

    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class Simple3DConfiguration
+extends D3C
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from interface neureka.ndim.config.NDConfiguration

      -NDConfiguration.IndexToIndexFunction, NDConfiguration.Layout, NDConfiguration.Utility
      -
      + -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      protected final int
      -
      _shape1
      -
      +
        +
      • + + +

        Field Summary

        + + + + + + + + + + + + + + + + + + +
        Fields 
        Modifier and TypeField and Description
        protected int_shape1
        The shape of the NDArray.
        - -
        protected final int
        -
        _shape2
        -
         
        -
        protected final int
        -
        _shape3
        -
         
        - - +
        protected int_shape2 
        protected int_shape3 
        • +
      -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Modifier
      -
      Constructor
      -
      Description
      -
      protected
      -
      Simple3DConfiguration(int[] shape, - int[] strides)
      -
       
      -
      -
      +
        +
      • + + +

        Constructor Summary

        + + + + + + + + + + +
        Constructors 
        ModifierConstructor and Description
        protected Simple3DConfiguration(int[] shape, + int[] strides) 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static Simple3DConfiguration
      -
      construct(int[] shape, - int[] strides)
      -
       
      -
      final int
      -
      indexOfIndex(int index)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static Simple3DConfigurationconstruct(int[] shape, + int[] strides) 
        intindexOfIndex(int index)
        Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
        - -
        final int
        -
        indexOfIndices(int[] indices)
        -
        +
        intindexOfIndices(int[] indices)
        The following method calculates the true index for an element in the data array based on a provided index array.
        - -
        final int
        -
        indexOfIndices(int d1, - int d2, - int d3)
        -
        -
        final int[]
        -
        indicesMap()
        -
        +
        intindexOfIndices(int d1, + int d2, + int d3)
        int[]indicesMap()
        If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
        - -
        final int
        -
        indicesMap(int i)
        -
        + for every axis of the tensor represented by this NDConfiguration.
        +
        intindicesMap(int i)
        This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
        - -
        final int[]
        -
        indicesOfIndex(int index)
        -
        +
        int[]indicesOfIndex(int index)
        The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
        - -
        final int[]
        -
        offset()
        -
        +
        int[]offset()
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        offset(int i)
        -
        +
        intoffset(int i)
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        rank()
        -
        +
        intrank()
        This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
        - -
        final int[]
        -
        shape()
        -
        + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
        +
        int[]shape()
        This method returns an array of axis sizes.
        - -
        final int
        -
        shape(int i)
        -
        +
        intshape(int i)
        This method receives an axis index and return the size of the axis.
        - -
        final int[]
        -
        spread()
        -
        +
        int[]spread()
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        spread(int i)
        -
        +
        intspread(int i)
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int[]
        -
        strides()
        -
        +
        int[]strides()
        The array returned by this method is used to translate an array of axis indices to a single ata array index.
        - -
        final int
        -
        strides(int i)
        -
        +
        intstrides(int i)
        This method receives an axis index and returns the translation value for the targeted axis.
        +
        + +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, finalize, getClass, notify, notifyAll, wait, wait, wait
          • +
        + +
        • +
      +
      • +
    -
    -
    -
    -
    -

    Methods inherited from class neureka.ndim.config.AbstractNDC

    -_cacheArray, _cached, _simpleReshape, equals, equals, hashCode, newReshaped, toString
    -
    -

    Methods inherited from class java.lang.Object

    -clone, finalize, getClass, notify, notifyAll, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.ndim.config.NDConfiguration

    -asInlineArray, getIndexToIndexAccessPattern, getLayout, getTraits, has, isCompact, isSimple, isVirtual, size
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Field Details

        -
          -
        • -
          -

          _shape1

          -
          protected final int _shape1
          +
            +
          • + + +

            Field Detail

            + + + +
              +
            • +

              _shape1

              +
              protected final int _shape1
              The shape of the NDArray.
              -
          • -
        • -
          -

          _shape2

          -
          protected final int _shape2
          -
          +
        + + + +
          +
        • +

          _shape2

          +
          protected final int _shape2
          • -
        • -
          -

          _shape3

          -
          protected final int _shape3
          -
          +
        + + + +
          +
        • +

          _shape3

          +
          protected final int _shape3
        -
        • +
      -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Simple3DConfiguration

        -
        protected Simple3DConfiguration(int[] shape, - int[] strides)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Simple3DConfiguration

            +
            protected Simple3DConfiguration(int[] shape,
+                                int[] strides)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static Simple3DConfiguration construct(int[] shape, - int[] strides)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static Simple3DConfiguration construct(int[] shape,
+                                              int[] strides)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -490,65 +627,133 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int d1, - int d2, - int d3)
    -
    -
    Specified by:
    -
    indexOfIndices in class D3C
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int d1,
+                                int d2,
+                                int d3)
      +
      +
      Specified by:
      +
      indexOfIndices in class D3C
      -
    • - - + + + + - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/simple/SimpleNDConfiguration.html b/docs/jdocs/neureka/ndim/config/types/simple/SimpleNDConfiguration.html index 30f32a8c9..8d1403c64 100644 --- a/docs/jdocs/neureka/ndim/config/types/simple/SimpleNDConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/simple/SimpleNDConfiguration.html @@ -1,426 +1,544 @@ - + + - -SimpleNDConfiguration (neureka 1.0.0 API) - - - - + +SimpleNDConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.simple
    -

    Class SimpleNDConfiguration

    +
    neureka.ndim.config.types.simple
    +

    Class SimpleNDConfiguration

    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.simple.SimpleNDConfiguration
    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public final class SimpleNDConfiguration
+extends AbstractNDC
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        SimpleNDConfiguration

        -
        protected SimpleNDConfiguration(int[] shape, - int[] strides)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            SimpleNDConfiguration

            +
            protected SimpleNDConfiguration(int[] shape,
+                                int[] strides)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static SimpleNDConfiguration construct(int[] shape, - int[] strides)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static SimpleNDConfiguration construct(int[] shape,
+                                              int[] strides)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration. +
      +
      Parameters:
      i - The index of the axis whose indices map value ought to be returned.
      -
      Returns:
      +
      Returns:
      The indices map value targeted by the provided index.
      -
    • -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -429,53 +547,118 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • - - + + + + - + +
    + + +
    Skip navigation links
    + + + +
    +
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    + +
    +
    +
      +
    • Summary: 
      • +
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    • Method
      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/simple/package-frame.html b/docs/jdocs/neureka/ndim/config/types/simple/package-frame.html new file mode 100644 index 000000000..477a77dc6 --- /dev/null +++ b/docs/jdocs/neureka/ndim/config/types/simple/package-frame.html @@ -0,0 +1,23 @@ + + + + + +neureka.ndim.config.types.simple (neureka 1.0.1 API) + + + + +

    neureka.ndim.config.types.simple

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/config/types/simple/package-summary.html b/docs/jdocs/neureka/ndim/config/types/simple/package-summary.html index 83c4e53d6..89359a276 100644 --- a/docs/jdocs/neureka/ndim/config/types/simple/package-summary.html +++ b/docs/jdocs/neureka/ndim/config/types/simple/package-summary.html @@ -1,107 +1,155 @@ - + + - -neureka.ndim.config.types.simple (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types.simple (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.config.types.simple

    -
    -
    -
    package neureka.ndim.config.types.simple
    -
    -
    -
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/simple/package-tree.html b/docs/jdocs/neureka/ndim/config/types/simple/package-tree.html index 4657bfa50..2a432d410 100644 --- a/docs/jdocs/neureka/ndim/config/types/simple/package-tree.html +++ b/docs/jdocs/neureka/ndim/config/types/simple/package-tree.html @@ -1,91 +1,154 @@ - + + - -neureka.ndim.config.types.simple Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types.simple Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.config.types.simple

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/sliced/Sliced0DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/sliced/Sliced0DConfiguration.html index 4f7be58f8..f0fb1fc84 100644 --- a/docs/jdocs/neureka/ndim/config/types/sliced/Sliced0DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/sliced/Sliced0DConfiguration.html @@ -1,427 +1,545 @@ - + + - -Sliced0DConfiguration (neureka 1.0.0 API) - - - - + +Sliced0DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.sliced
    -

    Class Sliced0DConfiguration

    +
    neureka.ndim.config.types.sliced
    +

    Class Sliced0DConfiguration

    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.sliced.Sliced0DConfiguration
    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public final class Sliced0DConfiguration
+extends AbstractNDC
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Sliced0DConfiguration

        -
        protected Sliced0DConfiguration(int shape, - int offset)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Sliced0DConfiguration

            +
            protected Sliced0DConfiguration(int shape,
+                                int offset)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static Sliced0DConfiguration construct(int[] shape, - int[] offset)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static Sliced0DConfiguration construct(int[] shape,
+                                              int[] offset)
          • -
        • -
          -

          rank

          -
          public int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public int indicesMap(int i)
    -
    Description copied from interface: NDConfiguration
    + + + + +
      +
    • +

      indicesMap

      +
      public int indicesMap(int i)
      +
      Description copied from interface: NDConfiguration
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    public int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -430,53 +548,118 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • - - + + + + - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/sliced/Sliced1DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/sliced/Sliced1DConfiguration.html index fde9d1461..0dd738b1b 100644 --- a/docs/jdocs/neureka/ndim/config/types/sliced/Sliced1DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/sliced/Sliced1DConfiguration.html @@ -1,479 +1,608 @@ - + + - -Sliced1DConfiguration (neureka 1.0.0 API) - - - - + +Sliced1DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.sliced
    -

    Class Sliced1DConfiguration

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D1C -
    neureka.ndim.config.types.sliced.Sliced1DConfiguration
    +
    neureka.ndim.config.types.sliced
    +

    Class Sliced1DConfiguration

    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class Sliced1DConfiguration
+extends D1C
      + +
    +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static Sliced1DConfiguration construct(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static Sliced1DConfiguration construct(int[] shape,
+                                              int[] strides,
+                                              int[] indicesMap,
+                                              int[] spread,
+                                              int[] offset)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration. +
      +
      Parameters:
      i - The index of the axis whose indices map value ought to be returned.
      -
      Returns:
      +
      Returns:
      The indices map value targeted by the provided index.
      -
    • -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -482,63 +611,131 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int d1)
    -
    -
    Specified by:
    -
    indexOfIndices in class D1C
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int d1)
      +
      +
      Specified by:
      +
      indexOfIndices in class D1C
      -
    • - - + + + + - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/sliced/Sliced2DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/sliced/Sliced2DConfiguration.html index 8b4964ebf..2fabf447f 100644 --- a/docs/jdocs/neureka/ndim/config/types/sliced/Sliced2DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/sliced/Sliced2DConfiguration.html @@ -1,489 +1,622 @@ - + + - -Sliced2DConfiguration (neureka 1.0.0 API) - - - - + +Sliced2DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.sliced
    -

    Class Sliced2DConfiguration

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D2C -
    neureka.ndim.config.types.sliced.Sliced2DConfiguration
    +
    neureka.ndim.config.types.sliced
    +

    Class Sliced2DConfiguration

    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class Sliced2DConfiguration
+extends D2C
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from interface neureka.ndim.config.NDConfiguration

      -NDConfiguration.IndexToIndexFunction, NDConfiguration.Layout, NDConfiguration.Utility
      -
      + -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      protected final int
      -
      _shape1
      -
      +
        +
      • + + +

        Field Summary

        + + + + + + + + + + + + + + +
        Fields 
        Modifier and TypeField and Description
        protected int_shape1
        The shape of the NDArray.
        - -
        protected final int
        -
        _shape2
        -
         
        - - +
        protected int_shape2 
        • +
      -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Modifier
      -
      Constructor
      -
      Description
      -
      protected
      -
      Sliced2DConfiguration(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
      -
       
      -
      -
      +
        +
      • + + +

        Constructor Summary

        + + + + + + + + + + +
        Constructors 
        ModifierConstructor and Description
        protected Sliced2DConfiguration(int[] shape, + int[] strides, + int[] indicesMap, + int[] spread, + int[] offset) 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static Sliced2DConfiguration
      -
      construct(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
      -
       
      -
      final int
      -
      indexOfIndex(int index)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static Sliced2DConfigurationconstruct(int[] shape, + int[] strides, + int[] indicesMap, + int[] spread, + int[] offset) 
        intindexOfIndex(int index)
        Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
        - -
        final int
        -
        indexOfIndices(int[] indices)
        -
        +
        intindexOfIndices(int[] indices)
        The following method calculates the true index for an element in the data array based on a provided index array.
        - -
        final int
        -
        indexOfIndices(int d1, - int d2)
        -
        -
        final int[]
        -
        indicesMap()
        -
        +
        intindexOfIndices(int d1, + int d2)
        int[]indicesMap()
        If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
        - -
        final int
        -
        indicesMap(int i)
        -
        + for every axis of the tensor represented by this NDConfiguration.
        +
        intindicesMap(int i)
        This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
        - -
        final int[]
        -
        indicesOfIndex(int index)
        -
        +
        int[]indicesOfIndex(int index)
        The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
        - -
        final int[]
        -
        offset()
        -
        +
        int[]offset()
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        offset(int i)
        -
        +
        intoffset(int i)
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        rank()
        -
        +
        intrank()
        This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
        - -
        final int[]
        -
        shape()
        -
        + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
        +
        int[]shape()
        This method returns an array of axis sizes.
        - -
        final int
        -
        shape(int i)
        -
        +
        intshape(int i)
        This method receives an axis index and return the size of the axis.
        - -
        final int[]
        -
        spread()
        -
        +
        int[]spread()
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        spread(int i)
        -
        +
        intspread(int i)
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int[]
        -
        strides()
        -
        +
        int[]strides()
        The array returned by this method is used to translate an array of axis indices to a single ata array index.
        - -
        final int
        -
        strides(int i)
        -
        +
        intstrides(int i)
        This method receives an axis index and returns the translation value for the targeted axis.
        +
        + +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, finalize, getClass, notify, notifyAll, wait, wait, wait
          • +
        + +
        • +
      +
      • +
    -
    - - -
    -

    Methods inherited from class neureka.ndim.config.AbstractNDC

    -_cacheArray, _cached, _simpleReshape, equals, equals, hashCode, newReshaped, toString
    -
    -

    Methods inherited from class java.lang.Object

    -clone, finalize, getClass, notify, notifyAll, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.ndim.config.NDConfiguration

    -asInlineArray, getIndexToIndexAccessPattern, getLayout, getTraits, has, isCompact, isSimple, isVirtual, size
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Field Details

        -
          -
        • -
          -

          _shape1

          -
          protected final int _shape1
          +
            +
          • + + +

            Field Detail

            + + + +
              +
            • +

              _shape1

              +
              protected final int _shape1
              The shape of the NDArray.
              -
          • -
        • -
          -

          _shape2

          -
          protected final int _shape2
          -
          +
        + + + +
          +
        • +

          _shape2

          +
          protected final int _shape2
        -
        • +
      -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Sliced2DConfiguration

        -
        protected Sliced2DConfiguration(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Sliced2DConfiguration

            +
            protected Sliced2DConfiguration(int[] shape,
+                                int[] strides,
+                                int[] indicesMap,
+                                int[] spread,
+                                int[] offset)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static Sliced2DConfiguration construct(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static Sliced2DConfiguration construct(int[] shape,
+                                              int[] strides,
+                                              int[] indicesMap,
+                                              int[] spread,
+                                              int[] offset)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor. +
          +
          Returns:
          The number of axis of an nd-array.
          -
    • -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration. +
      +
      Returns:
      An array of values which are used to map an index to an indices array.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration. +
      +
      Parameters:
      i - The index of the axis whose indices map value ought to be returned.
      -
      Returns:
      +
      Returns:
      The indices map value targeted by the provided index.
      -
    • -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -492,64 +625,132 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int d1, - int d2)
    -
    -
    Specified by:
    -
    indexOfIndices in class D2C
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int d1,
+                                int d2)
      +
      +
      Specified by:
      +
      indexOfIndices in class D2C
      -
    • -
    - + + + + - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/sliced/Sliced3DConfiguration.html b/docs/jdocs/neureka/ndim/config/types/sliced/Sliced3DConfiguration.html index 528d02046..68a7df850 100644 --- a/docs/jdocs/neureka/ndim/config/types/sliced/Sliced3DConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/sliced/Sliced3DConfiguration.html @@ -1,499 +1,636 @@ - + + - -Sliced3DConfiguration (neureka 1.0.0 API) - - - - + +Sliced3DConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.sliced
    -

    Class Sliced3DConfiguration

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D3C -
    neureka.ndim.config.types.sliced.Sliced3DConfiguration
    +
    neureka.ndim.config.types.sliced
    +

    Class Sliced3DConfiguration

    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class Sliced3DConfiguration
+extends D3C
      + +
    +
    +
    +
      +
    • -
    • -
      -

      Nested Class Summary

      -
      -

      Nested classes/interfaces inherited from interface neureka.ndim.config.NDConfiguration

      -NDConfiguration.IndexToIndexFunction, NDConfiguration.Layout, NDConfiguration.Utility
      -
      + -
    • -
      -

      Field Summary

      -
      Fields
      -
      -
      Modifier and Type
      -
      Field
      -
      Description
      -
      protected final int
      -
      _shape1
      -
      +
        +
      • + + +

        Field Summary

        + + + + + + + + + + + + + + + + + + +
        Fields 
        Modifier and TypeField and Description
        protected int_shape1
        The shape of the NDArray.
        - -
        protected final int
        -
        _shape2
        -
         
        -
        protected final int
        -
        _shape3
        -
         
        - - +
        protected int_shape2 
        protected int_shape3 
        • +
      -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Modifier
      -
      Constructor
      -
      Description
      -
      protected
      -
      Sliced3DConfiguration(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
      -
       
      -
      -
      +
        +
      • + + +

        Constructor Summary

        + + + + + + + + + + +
        Constructors 
        ModifierConstructor and Description
        protected Sliced3DConfiguration(int[] shape, + int[] strides, + int[] indicesMap, + int[] spread, + int[] offset) 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static Sliced3DConfiguration
      -
      construct(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
      -
       
      -
      final int
      -
      indexOfIndex(int index)
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Instance Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static Sliced3DConfigurationconstruct(int[] shape, + int[] strides, + int[] indicesMap, + int[] spread, + int[] offset) 
        intindexOfIndex(int index)
        Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index".
        - -
        final int
        -
        indexOfIndices(int[] indices)
        -
        +
        intindexOfIndices(int[] indices)
        The following method calculates the true index for an element in the data array based on a provided index array.
        - -
        final int
        -
        indexOfIndices(int d1, - int d2, - int d3)
        -
        -
        final int[]
        -
        indicesMap()
        -
        +
        intindexOfIndices(int d1, + int d2, + int d3)
        int[]indicesMap()
        If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
        - -
        final int
        -
        indicesMap(int i)
        -
        + for every axis of the tensor represented by this NDConfiguration.
        +
        intindicesMap(int i)
        This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration.
        - -
        final int[]
        -
        indicesOfIndex(int index)
        -
        +
        int[]indicesOfIndex(int index)
        The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index".
        - -
        final int[]
        -
        offset()
        -
        +
        int[]offset()
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        offset(int i)
        -
        +
        intoffset(int i)
        The offset is the position of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        rank()
        -
        +
        intrank()
        This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
        - -
        final int[]
        -
        shape()
        -
        + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
        +
        int[]shape()
        This method returns an array of axis sizes.
        - -
        final int
        -
        shape(int i)
        -
        +
        intshape(int i)
        This method receives an axis index and return the size of the axis.
        - -
        final int[]
        -
        spread()
        -
        +
        int[]spread()
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int
        -
        spread(int i)
        -
        +
        intspread(int i)
        The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
        - -
        final int[]
        -
        strides()
        -
        +
        int[]strides()
        The array returned by this method is used to translate an array of axis indices to a single ata array index.
        - -
        final int
        -
        strides(int i)
        -
        +
        intstrides(int i)
        This method receives an axis index and returns the translation value for the targeted axis.
        +
        + +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, finalize, getClass, notify, notifyAll, wait, wait, wait
          • +
        + +
        • +
      +
      • +
    -
    - - -
    -

    Methods inherited from class neureka.ndim.config.AbstractNDC

    -_cacheArray, _cached, _simpleReshape, equals, equals, hashCode, newReshaped, toString
    -
    -

    Methods inherited from class java.lang.Object

    -clone, finalize, getClass, notify, notifyAll, wait, wait, wait
    -
    -

    Methods inherited from interface neureka.ndim.config.NDConfiguration

    -asInlineArray, getIndexToIndexAccessPattern, getLayout, getTraits, has, isCompact, isSimple, isVirtual, size
    - - - - -
    -
      +
      +
        +
      • -
      • -
        -

        Field Details

        -
          -
        • -
          -

          _shape1

          -
          protected final int _shape1
          +
            +
          • + + +

            Field Detail

            + + + +
              +
            • +

              _shape1

              +
              protected final int _shape1
              The shape of the NDArray.
              -
          • -
        • -
          -

          _shape2

          -
          protected final int _shape2
          -
          +
        + + + +
          +
        • +

          _shape2

          +
          protected final int _shape2
          • -
        • -
          -

          _shape3

          -
          protected final int _shape3
          -
          +
        + + + +
          +
        • +

          _shape3

          +
          protected final int _shape3
        -
        • +
      -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Sliced3DConfiguration

        -
        protected Sliced3DConfiguration(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Sliced3DConfiguration

            +
            protected Sliced3DConfiguration(int[] shape,
+                                int[] strides,
+                                int[] indicesMap,
+                                int[] spread,
+                                int[] offset)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static Sliced3DConfiguration construct(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static Sliced3DConfiguration construct(int[] shape,
+                                              int[] strides,
+                                              int[] indicesMap,
+                                              int[] spread,
+                                              int[] offset)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor. +
          +
          Returns:
          The number of axis of an nd-array.
          -
    • -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration. +
      +
      Returns:
      An array of values which are used to map an index to an indices array.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration. +
      +
      Parameters:
      i - The index of the axis whose indices map value ought to be returned.
      -
      Returns:
      +
      Returns:
      The indices map value targeted by the provided index.
      -
    • -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -502,65 +639,133 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int d1, - int d2, - int d3)
    -
    -
    Specified by:
    -
    indexOfIndices in class D3C
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int d1,
+                                int d2,
+                                int d3)
      +
      +
      Specified by:
      +
      indexOfIndices in class D3C
      -
    • -
    - + + + + - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/sliced/SlicedNDConfiguration.html b/docs/jdocs/neureka/ndim/config/types/sliced/SlicedNDConfiguration.html index 9be4bd227..34f3fc356 100644 --- a/docs/jdocs/neureka/ndim/config/types/sliced/SlicedNDConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/sliced/SlicedNDConfiguration.html @@ -1,438 +1,556 @@ - + + - -SlicedNDConfiguration (neureka 1.0.0 API) - - - - + +SlicedNDConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.sliced
    -

    Class SlicedNDConfiguration

    +
    neureka.ndim.config.types.sliced
    +

    Class SlicedNDConfiguration

    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.sliced.SlicedNDConfiguration
    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class SlicedNDConfiguration
+extends AbstractNDC
      + +
    +
    +
    +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        SlicedNDConfiguration

        -
        protected SlicedNDConfiguration(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            SlicedNDConfiguration

            +
            protected SlicedNDConfiguration(int[] shape,
+                                int[] strides,
+                                int[] indicesMap,
+                                int[] spread,
+                                int[] offset)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      construct

      -
      public static SlicedNDConfiguration construct(int[] shape, - int[] strides, - int[] indicesMap, - int[] spread, - int[] offset)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          construct

          +
          public static SlicedNDConfiguration construct(int[] shape,
+                                              int[] strides,
+                                              int[] indicesMap,
+                                              int[] spread,
+                                              int[] offset)
          • -
        • -
          -

          rank

          -
          public final int rank()
          +
        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration. +
      +
      Parameters:
      i - The index of the axis whose indices map value ought to be returned.
      -
      Returns:
      +
      Returns:
      The indices map value targeted by the provided index.
      -
    • -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -441,53 +559,118 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • - - + + + + - + +
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    + diff --git a/docs/jdocs/neureka/ndim/config/types/sliced/package-frame.html b/docs/jdocs/neureka/ndim/config/types/sliced/package-frame.html new file mode 100644 index 000000000..d95d2d588 --- /dev/null +++ b/docs/jdocs/neureka/ndim/config/types/sliced/package-frame.html @@ -0,0 +1,23 @@ + + + + + +neureka.ndim.config.types.sliced (neureka 1.0.1 API) + + + + +

    neureka.ndim.config.types.sliced

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/config/types/sliced/package-summary.html b/docs/jdocs/neureka/ndim/config/types/sliced/package-summary.html index 60f552fbb..ccdfa7583 100644 --- a/docs/jdocs/neureka/ndim/config/types/sliced/package-summary.html +++ b/docs/jdocs/neureka/ndim/config/types/sliced/package-summary.html @@ -1,107 +1,155 @@ - + + - -neureka.ndim.config.types.sliced (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types.sliced (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.config.types.sliced

    -
    -
    -
    package neureka.ndim.config.types.sliced
    -
    -
    -
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/sliced/package-tree.html b/docs/jdocs/neureka/ndim/config/types/sliced/package-tree.html index aa0ebe2b7..454636f58 100644 --- a/docs/jdocs/neureka/ndim/config/types/sliced/package-tree.html +++ b/docs/jdocs/neureka/ndim/config/types/sliced/package-tree.html @@ -1,91 +1,154 @@ - + + - -neureka.ndim.config.types.sliced Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types.sliced Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.config.types.sliced

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
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    + +
    + + +
    Skip navigation links
    + + + + +
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    + diff --git a/docs/jdocs/neureka/ndim/config/types/views/SimpleReshapeView.html b/docs/jdocs/neureka/ndim/config/types/views/SimpleReshapeView.html index 616b24543..7a1b77ea3 100644 --- a/docs/jdocs/neureka/ndim/config/types/views/SimpleReshapeView.html +++ b/docs/jdocs/neureka/ndim/config/types/views/SimpleReshapeView.html @@ -1,413 +1,527 @@ - + + - -SimpleReshapeView (neureka 1.0.0 API) - - - - + +SimpleReshapeView (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.views
    -

    Class SimpleReshapeView

    +
    neureka.ndim.config.types.views
    +

    Class SimpleReshapeView

    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.views.SimpleReshapeView
    -
    -
    -
    -
    +
    + +
    +
    -
    -
      +
      +
      public class SimpleReshapeView
+extends AbstractNDC
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        SimpleReshapeView

        -
        public SimpleReshapeView(int[] form, - NDConfiguration toBeViewed)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            SimpleReshapeView

            +
            public SimpleReshapeView(int[] form,
+                         NDConfiguration toBeViewed)
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      rank

      -
      public final int rank()
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          rank

          +
          public final int rank()
          This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
          -
          -
          Returns:
          + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    • +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -416,53 +530,118 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • - - + + + + - + +
    + + +
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    • Prev Class
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    + diff --git a/docs/jdocs/neureka/ndim/config/types/views/package-frame.html b/docs/jdocs/neureka/ndim/config/types/views/package-frame.html new file mode 100644 index 000000000..2a1eda906 --- /dev/null +++ b/docs/jdocs/neureka/ndim/config/types/views/package-frame.html @@ -0,0 +1,19 @@ + + + + + +neureka.ndim.config.types.views (neureka 1.0.1 API) + + + + +

    neureka.ndim.config.types.views

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/config/types/views/package-summary.html b/docs/jdocs/neureka/ndim/config/types/views/package-summary.html index 43e5a798b..f2fa900ae 100644 --- a/docs/jdocs/neureka/ndim/config/types/views/package-summary.html +++ b/docs/jdocs/neureka/ndim/config/types/views/package-summary.html @@ -1,101 +1,139 @@ - + + - -neureka.ndim.config.types.views (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types.views (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.config.types.views

    -
    -
    -
    package neureka.ndim.config.types.views
    -
    -
    -
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/views/package-tree.html b/docs/jdocs/neureka/ndim/config/types/views/package-tree.html index eeab1f9ac..6b81d167c 100644 --- a/docs/jdocs/neureka/ndim/config/types/views/package-tree.html +++ b/docs/jdocs/neureka/ndim/config/types/views/package-tree.html @@ -1,75 +1,138 @@ - + + - -neureka.ndim.config.types.views Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types.views Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.config.types.views

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/config/types/views/virtual/VirtualNDConfiguration.html b/docs/jdocs/neureka/ndim/config/types/views/virtual/VirtualNDConfiguration.html index 68e7d0020..2e21c3dc8 100644 --- a/docs/jdocs/neureka/ndim/config/types/views/virtual/VirtualNDConfiguration.html +++ b/docs/jdocs/neureka/ndim/config/types/views/virtual/VirtualNDConfiguration.html @@ -1,401 +1,513 @@ - + + - -VirtualNDConfiguration (neureka 1.0.0 API) - - - - + +VirtualNDConfiguration (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.config.types.views.virtual
    -

    Class VirtualNDConfiguration

    +
    neureka.ndim.config.types.views.virtual
    +

    Class VirtualNDConfiguration

    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.views.virtual.VirtualNDConfiguration
    -
    -
    -
    -
    +
    + +
    +
    -
    -
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        construct

        -
        public static VirtualNDConfiguration construct(int[] shape)
        -
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            construct

            +
            public static VirtualNDConfiguration construct(int[] shape)
            • -
          • -
            -

            rank

            -
            public final int rank()
            +
          + + + +
            +
          • +

            rank

            +
            public final int rank()
            This method returns the number of axis of - a nd-array / Tensor which is equal to the - length of the shape of an nd-array / Tensor.
            -
            -
            Returns:
            + a nd-array / Tensor which is equal to the + length of the shape of an nd-array / Tensor.
    +
    +
    Returns:
    The number of axis of an nd-array.
    - -
  • -
    -

    shape

    -
    public final int[] shape()
    + + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      This method returns an array of axis sizes.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of axis sizes.
      -
    • -
  • -
    -

    shape

    -
    public final int shape(int i)
    + + + + +
      +
    • +

      shape

      +
      public final int shape(int i)
      This method receives an axis index and return the size of the axis. It enables readable access to the shape of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose size ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis size targeted by the provided index.
      -
    • -
  • -
    -

    indicesMap

    -
    public final int[] indicesMap()
    + + + + +
      +
    • +

      indicesMap

      +
      public final int[] indicesMap()
      If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Returns:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Returns:
    An array of values which are used to map an index to an indices array.
    - -
  • -
    -

    indicesMap

    -
    public final int indicesMap(int i)
    + + + + +
      +
    • +

      indicesMap

      +
      public final int indicesMap(int i)
      This method receives an axis index and return the indices mapping value of said axis to enable readable access to the indices map of this configuration. If one wants to for example access the fourth last item of all items - within a tensor based on a scalar index x then the NDConfiguration.indicesMap() + within a tensor based on a scalar index x then the NDConfiguration.indicesMap() is needed as a basis for translating said scalar index x to an array of indices - for every axis of the tensor represented by this NDConfiguration.
      -
      -
      Parameters:
      + for every axis of the tensor represented by this NDConfiguration.
    • +
    +
    Parameters:
    i - The index of the axis whose indices map value ought to be returned.
    -
    Returns:
    +
    Returns:
    The indices map value targeted by the provided index.
    - -
  • -
    -

    strides

    -
    public final int[] strides()
    + + + + +
    • +
    +
    Returns:
    An array of values used to translate the axes indices to a data array index.
    - -
  • -
    -

    strides

    -
    public final int strides(int i)
    + + + + +
      +
    • +

      strides

      +
      public final int strides(int i)
      This method receives an axis index and returns the translation value for the targeted axis. It enables readable and fast access to the translation of this configuration.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The index of the axis whose translation ought to be returned.
      -
      Returns:
      +
      Returns:
      The axis translation targeted by the provided index.
      -
    • -
  • -
    -

    spread

    -
    public final int[] spread()
    + + + + +
      +
    • +

      spread

      +
      public final int[] spread()
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor.
      -
      -
      Returns:
      +
      +
      Returns:
      An array of index step sizes for each tensor dimension / axis.
      -
    • -
  • -
    -

    spread

    -
    public final int spread(int i)
    + + + + +
      +
    • +

      spread

      +
      public final int spread(int i)
      The spread is the access step size of a slice within the n-dimensional data array of its parent tensor. Use this to look up the spread in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose spread should be returned.
      -
      Returns:
      +
      Returns:
      The spread of the targeted dimension.
      -
    • -
  • -
    -

    offset

    -
    public final int[] offset()
    + + + + +
      +
    • +

      offset

      +
      public final int[] offset()
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to get the offsets of all slice dimension.
      -
      -
      Returns:
      +
      +
      Returns:
      The offset position of the slice tensor inside the n-dimensional data array of the parent tensor.
      -
    • -
  • -
    -

    offset

    -
    public final int offset(int i)
    + + + + +
      +
    • +

      offset

      +
      public final int offset(int i)
      The offset is the position of a slice within the n-dimensional data array of its parent tensor. Use this to look up the offset in a particular dimension / axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      i - The dimension / axis index of the dimension / axis whose offset should be returned.
      -
      Returns:
      +
      Returns:
      The offset of the targeted dimension.
      -
    • -
  • -
    -

    indexOfIndex

    -
    public final int indexOfIndex(int index)
    + + + + +
      +
    • +

      indexOfIndex

      +
      public final int indexOfIndex(int index)
      Use this to calculate the true index for an element in the data array (data array index) based on a provided "virtual index", or "value array index". This virtual index may be different from the true index depending on the type of nd-array, @@ -404,64 +516,132 @@

      indexOfIndex

      This virtual index ought to be turned into an index array which defines the position for every axis. Then this indices array will be converted into the final and true index targeting an underlying item. The information needed for performing this translation is expressed by individual implementations of - this NDConfiguration interface, which contain everything + this NDConfiguration interface, which contain everything needed to treat a given block of data as a nd-array!
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The true index which targets the actual data within the underlying data array of an nd-array / tensor.
      -
    • -
  • -
    -

    indicesOfIndex

    -
    public final int[] indicesOfIndex(int index)
    + + + + +
      +
    • +

      indicesOfIndex

      +
      public final int[] indicesOfIndex(int index)
      The following method calculates the axis indices for an element in the nd-array array based on a provided "virtual index". The resulting index defines the position of the element for every axis.
      -
      -
      Parameters:
      +
      +
      Parameters:
      index - The virtual index of the tensor having this configuration.
      -
      Returns:
      +
      Returns:
      The position of the (virtually) targeted element represented as an array of axis indices.
      -
    • -
  • -
    -

    indexOfIndices

    -
    public final int indexOfIndices(int[] indices)
    + + + + +
      +
    • +

      indexOfIndices

      +
      public final int indexOfIndices(int[] indices)
      The following method calculates the true index for an element in the data array based on a provided index array.
      -
      -
      Parameters:
      +
      +
      Parameters:
      indices - The indices for every axis of a given nd-array.
      -
      Returns:
      +
      Returns:
      The true index targeting the underlying data array of a given nd-array.
      -
    • -
  • -
    -

    isVirtual

    -
    public final boolean isVirtual()
    -
    -
    Returns:
    -
    The truth value determining if this NDConfiguration - represents virtual tensors (see Tensor.isVirtual()).
    + + + + +
      +
    • +

      isVirtual

      +
      public final boolean isVirtual()
      +
      +
      Returns:
      +
      The truth value determining if this NDConfiguration + represents virtual tensors (see Tensor.isVirtual()).
      -
    • - - + + + + - + +
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    + diff --git a/docs/jdocs/neureka/ndim/config/types/views/virtual/package-frame.html b/docs/jdocs/neureka/ndim/config/types/views/virtual/package-frame.html new file mode 100644 index 000000000..a525ccd9d --- /dev/null +++ b/docs/jdocs/neureka/ndim/config/types/views/virtual/package-frame.html @@ -0,0 +1,19 @@ + + + + + +neureka.ndim.config.types.views.virtual (neureka 1.0.1 API) + + + + +

    neureka.ndim.config.types.views.virtual

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/config/types/views/virtual/package-summary.html b/docs/jdocs/neureka/ndim/config/types/views/virtual/package-summary.html index 2e11a09e4..d0a11f0fd 100644 --- a/docs/jdocs/neureka/ndim/config/types/views/virtual/package-summary.html +++ b/docs/jdocs/neureka/ndim/config/types/views/virtual/package-summary.html @@ -1,97 +1,143 @@ - + + - -neureka.ndim.config.types.views.virtual (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types.views.virtual (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.config.types.views.virtual

    +

    Package neureka.ndim.config.types.views.virtual

    -
    -
    package neureka.ndim.config.types.views.virtual
    -
    -
    -
    + +
    + + +
    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/ndim/config/types/views/virtual/package-tree.html b/docs/jdocs/neureka/ndim/config/types/views/virtual/package-tree.html index fc192fc43..880a3a352 100644 --- a/docs/jdocs/neureka/ndim/config/types/views/virtual/package-tree.html +++ b/docs/jdocs/neureka/ndim/config/types/views/virtual/package-tree.html @@ -1,75 +1,138 @@ - + + - -neureka.ndim.config.types.views.virtual Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.config.types.views.virtual Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.config.types.views.virtual

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

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    + diff --git a/docs/jdocs/neureka/ndim/iterator/NDIterator.NonVirtual.html b/docs/jdocs/neureka/ndim/iterator/NDIterator.NonVirtual.html index 633a97aab..564f9fb38 100644 --- a/docs/jdocs/neureka/ndim/iterator/NDIterator.NonVirtual.html +++ b/docs/jdocs/neureka/ndim/iterator/NDIterator.NonVirtual.html @@ -1,222 +1,343 @@ - + + - -NDIterator.NonVirtual (neureka 1.0.0 API) - - - - + +NDIterator.NonVirtual (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator
    -

    Enum Class NDIterator.NonVirtual

    -
    -
    java.lang.Object -
    java.lang.Enum<NDIterator.NonVirtual> -
    neureka.ndim.iterator.NDIterator.NonVirtual
    +
    neureka.ndim.iterator
    +

    Enum NDIterator.NonVirtual

    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • + +
      • +
    +
    +
    -
    -
    - +
    + - -
    -
      +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      values

      -
      public static NDIterator.NonVirtual[] values()
      -
      Returns an array containing the constants of this enum class, in -the order they are declared.
      -
      -
      Returns:
      -
      an array containing the constants of this enum class, in the order they are declared
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          values

          +
          public static NDIterator.NonVirtual[] values()
          +
          Returns an array containing the constants of this enum type, in +the order they are declared. This method may be used to iterate +over the constants as follows: +
          +for (NDIterator.NonVirtual c : NDIterator.NonVirtual.values())
+    System.out.println(c);
+
          +
          +
          Returns:
          +
          an array containing the constants of this enum type, in the order they are declared
          -
      • -
    • -
      -

      valueOf

      -
      public static NDIterator.NonVirtual valueOf(String name)
      -
      Returns the enum constant of this class with the specified name. +
    + + + +
      +
    • +

      valueOf

      +
      public static NDIterator.NonVirtual valueOf(java.lang.String name)
      +
      Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an -enum constant in this class. (Extraneous whitespace characters are +enum constant in this type. (Extraneous whitespace characters are not permitted.)
      -
      -
      Parameters:
      +
      +
      Parameters:
      name - the name of the enum constant to be returned.
      -
      Returns:
      +
      Returns:
      the enum constant with the specified name
      -
      Throws:
      -
      IllegalArgumentException - if this enum class has no constant with the specified name
      -
      NullPointerException - if the argument is null
      +
      Throws:
      +
      java.lang.IllegalArgumentException - if this enum type has no constant with the specified name
      +
      java.lang.NullPointerException - if the argument is null
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/NDIterator.html b/docs/jdocs/neureka/ndim/iterator/NDIterator.html index 13060c53b..e7fb9a5d3 100644 --- a/docs/jdocs/neureka/ndim/iterator/NDIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/NDIterator.html @@ -1,307 +1,457 @@ - + + - -NDIterator (neureka 1.0.0 API) - - - - + +NDIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator
    -

    Interface NDIterator

    +
    neureka.ndim.iterator
    +

    Interface NDIterator

    -
    -
    +
    +
    +
    -
    -
    - +
    +
    +
    + + + + + + + + +
      +
    • +

      shape

      +
      int shape(int i)
      • -
    • -
      -

      shape

      -
      int[] shape()
      -
      +
    + + + +
      +
    • +

      shape

      +
      int[] shape()
      • -
    • -
      -

      increment

      -
      void increment()
      -
      +
    + + + +
      +
    • +

      increment

      +
      void increment()
      +
      • +
    + + + +
      +
    • +

      getIndexAndIncrement

      +
      default int getIndexAndIncrement()
      • -
    • -
      -

      getIndexAndIncrement

      -
      default int getIndexAndIncrement()
      -
      +
    + + + +
      +
    • +

      decrement

      +
      void decrement()
      • -
    • -
      -

      decrement

      -
      void decrement()
      -
      +
    + + + +
      +
    • +

      i

      +
      int i()
      • -
    • -
      -

      i

      -
      int i()
      -
      +
    + + + +
      +
    • +

      get

      +
      int get(int axis)
      • -
    • -
      -

      get

      -
      int get(int axis)
      -
      +
    + + + +
      +
    • +

      get

      +
      int[] get()
      • -
    • -
      -

      get

      -
      int[] get()
      -
      +
    + + + +
      +
    • +

      set

      +
      void set(int axis,
+         int position)
      • -
    • -
      -

      set

      -
      void set(int axis, - int position)
      -
      +
    + + + +
      +
    • +

      set

      +
      void set(int[] indices)
      • -
    • -
      -

      set

      -
      void set(int[] indices)
      -
      +
    + + + +
      +
    • +

      rank

      +
      int rank()
      • -
    • -
      -

      rank

      -
      int rank()
      -
      +
    - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
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      • +
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      • +
    • Method
      • +
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    • Detail: 
      • +
    • Field | 
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      • +
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    + diff --git a/docs/jdocs/neureka/ndim/iterator/package-frame.html b/docs/jdocs/neureka/ndim/iterator/package-frame.html new file mode 100644 index 000000000..1428227a6 --- /dev/null +++ b/docs/jdocs/neureka/ndim/iterator/package-frame.html @@ -0,0 +1,23 @@ + + + + + +neureka.ndim.iterator (neureka 1.0.1 API) + + + + +

    neureka.ndim.iterator

    +
    +

    Interfaces

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/iterator/package-summary.html b/docs/jdocs/neureka/ndim/iterator/package-summary.html index e032939ce..b001d91c1 100644 --- a/docs/jdocs/neureka/ndim/iterator/package-summary.html +++ b/docs/jdocs/neureka/ndim/iterator/package-summary.html @@ -1,107 +1,158 @@ - + + - -neureka.ndim.iterator (neureka 1.0.0 API) - - - - + +neureka.ndim.iterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.iterator

    -
    -
    -
    package neureka.ndim.iterator
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
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    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/package-tree.html b/docs/jdocs/neureka/ndim/iterator/package-tree.html index a6be46e28..c9c75da10 100644 --- a/docs/jdocs/neureka/ndim/iterator/package-tree.html +++ b/docs/jdocs/neureka/ndim/iterator/package-tree.html @@ -1,81 +1,142 @@ - + + - -neureka.ndim.iterator Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.iterator Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.iterator

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Interface Hierarchy

    -
    -
    -

    Enum Class Hierarchy

    +

    Enum Hierarchy

    -
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    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/permuted/Permuted2DCIterator.html b/docs/jdocs/neureka/ndim/iterator/types/permuted/Permuted2DCIterator.html index 0e3403126..6742fbd3c 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/permuted/Permuted2DCIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/types/permuted/Permuted2DCIterator.html @@ -1,286 +1,461 @@ - + + - -Permuted2DCIterator (neureka 1.0.0 API) - - - - + +Permuted2DCIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator.types.permuted
    -

    Class Permuted2DCIterator

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D2C -
    neureka.ndim.config.types.permuted.Permuted2DConfiguration -
    neureka.ndim.iterator.types.permuted.Permuted2DCIterator
    -
    -
    -
    +
    neureka.ndim.iterator.types.permuted
    +

    Class Permuted2DCIterator

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      increment

      -
      public final void increment()
      -
      -
      Specified by:
      -
      increment in interface NDIterator
      +
      • -
    • -
      -

      decrement

      -
      public final void decrement()
      -
      -
      Specified by:
      -
      decrement in interface NDIterator
      +
    + + + +
      +
    • +

      decrement

      +
      public final void decrement()
      +
      +
      Specified by:
      +
      decrement in interface NDIterator
      -
    • -
  • -
    -

    i

    -
    public final int i()
    -
    -
    Specified by:
    -
    i in interface NDIterator
    + + + + +
      +
    • +

      i

      +
      public final int i()
      +
      +
      Specified by:
      +
      i in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int get(int axis)
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int get(int axis)
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int[] get()
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int[] get()
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int axis, - int position)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int axis,
+                      int position)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int[] indices)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int[] indices)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/permuted/Permuted3DCIterator.html b/docs/jdocs/neureka/ndim/iterator/types/permuted/Permuted3DCIterator.html index a718c2424..5a4ce7c59 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/permuted/Permuted3DCIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/types/permuted/Permuted3DCIterator.html @@ -1,286 +1,461 @@ - + + - -Permuted3DCIterator (neureka 1.0.0 API) - - - - + +Permuted3DCIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator.types.permuted
    -

    Class Permuted3DCIterator

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D3C -
    neureka.ndim.config.types.permuted.Permuted3DConfiguration -
    neureka.ndim.iterator.types.permuted.Permuted3DCIterator
    -
    -
    -
    +
    neureka.ndim.iterator.types.permuted
    +

    Class Permuted3DCIterator

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      increment

      -
      public final void increment()
      -
      -
      Specified by:
      -
      increment in interface NDIterator
      +
      • -
    • -
      -

      decrement

      -
      public final void decrement()
      -
      -
      Specified by:
      -
      decrement in interface NDIterator
      +
    + + + +
      +
    • +

      decrement

      +
      public final void decrement()
      +
      +
      Specified by:
      +
      decrement in interface NDIterator
      -
    • -
  • -
    -

    i

    -
    public final int i()
    -
    -
    Specified by:
    -
    i in interface NDIterator
    + + + + +
      +
    • +

      i

      +
      public final int i()
      +
      +
      Specified by:
      +
      i in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int get(int axis)
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int get(int axis)
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int[] get()
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int[] get()
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int axis, - int position)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int axis,
+                      int position)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int[] indices)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int[] indices)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/permuted/package-frame.html b/docs/jdocs/neureka/ndim/iterator/types/permuted/package-frame.html new file mode 100644 index 000000000..5b447e4c8 --- /dev/null +++ b/docs/jdocs/neureka/ndim/iterator/types/permuted/package-frame.html @@ -0,0 +1,20 @@ + + + + + +neureka.ndim.iterator.types.permuted (neureka 1.0.1 API) + + + + +

    neureka.ndim.iterator.types.permuted

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/iterator/types/permuted/package-summary.html b/docs/jdocs/neureka/ndim/iterator/types/permuted/package-summary.html index cb081a131..9ea1d6087 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/permuted/package-summary.html +++ b/docs/jdocs/neureka/ndim/iterator/types/permuted/package-summary.html @@ -1,84 +1,143 @@ - + + - -neureka.ndim.iterator.types.permuted (neureka 1.0.0 API) - - - - + +neureka.ndim.iterator.types.permuted (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.iterator.types.permuted

    -
    -
    -
    package neureka.ndim.iterator.types.permuted
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/permuted/package-tree.html b/docs/jdocs/neureka/ndim/iterator/types/permuted/package-tree.html index e23608a70..48ec90250 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/permuted/package-tree.html +++ b/docs/jdocs/neureka/ndim/iterator/types/permuted/package-tree.html @@ -1,80 +1,100 @@ - + + - -neureka.ndim.iterator.types.permuted Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.iterator.types.permuted Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.iterator.types.permuted

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/simple/Simple1DCIterator.html b/docs/jdocs/neureka/ndim/iterator/types/simple/Simple1DCIterator.html index cddd3f0b2..b3cf5b4c9 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/simple/Simple1DCIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/types/simple/Simple1DCIterator.html @@ -1,286 +1,461 @@ - + + - -Simple1DCIterator (neureka 1.0.0 API) - - - - + +Simple1DCIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator.types.simple
    -

    Class Simple1DCIterator

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D1C -
    neureka.ndim.config.types.simple.Simple1DConfiguration -
    neureka.ndim.iterator.types.simple.Simple1DCIterator
    -
    -
    -
    +
    neureka.ndim.iterator.types.simple
    +

    Class Simple1DCIterator

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    +
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    +
    -
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    +
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    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      increment

      -
      public final void increment()
      -
      -
      Specified by:
      -
      increment in interface NDIterator
      +
      • -
    • -
      -

      decrement

      -
      public final void decrement()
      -
      -
      Specified by:
      -
      decrement in interface NDIterator
      +
    + + + +
      +
    • +

      decrement

      +
      public final void decrement()
      +
      +
      Specified by:
      +
      decrement in interface NDIterator
      -
    • -
  • -
    -

    i

    -
    public final int i()
    -
    -
    Specified by:
    -
    i in interface NDIterator
    + + + + +
      +
    • +

      i

      +
      public final int i()
      +
      +
      Specified by:
      +
      i in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int get(int axis)
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int get(int axis)
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int[] get()
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int[] get()
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int axis, - int position)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int axis,
+                      int position)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int[] indices)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int[] indices)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/simple/Simple2DCIterator.html b/docs/jdocs/neureka/ndim/iterator/types/simple/Simple2DCIterator.html index e6ec3a299..53a576fd4 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/simple/Simple2DCIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/types/simple/Simple2DCIterator.html @@ -1,286 +1,461 @@ - + + - -Simple2DCIterator (neureka 1.0.0 API) - - - - + +Simple2DCIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator.types.simple
    -

    Class Simple2DCIterator

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D2C -
    neureka.ndim.config.types.simple.Simple2DConfiguration -
    neureka.ndim.iterator.types.simple.Simple2DCIterator
    -
    -
    -
    +
    neureka.ndim.iterator.types.simple
    +

    Class Simple2DCIterator

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    Method Details

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      increment

      -
      public final void increment()
      -
      -
      Specified by:
      -
      increment in interface NDIterator
      +
      • -
    • -
      -

      decrement

      -
      public final void decrement()
      -
      -
      Specified by:
      -
      decrement in interface NDIterator
      +
    + + + +
      +
    • +

      decrement

      +
      public final void decrement()
      +
      +
      Specified by:
      +
      decrement in interface NDIterator
      -
    • -
  • -
    -

    i

    -
    public final int i()
    -
    -
    Specified by:
    -
    i in interface NDIterator
    + + + + +
      +
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      i

      +
      public final int i()
      +
      +
      Specified by:
      +
      i in interface NDIterator
      -
    • -
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    get

    -
    public final int get(int axis)
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int get(int axis)
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int[] get()
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int[] get()
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
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    -

    set

    -
    public final void set(int axis, - int position)
    -
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    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int axis,
+                      int position)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int[] indices)
    -
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    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int[] indices)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/simple/Simple3DCIterator.html b/docs/jdocs/neureka/ndim/iterator/types/simple/Simple3DCIterator.html index 964be829e..63ab53c19 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/simple/Simple3DCIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/types/simple/Simple3DCIterator.html @@ -1,286 +1,461 @@ - + + - -Simple3DCIterator (neureka 1.0.0 API) - - - - + +Simple3DCIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator.types.simple
    -

    Class Simple3DCIterator

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D3C -
    neureka.ndim.config.types.simple.Simple3DConfiguration -
    neureka.ndim.iterator.types.simple.Simple3DCIterator
    -
    -
    -
    +
    neureka.ndim.iterator.types.simple
    +

    Class Simple3DCIterator

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    Method Details

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      increment

      -
      public final void increment()
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      -
      Specified by:
      -
      increment in interface NDIterator
      +
      • -
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      decrement

      -
      public final void decrement()
      -
      -
      Specified by:
      -
      decrement in interface NDIterator
      +
    + + + +
      +
    • +

      decrement

      +
      public final void decrement()
      +
      +
      Specified by:
      +
      decrement in interface NDIterator
      -
    • -
  • -
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    i

    -
    public final int i()
    -
    -
    Specified by:
    -
    i in interface NDIterator
    + + + + +
      +
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      i

      +
      public final int i()
      +
      +
      Specified by:
      +
      i in interface NDIterator
      -
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    get

    -
    public final int get(int axis)
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int get(int axis)
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
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    get

    -
    public final int[] get()
    -
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    Specified by:
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    get in interface NDIterator
    + + + + +
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    • +

      get

      +
      public final int[] get()
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
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    -

    set

    -
    public final void set(int axis, - int position)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int axis,
+                      int position)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
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    set

    -
    public final void set(int[] indices)
    -
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    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int[] indices)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/simple/package-frame.html b/docs/jdocs/neureka/ndim/iterator/types/simple/package-frame.html new file mode 100644 index 000000000..3abfb5958 --- /dev/null +++ b/docs/jdocs/neureka/ndim/iterator/types/simple/package-frame.html @@ -0,0 +1,21 @@ + + + + + +neureka.ndim.iterator.types.simple (neureka 1.0.1 API) + + + + +

    neureka.ndim.iterator.types.simple

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/iterator/types/simple/package-summary.html b/docs/jdocs/neureka/ndim/iterator/types/simple/package-summary.html index 21e036838..d154c99c3 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/simple/package-summary.html +++ b/docs/jdocs/neureka/ndim/iterator/types/simple/package-summary.html @@ -1,86 +1,147 @@ - + + - -neureka.ndim.iterator.types.simple (neureka 1.0.0 API) - - - - + +neureka.ndim.iterator.types.simple (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.iterator.types.simple

    -
    -
    -
    package neureka.ndim.iterator.types.simple
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/simple/package-tree.html b/docs/jdocs/neureka/ndim/iterator/types/simple/package-tree.html index 5e5e35a2c..625f4d125 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/simple/package-tree.html +++ b/docs/jdocs/neureka/ndim/iterator/types/simple/package-tree.html @@ -1,89 +1,109 @@ - + + - -neureka.ndim.iterator.types.simple Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.iterator.types.simple Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.iterator.types.simple

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced1DCIterator.html b/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced1DCIterator.html index a85800f57..c11bec51b 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced1DCIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced1DCIterator.html @@ -1,286 +1,461 @@ - + + - -Sliced1DCIterator (neureka 1.0.0 API) - - - - + +Sliced1DCIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator.types.sliced
    -

    Class Sliced1DCIterator

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D1C -
    neureka.ndim.config.types.sliced.Sliced1DConfiguration -
    neureka.ndim.iterator.types.sliced.Sliced1DCIterator
    -
    -
    -
    +
    neureka.ndim.iterator.types.sliced
    +

    Class Sliced1DCIterator

    -
    -
    +
    + +
    +
    -
    - +
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    +
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    + -
  • -
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    Method Details

    -
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    • -
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      increment

      -
      public final void increment()
      -
      -
      Specified by:
      -
      increment in interface NDIterator
      +
      • -
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      decrement

      -
      public final void decrement()
      -
      -
      Specified by:
      -
      decrement in interface NDIterator
      +
    + + + +
      +
    • +

      decrement

      +
      public final void decrement()
      +
      +
      Specified by:
      +
      decrement in interface NDIterator
      -
    • -
  • -
    -

    i

    -
    public final int i()
    -
    -
    Specified by:
    -
    i in interface NDIterator
    + + + + +
      +
    • +

      i

      +
      public final int i()
      +
      +
      Specified by:
      +
      i in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int get(int axis)
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int get(int axis)
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int[] get()
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int[] get()
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int axis, - int position)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int axis,
+                      int position)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int[] indices)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int[] indices)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced2DCIterator.html b/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced2DCIterator.html index 7803d27e9..05d46f5b2 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced2DCIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced2DCIterator.html @@ -1,286 +1,461 @@ - + + - -Sliced2DCIterator (neureka 1.0.0 API) - - - - + +Sliced2DCIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator.types.sliced
    -

    Class Sliced2DCIterator

    -
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    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D2C -
    neureka.ndim.config.types.sliced.Sliced2DConfiguration -
    neureka.ndim.iterator.types.sliced.Sliced2DCIterator
    -
    -
    -
    +
    neureka.ndim.iterator.types.sliced
    +

    Class Sliced2DCIterator

    -
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    + +
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    -
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    Method Details

    -
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      increment

      -
      public final void increment()
      -
      -
      Specified by:
      -
      increment in interface NDIterator
      +
      • -
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      -

      decrement

      -
      public final void decrement()
      -
      -
      Specified by:
      -
      decrement in interface NDIterator
      +
    + + + +
      +
    • +

      decrement

      +
      public final void decrement()
      +
      +
      Specified by:
      +
      decrement in interface NDIterator
      -
    • -
  • -
    -

    i

    -
    public final int i()
    -
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    Specified by:
    -
    i in interface NDIterator
    + + + + +
      +
    • +

      i

      +
      public final int i()
      +
      +
      Specified by:
      +
      i in interface NDIterator
      -
    • -
  • -
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    get

    -
    public final int get(int axis)
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int get(int axis)
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int[] get()
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int[] get()
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int axis, - int position)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int axis,
+                      int position)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int[] indices)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int[] indices)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced3DCIterator.html b/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced3DCIterator.html index b68155b2c..adbb54064 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced3DCIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/types/sliced/Sliced3DCIterator.html @@ -1,286 +1,461 @@ - + + - -Sliced3DCIterator (neureka 1.0.0 API) - - - - + +Sliced3DCIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator.types.sliced
    -

    Class Sliced3DCIterator

    -
    -
    java.lang.Object -
    neureka.ndim.config.AbstractNDC -
    neureka.ndim.config.types.D3C -
    neureka.ndim.config.types.sliced.Sliced3DConfiguration -
    neureka.ndim.iterator.types.sliced.Sliced3DCIterator
    -
    -
    -
    +
    neureka.ndim.iterator.types.sliced
    +

    Class Sliced3DCIterator

    -
    -
    +
    + +
    +
    -
    - +
    +
    +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      increment

      -
      public final void increment()
      -
      -
      Specified by:
      -
      increment in interface NDIterator
      +
      • -
    • -
      -

      decrement

      -
      public final void decrement()
      -
      -
      Specified by:
      -
      decrement in interface NDIterator
      +
    + + + +
      +
    • +

      decrement

      +
      public final void decrement()
      +
      +
      Specified by:
      +
      decrement in interface NDIterator
      -
    • -
  • -
    -

    i

    -
    public final int i()
    -
    -
    Specified by:
    -
    i in interface NDIterator
    + + + + +
      +
    • +

      i

      +
      public final int i()
      +
      +
      Specified by:
      +
      i in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int get(int axis)
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int get(int axis)
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int[] get()
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int[] get()
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int axis, - int position)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int axis,
+                      int position)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int[] indices)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int[] indices)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    + +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/sliced/SlicedNDIterator.html b/docs/jdocs/neureka/ndim/iterator/types/sliced/SlicedNDIterator.html index df6311ffe..0458d4311 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/sliced/SlicedNDIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/types/sliced/SlicedNDIterator.html @@ -1,298 +1,454 @@ - + + - -SlicedNDIterator (neureka 1.0.0 API) - - - - + +SlicedNDIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator.types.sliced
    -

    Class SlicedNDIterator

    -
    -
    java.lang.Object -
    neureka.ndim.iterator.types.sliced.SlicedNDIterator
    +
    neureka.ndim.iterator.types.sliced
    +

    Class SlicedNDIterator

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.ndim.iterator.types.sliced.SlicedNDIterator
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class SlicedNDIterator
+extends java.lang.Object
+implements NDIterator
      + +
    +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      shape

      -
      public final int shape(int i)
      -
      -
      Specified by:
      -
      shape in interface NDIterator
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          shape

          +
          public final int shape(int i)
          +
          +
          Specified by:
          +
          shape in interface NDIterator
          -
      • -
    • -
      -

      shape

      -
      public final int[] shape()
      -
      -
      Specified by:
      -
      shape in interface NDIterator
      +
    + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      +
      +
      Specified by:
      +
      shape in interface NDIterator
      -
    • -
  • -
    -

    increment

    -
    public final void increment()
    -
    -
    Specified by:
    -
    increment in interface NDIterator
    + + + + +
      +
    • +

      increment

      +
      public final void increment()
      +
      +
      Specified by:
      +
      increment in interface NDIterator
      -
    • -
  • -
    -

    decrement

    -
    public final void decrement()
    -
    -
    Specified by:
    -
    decrement in interface NDIterator
    + + + + +
      +
    • +

      decrement

      +
      public final void decrement()
      +
      +
      Specified by:
      +
      decrement in interface NDIterator
      -
    • -
  • -
    -

    i

    -
    public final int i()
    -
    -
    Specified by:
    -
    i in interface NDIterator
    + + + + +
      +
    • +

      i

      +
      public final int i()
      +
      +
      Specified by:
      +
      i in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int get(int axis)
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int get(int axis)
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int[] get()
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int[] get()
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int axis, - int position)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int axis,
+                      int position)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int[] indices)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int[] indices)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    rank

    -
    public final int rank()
    -
    -
    Specified by:
    -
    rank in interface NDIterator
    + + + + +
      +
    • +

      rank

      +
      public final int rank()
      +
      +
      Specified by:
      +
      rank in interface NDIterator
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/sliced/package-frame.html b/docs/jdocs/neureka/ndim/iterator/types/sliced/package-frame.html new file mode 100644 index 000000000..89d9abd3f --- /dev/null +++ b/docs/jdocs/neureka/ndim/iterator/types/sliced/package-frame.html @@ -0,0 +1,22 @@ + + + + + +neureka.ndim.iterator.types.sliced (neureka 1.0.1 API) + + + + +

    neureka.ndim.iterator.types.sliced

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/iterator/types/sliced/package-summary.html b/docs/jdocs/neureka/ndim/iterator/types/sliced/package-summary.html index 4850a6769..f2914aad6 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/sliced/package-summary.html +++ b/docs/jdocs/neureka/ndim/iterator/types/sliced/package-summary.html @@ -1,88 +1,151 @@ - + + - -neureka.ndim.iterator.types.sliced (neureka 1.0.0 API) - - - - + +neureka.ndim.iterator.types.sliced (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.iterator.types.sliced

    -
    -
    -
    package neureka.ndim.iterator.types.sliced
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/sliced/package-tree.html b/docs/jdocs/neureka/ndim/iterator/types/sliced/package-tree.html index da6dadcec..aca377105 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/sliced/package-tree.html +++ b/docs/jdocs/neureka/ndim/iterator/types/sliced/package-tree.html @@ -1,102 +1,165 @@ - + + - -neureka.ndim.iterator.types.sliced Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.iterator.types.sliced Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.iterator.types.sliced

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/virtual/VirtualNDIterator.html b/docs/jdocs/neureka/ndim/iterator/types/virtual/VirtualNDIterator.html index 09158fafe..6d14ab5a6 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/virtual/VirtualNDIterator.html +++ b/docs/jdocs/neureka/ndim/iterator/types/virtual/VirtualNDIterator.html @@ -1,298 +1,454 @@ - + + - -VirtualNDIterator (neureka 1.0.0 API) - - - - + +VirtualNDIterator (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.ndim.iterator.types.virtual
    -

    Class VirtualNDIterator

    -
    -
    java.lang.Object -
    neureka.ndim.iterator.types.virtual.VirtualNDIterator
    +
    neureka.ndim.iterator.types.virtual
    +

    Class VirtualNDIterator

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.ndim.iterator.types.virtual.VirtualNDIterator
        • +
      +
      • +
    +
    +
    -
    -
      +
      +
      public final class VirtualNDIterator
+extends java.lang.Object
+implements NDIterator
      + +
    +
    +
    + - -
    -
      +
    +
    + -
  • -
    -

    Method Details

    -
      -
    • -
      -

      shape

      -
      public final int shape(int i)
      -
      -
      Specified by:
      -
      shape in interface NDIterator
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          shape

          +
          public final int shape(int i)
          +
          +
          Specified by:
          +
          shape in interface NDIterator
          -
      • -
    • -
      -

      shape

      -
      public final int[] shape()
      -
      -
      Specified by:
      -
      shape in interface NDIterator
      +
    + + + +
      +
    • +

      shape

      +
      public final int[] shape()
      +
      +
      Specified by:
      +
      shape in interface NDIterator
      -
    • -
  • -
    -

    increment

    -
    public final void increment()
    -
    -
    Specified by:
    -
    increment in interface NDIterator
    + + + + +
      +
    • +

      increment

      +
      public final void increment()
      +
      +
      Specified by:
      +
      increment in interface NDIterator
      -
    • -
  • -
    -

    decrement

    -
    public final void decrement()
    -
    -
    Specified by:
    -
    decrement in interface NDIterator
    + + + + +
      +
    • +

      decrement

      +
      public final void decrement()
      +
      +
      Specified by:
      +
      decrement in interface NDIterator
      -
    • -
  • -
    -

    i

    -
    public final int i()
    -
    -
    Specified by:
    -
    i in interface NDIterator
    + + + + +
      +
    • +

      i

      +
      public final int i()
      +
      +
      Specified by:
      +
      i in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int get(int axis)
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int get(int axis)
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    get

    -
    public final int[] get()
    -
    -
    Specified by:
    -
    get in interface NDIterator
    + + + + +
      +
    • +

      get

      +
      public final int[] get()
      +
      +
      Specified by:
      +
      get in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int axis, - int position)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int axis,
+                      int position)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    set

    -
    public final void set(int[] indices)
    -
    -
    Specified by:
    -
    set in interface NDIterator
    + + + + +
      +
    • +

      set

      +
      public final void set(int[] indices)
      +
      +
      Specified by:
      +
      set in interface NDIterator
      -
    • -
  • -
    -

    rank

    -
    public final int rank()
    -
    -
    Specified by:
    -
    rank in interface NDIterator
    + + + + +
      +
    • +

      rank

      +
      public final int rank()
      +
      +
      Specified by:
      +
      rank in interface NDIterator
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    +
      +
    • Prev Class
      • +
    • Next Class
      • +
    + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/virtual/package-frame.html b/docs/jdocs/neureka/ndim/iterator/types/virtual/package-frame.html new file mode 100644 index 000000000..85b5c3722 --- /dev/null +++ b/docs/jdocs/neureka/ndim/iterator/types/virtual/package-frame.html @@ -0,0 +1,19 @@ + + + + + +neureka.ndim.iterator.types.virtual (neureka 1.0.1 API) + + + + +

    neureka.ndim.iterator.types.virtual

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/ndim/iterator/types/virtual/package-summary.html b/docs/jdocs/neureka/ndim/iterator/types/virtual/package-summary.html index 82131dac6..cd2ad1b49 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/virtual/package-summary.html +++ b/docs/jdocs/neureka/ndim/iterator/types/virtual/package-summary.html @@ -1,82 +1,139 @@ - + + - -neureka.ndim.iterator.types.virtual (neureka 1.0.0 API) - - - - + +neureka.ndim.iterator.types.virtual (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.ndim.iterator.types.virtual

    -
    -
    -
    package neureka.ndim.iterator.types.virtual
    -
    -
      -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      VirtualNDIterator
      -
       
      -
      +

      Package neureka.ndim.iterator.types.virtual

      +
      + -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
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    + + + +
    +
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    + diff --git a/docs/jdocs/neureka/ndim/iterator/types/virtual/package-tree.html b/docs/jdocs/neureka/ndim/iterator/types/virtual/package-tree.html index bacf202aa..fc05d66bd 100644 --- a/docs/jdocs/neureka/ndim/iterator/types/virtual/package-tree.html +++ b/docs/jdocs/neureka/ndim/iterator/types/virtual/package-tree.html @@ -1,71 +1,134 @@ - + + - -neureka.ndim.iterator.types.virtual Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.ndim.iterator.types.virtual Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.ndim.iterator.types.virtual

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
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    +
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    + diff --git a/docs/jdocs/neureka/ndim/package-frame.html b/docs/jdocs/neureka/ndim/package-frame.html new file mode 100644 index 000000000..abc76917d --- /dev/null +++ b/docs/jdocs/neureka/ndim/package-frame.html @@ -0,0 +1,25 @@ + + + + + +neureka.ndim (neureka 1.0.1 API) + + + + +

    neureka.ndim

    +
    +

    Interfaces

    + +

    Classes

    + +
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    Package neureka.ndim

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        Interface Summary 
        InterfaceDescription
        Filler<T>
        Implementations of this ought to map the index of a tensor entry to a value which should be placed at that entry position.
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        NDConstructor
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        NDConstructor 
        NDimensional
        This interface defines the most essential methods of the nd-array/tensor API, which describe them with respect to their dimensionality.
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        NDUtil
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        Class Summary 
        ClassDescription
        NDUtil
        Static utility methods for the NDArray.
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    Hierarchy For Package neureka.ndim

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    + diff --git a/docs/jdocs/neureka/optimization/Optimization.html b/docs/jdocs/neureka/optimization/Optimization.html index d0f087b60..aa1ce6c49 100644 --- a/docs/jdocs/neureka/optimization/Optimization.html +++ b/docs/jdocs/neureka/optimization/Optimization.html @@ -1,132 +1,226 @@ - + + - -Optimization (neureka 1.0.0 API) - - - - + +Optimization (neureka 1.0.1 API) - - - - - - + + -
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    Interface Optimization<V>

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    Interface Optimization<V>

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    Interface Optimizer<V>

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    Interface Optimizer<V>

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      ofGradient

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      static <T> Optimizer<T> ofGradient(Optimization<T> o)
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      Type Parameters:
      T - The value type parameter of the tensors processed by this optimizer.
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      o - The Optimization lambda which receives the gradient of a tensor for optimization.
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      An Optimizer which will process the gradient of any passed tensor (see of(Optimization) which processes tensors directly).
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      Parameters:
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      o - The Optimization lambda which receives the gradient of a tensor for optimization.
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      An Optimizer which will process the gradient of any passed tensor (see of(Optimization) which processes tensors directly).
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    Interface OptimizerFactory

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      <V extends Number>
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    + diff --git a/docs/jdocs/neureka/optimization/implementations/ADAM.html b/docs/jdocs/neureka/optimization/implementations/ADAM.html index 94b4015ff..3c67231b1 100644 --- a/docs/jdocs/neureka/optimization/implementations/ADAM.html +++ b/docs/jdocs/neureka/optimization/implementations/ADAM.html @@ -1,205 +1,347 @@ - + + - -ADAM (neureka 1.0.0 API) - - - - + +ADAM (neureka 1.0.1 API) - - - - - - + + -
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    Class ADAM<V extends Number>

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    neureka.optimization.implementations.ADAM<V>
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    Class ADAM<V extends java.lang.Number>

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      Type Parameters:
      V - The value type parameter of the tensor whose gradients are being optimized.
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      All Implemented Interfaces:
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      Component<Tensor<V>>, Optimization<V>, Optimizer<V>
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      Component<Tensor<V>>, Optimization<V>, Optimizer<V>
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      public final class ADAM<V extends Number> -extends Object -implements Optimizer<V>
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      public final class ADAM<V extends java.lang.Number>
+extends java.lang.Object
+implements Optimizer<V>
      ADAM (short for Adaptive Moment Estimation) is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of RMSProp and SGD with respect to Momentum. The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients.
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        getMomentum

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        public final Tensor<V> getMomentum()
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        getVelocity

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        getTime

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        getLearningRate

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        getLearningRate

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        public final double getLearningRate()
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    Class ADAMFactory

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    neureka.optimization.implementations.ADAMFactory
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    Class ADAMFactory

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      Constructor Summary

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      Constructor
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      Description
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      ADAMFactory()
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+extends java.lang.Object
+implements OptimizerFactory
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          Constructor Detail

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    create

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      public <V extends java.lang.Number> ADAM<V> create(Tensor<V> momentum,
+                                                   Tensor<V> velocity)
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    + diff --git a/docs/jdocs/neureka/optimization/implementations/AdaGrad.html b/docs/jdocs/neureka/optimization/implementations/AdaGrad.html index 75ae4c2da..f2b8ebc26 100644 --- a/docs/jdocs/neureka/optimization/implementations/AdaGrad.html +++ b/docs/jdocs/neureka/optimization/implementations/AdaGrad.html @@ -1,168 +1,294 @@ - + + - -AdaGrad (neureka 1.0.0 API) - - - - + +AdaGrad (neureka 1.0.1 API) - - - - - - + + -
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    Class AdaGrad<V extends Number>

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    neureka.optimization.implementations
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    Class AdaGrad<V extends java.lang.Number>

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    neureka.optimization.implementations.AdaGrad<V>
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      Type Parameters:
      V - The super type of the value item type for the tensors whose gradients can be optimized by this.
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      All Implemented Interfaces:
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      public class AdaGrad<V extends Number> -extends Object -implements Optimizer<V>
      +
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      public class AdaGrad<V extends java.lang.Number>
+extends java.lang.Object
+implements Optimizer<V>
      Adaptive Gradients, or AdaGrad for short, is an extension of the gradient descent optimization algorithm that adjusts the step size for each parameter based on the squared gradients seen over the course of previous optimization steps.
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    Class AdaGradFactory

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    neureka.optimization.implementations
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    Class AdaGradFactory

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    java.lang.Object -
    neureka.optimization.implementations.AdaGradFactory
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      Constructor Summary

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      AdaGradFactory()
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+extends java.lang.Object
+implements OptimizerFactory
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    + diff --git a/docs/jdocs/neureka/optimization/implementations/Momentum.html b/docs/jdocs/neureka/optimization/implementations/Momentum.html index 4fc7902a6..ea8e9836d 100644 --- a/docs/jdocs/neureka/optimization/implementations/Momentum.html +++ b/docs/jdocs/neureka/optimization/implementations/Momentum.html @@ -1,161 +1,287 @@ - + + - -Momentum (neureka 1.0.0 API) - - - - + +Momentum (neureka 1.0.1 API) - - - - - - + + -
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    Class Momentum<V extends Number>

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    neureka.optimization.implementations
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    Class Momentum<V extends java.lang.Number>

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    neureka.optimization.implementations.Momentum<V>
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      public class Momentum<V extends java.lang.Number>
+extends java.lang.Object
+implements Optimizer<V>
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    + diff --git a/docs/jdocs/neureka/optimization/implementations/MomentumFactory.html b/docs/jdocs/neureka/optimization/implementations/MomentumFactory.html index 0a425644e..ec759d891 100644 --- a/docs/jdocs/neureka/optimization/implementations/MomentumFactory.html +++ b/docs/jdocs/neureka/optimization/implementations/MomentumFactory.html @@ -1,185 +1,303 @@ - + + - -MomentumFactory (neureka 1.0.0 API) - - - - + +MomentumFactory (neureka 1.0.1 API) - - - - - - + + -
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    Class MomentumFactory

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    neureka.optimization.implementations.MomentumFactory
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    neureka.optimization.implementations
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    Class MomentumFactory

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      Constructor Summary

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      Constructor
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      Description
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      MomentumFactory()
      -
       
      +
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      public class MomentumFactory
+extends java.lang.Object
+implements OptimizerFactory
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        MomentumFactory

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          Constructor Detail

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            MomentumFactory

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    + diff --git a/docs/jdocs/neureka/optimization/implementations/RMSProp.html b/docs/jdocs/neureka/optimization/implementations/RMSProp.html index 4007aec1d..af593280c 100644 --- a/docs/jdocs/neureka/optimization/implementations/RMSProp.html +++ b/docs/jdocs/neureka/optimization/implementations/RMSProp.html @@ -1,171 +1,297 @@ - + + - -RMSProp (neureka 1.0.0 API) - - - - + +RMSProp (neureka 1.0.1 API) - - - - - - + + -
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    Class RMSProp<V extends Number>

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    neureka.optimization.implementations
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    Class RMSProp<V extends java.lang.Number>

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    neureka.optimization.implementations.RMSProp<V>
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    • +
        +
      • neureka.optimization.implementations.RMSProp<V>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The super type of the value item type for the tensors whose gradients can be optimized by this.
      -
      +
      All Implemented Interfaces:
      -
      Component<Tensor<V>>, Optimization<V>, Optimizer<V>
      +
      Component<Tensor<V>>, Optimization<V>, Optimizer<V>
      -
      public class RMSProp<V extends Number> -extends Object -implements Optimizer<V>
      +
      +
      public class RMSProp<V extends java.lang.Number>
+extends java.lang.Object
+implements Optimizer<V>
      Root Mean Squared Propagation, or RMSProp, is an extension of gradient descent and the AdaGrad version of gradient descent that uses a decaying average of partial gradients in the adaptation of the step size for each parameter. - It is similar to AdaGrad in that it uses a moving average of + It is similar to AdaGrad in that it uses a moving average of the squared gradients to scale the learning rate.
      -
    -
    -
      + +
    +
    +
    +
    +
    + - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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    • Summary: 
      • +
    • Nested | 
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    • Constr | 
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    • Method
      • +
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    • Detail: 
      • +
    • Field | 
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    • Method
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    + + +
    + diff --git a/docs/jdocs/neureka/optimization/implementations/RMSPropFactory.html b/docs/jdocs/neureka/optimization/implementations/RMSPropFactory.html index b79d1ea93..ff6cd7a2f 100644 --- a/docs/jdocs/neureka/optimization/implementations/RMSPropFactory.html +++ b/docs/jdocs/neureka/optimization/implementations/RMSPropFactory.html @@ -1,185 +1,303 @@ - + + - -RMSPropFactory (neureka 1.0.0 API) - - - - + +RMSPropFactory (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.optimization.implementations
    -

    Class RMSPropFactory

    -
    -
    java.lang.Object -
    neureka.optimization.implementations.RMSPropFactory
    +
    neureka.optimization.implementations
    +

    Class RMSPropFactory

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.optimization.implementations.RMSPropFactory
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      RMSPropFactory()
      -
       
      +
      +
      public class RMSPropFactory
+extends java.lang.Object
+implements OptimizerFactory
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        RMSPropFactory

        -
        public RMSPropFactory()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            RMSPropFactory

            +
            public RMSPropFactory()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • - - + + +
    +
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    + + +
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    + + + +
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/optimization/implementations/SGD.html b/docs/jdocs/neureka/optimization/implementations/SGD.html index 9d6b477ba..4ded6dac2 100644 --- a/docs/jdocs/neureka/optimization/implementations/SGD.html +++ b/docs/jdocs/neureka/optimization/implementations/SGD.html @@ -1,92 +1,123 @@ - + + - -SGD (neureka 1.0.0 API) - - - - + +SGD (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.optimization.implementations
    -

    Class SGD<V>

    +
    neureka.optimization.implementations
    +

    Class SGD<V>

    -
    java.lang.Object -
    neureka.optimization.implementations.SGD<V>
    -
    -
    -
    -
    Type Parameters:
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.optimization.implementations.SGD<V>
        • +
      +
      • +
    +
    +
      +
    • +
      +
      Type Parameters:
      V - The value type parameter of the tensor whose gradients are being optimized.
      -
      +
      All Implemented Interfaces:
      -
      Component<Tensor<V>>, Optimization<V>, Optimizer<V>
      +
      Component<Tensor<V>>, Optimization<V>, Optimizer<V>
      -
      public class SGD<V> -extends Object -implements Optimizer<V>
      +
      +
      public class SGD<V>
+extends java.lang.Object
+implements Optimizer<V>
      Stochastic Gradient Descent is an iterative optimization technique that uses the gradient of a weight variable to adjust said variable, in order to reduce the error used to calculate said gradient. @@ -94,88 +125,187 @@

      Class SGD<V>

      optimize the gradient based on previous gradients (network forward and backward passes) but simply applies the gradient value based on each example within the training dataset.
      -
    -
    -
      + +
    +
    +
    +
    +
    + - - +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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    + diff --git a/docs/jdocs/neureka/optimization/implementations/SGDFactory.html b/docs/jdocs/neureka/optimization/implementations/SGDFactory.html index 7d47aa73d..a613909e7 100644 --- a/docs/jdocs/neureka/optimization/implementations/SGDFactory.html +++ b/docs/jdocs/neureka/optimization/implementations/SGDFactory.html @@ -1,176 +1,290 @@ - + + - -SGDFactory (neureka 1.0.0 API) - - - - + +SGDFactory (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.optimization.implementations
    -

    Class SGDFactory

    +
    neureka.optimization.implementations
    +

    Class SGDFactory

    -
    java.lang.Object -
    neureka.optimization.implementations.SGDFactory
    -
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.optimization.implementations.SGDFactory
        • +
      +
      • +
    +
    +
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      SGDFactory()
      -
       
      +
      +
      public class SGDFactory
+extends java.lang.Object
+implements OptimizerFactory
      +
      • +
    - +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        SGDFactory

        -
        public SGDFactory()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            SGDFactory

            +
            public SGDFactory()
          -
      • +
    -
  • -
    -

    Method Details

    -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
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    • Summary: 
      • +
    • Nested | 
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      • +
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    + + +
    + diff --git a/docs/jdocs/neureka/optimization/implementations/package-frame.html b/docs/jdocs/neureka/optimization/implementations/package-frame.html new file mode 100644 index 000000000..d24b06ba1 --- /dev/null +++ b/docs/jdocs/neureka/optimization/implementations/package-frame.html @@ -0,0 +1,28 @@ + + + + + +neureka.optimization.implementations (neureka 1.0.1 API) + + + + +

    neureka.optimization.implementations

    +
    +

    Classes

    + +
    + + diff --git a/docs/jdocs/neureka/optimization/implementations/package-summary.html b/docs/jdocs/neureka/optimization/implementations/package-summary.html index 30eb2be5a..f7af10d6f 100644 --- a/docs/jdocs/neureka/optimization/implementations/package-summary.html +++ b/docs/jdocs/neureka/optimization/implementations/package-summary.html @@ -1,127 +1,191 @@ - + + - -neureka.optimization.implementations (neureka 1.0.0 API) - - - - + +neureka.optimization.implementations (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.optimization.implementations

    -
    -
    -
    package neureka.optimization.implementations
    -
    -
      -
    • - -
      • -
    • -
      -
      Classes
      -
      -
      Class
      -
      Description
      -
      AdaGrad<V extends Number>
      -
      +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        AdaGrad<V extends java.lang.Number>
        Adaptive Gradients, or AdaGrad for short, is an extension of the gradient descent optimization algorithm that adjusts the step size for each parameter based on the squared gradients seen over the course of previous optimization steps.
        - -
        AdaGradFactory
        -
         
        -
        ADAM<V extends Number>
        -
        +
        AdaGradFactory 
        ADAM<V extends java.lang.Number>
        ADAM (short for Adaptive Moment Estimation) is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of RMSProp and SGD with respect to Momentum.
        - -
        ADAMFactory
        -
         
        -
        Momentum<V extends Number>
        -
         
        -
        MomentumFactory
        -
         
        -
        RMSProp<V extends Number>
        -
        +
        ADAMFactory 
        Momentum<V extends java.lang.Number> 
        MomentumFactory 
        RMSProp<V extends java.lang.Number>
        Root Mean Squared Propagation, or RMSProp, is an extension of gradient descent and the AdaGrad version of gradient descent that uses a decaying average of partial gradients in the adaptation of the step size for each parameter.
        - -
        RMSPropFactory
        -
         
        -
        SGD<V>
        -
        +
        RMSPropFactory 
        SGD<V>
        Stochastic Gradient Descent is an iterative optimization technique that uses the gradient of a weight variable to adjust said variable, in order to reduce the error used to calculate said gradient.
        - -
        SGDFactory
        -
         
        - - +
        SGDFactory 
      -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/optimization/implementations/package-tree.html b/docs/jdocs/neureka/optimization/implementations/package-tree.html index c7d9e168a..e0280123c 100644 --- a/docs/jdocs/neureka/optimization/implementations/package-tree.html +++ b/docs/jdocs/neureka/optimization/implementations/package-tree.html @@ -1,80 +1,143 @@ - + + - -neureka.optimization.implementations Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.optimization.implementations Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka.optimization.implementations

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
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    + +
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    Skip navigation links
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    +
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    + diff --git a/docs/jdocs/neureka/optimization/package-frame.html b/docs/jdocs/neureka/optimization/package-frame.html new file mode 100644 index 000000000..ca2c0eb06 --- /dev/null +++ b/docs/jdocs/neureka/optimization/package-frame.html @@ -0,0 +1,21 @@ + + + + + +neureka.optimization (neureka 1.0.1 API) + + + + +

    neureka.optimization

    +
    +

    Interfaces

    + +
    + + diff --git a/docs/jdocs/neureka/optimization/package-summary.html b/docs/jdocs/neureka/optimization/package-summary.html index 869d6a3ef..6d61f2306 100644 --- a/docs/jdocs/neureka/optimization/package-summary.html +++ b/docs/jdocs/neureka/optimization/package-summary.html @@ -1,102 +1,150 @@ - + + - -neureka.optimization (neureka 1.0.0 API) - - - - + +neureka.optimization (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka.optimization

    +

    Package neureka.optimization

    -
    -
    package neureka.optimization
    -
    -
    -
    + +
    + + +
    Skip navigation links
    + + + + +
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    + diff --git a/docs/jdocs/neureka/optimization/package-tree.html b/docs/jdocs/neureka/optimization/package-tree.html index 622b12f34..9c445a854 100644 --- a/docs/jdocs/neureka/optimization/package-tree.html +++ b/docs/jdocs/neureka/optimization/package-tree.html @@ -1,77 +1,140 @@ - + + - -neureka.optimization Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka.optimization Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
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    Hierarchy For Package neureka.optimization

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    neureka

    +
    +

    Interfaces

    + +

    Classes

    + +

    Enums

    + +
    + + diff --git a/docs/jdocs/neureka/package-summary.html b/docs/jdocs/neureka/package-summary.html index d26ca1bf7..88f28517c 100644 --- a/docs/jdocs/neureka/package-summary.html +++ b/docs/jdocs/neureka/package-summary.html @@ -1,163 +1,231 @@ - + + - -neureka (neureka 1.0.0 API) - - - - + +neureka (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -

    Package neureka

    -
    -
    -
    package neureka
    -
    -
      -
    • - -
      • -
    • -
      -
      -
      -
      -
      Class
      -
      Description
      -
      Data<V>
      -
      +

      Package neureka

      +
      +
      +
        +
      • + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        Interface Summary 
        InterfaceDescription
        Data<V>
        A wrapper type for the raw data array of a tensor/nd-array, - which is typically provided by implementations of the Device interface.
        - -
        MutateNda<T>
        -
        + which is typically provided by implementations of the Device interface.
        +
        MutateNda<T>
        Nd-arrays should be used as immutable data structures mostly, however sometimes it is important to mutate their state for performance reasons.
        - -
        MutateNda.Item<V>
        -
        -
        Instances of this are being returned by the Nda.at(int...) method, +
        MutateNda.Item<V> +
        Instances of this are being returned by the Nda.at(int...) method, and they allow you to get or set individual nd-array items
        - -
        MutateTensor<T>
        -
        +
        MutateTensor<T>
        Tensors should be considered immutable, however sometimes it is important to mutate their state for performance reasons.
        - -
        Nda<V>
        -
        -
        Nda, which is an abbreviation of 'N-Dimensional-Array', represents +
        Nda<V> +
        Nda, which is an abbreviation of 'N-Dimensional-Array', represents a multidimensional, homogeneously filled fixed-size array of items.
        - -
        Nda.Item<V>
        -
        -
        Instances of this are being returned by the Nda.at(int...) method, +
        Nda.Item<V> +
        Instances of this are being returned by the Nda.at(int...) method, and they allow you to get individual nd-array items
        - -
        Neureka
        -
        -
        Neureka is the key access point for thread local / global library settings ( seeNeureka.Settings) - as well as execution contexts (see BackendContext) - and pre-instantiated Functions.
        -
        -
        Neureka.Utility
        -
         
        -
        Shape
        -
        +
        Shape
        Basically a tuple of integers which is used to describe the shape of an array.
        - -
        Tensor<V>
        -
        -
        A Tensor is a mathematical concept and type of multidimensional +
        Tensor<V> +
        A Tensor is a mathematical concept and type of multidimensional data-structure with certain transformation properties.
        - -
        Tensor.ImageType
        -
        -
        Use this enum as argument for the Tensor.asImage(Tensor.ImageType) method to +
        +
        • +
      • + + + + + + + + + + + + + + + + +
        Class Summary 
        ClassDescription
        Neureka +
        Neureka is the key access point for thread local / global library settings ( seeNeureka.Settings) + as well as execution contexts (see BackendContext) + and pre-instantiated Functions.
        +
        Neureka.Utility 
        +
        • +
      • + + + + + + + + + + + + +
        Enum Summary 
        EnumDescription
        Tensor.ImageType +
        Use this enum as argument for the Tensor.asImage(Tensor.ImageType) method to specify the type of image that should be returned.
        - - - - +
      -
    -
    + +
    + + +
    Skip navigation links
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    + diff --git a/docs/jdocs/neureka/package-tree.html b/docs/jdocs/neureka/package-tree.html index 3e90c7115..36e831a39 100644 --- a/docs/jdocs/neureka/package-tree.html +++ b/docs/jdocs/neureka/package-tree.html @@ -1,137 +1,196 @@ - + + - -neureka Class Hierarchy (neureka 1.0.0 API) - - - - + +neureka Class Hierarchy (neureka 1.0.1 API) - - - - - - + + -
    - -
    -

    Hierarchy For Package neureka

    -Package Hierarchies: +Package Hierarchies:
    -
    +

    Class Hierarchy

    -
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    Interface Hierarchy

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    +

    Enum Hierarchy

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    + diff --git a/docs/jdocs/neureka/view/NDPrintSettings.html b/docs/jdocs/neureka/view/NDPrintSettings.html index 7eef2ed48..0b23f1530 100644 --- a/docs/jdocs/neureka/view/NDPrintSettings.html +++ b/docs/jdocs/neureka/view/NDPrintSettings.html @@ -1,614 +1,852 @@ - + + - -NDPrintSettings (neureka 1.0.0 API) - - - - + +NDPrintSettings (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.view
    -

    Class NDPrintSettings

    -
    -
    java.lang.Object -
    neureka.view.NDPrintSettings
    +
    neureka.view
    +

    Class NDPrintSettings

    -
    -
    -
    public final class NDPrintSettings -extends Object
    -
    This is simply a mutable container for configuring how Tensor - instances ought to be converted to Strings.
    -
    -
    -
      - +
      +
        +
      • java.lang.Object
      • -
        -

        Constructor Summary

        -
        Constructors
        -
        -
        Constructor
        -
        Description
        -
        NDPrintSettings(Supplier<Boolean> notModifiable)
        -
         
        +
          +
        • neureka.view.NDPrintSettings
          • +
        +
        • +
      +
      +
        +
      • +
        +
        +
        public final class NDPrintSettings
+extends java.lang.Object
        +
        This is simply a mutable container for configuring how Tensor + instances ought to be converted to Strings.
        +
        • +
      -
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        NDPrintSettings

        -
        public NDPrintSettings(Supplier<Boolean> notModifiable)
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            NDPrintSettings

            +
            public NDPrintSettings(java.util.function.Supplier<java.lang.Boolean> notModifiable)
          -
      • +
    -
  • -
    -

    Method Details

    - + + + + + + + +
      +
    • +

      getCellSize

      +
      public int getCellSize()
      A cell size refers to the number of characters reserved to - the String representation of a single element. - This property only becomes relevant when the getIsCellBound() + the String representation of a single element. + This property only becomes relevant when the getIsCellBound() flag is set. This will then cause the width of the cell to be always of the specified size.
      -
      -
      Returns:
      +
      +
      Returns:
      The width of the cell in terms of numbers of characters.
      -
    • -
  • -
    -

    setCellSize

    -
    public NDPrintSettings setCellSize(int cellSize)
    + + + + +
      +
    • +

      setCellSize

      +
      public NDPrintSettings setCellSize(int cellSize)
      A cell size refers to the number of characters reserved to - the String representation of a single element. - This property only becomes relevant when the getIsCellBound() + the String representation of a single element. + This property only becomes relevant when the getIsCellBound() flag is set. This will then cause the width of the cell to be always of the specified size.
      -
      -
      Parameters:
      +
      +
      Parameters:
      cellSize - The width of the cell in terms of numbers of characters.
      -
    • -
  • -
    -

    getRowLimit

    -
    public int getRowLimit()
    + + + + +
      +
    • +

      getRowLimit

      +
      public int getRowLimit()
      Very large tensors with a rank larger than 1 might take a lot - of vertical space when converted to a String. + of vertical space when converted to a String. This property is the maximum number of matrix rows printed. It determines at which point the number of rows ought to be pruned.
      -
      -
      Returns:
      -
      The maximum number of rows in the String representation of the tensor.
      +
      +
      Returns:
      +
      The maximum number of rows in the String representation of the tensor.
      -
    • -
  • -
    -

    setRowLimit

    -
    public NDPrintSettings setRowLimit(int shortage)
    + + + + +
      +
    • +

      setRowLimit

      +
      public NDPrintSettings setRowLimit(int shortage)
      Very large tensors with a rank larger than 1 might take a lot - of vertical space when converted to a String. + of vertical space when converted to a String. This property is the maximum number of matrix rows printed. It determines at which point the number of rows ought to be pruned.
      -
      -
      Parameters:
      -
      shortage - The maximum number of rows in the String representation of the tensor.
      +
      +
      Parameters:
      +
      shortage - The maximum number of rows in the String representation of the tensor.
      -
    • -
  • -
    -

    getHasGradient

    -
    public boolean getHasGradient()
    -
    -
    Returns:
    + + + + +
      +
    • +

      getHasGradient

      +
      public boolean getHasGradient()
      +
      +
      Returns:
      The truth value determining if the tensor should also print its gradient.
      -
    • -
  • -
    -

    setHasGradient

    -
    public NDPrintSettings setHasGradient(boolean hasGradient)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      setHasGradient

      +
      public NDPrintSettings setHasGradient(boolean hasGradient)
      +
      +
      Parameters:
      hasGradient - The truth value determining if the tensor should also print its gradient.
      -
    • -
  • -
    -

    getIsScientific

    -
    public boolean getIsScientific()
    -
    -
    Returns:
    + + + + +
      +
    • +

      getIsScientific

      +
      public boolean getIsScientific()
      +
      +
      Returns:
      The truth value determining if numeric values should be formatted in scientific notation.
      -
    • -
  • -
    -

    setIsScientific

    -
    public NDPrintSettings setIsScientific(boolean isScientific)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      setIsScientific

      +
      public NDPrintSettings setIsScientific(boolean isScientific)
      +
      +
      Parameters:
      isScientific - The truth value determining if numeric values should be formatted in scientific notation.
      -
    • -
  • -
    -

    getIsMultiline

    -
    public boolean getIsMultiline()
    -
    -
    Returns:
    + + + + +
      +
    • +

      getIsMultiline

      +
      public boolean getIsMultiline()
      +
      +
      Returns:
      The truth value determining if the tensor should be printed in one line or across multiple lines.
      -
    • -
  • -
    -

    setIsMultiline

    -
    public NDPrintSettings setIsMultiline(boolean isMultiline)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      setIsMultiline

      +
      public NDPrintSettings setIsMultiline(boolean isMultiline)
      +
      +
      Parameters:
      isMultiline - The truth value determining if the tensor should be printed in one line or across multiple lines.
      -
    • -
  • -
    -

    getHasSlimNumbers

    -
    public boolean getHasSlimNumbers()
    -
    -
    Returns:
    + + + + +
      +
    • +

      getHasSlimNumbers

      +
      public boolean getHasSlimNumbers()
      +
      +
      Returns:
      The truth value determining if numbers should be formatted more compactly (1.0 to 1).
      -
    • -
  • -
    -

    setHasSlimNumbers

    -
    public NDPrintSettings setHasSlimNumbers(boolean haveSlimNumbers)
    -
    -
    Parameters:
    + + + + +
      +
    • +

      setHasSlimNumbers

      +
      public NDPrintSettings setHasSlimNumbers(boolean haveSlimNumbers)
      +
      +
      Parameters:
      haveSlimNumbers - The truth value determining if numbers should be formatted more compactly (1.0 to 1).
      -
    • -
  • -
    -

    getHasValue

    -
    public boolean getHasValue()
    -
    -
    Returns:
    -
    The truth value determining if the values of the tensor should be included in the String representation.
    + + + + +
      +
    • +

      getHasValue

      +
      public boolean getHasValue()
      +
      +
      Returns:
      +
      The truth value determining if the values of the tensor should be included in the String representation.
      -
    • -
  • -
    -

    setHasValue

    -
    public NDPrintSettings setHasValue(boolean hasValue)
    -
    -
    Parameters:
    -
    hasValue - The truth value determining if the values of the tensor should be included in the String representation.
    + + + + +
      +
    • +

      setHasValue

      +
      public NDPrintSettings setHasValue(boolean hasValue)
      +
      +
      Parameters:
      +
      hasValue - The truth value determining if the values of the tensor should be included in the String representation.
      -
    • -
  • -
    -

    getHasShape

    -
    public boolean getHasShape()
    -
    -
    Returns:
    -
    The truth value determining if the tensor should have its shape included in the String.
    + + + + +
      +
    • +

      getHasShape

      +
      public boolean getHasShape()
      +
      +
      Returns:
      +
      The truth value determining if the tensor should have its shape included in the String.
      -
    • -
  • -
    -

    setHasShape

    -
    public NDPrintSettings setHasShape(boolean hasShape)
    -
    -
    Parameters:
    -
    hasShape - The truth value determining if the tensor should have its shape included in the String.
    + + + + +
      +
    • +

      setHasShape

      +
      public NDPrintSettings setHasShape(boolean hasShape)
      +
      +
      Parameters:
      +
      hasShape - The truth value determining if the tensor should have its shape included in the String.
      -
    • -
  • -
    -

    getHasRecursiveGraph

    -
    public boolean getHasRecursiveGraph()
    -
    -
    Returns:
    -
    The truth value determining if the String representation of the + + + + +
      +
    • +

      getHasRecursiveGraph

      +
      public boolean getHasRecursiveGraph()
      +
      +
      Returns:
      +
      The truth value determining if the String representation of the tensor should have its computation graph attached (if present).
      -
    • -
  • -
    -

    setHasRecursiveGraph

    -
    public NDPrintSettings setHasRecursiveGraph(boolean hasRecursiveGraph)
    -
    -
    Parameters:
    -
    hasRecursiveGraph - The truth value determining if the String representation of the + + + + +
      +
    • +

      setHasRecursiveGraph

      +
      public NDPrintSettings setHasRecursiveGraph(boolean hasRecursiveGraph)
      +
      +
      Parameters:
      +
      hasRecursiveGraph - The truth value determining if the String representation of the tensor should have its computation graph attached (if present).
      -
    • -
  • -
    -

    getHasDerivatives

    -
    public boolean getHasDerivatives()
    -
    + + + + +
      +
    • +

      getHasDerivatives

      +
      public boolean getHasDerivatives()
      • -
    • -
      -

      setHasDerivatives

      -
      public NDPrintSettings setHasDerivatives(boolean hasDerivatives)
      -
      +
    + + + +
      +
    • +

      setHasDerivatives

      +
      public NDPrintSettings setHasDerivatives(boolean hasDerivatives)
      • -
    • -
      -

      getIsCellBound

      -
      public boolean getIsCellBound()
      -
      +
    + + + +
      +
    • +

      getIsCellBound

      +
      public boolean getIsCellBound()
      • -
    • -
      -

      setIsCellBound

      -
      public NDPrintSettings setIsCellBound(boolean isCellBound)
      -
      +
    + + + +
      +
    • +

      setIsCellBound

      +
      public NDPrintSettings setIsCellBound(boolean isCellBound)
      • -
    • -
      -

      getPrefix

      -
      public String getPrefix()
      -
      -
      Returns:
      -
      The String which will be prepended at the beginning of a Tensor string representation.
      +
    + + + +
      +
    • +

      getPrefix

      +
      public java.lang.String getPrefix()
      +
      +
      Returns:
      +
      The String which will be prepended at the beginning of a Tensor string representation.
      -
      • -
    • -
      -

      setPrefix

      -
      public NDPrintSettings setPrefix(String prefix)
      -
      -
      Parameters:
      -
      prefix - The String which will be prepended at the beginning of a Tensor string representation.
      +
    + + + +
      +
    • +

      setPrefix

      +
      public NDPrintSettings setPrefix(java.lang.String prefix)
      +
      +
      Parameters:
      +
      prefix - The String which will be prepended at the beginning of a Tensor string representation.
      -
      • -
    • -
      -

      getPostfix

      -
      public String getPostfix()
      -
      -
      Returns:
      -
      The String which will be appended at the end of a Tensor string representation.
      +
    + + + +
      +
    • +

      getPostfix

      +
      public java.lang.String getPostfix()
      +
      +
      Returns:
      +
      The String which will be appended at the end of a Tensor string representation.
      -
      • -
    • -
      -

      setPostfix

      -
      public NDPrintSettings setPostfix(String postfix)
      -
      -
      Parameters:
      -
      postfix - The String which will be appended at the end of a Tensor string representation.
      +
    + + + +
      +
    • +

      setPostfix

      +
      public NDPrintSettings setPostfix(java.lang.String postfix)
      +
      +
      Parameters:
      +
      postfix - The String which will be appended at the end of a Tensor string representation.
      -
      • -
    • -
      -

      getIndent

      -
      public String getIndent()
      -
      -
      Returns:
      +
    + + + +
      +
    • +

      getIndent

      +
      public java.lang.String getIndent()
      +
      +
      Returns:
      The indent step for a single level of nesting for - String representation where - the getIsMultiline() is set to true.
      + String representation where + the getIsMultiline() is set to true.
      -
      • -
    • -
      -

      setIndent

      -
      public NDPrintSettings setIndent(String indent)
      -
      -
      Parameters:
      +
    + + + +
      +
    • +

      setIndent

      +
      public NDPrintSettings setIndent(java.lang.String indent)
      +
      +
      Parameters:
      indent - The indent step for a single level of nesting for - String representation where - the getIsMultiline() is set to true.
      + String representation where + the getIsMultiline() is set to true.
      -
      • -
    • -
      -

      getIsLegacy

      -
      public boolean getIsLegacy()
      +
    + + + +
      +
    • +

      getIsLegacy

      +
      public boolean getIsLegacy()
      This flag determines the usage of bracket types, where "[1x3]:(1, 2, 3)" would be the legacy version of "(1x3):[1, 2, 3]".
      -
      -
      Returns:
      +
      +
      Returns:
      The truth value determining the type of brackets used.
      -
      • -
    • -
      -

      setIsLegacy

      -
      public NDPrintSettings setIsLegacy(boolean legacy)
      +
    + + + +
      +
    • +

      setIsLegacy

      +
      public NDPrintSettings setIsLegacy(boolean legacy)
      This flag determines the usage of bracket types, where "[1x3]:(1, 2, 3)" would be the legacy version of "(1x3):[1, 2, 3]".
      -
      -
      Parameters:
      +
      +
      Parameters:
      legacy - The truth value determining the type of brackets used.
      -
      • -
    • -
      -

      with

      -
      public NDPrintSettings with(String modes)
      -
      -
      Parameters:
      -
      modes - A String in which letters will be translated to settings.
      -
      Returns:
      -
      A NDPrintSettings configuration based on the provided modes.
      +
    + + + +
      +
    • +

      with

      +
      public NDPrintSettings with(java.lang.String modes)
      +
      +
      Parameters:
      +
      modes - A String in which letters will be translated to settings.
      +
      Returns:
      +
      A NDPrintSettings configuration based on the provided modes.
      -
    -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/view/NdaAsString.Builder.html b/docs/jdocs/neureka/view/NdaAsString.Builder.html index af21800b0..e1dd30e41 100644 --- a/docs/jdocs/neureka/view/NdaAsString.Builder.html +++ b/docs/jdocs/neureka/view/NdaAsString.Builder.html @@ -1,164 +1,266 @@ - + + - -NdaAsString.Builder (neureka 1.0.0 API) - - - - + +NdaAsString.Builder (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.view
    -

    Interface NdaAsString.Builder

    +
    neureka.view
    +

    Interface NdaAsString.Builder

    -
    -
    +
    +
    +
      +
    • +
      Enclosing class:
      -
      NdaAsString
      +
      NdaAsString
      -
      public static interface NdaAsString.Builder
      +
      +
      public static interface NdaAsString.Builder
      A builder interface providing multiple different options for building - a NdaAsString instance in a fluent way.
      -
    -
    -
    - +
    +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
        -
      • -
        -

        withConfig

        -
        NdaAsString withConfig(NDPrintSettings configMap)
        -
        -
        Parameters:
        -
        configMap - The configuration map used as basis for turning the wrapped Tensor to a String.
        -
        Returns:
        -
        A new NdaAsString based on the provided configuration.
        +
          +
        • + + +

          Method Detail

          + + + +
            +
          • +

            withConfig

            +
            NdaAsString withConfig(NDPrintSettings configMap)
            +
            +
            Parameters:
            +
            configMap - The configuration map used as basis for turning the wrapped Tensor to a String.
            +
            Returns:
            +
            A new NdaAsString based on the provided configuration.
            -
        • -
      • -
        -

        withConfig

        -
        NdaAsString withConfig(String config)
        -
        -
        Parameters:
        -
        config - The configuration used as basis for turning the wrapped Tensor to a String.
        -
        Returns:
        -
        A new NdaAsString based on the provided configuration.
        +
      + + + +
        +
      • +

        withConfig

        +
        NdaAsString withConfig(java.lang.String config)
        +
        +
        Parameters:
        +
        config - The configuration used as basis for turning the wrapped Tensor to a String.
        +
        Returns:
        +
        A new NdaAsString based on the provided configuration.
        -
      • -
    • -
      -

      byDefaults

      -
      NdaAsString byDefaults()
      -
      -
      Returns:
      -
      A new NdaAsString based on the default configuration.
      +
    + + + +
      +
    • +

      byDefaults

      +
      NdaAsString byDefaults()
      +
      +
      Returns:
      +
      A new NdaAsString based on the default configuration.
      -
    - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + + +
    +
    + + + +
    +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    + + +
    + diff --git a/docs/jdocs/neureka/view/NdaAsString.Util.html b/docs/jdocs/neureka/view/NdaAsString.Util.html index b551b8430..eea764b0c 100644 --- a/docs/jdocs/neureka/view/NdaAsString.Util.html +++ b/docs/jdocs/neureka/view/NdaAsString.Util.html @@ -1,196 +1,318 @@ - + + - -NdaAsString.Util (neureka 1.0.0 API) - - - - + +NdaAsString.Util (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.view
    -

    Class NdaAsString.Util

    -
    -
    java.lang.Object -
    neureka.view.NdaAsString.Util
    +
    neureka.view
    +

    Class NdaAsString.Util

    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.view.NdaAsString.Util
        • +
      +
      • +
    +
    +
      +
    • +
      Enclosing class:
      -
      NdaAsString
      +
      NdaAsString
      -
      public static class NdaAsString.Util -extends Object
      +
      +
      public static class NdaAsString.Util
+extends java.lang.Object
      This class is a simple utility class which contains a collection of static and stateless methods containing useful functionalities for tensor stringification.
      -
    -
    -
      - -
    • -
      -

      Constructor Summary

      -
      Constructors
      -
      -
      Constructor
      -
      Description
      -
      Util()
      -
       
      +
      • +
    - +
    +
      +
    • + +
        +
      • + + +

        Constructor Summary

        + + + + + + + + +
        Constructors 
        Constructor and Description
        Util() 
        • +
      -
    • -
      -

      Method Summary

      -
      -
      -
      -
      -
      Modifier and Type
      -
      Method
      -
      Description
      -
      static String
      -
      indent(int n)
      -
       
      -
      static String
      -
      pad(int left, - String s)
      -
       
      -
      static String
      -
      pad(String s, - int right)
      -
       
      -
      static String
      -
      spaces(int n)
      -
       
      -
      -
      -
      -
      -

      Methods inherited from class java.lang.Object

      -clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
      -
      +
        +
      • + + +

        Method Summary

        + + + + + + + + + + + + + + + + + + + + + + +
        All Methods Static Methods Concrete Methods 
        Modifier and TypeMethod and Description
        static java.lang.Stringindent(int n) 
        static java.lang.Stringpad(int left, + java.lang.String s) 
        static java.lang.Stringpad(java.lang.String s, + int right) 
        static java.lang.Stringspaces(int n) 
        +
          +
        • + + +

          Methods inherited from class java.lang.Object

          +clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
          • +
      - -
      -
        + +
      +
    +
    +
      +
    • -
    • -
      -

      Constructor Details

      -
        -
      • -
        -

        Util

        -
        public Util()
        -
        +
          +
        • + + +

          Constructor Detail

          + + + +
            +
          • +

            Util

            +
            public Util()
          -
      • +
    -
  • -
    -

    Method Details

    -
      -
    • -
      -

      indent

      -
      public static String indent(int n)
      -
      +
        +
      • + + +

        Method Detail

        + + + +
          +
        • +

          indent

          +
          public static java.lang.String indent(int n)
          • -
        • -
          -

          spaces

          -
          public static String spaces(int n)
          -
          +
        + + + +
          +
        • +

          spaces

          +
          public static java.lang.String spaces(int n)
          • -
        • -
          -

          pad

          -
          public static String pad(int left, - String s)
          -
          +
        + + + +
          +
        • +

          pad

          +
          public static java.lang.String pad(int left,
+                                   java.lang.String s)
          • -
        • -
          -

          pad

          -
          public static String pad(String s, - int right)
          -
          +
        + + + +
          +
        • +

          pad

          +
          public static java.lang.String pad(java.lang.String s,
+                                   int right)
        -
    • - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
    + + +
    + diff --git a/docs/jdocs/neureka/view/NdaAsString.html b/docs/jdocs/neureka/view/NdaAsString.html index 2fe0b49f9..2807051d1 100644 --- a/docs/jdocs/neureka/view/NdaAsString.html +++ b/docs/jdocs/neureka/view/NdaAsString.html @@ -1,179 +1,291 @@ - + + - -NdaAsString (neureka 1.0.0 API) - - - - + +NdaAsString (neureka 1.0.1 API) - - - - - - + + -
    - -
    -
    -
    Package neureka.view
    -

    Class NdaAsString

    +
    neureka.view
    +

    Class NdaAsString

    -
    java.lang.Object -
    neureka.view.NdaAsString
    -
    -
    +
    +
      +
    • java.lang.Object
      • +
    • +
        +
      • neureka.view.NdaAsString
        • +
      +
      • +
    +
    +
      +
    • -
      public final class NdaAsString -extends Object
      +
      +
      public final class NdaAsString
+extends java.lang.Object
      This class is in essence a simple wrapper class for a tensor and a StringBuilder Methods in this class use the builder in order to construct a String representation for said tensor. These methods perform the String building based on a set of certain - configurations provided by the NDPrintSettings!
      -
    -
    -
    + + +
    +
    + - -
    -
      +
    +
    +
      +
    • -
    • -
      -

      Method Details

      -
    -
  • -
    -

    toString

    -
    public String toString()
    -
    -
    Overrides:
    -
    toString in class Object
    + + + + +
      +
    • +

      toString

      +
      public java.lang.String toString()
      +
      +
      Overrides:
      +
      toString in class java.lang.Object
      -
    • - - + + +
    +
    - + +
    + + +
    Skip navigation links
    + + + +
    +
    + + + +
    + +
    +
    +
      +
    • Summary: 
      • +
    • Nested | 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
    +
      +
    • Detail: 
      • +
    • Field | 
      • +
    • Constr | 
      • +
    • Method
      • +
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      NdaAsString.Builder +
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      NdaAsString
      This class is in essence a simple wrapper class for a tensor and a StringBuilder Methods in this class use the builder in order to construct a String representation for said tensor.
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      NdaAsString.Builder
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      A builder interface providing multiple different options for building - a NdaAsString instance in a fluent way.
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      NdaAsString.Util
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      +
      NdaAsString.Util
      This class is a simple utility class which contains a collection of static and stateless methods containing useful functionalities for tensor stringification.
      - -
      NDPrintSettings
      -
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      This is simply a mutable container for configuring how Tensor - instances ought to be converted to Strings.
      -
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      NDPrintSettings +
      This is simply a mutable container for configuring how Tensor + instances ought to be converted to Strings.
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  • neureka.math.args.Arg.Axis
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  • neureka.Neureka
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    • +
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  • neureka.framing.Relation<V> (implements neureka.common.composition.Component<O>)
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    • +
  • neureka.backend.main.implementations.fun.ScalarAbsolute (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarCbrt (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarCosinus (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarExp (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
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    • +
  • neureka.backend.main.implementations.fun.ScalarGaTU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
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    • +
  • neureka.backend.main.implementations.fun.ScalarGaussianFast (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarGeLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarIdentity (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarLog10 (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
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    • +
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    • +
  • neureka.backend.main.implementations.fun.ScalarReLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarSeLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarSigmoid (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarSiLU (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarSinus (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarSoftplus (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarSoftsign (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarSqrt (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
  • neureka.backend.main.implementations.fun.ScalarTanh (implements neureka.backend.main.implementations.fun.api.ScalarFun)
    • +
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All rights reserved. - * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. - * - * This code is free software; you can redistribute it and/or modify it - * under the terms of the GNU General Public License version 2 only, as - * published by the Free Software Foundation. Oracle designates this - * particular file as subject to the "Classpath" exception as provided - * by Oracle in the LICENSE file that accompanied this code. - * - * This code is distributed in the hope that it will be useful, but WITHOUT - * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or - * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License - * version 2 for more details (a copy is included in the LICENSE file that - * accompanied this code). - * - * You should have received a copy of the GNU General Public License version - * 2 along with this work; if not, write to the Free Software Foundation, - * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. - * - * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA - * or visit www.oracle.com if you need additional information or have any - * questions. - */ - -var moduleSearchIndex; -var packageSearchIndex; -var typeSearchIndex; -var memberSearchIndex; -var tagSearchIndex; -function loadScripts(doc, tag) { - createElem(doc, tag, 'search.js'); - - createElem(doc, tag, 'module-search-index.js'); - createElem(doc, tag, 'package-search-index.js'); - createElem(doc, tag, 'type-search-index.js'); - createElem(doc, tag, 'member-search-index.js'); - createElem(doc, tag, 'tag-search-index.js'); -} - -function createElem(doc, tag, path) { - var script = doc.createElement(tag); - var scriptElement = doc.getElementsByTagName(tag)[0]; - script.src = pathtoroot + path; - scriptElement.parentNode.insertBefore(script, scriptElement); -} - -function show(tableId, selected, columns) { - if (tableId !== selected) { - document.querySelectorAll('div.' + tableId + ':not(.' + selected + ')') - .forEach(function(elem) { - elem.style.display = 'none'; - }); - } - document.querySelectorAll('div.' + selected) - .forEach(function(elem, index) { - elem.style.display = ''; - var isEvenRow = index % (columns * 2) < columns; - elem.classList.remove(isEvenRow ? oddRowColor : evenRowColor); - elem.classList.add(isEvenRow ? evenRowColor : oddRowColor); - }); - updateTabs(tableId, selected); -} - -function updateTabs(tableId, selected) { - document.querySelector('div#' + tableId +' .summary-table') - .setAttribute('aria-labelledby', selected); - document.querySelectorAll('button[id^="' + tableId + '"]') - .forEach(function(tab, index) { - if (selected === tab.id || (tableId === selected && index === 0)) { - tab.className = activeTableTab; - tab.setAttribute('aria-selected', true); - tab.setAttribute('tabindex',0); - } else { - tab.className = tableTab; - tab.setAttribute('aria-selected', false); - tab.setAttribute('tabindex',-1); - } - }); -} - -function switchTab(e) { - var selected = document.querySelector('[aria-selected=true]'); - if (selected) { - if ((e.keyCode === 37 || e.keyCode === 38) && selected.previousSibling) { - // left or up arrow key pressed: move focus to previous tab - selected.previousSibling.click(); - selected.previousSibling.focus(); - e.preventDefault(); - } else if ((e.keyCode === 39 || e.keyCode === 40) && selected.nextSibling) { - // right or down arrow key pressed: move focus to next tab - selected.nextSibling.click(); - selected.nextSibling.focus(); - e.preventDefault(); +function show(type) +{ + count = 0; + for (var key in methods) { + var row = document.getElementById(key); + if ((methods[key] & type) != 0) { + row.style.display = ''; + row.className = (count++ % 2) ? rowColor : altColor; } + else + row.style.display = 'none'; } + updateTabs(type); } -var updateSearchResults = function() {}; - -function indexFilesLoaded() { - return moduleSearchIndex - && packageSearchIndex - && typeSearchIndex - && memberSearchIndex - && tagSearchIndex; -} - -// Workaround for scroll position not being included in browser history (8249133) -document.addEventListener("DOMContentLoaded", function(e) { - var contentDiv = document.querySelector("div.flex-content"); - window.addEventListener("popstate", function(e) { - if (e.state !== null) { - contentDiv.scrollTop = e.state; +function updateTabs(type) +{ + for (var value in tabs) { + var sNode = document.getElementById(tabs[value][0]); + var spanNode = sNode.firstChild; + if (value == type) { + sNode.className = activeTableTab; + spanNode.innerHTML = tabs[value][1]; } - }); - window.addEventListener("hashchange", function(e) { - history.replaceState(contentDiv.scrollTop, document.title); - }); - contentDiv.addEventListener("scroll", function(e) { - var timeoutID; - if (!timeoutID) { - timeoutID = setTimeout(function() { - history.replaceState(contentDiv.scrollTop, document.title); - timeoutID = null; - }, 100); + else { + sNode.className = tableTab; + spanNode.innerHTML = "" + tabs[value][1] + ""; } - }); - if (!location.hash) { - history.replaceState(contentDiv.scrollTop, document.title); } -}); +} diff --git a/docs/jdocs/search.js b/docs/jdocs/search.js deleted file mode 100644 index db3b2f4a6..000000000 --- a/docs/jdocs/search.js +++ /dev/null @@ -1,354 +0,0 @@ -/* - * Copyright (c) 2015, 2020, Oracle and/or its affiliates. All rights reserved. - * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. - * - * This code is free software; you can redistribute it and/or modify it - * under the terms of the GNU General Public License version 2 only, as - * published by the Free Software Foundation. Oracle designates this - * particular file as subject to the "Classpath" exception as provided - * by Oracle in the LICENSE file that accompanied this code. - * - * This code is distributed in the hope that it will be useful, but WITHOUT - * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or - * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License - * version 2 for more details (a copy is included in the LICENSE file that - * accompanied this code). - * - * You should have received a copy of the GNU General Public License version - * 2 along with this work; if not, write to the Free Software Foundation, - * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. - * - * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA - * or visit www.oracle.com if you need additional information or have any - * questions. - */ - -var noResult = {l: "No results found"}; -var loading = {l: "Loading search index..."}; -var catModules = "Modules"; -var catPackages = "Packages"; -var catTypes = "Classes and Interfaces"; -var catMembers = "Members"; -var catSearchTags = "Search Tags"; -var highlight = "$&"; -var searchPattern = ""; -var fallbackPattern = ""; -var RANKING_THRESHOLD = 2; -var NO_MATCH = 0xffff; -var MIN_RESULTS = 3; -var MAX_RESULTS = 500; -var UNNAMED = ""; -function escapeHtml(str) { - return str.replace(//g, ">"); -} -function getHighlightedText(item, matcher, fallbackMatcher) { - var escapedItem = escapeHtml(item); - var highlighted = escapedItem.replace(matcher, highlight); - if (highlighted === escapedItem) { - highlighted = escapedItem.replace(fallbackMatcher, highlight) - } - return highlighted; -} -function getURLPrefix(ui) { - var urlPrefix=""; - var slash = "/"; - if (ui.item.category === catModules) { - return ui.item.l + slash; - } else if (ui.item.category === catPackages && ui.item.m) { - return ui.item.m + slash; - } else if (ui.item.category === catTypes || ui.item.category === catMembers) { - if (ui.item.m) { - urlPrefix = ui.item.m + slash; - } else { - $.each(packageSearchIndex, function(index, item) { - if (item.m && ui.item.p === item.l) { - urlPrefix = item.m + slash; - } - }); - } - } - return urlPrefix; -} -function createSearchPattern(term) { - var pattern = ""; - var isWordToken = false; - term.replace(/,\s*/g, ", ").trim().split(/\s+/).forEach(function(w, index) { - if (index > 0) { - // whitespace between identifiers is significant - pattern += (isWordToken && /^\w/.test(w)) ? "\\s+" : "\\s*"; - } - var tokens = w.split(/(?=[A-Z,.()<>[\/])/); - for (var i = 0; i < tokens.length; i++) { - var s = tokens[i]; - if (s === "") { - continue; - } - pattern += $.ui.autocomplete.escapeRegex(s); - isWordToken = /\w$/.test(s); - if (isWordToken) { - pattern += "([a-z0-9_$<>\\[\\]]*?)"; - } - } - }); - return pattern; -} -function createMatcher(pattern, flags) { - var isCamelCase = /[A-Z]/.test(pattern); - return new RegExp(pattern, flags + (isCamelCase ? "" : "i")); -} -var watermark = 'Search'; -$(function() { - var search = $("#search-input"); - var reset = $("#reset-button"); - search.val(''); - search.prop("disabled", false); - reset.prop("disabled", false); - search.val(watermark).addClass('watermark'); - search.blur(function() { - if ($(this).val().length === 0) { - $(this).val(watermark).addClass('watermark'); - } - }); - search.on('click keydown paste', function() { - if ($(this).val() === watermark) { - $(this).val('').removeClass('watermark'); - } - }); - reset.click(function() { - search.val('').focus(); - }); - search.focus()[0].setSelectionRange(0, 0); -}); -$.widget("custom.catcomplete", $.ui.autocomplete, { - _create: function() { - this._super(); - this.widget().menu("option", "items", "> :not(.ui-autocomplete-category)"); - }, - _renderMenu: function(ul, items) { - var rMenu = this; - var currentCategory = ""; - rMenu.menu.bindings = $(); - $.each(items, function(index, item) { - var li; - if (item.category && item.category !== currentCategory) { - ul.append("
      • " + item.category + "
        • "); - currentCategory = item.category; - } - li = rMenu._renderItemData(ul, item); - if (item.category) { - li.attr("aria-label", item.category + " : " + item.l); - li.attr("class", "result-item"); - } else { - li.attr("aria-label", item.l); - li.attr("class", "result-item"); - } - }); - }, - _renderItem: function(ul, item) { - var label = ""; - var matcher = createMatcher(escapeHtml(searchPattern), "g"); - var fallbackMatcher = new RegExp(fallbackPattern, "gi") - if (item.category === catModules) { - label = getHighlightedText(item.l, matcher, fallbackMatcher); - } else if (item.category === catPackages) { - label = getHighlightedText(item.l, matcher, fallbackMatcher); - } else if (item.category === catTypes) { - label = (item.p && item.p !== UNNAMED) - ? getHighlightedText(item.p + "." + item.l, matcher, fallbackMatcher) - : getHighlightedText(item.l, matcher, fallbackMatcher); - } else if (item.category === catMembers) { - label = (item.p && item.p !== UNNAMED) - ? getHighlightedText(item.p + "." + item.c + "." + item.l, matcher, fallbackMatcher) - : getHighlightedText(item.c + "." + item.l, matcher, fallbackMatcher); - } else if (item.category === catSearchTags) { - label = getHighlightedText(item.l, matcher, fallbackMatcher); - } else { - label = item.l; - } - var li = $("
      • ").appendTo(ul); - var div = $("
        ").appendTo(li); - if (item.category === catSearchTags && item.h) { - if (item.d) { - div.html(label + " (" + item.h + ")
        " - + item.d + "
        "); - } else { - div.html(label + " (" + item.h + ")"); - } - } else { - if (item.m) { - div.html(item.m + "/" + label); - } else { - div.html(label); - } - } - return li; - } -}); -function rankMatch(match, category) { - if (!match) { - return NO_MATCH; - } - var index = match.index; - var input = match.input; - var leftBoundaryMatch = 2; - var periferalMatch = 0; - // make sure match is anchored on a left word boundary - if (index === 0 || /\W/.test(input[index - 1]) || "_" === input[index]) { - leftBoundaryMatch = 0; - } else if ("_" === input[index - 1] || (input[index] === input[index].toUpperCase() && !/^[A-Z0-9_$]+$/.test(input))) { - leftBoundaryMatch = 1; - } - var matchEnd = index + match[0].length; - var leftParen = input.indexOf("("); - var endOfName = leftParen > -1 ? leftParen : input.length; - // exclude peripheral matches - if (category !== catModules && category !== catSearchTags) { - var delim = category === catPackages ? "/" : "."; - if (leftParen > -1 && leftParen < index) { - periferalMatch += 2; - } else if (input.lastIndexOf(delim, endOfName) >= matchEnd) { - periferalMatch += 2; - } - } - var delta = match[0].length === endOfName ? 0 : 1; // rank full match higher than partial match - for (var i = 1; i < match.length; i++) { - // lower ranking if parts of the name are missing - if (match[i]) - delta += match[i].length; - } - if (category === catTypes) { - // lower ranking if a type name contains unmatched camel-case parts - if (/[A-Z]/.test(input.substring(matchEnd))) - delta += 5; - if (/[A-Z]/.test(input.substring(0, index))) - delta += 5; - } - return leftBoundaryMatch + periferalMatch + (delta / 200); - -} -function doSearch(request, response) { - var result = []; - searchPattern = createSearchPattern(request.term); - fallbackPattern = createSearchPattern(request.term.toLowerCase()); - if (searchPattern === "") { - return this.close(); - } - var camelCaseMatcher = createMatcher(searchPattern, ""); - var fallbackMatcher = new RegExp(fallbackPattern, "i"); - - function searchIndexWithMatcher(indexArray, matcher, category, nameFunc) { - if (indexArray) { - var newResults = []; - $.each(indexArray, function (i, item) { - item.category = category; - var ranking = rankMatch(matcher.exec(nameFunc(item)), category); - if (ranking < RANKING_THRESHOLD) { - newResults.push({ranking: ranking, item: item}); - } - return newResults.length <= MAX_RESULTS; - }); - return newResults.sort(function(e1, e2) { - return e1.ranking - e2.ranking; - }).map(function(e) { - return e.item; - }); - } - return []; - } - function searchIndex(indexArray, category, nameFunc) { - var primaryResults = searchIndexWithMatcher(indexArray, camelCaseMatcher, category, nameFunc); - result = result.concat(primaryResults); - if (primaryResults.length <= MIN_RESULTS && !camelCaseMatcher.ignoreCase) { - var secondaryResults = searchIndexWithMatcher(indexArray, fallbackMatcher, category, nameFunc); - result = result.concat(secondaryResults.filter(function (item) { - return primaryResults.indexOf(item) === -1; - })); - } - } - - searchIndex(moduleSearchIndex, catModules, function(item) { return item.l; }); - searchIndex(packageSearchIndex, catPackages, function(item) { - return (item.m && request.term.indexOf("/") > -1) - ? (item.m + "/" + item.l) : item.l; - }); - searchIndex(typeSearchIndex, catTypes, function(item) { - return request.term.indexOf(".") > -1 ? item.p + "." + item.l : item.l; - }); - searchIndex(memberSearchIndex, catMembers, function(item) { - return request.term.indexOf(".") > -1 - ? item.p + "." + item.c + "." + item.l : item.l; - }); - searchIndex(tagSearchIndex, catSearchTags, function(item) { return item.l; }); - - if (!indexFilesLoaded()) { - updateSearchResults = function() { - doSearch(request, response); - } - result.unshift(loading); - } else { - updateSearchResults = function() {}; - } - response(result); -} -$(function() { - $("#search-input").catcomplete({ - minLength: 1, - delay: 300, - source: doSearch, - response: function(event, ui) { - if (!ui.content.length) { - ui.content.push(noResult); - } else { - $("#search-input").empty(); - } - }, - autoFocus: true, - focus: function(event, ui) { - return false; - }, - position: { - collision: "flip" - }, - select: function(event, ui) { - if (ui.item.category) { - var url = getURLPrefix(ui); - if (ui.item.category === catModules) { - url += "module-summary.html"; - } else if (ui.item.category === catPackages) { - if (ui.item.u) { - url = ui.item.u; - } else { - url += ui.item.l.replace(/\./g, '/') + "/package-summary.html"; - } - } else if (ui.item.category === catTypes) { - if (ui.item.u) { - url = ui.item.u; - } else if (ui.item.p === UNNAMED) { - url += ui.item.l + ".html"; - } else { - url += ui.item.p.replace(/\./g, '/') + "/" + ui.item.l + ".html"; - } - } else if (ui.item.category === catMembers) { - if (ui.item.p === UNNAMED) { - url += ui.item.c + ".html" + "#"; - } else { - url += ui.item.p.replace(/\./g, '/') + "/" + ui.item.c + ".html" + "#"; - } - if (ui.item.u) { - url += ui.item.u; - } else { - url += ui.item.l; - } - } else if (ui.item.category === catSearchTags) { - url += ui.item.u; - } - if (top !== window) { - parent.classFrame.location = pathtoroot + url; - } else { - window.location.href = pathtoroot + url; - } - $("#search-input").focus(); - } - } - }); -}); diff --git a/docs/jdocs/stylesheet.css b/docs/jdocs/stylesheet.css index 836c62da8..98055b22d 100644 --- a/docs/jdocs/stylesheet.css +++ b/docs/jdocs/stylesheet.css @@ -1,45 +1,36 @@ +/* Javadoc style sheet */ /* - * Javadoc style sheet - */ +Overall document style +*/ @import url('resources/fonts/dejavu.css'); -/* - * Styles for individual HTML elements. - * - * These are styles that are specific to individual HTML elements. Changing them affects the style of a particular - * HTML element throughout the page. - */ - body { background-color:#ffffff; color:#353833; font-family:'DejaVu Sans', Arial, Helvetica, sans-serif; font-size:14px; margin:0; - padding:0; - height:100%; - width:100%; -} -iframe { - margin:0; - padding:0; - height:100%; - width:100%; - overflow-y:scroll; - border:none; } a:link, a:visited { text-decoration:none; color:#4A6782; } -a[href]:hover, a[href]:focus { +a:hover, a:focus { text-decoration:none; color:#bb7a2a; } +a:active { + text-decoration:none; + color:#4A6782; +} a[name] { color:#353833; } +a[name]:hover { + text-decoration:none; + color:#353833; +} pre { font-family:'DejaVu Sans Mono', monospace; font-size:14px; @@ -52,24 +43,22 @@ h2 { } h3 { font-size:16px; + font-style:italic; } h4 { - font-size:15px; + font-size:13px; } h5 { - font-size:14px; + font-size:12px; } h6 { - font-size:13px; + font-size:11px; } ul { list-style-type:disc; } code, tt { font-family:'DejaVu Sans Mono', monospace; -} -:not(h1, h2, h3, h4, h5, h6) > code, -:not(h1, h2, h3, h4, h5, h6) > tt { font-size:14px; padding-top:4px; margin-top:8px; @@ -80,7 +69,7 @@ dt code { font-size:14px; padding-top:4px; } -.summary-table dt code { +table tr td dt code { font-family:'DejaVu Sans Mono', monospace; font-size:14px; vertical-align:top; @@ -89,34 +78,31 @@ dt code { sup { font-size:8px; } -button { - font-family: 'DejaVu Sans', Arial, Helvetica, sans-serif; - font-size: 14px; -} -/* - * Styles for HTML generated by javadoc. - * - * These are style classes that are used by the standard doclet to generate HTML documentation. - */ - /* - * Styles for document title and copyright. - */ +Document title and Copyright styles +*/ .clear { clear:both; - height:0; + height:0px; overflow:hidden; } -.about-language { +.aboutLanguage { float:right; - padding:0 21px 8px 8px; + padding:0px 21px; font-size:11px; + z-index:200; margin-top:-9px; - height:2.9em; } -.legal-copy { +.legalCopy { margin-left:.5em; } +.bar a, .bar a:link, .bar a:visited, .bar a:active { + color:#FFFFFF; + text-decoration:none; +} +.bar a:hover, .bar a:focus { + color:#bb7a2a; +} .tab { background-color:#0066FF; color:#ffffff; @@ -125,240 +111,263 @@ button { font-weight:bold; } /* - * Styles for navigation bar. - */ -@media screen { - .flex-box { - position:fixed; - display:flex; - flex-direction:column; - height: 100%; - width: 100%; - } - .flex-header { - flex: 0 0 auto; - } - .flex-content { - flex: 1 1 auto; - overflow-y: auto; - } -} -.top-nav { +Navigation bar styles +*/ +.bar { + background-color:#4D7A97; + color:#FFFFFF; + padding:.8em .5em .4em .8em; + height:auto;/*height:1.8em;*/ + font-size:11px; + margin:0; +} +.topNav { + background-color:#4D7A97; + color:#FFFFFF; + float:left; + padding:0; + width:100%; + clear:right; + height:2.8em; + padding-top:10px; + overflow:hidden; + font-size:12px; +} +.bottomNav { + margin-top:10px; background-color:#4D7A97; color:#FFFFFF; float:left; padding:0; width:100%; clear:right; - min-height:2.8em; + height:2.8em; padding-top:10px; overflow:hidden; font-size:12px; } -.sub-nav { +.subNav { background-color:#dee3e9; float:left; width:100%; overflow:hidden; font-size:12px; } -.sub-nav div { +.subNav div { clear:left; float:left; padding:0 0 5px 6px; text-transform:uppercase; } -.sub-nav .nav-list { - padding-top:5px; -} -ul.nav-list { - display:block; - margin:0 25px 0 0; - padding:0; -} -ul.sub-nav-list { +ul.navList, ul.subNavList { float:left; margin:0 25px 0 0; padding:0; } -ul.nav-list li { +ul.navList li{ list-style:none; float:left; padding: 5px 6px; text-transform:uppercase; } -.sub-nav .nav-list-search { - float:right; - margin:0 0 0 0; - padding:5px 6px; - clear:none; -} -.nav-list-search label { - position:relative; - right:-16px; -} -ul.sub-nav-list li { +ul.subNavList li{ list-style:none; float:left; - padding-top:10px; } -.top-nav a:link, .top-nav a:active, .top-nav a:visited { +.topNav a:link, .topNav a:active, .topNav a:visited, .bottomNav a:link, .bottomNav a:active, .bottomNav a:visited { color:#FFFFFF; text-decoration:none; text-transform:uppercase; } -.top-nav a:hover { +.topNav a:hover, .bottomNav a:hover { text-decoration:none; color:#bb7a2a; text-transform:uppercase; } -.nav-bar-cell1-rev { +.navBarCell1Rev { background-color:#F8981D; color:#253441; margin: auto 5px; } -.skip-nav { +.skipNav { position:absolute; top:auto; left:-9999px; overflow:hidden; } /* - * Hide navigation links and search box in print layout - */ -@media print { - ul.nav-list, div.sub-nav { - display:none; - } +Page header and footer styles +*/ +.header, .footer { + clear:both; + margin:0 20px; + padding:5px 0 0 0; +} +.indexHeader { + margin:10px; + position:relative; +} +.indexHeader span{ + margin-right:15px; +} +.indexHeader h1 { + font-size:13px; } -/* - * Styles for page header and footer. - */ .title { color:#2c4557; margin:10px 0; } -.sub-title { +.subTitle { margin:5px 0 0 0; } .header ul { margin:0 0 15px 0; padding:0; } +.footer ul { + margin:20px 0 5px 0; +} .header ul li, .footer ul li { list-style:none; font-size:13px; } /* - * Styles for headings. - */ -body.class-declaration-page .summary h2, -body.class-declaration-page .details h2, -body.class-use-page h2, -body.module-declaration-page .block-list h2 { - font-style: italic; - padding:0; - margin:15px 0; +Heading styles +*/ +div.details ul.blockList ul.blockList ul.blockList li.blockList h4, div.details ul.blockList ul.blockList ul.blockListLast li.blockList h4 { + background-color:#dee3e9; + border:1px solid #d0d9e0; + margin:0 0 6px -8px; + padding:7px 5px; } -body.class-declaration-page .summary h3, -body.class-declaration-page .details h3, -body.class-declaration-page .summary .inherited-list h2 { +ul.blockList ul.blockList ul.blockList li.blockList h3 { background-color:#dee3e9; border:1px solid #d0d9e0; margin:0 0 6px -8px; padding:7px 5px; } +ul.blockList ul.blockList li.blockList h3 { + padding:0; + margin:15px 0; +} +ul.blockList li.blockList h2 { + padding:0px 0 20px 0; +} /* - * Styles for page layout containers. - */ -main { +Page layout container styles +*/ +.contentContainer, .sourceContainer, .classUseContainer, .serializedFormContainer, .constantValuesContainer { clear:both; padding:10px 20px; position:relative; } -dl.notes > dt { - font-family: 'DejaVu Sans', Arial, Helvetica, sans-serif; +.indexContainer { + margin:10px; + position:relative; + font-size:12px; +} +.indexContainer h2 { + font-size:13px; + padding:0 0 3px 0; +} +.indexContainer ul { + margin:0; + padding:0; +} +.indexContainer ul li { + list-style:none; + padding-top:2px; +} +.contentContainer .description dl dt, .contentContainer .details dl dt, .serializedFormContainer dl dt { font-size:12px; font-weight:bold; margin:10px 0 0 0; color:#4E4E4E; } -dl.notes > dd { - margin:5px 10px 10px 0; +.contentContainer .description dl dd, .contentContainer .details dl dd, .serializedFormContainer dl dd { + margin:5px 0 10px 0px; font-size:14px; - font-family:'DejaVu Serif', Georgia, "Times New Roman", Times, serif; + font-family:'DejaVu Sans Mono',monospace; } -dl.name-value > dt { +.serializedFormContainer dl.nameValue dt { margin-left:1px; font-size:1.1em; display:inline; font-weight:bold; } -dl.name-value > dd { +.serializedFormContainer dl.nameValue dd { margin:0 0 0 1px; font-size:1.1em; display:inline; } /* - * Styles for lists. - */ -li.circle { - list-style:circle; -} +List styles +*/ ul.horizontal li { display:inline; font-size:0.9em; } -div.inheritance { +ul.inheritance { margin:0; padding:0; } -div.inheritance div.inheritance { - margin-left:2em; +ul.inheritance li { + display:inline; + list-style:none; } -ul.block-list, -ul.details-list, -ul.member-list, -ul.summary-list { +ul.inheritance li ul.inheritance { + margin-left:15px; + padding-left:15px; + padding-top:1px; +} +ul.blockList, ul.blockListLast { margin:10px 0 10px 0; padding:0; } -ul.block-list > li, -ul.details-list > li, -ul.member-list > li, -ul.summary-list > li { +ul.blockList li.blockList, ul.blockListLast li.blockList { list-style:none; margin-bottom:15px; line-height:1.4; } -.summary-table dl, .summary-table dl dt, .summary-table dl dd { - margin-top:0; - margin-bottom:1px; +ul.blockList ul.blockList li.blockList, ul.blockList ul.blockListLast li.blockList { + padding:0px 20px 5px 10px; + border:1px solid #ededed; + background-color:#f8f8f8; +} +ul.blockList ul.blockList ul.blockList li.blockList, ul.blockList ul.blockList ul.blockListLast li.blockList { + padding:0 0 5px 8px; + background-color:#ffffff; + border:none; } -ul.see-list, ul.see-list-long { - padding-left: 0; - list-style: none; +ul.blockList ul.blockList ul.blockList ul.blockList li.blockList { + margin-left:0; + padding-left:0; + padding-bottom:15px; + border:none; } -ul.see-list li { - display: inline; +ul.blockList ul.blockList ul.blockList ul.blockList li.blockListLast { + list-style:none; + border-bottom:none; + padding-bottom:0; } -ul.see-list li:not(:last-child):after, -ul.see-list-long li:not(:last-child):after { - content: ", "; - white-space: pre-wrap; +table tr td dl, table tr td dl dt, table tr td dl dd { + margin-top:0; + margin-bottom:1px; } /* - * Styles for tables. - */ -.summary-table, .details-table { +Table styles +*/ +.overviewSummary, .memberSummary, .typeSummary, .useSummary, .constantsSummary, .deprecatedSummary { width:100%; - border-spacing:0; - border-left:1px solid #EEE; - border-right:1px solid #EEE; - border-bottom:1px solid #EEE; - padding:0; + border-left:1px solid #EEE; + border-right:1px solid #EEE; + border-bottom:1px solid #EEE; } -.caption { +.overviewSummary, .memberSummary { + padding:0px; +} +.overviewSummary caption, .memberSummary caption, .typeSummary caption, +.useSummary caption, .constantsSummary caption, .deprecatedSummary caption { position:relative; text-align:left; background-repeat:no-repeat; @@ -366,20 +375,24 @@ ul.see-list-long li:not(:last-child):after { font-weight:bold; clear:none; overflow:hidden; - padding:0; + padding:0px; padding-top:10px; padding-left:1px; - margin:0; + margin:0px; white-space:pre; } -.caption a:link, .caption a:visited { - color:#1f389c; -} -.caption a:hover, -.caption a:active { +.overviewSummary caption a:link, .memberSummary caption a:link, .typeSummary caption a:link, +.useSummary caption a:link, .constantsSummary caption a:link, .deprecatedSummary caption a:link, +.overviewSummary caption a:hover, .memberSummary caption a:hover, .typeSummary caption a:hover, +.useSummary caption a:hover, .constantsSummary caption a:hover, .deprecatedSummary caption a:hover, +.overviewSummary caption a:active, .memberSummary caption a:active, .typeSummary caption a:active, +.useSummary caption a:active, .constantsSummary caption a:active, .deprecatedSummary caption a:active, +.overviewSummary caption a:visited, .memberSummary caption a:visited, .typeSummary caption a:visited, +.useSummary caption a:visited, .constantsSummary caption a:visited, .deprecatedSummary caption a:visited { color:#FFFFFF; } -.caption span { +.overviewSummary caption span, .memberSummary caption span, .typeSummary caption span, +.useSummary caption span, .constantsSummary caption span, .deprecatedSummary caption span { white-space:nowrap; padding-top:5px; padding-left:12px; @@ -391,150 +404,144 @@ ul.see-list-long li:not(:last-child):after { border: none; height:16px; } -div.table-tabs { - padding:10px 0 0 1px; - margin:0; +.memberSummary caption span.activeTableTab span { + white-space:nowrap; + padding-top:5px; + padding-left:12px; + padding-right:12px; + margin-right:3px; + display:inline-block; + float:left; + background-color:#F8981D; + height:16px; +} +.memberSummary caption span.tableTab span { + white-space:nowrap; + padding-top:5px; + padding-left:12px; + padding-right:12px; + margin-right:3px; + display:inline-block; + float:left; + background-color:#4D7A97; + height:16px; +} +.memberSummary caption span.tableTab, .memberSummary caption span.activeTableTab { + padding-top:0px; + padding-left:0px; + padding-right:0px; + background-image:none; + float:none; + display:inline; +} +.overviewSummary .tabEnd, .memberSummary .tabEnd, .typeSummary .tabEnd, +.useSummary .tabEnd, .constantsSummary .tabEnd, .deprecatedSummary .tabEnd { + display:none; + width:5px; + position:relative; + float:left; + background-color:#F8981D; } -div.table-tabs > button { - border: none; - cursor: pointer; - padding: 5px 12px 7px 12px; - font-weight: bold; - margin-right: 3px; -} -div.table-tabs > button.active-table-tab { - background: #F8981D; - color: #253441; -} -div.table-tabs > button.table-tab { - background: #4D7A97; - color: #FFFFFF; -} -.two-column-summary { - display: grid; - grid-template-columns: minmax(15%, max-content) minmax(15%, auto); -} -.three-column-summary { - display: grid; - grid-template-columns: minmax(10%, max-content) minmax(15%, max-content) minmax(15%, auto); -} -.four-column-summary { - display: grid; - grid-template-columns: minmax(10%, max-content) minmax(10%, max-content) minmax(10%, max-content) minmax(10%, auto); -} -@media screen and (max-width: 600px) { - .two-column-summary { - display: grid; - grid-template-columns: 1fr; - } -} -@media screen and (max-width: 800px) { - .three-column-summary { - display: grid; - grid-template-columns: minmax(10%, max-content) minmax(25%, auto); - } - .three-column-summary .col-last { - grid-column-end: span 2; - } -} -@media screen and (max-width: 1000px) { - .four-column-summary { - display: grid; - grid-template-columns: minmax(15%, max-content) minmax(15%, auto); - } -} -.summary-table > div, .details-table > div { +.memberSummary .activeTableTab .tabEnd { + display:none; + width:5px; + margin-right:3px; + position:relative; + float:left; + background-color:#F8981D; +} +.memberSummary .tableTab .tabEnd { + display:none; + width:5px; + margin-right:3px; + position:relative; + background-color:#4D7A97; + float:left; + +} +.overviewSummary td, .memberSummary td, .typeSummary td, +.useSummary td, .constantsSummary td, .deprecatedSummary td { text-align:left; - padding: 8px 3px 3px 7px; + padding:0px 0px 12px 10px; } -.col-first, .col-second, .col-last, .col-constructor-name, .col-summary-item-name { +th.colOne, th.colFirst, th.colLast, .useSummary th, .constantsSummary th, +td.colOne, td.colFirst, td.colLast, .useSummary td, .constantsSummary td{ vertical-align:top; - padding-right:0; + padding-right:0px; padding-top:8px; padding-bottom:3px; } -.table-header { +th.colFirst, th.colLast, th.colOne, .constantsSummary th { background:#dee3e9; - font-weight: bold; + text-align:left; + padding:8px 3px 3px 7px; +} +td.colFirst, th.colFirst { + white-space:nowrap; + font-size:13px; } -.col-first, .col-first { +td.colLast, th.colLast { font-size:13px; } -.col-second, .col-second, .col-last, .col-constructor-name, .col-summary-item-name, .col-last { +td.colOne, th.colOne { font-size:13px; } -.col-first, .col-second, .col-constructor-name { +.overviewSummary td.colFirst, .overviewSummary th.colFirst, +.useSummary td.colFirst, .useSummary th.colFirst, +.overviewSummary td.colOne, .overviewSummary th.colOne, +.memberSummary td.colFirst, .memberSummary th.colFirst, +.memberSummary td.colOne, .memberSummary th.colOne, +.typeSummary td.colFirst{ + width:25%; vertical-align:top; - overflow: auto; -} -.col-last { - white-space:normal; -} -.col-first a:link, .col-first a:visited, -.col-second a:link, .col-second a:visited, -.col-first a:link, .col-first a:visited, -.col-second a:link, .col-second a:visited, -.col-constructor-name a:link, .col-constructor-name a:visited, -.col-summary-item-name a:link, .col-summary-item-name a:visited, -.constant-values-container a:link, .constant-values-container a:visited, -.all-classes-container a:link, .all-classes-container a:visited, -.all-packages-container a:link, .all-packages-container a:visited { +} +td.colOne a:link, td.colOne a:active, td.colOne a:visited, td.colOne a:hover, td.colFirst a:link, td.colFirst a:active, td.colFirst a:visited, td.colFirst a:hover, td.colLast a:link, td.colLast a:active, td.colLast a:visited, td.colLast a:hover, .constantValuesContainer td a:link, .constantValuesContainer td a:active, .constantValuesContainer td a:visited, .constantValuesContainer td a:hover { font-weight:bold; } -.table-sub-heading-color { +.tableSubHeadingColor { background-color:#EEEEFF; } -.even-row-color, .even-row-color .table-header { +.altColor { background-color:#FFFFFF; } -.odd-row-color, .odd-row-color .table-header { +.rowColor { background-color:#EEEEEF; } /* - * Styles for contents. - */ -.deprecated-content { +Content styles +*/ +.description pre { + margin-top:0; +} +.deprecatedContent { margin:0; padding:10px 0; } +.docSummary { + padding:0; +} + +ul.blockList ul.blockList ul.blockList li.blockList h3 { + font-style:normal; +} + div.block { font-size:14px; font-family:'DejaVu Serif', Georgia, "Times New Roman", Times, serif; } -.col-last div { - padding-top:0; + +td.colLast div { + padding-top:0px; } -.col-last a { + + +td.colLast a { padding-bottom:3px; } -.module-signature, -.package-signature, -.type-signature, -.member-signature { - font-family:'DejaVu Sans Mono', monospace; - font-size:14px; - margin:14px 0; - white-space: pre-wrap; -} -.module-signature, -.package-signature, -.type-signature { - margin-top: 0; -} -.member-signature .type-parameters-long, -.member-signature .parameters, -.member-signature .exceptions { - display: inline-block; - vertical-align: top; - white-space: pre; -} -.member-signature .type-parameters { - white-space: normal; -} /* - * Styles for formatting effect. - */ -.source-line-no { +Formatting effect styles +*/ +.sourceLineNo { color:green; padding:0 30px 0 0; } @@ -545,321 +552,23 @@ h1.hidden { } .block { display:block; - margin:0 10px 5px 0; + margin:3px 10px 2px 0px; color:#474747; } -.deprecated-label, .descfrm-type-label, .implementation-label, .member-name-label, .member-name-link, -.module-label-in-package, .module-label-in-type, .override-specify-label, .package-label-in-type, -.package-hierarchy-label, .type-name-label, .type-name-link, .search-tag-link, .preview-label { +.deprecatedLabel, .descfrmTypeLabel, .memberNameLabel, .memberNameLink, +.overrideSpecifyLabel, .packageHierarchyLabel, .paramLabel, .returnLabel, +.seeLabel, .simpleTagLabel, .throwsLabel, .typeNameLabel, .typeNameLink { font-weight:bold; } -.deprecation-comment, .help-footnote, .preview-comment { +.deprecationComment, .emphasizedPhrase, .interfaceName { font-style:italic; } -.deprecation-block { - font-size:14px; - font-family:'DejaVu Serif', Georgia, "Times New Roman", Times, serif; - border-style:solid; - border-width:thin; - border-radius:10px; - padding:10px; - margin-bottom:10px; - margin-right:10px; - display:inline-block; -} -.preview-block { - font-size:14px; - font-family:'DejaVu Serif', Georgia, "Times New Roman", Times, serif; - border-style:solid; - border-width:thin; - border-radius:10px; - padding:10px; - margin-bottom:10px; - margin-right:10px; - display:inline-block; -} -div.block div.deprecation-comment { + +div.block div.block span.deprecationComment, div.block div.block span.emphasizedPhrase, +div.block div.block span.interfaceName { font-style:normal; } -/* - * Styles specific to HTML5 elements. - */ -main, nav, header, footer, section { - display:block; -} -/* - * Styles for javadoc search. - */ -.ui-autocomplete-category { - font-weight:bold; - font-size:15px; - padding:7px 0 7px 3px; - background-color:#4D7A97; - color:#FFFFFF; -} -.result-item { - font-size:13px; -} -.ui-autocomplete { - max-height:85%; - max-width:65%; - overflow-y:scroll; - overflow-x:scroll; - white-space:nowrap; - box-shadow: 0 3px 6px rgba(0,0,0,0.16), 0 3px 6px rgba(0,0,0,0.23); -} -ul.ui-autocomplete { - position:fixed; - z-index:999999; -} -ul.ui-autocomplete li { - float:left; - clear:both; - width:100%; -} -.result-highlight { - font-weight:bold; -} -#search-input { - background-image:url('resources/glass.png'); - background-size:13px; - background-repeat:no-repeat; - background-position:2px 3px; - padding-left:20px; - position:relative; - right:-18px; - width:400px; -} -#reset-button { - background-color: rgb(255,255,255); - background-image:url('resources/x.png'); - background-position:center; - background-repeat:no-repeat; - background-size:12px; - border:0 none; - width:16px; - height:16px; - position:relative; - left:-4px; - top:-4px; - font-size:0px; -} -.watermark { - color:#545454; -} -.search-tag-desc-result { - font-style:italic; - font-size:11px; -} -.search-tag-holder-result { - font-style:italic; - font-size:12px; -} -.search-tag-result:target { - background-color:yellow; -} -.module-graph span { - display:none; - position:absolute; -} -.module-graph:hover span { - display:block; - margin: -100px 0 0 100px; - z-index: 1; -} -.inherited-list { - margin: 10px 0 10px 0; -} -section.class-description 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project.\n This is a simple introduction as to how to get started writing Spock specifications.\n \n Spock works on top of Groovy which is in essence a syntactic super-set of Java.\n That means that one can write Java code in Groovy, and 99% of the time it will \n work the exact same way."},"it.Calculus_Stress_Test":{"executedFeatures":["Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors.","Activation functions work across types.","Dot operation stress test runs error free and produces expected result","Stress test runs error free and produces expected result","The broadcast operation stress test runs error free and produces expected result"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":756},"title":"","narrative":""},"it.Cross_Device_Sliced_Tensor_System_Test":{"executedFeatures":["Cross device sliced tensor integration test runs without errors.","Slices can be created using the SliceBuilder."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":70},"title":"Cross Device Tensor Slicing","narrative":""},"it.Cross_Device_Spec":{"executedFeatures":["A gradient of ones can be set by calling the backward method on a tensor sitting on any device.","Convolution can model matrix multiplications across devices.","Cross device system test runs successfully.","Mapping tensors works for every device (even if they are not used).","Test simple NN implementation with manual backprop"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":9201},"title":"Cross Device Stress Test Specification","narrative":"This specification is pretty much a system test which covers\n the behavior of the library as a whole across multiple devices!\n No matter which device is being used for a given stress test, the result should be the same..."},"it.Eleven_Lines_NN_System_Spec":{"executedFeatures":["One can write a simple double based neural network in less than 11 lines of java like code using the \"@\" operator!","One can write a simple float based neural network in less than 11 lines of java like code!","One can write a simple neural network in less than 11 lines of code!","One can write a simple neural network with custom back-prop in 11 lines of code!","The pseudo random number generator works as expected for the weights used in the 11 line NN examples!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":295},"title":"NN Code Golfing!","narrative":"This system test specification uses the following Numpy\n code as reference implementation for the equivalent in Neureka\n or similar implementations and variations.\n The code below is a simple neural network in only 11 lines of code.\n\n \u00b4\u00b4\u00b4\n X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])\n y = np.array([[0,1,1,0]]).T\n W1 = 2*np.random.random((3,4)) - 1\n W2 = 2*np.random.random((4,1)) - 1\n for j in xrange(60000):\n l1 = 1/(1+np.exp(-(np.dot(X,W1))))\n l2 = 1/(1+np.exp(-(np.dot(l1,W2))))\n l2_delta = (y - l2)*(l2*(1-l2))\n l1_delta = l2_delta.dot(W2.T) * (l1 * (1-l1))\n W2 += l1.T.dot(l2_delta)\n W1 += X.T.dot(l1_delta)\n \u00b4\u00b4\u00b4"},"st.Benchmark_System_Test":{"executedFeatures":["Tensor can be constructed by passing List instances.","Test benchmark script and simple tensor constructor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":1281},"title":"","narrative":""},"st.Broad_System_Test":{"executedFeatures":["The long broad integration test runs successfully."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":33},"title":"","narrative":""},"st.NN_Concepts_Spec":{"executedFeatures":["The attention mechanism (found in the commonly known transformer) demonstrated."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":135},"title":"Examining Neural Network Architecture Snippets","narrative":"This specification is intended to showcase some basic building blocks of \n various neural network architectures."},"st.Training_NNs_Spec":{"executedFeatures":["A simple 3 layer neural network converges.","A very simple 1 layer NN converges.","We can run the attention head test model."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":6994},"title":"Training a Neural Network Class","narrative":"When designing larger neural network architectures, what you would usually do is\n to create a class that represents the whole model (which itself might be composed\n of smaller models). \n \n This class would then represent something that can be executed and then trained.\n This Specification shows how to instantiate, execute and train various \n pre-defined example neural network models."},"ut.autograd.AD_And_Computation_Graph_Spec":{"executedFeatures":["Payloads and derivatives are null after garbage collection.","Reshaping produces expected computation graph and also works with reverse mode AD."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":621},"title":"","narrative":""},"ut.autograd.Autograd_Explained":{"executedFeatures":["Simple automatic differentiation and propagation."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":7},"title":"Autograd - Automatic Differentiation","narrative":"Central to all neural networks in Neureka is the autograd package. \n The autograd package provides automatic differentiation for all default operations on Tensors. \n Neureka is a define-by-run library, which means that your backpropagation is defined by how \n your code is run, and that every single iteration can be different. \n \n The class neureka.Tensor is the central class of the main package. \n If you set its attribute 'rqsGradient' to True, Neureka starts to track all operations on it. \n When you finish the forward pass of your network \n you can call .backward() and have all the gradients computed \n and distributed to the tensors requiring them automatically. \n \n The gradient for a tensor will be accumulated into a child tensor (component) which \n can be accessed via the '.getGradient()' method. \n \n To stop a tensor from tracking history, you can call '.detach()' to detach it from the \n computation history, and to prevent future computation from being tracked."},"ut.autograd.Autograd_Flags_Explained":{"executedFeatures":["Advanced backpropagation on all AD-Modes ","We can create a shallow copy of a tensor detached from the computation graph."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":9,"totalFeatures":2,"passed":9,"successRate":1.0,"time":48},"title":"","narrative":""},"ut.autograd.Autograd_NN_Spec":{"executedFeatures":["Autograd work for simple matrix multiplications.","Autograd works for 2 matrix multiplications in a row.","Autograd works in a simple convolutional dot product and float based feed forward neural network.","Autograd works in a simple convolutional dot product based feed forward neural network.","Autograd works in a simple mat-mul based feed forward neural network."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":79},"title":"Simple Neural Network autograd integration test","narrative":"The integration test below has been implemented by using\n the following code and the result it produces as reference : \n https://medium.com/dair-ai/a-simple-neural-network-from-scratch-with-pytorch-and-google-colab-c7f3830618e0 \n \n The following seed has been used to assure reproducibility :\n 'torch.manual_seed(503672689411)'"},"ut.autograd.Autograd_Tensor_Spec":{"executedFeatures":["A tensor used as derivative within a computation graph will throw exception when trying to deleting it.","Second-Test \"x-mul\" autograd behaviour. (Not on device)","Test basic autograd behaviour. (Not on device)"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":20},"title":"","narrative":""},"ut.autograd.JITProp_Autograd_Tensor_Spec":{"executedFeatures":["Gradient auto-apply kicks in when used AD uses JIT prop","Test JIT propagation variant one.","Test JIT propagation variant two.","Test autograd without JIT and auto apply.","Test in-differential and JIT with auto apply","Test no JIT prop when forward AD","Test no preemptive gradient apply when not requested and auto apply and JIT_prop","Test pending error optimization"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":132},"title":"","narrative":""},"ut.autograd.internal.GraphNode_Instantiation_Exception_Unit_Tests":{"executedFeatures":["GraphNode instantiation throws exception because tensors of ExecutionCall do not return GraphNode instances.","GraphNode throws an exception when trying to execute an inline operation on inputs with active autograd."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":139},"title":"","narrative":""},"ut.autograd.internal.GraphNode_Tensor_Exception_Unit_Tests":{"executedFeatures":["A tensor cannot be deleted if it is part of a graph and the tensor is used as derivative."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":4},"title":"","narrative":""},"ut.backend.Backend_Extension_Spec":{"executedFeatures":["Mock operation interacts with FunctionNode (AbstractFunction) instance as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":53},"title":"","narrative":""},"ut.backend.Backend_MatMul_Extension_Spec":{"executedFeatures":["GEMM matrix multiplication reference implementation can be set as custom OperationType and works as expected.","Test context mock for opencl reference implementations.","Tile parsing for kernel parameter calculation yields expected tile dimensions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":54},"title":"","narrative":""},"ut.backend.Matrix_Multiplication_Spec":{"executedFeatures":["The \"matMul\" method allows us to perform matrix multiplication.","The simple CPU matrix multiplication implementation works as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":27},"title":"Matrix Multiplication","narrative":"The tensor API exposes a useful method for Matrix Multiplication.\n This specification not only demonstrates how to use this method\n but also shows how matrix multiplication work \n for tensors with both row and column major layouts.\n (typically, column major is faster)"},"ut.backend.core.Backend_Algorithm_AD_Spec":{"executedFeatures":["Activation implementations behave as expected.","Broadcast implementations have expected properties.","Convolution implementations behave as expected.","Operator implementations behave as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":52},"title":"","narrative":""},"ut.backend.core.Backend_Algorithm_Implementation_Spec":{"executedFeatures":["Activation implementations have expected Executor instances.","HostExecutors of Operator implementations behave as expected.","Operator implementations have expected Executor instances."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":65},"title":"","narrative":""},"ut.backend.core.Backend_Functional_Algorithm_Spec":{"executedFeatures":["A functional algorithm cannot be used if it was not built properly!","A functional algorithm does not accept null as an answer!","A functional algorithm warns us when modified after it has been built!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":11},"title":"","narrative":""},"ut.backend.core.Matrix_Multiplication_Spec":{"executedFeatures":["The CPU matrix multiplication implementation works as expected.","The internal matrix multiplication test script runs!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":1154},"title":"Internal CPU based Matrix Multiplication","narrative":"This specification covers library internal matrix multiplication logic,\n specifically the CPU implementation.\n Do not depend on the API used in this specification as it is subject to change!"},"ut.backend.core.OpenCL_Backend_Spec":{"executedFeatures":["The OpenCL backend context can load implementations."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":7},"title":"","narrative":""},"ut.backend.core.Randomization_Spec":{"executedFeatures":["Randomization is in essence the same algorithm as JDKs \"Random\".","The Randomization class can fill various types of arrays with pseudo random numbers.","We can make slices of tensors random."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":22},"title":"","narrative":""},"ut.device.CPU_Spec":{"executedFeatures":["CPU knows the current number of available processor cores!","The CPU device will keep track of the amount of tensors it stores.","The CPU exposes a non null API for executing workloads in parallel.","Thread pool executes given workload in parallel"],"ignoredFeatures":[],"stats":{"failures":1,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":3,"successRate":0.75,"time":5449},"title":"The CPU device, an API for CPU based execution","narrative":"The CPU class, one of many implementations of the Device interface, \n is simply supposed to be an API for dispatching threaded workloads onto the CPU.\n Contrary to other types of device, the CPU will host tensor data by default, simply\n because the tensors will be stored in RAM if no device was specified."},"ut.device.Cross_Device_IO_Spec":{"executedFeatures":["We can use the access device API to read from a tensor.","We can use the access device API to write to a tensor"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":16},"title":"Devices manage the states of the tensors they store!","narrative":"Tensors should not manage their states\n themselves, simply because the type and location\n of the data is dependent on the device onto which they are stored.\n This specification tests of various device implementations\n enable reading to or writing from the tensors they store."},"ut.device.Cross_Device_Type_Spec":{"executedFeatures":["A device will keep track of the amount of tensors and data objects it stores.","Devices expose an API for accessing (reading and writing) the data of a tensor.","Devices store slices which can also be restored just like any other tensor.","Devices store tensors which can also be restored.","Execution calls containing null arguments will cause an exception to be thrown in device instances.","In total there are 3 different types of methods for finding device instances.","Passing a numeric array to a tensor should modify its contents!","Virtual tensors stay virtual when outsourced.","We can find Device implementations or null by passing search keys to the \"get\" method.","We can query the backend for devices by specifying both the requested type and a key word."],"ignoredFeatures":["Devices cannot store slices whose parents are not already stored."],"stats":{"failures":1,"errors":0,"skipped":1,"totalRuns":10,"totalFeatures":11,"passed":9,"successRate":0.9,"time":10184},"title":"Finding Device Types","narrative":"Neureka introduces a the concept of a `Device` which is an interface\n that represents a computational device used for executing tensor / nd-array operations on them.\n The `Device` interface is implemented by various classes which represent\n different types of accelerator hardware such as `CPUs`, `GPUs`, `TPUs`, `FPGAs`, etc.\n These various `Device` types can not be instantiated directly because they model \n the concrete and finite hardware that is available on any given system Neureka is running on.\n This means that they are usually instantiated lazily upon access request or \n upfront by the library backend (usually a backend extension built fo a specific device).\n In order to find these instances embedded in the library backend the `Device` interface\n exposes various static methods which can be used to find a device instance by name or type."},"ut.device.FileDevice_Spec":{"executedFeatures":["A file device stores tensors in idx files by default.","A file device stores tensors in various file formats.","A tensor loaded from a file device can be loaded again.","The file device can load known files in a directory."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":54},"title":"FileDevice, Storing Tensors in Files","narrative":"The `FileDevice` class, one of many implementations of the `Device` interface, \n represents a file directory which can store and load tensors as files (`idx`, `jpg`, `png`...)."},"ut.device.OpenCLDevice_Exception_Spec":{"executedFeatures":["Ad hoc compilation produces expected exceptions when duplication is found.","Ad hoc compilation produces expected exceptions.","An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.","Trying to restore a tensor which is not on a device raises exception."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":77},"title":"OpenCLDevice Exception Handling","narrative":"The OpenCLDevice class, one of many implementations of the Device interface, \n represents physical OpenCL devices.\n This specification defines how instances of this class deal with exceptional information."},"ut.device.OpenCLDevice_Spec":{"executedFeatures":["Ad hoc compilation produces executable kernel.","Ad hoc compilation works for WIP general purpose matrix multiplication.","Ad hoc compilation works for custom column major based tiled matrix multiplication.","Ad hoc compilation works for custom simple row major based matrix multiplication.","Ad hoc matrix multiplication works for multiple of 16 matrices.","An OpenCLDevice loads tensors in a provided lambda temporarily.","We can get the items of an outsourced tensor as a primitive array.","We can take a look at the underlying data array of an outsourced tensor through the unsafe API."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":2269},"title":"The OpenCLDevice Specification","narrative":"Tensors need devices for execution!\n By default tensors use the `CPU` device, but sometimes we want to\n use something more suitable for large amounts of data and a high degree of parallelization.\n This is were the `OpenCLDevice` comes into play!\n It is a `Device` implementation built on top of the JOCL library, a thin OpenCL API.\n We expect the `OpenCLDevice` to store tensors as well as being able to read and write\n data from and to stored tensors.\n Also, an `OpenCLDevice` should allows us to compile OpenCL kernel code on the fly..."},"ut.device.OpenCL_Spec":{"executedFeatures":["A given OpenCL context can be disposed!","An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.","First found OpenCLDevice will have realistic numeric properties.","First found OpenCLDevice will have realistic properties inside summary query.","First found OpenCLDevice will have realistic text properties."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":2988},"title":"Working with OpenCL","narrative":"Neureka models the OpenCL API through various types of classes.\n The most fundamental of these is the `OpenCLDevice` class which\n represents a single device with OpenCL support.\n Besides that, there is also the `OpenCLContext` class which\n represents a OpenCL contexts, platforms and multiple devices on said platforms..."},"ut.device.internal.CLFunctionCompiler_Spec":{"executedFeatures":["The CLFunctionCompiler produces an operation which properly integrates to the backend.","The CLFunctionCompiler produces the expected \"ad hoc\" kernel.","The OpenCLDevice produces a working optimized Function for doubles.","The OpenCLDevice produces a working optimized Function for floats."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":173},"title":"Turning functions into kernels.","narrative":"Neureka parses mathematical expressions into an AST representation\n hidden behind the Function interface...\n This feature does not exist without reason, we can use\n this abstract syntax tree to compile to OpenCL kernels\n for optimal execution speed!"},"ut.device.internal.CPU_Kernel_Spec":{"executedFeatures":["The Reduce implementation for the CPU has realistic behaviour","The Sum implementation for the CPU has realistic behaviour"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":28},"title":"","narrative":""},"ut.device.internal.OpenCL_Data_Spec":{"executedFeatures":["The \"Data\" class can represent various OpenCL data types.","The OpenCLDevice specific Data class represents JVM data for OpenCL."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":20},"title":"","narrative":""},"ut.device.internal.OpenCL_Kernel_Unit_Spec":{"executedFeatures":["The CLDot implementation for the OpenCLDevice has realistic behaviour","The GEMM implementation for the OpenCLDevice has realistic behaviour","The Reduce implementation for the OpenCLDevice has realistic behaviour","The Sum implementation for the OpenCLDevice has realistic behaviour","The Sum implementation for the OpenCLDevice has realistic behaviour for when the number of elements is a prime."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":13},"title":"","narrative":""},"ut.dtype.DataType_Spec":{"executedFeatures":["DataType multi-ton instances behave as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":5},"title":"","narrative":""},"ut.dtype.NumericType_Spec":{"executedFeatures":["Conversion goes both ways and produces expected numeric values.","NumericType conversion to holder types yields expected results.","NumericType implementations behave as expected.","NumericType implementations return their expected properties."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":23},"title":"The NumericType and its implementations model their respective numeric data types.","narrative":"This specification covers the behavior of the NumericType interface\n which is responsible for modelling numeric data types which may or may not be native to the JVM. \n These implementations however do not model them in the traditional OO style\n but merely expose useful utility method for converting and representing \n these numeric data types using JVM types."},"ut.framing.Tensor_Framing_Spec":{"executedFeatures":["A matrix (rank 2 tensor) can be labeled and their labels can be used to extract slices / subsets.","A tensor can be labeled partially.","Rank 3 tensors can be labeled and their labels can be used to extract slices / subsets of tensors.","We can add labels to tensors through lists or maps passed to the \"label()\" method."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":16},"title":"Naming Tensors and their Dimensions.","narrative":"A powerful concept in the data science as well as machine learning\n world is something usually referred to as \"Data Frames\".\n These are highly flexible 2D data structures\n used to load and store CSV, CRV, etc... files for \n data exploration and further processing.\n Data frames are so powerful because\n their indices are labeled and therefore human readable.\n Neureka's tensors are general purpose data containers\n which may also stored data in 2 dimensions whose\n indices may also be something other than integers."},"ut.introductions.Tensor_NDArray_Spec":{"executedFeatures":["Tensor is a subtype of NdArray.","We can use tensors for numeric calculations (but not nd-arrays)."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":4},"title":"Tensors or Nd-arrays","narrative":"*What is the difference?*\n\nIn the world of machine learning we use something called a **'tensor'** to represent data.\nThey might be called **'nd-arrays'** in some other frameworks,\nbut although they are very similar, \nthere are also some important distinctions to be made between these two concepts.\nBoth are at their core merely multidimensional arrays, however,\nthey are different in their typical usage and API.\nnd-arrays are merely used to represent any type of data as a \ncollection of elements in a multidimensional grid, \ntensors on the other hand have additional requirements.\nThey are a type of nd-array which stores numeric data \nas well as expose various mathematical operations for said data.\nIn that sense it is actually merely a more complex kind of number.\nThis concept actually comes from the field of physics, \nwhere it is used to represent a physical quantity.\n\nNeureka models both concepts through the `Tensor` and the `Nda` interfaces.\n`Nda` is an abbreviation of `NdArray`, and `Tensor` is an abbreviation of `Tensor`.\nThe `Tensor` type is a subtype of the `Nda` type, exposing additional methods\nlike for example `plus`, `minus`, `times` and `divide`.\nBoth can be instantiated through static factory methods (and a fluent builder API)."},"ut.math.BackendContext_Spec":{"executedFeatures":["BackendContext instances can be created by cloning from Singleton instance.","BackendContext instances return Runner instances for easy visiting with return values.","BackendContext instances return Runner instances for easy visiting."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":7},"title":"The BackendContext is a cloneable context which can run Tasks.","narrative":"This specification defines the expected behaviour of the backend context\n which should expose a convenient API to work with.\n This API should allow for tasks to be running on a given context\n which is important for testing and modularity not only\n during library startup but also throughout the runtime."},"ut.math.ConCat_Spec":{"executedFeatures":["We can concatenate 2 float tensors alongside a specified axis!","We can concatenate 2 string tensors alongside a specified axis!","We can concatenate 2 tensors alongside a specified axis!","We can concatenate and then back-propagate 2 simple float tensors alongside a specified axis!","We can concatenate and then back-propagate 3 simple float tensors alongside a specified axis!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":17},"title":"Merging Tensors","narrative":"Tensors can not only be sliced, but also merged.\n This is most easily achieved through the concatenation operation, \n which stacks 2 tensors alongside a specified axis.\n This specification not only covers how you can concatenate tensors,\n but also how this works alongside autograd and non-numeric tensors."},"ut.math.Function_Exception_Spec":{"executedFeatures":["Function throws exception when arity does not match input number.","Function throws exception when not enough inputs provided."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":2},"title":"","narrative":""},"ut.math.Function_Parsing_Spec":{"executedFeatures":["Functions can derive themselves according to the provided index of the input which ought to be derived.","Parsed equations throw expected error messages.","Test parsed equations when building Function instances."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":17},"title":"Parsing Expressions into Functions","narrative":"Neureka uses the 'Function' interface as a representation of a\n nested structure of operations.\n This means that a 'Function' is simply an abstract syntax trees made up of other 'Function' implementations\n which are assembled together by a parser receiving a string expression.\n In this specification we ensure that function expressions will be properly parsed into\n 'Function' implementations."},"ut.math.Function_Scalar_Spec":{"executedFeatures":["Function \"(I[0]+1/I[0])**-I[0]\" instance returns expected scalar result.","Function \"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\" instance returns expected scalars.","Function \"1/I[0]\" instance returns expected scalar results.","Function \"I[0]+1/I[0]\" instance returns expected scalar results.","Test scalar results of Function \"sumjs((cos(I[j]*5)/5+I[j])*(1+sin(I[j])/2))\" instance.","Test scalar results of various Function instances."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":22},"title":"Functions for Scalars","narrative":"The Function API and it's implementations \n receive and process arrays of scalars as arguments.\n Functions don't have to be used alongside tensors / nd-arrays,\n they can also compute derivatives based on scalar values."},"ut.math.Function_Spec":{"executedFeatures":["Function implementations ensure that internally created tensors are flagged as \"intermediate\" initially!","Function implementations ensure that outputs which are input members are not flagged as \"intermediate\"!","Function implementations will ensure the \"call\" and \"invoke\" does not return tensors flagged as \"intermediate\".","The library context exposes a set of useful functions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":23},"title":"Testing Default Methods on Functions","narrative":"This specification tests the default methods on functions\n through a simple dummy implementation of the Function interface."},"ut.math.Tensor_Function_Spec":{"executedFeatures":["Executed tensors are intermediate tensors.","Reshaping on 3D tensors works by instantiate a Function instance built from a String.","Tensor results of various Function instances return expected results.","The \"DimTrim\" operation works forward as well as backward!","The optimization function for the SGD algorithm produces the expected result","The softmax can be calculated alongside multiple axes.","The softmax can be calculated for a particular axis.","The softmax function can be applied to tensors with more than one dimension.","The tensor API has built-in methods for applying functions.","We can collect a stream into a tensor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":10,"totalFeatures":10,"passed":10,"successRate":1.0,"time":78},"title":"Applying Functions to Tensors","narrative":"A tensor would be nothing without being able to apply operations on them.\n However, calling operations manually in order to process your\n tensors can be a verbose and error prone task.\n This is where functions come into play.\n Neureka's functions are composed of operations forming an abstract syntax tree.\n Passing tensors to a function will route them trough this tree and apply\n all of the operations on the tensors for you."},"ut.miscellaneous.Weired_NN_Spec":{"executedFeatures":["Dot based feed forward and activation produces expected result."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":21},"title":"","narrative":"This specification is meant less as feature documentation and more as a\n chaos test for weired neural network architectures\n an unusual usages of the Neureka library."},"ut.ndas.Nda_Assign_Spec":{"executedFeatures":["Assignment can be easily achieved through subscription operators.","We can assign one slice into another one.","We can use the \"mut\" API to assign the contents of one nd-array into another one."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":3},"title":"Nda Inline Assignment","narrative":"In this specification we cover the behaviour of nda's with respect to the assignment operation\n as well as the assignment of individual Nda items."},"ut.ndas.Nda_Framing_Spec":{"executedFeatures":["An Nda can be labeled.","Concatenating 2 labeled nd-arrays will produce a nd-array which is also labeled.","The slice of a labeled vector is labeled too.","We can label the columns and rows of a rank 3 nd-array.","We can label the columns of a rank 2 nd-array.","We can use labels as selectors for slicing."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":9},"title":"Nda framing","narrative":"Immutability is a core concept of the Neureka library.\n This means that the Nda API does not expose mutability directly.\n Instead, the API exposes methods that return new instances of Nda\n that are derived from the original instance.\n \n This is also true for labeling operations, \n meaning that the Nda API does not directly expose methods that mutate labels of an Nda\n but instead provides methods that return new instances of Nda\n with different labels.\n \n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!"},"ut.ndas.Nda_Inplace_Framing_Spec":{"executedFeatures":["Concatenating 2 labeled nd-arrays will produce a nd-array which is also labeled.","The slice of a labeled vector is labeled too.","We can concatenate more than 2 nd-arrays.","We can label the columns and rows of a rank 3 nd-array.","We can label the columns of a rank 2 nd-array.","We can use labels as selectors for slicing."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":5},"title":"NDA Framing","narrative":"Framing an nd-array is all about naming its axes and then using those names to\n access, read or write its values in a more convenient and human readable way."},"ut.ndas.Nda_Instantiation_Spec":{"executedFeatures":["A vector can be created from an array of values through the \"of\" method.","Common types of nd-arrays are best instantiated using type specific convenience methods.","ND-arrays can be created fluently."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":4},"title":"ND-Array Instantiation","narrative":"In this specification we cover how ND-arrays can be instantiated."},"ut.ndas.Nda_Items_Spec":{"executedFeatures":["An item can be converted to an Optional object.","Other than the \"orElse(T)\" method of the Optional class, the same method of an Item will throw an exception if the provided value is null.","The \"get\" method of an Item object will throw an exception if the item is missing.","We can check if items of a tensor is present or not.","We can get the value of an item.","We can use the \"orElse(T)\" method to avoid null values."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":4},"title":"The Nds Items API","narrative":"Nd-arrays are collections of items similar to other\n collection types in Java. \n One useful way to access the items of an nd-array is\n to use the items API.\n \n Using the `at` methods we can access an `Item` object\n which is a wrapper around the item's value and its\n index in the nd-array.\n \n The `Item` object is a simple data class which\n is very similar to the `Optional` class, meaning\n that it can either be empty or contain a value."},"ut.ndas.Nda_Mutation_Spec":{"executedFeatures":["A ND-Array can be mutated simply using the \"set\" method.","A ND-Array can be mutated using the \"at(..).set(..)\" methods.","A simple vector ND-Array can be mutated using the \"at(..).set(..)\" methods.","A simple vector ND-Array can be mutated using the \"setItemAt\" method.","We can use the subscription operator to mutate a simple vector ND-Array.","We can use the subscription operator to mutate an ND-Array."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":3},"title":"Mutating ND-Arrays","narrative":"ND-Arrays should be considered immutable, so we should prefer creating new \n ND-Arrays from existing ones using wither methods.\n However this is not always a good idea as it can be expensive to create new\n ND-Arrays, especially if the ND-Array is very large.\n The ability to mutate ND-Arrays is therefore provided, but only\n accessible via the mutation API exposed by the `getMut()` method."},"ut.ndas.Nda_Reshape_Spec":{"executedFeatures":["We can create a new Nda instance with a different shape."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":1},"title":"Nda Reshaping","narrative":"Immutability is a core concept of the Neureka library.\n This means that the Nda API does not expose mutability directly.\n Instead, the API exposes methods that return new instances of Nda\n that are derived from the original instance.\n \n This is also true for reshaping operations, \n meaning that the Nda API does not expose methods that mutate the shape of an Nda\n but instead provides methods that return new instances of Nda\n with a different shape.\n \n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!"},"ut.ndim.NDConfiguration_Spec":{"executedFeatures":["Various NDConfigurations behave exactly like their general purpose implementation."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":28},"title":"Making Arrays N-Dimensional","narrative":"Under the hood Neureka implements powerful indexing \n abstractions through the `NDConfiguration` interface and its various implementations.\n This allows for the creation of tensors/nd-arrays with arbitrary dimensions, \n the ability to slice them into smaller tensors/nd-arrays with the same underlying data,\n and finally the ability to permute their axes (like transposing them for example).\n \n This specification however only focuses on the behaviour of the `NDConfiguration` interface\n which translates various types of indices."},"ut.ndim.Nda_Permute_Spec":{"executedFeatures":["We can use the \"permute\" method to rearrange the dimensions of an nd-array.","We can use the \"transpose\" method to transpose swap 2 dimensions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":2},"title":"Reshaping Nd-Arrays","narrative":"Permuting an N-dimensional array means rearranging the dimensions/axes of the N-dimensional array.\n It returns a new nd-array with the same data as the original nd-array, \n but with the specified dimensions rearranged. \n It is very useful for example when you want to\n change the order of dimensions, for example, if you have a nd-array with dimensions (batch_size, channels, height, width), \n you can use permute() to rearrange the dimensions to (batch_size, height, width, channels).\n Another useful application of permute() is transposing a matrix.\n For example, if you have a matrix with dimensions (rows, columns), \n you can use permute() to rearrange the dimensions to (columns, rows).\n \n Permuting is a very cheap operation because it does not copy any data but merely\n creates a new view on the same data with a different access pattern."},"ut.ndim.Shape_Spec":{"executedFeatures":["A shape can be created from a list of integers.","A shape can be created from a stream of ints.","A shape can be created from an iterable.","A shape can be mapped to a new shape.","A shape can be sliced.","Use the \"any\" or \"every\" method to check if a predicate holds for any or every value of the shape.","You can use the \"count(Predicate)\" method to count the number of values that satisfy a predicate."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":4},"title":"The Shape Tuple","narrative":"The `Shape` of an nd-array/tensor is in essence merely an immutable tuple of integers\n which define the size of each dimension of the tensor.\n So if you think of an nd-array as a grid of numbers, then the shape of the\n tensor is the size of the grid in each dimension.\n \n This specifications shows you how to create a shape and how to use it."},"ut.ndim.Tensor_NDConfiguration_Spec":{"executedFeatures":["NDConfiguration instances of tensors have expected state and behaviour.","NDConfiguration instances of tensors have expected state."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":3},"title":"What it means to be N-Dimensional","narrative":"This specification covers how implementations\n of the `NDConfiguration` interface manage to define\n what it means to be a n-dimensional tensor/nd-array."},"ut.ndim.Tensor_Permute_Spec":{"executedFeatures":["We can use the \"permute\" method to rearrange the dimensions of a tensor.","When matrices are transpose, they will change their layout type as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":5},"title":"Reshaping Tensors","narrative":"Permuting an N-dimensional array means rearranging the dimensions/axes of the N-dimensional array.\n It produces a new tensor with the same data as the original tensor, \n but with the specified dimensions rearranged. \n \n This is very useful for example when you want to\n change the order of dimensions, for example, if you have a tensor with dimensions (batch_size, channels, height, width), \n you can use permute() to rearrange the dimensions to (batch_size, height, width, channels).\n Another useful application of permute() is transposing a matrix.\n For example, if you have a matrix with dimensions (rows, columns), \n you can use permute() to rearrange the dimensions to (columns, rows).\n \n Permuting is a very cheap operation because it does not copy any data but merely\n creates a new view on the same data with a different access pattern."},"ut.ndim.Tensor_Slice_Permute_Spec":{"executedFeatures":["A slice of a tensor changes as expected when reshaping it.","Reshaping a slice works as expected.","Two slices of one big tensor perform matrix multiplication flawless."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":5},"title":"Permuting Slices of Tensors","narrative":"Neureka provides a convenient way to permuting tensors\n even if they are slices of other tensors sharing the same underlying data.\n This is possible because of the under the hood indexing \n abstractions provided by the `NDConfiguration` interface and its various implementations."},"ut.neureka.Neureka_Spec":{"executedFeatures":["Backend related library objects adhere to the same toString formatting convention!","Every Thread instance has their own Neureka instance.","Neureka class instance has expected behaviour.","Neureka settings class can be locked causing its properties to be immutable.","OpenCL related library objects adhere to the same toString formatting convention!","Various library objects adhere to the same toString formatting convention!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":8863},"title":"The Neureka context can be used and configured as expected.","narrative":"This specification covers the behavior of the Neureka class which\n exposes a global API for configuring thread local contexts and library settings.\n The purpose of this is to assert that the API exposed by the Neureka class \n is both thread local and configurable.\n This specification also exists to cover standards for the Neureka library in general."},"ut.optimization.ADAM_Spec":{"executedFeatures":["ADAM optimizes according to expected inputs","Equations \"I[0]*I[1]+(1-I[2])*I[3]\" and \"(1-I[0])*I[1]\" used within ADAM return expected results.","Equations used by ADAM return expected result."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":46},"title":"","narrative":"ADAM is a more powerful alternative to the classical stochastic gradient descent. \n It combines the best properties of the AdaGrad and the RMSProp algorithms, which makes \n it especially well suited for sparse gradients and noisy data.\n Adam is the most popular among the adaptive optimizers\n because its adaptive learning rate working so well with sparse datasets."},"ut.optimization.AdaGrad_Spec":{"executedFeatures":["AdaGrad optimizes according to expected inputs"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":15},"title":"","narrative":""},"ut.optimization.Momentum_Spec":{"executedFeatures":["Momentum optimizes according to expected inputs"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":8},"title":"","narrative":"Momentum is an extension to the gradient descent optimization \n algorithm that allows the search to build inertia in a direction \n in the search space and overcome the oscillations of noisy \n gradients and coast across flat spots of the search space."},"ut.optimization.RMSProp_Spec":{"executedFeatures":["RMSprop optimizes according to expected inputs"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":14},"title":"","narrative":"**Root Mean Squared Propagation**, or RMSProp, is an extension of gradient \n descent and the AdaGrad version of gradient descent that uses a \n decaying average of partial gradients in the adaptation of the \n step size for each parameter."},"ut.tensors.Copy_Spec":{"executedFeatures":["A deep copy of a slice tensor is also a deep copy of the underlying data array.","A deep copy of a tensor is also a deep copy of the underlying data array.","A shallow copy of a tensor will be flagged as such.","A shallow copy will share the same underlying data as its original tensor.","We can deep copy various types of tensors."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":15},"title":"To Copy or Not to Copy","narrative":"In this specification we cover the behaviour of tensors with respect to their copy methods.\n There are to main ways to copy a tensor:
        \n 1. .shallowCopy()
        \n 2. .deepCopy()
        \n
        \n The first method creates a new tensor with the same underlying data array as the original tensor.
        \n The second method on the other hand creates a new tensor with a new data array.
        \n
        \n The first method is the most efficient, but it is not as safe as the second method.
        \n The second method is the most safe, but it is not as efficient.
        \n
        \n Besides these 2 main requirements, there are als some corner cases with respect to\n the components of a tensor (like for example its computation graph) which\n will be covered in this specification as well."},"ut.tensors.DimTrim_Spec":{"executedFeatures":["The \"dimTrim\" operation works on slices too!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":2},"title":"The 'dimTrim' Method","narrative":"The 'dimTrim' method is used to remove training and leading dimensions of length 1 from a tensor.\n This is useful when you want to perform operations on tensors of different ranks.\n For example, if you want to perform a dot product on two vectors, you can use the 'dimTrim' method\n to remove the dimension of length 1 from the vector, so that it becomes a scalar.\n This way you can perform the dot product on two scalars."},"ut.tensors.Expression_Based_Tensor_Instantiation_Spec":{"executedFeatures":["A tensor can be created from a function as expression.","We can instantiate tensors from various simple string expressions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":3},"title":"Expression based Tensor Instantiation","narrative":"This specification defines how a tensor can be instantiated\n using string expressions, which define operations to be executed.\n This form of tensor instantiation is very useful to avoid boilerplate code."},"ut.tensors.Fluent_Tensor_Creation_Spec":{"executedFeatures":["Initialization lambda based tensors can be created fluently.","Range based tensors can be created fluently.","Scalars can be created fluently.","Seed based tensors can be created fluently.","Tensors can be created fluently.","Value based tensors can be created fluently.","Vectors can be created fluently."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":20},"title":"","narrative":""},"ut.tensors.Functional_Nda_Spec":{"executedFeatures":["ND-Array mapping lambdas produce expected nd-arrays.","The \"map\" method is a shorter convenience method for mapping to the same type.","We can analyse the values of a nd-array using various predicate receiving methods","We can collect a stream into a nd-array.","We can find both min and max items in a tensor by providing a comparator.","We can find both min and max items in an ND-array by providing a comparator.","We can initialize an ND-Array using a filler lambda mapping indices to items.","We can use the \"filter\" method as a shortcut for \"stream().filter(..)\".","We can use the \"flatMap\" method as a shortcut for \"stream().flatMap(..)\"."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":9,"totalFeatures":9,"passed":9,"successRate":1.0,"time":300},"title":"Functional ND-Arrays","narrative":"ND-Arrays expose a powerful API for performing operations on them\n in a functional style."},"ut.tensors.Functional_Tensor_Spec":{"executedFeatures":["Tensor mapping lambdas produce expected tensors.","The \"map\" method is a shorter convenience method for mapping to the same type.","We can analyse the values of a tensor using various predicate receiving methods","We can find both min and max items in a tensor by providing a comparator.","We can initialize a tensor using a filler lambda mapping indices to items.","We can use the \"filter\" method as a shortcut for \"stream().filter(..)\".","We can use the \"flatMap\" method as a shortcut for \"stream().flatMap(..)\"."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":303},"title":"Functional Tensors","narrative":"Tensors expose a powerful API for performing operations on them\n in a functional style."},"ut.tensors.Reshape_Spec":{"executedFeatures":["The reshape operation supports autograd!","We can create a new tensor with a different shape.","We can use `-1` in the desired shape if we want the axis size to be determined automatically."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":2},"title":"Tensor Reshaping","narrative":"This specification demonstrates how to reshape tensors,\n which means to change the shape of a tensor.\n\n Note that immutability is a core concept of the Neureka library.\n This means that the `Tensor` API does not expose mutability directly.\n Instead, it exposes methods that return new instances of `Tensor`\n that are derived from the original instance.\n \n This is also true for reshaping operations.\n \n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!"},"ut.tensors.Tensor_As_Container_Spec":{"executedFeatures":["More tensor operations translate to custom data type \"ComplexNumber\".","Plus operator on String tensors works element-wise.","Tensor operations translate to custom data type \"ComplexNumber\".","We can apply predicates on the values of a tensor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":9},"title":"Why not have a tensor of words?","narrative":"Technically, tensors are merely fancy ND-arrays with some useful mathematical operations\n applicable to them...\n Therefore, there is no reason why a tensor would not also be able to store\n other kinds of objects besides numbers like strings for example.\n This specification ensures that tensors can hold and index many other things..."},"ut.tensors.Tensor_Assign_Spec":{"executedFeatures":["Assignment can be easily achieved through subscription operators.","We can assign one slice into another one.","We can use the \"mut\" API to assign the contents of one tensor into another one."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":3},"title":"Tensor Inline Assignment","narrative":"In this specification we cover the behaviour of tensors with respect to the assignment operation\n as well as the assignment of individual tensor items."},"ut.tensors.Tensor_Conversion_Spec":{"executedFeatures":["Tensors value type can be changed by calling \"toType(...)\".","We can change the data type of all kinds of tensors.","We turn a tensor into a scalar value or string through the \"as\" operator!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":11},"title":"Tensor Type Conversion","narrative":"Here we specify how a tensor can be converted to other data types\n like for example another tensor of a different data type."},"ut.tensors.Tensor_Convolution_Spec":{"executedFeatures":["Autograd works with simple 2D convolution.","Convolution can be performed using non-quadratic matrix tensors.","Convolution can be performed using tensors with an additional dimension as batch size.","Convolution with tensors of the same shape is equivalent to a dot product.","Manual convolution produces expected result.","Sime convolution works as expected eith autograd.","Tensors have the correct layout after convolution.","The \"x\" (convolution) operator produces expected results (On the CPU).","Very simple manual convolution produces expected result.","We can perform a convolution operation on a 2D tensor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":10,"totalFeatures":10,"passed":10,"successRate":1.0,"time":653},"title":"Tensor Convolution","narrative":"This specification shows how convolution can be performed on tensors.\n\n Convolution is a linear operation which is not only important for image processing but also\n a central player in the field of machine learning (especially for computer vision).\n It is used to extract features from images and other typically ~2 dimensional data.\n Other than that it is extremely important in the field of signal processing."},"ut.tensors.Tensor_Device_Spec":{"executedFeatures":["Adding OpenCL device to tensor makes tensor be \"outsourced\" and contain the Device instance as component.","Tensors try to migrate themselves to a device that is being added to them as component.","The device of a tensor can be accessed via the \"device()\" method.","When creating slices of tensors then this should trigger a \"parent - child\" relation noticeable to the device!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":4},"title":"Tensors on Devices","narrative":"This unit test specification covers \n the expected behavior of tensors when interacting\n with instances of implementations of the Device interface."},"ut.tensors.Tensor_Dot_Product_Spec":{"executedFeatures":["The \"dot\" method calculates the dot product between vectors.","The \"dot\" operation supports autograd.","The dot operation work even when one tensor is virtual.","The dot operation works for virtual tensors as well.","The dot product operation runs on any device.","The dot product works across different types and devices.","You can slice a Matrix into vectors and then used them for dot products."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":135},"title":"Tensor Dot Products","narrative":"A tensor can also be a simple vector, which is a tensor of rank 1.\n This specification demonstrates how to perform dot products on tensors of rank 1."},"ut.tensors.Tensor_Generics_Spec":{"executedFeatures":["1D tensors can be created from primitive arrays.","Anonymous tensor instance has the default datatype class as defined in Neureka settings.","We can create a tensor of strings."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":3},"title":"Tensors as Generic Containers","narrative":"Tensors do not just store numeric data.\n They can hold anything which can be stuffed into a \"Object[]\" array.\n You could even create a tensor of tensors!"},"ut.tensors.Tensor_Gradient_Spec":{"executedFeatures":["Gradient of tensor is being applies regardless of the tensor requiring gradient or not","Tensors can have gradients but not require them.","Tensors that have gradients but do not require them still print them."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":3},"title":"Gradients are Tensors which are Components of other Tensors","narrative":"This specification defines the gradient API on tensors.\n So one ought to be able to check wetter or not a tensor has a gradient attached to it or not.\n In that case one should be able to get this gradient and then work with\n it independently of the original tensor to which it belongs to..."},"ut.tensors.Tensor_IO_Spec":{"executedFeatures":["A tensor produced by the static \"Tensor.newRandom(shape)\" has expected \"random\" value.","Indexing after reshaping works as expected.","Tensor value type can not be changed by passing float or double arrays to it.","Tensor values can be manipulated","The tensor data array can be modified by targeting them with an index.","We can manipulate the underlying data array of a tensor through the mut API.","We can re-populate a tensor of shorts from a single scalar value!","When we try to manipulate the underlying data array of a virtual tensor then it will become actual."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":29},"title":"Reading and Writing Tensor Items","narrative":"Tensors are complicated data structures with a wide range of different possible states.\n They can host elements of different types residing on many kinds of different devices.\n Here we want to read from and write to the state of a tensor."},"ut.tensors.Tensor_Instantiation_Spec":{"executedFeatures":["A matrix tensor can be instantiated using lists for it's shape and values.","A simple 2D vector can be instantiated using lists for it's shape and values.","Passing a seed in the form of a String to a tensor produces pseudo random items.","Scalar tensors can be created via static factory methods","Tensors can be instantiated based on arrays for both shapes and values.","Tensors can be instantiated with String seed.","Vector tensors can be instantiated via factory methods."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":11},"title":"Instantiating Tensors","narrative":"Tensors are complicated data structures with a wide range of different possible states.\n They can host elements of different types residing on many kinds of different devices.\n Here we want to show how a tensor can be instantiated in different ways."},"ut.tensors.Tensor_Interop_Spec":{"executedFeatures":["Not all tensor can be converted to images.","Tensor can be converted to buffered images."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":7},"title":"Tensors play well with other data structures!","narrative":"Tensors should have good interoperability with other JDK data structures like images.\n In this specification we define these interoperability requirements."},"ut.tensors.Tensor_Layout_Spec":{"executedFeatures":["A new transposed version of a given tensor will be returned by the \"T()\" method.","Matrix multiplication works for both column and row major matrices across devices."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":144},"title":"Row or Column Major. Why not both?","narrative":"Although Neureka exposes tensors as row major tensors from \n a users point of view, it does in fact support both row major and column major \n based tensor layout under the hood.\n Here we cover how the layout of tensors can be modified\n and we ensure the different tensor types still work as expected...\n (The features in this specification involve mutating tensors, be careful when playing around with this yourself)"},"ut.tensors.Tensor_Operation_Spec":{"executedFeatures":["Activation functions work across types on slices and non sliced tensors.","Auto reshaping and broadcasting works and the result can be back propagated.","New method \"asFunction\" of String added at runtime is callable by groovy and also works.","New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work.","Operators \"+,*,**\" produce expected results with gradients which can be accessed via a \"Ig[0]\" Function instance","Overloaded operation methods on tensors produce expected results when called.","Scalar broadcasting works across devices.","Simple slice addition produces expected result.","The \"dot\" operation reshapes and produces valid \"x\" operation result.","The \"matMul\" operation produces the expected result.","The \"random\" function/operation populates tensors randomly.","The transpose operation exposed by the \"T()\" method, supports autograd.","The values of a randomly populated tensor seems to adhere to a gaussian distribution.","You can do matrix multiplication using 2 transposed matrices.","You can do matrix multiplication using transposed matrices as second operand.","You can do matrix multiplication using transposed matrices."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":16,"totalFeatures":16,"passed":16,"successRate":1.0,"time":5000},"title":"Running Tensors through operations","narrative":"This specification shows how to use the tensor API to run tensors through various operations.\n Operations are triggered either by simply calling methods on tensors or by using \n `Function` objects which are used to define custom operations in the form \n of a syntax tree."},"ut.tensors.Tensor_Slicing_Spec":{"executedFeatures":["A tensor can be sliced by passing ranges in the form of lists (Groovy ranges).","A tensor can be sliced by passing ranges in the form of primitive arrays.","Normal slicing will try to do autograd.","Slicing is also a Function with autograd support!","The \"at\" method and the \"from\" / \"to\" methods can be mixed when slicing a tensor.","The slice builder also supports slicing with custom step sizes.","We can avoid autograd when slicing by using the \"detached\" instead of the \"get\" method.","We can slice a scalar tensor from a larger tensor of rank 4.","When Slicing only one axis using the SliceBuilder API, the other axes will be sliced implicitly."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":9,"totalFeatures":9,"passed":9,"successRate":1.0,"time":13},"title":"Tensors within Tensors","narrative":"ND-Array data structures can be \"sliced\" in the sense\n that one can create a subset view of the underlying data inside a tensor\n through a new tensor instance...\n This can be a tedious and complicated procedure.\n Therefore a tensor should expose a various user friendly API for slicing which\n are also fit for various languages.\n This specification covers these APIs for tensor slicing."},"ut.tensors.Tensor_State_Spec":{"executedFeatures":["A tensor can be instantiated from a item type class and nested lists.","Numeric tensors as String can be formatted on an entry based level.","Tensor created from shape and datatype has expected state.","Tensors as String can be formatted depending on shape.","Tensors as String can be formatted on an entry based level.","The data and the value of a tensor a 2 different things!","We can create scalar tensors."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":16},"title":"The Tensor Initialization and State Specification","narrative":"This specification defines the expected states of freshly instantiated\n and initialized tensors.\n After a tensor was created successfully we expect it \n to have certain properties like a shape, rank, type and data array\n among other things."},"ut.tensors.Tensor_Stats_Spec":{"executedFeatures":["A tensor can be summed alongside a specific axis.","Both the min and max operation support autograd (back-propagation).","Multiple dimensions of a tensor can selectively be summed up.","The sum operation support autograd (back-propagation).","There is no need to use a function, we can use the min() and max() methods on tensors instead.","We can get pre-instantiated min and max functions from the library context.","We can use the \"sum\" method to sum the items of a tensor.","We can use the max operation as a function"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":228},"title":"Reducing Tensors","narrative":"Various kinds of operations reduce tensors to scalars,\n the most common ones being the min and max operations \n which find the smallest as well as largest number among all \n items of a tensor.\n Neureka exposes various different ways to achieve this,\n all of which are also differential (autograd support)."},"ut.tensors.Tensor_Version_Spec":{"executedFeatures":["Inline operations cause illegal state exceptions.","Inline operations causes version incrementation.","Non-inline operations do not cause version incrementation.","Storing a tensor on a device should not change the version of a tensor (Even though its data changed technically)."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":57},"title":"Tensor (Data Array) Version","narrative":"There are two fundamental categories of operations\n which can be applied to tensors : \n Inline operations and Non-Inline operations! \n \n Inline operations are often times problematic because they produce\n side effects by changing passed tensors instead of producing new ones... \n One such bad side effect can easily occur for tensors involved in the\n autograd system, more specifically: the recorded computation graph. \n Inline operations can break the mathematically pureness of the back-propagation\n procedure by for example changing partial derivatives...
        \n In order to prevent said errors from occurring unnoticed tensors\n have versions which will increment when the underlying data of the tensor changes. \n This version will be tracked by the computation graph as well in order to\n match it with the ones stored inside the tensor. \n A mismatch would then yield an exception! \n \n This specification is responsible for defining the behaviour of this\n version number with respect to their wrapping tensors as well as computation graph nodes."},"ut.tensors.exceptions.Tensor_Delete_Exception_Spec":{"executedFeatures":["A deleted tensor will tell you that it has been deleted.","A deleted tensor will throw an exception when accessing its configuration.","A deleted tensor will throw an exception when accessing its data type.","A deleted tensor will throw an exception when accessing its data.","A deleted tensor will throw an exception when modifying its data type.","A deleted tensor will throw an exception when trying to modify its data.","A deleted tensor will throw an exception when trying to set its configuration."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":3},"title":"","narrative":""},"ut.tensors.exceptions.Tensor_Exception_Spec":{"executedFeatures":["Building a tensor with \"null\" as shape argument throws an exception.","Building a tensor with 0 shape arguments throws an exception.","Casting a tensor as something unusual will cuas an exception to be thrown.","Out of dimension bound causes descriptive exception!","Passing an invalid key object into the \"getAt\" method causes a descriptive exception.","Passing an invalid object into Tensor constructor causes descriptive exception.","Passing null to various methods of the tensor API will throw exceptions.","Trying to inject an empty tensor into another causes fitting exception."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":13},"title":"Tensors Exception Behavior","narrative":"This specification covers the behavior of the Tensor class in\n exceptional scenarios which are contrary to its intended use.\n The purpose of this is to assert that the Tensor class will provide\n useful feedback to a user to explain that a misuse of its API\n occurred so that the user can correct this misuse."},"ut.utility.Cleaner_Testing":{"executedFeatures":["The DeviceCleaner triggers registered cleaner actions when things are eligible for GC."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":252},"title":"How Neureka Cleans Up","narrative":"Under the hood \n Neureka deals whith large arrays of\n data, which are often times \n native data arrays requiring explicit\n memory freeing!\n This freeing of memory can happen at any time\n during the livetime of a nd-array, however\n it should happen at least up until the nd-arra/tensor\n objects representing their referenced data arrays become\n eligible for garbage collection.\n This specification ensures that the custom garbage\n cleaner implementation used by Neureka fulfills this role"},"ut.utility.DataConverter_Spec":{"executedFeatures":["An array of any type of object may be converted to a array of primitives.","The DataConverter can convert the given array data."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":2},"title":"","narrative":""},"ut.utility.FileHandle_Spec":{"executedFeatures":["Fully labeled tenors will be stored with their labels included when saving them as CSV.","Partially labeled tenors will be stored with their labels included when saving them as CSV.","Test reading IDX file format.","Test writing IDX file format.","The FileDevice component \"CSVHead\" can read CSV file formats and load them as tensors.","We can load image files as tensors."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":617},"title":"","narrative":""},"ut.utility.ListReader_Exception_Spec":{"executedFeatures":["The ListReader will detect inconsistent degrees of nesting in the provided data.","The ListReader will detect inconsistent types in the provided data."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":2},"title":"","narrative":""},"ut.utility.ListReader_Spec":{"executedFeatures":["The ListReader can interpret nested lists into a shape list and value list.","The ListReader can interpret nested lists resembling a 3D tensor into a shape list and value list.","The ListReader can interpret nested lists resembling a matrix into a shape list and value list."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":3},"title":"The Internal ListReader turning lists into flat arrays with shape and type data","narrative":"This specification covers an internal class which should not be used\n outside this library, namely the ListReader class.\n This class is simply a converter which turns nested lists\n into flat arrays alongside the type of the elements and the shape of this \"tensor\"."},"ut.utility.Utility_Spec":{"executedFeatures":["Object arrays can be converted to primitive arrays."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":6},"title":"","narrative":""},"ut.device.CPU_Spec":{"executedFeatures":["CPU knows the current number of available processor cores!","The CPU device will keep track of the amount of tensors it stores.","The CPU exposes a non null API for executing workloads in parallel.","Thread pool executes given workload in parallel"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":704},"title":"The CPU device, an API for CPU based execution","narrative":"The CPU class, one of many implementations of the Device interface, \n is simply supposed to be an API for dispatching threaded workloads onto the CPU.\n Contrary to other types of device, the CPU will host tensor data by default, simply\n because the tensors will be stored in RAM if no device was specified."},"ut.device.Cross_Device_Type_Spec":{"executedFeatures":["A device will keep track of the amount of tensors and data objects it stores.","Devices expose an API for accessing (reading and writing) the data of a tensor.","Devices store slices which can also be restored just like any other tensor.","Devices store tensors which can also be restored.","Execution calls containing null arguments will cause an exception to be thrown in device instances.","In total there are 3 different types of methods for finding device instances.","Passing a numeric array to a tensor should modify its contents!","Virtual tensors stay virtual when outsourced.","We can find Device implementations or null by passing search keys to the \"get\" method.","We can query the backend for devices by specifying both the requested type and a key word."],"ignoredFeatures":["Devices cannot store slices whose parents are not already stored."],"stats":{"failures":0,"errors":0,"skipped":1,"totalRuns":10,"totalFeatures":11,"passed":10,"successRate":1.0,"time":834},"title":"Finding Device Types","narrative":"Neureka introduces a the concept of a `Device` which is an interface\n that represents a computational device used for executing tensor / nd-array operations on them.\n The `Device` interface is implemented by various classes which represent\n different types of accelerator hardware such as `CPUs`, `GPUs`, `TPUs`, `FPGAs`, etc.\n These various `Device` types can not be instantiated directly because they model \n the concrete and finite hardware that is available on any given system Neureka is running on.\n This means that they are usually instantiated lazily upon access request or \n upfront by the library backend (usually a backend extension built fo a specific device).\n In order to find these instances embedded in the library backend the `Device` interface\n exposes various static methods which can be used to find a device instance by name or type."},"Example_Spec.Example_Spec":{"executedFeatures":["Call me feature not unit test!","I am readable and also best practice!","Numbers to the power of two with a fancy data table!","Should be able to remove from list","iAmNotSoReadable"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":5},"title":"An Introduction to writing Spock Specifications","narrative":"Hello and welcome to the example / template specification of this project.\n This is a simple introduction as to how to get started writing Spock specifications.\n \n Spock works on top of Groovy which is in essence a syntactic super-set of Java.\n That means that one can write Java code in Groovy, and 99% of the time it will \n work the exact same way."},"it.Calculus_Stress_Test":{"executedFeatures":["Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors.","Activation functions work across types.","Dot operation stress test runs error free and produces expected result","Stress test runs error free and produces expected result","The broadcast operation stress test runs error free and produces expected result"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":589},"title":"","narrative":""},"it.Cross_Device_Sliced_Tensor_System_Test":{"executedFeatures":["Cross device sliced tensor integration test runs without errors.","Slices can be created using the SliceBuilder."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":70},"title":"Cross Device Tensor Slicing","narrative":""},"it.Cross_Device_Spec":{"executedFeatures":["A gradient of ones can be set by calling the backward method on a tensor sitting on any device.","Convolution can model matrix multiplications across devices.","Cross device system test runs successfully.","Mapping tensors works for every device (even if they are not used).","Test simple NN implementation with manual backprop"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":8704},"title":"Cross Device Stress Test Specification","narrative":"This specification is pretty much a system test which covers\n the behavior of the library as a whole across multiple devices!\n No matter which device is being used for a given stress test, the result should be the same..."},"it.Eleven_Lines_NN_System_Spec":{"executedFeatures":["One can write a simple double based neural network in less than 11 lines of java like code using the \"@\" operator!","One can write a simple float based neural network in less than 11 lines of java like code!","One can write a simple neural network in less than 11 lines of code!","One can write a simple neural network with custom back-prop in 11 lines of code!","The pseudo random number generator works as expected for the weights used in the 11 line NN examples!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":281},"title":"NN Code Golfing!","narrative":"This system test specification uses the following Numpy\n code as reference implementation for the equivalent in Neureka\n or similar implementations and variations.\n The code below is a simple neural network in only 11 lines of code.\n\n \u00b4\u00b4\u00b4\n X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])\n y = np.array([[0,1,1,0]]).T\n W1 = 2*np.random.random((3,4)) - 1\n W2 = 2*np.random.random((4,1)) - 1\n for j in xrange(60000):\n l1 = 1/(1+np.exp(-(np.dot(X,W1))))\n l2 = 1/(1+np.exp(-(np.dot(l1,W2))))\n l2_delta = (y - l2)*(l2*(1-l2))\n l1_delta = l2_delta.dot(W2.T) * (l1 * (1-l1))\n W2 += l1.T.dot(l2_delta)\n W1 += X.T.dot(l1_delta)\n \u00b4\u00b4\u00b4"},"st.Benchmark_System_Test":{"executedFeatures":["Tensor can be constructed by passing List instances.","Test benchmark script and simple tensor constructor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":910},"title":"","narrative":""},"st.Broad_System_Test":{"executedFeatures":["The long broad integration test runs successfully."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":33},"title":"","narrative":""},"st.NN_Concepts_Spec":{"executedFeatures":["The attention mechanism (found in the commonly known transformer) demonstrated."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":140},"title":"Examining Neural Network Architecture Snippets","narrative":"This specification is intended to showcase some basic building blocks of \n various neural network architectures."},"st.Training_NNs_Spec":{"executedFeatures":["A simple 3 layer neural network converges.","A very simple 1 layer NN converges.","We can run the attention head test model."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":7071},"title":"Training a Neural Network Class","narrative":"When designing larger neural network architectures, what you would usually do is\n to create a class that represents the whole model (which itself might be composed\n of smaller models). \n \n This class would then represent something that can be executed and then trained.\n This Specification shows how to instantiate, execute and train various \n pre-defined example neural network models."},"ut.autograd.AD_And_Computation_Graph_Spec":{"executedFeatures":["Payloads and derivatives are null after garbage collection.","Reshaping produces expected computation graph and also works with reverse mode AD."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":605},"title":"","narrative":""},"ut.autograd.Autograd_Explained":{"executedFeatures":["Simple automatic differentiation and propagation."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":6},"title":"Autograd - Automatic Differentiation","narrative":"Central to all neural networks in Neureka is the autograd package. \n The autograd package provides automatic differentiation for all default operations on Tensors. \n Neureka is a define-by-run library, which means that your backpropagation is defined by how \n your code is run, and that every single iteration can be different. \n \n The class neureka.Tensor is the central class of the main package. \n If you set its attribute 'rqsGradient' to True, Neureka starts to track all operations on it. \n When you finish the forward pass of your network \n you can call .backward() and have all the gradients computed \n and distributed to the tensors requiring them automatically. \n \n The gradient for a tensor will be accumulated into a child tensor (component) which \n can be accessed via the '.getGradient()' method. \n \n To stop a tensor from tracking history, you can call '.detach()' to detach it from the \n computation history, and to prevent future computation from being tracked."},"ut.autograd.Autograd_Flags_Explained":{"executedFeatures":["Advanced backpropagation on all AD-Modes ","We can create a shallow copy of a tensor detached from the computation graph."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":9,"totalFeatures":2,"passed":9,"successRate":1.0,"time":48},"title":"","narrative":""},"ut.autograd.Autograd_NN_Spec":{"executedFeatures":["Autograd work for simple matrix multiplications.","Autograd works for 2 matrix multiplications in a row.","Autograd works in a simple convolutional dot product and float based feed forward neural network.","Autograd works in a simple convolutional dot product based feed forward neural network.","Autograd works in a simple mat-mul based feed forward neural network."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":76},"title":"Simple Neural Network autograd integration test","narrative":"The integration test below has been implemented by using\n the following code and the result it produces as reference : \n https://medium.com/dair-ai/a-simple-neural-network-from-scratch-with-pytorch-and-google-colab-c7f3830618e0 \n \n The following seed has been used to assure reproducibility :\n 'torch.manual_seed(503672689411)'"},"ut.autograd.Autograd_Tensor_Spec":{"executedFeatures":["A tensor used as derivative within a computation graph will throw exception when trying to deleting it.","Second-Test \"x-mul\" autograd behaviour. (Not on device)","Test basic autograd behaviour. (Not on device)"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":15},"title":"","narrative":""},"ut.autograd.JITProp_Autograd_Tensor_Spec":{"executedFeatures":["Gradient auto-apply kicks in when used AD uses JIT prop","Test JIT propagation variant one.","Test JIT propagation variant two.","Test autograd without JIT and auto apply.","Test in-differential and JIT with auto apply","Test no JIT prop when forward AD","Test no preemptive gradient apply when not requested and auto apply and JIT_prop","Test pending error optimization"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":121},"title":"","narrative":""},"ut.autograd.internal.GraphNode_Instantiation_Exception_Unit_Tests":{"executedFeatures":["GraphNode instantiation throws exception because tensors of ExecutionCall do not return GraphNode instances.","GraphNode throws an exception when trying to execute an inline operation on inputs with active autograd."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":71},"title":"","narrative":""},"ut.autograd.internal.GraphNode_Tensor_Exception_Unit_Tests":{"executedFeatures":["A tensor cannot be deleted if it is part of a graph and the tensor is used as derivative."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":4},"title":"","narrative":""},"ut.backend.Backend_Extension_Spec":{"executedFeatures":["Mock operation interacts with FunctionNode (AbstractFunction) instance as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":58},"title":"","narrative":""},"ut.backend.Backend_MatMul_Extension_Spec":{"executedFeatures":["GEMM matrix multiplication reference implementation can be set as custom OperationType and works as expected.","Test context mock for opencl reference implementations.","Tile parsing for kernel parameter calculation yields expected tile dimensions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":53},"title":"","narrative":""},"ut.backend.Matrix_Multiplication_Spec":{"executedFeatures":["The \"matMul\" method allows us to perform matrix multiplication.","The simple CPU matrix multiplication implementation works as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":21},"title":"Matrix Multiplication","narrative":"The tensor API exposes a useful method for Matrix Multiplication.\n This specification not only demonstrates how to use this method\n but also shows how matrix multiplication work \n for tensors with both row and column major layouts.\n (typically, column major is faster)"},"ut.backend.core.Backend_Algorithm_AD_Spec":{"executedFeatures":["Activation implementations behave as expected.","Broadcast implementations have expected properties.","Convolution implementations behave as expected.","Operator implementations behave as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":46},"title":"","narrative":""},"ut.backend.core.Backend_Algorithm_Implementation_Spec":{"executedFeatures":["Activation implementations have expected Executor instances.","HostExecutors of Operator implementations behave as expected.","Operator implementations have expected Executor instances."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":60},"title":"","narrative":""},"ut.backend.core.Backend_Functional_Algorithm_Spec":{"executedFeatures":["A functional algorithm cannot be used if it was not built properly!","A functional algorithm does not accept null as an answer!","A functional algorithm warns us when modified after it has been built!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":11},"title":"","narrative":""},"ut.backend.core.Matrix_Multiplication_Spec":{"executedFeatures":["The CPU matrix multiplication implementation works as expected.","The internal matrix multiplication test script runs!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":1154},"title":"Internal CPU based Matrix Multiplication","narrative":"This specification covers library internal matrix multiplication logic,\n specifically the CPU implementation.\n Do not depend on the API used in this specification as it is subject to change!"},"ut.backend.core.OpenCL_Backend_Spec":{"executedFeatures":["The OpenCL backend context can load implementations."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":6},"title":"","narrative":""},"ut.backend.core.Randomization_Spec":{"executedFeatures":["Randomization is in essence the same algorithm as JDKs \"Random\".","The Randomization class can fill various types of arrays with pseudo random numbers.","We can make slices of tensors random."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":18},"title":"","narrative":""},"ut.device.Cross_Device_IO_Spec":{"executedFeatures":["We can use the access 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directory."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":48},"title":"FileDevice, Storing Tensors in Files","narrative":"The `FileDevice` class, one of many implementations of the `Device` interface, \n represents a file directory which can store and load tensors as files (`idx`, `jpg`, `png`...)."},"ut.device.OpenCLDevice_Exception_Spec":{"executedFeatures":["Ad hoc compilation produces expected exceptions when duplication is found.","Ad hoc compilation produces expected exceptions.","An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.","Trying to restore a tensor which is not on a device raises exception."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":79},"title":"OpenCLDevice Exception Handling","narrative":"The OpenCLDevice class, one of many implementations of the Device interface, \n represents physical OpenCL devices.\n This specification defines how instances of this class deal with exceptional information."},"ut.device.OpenCLDevice_Spec":{"executedFeatures":["Ad hoc compilation produces executable kernel.","Ad hoc compilation works for WIP general purpose matrix multiplication.","Ad hoc compilation works for custom column major based tiled matrix multiplication.","Ad hoc compilation works for custom simple row major based matrix multiplication.","Ad hoc matrix multiplication works for multiple of 16 matrices.","An OpenCLDevice loads tensors in a provided lambda temporarily.","We can get the items of an outsourced tensor as a primitive array.","We can take a look at the underlying data array of an outsourced tensor through the unsafe API."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":2217},"title":"The OpenCLDevice Specification","narrative":"Tensors need devices for execution!\n By default tensors use the `CPU` device, but sometimes we want to\n use something more suitable for large amounts of data and a high degree of parallelization.\n This is were the `OpenCLDevice` comes into play!\n It is a `Device` implementation built on top of the JOCL library, a thin OpenCL API.\n We expect the `OpenCLDevice` to store tensors as well as being able to read and write\n data from and to stored tensors.\n Also, an `OpenCLDevice` should allows us to compile OpenCL kernel code on the fly..."},"ut.device.OpenCL_Spec":{"executedFeatures":["A given OpenCL context can be disposed!","An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.","First found OpenCLDevice will have realistic numeric properties.","First found OpenCLDevice will have realistic properties inside summary query.","First found OpenCLDevice will have realistic text properties."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":3009},"title":"Working with OpenCL","narrative":"Neureka models the OpenCL API through various types of classes.\n The most fundamental of these is the `OpenCLDevice` class which\n represents a single device with OpenCL support.\n Besides that, there is also the `OpenCLContext` class which\n represents a OpenCL contexts, platforms and multiple devices on said platforms..."},"ut.device.internal.CLFunctionCompiler_Spec":{"executedFeatures":["The CLFunctionCompiler produces an operation which properly integrates to the backend.","The CLFunctionCompiler produces the expected \"ad hoc\" kernel.","The OpenCLDevice produces a working optimized Function for doubles.","The OpenCLDevice produces a working optimized Function for floats."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":170},"title":"Turning functions into kernels.","narrative":"Neureka parses mathematical expressions into an AST representation\n hidden behind the Function interface...\n This feature does not exist without reason, we can use\n this abstract syntax tree to compile to OpenCL kernels\n for optimal execution speed!"},"ut.device.internal.CPU_Kernel_Spec":{"executedFeatures":["The Reduce implementation for the CPU has realistic behaviour","The Sum implementation for the CPU has realistic behaviour"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":27},"title":"","narrative":""},"ut.device.internal.OpenCL_Data_Spec":{"executedFeatures":["The \"Data\" class can represent various OpenCL data types.","The OpenCLDevice specific Data class represents JVM data for OpenCL."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":19},"title":"","narrative":""},"ut.device.internal.OpenCL_Kernel_Unit_Spec":{"executedFeatures":["The CLDot implementation for the OpenCLDevice has realistic behaviour","The GEMM implementation for the OpenCLDevice has realistic behaviour","The Reduce implementation for the OpenCLDevice has realistic behaviour","The Sum implementation for the OpenCLDevice has realistic behaviour","The Sum implementation for the OpenCLDevice has realistic behaviour for when the number of elements is a prime."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":12},"title":"","narrative":""},"ut.dtype.DataType_Spec":{"executedFeatures":["DataType multi-ton instances behave as 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method for converting and representing \n these numeric data types using JVM types."},"ut.framing.Tensor_Framing_Spec":{"executedFeatures":["A matrix (rank 2 tensor) can be labeled and their labels can be used to extract slices / subsets.","A tensor can be labeled partially.","Rank 3 tensors can be labeled and their labels can be used to extract slices / subsets of tensors.","We can add labels to tensors through lists or maps passed to the \"label()\" method."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":14},"title":"Naming Tensors and their Dimensions.","narrative":"A powerful concept in the data science as well as machine learning\n world is something usually referred to as \"Data Frames\".\n These are highly flexible 2D data structures\n used to load and store CSV, CRV, etc... files for \n data exploration and further processing.\n Data frames are so powerful because\n their indices are labeled and therefore human readable.\n Neureka's tensors are general purpose data containers\n which may also stored data in 2 dimensions whose\n indices may also be something other than integers."},"ut.introductions.Tensor_NDArray_Spec":{"executedFeatures":["Tensor is a subtype of NdArray.","We can use tensors for numeric calculations (but not nd-arrays)."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":3},"title":"Tensors or Nd-arrays","narrative":"*What is the difference?*\n\nIn the world of machine learning we use something called a **'tensor'** to represent data.\nThey might be called **'nd-arrays'** in some other frameworks,\nbut although they are very similar, \nthere are also some important distinctions to be made between these two concepts.\nBoth are at their core merely multidimensional arrays, however,\nthey are different in their typical usage and API.\nnd-arrays are merely used to represent any type of data as a \ncollection of elements in a multidimensional grid, \ntensors on the other hand have additional requirements.\nThey are a type of nd-array which stores numeric data \nas well as expose various mathematical operations for said data.\nIn that sense it is actually merely a more complex kind of number.\nThis concept actually comes from the field of physics, \nwhere it is used to represent a physical quantity.\n\nNeureka models both concepts through the `Tensor` and the `Nda` interfaces.\n`Nda` is an abbreviation of `NdArray`, and `Tensor` is an abbreviation of `Tensor`.\nThe `Tensor` type is a subtype of the `Nda` type, exposing additional methods\nlike for example `plus`, `minus`, `times` and `divide`.\nBoth can be instantiated through static factory methods (and a fluent builder API)."},"ut.math.BackendContext_Spec":{"executedFeatures":["BackendContext instances can be created by cloning from Singleton instance.","BackendContext instances return Runner instances for 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back-propagate 3 simple float tensors alongside a specified axis!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":17},"title":"Merging Tensors","narrative":"Tensors can not only be sliced, but also merged.\n This is most easily achieved through the concatenation operation, \n which stacks 2 tensors alongside a specified axis.\n This specification not only covers how you can concatenate tensors,\n but also how this works alongside autograd and non-numeric tensors."},"ut.math.Function_Exception_Spec":{"executedFeatures":["Function throws exception when arity does not match input number.","Function throws exception when not enough inputs provided."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":1},"title":"","narrative":""},"ut.math.Function_Parsing_Spec":{"executedFeatures":["Functions can derive themselves according to the provided index of the input which ought to be derived.","Parsed equations throw expected error messages.","Test parsed equations when building Function instances."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":20},"title":"Parsing Expressions into Functions","narrative":"Neureka uses the 'Function' interface as a representation of a\n nested structure of operations.\n This means that a 'Function' is simply an abstract syntax trees made up of other 'Function' implementations\n which are assembled together by a parser receiving a string expression.\n In this specification we ensure that function expressions will be properly parsed into\n 'Function' implementations."},"ut.math.Function_Scalar_Spec":{"executedFeatures":["Function \"(I[0]+1/I[0])**-I[0]\" instance returns expected scalar result.","Function \"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\" instance returns expected scalars.","Function \"1/I[0]\" instance returns expected scalar results.","Function \"I[0]+1/I[0]\" instance returns expected scalar results.","Test scalar results of Function \"sumjs((cos(I[j]*5)/5+I[j])*(1+sin(I[j])/2))\" instance.","Test scalar results of various Function instances."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":27},"title":"Functions for Scalars","narrative":"The Function API and it's implementations \n receive and process arrays of scalars as arguments.\n Functions don't have to be used alongside tensors / nd-arrays,\n they can also compute derivatives based on scalar values."},"ut.math.Function_Spec":{"executedFeatures":["Function implementations ensure that internally created tensors are flagged as \"intermediate\" initially!","Function implementations ensure that outputs which are input members are not flagged as \"intermediate\"!","Function implementations will ensure the \"call\" and \"invoke\" does not return tensors flagged as \"intermediate\".","The library context exposes a set of useful functions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":23},"title":"Testing Default Methods on Functions","narrative":"This specification tests the default methods on functions\n through a simple dummy implementation of the Function interface."},"ut.math.Tensor_Function_Spec":{"executedFeatures":["Executed tensors are intermediate tensors.","Reshaping on 3D tensors works by instantiate a Function instance built from a String.","Tensor results of various Function instances return expected results.","The \"DimTrim\" operation works forward as well as backward!","The optimization function for the SGD algorithm produces the expected result","The softmax can be calculated alongside multiple axes.","The softmax can be calculated for a particular axis.","The softmax function can be applied to tensors with more than one dimension.","The tensor API has built-in methods for applying functions.","We can collect a stream into a tensor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":10,"totalFeatures":10,"passed":10,"successRate":1.0,"time":80},"title":"Applying Functions to Tensors","narrative":"A tensor would be nothing without being able to apply operations on them.\n However, calling operations manually in order to process your\n tensors can be a verbose and error prone task.\n This is where functions come into play.\n Neureka's functions are composed of operations forming an abstract syntax tree.\n Passing tensors to a function will route them trough this tree and apply\n all of the operations on the tensors for you."},"ut.miscellaneous.Weired_NN_Spec":{"executedFeatures":["Dot based feed forward and activation produces expected result."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":19},"title":"","narrative":"This specification is meant less as feature documentation and more as a\n chaos test for weired neural network architectures\n an unusual usages of the Neureka library."},"ut.ndas.Nda_Assign_Spec":{"executedFeatures":["Assignment can be easily achieved through subscription operators.","We can assign one slice into another one.","We can use the \"mut\" API to assign the contents of one nd-array into another one."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":2},"title":"Nda Inline Assignment","narrative":"In this specification we cover the behaviour of nda's with respect to the assignment operation\n as well as the assignment of individual Nda items."},"ut.ndas.Nda_Framing_Spec":{"executedFeatures":["An Nda can be labeled.","Concatenating 2 labeled nd-arrays will produce a nd-array which is also labeled.","The slice of a labeled vector is labeled too.","We can label the columns and rows of a rank 3 nd-array.","We can label the columns of a rank 2 nd-array.","We can use labels as selectors for slicing."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":15},"title":"Nda framing","narrative":"Immutability is a core concept of the Neureka library.\n This means that the Nda API does not expose mutability directly.\n Instead, the API exposes methods that return new instances of Nda\n that are derived from the original instance.\n \n This is also true for labeling operations, \n meaning that the Nda API does not directly expose methods that mutate labels of an Nda\n but instead provides methods that return new instances of Nda\n with different labels.\n \n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!"},"ut.ndas.Nda_Inplace_Framing_Spec":{"executedFeatures":["Concatenating 2 labeled nd-arrays will produce a nd-array which is also labeled.","The slice of a labeled vector is labeled too.","We can concatenate more than 2 nd-arrays.","We can label the columns and rows of a rank 3 nd-array.","We can label the columns of a rank 2 nd-array.","We can use labels as selectors for slicing."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":5},"title":"NDA Framing","narrative":"Framing an nd-array is all about naming its axes and then using those names to\n access, read or write its values in a more convenient and human readable way."},"ut.ndas.Nda_Instantiation_Spec":{"executedFeatures":["A vector can be created from an array of values through the \"of\" method.","Common types of nd-arrays are best instantiated using type specific convenience methods.","ND-arrays can be created fluently."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":4},"title":"ND-Array Instantiation","narrative":"In this specification we cover how ND-arrays can be instantiated."},"ut.ndas.Nda_Items_Spec":{"executedFeatures":["An item can be converted to an Optional object.","Other than the \"orElse(T)\" method of the Optional class, the same method of an Item will throw an exception if the provided value is null.","The \"get\" method of an Item object will throw an exception if the item is missing.","We can check if items of a tensor is present or not.","We can get the value of an item.","We can use the \"orElse(T)\" method to avoid null values."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":4},"title":"The Nds Items API","narrative":"Nd-arrays are collections of items similar to other\n collection types in Java. \n One useful way to access the items of an nd-array is\n to use the items API.\n \n Using the `at` methods we can access an `Item` object\n which is a wrapper around the item's value and its\n index in the nd-array.\n \n The `Item` object is a simple data class which\n is very similar to the `Optional` class, meaning\n that it can either be empty or contain a value."},"ut.ndas.Nda_Mutation_Spec":{"executedFeatures":["A ND-Array can be mutated simply using the \"set\" method.","A ND-Array can be mutated using the \"at(..).set(..)\" methods.","A simple vector ND-Array can be mutated using the \"at(..).set(..)\" methods.","A simple vector ND-Array can be mutated using the \"setItemAt\" method.","We can use the subscription operator to mutate a simple vector ND-Array.","We can use the subscription operator to mutate an ND-Array."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":4},"title":"Mutating ND-Arrays","narrative":"ND-Arrays should be considered immutable, so we should prefer creating new \n ND-Arrays from existing ones using wither methods.\n However this is not always a good idea as it can be expensive to create new\n ND-Arrays, especially if the ND-Array is very large.\n The ability to mutate ND-Arrays is therefore provided, but only\n accessible via the mutation API exposed by the `getMut()` method."},"ut.ndas.Nda_Reshape_Spec":{"executedFeatures":["We can create a new Nda instance with a different shape."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":1},"title":"Nda Reshaping","narrative":"Immutability is a core concept of the Neureka library.\n This means that the Nda API does not expose mutability directly.\n Instead, the API exposes methods that return new instances of Nda\n that are derived from the original instance.\n \n This is also true for reshaping operations, \n meaning that the Nda API does not expose methods that mutate the shape of an Nda\n but instead provides methods that return new instances of Nda\n with a different shape.\n \n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!"},"ut.ndim.NDConfiguration_Spec":{"executedFeatures":["Various NDConfigurations behave exactly like their general purpose implementation."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":24},"title":"Making Arrays N-Dimensional","narrative":"Under the hood Neureka implements powerful indexing \n abstractions through the `NDConfiguration` interface and its various implementations.\n This allows for the creation of tensors/nd-arrays with arbitrary dimensions, \n the ability to slice them into smaller tensors/nd-arrays with the same underlying data,\n and finally the ability to permute their axes (like transposing them for example).\n \n This specification however only focuses on the behaviour of the `NDConfiguration` interface\n which translates various types of indices."},"ut.ndim.Nda_Permute_Spec":{"executedFeatures":["We can use the \"permute\" method to rearrange the dimensions of an nd-array.","We can use the \"transpose\" method to transpose swap 2 dimensions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":3},"title":"Reshaping Nd-Arrays","narrative":"Permuting an N-dimensional array means rearranging the dimensions/axes of the N-dimensional array.\n It returns a new nd-array with the same data as the original nd-array, \n but with the specified dimensions rearranged. \n It is very useful for example when you want to\n change the order of dimensions, for example, if you have a nd-array with dimensions (batch_size, channels, height, width), \n you can use permute() to rearrange the dimensions to (batch_size, height, width, channels).\n Another useful application of permute() is transposing a matrix.\n For example, if you have a matrix with dimensions (rows, columns), \n you can use permute() to rearrange the dimensions to (columns, rows).\n \n Permuting is a very cheap operation because it does not copy any data but merely\n creates a new view on the same data with a different access pattern."},"ut.ndim.Shape_Spec":{"executedFeatures":["A shape can be created from a list of integers.","A shape can be created from a stream of ints.","A shape can be created from an iterable.","A shape can be mapped to a new shape.","A shape can be sliced.","Use the \"any\" or \"every\" method to check if a predicate holds for any or every value of the shape.","You can use the \"count(Predicate)\" method to count the number of values that satisfy a predicate."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":3},"title":"The Shape Tuple","narrative":"The `Shape` of an nd-array/tensor is in essence merely an immutable tuple of integers\n which define the size of each dimension of the tensor.\n So if you think of an nd-array as a grid of numbers, then the shape of the\n tensor is the size of the grid in each dimension.\n \n This specifications shows you how to create a shape and how to use it."},"ut.ndim.Tensor_NDConfiguration_Spec":{"executedFeatures":["NDConfiguration instances of tensors have expected state and behaviour.","NDConfiguration instances of tensors have expected state."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":3},"title":"What it means to be N-Dimensional","narrative":"This specification covers how implementations\n of the `NDConfiguration` interface manage to define\n what it means to be a n-dimensional tensor/nd-array."},"ut.ndim.Tensor_Permute_Spec":{"executedFeatures":["We can use the \"permute\" method to rearrange the dimensions of a tensor.","When matrices are transpose, they will change their layout type as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":3},"title":"Reshaping Tensors","narrative":"Permuting an N-dimensional array means rearranging the dimensions/axes of the N-dimensional array.\n It produces a new tensor with the same data as the original tensor, \n but with the specified dimensions rearranged. \n \n This is very useful for example when you want to\n change the order of dimensions, for example, if you have a tensor with dimensions (batch_size, channels, height, width), \n you can use permute() to rearrange the dimensions to (batch_size, height, width, channels).\n Another useful application of permute() is transposing a matrix.\n For example, if you have a matrix with dimensions (rows, columns), \n you can use permute() to rearrange the dimensions to (columns, rows).\n \n Permuting is a very cheap operation because it does not copy any data but merely\n creates a new view on the same data with a different access pattern."},"ut.ndim.Tensor_Slice_Permute_Spec":{"executedFeatures":["A slice of a tensor changes as expected when reshaping it.","Reshaping a slice works as expected.","Two slices of one big tensor perform matrix multiplication flawless."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":5},"title":"Permuting Slices of Tensors","narrative":"Neureka provides a convenient way to permuting tensors\n even if they are slices of other tensors sharing the same underlying data.\n This is possible because of the under the hood indexing \n abstractions provided by the `NDConfiguration` interface and its various implementations."},"ut.neureka.Neureka_Spec":{"executedFeatures":["Backend related library objects adhere to the same toString formatting convention!","Every Thread instance has their own Neureka instance.","Neureka class instance has expected behaviour.","Neureka settings class can be locked causing its properties to be immutable.","OpenCL related library objects adhere to the same toString formatting convention!","Various library objects adhere to the same toString formatting convention!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":8598},"title":"The Neureka context can be used and configured as expected.","narrative":"This specification covers the behavior of the Neureka class which\n exposes a global API for configuring thread local contexts and library settings.\n The purpose of this is to assert that the API exposed by the Neureka class \n is both thread local and configurable.\n This specification also exists to cover standards for the Neureka library in general."},"ut.optimization.ADAM_Spec":{"executedFeatures":["ADAM optimizes according to expected inputs","Equations \"I[0]*I[1]+(1-I[2])*I[3]\" and \"(1-I[0])*I[1]\" used within ADAM return expected results.","Equations used by ADAM return expected result."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":50},"title":"","narrative":"ADAM is a more powerful alternative to the classical stochastic gradient descent. \n It combines the best properties of the AdaGrad and the RMSProp algorithms, which makes \n it especially well suited for sparse gradients and noisy data.\n Adam is the most popular among the adaptive optimizers\n because its adaptive learning rate working so well with sparse datasets."},"ut.optimization.AdaGrad_Spec":{"executedFeatures":["AdaGrad optimizes according to expected inputs"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":13},"title":"","narrative":""},"ut.optimization.Momentum_Spec":{"executedFeatures":["Momentum optimizes according to expected inputs"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":7},"title":"","narrative":"Momentum is an extension to the gradient descent optimization \n algorithm that allows the search to build inertia in a direction \n in the search space and overcome the oscillations of noisy \n gradients and coast across flat spots of the search space."},"ut.optimization.RMSProp_Spec":{"executedFeatures":["RMSprop optimizes according to expected inputs"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":14},"title":"","narrative":"**Root Mean Squared Propagation**, or RMSProp, is an extension of gradient \n descent and the AdaGrad version of gradient descent that uses a \n decaying average of partial gradients in the adaptation of the \n step size for each parameter."},"ut.tensors.Copy_Spec":{"executedFeatures":["A deep copy of a slice tensor is also a deep copy of the underlying data array.","A deep copy of a tensor is also a deep copy of the underlying data array.","A shallow copy of a tensor will be flagged as such.","A shallow copy will share the same underlying data as its original tensor.","We can deep copy various types of tensors."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":13},"title":"To Copy or Not to Copy","narrative":"In this specification we cover the behaviour of tensors with respect to their copy methods.\n There are to main ways to copy a tensor:
        \n 1. .shallowCopy()
        \n 2. .deepCopy()
        \n
        \n The first method creates a new tensor with the same underlying data array as the original tensor.
        \n The second method on the other hand creates a new tensor with a new data array.
        \n
        \n The first method is the most efficient, but it is not as safe as the second method.
        \n The second method is the most safe, but it is not as efficient.
        \n
        \n Besides these 2 main requirements, there are als some corner cases with respect to\n the components of a tensor (like for example its computation graph) which\n will be covered in this specification as well."},"ut.tensors.DimTrim_Spec":{"executedFeatures":["The \"dimTrim\" operation works on slices too!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":2},"title":"The 'dimTrim' Method","narrative":"The 'dimTrim' method is used to remove training and leading dimensions of length 1 from a tensor.\n This is useful when you want to perform operations on tensors of different ranks.\n For example, if you want to perform a dot product on two vectors, you can use the 'dimTrim' method\n to remove the dimension of length 1 from the vector, so that it becomes a scalar.\n This way you can perform the dot product on two scalars."},"ut.tensors.Expression_Based_Tensor_Instantiation_Spec":{"executedFeatures":["A tensor can be created from a function as expression.","We can instantiate tensors from various simple string expressions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":3},"title":"Expression based Tensor Instantiation","narrative":"This specification defines how a tensor can be instantiated\n using string expressions, which define operations to be executed.\n This form of tensor instantiation is very useful to avoid boilerplate code."},"ut.tensors.Fluent_Tensor_Creation_Spec":{"executedFeatures":["Initialization lambda based tensors can be created fluently.","Range based tensors can be created fluently.","Scalars can be created fluently.","Seed based tensors can be created fluently.","Tensors can be created fluently.","Value based tensors can be created fluently.","Vectors can be created fluently."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":18},"title":"","narrative":""},"ut.tensors.Functional_Nda_Spec":{"executedFeatures":["ND-Array mapping lambdas produce expected nd-arrays.","The \"map\" method is a shorter convenience method for mapping to the same type.","We can analyse the values of a nd-array using various predicate receiving methods","We can collect a stream into a nd-array.","We can find both min and max items in a tensor by providing a comparator.","We can find both min and max items in an ND-array by providing a comparator.","We can initialize an ND-Array using a filler lambda mapping indices to items.","We can use the \"filter\" method as a shortcut for \"stream().filter(..)\".","We can use the \"flatMap\" method as a shortcut for \"stream().flatMap(..)\"."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":9,"totalFeatures":9,"passed":9,"successRate":1.0,"time":273},"title":"Functional ND-Arrays","narrative":"ND-Arrays expose a powerful API for performing operations on them\n in a functional style."},"ut.tensors.Functional_Tensor_Spec":{"executedFeatures":["Tensor mapping lambdas produce expected tensors.","The \"map\" method is a shorter convenience method for mapping to the same type.","We can analyse the values of a tensor using various predicate receiving methods","We can find both min and max items in a tensor by providing a comparator.","We can initialize a tensor using a filler lambda mapping indices to items.","We can use the \"filter\" method as a shortcut for \"stream().filter(..)\".","We can use the \"flatMap\" method as a shortcut for \"stream().flatMap(..)\"."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":301},"title":"Functional Tensors","narrative":"Tensors expose a powerful API for performing operations on them\n in a functional style."},"ut.tensors.Reshape_Spec":{"executedFeatures":["The reshape operation supports autograd!","We can create a new tensor with a different shape.","We can use `-1` in the desired shape if we want the axis size to be determined automatically."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":2},"title":"Tensor Reshaping","narrative":"This specification demonstrates how to reshape tensors,\n which means to change the shape of a tensor.\n\n Note that immutability is a core concept of the Neureka library.\n This means that the `Tensor` API does not expose mutability directly.\n Instead, it exposes methods that return new instances of `Tensor`\n that are derived from the original instance.\n \n This is also true for reshaping operations.\n \n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!"},"ut.tensors.Tensor_As_Container_Spec":{"executedFeatures":["More tensor operations translate to custom data type \"ComplexNumber\".","Plus operator on String tensors works element-wise.","Tensor operations translate to custom data type \"ComplexNumber\".","We can apply predicates on the values of a tensor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":9},"title":"Why not have a tensor of words?","narrative":"Technically, tensors are merely fancy ND-arrays with some useful mathematical operations\n applicable to them...\n Therefore, there is no reason why a tensor would not also be able to store\n other kinds of objects besides numbers like strings for example.\n This specification ensures that tensors can hold and index many other things..."},"ut.tensors.Tensor_Assign_Spec":{"executedFeatures":["Assignment can be easily achieved through subscription operators.","We can assign one slice into another one.","We can use the \"mut\" API to assign the contents of one tensor into another one."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":3},"title":"Tensor Inline Assignment","narrative":"In this specification we cover the behaviour of tensors with respect to the assignment operation\n as well as the assignment of individual tensor items."},"ut.tensors.Tensor_Conversion_Spec":{"executedFeatures":["Tensors value type can be changed by calling \"toType(...)\".","We can change the data type of all kinds of tensors.","We turn a tensor into a scalar value or string through the \"as\" operator!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":11},"title":"Tensor Type Conversion","narrative":"Here we specify how a tensor can be converted to other data types\n like for example another tensor of a different data type."},"ut.tensors.Tensor_Convolution_Spec":{"executedFeatures":["Autograd works with simple 2D convolution.","Convolution can be performed using non-quadratic matrix tensors.","Convolution can be performed using tensors with an additional dimension as batch size.","Convolution with tensors of the same shape is equivalent to a dot product.","Manual convolution produces expected result.","Sime convolution works as expected eith autograd.","Tensors have the correct layout after convolution.","The \"x\" (convolution) operator produces expected results (On the CPU).","Very simple manual convolution produces expected result.","We can perform a convolution operation on a 2D tensor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":10,"totalFeatures":10,"passed":10,"successRate":1.0,"time":639},"title":"Tensor Convolution","narrative":"This specification shows how convolution can be performed on tensors.\n\n Convolution is a linear operation which is not only important for image processing but also\n a central player in the field of machine learning (especially for computer vision).\n It is used to extract features from images and other typically ~2 dimensional data.\n Other than that it is extremely important in the field of signal processing."},"ut.tensors.Tensor_Device_Spec":{"executedFeatures":["Adding OpenCL device to tensor makes tensor be \"outsourced\" and contain the Device instance as component.","Tensors try to migrate themselves to a device that is being added to them as component.","The device of a tensor can be accessed via the \"device()\" method.","When creating slices of tensors then this should trigger a \"parent - child\" relation noticeable to the device!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":4},"title":"Tensors on Devices","narrative":"This unit test specification covers \n the expected behavior of tensors when interacting\n with instances of implementations of the Device interface."},"ut.tensors.Tensor_Dot_Product_Spec":{"executedFeatures":["The \"dot\" method calculates the dot product between vectors.","The \"dot\" operation supports autograd.","The dot operation work even when one tensor is virtual.","The dot operation works for virtual tensors as well.","The dot product operation runs on any device.","The dot product works across different types and devices.","You can slice a Matrix into vectors and then used them for dot products."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":137},"title":"Tensor Dot Products","narrative":"A tensor can also be a simple vector, which is a tensor of rank 1.\n This specification demonstrates how to perform dot products on tensors of rank 1."},"ut.tensors.Tensor_Generics_Spec":{"executedFeatures":["1D tensors can be created from primitive arrays.","Anonymous tensor instance has the default datatype class as defined in Neureka settings.","We can create a tensor of strings."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":4},"title":"Tensors as Generic Containers","narrative":"Tensors do not just store numeric data.\n They can hold anything which can be stuffed into a \"Object[]\" array.\n You could even create a tensor of tensors!"},"ut.tensors.Tensor_Gradient_Spec":{"executedFeatures":["Gradient of tensor is being applies regardless of the tensor requiring gradient or not","Tensors can have gradients but not require them.","Tensors that have gradients but do not require them still print them."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":3},"title":"Gradients are Tensors which are Components of other Tensors","narrative":"This specification defines the gradient API on tensors.\n So one ought to be able to check wetter or not a tensor has a gradient attached to it or not.\n In that case one should be able to get this gradient and then work with\n it independently of the original tensor to which it belongs to..."},"ut.tensors.Tensor_IO_Spec":{"executedFeatures":["A tensor produced by the static \"Tensor.newRandom(shape)\" has expected \"random\" value.","Indexing after reshaping works as expected.","Tensor value type can not be changed by passing float or double arrays to it.","Tensor values can be manipulated","The tensor data array can be modified by targeting them with an index.","We can manipulate the underlying data array of a tensor through the mut API.","We can re-populate a tensor of shorts from a single scalar value!","When we try to manipulate the underlying data array of a virtual tensor then it will become actual."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":31},"title":"Reading and Writing Tensor Items","narrative":"Tensors are complicated data structures with a wide range of different possible states.\n They can host elements of different types residing on many kinds of different devices.\n Here we want to read from and write to the state of a tensor."},"ut.tensors.Tensor_Instantiation_Spec":{"executedFeatures":["A matrix tensor can be instantiated using lists for it's shape and values.","A simple 2D vector can be instantiated using lists for it's shape and values.","Passing a seed in the form of a String to a tensor produces pseudo random items.","Scalar tensors can be created via static factory methods","Tensors can be instantiated based on arrays for both shapes and values.","Tensors can be instantiated with String seed.","Vector tensors can be instantiated via factory methods."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":11},"title":"Instantiating Tensors","narrative":"Tensors are complicated data structures with a wide range of different possible states.\n They can host elements of different types residing on many kinds of different devices.\n Here we want to show how a tensor can be instantiated in different ways."},"ut.tensors.Tensor_Interop_Spec":{"executedFeatures":["Not all tensor can be converted to images.","Tensor can be converted to buffered images."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":8},"title":"Tensors play well with other data structures!","narrative":"Tensors should have good interoperability with other JDK data structures like images.\n In this specification we define these interoperability requirements."},"ut.tensors.Tensor_Layout_Spec":{"executedFeatures":["A new transposed version of a given tensor will be returned by the \"T()\" method.","Matrix multiplication works for both column and row major matrices across devices."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":133},"title":"Row or Column Major. Why not both?","narrative":"Although Neureka exposes tensors as row major tensors from \n a users point of view, it does in fact support both row major and column major \n based tensor layout under the hood.\n Here we cover how the layout of tensors can be modified\n and we ensure the different tensor types still work as expected...\n (The features in this specification involve mutating tensors, be careful when playing around with this yourself)"},"ut.tensors.Tensor_Operation_Spec":{"executedFeatures":["Activation functions work across types on slices and non sliced tensors.","Auto reshaping and broadcasting works and the result can be back propagated.","New method \"asFunction\" of String added at runtime is callable by groovy and also works.","New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work.","Operators \"+,*,**\" produce expected results with gradients which can be accessed via a \"Ig[0]\" Function instance","Overloaded operation methods on tensors produce expected results when called.","Scalar broadcasting works across devices.","Simple slice addition produces expected result.","The \"dot\" operation reshapes and produces valid \"x\" operation result.","The \"matMul\" operation produces the expected result.","The \"random\" function/operation populates tensors randomly.","The transpose operation exposed by the \"T()\" method, supports autograd.","The values of a randomly populated tensor seems to adhere to a gaussian distribution.","You can do matrix multiplication using 2 transposed matrices.","You can do matrix multiplication using transposed matrices as second operand.","You can do matrix multiplication using transposed matrices."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":16,"totalFeatures":16,"passed":16,"successRate":1.0,"time":5029},"title":"Running Tensors through operations","narrative":"This specification shows how to use the tensor API to run tensors through various operations.\n Operations are triggered either by simply calling methods on tensors or by using \n `Function` objects which are used to define custom operations in the form \n of a syntax tree."},"ut.tensors.Tensor_Slicing_Spec":{"executedFeatures":["A tensor can be sliced by passing ranges in the form of lists (Groovy ranges).","A tensor can be sliced by passing ranges in the form of primitive arrays.","Normal slicing will try to do autograd.","Slicing is also a Function with autograd support!","The \"at\" method and the \"from\" / \"to\" methods can be mixed when slicing a tensor.","The slice builder also supports slicing with custom step sizes.","We can avoid autograd when slicing by using the \"detached\" instead of the \"get\" method.","We can slice a scalar tensor from a larger tensor of rank 4.","When Slicing only one axis using the SliceBuilder API, the other axes will be sliced implicitly."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":9,"totalFeatures":9,"passed":9,"successRate":1.0,"time":13},"title":"Tensors within Tensors","narrative":"ND-Array data structures can be \"sliced\" in the sense\n that one can create a subset view of the underlying data inside a tensor\n through a new tensor instance...\n This can be a tedious and complicated procedure.\n Therefore a tensor should expose a various user friendly API for slicing which\n are also fit for various languages.\n This specification covers these APIs for tensor slicing."},"ut.tensors.Tensor_State_Spec":{"executedFeatures":["A tensor can be instantiated from a item type class and nested lists.","Numeric tensors as String can be formatted on an entry based level.","Tensor created from shape and datatype has expected state.","Tensors as String can be formatted depending on shape.","Tensors as String can be formatted on an entry based level.","The data and the value of a tensor a 2 different things!","We can create scalar tensors."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":16},"title":"The Tensor Initialization and State Specification","narrative":"This specification defines the expected states of freshly instantiated\n and initialized tensors.\n After a tensor was created successfully we expect it \n to have certain properties like a shape, rank, type and data array\n among other things."},"ut.tensors.Tensor_Stats_Spec":{"executedFeatures":["A tensor can be summed alongside a specific axis.","Both the min and max operation support autograd (back-propagation).","Multiple dimensions of a tensor can selectively be summed up.","The sum operation support autograd (back-propagation).","There is no need to use a function, we can use the min() and max() methods on tensors instead.","We can get pre-instantiated min and max functions from the library context.","We can use the \"sum\" method to sum the items of a tensor.","We can use the max operation as a function"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":230},"title":"Reducing Tensors","narrative":"Various kinds of operations reduce tensors to scalars,\n the most common ones being the min and max operations \n which find the smallest as well as largest number among all \n items of a tensor.\n Neureka exposes various different ways to achieve this,\n all of which are also differential (autograd support)."},"ut.tensors.Tensor_Version_Spec":{"executedFeatures":["Inline operations cause illegal state exceptions.","Inline operations causes version incrementation.","Non-inline operations do not cause version incrementation.","Storing a tensor on a device should not change the version of a tensor (Even though its data changed technically)."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":50},"title":"Tensor (Data Array) Version","narrative":"There are two fundamental categories of operations\n which can be applied to tensors : \n Inline operations and Non-Inline operations! \n \n Inline operations are often times problematic because they produce\n side effects by changing passed tensors instead of producing new ones... \n One such bad side effect can easily occur for tensors involved in the\n autograd system, more specifically: the recorded computation graph. \n Inline operations can break the mathematically pureness of the back-propagation\n procedure by for example changing partial derivatives...
        \n In order to prevent said errors from occurring unnoticed tensors\n have versions which will increment when the underlying data of the tensor changes. \n This version will be tracked by the computation graph as well in order to\n match it with the ones stored inside the tensor. \n A mismatch would then yield an exception! \n \n This specification is responsible for defining the behaviour of this\n version number with respect to their wrapping tensors as well as computation graph nodes."},"ut.tensors.exceptions.Tensor_Delete_Exception_Spec":{"executedFeatures":["A deleted tensor will tell you that it has been deleted.","A deleted tensor will throw an exception when accessing its configuration.","A deleted tensor will throw an exception when accessing its data type.","A deleted tensor will throw an exception when accessing its data.","A deleted tensor will throw an exception when modifying its data type.","A deleted tensor will throw an exception when trying to modify its data.","A deleted tensor will throw an exception when trying to set its configuration."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":3},"title":"","narrative":""},"ut.tensors.exceptions.Tensor_Exception_Spec":{"executedFeatures":["Building a tensor with \"null\" as shape argument throws an exception.","Building a tensor with 0 shape arguments throws an exception.","Casting a tensor as something unusual will cuas an exception to be thrown.","Out of dimension bound causes descriptive exception!","Passing an invalid key object into the \"getAt\" method causes a descriptive exception.","Passing an invalid object into Tensor constructor causes descriptive exception.","Passing null to various methods of the tensor API will throw exceptions.","Trying to inject an empty tensor into another causes fitting exception."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":14},"title":"Tensors Exception Behavior","narrative":"This specification covers the behavior of the Tensor class in\n exceptional scenarios which are contrary to its intended use.\n The purpose of this is to assert that the Tensor class will provide\n useful feedback to a user to explain that a misuse of its API\n occurred so that the user can correct this misuse."},"ut.utility.Cleaner_Testing":{"executedFeatures":["The DeviceCleaner triggers registered cleaner actions when things are eligible for GC."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":260},"title":"How Neureka Cleans Up","narrative":"Under the hood \n Neureka deals whith large arrays of\n data, which are often times \n native data arrays requiring explicit\n memory freeing!\n This freeing of memory can happen at any time\n during the livetime of a nd-array, however\n it should happen at least up until the nd-arra/tensor\n objects representing their referenced data arrays become\n eligible for garbage collection.\n This specification ensures that the custom garbage\n cleaner implementation used by Neureka fulfills this role"},"ut.utility.DataConverter_Spec":{"executedFeatures":["An array of any type of object may be converted to a array of primitives.","The DataConverter can convert the given array data."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":2},"title":"","narrative":""},"ut.utility.FileHandle_Spec":{"executedFeatures":["Fully labeled tenors will be stored with their labels included when saving them as CSV.","Partially labeled tenors will be stored with their labels included when saving them as CSV.","Test reading IDX file format.","Test writing IDX file format.","The FileDevice component \"CSVHead\" can read CSV file formats and load them as tensors.","We can load image files as tensors."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":536},"title":"","narrative":""},"ut.utility.ListReader_Exception_Spec":{"executedFeatures":["The ListReader will detect inconsistent degrees of nesting in the provided data.","The ListReader will detect inconsistent types in the provided data."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":3},"title":"","narrative":""},"ut.utility.ListReader_Spec":{"executedFeatures":["The ListReader can interpret nested lists into a shape list and value list.","The ListReader can interpret nested lists resembling a 3D tensor into a shape list and value list.","The ListReader can interpret nested lists resembling a matrix into a shape list and value list."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":3},"title":"The Internal ListReader turning lists into flat arrays with shape and type data","narrative":"This specification covers an internal class which should not be used\n outside this library, namely the ListReader class.\n This class is simply a converter which turns nested lists\n into flat arrays alongside the type of the elements and the shape of this \"tensor\"."},"ut.utility.Utility_Spec":{"executedFeatures":["Object arrays can be converted to primitive arrays."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":6},"title":"","narrative":""}} \ No newline at end of file +{"Example_Spec.Example_Spec":{"executedFeatures":["Call me feature not unit test!","I am readable and also best practice!","Numbers to the power of two with a fancy data table!","Should be able to remove from list","iAmNotSoReadable"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":5,"passed":7,"successRate":1.0,"time":68},"title":"An Introduction to writing Spock Specifications","narrative":"Hello and welcome to the example / template specification of this project.\n This is a simple introduction as to how to get started writing Spock specifications.\n \n Spock works on top of Groovy which is in essence a syntactic super-set of Java.\n That means that one can write Java code in Groovy, and 99% of the time it will \n work the exact same way."},"it.Calculus_Stress_Test":{"executedFeatures":["Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors.","Activation functions work across types.","Dot operation stress test runs error free and produces expected result","Stress test runs error free and produces expected result","The broadcast operation stress test runs error free and produces expected result"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":69,"totalFeatures":5,"passed":69,"successRate":1.0,"time":1086},"title":"","narrative":""},"it.Cross_Device_Sliced_Tensor_System_Test":{"executedFeatures":["Cross device sliced tensor integration test runs without errors.","Slices can be created using the SliceBuilder."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":2,"passed":4,"successRate":1.0,"time":146},"title":"Cross Device Tensor Slicing","narrative":""},"it.Cross_Device_Spec":{"executedFeatures":["A gradient of ones can be set by calling the backward method on a tensor sitting on any device.","Convolution can model matrix multiplications across devices.","Cross device system test runs successfully.","Mapping tensors works for every device (even if they are not used).","Test simple NN implementation with manual backprop"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":79,"totalFeatures":5,"passed":79,"successRate":1.0,"time":6066},"title":"Cross Device Stress Test Specification","narrative":"This specification is pretty much a system test which covers\n the behavior of the library as a whole across multiple devices!\n No matter which device is being used for a given stress test, the result should be the same..."},"it.Eleven_Lines_NN_System_Spec":{"executedFeatures":["One can write a simple double based neural network in less than 11 lines of java like code using the \"@\" operator!","One can write a simple float based neural network in less than 11 lines of java like code!","One can write a simple neural network in less than 11 lines of code!","One can write a simple neural network with custom back-prop in 11 lines of code!","The pseudo random number generator works as expected for the weights used in the 11 line NN examples!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":5,"passed":5,"successRate":1.0,"time":491},"title":"NN Code Golfing!","narrative":"This system test specification uses the following Numpy\n code as reference implementation for the equivalent in Neureka\n or similar implementations and variations.\n The code below is a simple neural network in only 11 lines of code.\n\n \u00b4\u00b4\u00b4\n X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])\n y = np.array([[0,1,1,0]]).T\n W1 = 2*np.random.random((3,4)) - 1\n W2 = 2*np.random.random((4,1)) - 1\n for j in xrange(60000):\n l1 = 1/(1+np.exp(-(np.dot(X,W1))))\n l2 = 1/(1+np.exp(-(np.dot(l1,W2))))\n l2_delta = (y - l2)*(l2*(1-l2))\n l1_delta = l2_delta.dot(W2.T) * (l1 * (1-l1))\n W2 += l1.T.dot(l2_delta)\n W1 += X.T.dot(l1_delta)\n \u00b4\u00b4\u00b4"},"st.Benchmark_System_Test":{"executedFeatures":["Tensor can be constructed by passing List instances.","Test benchmark script and simple tensor constructor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":1900},"title":"","narrative":""},"st.Broad_System_Test":{"executedFeatures":["A function with expression \"(-3*(2*(i0*-1)))*(-1*i0)\" can be backpropagated.","A function with expression \"softplus((I[0]xI[1])*-100)\" can be backpropagated.","A function with expression \"softplus(tanh(I[0]*I[1]*2)*I[1])\" can be backpropagated.","The long broad integration test runs successfully."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":83},"title":"","narrative":""},"st.NN_Concepts_Spec":{"executedFeatures":["The attention mechanism (found in the commonly known transformer) demonstrated."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":201},"title":"Examining Neural Network Architecture Snippets","narrative":"This specification is intended to showcase some basic building blocks of \n various neural network architectures."},"st.Training_NNs_Spec":{"executedFeatures":["A simple 3 layer neural network converges.","A very simple 1 layer NN converges.","We can run the attention head test model."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":3,"passed":5,"successRate":1.0,"time":12622},"title":"Training a Neural Network Class","narrative":"When designing larger neural network architectures, what you would usually do is\n to create a class that represents the whole model (which itself might be composed\n of smaller models). \n \n This class would then represent something that can be executed and then trained.\n This Specification shows how to instantiate, execute and train various \n pre-defined example neural network models."},"ut.autograd.AD_And_Computation_Graph_Spec":{"executedFeatures":["Payloads and derivatives are null after garbage collection.","Reshaping produces expected computation graph and also works with reverse mode AD."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":1288},"title":"","narrative":""},"ut.autograd.Autograd_Explained":{"executedFeatures":["Simple automatic differentiation and propagation."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":11},"title":"Autograd - Automatic Differentiation","narrative":"Central to all neural networks in Neureka is the autograd package. \n The autograd package provides automatic differentiation for all default operations on Tensors. \n Neureka is a define-by-run library, which means that your backpropagation is defined by how \n your code is run, and that every single iteration can be different. \n \n The class neureka.Tensor is the central class of the main package. \n If you set its attribute 'rqsGradient' to True, Neureka starts to track all operations on it. \n When you finish the forward pass of your network \n you can call .backward() and have all the gradients computed \n and distributed to the tensors requiring them automatically. \n \n The gradient for a tensor will be accumulated into a child tensor (component) which \n can be accessed via the '.getGradient()' method. \n \n To stop a tensor from tracking history, you can call '.detach()' to detach it from the \n computation history, and to prevent future computation from being tracked."},"ut.autograd.Autograd_Flags_Explained":{"executedFeatures":["Advanced backpropagation on all AD-Modes ","We can create a shallow copy of a tensor detached from the computation graph."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":9,"totalFeatures":2,"passed":9,"successRate":1.0,"time":149},"title":"","narrative":""},"ut.autograd.Autograd_NN_Spec":{"executedFeatures":["Autograd work for simple matrix multiplications.","Autograd works for 2 matrix multiplications in a row.","Autograd works in a simple convolutional dot product and float based feed forward neural network.","Autograd works in a simple convolutional dot product based feed forward neural network.","Autograd works in a simple mat-mul based feed forward neural network."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":5,"passed":7,"successRate":1.0,"time":148},"title":"Simple Neural Network autograd integration test","narrative":"The integration test below has been implemented by using\n the following code and the result it produces as reference : \n https://medium.com/dair-ai/a-simple-neural-network-from-scratch-with-pytorch-and-google-colab-c7f3830618e0 \n \n The following seed has been used to assure reproducibility :\n 'torch.manual_seed(503672689411)'"},"ut.autograd.Autograd_Tensor_Spec":{"executedFeatures":["A tensor used as derivative within a computation graph will throw exception when trying to deleting it.","Second-Test \"x-mul\" autograd behaviour. (Not on device)","Test basic autograd behaviour. (Not on device)"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":3,"passed":4,"successRate":1.0,"time":52},"title":"","narrative":""},"ut.autograd.JITProp_Autograd_Tensor_Spec":{"executedFeatures":["Gradient auto-apply kicks in when used AD uses JIT prop","Test JIT propagation variant one.","Test JIT propagation variant two.","Test autograd without JIT and auto apply.","Test in-differential and JIT with auto apply","Test no JIT prop when forward AD","Test no preemptive gradient apply when not requested and auto apply and JIT_prop","Test pending error optimization"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":8,"passed":8,"successRate":1.0,"time":638},"title":"","narrative":""},"ut.autograd.internal.GraphNode_Instantiation_Exception_Unit_Tests":{"executedFeatures":["GraphNode instantiation throws exception because tensors of ExecutionCall do not return GraphNode instances.","GraphNode throws an exception when trying to execute an inline operation on inputs with active autograd."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":189},"title":"","narrative":""},"ut.autograd.internal.GraphNode_Tensor_Exception_Unit_Tests":{"executedFeatures":["A tensor cannot be deleted if it is part of a graph and the tensor is used as derivative."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":8},"title":"","narrative":""},"ut.backend.Backend_Extension_Spec":{"executedFeatures":["Mock operation interacts with FunctionNode (AbstractFunction) instance as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":71},"title":"","narrative":""},"ut.backend.Backend_MatMul_Extension_Spec":{"executedFeatures":["GEMM matrix multiplication reference implementation can be set as custom OperationType and works as expected.","Test context mock for opencl reference implementations.","Tile parsing for kernel parameter calculation yields expected tile dimensions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":17,"totalFeatures":3,"passed":17,"successRate":1.0,"time":83},"title":"","narrative":""},"ut.backend.Matrix_Multiplication_Spec":{"executedFeatures":["The \"matMul\" method allows us to perform matrix multiplication.","The simple CPU matrix multiplication implementation works as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":16,"totalFeatures":2,"passed":16,"successRate":1.0,"time":63},"title":"Matrix Multiplication","narrative":"The tensor API exposes a useful method for Matrix Multiplication.\n This specification not only demonstrates how to use this method\n but also shows how matrix multiplication work \n for tensors with both row and column major layouts.\n (typically, column major is faster)"},"ut.backend.core.Backend_Algorithm_AD_Spec":{"executedFeatures":["Activation implementations behave as expected.","Broadcast implementations have expected properties.","Convolution implementations behave as expected.","Operator implementations behave as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":37,"totalFeatures":4,"passed":37,"successRate":1.0,"time":91},"title":"","narrative":""},"ut.backend.core.Backend_Algorithm_Implementation_Spec":{"executedFeatures":["Activation implementations have expected Executor instances.","HostExecutors of Operator implementations behave as expected.","Operator implementations have expected Executor instances."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":35,"totalFeatures":3,"passed":35,"successRate":1.0,"time":97},"title":"","narrative":""},"ut.backend.core.Backend_Functional_Algorithm_Spec":{"executedFeatures":["A functional algorithm cannot be used if it was not built properly!","A functional algorithm does not accept null as an answer!","A functional algorithm warns us when modified after it has been built!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":3,"passed":7,"successRate":1.0,"time":19},"title":"","narrative":""},"ut.backend.core.Matrix_Multiplication_Spec":{"executedFeatures":["The CPU matrix multiplication implementation works as expected.","The internal matrix multiplication test script runs!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":13,"totalFeatures":2,"passed":13,"successRate":1.0,"time":10522},"title":"Internal CPU based Matrix Multiplication","narrative":"This specification covers library internal matrix multiplication logic,\n specifically the CPU implementation.\n Do not depend on the API used in this specification as it is subject to change!"},"ut.backend.core.OpenCL_Backend_Spec":{"executedFeatures":["The OpenCL backend context can load implementations."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":21},"title":"","narrative":""},"ut.backend.core.Randomization_Spec":{"executedFeatures":["Randomization is in essence the same algorithm as JDKs \"Random\".","The Randomization class can fill various types of arrays with pseudo random numbers.","We can make slices of tensors random."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":10,"totalFeatures":3,"passed":10,"successRate":1.0,"time":39},"title":"","narrative":""},"ut.device.CPU_Spec":{"executedFeatures":["CPU knows the current number of available processor cores!","The CPU device will keep track of the amount of tensors it stores.","The CPU exposes a non null API for executing workloads in parallel.","Thread pool executes given workload in parallel"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":5707},"title":"The CPU device, an API for CPU based execution","narrative":"The CPU class, one of many implementations of the Device interface, \n is simply supposed to be an API for dispatching threaded workloads onto the CPU.\n Contrary to other types of device, the CPU will host tensor data by default, simply\n because the tensors will be stored in RAM if no device was specified."},"ut.device.Cross_Device_IO_Spec":{"executedFeatures":["We can use the access device API to read from a tensor.","We can use the access device API to write to a tensor"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":15,"totalFeatures":2,"passed":15,"successRate":1.0,"time":72},"title":"Devices manage the states of the tensors they store!","narrative":"Tensors should not manage their states\n themselves, simply because the type and location\n of the data is dependent on the device onto which they are stored.\n This specification tests of various device implementations\n enable reading to or writing from the tensors they store."},"ut.device.Cross_Device_Type_Spec":{"executedFeatures":["A device will keep track of the amount of tensors and data objects it stores.","Devices expose an API for accessing (reading and writing) the data of a tensor.","Devices store slices which can also be restored just like any other tensor.","Devices store tensors which can also be restored.","Execution calls containing null arguments will cause an exception to be thrown in device instances.","In total there are 3 different types of methods for finding device instances.","Passing a numeric array to a tensor should modify its contents!","Virtual tensors stay virtual when outsourced.","We can find Device implementations or null by passing search keys to the \"get\" method.","We can query the backend for devices by specifying both the requested type and a key word."],"ignoredFeatures":["Devices cannot store slices whose parents are not already stored."],"stats":{"failures":0,"errors":0,"skipped":1,"totalRuns":53,"totalFeatures":11,"passed":53,"successRate":1.0,"time":6152},"title":"Finding Device Types","narrative":"Neureka introduces a the concept of a `Device` which is an interface\n that represents a computational device used for executing tensor / nd-array operations on them.\n The `Device` interface is implemented by various classes which represent\n different types of accelerator hardware such as `CPUs`, `GPUs`, `TPUs`, `FPGAs`, etc.\n These various `Device` types can not be instantiated directly because they model \n the concrete and finite hardware that is available on any given system Neureka is running on.\n This means that they are usually instantiated lazily upon access request or \n upfront by the library backend (usually a backend extension built fo a specific device).\n In order to find these instances embedded in the library backend the `Device` interface\n exposes various static methods which can be used to find a device instance by name or type."},"ut.device.FileDevice_Spec":{"executedFeatures":["A file device stores tensors in idx files by default.","A file device stores tensors in various file formats.","A tensor loaded from a file device can be loaded again.","The file device can load known files in a directory."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":12,"totalFeatures":4,"passed":12,"successRate":1.0,"time":161},"title":"FileDevice, Storing Tensors in Files","narrative":"The `FileDevice` class, one of many implementations of the `Device` interface, \n represents a file directory which can store and load tensors as files (`idx`, `jpg`, `png`...)."},"ut.device.OpenCLDevice_Exception_Spec":{"executedFeatures":[],"ignoredFeatures":["Ad hoc compilation produces expected exceptions when duplication is found.","Ad hoc compilation produces expected exceptions.","An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.","Trying to restore a tensor which is not on a device raises exception."],"stats":{"failures":0,"errors":0,"skipped":4,"totalRuns":0,"totalFeatures":4,"passed":0,"successRate":1.0,"time":6},"title":"OpenCLDevice Exception Handling","narrative":"The OpenCLDevice class, one of many implementations of the Device interface, \n represents physical OpenCL devices.\n This specification defines how instances of this class deal with exceptional information."},"ut.device.OpenCLDevice_Spec":{"executedFeatures":[],"ignoredFeatures":["Ad hoc compilation produces executable kernel.","Ad hoc compilation works for WIP general purpose matrix multiplication.","Ad hoc compilation works for custom column major based tiled matrix multiplication.","Ad hoc compilation works for custom simple row major based matrix multiplication.","Ad hoc matrix multiplication works for multiple of 16 matrices.","An OpenCLDevice loads tensors in a provided lambda temporarily.","We can get the items of an outsourced tensor as a primitive array.","We can take a look at the underlying data array of an outsourced tensor through the unsafe API."],"stats":{"failures":0,"errors":0,"skipped":8,"totalRuns":0,"totalFeatures":8,"passed":0,"successRate":1.0,"time":6},"title":"The OpenCLDevice Specification","narrative":"Tensors need devices for execution!\n By default tensors use the `CPU` device, but sometimes we want to\n use something more suitable for large amounts of data and a high degree of parallelization.\n This is were the `OpenCLDevice` comes into play!\n It is a `Device` implementation built on top of the JOCL library, a thin OpenCL API.\n We expect the `OpenCLDevice` to store tensors as well as being able to read and write\n data from and to stored tensors.\n Also, an `OpenCLDevice` should allows us to compile OpenCL kernel code on the fly..."},"ut.device.OpenCL_Spec":{"executedFeatures":[],"ignoredFeatures":["A given OpenCL context can be disposed!","An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.","First found OpenCLDevice will have realistic numeric properties.","First found OpenCLDevice will have realistic properties inside summary query.","First found OpenCLDevice will have realistic text properties."],"stats":{"failures":0,"errors":0,"skipped":5,"totalRuns":0,"totalFeatures":5,"passed":0,"successRate":1.0,"time":1},"title":"Working with OpenCL","narrative":"Neureka models the OpenCL API through various types of classes.\n The most fundamental of these is the `OpenCLDevice` class which\n represents a single device with OpenCL support.\n Besides that, there is also the `OpenCLContext` class which\n represents a OpenCL contexts, platforms and multiple devices on said platforms..."},"ut.device.internal.CLFunctionCompiler_Spec":{"executedFeatures":["The CLFunctionCompiler produces an operation which properly integrates to the backend.","The CLFunctionCompiler produces the expected \"ad hoc\" kernel."],"ignoredFeatures":["The OpenCLDevice produces a working optimized Function for doubles.","The OpenCLDevice produces a working optimized Function for floats."],"stats":{"failures":0,"errors":0,"skipped":2,"totalRuns":2,"totalFeatures":4,"passed":2,"successRate":1.0,"time":206},"title":"Turning functions into kernels.","narrative":"Neureka parses mathematical expressions into an AST representation\n hidden behind the Function interface...\n This feature does not exist without reason, we can use\n this abstract syntax tree to compile to OpenCL kernels\n for optimal execution speed!"},"ut.device.internal.CPU_Kernel_Spec":{"executedFeatures":["The Reduce implementation for the CPU has realistic behaviour","The Sum implementation for the CPU has realistic behaviour"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":20,"totalFeatures":2,"passed":20,"successRate":1.0,"time":83},"title":"","narrative":""},"ut.device.internal.OpenCL_Data_Spec":{"executedFeatures":["The \"Data\" class can represent various OpenCL data types.","The OpenCLDevice specific Data class represents JVM data for OpenCL."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":39,"totalFeatures":2,"passed":39,"successRate":1.0,"time":89},"title":"","narrative":""},"ut.device.internal.OpenCL_Kernel_Unit_Spec":{"executedFeatures":["The CLDot implementation for the OpenCLDevice has realistic behaviour","The GEMM implementation for the OpenCLDevice has realistic behaviour","The Reduce implementation for the OpenCLDevice has realistic behaviour","The Sum implementation for the OpenCLDevice has realistic behaviour","The Sum implementation for the OpenCLDevice has realistic behaviour for when the number of elements is a prime."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":5,"passed":6,"successRate":1.0,"time":55},"title":"","narrative":""},"ut.dtype.DataType_Spec":{"executedFeatures":["DataType multi-ton instances behave as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":10,"totalFeatures":1,"passed":10,"successRate":1.0,"time":10},"title":"","narrative":""},"ut.dtype.NumericType_Spec":{"executedFeatures":["Conversion goes both ways and produces expected numeric values.","NumericType conversion to holder types yields expected results.","NumericType implementations behave as expected.","NumericType implementations return their expected properties."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":58,"totalFeatures":4,"passed":58,"successRate":1.0,"time":61},"title":"The NumericType and its implementations model their respective numeric data types.","narrative":"This specification covers the behavior of the NumericType interface\n which is responsible for modelling numeric data types which may or may not be native to the JVM. \n These implementations however do not model them in the traditional OO style\n but merely expose useful utility method for converting and representing \n these numeric data types using JVM types."},"ut.framing.Tensor_Framing_Spec":{"executedFeatures":["A matrix (rank 2 tensor) can be labeled and their labels can be used to extract slices / subsets.","A tensor can be labeled partially.","Rank 3 tensors can be labeled and their labels can be used to extract slices / subsets of tensors.","We can add labels to tensors through lists or maps passed to the \"label()\" method."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":35},"title":"Naming Tensors and their Dimensions.","narrative":"A powerful concept in the data science as well as machine learning\n world is something usually referred to as \"Data Frames\".\n These are highly flexible 2D data structures\n used to load and store CSV, CRV, etc... files for \n data exploration and further processing.\n Data frames are so powerful because\n their indices are labeled and therefore human readable.\n Neureka's tensors are general purpose data containers\n which may also stored data in 2 dimensions whose\n indices may also be something other than integers."},"ut.introductions.Tensor_NDArray_Spec":{"executedFeatures":["Tensor is a subtype of NdArray.","We can use tensors for numeric calculations (but not nd-arrays)."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":9},"title":"Tensors or Nd-arrays","narrative":"*What is the difference?*\n\nIn the world of machine learning we use something called a **'tensor'** to represent data.\nThey might be called **'nd-arrays'** in some other frameworks,\nbut although they are very similar, \nthere are also some important distinctions to be made between these two concepts.\nBoth are at their core merely multidimensional arrays, however,\nthey are different in their typical usage and API.\nnd-arrays are merely used to represent any type of data as a \ncollection of elements in a multidimensional grid, \ntensors on the other hand have additional requirements.\nThey are a type of nd-array which stores numeric data \nas well as expose various mathematical operations for said data.\nIn that sense it is actually merely a more complex kind of number.\nThis concept actually comes from the field of physics, \nwhere it is used to represent a physical quantity.\n\nNeureka models both concepts through the `Tensor` and the `Nda` interfaces.\n`Nda` is an abbreviation of `NdArray`, and `Tensor` is an abbreviation of `Tensor`.\nThe `Tensor` type is a subtype of the `Nda` type, exposing additional methods\nlike for example `plus`, `minus`, `times` and `divide`.\nBoth can be instantiated through static factory methods (and a fluent builder API)."},"ut.math.BackendContext_Spec":{"executedFeatures":["BackendContext instances can be created by cloning from Singleton instance.","BackendContext instances return Runner instances for easy visiting with return values.","BackendContext instances return Runner instances for easy visiting."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":5,"totalFeatures":3,"passed":5,"successRate":1.0,"time":13},"title":"The BackendContext is a cloneable context which can run Tasks.","narrative":"This specification defines the expected behaviour of the backend context\n which should expose a convenient API to work with.\n This API should allow for tasks to be running on a given context\n which is important for testing and modularity not only\n during library startup but also throughout the runtime."},"ut.math.ConCat_Spec":{"executedFeatures":["We can concatenate 2 float tensors alongside a specified axis!","We can concatenate 2 string tensors alongside a specified axis!","We can concatenate 2 tensors alongside a specified axis!","We can concatenate and then back-propagate 2 simple float tensors alongside a specified axis!","We can concatenate and then back-propagate 3 simple float tensors alongside a specified axis!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":5,"passed":7,"successRate":1.0,"time":60},"title":"Merging Tensors","narrative":"Tensors can not only be sliced, but also merged.\n This is most easily achieved through the concatenation operation, \n which stacks 2 tensors alongside a specified axis.\n This specification not only covers how you can concatenate tensors,\n but also how this works alongside autograd and non-numeric tensors."},"ut.math.Function_Exception_Spec":{"executedFeatures":["Function throws exception when arity does not match input number.","Function throws exception when not enough inputs provided."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":6},"title":"","narrative":""},"ut.math.Function_Parsing_Spec":{"executedFeatures":["Functions can derive themselves according to the provided index of the input which ought to be derived.","Parsed equations throw expected error messages.","Test parsed equations when building Function instances."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":37,"totalFeatures":3,"passed":37,"successRate":1.0,"time":39},"title":"Parsing Expressions into Functions","narrative":"Neureka uses the 'Function' interface as a representation of a\n nested structure of operations.\n This means that a 'Function' is simply an abstract syntax trees made up of other 'Function' implementations\n which are assembled together by a parser receiving a string expression.\n In this specification we ensure that function expressions will be properly parsed into\n 'Function' implementations."},"ut.math.Function_Scalar_Spec":{"executedFeatures":["Function \"(I[0]+1/I[0])**-I[0]\" instance returns expected scalar result.","Function \"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\" instance returns expected scalars.","Function \"1/I[0]\" instance returns expected scalar results.","Function \"I[0]+1/I[0]\" instance returns expected scalar results.","Test scalar results of Function \"sumjs((cos(I[j]*5)/5+I[j])*(1+sin(I[j])/2))\" instance.","Test scalar results of various Function instances."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":66,"totalFeatures":6,"passed":66,"successRate":1.0,"time":42},"title":"Functions for Scalars","narrative":"The Function API and it's implementations \n receive and process arrays of scalars as arguments.\n Functions don't have to be used alongside tensors / nd-arrays,\n they can also compute derivatives based on scalar values."},"ut.math.Function_Spec":{"executedFeatures":["Function implementations ensure that internally created tensors are flagged as \"intermediate\" initially!","Function implementations ensure that outputs which are input members are not flagged as \"intermediate\"!","Function implementations will ensure the \"call\" and \"invoke\" does not return tensors flagged as \"intermediate\".","The library context exposes a set of useful functions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":40,"totalFeatures":4,"passed":40,"successRate":1.0,"time":46},"title":"Testing Default Methods on Functions","narrative":"This specification tests the default methods on functions\n through a simple dummy implementation of the Function interface."},"ut.math.Tensor_Function_Spec":{"executedFeatures":["Executed tensors are intermediate tensors.","Reshaping on 3D tensors works by instantiate a Function instance built from a String.","Tensor results of various Function instances return expected results.","The \"DimTrim\" operation works forward as well as backward!","The optimization function for the SGD algorithm produces the expected result","The softmax can be calculated alongside multiple axes.","The softmax can be calculated for a particular axis.","The softmax function can be applied to tensors with more than one dimension.","The tensor API has built-in methods for applying functions.","We can collect a stream into a tensor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":52,"totalFeatures":10,"passed":52,"successRate":1.0,"time":179},"title":"Applying Functions to Tensors","narrative":"A tensor would be nothing without being able to apply operations on them.\n However, calling operations manually in order to process your\n tensors can be a verbose and error prone task.\n This is where functions come into play.\n Neureka's functions are composed of operations forming an abstract syntax tree.\n Passing tensors to a function will route them trough this tree and apply\n all of the operations on the tensors for you."},"ut.miscellaneous.Weired_NN_Spec":{"executedFeatures":["Dot based feed forward and activation produces expected result."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":36},"title":"","narrative":"This specification is meant less as feature documentation and more as a\n chaos test for weired neural network architectures\n an unusual usages of the Neureka library."},"ut.ndas.Nda_Assign_Spec":{"executedFeatures":["Assignment can be easily achieved through subscription operators.","We can assign one slice into another one.","We can use the \"mut\" API to assign the contents of one nd-array into another one."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":7},"title":"Nda Inline Assignment","narrative":"In this specification we cover the behaviour of nda's with respect to the assignment operation\n as well as the assignment of individual Nda items."},"ut.ndas.Nda_Framing_Spec":{"executedFeatures":["An Nda can be labeled.","Concatenating 2 labeled nd-arrays will produce a nd-array which is also labeled.","The slice of a labeled vector is labeled too.","We can label the columns and rows of a rank 3 nd-array.","We can label the columns of a rank 2 nd-array.","We can use labels as selectors for slicing."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":28},"title":"Nda framing","narrative":"Immutability is a core concept of the Neureka library.\n This means that the Nda API does not expose mutability directly.\n Instead, the API exposes methods that return new instances of Nda\n that are derived from the original instance.\n \n This is also true for labeling operations, \n meaning that the Nda API does not directly expose methods that mutate labels of an Nda\n but instead provides methods that return new instances of Nda\n with different labels.\n \n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!"},"ut.ndas.Nda_Inplace_Framing_Spec":{"executedFeatures":["Concatenating 2 labeled nd-arrays will produce a nd-array which is also labeled.","The slice of a labeled vector is labeled too.","We can concatenate more than 2 nd-arrays.","We can label the columns and rows of a rank 3 nd-array.","We can label the columns of a rank 2 nd-array.","We can use labels as selectors for slicing."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":23},"title":"NDA Framing","narrative":"Framing an nd-array is all about naming its axes and then using those names to\n access, read or write its values in a more convenient and human readable way."},"ut.ndas.Nda_Instantiation_Spec":{"executedFeatures":["A vector can be created from an array of values through the \"of\" method.","Common types of nd-arrays are best instantiated using type specific convenience methods.","ND-arrays can be created fluently."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":3,"passed":8,"successRate":1.0,"time":16},"title":"ND-Array Instantiation","narrative":"In this specification we cover how ND-arrays can be instantiated."},"ut.ndas.Nda_Items_Spec":{"executedFeatures":["An item can be converted to an Optional object.","Other than the \"orElse(T)\" method of the Optional class, the same method of an Item will throw an exception if the provided value is null.","The \"get\" method of an Item object will throw an exception if the item is missing.","We can check if items of a tensor is present or not.","We can get the value of an item.","We can use the \"orElse(T)\" method to avoid null values."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":10},"title":"The Nds Items API","narrative":"Nd-arrays are collections of items similar to other\n collection types in Java. \n One useful way to access the items of an nd-array is\n to use the items API.\n \n Using the `at` methods we can access an `Item` object\n which is a wrapper around the item's value and its\n index in the nd-array.\n \n The `Item` object is a simple data class which\n is very similar to the `Optional` class, meaning\n that it can either be empty or contain a value."},"ut.ndas.Nda_Mutation_Spec":{"executedFeatures":["A ND-Array can be mutated simply using the \"set\" method.","A ND-Array can be mutated using the \"at(..).set(..)\" methods.","A simple vector ND-Array can be mutated using the \"at(..).set(..)\" methods.","A simple vector ND-Array can be mutated using the \"setItemAt\" method.","We can use the subscription operator to mutate a simple vector ND-Array.","We can use the subscription operator to mutate an ND-Array."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":6,"totalFeatures":6,"passed":6,"successRate":1.0,"time":11},"title":"Mutating ND-Arrays","narrative":"ND-Arrays should be considered immutable, so we should prefer creating new \n ND-Arrays from existing ones using wither methods.\n However this is not always a good idea as it can be expensive to create new\n ND-Arrays, especially if the ND-Array is very large.\n The ability to mutate ND-Arrays is therefore provided, but only\n accessible via the mutation API exposed by the `getMut()` method."},"ut.ndas.Nda_Reshape_Spec":{"executedFeatures":["We can create a new Nda instance with a different shape."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":3},"title":"Nda Reshaping","narrative":"Immutability is a core concept of the Neureka library.\n This means that the Nda API does not expose mutability directly.\n Instead, the API exposes methods that return new instances of Nda\n that are derived from the original instance.\n \n This is also true for reshaping operations, \n meaning that the Nda API does not expose methods that mutate the shape of an Nda\n but instead provides methods that return new instances of Nda\n with a different shape.\n \n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!"},"ut.ndim.NDConfiguration_Spec":{"executedFeatures":["Various NDConfigurations behave exactly like their general purpose implementation."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":20,"totalFeatures":1,"passed":20,"successRate":1.0,"time":55},"title":"Making Arrays N-Dimensional","narrative":"Under the hood Neureka implements powerful indexing \n abstractions through the `NDConfiguration` interface and its various implementations.\n This allows for the creation of tensors/nd-arrays with arbitrary dimensions, \n the ability to slice them into smaller tensors/nd-arrays with the same underlying data,\n and finally the ability to permute their axes (like transposing them for example).\n \n This specification however only focuses on the behaviour of the `NDConfiguration` interface\n which translates various types of indices."},"ut.ndim.Nda_Permute_Spec":{"executedFeatures":["We can use the \"permute\" method to rearrange the dimensions of an nd-array.","We can use the \"transpose\" method to transpose swap 2 dimensions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":6},"title":"Reshaping Nd-Arrays","narrative":"Permuting an N-dimensional array means rearranging the dimensions/axes of the N-dimensional array.\n It returns a new nd-array with the same data as the original nd-array, \n but with the specified dimensions rearranged. \n It is very useful for example when you want to\n change the order of dimensions, for example, if you have a nd-array with dimensions (batch_size, channels, height, width), \n you can use permute() to rearrange the dimensions to (batch_size, height, width, channels).\n Another useful application of permute() is transposing a matrix.\n For example, if you have a matrix with dimensions (rows, columns), \n you can use permute() to rearrange the dimensions to (columns, rows).\n \n Permuting is a very cheap operation because it does not copy any data but merely\n creates a new view on the same data with a different access pattern."},"ut.ndim.Shape_Spec":{"executedFeatures":["A shape can be created from a list of integers.","A shape can be created from a stream of ints.","A shape can be created from an iterable.","A shape can be mapped to a new shape.","A shape can be sliced.","Use the \"any\" or \"every\" method to check if a predicate holds for any or every value of the shape.","You can use the \"count(Predicate)\" method to count the number of values that satisfy a predicate."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":19},"title":"The Shape Tuple","narrative":"The `Shape` of an nd-array/tensor is in essence merely an immutable tuple of integers\n which define the size of each dimension of the tensor.\n So if you think of an nd-array as a grid of numbers, then the shape of the\n tensor is the size of the grid in each dimension.\n \n This specifications shows you how to create a shape and how to use it."},"ut.ndim.Tensor_NDConfiguration_Spec":{"executedFeatures":["NDConfiguration instances of tensors have expected state and behaviour.","NDConfiguration instances of tensors have expected state."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":11},"title":"What it means to be N-Dimensional","narrative":"This specification covers how implementations\n of the `NDConfiguration` interface manage to define\n what it means to be a n-dimensional tensor/nd-array."},"ut.ndim.Tensor_Permute_Spec":{"executedFeatures":["We can use the \"permute\" method to rearrange the dimensions of a tensor.","When matrices are transpose, they will change their layout type as expected."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":9},"title":"Reshaping Tensors","narrative":"Permuting an N-dimensional array means rearranging the dimensions/axes of the N-dimensional array.\n It produces a new tensor with the same data as the original tensor, \n but with the specified dimensions rearranged. \n \n This is very useful for example when you want to\n change the order of dimensions, for example, if you have a tensor with dimensions (batch_size, channels, height, width), \n you can use permute() to rearrange the dimensions to (batch_size, height, width, channels).\n Another useful application of permute() is transposing a matrix.\n For example, if you have a matrix with dimensions (rows, columns), \n you can use permute() to rearrange the dimensions to (columns, rows).\n \n Permuting is a very cheap operation because it does not copy any data but merely\n creates a new view on the same data with a different access pattern."},"ut.ndim.Tensor_Slice_Permute_Spec":{"executedFeatures":["A slice of a tensor changes as expected when reshaping it.","Reshaping a slice works as expected.","Two slices of one big tensor perform matrix multiplication flawless."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":11},"title":"Permuting Slices of Tensors","narrative":"Neureka provides a convenient way to permuting tensors\n even if they are slices of other tensors sharing the same underlying data.\n This is possible because of the under the hood indexing \n abstractions provided by the `NDConfiguration` interface and its various implementations."},"ut.neureka.Neureka_Spec":{"executedFeatures":["Backend related library objects adhere to the same toString formatting convention!","Every Thread instance has their own Neureka instance.","Neureka class instance has expected behaviour.","Neureka settings class can be locked causing its properties to be immutable.","Various library objects adhere to the same toString formatting convention!"],"ignoredFeatures":["OpenCL related library objects adhere to the same toString formatting convention!"],"stats":{"failures":0,"errors":0,"skipped":1,"totalRuns":82,"totalFeatures":6,"passed":82,"successRate":1.0,"time":4898},"title":"The Neureka context can be used and configured as expected.","narrative":"This specification covers the behavior of the Neureka class which\n exposes a global API for configuring thread local contexts and library settings.\n The purpose of this is to assert that the API exposed by the Neureka class \n is both thread local and configurable.\n This specification also exists to cover standards for the Neureka library in general."},"ut.optimization.ADAM_Spec":{"executedFeatures":["ADAM optimizes according to expected inputs","Equations \"I[0]*I[1]+(1-I[2])*I[3]\" and \"(1-I[0])*I[1]\" used within ADAM return expected results.","Equations used by ADAM return expected result."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":19,"totalFeatures":3,"passed":19,"successRate":1.0,"time":92},"title":"","narrative":"ADAM is a more powerful alternative to the classical stochastic gradient descent. \n It combines the best properties of the AdaGrad and the RMSProp algorithms, which makes \n it especially well suited for sparse gradients and noisy data.\n Adam is the most popular among the adaptive optimizers\n because its adaptive learning rate working so well with sparse datasets."},"ut.optimization.AdaGrad_Spec":{"executedFeatures":["AdaGrad optimizes according to expected inputs"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":10,"totalFeatures":1,"passed":10,"successRate":1.0,"time":28},"title":"","narrative":""},"ut.optimization.Momentum_Spec":{"executedFeatures":["Momentum optimizes according to expected inputs"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":10,"totalFeatures":1,"passed":10,"successRate":1.0,"time":18},"title":"","narrative":"Momentum is an extension to the gradient descent optimization \n algorithm that allows the search to build inertia in a direction \n in the search space and overcome the oscillations of noisy \n gradients and coast across flat spots of the search space."},"ut.optimization.RMSProp_Spec":{"executedFeatures":["RMSprop optimizes according to expected inputs"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":10,"totalFeatures":1,"passed":10,"successRate":1.0,"time":28},"title":"","narrative":"**Root Mean Squared Propagation**, or RMSProp, is an extension of gradient \n descent and the AdaGrad version of gradient descent that uses a \n decaying average of partial gradients in the adaptation of the \n step size for each parameter."},"ut.tensors.Copy_Spec":{"executedFeatures":["A deep copy of a slice tensor is also a deep copy of the underlying data array.","A deep copy of a tensor is also a deep copy of the underlying data array.","A shallow copy of a tensor will be flagged as such.","A shallow copy will share the same underlying data as its original tensor.","We can deep copy various types of tensors."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":17,"totalFeatures":5,"passed":17,"successRate":1.0,"time":38},"title":"To Copy or Not to Copy","narrative":"In this specification we cover the behaviour of tensors with respect to their copy methods.\n There are to main ways to copy a tensor:
        \n 1. .shallowCopy()
        \n 2. .deepCopy()
        \n
        \n The first method creates a new tensor with the same underlying data array as the original tensor.
        \n The second method on the other hand creates a new tensor with a new data array.
        \n
        \n The first method is the most efficient, but it is not as safe as the second method.
        \n The second method is the most safe, but it is not as efficient.
        \n
        \n Besides these 2 main requirements, there are als some corner cases with respect to\n the components of a tensor (like for example its computation graph) which\n will be covered in this specification as well."},"ut.tensors.DimTrim_Spec":{"executedFeatures":["The \"dimTrim\" operation works on slices too!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":5},"title":"The 'dimTrim' Method","narrative":"The 'dimTrim' method is used to remove training and leading dimensions of length 1 from a tensor.\n This is useful when you want to perform operations on tensors of different ranks.\n For example, if you want to perform a dot product on two vectors, you can use the 'dimTrim' method\n to remove the dimension of length 1 from the vector, so that it becomes a scalar.\n This way you can perform the dot product on two scalars."},"ut.tensors.Expression_Based_Tensor_Instantiation_Spec":{"executedFeatures":["A tensor can be created from a function as expression.","We can instantiate tensors from various simple string expressions."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":14},"title":"Expression based Tensor Instantiation","narrative":"This specification defines how a tensor can be instantiated\n using string expressions, which define operations to be executed.\n This form of tensor instantiation is very useful to avoid boilerplate code."},"ut.tensors.Fluent_Tensor_Creation_Spec":{"executedFeatures":["Initialization lambda based tensors can be created fluently.","Range based tensors can be created fluently.","Scalars can be created fluently.","Seed based tensors can be created fluently.","Tensors can be created fluently.","Value based tensors can be created fluently.","Vectors can be created fluently."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":46,"totalFeatures":7,"passed":46,"successRate":1.0,"time":46},"title":"","narrative":""},"ut.tensors.Functional_Nda_Spec":{"executedFeatures":["ND-Array mapping lambdas produce expected nd-arrays.","The \"map\" method is a shorter convenience method for mapping to the same type.","We can analyse the values of a nd-array using various predicate receiving methods","We can collect a stream into a nd-array.","We can find both min and max items in a tensor by providing a comparator.","We can find both min and max items in an ND-array by providing a comparator.","We can initialize an ND-Array using a filler lambda mapping indices to items.","We can use the \"filter\" method as a shortcut for \"stream().filter(..)\".","We can use the \"flatMap\" method as a shortcut for \"stream().flatMap(..)\"."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":9,"totalFeatures":9,"passed":9,"successRate":1.0,"time":558},"title":"Functional ND-Arrays","narrative":"ND-Arrays expose a powerful API for performing operations on them\n in a functional style."},"ut.tensors.Functional_Tensor_Spec":{"executedFeatures":["We can find both min and max items in a tensor by providing a comparator.","We can initialize a tensor using a filler lambda mapping indices to items.","We can use the \"filter\" method as a shortcut for \"stream().filter(..)\".","We can use the \"flatMap\" method as a shortcut for \"stream().flatMap(..)\"."],"ignoredFeatures":["Tensor mapping lambdas produce expected tensors.","The \"map\" method is a shorter convenience method for mapping to the same type.","We can analyse the values of a tensor using various predicate receiving methods"],"stats":{"failures":0,"errors":0,"skipped":3,"totalRuns":4,"totalFeatures":7,"passed":4,"successRate":1.0,"time":230},"title":"Functional Tensors","narrative":"Tensors expose a powerful API for performing operations on them\n in a functional style."},"ut.tensors.Reshape_Spec":{"executedFeatures":["The reshape operation supports autograd!","We can create a new tensor with a different shape.","We can use `-1` in the desired shape if we want the axis size to be determined automatically."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":13},"title":"Tensor Reshaping","narrative":"This specification demonstrates how to reshape tensors,\n which means to change the shape of a tensor.\n\n Note that immutability is a core concept of the Neureka library.\n This means that the `Tensor` API does not expose mutability directly.\n Instead, it exposes methods that return new instances of `Tensor`\n that are derived from the original instance.\n \n This is also true for reshaping operations.\n \n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!"},"ut.tensors.Tensor_As_Container_Spec":{"executedFeatures":["More tensor operations translate to custom data type \"ComplexNumber\".","Plus operator on String tensors works element-wise.","Tensor operations translate to custom data type \"ComplexNumber\".","We can apply predicates on the values of a tensor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":4,"passed":4,"successRate":1.0,"time":55},"title":"Why not have a tensor of words?","narrative":"Technically, tensors are merely fancy ND-arrays with some useful mathematical operations\n applicable to them...\n Therefore, there is no reason why a tensor would not also be able to store\n other kinds of objects besides numbers like strings for example.\n This specification ensures that tensors can hold and index many other things..."},"ut.tensors.Tensor_Assign_Spec":{"executedFeatures":["Assignment can be easily achieved through subscription operators.","We can assign one slice into another one.","We can use the \"mut\" API to assign the contents of one tensor into another one."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":3,"passed":3,"successRate":1.0,"time":11},"title":"Tensor Inline Assignment","narrative":"In this specification we cover the behaviour of tensors with respect to the assignment operation\n as well as the assignment of individual tensor items."},"ut.tensors.Tensor_Conversion_Spec":{"executedFeatures":["Tensors value type can be changed by calling \"toType(...)\".","We can change the data type of all kinds of tensors.","We turn a tensor into a scalar value or string through the \"as\" operator!"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":38,"totalFeatures":3,"passed":38,"successRate":1.0,"time":24},"title":"Tensor Type Conversion","narrative":"Here we specify how a tensor can be converted to other data types\n like for example another tensor of a different data type."},"ut.tensors.Tensor_Convolution_Spec":{"executedFeatures":["Autograd works with simple 2D convolution.","Convolution can be performed using non-quadratic matrix tensors.","Convolution can be performed using tensors with an additional dimension as batch size.","Convolution with tensors of the same shape is equivalent to a dot product.","Manual convolution produces expected result.","Sime convolution works as expected eith autograd.","Tensors have the correct layout after convolution.","The \"x\" (convolution) operator produces expected results (On the CPU).","Very simple manual convolution produces expected result.","We can perform a convolution operation on a 2D tensor."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":20,"totalFeatures":10,"passed":20,"successRate":1.0,"time":1132},"title":"Tensor Convolution","narrative":"This specification shows how convolution can be performed on tensors.\n\n Convolution is a linear operation which is not only important for image processing but also\n a central player in the field of machine learning (especially for computer vision).\n It is used to extract features from images and other typically ~2 dimensional data.\n Other than that it is extremely important in the field of signal processing."},"ut.tensors.Tensor_Device_Spec":{"executedFeatures":["Tensors try to migrate themselves to a device that is being added to them as component.","The device of a tensor can be accessed via the \"device()\" method.","When creating slices of tensors then this should trigger a \"parent - child\" relation noticeable to the device!"],"ignoredFeatures":["Adding OpenCL device to tensor makes tensor be \"outsourced\" and contain the Device instance as component."],"stats":{"failures":0,"errors":0,"skipped":1,"totalRuns":3,"totalFeatures":4,"passed":3,"successRate":1.0,"time":15},"title":"Tensors on Devices","narrative":"This unit test specification covers \n the expected behavior of tensors when interacting\n with instances of implementations of the Device interface."},"ut.tensors.Tensor_Dot_Product_Spec":{"executedFeatures":["The \"dot\" method calculates the dot product between vectors.","The \"dot\" operation supports autograd.","The dot operation work even when one tensor is virtual.","The dot operation works for virtual tensors as well.","The dot product operation runs on any device.","The dot product works across different types and devices.","You can slice a Matrix into vectors and then used them for dot products."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":21,"totalFeatures":7,"passed":21,"successRate":1.0,"time":37},"title":"Tensor Dot Products","narrative":"A tensor can also be a simple vector, which is a tensor of rank 1.\n This specification demonstrates how to perform dot products on tensors of rank 1."},"ut.tensors.Tensor_Generics_Spec":{"executedFeatures":["1D tensors can be created from primitive arrays.","Anonymous tensor instance has the default datatype class as defined in Neureka settings.","We can create a tensor of strings."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":3,"passed":7,"successRate":1.0,"time":8},"title":"Tensors as Generic Containers","narrative":"Tensors do not just store numeric data.\n They can hold anything which can be stuffed into a \"Object[]\" array.\n You could even create a tensor of tensors!"},"ut.tensors.Tensor_Gradient_Spec":{"executedFeatures":["Gradient of tensor is being applies regardless of the tensor requiring gradient or not","Tensors can have gradients but not require them.","Tensors that have gradients but do not require them still print them."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":4,"totalFeatures":3,"passed":4,"successRate":1.0,"time":8},"title":"Gradients are Tensors which are Components of other Tensors","narrative":"This specification defines the gradient API on tensors.\n So one ought to be able to check wetter or not a tensor has a gradient attached to it or not.\n In that case one should be able to get this gradient and then work with\n it independently of the original tensor to which it belongs to..."},"ut.tensors.Tensor_IO_Spec":{"executedFeatures":["A tensor produced by the static \"Tensor.newRandom(shape)\" has expected \"random\" value.","Indexing after reshaping works as expected.","Tensor value type can not be changed by passing float or double arrays to it.","Tensor values can be manipulated","The tensor data array can be modified by targeting them with an index.","We can manipulate the underlying data array of a tensor through the mut API.","We can re-populate a tensor of shorts from a single scalar value!","When we try to manipulate the underlying data array of a virtual tensor then it will become actual."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":43,"totalFeatures":8,"passed":43,"successRate":1.0,"time":69},"title":"Reading and Writing Tensor Items","narrative":"Tensors are complicated data structures with a wide range of different possible states.\n They can host elements of different types residing on many kinds of different devices.\n Here we want to read from and write to the state of a tensor."},"ut.tensors.Tensor_Instantiation_Spec":{"executedFeatures":["A matrix tensor can be instantiated using lists for it's shape and values.","A simple 2D vector can be instantiated using lists for it's shape and values.","Passing a seed in the form of a String to a tensor produces pseudo random items.","Scalar tensors can be created via static factory methods","Tensors can be instantiated based on arrays for both shapes and values.","Tensors can be instantiated with String seed.","Vector tensors can be instantiated via factory methods."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":25,"totalFeatures":7,"passed":25,"successRate":1.0,"time":27},"title":"Instantiating Tensors","narrative":"Tensors are complicated data structures with a wide range of different possible states.\n They can host elements of different types residing on many kinds of different devices.\n Here we want to show how a tensor can be instantiated in different ways."},"ut.tensors.Tensor_Interop_Spec":{"executedFeatures":["Not all tensor can be converted to images.","Tensor can be converted to buffered images."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":2,"passed":8,"successRate":1.0,"time":35},"title":"Tensors play well with other data structures!","narrative":"Tensors should have good interoperability with other JDK data structures like images.\n In this specification we define these interoperability requirements."},"ut.tensors.Tensor_Layout_Spec":{"executedFeatures":["A new transposed version of a given tensor will be returned by the \"T()\" method.","Matrix multiplication works for both column and row major matrices across devices."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":3,"totalFeatures":2,"passed":3,"successRate":1.0,"time":184},"title":"Row or Column Major. Why not both?","narrative":"Although Neureka exposes tensors as row major tensors from \n a users point of view, it does in fact support both row major and column major \n based tensor layout under the hood.\n Here we cover how the layout of tensors can be modified\n and we ensure the different tensor types still work as expected...\n (The features in this specification involve mutating tensors, be careful when playing around with this yourself)"},"ut.tensors.Tensor_Operation_Spec":{"executedFeatures":["Activation functions work across types on slices and non sliced tensors.","Auto reshaping and broadcasting works and the result can be back propagated.","New method \"asFunction\" of String added at runtime is callable by groovy and also works.","New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work.","Operators \"+,*,**\" produce expected results with gradients which can be accessed via a \"Ig[0]\" Function instance","Overloaded operation methods on tensors produce expected results when called.","Scalar broadcasting works across devices.","Simple slice addition produces expected result.","The \"dot\" operation reshapes and produces valid \"x\" operation result.","The \"matMul\" operation produces the expected result.","The \"random\" function/operation populates tensors randomly.","The transpose operation exposed by the \"T()\" method, supports autograd.","The values of a randomly populated tensor seems to adhere to a gaussian distribution.","You can do matrix multiplication using 2 transposed matrices.","You can do matrix multiplication using transposed matrices as second operand.","You can do matrix multiplication using transposed matrices."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":164,"totalFeatures":16,"passed":164,"successRate":1.0,"time":10225},"title":"Running Tensors through operations","narrative":"This specification shows how to use the tensor API to run tensors through various operations.\n Operations are triggered either by simply calling methods on tensors or by using \n `Function` objects which are used to define custom operations in the form \n of a syntax tree."},"ut.tensors.Tensor_Slicing_Spec":{"executedFeatures":["A tensor can be sliced by passing ranges in the form of lists (Groovy ranges).","A tensor can be sliced by passing ranges in the form of primitive arrays.","Normal slicing will try to do autograd.","Slicing is also a Function with autograd support!","The \"at\" method and the \"from\" / \"to\" methods can be mixed when slicing a tensor.","The slice builder also supports slicing with custom step sizes.","We can avoid autograd when slicing by using the \"detached\" instead of the \"get\" method.","We can slice a scalar tensor from a larger tensor of rank 4.","When Slicing only one axis using the SliceBuilder API, the other axes will be sliced implicitly."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":14,"totalFeatures":9,"passed":14,"successRate":1.0,"time":36},"title":"Tensors within Tensors","narrative":"ND-Array data structures can be \"sliced\" in the sense\n that one can create a subset view of the underlying data inside a tensor\n through a new tensor instance...\n This can be a tedious and complicated procedure.\n Therefore a tensor should expose a various user friendly API for slicing which\n are also fit for various languages.\n This specification covers these APIs for tensor slicing."},"ut.tensors.Tensor_State_Spec":{"executedFeatures":["A tensor can be instantiated from a item type class and nested lists.","Numeric tensors as String can be formatted on an entry based level.","Tensor created from shape and datatype has expected state.","Tensors as String can be formatted depending on shape.","Tensors as String can be formatted on an entry based level.","The data and the value of a tensor a 2 different things!","We can create scalar tensors."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":19,"totalFeatures":7,"passed":19,"successRate":1.0,"time":52},"title":"The Tensor Initialization and State Specification","narrative":"This specification defines the expected states of freshly instantiated\n and initialized tensors.\n After a tensor was created successfully we expect it \n to have certain properties like a shape, rank, type and data array\n among other things."},"ut.tensors.Tensor_Stats_Spec":{"executedFeatures":["A tensor can be summed alongside a specific axis.","Both the min and max operation support autograd (back-propagation).","Multiple dimensions of a tensor can selectively be summed up.","The sum operation support autograd (back-propagation).","There is no need to use a function, we can use the min() and max() methods on tensors instead.","We can get pre-instantiated min and max functions from the library context.","We can use the \"sum\" method to sum the items of a tensor.","We can use the max operation as a function"],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":22,"totalFeatures":8,"passed":22,"successRate":1.0,"time":225},"title":"Reducing Tensors","narrative":"Various kinds of operations reduce tensors to scalars,\n the most common ones being the min and max operations \n which find the smallest as well as largest number among all \n items of a tensor.\n Neureka exposes various different ways to achieve this,\n all of which are also differential (autograd support)."},"ut.tensors.Tensor_Version_Spec":{"executedFeatures":["Inline operations cause illegal state exceptions.","Inline operations causes version incrementation.","Non-inline operations do not cause version incrementation."],"ignoredFeatures":["Storing a tensor on a device should not change the version of a tensor (Even though its data changed technically)."],"stats":{"failures":0,"errors":0,"skipped":1,"totalRuns":22,"totalFeatures":4,"passed":22,"successRate":1.0,"time":135},"title":"Tensor (Data Array) Version","narrative":"There are two fundamental categories of operations\n which can be applied to tensors : \n Inline operations and Non-Inline operations! \n \n Inline operations are often times problematic because they produce\n side effects by changing passed tensors instead of producing new ones... \n One such bad side effect can easily occur for tensors involved in the\n autograd system, more specifically: the recorded computation graph. \n Inline operations can break the mathematically pureness of the back-propagation\n procedure by for example changing partial derivatives...
        \n In order to prevent said errors from occurring unnoticed tensors\n have versions which will increment when the underlying data of the tensor changes. \n This version will be tracked by the computation graph as well in order to\n match it with the ones stored inside the tensor. \n A mismatch would then yield an exception! \n \n This specification is responsible for defining the behaviour of this\n version number with respect to their wrapping tensors as well as computation graph nodes."},"ut.tensors.exceptions.Tensor_Delete_Exception_Spec":{"executedFeatures":["A deleted tensor will tell you that it has been deleted.","A deleted tensor will throw an exception when accessing its configuration.","A deleted tensor will throw an exception when accessing its data type.","A deleted tensor will throw an exception when accessing its data.","A deleted tensor will throw an exception when modifying its data type.","A deleted tensor will throw an exception when trying to modify its data.","A deleted tensor will throw an exception when trying to set its configuration."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":7,"totalFeatures":7,"passed":7,"successRate":1.0,"time":14},"title":"","narrative":""},"ut.tensors.exceptions.Tensor_Exception_Spec":{"executedFeatures":["Building a tensor with \"null\" as shape argument throws an exception.","Building a tensor with 0 shape arguments throws an exception.","Casting a tensor as something unusual will cuas an exception to be thrown.","Out of dimension bound causes descriptive exception!","Passing an invalid key object into the \"getAt\" method causes a descriptive exception.","Passing an invalid object into Tensor constructor causes descriptive exception.","Passing null to various methods of the tensor API will throw exceptions.","Trying to inject an empty tensor into another causes fitting exception."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":25,"totalFeatures":8,"passed":25,"successRate":1.0,"time":47},"title":"Tensors Exception Behavior","narrative":"This specification covers the behavior of the Tensor class in\n exceptional scenarios which are contrary to its intended use.\n The purpose of this is to assert that the Tensor class will provide\n useful feedback to a user to explain that a misuse of its API\n occurred so that the user can correct this misuse."},"ut.utility.Cleaner_Testing":{"executedFeatures":["The DeviceCleaner triggers registered cleaner actions when things are eligible for GC."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":1,"totalFeatures":1,"passed":1,"successRate":1.0,"time":1304},"title":"How Neureka Cleans Up","narrative":"Under the hood \n Neureka deals whith large arrays of\n data, which are often times \n native data arrays requiring explicit\n memory freeing!\n This freeing of memory can happen at any time\n during the livetime of a nd-array, however\n it should happen at least up until the nd-arra/tensor\n objects representing their referenced data arrays become\n eligible for garbage collection.\n This specification ensures that the custom garbage\n cleaner implementation used by Neureka fulfills this role"},"ut.utility.DataConverter_Spec":{"executedFeatures":["An array of any type of object may be converted to a array of primitives.","The DataConverter can convert the given array data."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":9},"title":"","narrative":""},"ut.utility.FileHandle_Spec":{"executedFeatures":["Fully labeled tenors will be stored with their labels included when saving them as CSV.","Partially labeled tenors will be stored with their labels included when saving them as CSV.","Test reading IDX file format.","Test writing IDX file format.","The FileDevice component \"CSVHead\" can read CSV file formats and load them as tensors.","We can load image files as tensors."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":24,"totalFeatures":6,"passed":24,"successRate":1.0,"time":787},"title":"","narrative":""},"ut.utility.ListReader_Exception_Spec":{"executedFeatures":["The ListReader will detect inconsistent degrees of nesting in the provided data.","The ListReader will detect inconsistent types in the provided data."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":2,"totalFeatures":2,"passed":2,"successRate":1.0,"time":6},"title":"","narrative":""},"ut.utility.ListReader_Spec":{"executedFeatures":["The ListReader can interpret nested lists into a shape list and value list.","The ListReader can interpret nested lists resembling a 3D tensor into a shape list and value list.","The ListReader can interpret nested lists resembling a matrix into a shape list and value list."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":3,"passed":8,"successRate":1.0,"time":7},"title":"The Internal ListReader turning lists into flat arrays with shape and type data","narrative":"This specification covers an internal class which should not be used\n outside this library, namely the ListReader class.\n This class is simply a converter which turns nested lists\n into flat arrays alongside the type of the elements and the shape of this \"tensor\"."},"ut.utility.Utility_Spec":{"executedFeatures":["Object arrays can be converted to primitive arrays."],"ignoredFeatures":[],"stats":{"failures":0,"errors":0,"skipped":0,"totalRuns":8,"totalFeatures":1,"passed":8,"successRate":1.0,"time":26},"title":"","narrative":""}} \ No newline at end of file diff --git a/docs/spock/reports/Example_Spec.Example_Spec.json b/docs/spock/reports/Example_Spec.Example_Spec.json index f56f0713d..e0957a073 100644 --- a/docs/spock/reports/Example_Spec.Example_Spec.json +++ b/docs/spock/reports/Example_Spec.Example_Spec.json @@ -1,22 +1,22 @@ { "className":"Example_Spec.Example_Spec", "title":"An Introduction to writing Spock Specifications", - "narrative":"Hello and welcome to the example / template specification of this project.\n This is a simple introduction as to how to get started writing Spock specifications.\n\n Spock works on top of Groovy which is in essence a syntactic super-set of Java.\n That means that one can write Java code in Groovy, and 99% of the time it will\n work the exact same way.", + "narrative":"Hello and welcome to the example / template specification of this project.\n This is a simple introduction as to how to get started writing Spock specifications.\n\n Spock works on top of Groovy which is in essence a syntactic super-set of Java.\n That means that one can write Java code in Groovy, and 99% of the time it will \n work the exact same way.", "subjects":[], "statistics":{ - "runs":"5", + "runs":"7", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.005 seconds" + "duration":"0.068 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"iAmNotSoReadable", "result":"PASS", - "duration":"0", + "duration":"0.007 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -55,7 +55,7 @@ { "id":"Should be able to remove from list", "result":"PASS", - "duration":"0", + "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -70,7 +70,37 @@ }, { - "id":"Numbers to the power of two with a fancy data table!", + "id":"Numbers to the power of two with a fancy data table! [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"Math works!","code":[]}, + + {"kind":"where","text":"We use the following data:","code":{"a":["1"],"b":["2"],"c":["1"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Numbers to the power of two with a fancy data table! [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"Math works!","code":[]}, + + {"kind":"where","text":"We use the following data:","code":{"a":["2"],"b":["2"],"c":["4"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Numbers to the power of two with a fancy data table! [2]", "result":"PASS", "duration":"0", "iterations":{ @@ -79,7 +109,7 @@ "blocks":[ {"kind":"expect","text":"Math works!","code":[]}, - {"kind":"where","text":"We use the following data:","code":{"a":["1","2","3"],"b":["2","2","2"],"c":["1","4","9"]}} + {"kind":"where","text":"We use the following data:","code":{"a":["3"],"b":["2"],"c":["9"]}} ], "problems":{"dataValues":[], "errors":[]} } diff --git a/docs/spock/reports/it.Calculus_Stress_Test.json b/docs/spock/reports/it.Calculus_Stress_Test.json index 92de09fcd..c9993fb0b 100644 --- a/docs/spock/reports/it.Calculus_Stress_Test.json +++ b/docs/spock/reports/it.Calculus_Stress_Test.json @@ -4,19 +4,19 @@ "narrative":"", "subjects":[], "statistics":{ - "runs":"5", + "runs":"69", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.589 seconds" + "duration":"1.086 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"Stress test runs error free and produces expected result", + "id":"Stress test runs error free and produces expected result [0]", "result":"PASS", - "duration":"0.033 seconds", + "duration":"0.183 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -45,9 +45,78 @@ }, { - "id":"Dot operation stress test runs error free and produces expected result", + "id":"Stress test runs error free and produces expected result [1]", "result":"PASS", - "duration":"0.016 seconds", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["def stress = (Tensor t ) -> {"," t = t + Tensor.of( t.shape(), -3d..12d )"," t = t * Tensor.of( t.shape(), 2d..3d )"," t = t / Tensor.of( t.shape(), 1d..2d )"," t = t **Tensor.of( t.shape(), 2d..1d )"," t = t - Tensor.of( t.shape(), -2d..2d )"," return t","}"]}, + + {"kind":"and","text":"","code":["Tensor source = Tensor.of( [3, 3, 3, 3], -1d ).to( device )"]}, + + {"kind":"when","text":"","code":["source.mut[1..2, 0..2, 1..1, 0..2] = Tensor.of( [2, 3, 1, 3], -4d..2d )","Tensor s = source[1..2, 0..2, 1..1, 0d..2d]"]}, + + {"kind":"then","text":"","code":["s.toString() == Tensor.of( [2, 3, 1, 3], -4d..2d ).toString()"]}, + + {"kind":"when","text":"","code":["s = stress(s)"]}, + + {"kind":"then","text":"","code":["s.toString({it.hasSlimNumbers = true}) =="," \"(2x3x1x3):[\" +"," \"198, -6.5, \" +"," \"36, -2.5, \" +"," \"2, 6.5, \" +"," \"\" +"," \"101, 0, \" +"," \"15, 4, \" +"," \"146, 13, \" +"," \"\" +"," \"400, 17, \" +"," \"194, 15.5, \" +"," \"101, -4.5\" +"," \"]\""]}, + + {"kind":"and","text":"","code":["(device instanceof OpenCLDevice) || s.mut.data.get() == [198.0, -6.5, 36.0, -2.5, 2.0, 6.5, 101.0, 0.0, 15.0, 4.0, 146.0, 13.0, 400.0, 17.0, 194.0, 15.5, 101.0, -4.5]","(device instanceof OpenCLDevice) || source.mut.data.get() == [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -4.0, -3.0, -2.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 0.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 2.0, -4.0, -3.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -2.0, -1.0, 0.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 1.0, 2.0, -4.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -3.0, -2.0, -1.0, -1.0, -1.0, -1.0]"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","Device.get('gpu')"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Dot operation stress test runs error free and produces expected result [0]", + "result":"PASS", + "duration":"0.015 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of( shape, -4d..2d )"]}, + + {"kind":"when","text":"","code":["t = t.convDot( t.T() )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"where","text":"","code":{"shape":["[2, 3]","[2, 3]","[2, 1, 3]","[2, 1, 3]"],"expected":["\"(2x1x2):[29.0, 2.0, 2.0, 2.0]\"","\"(2x1x2):[29.0, 2.0, 2.0, 2.0]\"","\"(2x1x1x1x2):[29.0, 2.0, 2.0, 2.0]\"","\"(2x1x1x1x2):[29.0, 2.0, 2.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Dot operation stress test runs error free and produces expected result [1]", + "result":"PASS", + "duration":"0.007 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of( shape, -4d..2d )"]}, + + {"kind":"when","text":"","code":["t = t.convDot( t.T() )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"where","text":"","code":{"shape":["[2, 3]","[2, 3]","[2, 1, 3]","[2, 1, 3]"],"expected":["\"(2x1x2):[29.0, 2.0, 2.0, 2.0]\"","\"(2x1x2):[29.0, 2.0, 2.0, 2.0]\"","\"(2x1x1x1x2):[29.0, 2.0, 2.0, 2.0]\"","\"(2x1x1x1x2):[29.0, 2.0, 2.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Dot operation stress test runs error free and produces expected result [2]", + "result":"PASS", + "duration":"0.009 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -64,9 +133,28 @@ }, { - "id":"The broadcast operation stress test runs error free and produces expected result", + "id":"Dot operation stress test runs error free and produces expected result [3]", "result":"PASS", - "duration":"0.017 seconds", + "duration":"0.006 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of( shape, -4d..2d )"]}, + + {"kind":"when","text":"","code":["t = t.convDot( t.T() )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"where","text":"","code":{"shape":["[2, 3]","[2, 3]","[2, 1, 3]","[2, 1, 3]"],"expected":["\"(2x1x2):[29.0, 2.0, 2.0, 2.0]\"","\"(2x1x2):[29.0, 2.0, 2.0, 2.0]\"","\"(2x1x1x1x2):[29.0, 2.0, 2.0, 2.0]\"","\"(2x1x1x1x2):[29.0, 2.0, 2.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [0]", + "result":"PASS", + "duration":"0.008 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -87,38 +175,1688 @@ }, { - "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors.", + "id":"The broadcast operation stress test runs error free and produces expected result [1]", "result":"PASS", - "duration":"0.430 seconds", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, - {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, - {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, - {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + {"kind":"then","text":"","code":["t.toString() == expected"]}, - {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, - {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [2]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, - {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, - {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, - {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [3]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [5]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [6]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [7]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [8]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [9]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [10]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [11]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [12]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [13]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [14]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The broadcast operation stress test runs error free and produces expected result [15]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["Tensor t1 = Tensor.of( shape1, -4d..2d ).to( device )","Tensor t2 = Tensor.of( shape2, -3d..5d ).to( device )"]}, + + {"kind":"when","text":"","code":["Tensor t = Tensor.of( operation, [t1, t2] )"]}, + + {"kind":"then","text":"","code":["t.toString() == expected"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()","Device.get('gpu')","Device.get('gpu')","CPU.get()","CPU.get()"],"shape1":["[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]","[2, 1]","[2, 3, 1]"],"shape2":["[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]","[2, 2]","[1, 3, 2]"],"operation":["'i0%i1'","'i0%i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0*i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0+i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0-i1'","'i0/i1'","'i0/i1'"],"expected":["\"(2x2):[-1.0, -0.0, -0.0, NaN]\"","\"(2x3x2):[-1.0, -0.0, -0.0, NaN, -0.0, -0.0, -1.0, -1.0, 0.0, NaN, 0.0, 1.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[12.0, 8.0, 3.0, -0.0]\"","\"(2x3x2):[12.0, 8.0, 3.0, -0.0, -2.0, -4.0, 3.0, 2.0, -0.0, 0.0, 1.0, 2.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-7.0, -6.0, -4.0, -3.0]\"","\"(2x3x2):[-7.0, -6.0, -4.0, -3.0, -1.0, 0.0, -4.0, -3.0, -1.0, 0.0, 2.0, 3.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[-1.0, -2.0, -2.0, -3.0]\"","\"(2x3x2):[-1.0, -2.0, -2.0, -3.0, -3.0, -4.0, 2.0, 1.0, 1.0, 0.0, 0.0, -1.0]\"","\"(2x2):[1.33333, 2.0, 3.0, -∞]\"","\"(2x3x2):[1.33333, 2.0, 3.0, -∞, -2.0, -1.0, 0.33333, 0.5, -0.0, NaN, 1.0, 0.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [0]", + "result":"PASS", + "duration":"0.117 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [1]", + "result":"PASS", + "duration":"0.062 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [2]", + "result":"PASS", + "duration":"0.041 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [3]", + "result":"PASS", + "duration":"0.043 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [4]", + "result":"PASS", + "duration":"0.039 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [5]", + "result":"PASS", + "duration":"0.035 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [6]", + "result":"PASS", + "duration":"0.035 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [7]", + "result":"PASS", + "duration":"0.024 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [8]", + "result":"PASS", + "duration":"0.019 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [9]", + "result":"PASS", + "duration":"0.024 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [10]", + "result":"PASS", + "duration":"0.019 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [11]", + "result":"PASS", + "duration":"0.015 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [12]", + "result":"PASS", + "duration":"0.027 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [13]", + "result":"PASS", + "duration":"0.023 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [14]", + "result":"PASS", + "duration":"0.018 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [15]", + "result":"PASS", + "duration":"0.020 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types, on large prime sized 1D slices and non sliced 1D tensors. [16]", + "result":"PASS", + "duration":"0.016 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 7907","var PRIME_SIZE_2 = 7919"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[9..7915]","t2.mut[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)","var data1 = result1.mut.data.get()","var data2 = result2.mut.data.get()"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["data1.class == result1.dataType.dataArrayType()","data2.class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["data1.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[0]","data2.collect({(it as BigDecimal).round(3)}).sum().round(3) == expected[1]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'gaus(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_tanh(i0)*100 % i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'fast_gaus(i0)+i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'softsign(i0)*100 % i0'","'random(i0)'","'random(i0)'"],"expected":["[2840.044, 2840.044]","[2840.043, 2840.043]","[0.000, 0.000]","[625.914, 625.914]","[625.910, 625.910]","[-20900.000, -20900.000]","[637.151, 637.151]","[637.011, 637.011]","[-20900.000, -20900.000]","[6632.311, 6632.311]","[6632.311, 6632.311]","[-233722189118.000, -233722189118.000]","[647.562, 647.562]","[647.564, 647.564]","[-20900.000, -20900.000]","[93.662, 93.662]","[93.662, 93.662]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [0]", + "result":"PASS", + "duration":"0.009 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [1]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [2]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [3]", + "result":"PASS", + "duration":"0.006 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [4]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [5]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [6]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [7]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [8]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [9]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [10]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [11]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [12]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [13]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [14]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [15]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [16]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [17]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [18]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [19]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [20]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [21]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [22]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [23]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [24]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [25]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [26]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [27]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types. [28]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We use a large prime number to size our tensors in order to stress workload divisibility.","code":["var PRIME_SIZE_1 = 3","var PRIME_SIZE_2 = 5"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(PRIME_SIZE_1).andSeed(\"Seitan\")","var t2 = Tensor.of(type).withShape(PRIME_SIZE_2).all(0)[1..3]","t2[0..t2.size-1] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = ( !derive ? func(t1) : func.derive([t1], 0) )","var result2 = ( !derive ? func(t2) : func.derive([t2], 0) )"]}, + + {"kind":"then","text":"First we ensure that both tensors have the correct value/element type.","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The underlying data object should match the data array type as is defined by the data type!","code":["result1.mut.data.get().class == result1.dataType.dataArrayType()","result2.mut.data.get().class == result2.dataType.dataArrayType()"]}, + + {"kind":"and","text":"The data of the first non slice tensor as well as its slice should be as expected.","code":["result1.items == expected","result2.items == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer"],"funExpression":["'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'silu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'gelu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'selu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gatu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'","'gasu(i0)'"],"derive":["false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true","false","false","false","true","true","true"],"expected":["[1.0985150624263118, 2.331551300795844, 0.08745752408303246] as double[]","[1.098515, 2.3315513, 0.08745752] as float[]","[2124371342, 0, 0] as int[]","[1.0198659569612678, 1.0992238228008295, 0.5805713104936336] as double[]","[1.019866, 1.0992239, 0.5805713] as float[]","[1, 0, 0] as int[]","[1.2553019101258691, 2.48514859714065, 0.09199892841280806] as double[]","[1.255302, 2.4851487, 0.09199893] as float[]","[2124371342, 0, 0] as int[]","[1.09968352899801, 1.043758795430269, 0.6360091016582581] as double[]","[1.0996835, 1.0437589, 0.6360091] as float[]","[1, 0, 0] as int[]","[1.4457526798842053, 2.6470118580557593, 0.17005220305511268] as double[]","[1.4457527, 2.647012, 0.1700522] as float[]","[-2062888229, -2, -2] as int[]","[1.0507009873554805, 1.0507009873554805, 1.0507009873554805] as double[]","[1.050701, 1.050701, 1.050701] as float[]","[1, 0, 0] as int[]","[0.9891407665275838, 0.9999999999999742, 0.004239423130809827] as double[]","[0.98914075, 1.0, 0.0042394227] as float[]","[1, -1, -1] as int[]","[0.1226918386004856, 9.805489753489383E-13, 0.07858138767615172] as double[]","[0.12269211, 0.0, 0.078581385] as float[]","[0, 0, 0] as int[]","[0.7226245060456667, 0.9411395236107959, 0.004221551478848414] as double[]","[0.72262454, 0.9411396, 0.004221551] as float[]","[1, -1, -1] as int[]","[0.4370057619908791, 0.06596632547000601, 0.07792071781374522] as double[]","[0.43700573, 0.06596632, 0.07792072] as float[]","[0, 0, 0] as int[]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Activation functions work across types.", + "id":"Activation functions work across types. [29]", "result":"PASS", - "duration":"0.017 seconds", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/it.Cross_Device_Sliced_Tensor_System_Test.json b/docs/spock/reports/it.Cross_Device_Sliced_Tensor_System_Test.json index f633b7f52..79d8532ec 100644 --- a/docs/spock/reports/it.Cross_Device_Sliced_Tensor_System_Test.json +++ b/docs/spock/reports/it.Cross_Device_Sliced_Tensor_System_Test.json @@ -4,19 +4,19 @@ "narrative":"", "subjects":[], "statistics":{ - "runs":"2", + "runs":"4", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.070 seconds" + "duration":"0.146 seconds" }, "headers":[" \n

        \n This specification covers the behavior of tensors when being sliced\n on multiple different device types in conjunction with \n the autograd system.\n Autograd should work on slices as well. \n

        \n "],"tags":{},"see":[], "features":[ { - "id":"Slices can be created using the SliceBuilder.", + "id":"Slices can be created using the SliceBuilder. [0]", "result":"PASS", - "duration":"0.023 seconds", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -43,9 +43,107 @@ }, { - "id":"Cross device sliced tensor integration test runs without errors.", + "id":"Slices can be created using the SliceBuilder. [1]", "result":"PASS", - "duration":"0.040 seconds", + "duration":"0.039 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"and","text":"","code":["if ( device == null ) return","Neureka.get().settings().autograd().isApplyingGradientWhenTensorIsUsed = false","Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(false)","if ( device instanceof OpenCLDevice && !Neureka.get().canAccessOpenCLDevice() ) return"]}, + + {"kind":"and","text":"A tensor which ought to be sliced:","code":["var a = Tensor.of([4, 6], ["," 1d, 2d, 3d, 4d, 5d, 6d,"," 7d, 8d, 9d, 1d, 2d, 3d,"," 4d, 5d, 6d, 7d, 8d, 9d,"," 1d, 2d, 3d, 4d, 5d, 6d"," ])","device.store(a)"]}, + + {"kind":"when","text":"","code":["var b = a.slice() // [-1..-3, -6..-3]"," .axis(0).from(-1).to(-3)"," .axis(1).from(-6).to(-3)"," .get()","var s = a.slice() // [1, -2]"," .axis(0).at(1)"," .axis(1).at(-2)"," .get()","s.rqsGradient = true"]}, + + {"kind":"then","text":"","code":["s.toString() == \"(1x1):[2.0]:g:[null]\"","s.item(0) == 2.0","s.rqsGradient()","b.toString().contains(\"7.0, 8.0, 9.0, 1.0, 4.0, 5.0, 6.0, 7.0, 1.0, 2.0, 3.0, 4.0\")","b.spread() != null"]}, + + {"kind":"when","text":"","code":["var y = ( s * 4 ) ** 1.5"]}, + + {"kind":"then","text":"","code":["y.toString() == '(1x1):[22.6274]; ->d(1x1):[16.9706]'"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["Device.get('gpu')","CPU.get()"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Cross device sliced tensor integration test runs without errors. [0]", + "result":"PASS", + "duration":"0.089 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["if ( device == null ) return"]}, + + {"kind":"and","text":"For this test we tell the CL-Backend to auto-convert to floats.","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }","Neureka.get().settings().autograd().isApplyingGradientWhenTensorIsUsed = false","Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)","if ( device instanceof OpenCLDevice && !Neureka.get().canAccessOpenCLDevice() ) return"]}, + + {"kind":"when","text":"","code":["var x = Tensor.of([1], 3d).setRqsGradient(true)","var b = Tensor.of([1], -4d)","var w = Tensor.of([1], 2d)","device.store(x).store(b).store(w)","var y = Tensor.of(\"((i0+i1)*i2)**2\", [x, b, w])"]}, + + {"kind":"then","text":"","code":["y.indicesMap() != null","y.toString().contains(\"[1]:(4.0); ->d[1]:(-8.0)\")"]}, + + {"kind":"when","text":"","code":["y.backward(Tensor.of(2d))","y = ( ( x + b ) * w )**2"]}, + + {"kind":"then","text":"","code":["y.toString().contains(\"[1]:(4.0); ->d[1]:(-8.0)\")"]}, + + {"kind":"when","text":"","code":["y.backward(Tensor.of(2d))","x.toString().contains(\"-32.0\")","y = b + w * x","var a = Tensor.of([4, 6], ["," 1d, 2d, 3d, 4d, 5d, 6d,"," 7d, 8d, 9d, 1d, 2d, 3d,"," 4d, 5d, 6d, 7d, 8d, 9d,"," 1d, 2d, 3d, 4d, 5d, 6d"," ])","device.store(a)","b = a[[-1..-3, -6..-3]]","var s = a[[1, -2]]"]}, + + {"kind":"then","text":"","code":["s.toString() == \"[1x1]:(2.0)\"","s.item(0) == 2.0","b.toString().contains(\"7.0, 8.0, 9.0, 1.0, 4.0, 5.0, 6.0, 7.0, 1.0, 2.0, 3.0, 4.0\")","b.spread() != null"]}, + + {"kind":"when","text":"","code":["b = a[-3..-1, 0..3]","s = a[1, -2]"]}, + + {"kind":"then","text":"","code":["s.toString() == \"[1x1]:(2.0)\"","s.item(0) == 2.0","s.getDataAt(0) == 1.0","s.getDataAt(1) == 2.0","b.toString().contains(\"7.0, 8.0, 9.0, 1.0, 4.0, 5.0, 6.0, 7.0, 1.0, 2.0, 3.0, 4.0\")","b.spread() != null"]}, + + {"kind":"when","text":"","code":["if( device instanceof DummyDevice ) {"," a.getDataAs( double[].class )[1] = a.getDataAs( double[].class )[1] * 6"," a.getDataAs( double[].class )[7] = a.getDataAs( double[].class )[7] * 2","} else {"," var k = Tensor.of([4, 6], ["," 1d, 6d, 1d, 1d,"," 1d, 1d, 1d, 2d,"," 1d, 1d, 1d, 1d,"," 1d, 1d, 1d, 1d,"," 1d, 1d, 1d, 1d,"," 1d, 1d, 1d, 1d"," ])"," device.store( k )"," a.mut[] = a * k","}"]}, + + {"kind":"then","text":"","code":["b.toString().contains(\"7.0, 16.0, 9.0, 1.0, 4.0, 5.0, 6.0, 7.0, 1.0, 2.0, 3.0, 4.0\")"]}, + + {"kind":"when","text":"","code":["var c = Tensor.of([3, 4], ["," -3d, 2d, 3d,"," 5d, 6d, 2d,"," -1d, 1d, 2d,"," 3d, 4d, 2d,"," ])","var d = b + c"]}, + + {"kind":"then","text":"","code":["(d.NDConf.asInlineArray() as List) == ( [3, 4, 4, 1, 4, 1, 0, 0, 1, 1] )","(b.NDConf.asInlineArray() as List) == ( [3, 4, 6, 1, 4, 1, 1, 0, 1, 1] )","(c.NDConf.asInlineArray() as List) == ( [3, 4, 4, 1, 4, 1, 0, 0, 1, 1] )","d.toString().contains("," \"4.0, 18.0, 12.0, 6.0, \"+"," \"10.0, 7.0, 5.0, 8.0, \"+"," \"3.0, 5.0, 7.0, 6.0\"",")"]}, + + {"kind":"when","text":"","code":["b = a[1..3, 2..4]"]}, + + {"kind":"then","text":"","code":["b.toString().contains(\"9.0, 1.0, 2.0, 6.0, 7.0, 8.0, 3.0, 4.0, 5.0\")","b.spread() != null"]}, + + {"kind":"when","text":"","code":["b = a[[[0..3]:2, [1..4]:2]]"]}, + + {"kind":"then","text":"","code":["b.toString().contains( \"12.0, 4.0, 5.0, 7.0\" )","b.spread() != null"]}, + + {"kind":"when","text":"","code":["var p = Tensor.of([2, 2], [2d, 55d, 4d, 7d]).to((device instanceof DummyDevice)?null:device)","var u = Tensor.of([2, 2], [5d, 2d, 7d, 34d]).to((device instanceof DummyDevice)?null:device)","p.mut[] = u"]}, + + {"kind":"then","text":"","code":["p.toString().contains(\"5.0, 2.0, 7.0, 34.0\")"]}, + + {"kind":"when","text":"","code":["a.mut[[[0..3]:2, [1..4]:2]] = Tensor.of([2, 2], [1d, 2d, 3d, 4d])"]}, + + {"kind":"then","text":"","code":["b.toString().contains(\"1.0, 2.0, 3.0, 4.0\")","a.toString().contains("," \"1.0, 1.0, 3.0, 2.0, 5.0, 6.0, \" +"," \"7.0, 16.0, 9.0, 1.0, 2.0, 3.0, \" +"," \"4.0, 3.0, 6.0, 4.0, 8.0, 9.0, \" +"," \"1.0, 2.0, 3.0, 4.0, 5.0, 6.0\"",")"]}, + + {"kind":"when","text":"","code":["a.mut[1..2, 1..2] = Tensor.of([2, 2], [8, 8, 8, 8])"]}, + + {"kind":"then","text":"","code":["b.toString().contains("," \"1.0, 2.0, \"+"," \"8.0, 4.0\"",")","a.toString().contains("," \"1.0, 1.0, 3.0, 2.0, 5.0, 6.0, \" +"," \"7.0, 8.0, 8.0, 1.0, 2.0, 3.0, \" +"," \"4.0, 8.0, 8.0, 4.0, 8.0, 9.0, \" +"," \"1.0, 2.0, 3.0, 4.0, 5.0, 6.0\"",")"]}, + + {"kind":"when","text":"","code":["b.setRqsGradient(true)","c = Tensor.of([2, 2], ["," -2, 3,//-2 + 24 + 3 + 8"," 1, 2,"," ])","device.store(b).store(c) // -2 + 6 + 8 + 8 = 22","x = Tensor.of(b, \"x\", c) // This test is important because it tests convolution on slices!"]}, + + {"kind":"then","text":"","code":["x.item() == 20"]}, + + {"kind":"and","text":"","code":["x.toString().replace(\".0\", \"\").contains(\"->d[2x2]:(-2, 3, 1, 2)\")"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","Device.get('gpu')"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Cross device sliced tensor integration test runs without errors. [1]", + "result":"PASS", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/it.Cross_Device_Spec.json b/docs/spock/reports/it.Cross_Device_Spec.json index 08b813f26..6269df7ca 100644 --- a/docs/spock/reports/it.Cross_Device_Spec.json +++ b/docs/spock/reports/it.Cross_Device_Spec.json @@ -4,19 +4,19 @@ "narrative":"This specification is pretty much a system test which covers\n the behavior of the library as a whole across multiple devices!\n No matter which device is being used for a given stress test, the result should be the same...", "subjects":["neureka.devices.Device"], "statistics":{ - "runs":"5", + "runs":"79", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"8.704 seconds" + "duration":"6.066 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"Convolution can model matrix multiplications across devices.", + "id":"Convolution can model matrix multiplications across devices. [0]", "result":"PASS", - "duration":"0.009 seconds", + "duration":"0.033 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,9 +37,51 @@ }, { - "id":"Cross device system test runs successfully.", + "id":"Convolution can model matrix multiplications across devices. [1]", "result":"PASS", - "duration":"5.102 seconds", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A given device of any type and the settings configured for testing.","code":["Device device = ( deviceType == \"CPU\" ) ? CPU.get() : Device.get('first')","Neureka.get().reset()","Neureka.get().settings().debug().isKeepingDerivativeTargetPayloads = true","Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + + {"kind":"and","text":"Two tensors, one requiring gradients and the other one does not.","code":["var tensor1 = Tensor.of(Shape.of(2, 2, 1),"," Data.of("," 1f, 2f, // 3, 1,"," 2f, -3f, // -2, -1,"," ))"," .setRqsGradient( true )","var tensor2 = Tensor.of(Shape.of(1, 2, 2),"," Data.of("," -2f, 3f, // 0 7"," 1f, 2f, // -7 0"," ))","device.store(tensor1).store(tensor2)"]}, + + {"kind":"and","text":"","code":["Tensor product = Tensor.of(\"i0xi1\", tensor1, tensor2)","product.backward( Tensor.of(Shape.of(2, 1, 2), Data.of(1, 1, 1, 1)) )","String result = product.toString({"," it.rowLimit = 15 // \"rc\""," it.isScientific = false"," it.isMultiline = false"," it.hasGradient = false"," it.cellSize = 1"," it.hasValue = true"," it.hasRecursiveGraph = true"," it.hasDerivatives = false"," it.hasShape = true"," it.isCellBound = false"," it.postfix = \"\""," it.prefix = \"\""," it.hasSlimNumbers = false","})"]}, + + {"kind":"expect","text":"","code":["result.contains("," \"[2x1x2]:(0.0, 7.0, -7.0, 0.0); =>d|[ [1x2x2]:(-2.0, 3.0, 1.0, 2.0) ]|:t{ [2x2x1]:(1.0, 2.0, 2.0, -3.0) }\"",")"]}, + + {"kind":"cleanup","text":"","code":["product.mut.delete()","tensor1.mut.delete()","tensor2.mut.delete()"]}, + + {"kind":"where","text":"The following settings are being used: ","code":{"deviceType":["'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Cross device system test runs successfully. [0]", + "result":"PASS", + "duration":"3.278 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A given device of any type and the settings configured for testing.","code":["Device device = ( deviceType == \"CPU\" ) ? CPU.get() : Device.get('first')","Neureka.get().settings().debug().isKeepingDerivativeTargetPayloads = true","Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)","Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"expect","text":"The integration test runs successful.","code":["CrossDeviceSystemTest.on(device)"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"where","text":"The following settings are being used: ","code":{"deviceType":["'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Cross device system test runs successfully. [1]", + "result":"PASS", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -56,9 +98,32 @@ }, { - "id":"Test simple NN implementation with manual backprop", + "id":"Test simple NN implementation with manual backprop [0]", + "result":"PASS", + "duration":"2.579 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)","Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }"]}, + + {"kind":"expect","text":"","code":["device != null"]}, + + {"kind":"and","text":"","code":["new SimpleNNSystemTest(SimpleNNSystemTest.Mode.CONVOLUTION).on(device)"]}, + + {"kind":"and","text":"","code":["if ( !(device instanceof OpenCLDevice) )"," new SimpleNNSystemTest(SimpleNNSystemTest.Mode.MAT_MUL).on(device)"]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend().find(CLBackend).ifPresent{ it.getSettings().autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","Device.get('first gpu')"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test simple NN implementation with manual backprop [1]", "result":"PASS", - "duration":"3.512 seconds", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -79,7 +144,7 @@ }, { - "id":"A gradient of ones can be set by calling the backward method on a tensor sitting on any device.", + "id":"A gradient of ones can be set by calling the backward method on a tensor sitting on any device. [0]", "result":"PASS", "duration":"0.004 seconds", "iterations":{ @@ -106,7 +171,61 @@ }, { - "id":"Mapping tensors works for every device (even if they are not used).", + "id":"A gradient of ones can be set by calling the backward method on a tensor sitting on any device. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We use the legacy representation of tensors for this little test!","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + + {"kind":"and","text":"We create a small matrix of 4 fours which requires a gradient and is stored on the provided device!","code":["Tensor t = Tensor.of([2, 2], 4d).setRqsGradient(true).to(device)"]}, + + {"kind":"when","text":"We now call the backward method on the tensor directly without having done any operations...","code":["t.backward(1)"]}, + + {"kind":"and","text":"Then we take the gradient to see what happened.","code":["Tensor g = t.gradient.get()"]}, + + {"kind":"then","text":"We expect this gradient to be all ones with the shape of our matrix!","code":["g.toString().contains(\"[2x2]:(1.0, 1.0, 1.0, 1.0)\")","t.toString().contains(\"[2x2]:(4.0, 4.0, 4.0, 4.0):g:(1.0, 1.0, 1.0, 1.0)\")"]}, + + {"kind":"and","text":"","code":["t.isOutsourced() == !(device instanceof CPU)","g.isOutsourced() == !(device instanceof CPU)"]}, + + {"kind":"and","text":"","code":["t.device == device","g.device == device"]}, + + {"kind":"where","text":"","code":{"device":["new DummyDevice()","Device.get('first gpu')","CPU.get()"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A gradient of ones can be set by calling the backward method on a tensor sitting on any device. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We use the legacy representation of tensors for this little test!","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + + {"kind":"and","text":"We create a small matrix of 4 fours which requires a gradient and is stored on the provided device!","code":["Tensor t = Tensor.of([2, 2], 4d).setRqsGradient(true).to(device)"]}, + + {"kind":"when","text":"We now call the backward method on the tensor directly without having done any operations...","code":["t.backward(1)"]}, + + {"kind":"and","text":"Then we take the gradient to see what happened.","code":["Tensor g = t.gradient.get()"]}, + + {"kind":"then","text":"We expect this gradient to be all ones with the shape of our matrix!","code":["g.toString().contains(\"[2x2]:(1.0, 1.0, 1.0, 1.0)\")","t.toString().contains(\"[2x2]:(4.0, 4.0, 4.0, 4.0):g:(1.0, 1.0, 1.0, 1.0)\")"]}, + + {"kind":"and","text":"","code":["t.isOutsourced() == !(device instanceof CPU)","g.isOutsourced() == !(device instanceof CPU)"]}, + + {"kind":"and","text":"","code":["t.device == device","g.device == device"]}, + + {"kind":"where","text":"","code":{"device":["new DummyDevice()","Device.get('first gpu')","CPU.get()"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [0]", "result":"PASS", "duration":"0.004 seconds", "iterations":{ @@ -130,6 +249,1869 @@ {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 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], "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [2]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [6]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 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37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [8]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [11]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [12]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [13]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 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37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [14]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [15]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [16]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [17]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [18]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [19]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [20]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 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37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [21]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [22]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [23]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [24]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [25]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [26]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [27]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [28]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [29]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [30]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [31]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [32]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [33]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [34]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 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37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [35]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [36]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [37]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [38]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [39]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [40]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [41]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 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37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [42]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [43]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [44]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [45]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [46]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [47]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [48]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 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37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [49]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [50]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [51]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [52]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [53]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [54]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [55]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 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37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [56]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [57]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [58]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [59]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [60]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [61]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [62]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 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37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [63]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [64]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [65]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [66]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [67]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 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+ ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [68]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Mapping tensors works for every device (even if they are not used). [69]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We first make a note of the type we started with.","code":["var originalType = tensor.itemType()"]}, + + {"kind":"when","text":"","code":["when : \"\"\""," We start off by storing the provided tensor on the provided device."," This might be any kind of device like for example an $OpenCLDevice."," Which means the tensor might not be sitting in RAM!"," \"\"\"","tensor.to(device)"]}, + + {"kind":"then","text":"After the tensor is stored on the device, we expect it to be still of the original type.","code":["tensor.itemType == originalType"]}, + + {"kind":"when","text":"\n We call the mapping method which is supposed to create a new tensor of the provided type.\n This procedure is only supported when the tensor is stored in RAM, so when\n the tensor is outsourced (stored on a device), then we expect that the mapping method\n temporarily migrates the tensor back and forth internally...\n ","code":["Tensor result = tensor.mapTo(target, lambda)"]}, + + {"kind":"then","text":"We expect the String representation of the tensor to be as expected!","code":["result.toString() == expected"]}, + + {"kind":"and","text":"We expect the result to have the expected target class!","code":["result.itemType == target"]}, + + {"kind":"and","text":"Lastly, the original tensor used as mapping source should be stored on the original device!","code":["tensor.isOutsourced() == !(device instanceof CPU)","tensor.device == device"]}, + + {"kind":"where","text":"We use the following data to test this mapping for a wide range of types and values!","code":{"tensor":["Tensor.of(3.5)","Tensor.of(3.5)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofFloats().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofShorts().scalar(3.5f)","Tensor.ofBytes().scalar(2.7)","Tensor.ofBytes().scalar(2.7)","Tensor.ofInts().scalar(6.1f)","Tensor.ofInts().scalar(6.1f)","Tensor.of( 3.0 )","Tensor.of(-1.0 )","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.of(3.0 )","Tensor.of(-1.0)","Tensor.of(0.5)","Tensor.of(0.7)","Tensor.of(0.9)","Tensor.of(3.8)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofFloats().scalar( 3f )","Tensor.ofFloats().scalar(-1f )","Tensor.ofFloats().scalar(0.5f)","Tensor.ofFloats().scalar(0.7f)","Tensor.ofFloats().scalar(0.9f)","Tensor.ofFloats().scalar(3.8f)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofInts().scalar( 3 )","Tensor.ofInts().scalar(-1 )","Tensor.ofInts().scalar( 5 )","Tensor.ofInts().scalar( 70)","Tensor.ofInts().scalar( 90)","Tensor.ofInts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofShorts().scalar( 3 )","Tensor.ofShorts().scalar(-1 )","Tensor.ofShorts().scalar( 5 )","Tensor.ofShorts().scalar( 70)","Tensor.ofShorts().scalar( 90)","Tensor.ofShorts().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)","Tensor.ofBytes().scalar( 3 )","Tensor.ofBytes().scalar(-1 )","Tensor.ofBytes().scalar( 5 )","Tensor.ofBytes().scalar( 70)","Tensor.ofBytes().scalar( 90)","Tensor.ofBytes().scalar( 37)"],"device":["CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","Device.get('first')","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()","CPU.get()"],"target":["String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","String.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class","Double.class","Float.class","Integer.class","Long.class","Byte.class","Short.class"],"lambda":["{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{\"~$it\"}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}","{it*it}","{it/2}","{it*10}","{it*5}","{it*2}","{it/2}"],"expected":["'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3.5]'","'(1):[~3]'","'(1):[~3]'","'(1):[~2]'","'(1):[~2]'","'(1):[~6]'","'(1):[~6]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[5]'","'(1):[3]'","'(1):[1]'","'(1):[1]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'","'(1):[9.0]'","'(1):[-0.5]'","'(1):[50]'","'(1):[350]'","'(1):[-76]'","'(1):[18]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} } ], diff --git a/docs/spock/reports/it.Eleven_Lines_NN_System_Spec.json b/docs/spock/reports/it.Eleven_Lines_NN_System_Spec.json index bba1c7bd5..98734156a 100644 --- a/docs/spock/reports/it.Eleven_Lines_NN_System_Spec.json +++ b/docs/spock/reports/it.Eleven_Lines_NN_System_Spec.json @@ -9,21 +9,21 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.281 seconds" + "duration":"0.491 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"One can write a simple neural network with custom back-prop in 11 lines of code!", "result":"PASS", - "duration":"0.064 seconds", + "duration":"0.109 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ {"kind":"given","text":"","code":["var X = Tensor.of(Double, [[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]])","var y = Tensor.of(Double, [[0, 1, 1, 0]]).T","var W1 = Tensor.ofRandom(Double, 3, 4)","var W2 = Tensor.ofRandom(Double, 4, 1)","60.times {"," var l1 = Tensor.of('sig(', X.matMul(W1), ')')"," var l2 = Tensor.of('sig(', l1.matMul(W2), ')')"," var l2_delta = (y - l2) * (l2 * (-l2 + 1))"," var l1_delta = l2_delta.matMul(W2.T) * (l1 * (-l1 + 1))"," W2 += l1.T.matMul(l2_delta)"," W1 += X.T.matMul(l1_delta)","}"]}, - {"kind":"expect","text":"","code":["W1.mut.data.get().collect({it.round 14}) == RESULT_W1.collect({it.round 14})","W2.mut.data.get().collect({it.round 14}) == RESULT_W2.collect({it.round 14})"]} + {"kind":"expect","text":"","code":["W1.mut.data.get().collect({it.round 12}) == RESULT_W1.collect({it.round 12})","W2.mut.data.get().collect({it.round 12}) == RESULT_W2.collect({it.round 12})"]} ], "problems":{"dataValues":[], "errors":[]} }, @@ -31,14 +31,14 @@ { "id":"One can write a simple neural network in less than 11 lines of code!", "result":"PASS", - "duration":"0.078 seconds", + "duration":"0.122 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ {"kind":"given","text":"","code":["var X = Tensor.of(Double, [[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]])","var y = Tensor.of(Double, [[0, 1, 1, 0]]).T","var W1 = Tensor.ofRandom(Double, 3, 4).setRqsGradient(true)","var W2 = Tensor.ofRandom(Double, 4, 1).setRqsGradient(true)","60.times {"," var l2 = Tensor.of('sig(',Tensor.of('sig(',X.matMul(W1),')').matMul(W2),')')"," l2.backward(y - l2)"," W1.applyGradient(); W2.applyGradient()","}"]}, - {"kind":"expect","text":"","code":["W1.mut.data.get().collect({it.round 14}) == RESULT_W1.collect({it.round 14})","W2.mut.data.get().collect({it.round 14}) == RESULT_W2.collect({it.round 14})"]} + {"kind":"expect","text":"","code":["W1.mut.data.get().collect({it.round 12}) == RESULT_W1.collect({it.round 12})","W2.mut.data.get().collect({it.round 12}) == RESULT_W2.collect({it.round 12})"]} ], "problems":{"dataValues":[], "errors":[]} }, @@ -46,7 +46,7 @@ { "id":"One can write a simple float based neural network in less than 11 lines of java like code!", "result":"PASS", - "duration":"0.067 seconds", + "duration":"0.128 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -61,7 +61,7 @@ { "id":"One can write a simple double based neural network in less than 11 lines of java like code using the \"@\" operator!", "result":"PASS", - "duration":"0.068 seconds", + "duration":"0.125 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -76,7 +76,7 @@ { "id":"The pseudo random number generator works as expected for the weights used in the 11 line NN examples!", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/st.Benchmark_System_Test.json b/docs/spock/reports/st.Benchmark_System_Test.json index 6c445f79f..be0203252 100644 --- a/docs/spock/reports/st.Benchmark_System_Test.json +++ b/docs/spock/reports/st.Benchmark_System_Test.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.910 seconds" + "duration":"1.900 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"Tensor can be constructed by passing List instances.", "result":"PASS", - "duration":"0.150 seconds", + "duration":"0.822 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -39,7 +39,7 @@ { "id":"Test benchmark script and simple tensor constructor.", "result":"PASS", - "duration":"0.758 seconds", + "duration":"1.074 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/st.Broad_System_Test.json b/docs/spock/reports/st.Broad_System_Test.json index 68906cd38..fc89e3439 100644 --- a/docs/spock/reports/st.Broad_System_Test.json +++ b/docs/spock/reports/st.Broad_System_Test.json @@ -4,19 +4,19 @@ "narrative":"", "subjects":[], "statistics":{ - "runs":"1", + "runs":"4", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.033 seconds" + "duration":"0.083 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"The long broad integration test runs successfully.", "result":"PASS", - "duration":"0.030 seconds", + "duration":"0.040 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -24,6 +24,81 @@ {"kind":"expect","text":"The integration test runs without exceptions or assertion errors.","code":["BroadSystemTest.on() // This is the actual test."]} ], "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A function with expression \"softplus((I[0]xI[1])*-100)\" can be backpropagated.", + "result":"PASS", + "duration":"0.011 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true);","Tensor tensor1 = Tensor.of(Shape.of(1, 3), 2d);","Tensor tensor2 = Tensor.of(Double).withShape(2, 1).all(-1.0);","tensor1.setRqsGradient(true);","tensor2.setRqsGradient(true);"]}, + + {"kind":"when","text":"","code":["Tensor result1 = Tensor.of(\"softplus((I[0]xI[1])*-100)\", [tensor1, tensor2]);","Tensor result2 = (Tensor.of(\"i0 x i1\", tensor1, tensor2)*-100).softplus();"]}, + + {"kind":"then","text":"","code":["result1.toString() == \"[2x3]:(200.0, 200.0, 200.0, 200.0, 200.0, 200.0); ->d[2x3]:(-100.0, -100.0, -100.0, -100.0, -100.0, -100.0)\"","result2.toString() == \"[2x3]:(200.0, 200.0, 200.0, 200.0, 200.0, 200.0); ->d[2x3]:(-100.0, -100.0, -100.0, -100.0, -100.0, -100.0)\""]}, + + {"kind":"when","text":"We perform a backwards pass of a gradient of `-0.1`:","code":["result1.backward( -0.1 );"]}, + + {"kind":"then","text":"","code":["tensor1.gradient.get().toString() == \"[1x3]:(-20.0, -20.0, -20.0)\"","tensor2.gradient.get().toString() == \"[2x1]:(60.0, 60.0)\""]}, + + {"kind":"when","text":"We perform a backwards pass of a gradient of `-0.1`:","code":["result2.backward( -0.1 );"]}, + + {"kind":"then","text":"","code":["tensor1.gradient.get().toString() == \"[1x3]:(-40.0, -40.0, -40.0)\"","tensor2.gradient.get().toString() == \"[2x1]:(120.0, 120.0)\""]} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A function with expression \"softplus(tanh(I[0]*I[1]*2)*I[1])\" can be backpropagated.", + "result":"PASS", + "duration":"0.016 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true);","Tensor tensor1 = Tensor.of(Shape.of(2), 2d);","Tensor tensor2 = Tensor.of(Shape.of(2), 4d);","tensor1.setRqsGradient(true);","tensor2.setRqsGradient(true);"]}, + + {"kind":"when","text":"","code":["Tensor result1 = Tensor.of(\"softplus(tanh(I[0]*I[1]*2)*I[1])\", [tensor1, tensor2]);","Tensor result2 = ((tensor1 * tensor2 * 2).tanh()*tensor2).softplus();"]}, + + {"kind":"then","text":"","code":["result1.toString({it.hasDerivatives=false}) == \"[2]:(4.01815, 4.01815)\"","result2.toString({it.hasDerivatives=false}) == \"[2]:(4.01815, 4.01815)\""]}, + + {"kind":"when","text":"We perform a backwards pass of a gradient of `100`:","code":["result1.backward( 100 );"]}, + + {"kind":"then","text":"","code":["tensor1.gradient.get().toString() == \"[2]:(159.09e-12, 159.09e-12)\"","tensor2.gradient.get().toString() == \"[2]:(98.2014, 98.2014)\""]}, + + {"kind":"when","text":"We perform a backwards pass of a gradient of `100`:","code":["result2.backward( 100 );"]}, + + {"kind":"then","text":"","code":["tensor1.gradient.get().toString() == \"[2]:(318.18e-12, 318.18e-12)\"","tensor2.gradient.get().toString() == \"[2]:(196.403, 196.403)\""]} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A function with expression \"(-3*(2*(i0*-1)))*(-1*i0)\" can be backpropagated.", + "result":"PASS", + "duration":"0.010 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true);","Tensor tensor1 = Tensor.of(Shape.of(1), 2d);//-2*4 = 8 | *3 = -24","tensor1.setRqsGradient(true);"]}, + + {"kind":"when","text":"","code":["Tensor result1 = Tensor.of(\"(-3*(2*(i0*-1)))*(-1*i0)\", [tensor1]);","Tensor result2 = ((((tensor1*-1)*2)*-3)*(tensor1*-1));"]}, + + {"kind":"then","text":"","code":["result1.toString({it.hasDerivatives=false}) == \"[1]:(-24.0)\"","result2.toString({it.hasDerivatives=false}) == \"[1]:(-24.0)\""]}, + + {"kind":"when","text":"We perform a backwards pass of a gradient of `2`:","code":["result1.backward( 2 );"]}, + + {"kind":"then","text":"","code":["tensor1.gradient.get().toString() == \"[1]:(-48.0)\""]}, + + {"kind":"when","text":"We perform a backwards pass of a gradient of `2`:","code":["result2.backward( 2 );"]}, + + {"kind":"then","text":"","code":["tensor1.gradient.get().toString() == \"[1]:(-96.0)\""]} + ], + "problems":{"dataValues":[], "errors":[]} } ], diff --git a/docs/spock/reports/st.NN_Concepts_Spec.json b/docs/spock/reports/st.NN_Concepts_Spec.json index e507df792..29ee88929 100644 --- a/docs/spock/reports/st.NN_Concepts_Spec.json +++ b/docs/spock/reports/st.NN_Concepts_Spec.json @@ -1,7 +1,7 @@ { "className":"st.NN_Concepts_Spec", "title":"Examining Neural Network Architecture Snippets", - "narrative":"This specification is intended to showcase some basic building blocks of\n various neural network architectures.", + "narrative":"This specification is intended to showcase some basic building blocks of \n various neural network architectures.", "subjects":[], "statistics":{ "runs":"1", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.140 seconds" + "duration":"0.201 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"The attention mechanism (found in the commonly known transformer) demonstrated.", "result":"PASS", - "duration":"0.138 seconds", + "duration":"0.200 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n The attention mechanism is a core component of the transformer architecture and\n most likely the reason why it is so successful in natural language processing.\n\n Here you can see that the query and key weight matrices are trained\n if there is only one input vector.\n "] }, diff --git a/docs/spock/reports/st.Training_NNs_Spec.json b/docs/spock/reports/st.Training_NNs_Spec.json index bd027179a..fb1738bfc 100644 --- a/docs/spock/reports/st.Training_NNs_Spec.json +++ b/docs/spock/reports/st.Training_NNs_Spec.json @@ -1,22 +1,22 @@ { "className":"st.Training_NNs_Spec", "title":"Training a Neural Network Class", - "narrative":"When designing larger neural network architectures, what you would usually do is\n to create a class that represents the whole model (which itself might be composed\n of smaller models).\n\n This class would then represent something that can be executed and then trained.\n This Specification shows how to instantiate, execute and train various\n pre-defined example neural network models.", + "narrative":"When designing larger neural network architectures, what you would usually do is\n to create a class that represents the whole model (which itself might be composed\n of smaller models). \n\n This class would then represent something that can be executed and then trained.\n This Specification shows how to instantiate, execute and train various \n pre-defined example neural network models.", "subjects":[], "statistics":{ - "runs":"3", + "runs":"5", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"7.071 seconds" + "duration":"12.622 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can run the attention head test model.", "result":"PASS", - "duration":"5.276 seconds", + "duration":"9.452 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n This little test simply executes the `QuasiMultiHeadAttention` model class\n and checks if the loss is decreasing over time.\n You can check out how this is implemented in the `QuasiMultiHeadAttention` class.\n Here you will only see how the training is executed.\n "] }, @@ -35,7 +35,7 @@ { "id":"A simple 3 layer neural network converges.", "result":"PASS", - "duration":"1.331 seconds", + "duration":"2.328 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -50,9 +50,55 @@ }, { - "id":"A very simple 1 layer NN converges.", + "id":"A very simple 1 layer NN converges. [0]", "result":"PASS", - "duration":"0.458 seconds", + "duration":"0.144 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var inputs = Tensor.ofFloats().withShape( 2, 6 ).andFill(-4f..3f)","var weights = Tensor.ofRandom(Float, 6, 1)","var targets = Tensor.of( 0.2f, -0.1f, 0.5f, 1.2f, -0.3f, 0.2f ).reshape( 2, 1 )"]}, + + {"kind":"and","text":"","code":["weights.setRqsGradient( true )","applyOptimizer.accept(weights)"]}, + + {"kind":"and","text":"","code":["var pred","var losses = []"]}, + + {"kind":"when","text":"","code":["100.times {"," pred = inputs.matMul( weights ).tanh()"," var loss = ((pred - targets)**2).sum()"," loss.backward()"," weights.applyGradient()"," losses << loss.item()","}"]}, + + {"kind":"then","text":"","code":["pred.shape == [2, 1]","losses[0] > losses[losses.size()-1]","losses[0] > 2","losses[losses.size()-1] < 0.5"]}, + + {"kind":"where","text":"","code":{"applyOptimizer":["{ it.set(Optimizer.SGD.withLearningRate(0.03)) }","{ it.set(Optimizer.ADAM) }","{ it.set(Optimizer.RMSProp.withLearningRate(0.05)) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A very simple 1 layer NN converges. [1]", + "result":"PASS", + "duration":"0.434 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var inputs = Tensor.ofFloats().withShape( 2, 6 ).andFill(-4f..3f)","var weights = Tensor.ofRandom(Float, 6, 1)","var targets = Tensor.of( 0.2f, -0.1f, 0.5f, 1.2f, -0.3f, 0.2f ).reshape( 2, 1 )"]}, + + {"kind":"and","text":"","code":["weights.setRqsGradient( true )","applyOptimizer.accept(weights)"]}, + + {"kind":"and","text":"","code":["var pred","var losses = []"]}, + + {"kind":"when","text":"","code":["100.times {"," pred = inputs.matMul( weights ).tanh()"," var loss = ((pred - targets)**2).sum()"," loss.backward()"," weights.applyGradient()"," losses << loss.item()","}"]}, + + {"kind":"then","text":"","code":["pred.shape == [2, 1]","losses[0] > losses[losses.size()-1]","losses[0] > 2","losses[losses.size()-1] < 0.5"]}, + + {"kind":"where","text":"","code":{"applyOptimizer":["{ it.set(Optimizer.SGD.withLearningRate(0.03)) }","{ it.set(Optimizer.ADAM) }","{ it.set(Optimizer.RMSProp.withLearningRate(0.05)) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A very simple 1 layer NN converges. [2]", + "result":"PASS", + "duration":"0.255 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/summary.json b/docs/spock/reports/summary.json index 57d616f65..b4161edda 100644 --- a/docs/spock/reports/summary.json +++ b/docs/spock/reports/summary.json @@ -1,10 +1,10 @@ { "project": "Neureka", "version": "0.20.1", - "created": "Thu Jun 29 18:08:38 CEST 2023", + "created": "Mon Dec 16 14:38:28 CET 2024", "statistics":{ "runs":"99", - "passed":"99", + "passed":"96", "failed":"0", "featureFailures":"0", "successRate":"1.0", @@ -86,13 +86,13 @@ "className":"st.Broad_System_Test", "title":"", "narrative":"", - "featureCount":"1", + "featureCount":"4", "failures":"0", "errors":"0", "skipped":"0" , "successRate":"1.0", "duration":"?", - "executedFeatures":[{"id":"The long broad integration test runs successfully.","extraInfo":[]}], + "executedFeatures":[{"id":"A function with expression \"(-3*(2*(i0*-1)))*(-1*i0)\" can be backpropagated.","extraInfo":[]},{"id":"A function with expression \"softplus((I[0]xI[1])*-100)\" can be backpropagated.","extraInfo":[]},{"id":"A function with expression \"softplus(tanh(I[0]*I[1]*2)*I[1])\" can be backpropagated.","extraInfo":[]},{"id":"The long broad integration test runs successfully.","extraInfo":[]}], "ignoredFeatures":[] },{ "className":"st.NN_Concepts_Spec", @@ -377,11 +377,11 @@ "featureCount":"4", "failures":"0", "errors":"0", - "skipped":"0" , + "skipped":"4" , "successRate":"1.0", "duration":"?", - "executedFeatures":[{"id":"Ad hoc compilation produces expected exceptions when duplication is found.","extraInfo":[]},{"id":"Ad hoc compilation produces expected exceptions.","extraInfo":[]},{"id":"An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.","extraInfo":[]},{"id":"Trying to restore a tensor which is not on a device raises exception.","extraInfo":[]}], - "ignoredFeatures":[] + "executedFeatures":[], + "ignoredFeatures":[{"id":"Ad hoc compilation produces expected exceptions when duplication is found.","extraInfo":[]},{"id":"Ad hoc compilation produces expected exceptions.","extraInfo":[]},{"id":"An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.","extraInfo":[]},{"id":"Trying to restore a tensor which is not on a device raises exception.","extraInfo":[]}] },{ "className":"ut.device.OpenCLDevice_Spec", "title":"The OpenCLDevice Specification", @@ -389,11 +389,11 @@ "featureCount":"8", "failures":"0", "errors":"0", - "skipped":"0" , + "skipped":"8" , "successRate":"1.0", "duration":"?", - "executedFeatures":[{"id":"Ad hoc compilation produces executable kernel.","extraInfo":[]},{"id":"Ad hoc compilation works for WIP general purpose matrix multiplication.","extraInfo":[]},{"id":"Ad hoc compilation works for custom column major based tiled matrix multiplication.","extraInfo":[]},{"id":"Ad hoc compilation works for custom simple row major based matrix multiplication.","extraInfo":[]},{"id":"Ad hoc matrix multiplication works for multiple of 16 matrices.","extraInfo":[]},{"id":"An OpenCLDevice loads tensors in a provided lambda temporarily.","extraInfo":[]},{"id":"We can get the items of an outsourced tensor as a primitive array.","extraInfo":[]},{"id":"We can take a look at the underlying data array of an outsourced tensor through the unsafe API.","extraInfo":[]}], - "ignoredFeatures":[] + "executedFeatures":[], + "ignoredFeatures":[{"id":"Ad hoc compilation produces executable kernel.","extraInfo":[]},{"id":"Ad hoc compilation works for WIP general purpose matrix multiplication.","extraInfo":[]},{"id":"Ad hoc compilation works for custom column major based tiled matrix multiplication.","extraInfo":[]},{"id":"Ad hoc compilation works for custom simple row major based matrix multiplication.","extraInfo":[]},{"id":"Ad hoc matrix multiplication works for multiple of 16 matrices.","extraInfo":[]},{"id":"An OpenCLDevice loads tensors in a provided lambda temporarily.","extraInfo":[]},{"id":"We can get the items of an outsourced tensor as a primitive array.","extraInfo":[]},{"id":"We can take a look at the underlying data array of an outsourced tensor through the unsafe API.","extraInfo":[]}] },{ "className":"ut.device.OpenCL_Spec", "title":"Working with OpenCL", @@ -401,11 +401,11 @@ "featureCount":"5", "failures":"0", "errors":"0", - "skipped":"0" , + "skipped":"5" , "successRate":"1.0", "duration":"?", - "executedFeatures":[{"id":"A given OpenCL context can be disposed!","extraInfo":[]},{"id":"An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.","extraInfo":[]},{"id":"First found OpenCLDevice will have realistic numeric properties.","extraInfo":[]},{"id":"First found OpenCLDevice will have realistic properties inside summary query.","extraInfo":[]},{"id":"First found OpenCLDevice will have realistic text properties.","extraInfo":[]}], - "ignoredFeatures":[] + "executedFeatures":[], + "ignoredFeatures":[{"id":"A given OpenCL context can be disposed!","extraInfo":[]},{"id":"An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.","extraInfo":[]},{"id":"First found OpenCLDevice will have realistic numeric properties.","extraInfo":[]},{"id":"First found OpenCLDevice will have realistic properties inside summary query.","extraInfo":[]},{"id":"First found OpenCLDevice will have realistic text properties.","extraInfo":[]}] },{ "className":"ut.device.internal.CLFunctionCompiler_Spec", "title":"Turning functions into kernels.", @@ -413,11 +413,11 @@ "featureCount":"4", "failures":"0", "errors":"0", - "skipped":"0" , + "skipped":"2" , "successRate":"1.0", "duration":"?", - "executedFeatures":[{"id":"The CLFunctionCompiler produces an operation which properly integrates to the backend.","extraInfo":[]},{"id":"The CLFunctionCompiler produces the expected \"ad hoc\" kernel.","extraInfo":[]},{"id":"The OpenCLDevice produces a working optimized Function for doubles.","extraInfo":[]},{"id":"The OpenCLDevice produces a working optimized Function for floats.","extraInfo":[]}], - "ignoredFeatures":[] + "executedFeatures":[{"id":"The CLFunctionCompiler produces an operation which properly integrates to the backend.","extraInfo":[]},{"id":"The CLFunctionCompiler produces the expected \"ad hoc\" kernel.","extraInfo":[]}], + "ignoredFeatures":[{"id":"The OpenCLDevice produces a working optimized Function for doubles.","extraInfo":[]},{"id":"The OpenCLDevice produces a working optimized Function for floats.","extraInfo":[]}] },{ "className":"ut.device.internal.CPU_Kernel_Spec", "title":"", @@ -761,11 +761,11 @@ "featureCount":"6", "failures":"0", "errors":"0", - "skipped":"0" , + "skipped":"1" , "successRate":"1.0", "duration":"?", - "executedFeatures":[{"id":"Backend related library objects adhere to the same toString formatting convention!","extraInfo":[]},{"id":"Every Thread instance has their own Neureka instance.","extraInfo":[]},{"id":"Neureka class instance has expected behaviour.","extraInfo":[]},{"id":"Neureka settings class can be locked causing its properties to be immutable.","extraInfo":[]},{"id":"OpenCL related library objects adhere to the same toString formatting convention!","extraInfo":[]},{"id":"Various library objects adhere to the same toString formatting convention!","extraInfo":[]}], - "ignoredFeatures":[] + "executedFeatures":[{"id":"Backend related library objects adhere to the same toString formatting convention!","extraInfo":[]},{"id":"Every Thread instance has their own Neureka instance.","extraInfo":[]},{"id":"Neureka class instance has expected behaviour.","extraInfo":[]},{"id":"Neureka settings class can be locked causing its properties to be immutable.","extraInfo":[]},{"id":"Various library objects adhere to the same toString formatting convention!","extraInfo":[]}], + "ignoredFeatures":[{"id":"OpenCL related library objects adhere to the same toString formatting convention!","extraInfo":[]}] },{ "className":"ut.optimization.ADAM_Spec", "title":"", @@ -881,11 +881,11 @@ "featureCount":"7", "failures":"0", "errors":"0", - "skipped":"0" , + "skipped":"3" , "successRate":"1.0", "duration":"?", - "executedFeatures":[{"id":"Tensor mapping lambdas produce expected tensors.","extraInfo":[]},{"id":"The \"map\" method is a shorter convenience method for mapping to the same type.","extraInfo":[]},{"id":"We can analyse the values of a tensor using various predicate receiving methods","extraInfo":[]},{"id":"We can find both min and max items in a tensor by providing a comparator.","extraInfo":[]},{"id":"We can initialize a tensor using a filler lambda mapping indices to items.","extraInfo":[]},{"id":"We can use the \"filter\" method as a shortcut for \"stream().filter(..)\".","extraInfo":[]},{"id":"We can use the \"flatMap\" method as a shortcut for \"stream().flatMap(..)\".","extraInfo":[]}], - "ignoredFeatures":[] + "executedFeatures":[{"id":"We can find both min and max items in a tensor by providing a comparator.","extraInfo":[]},{"id":"We can initialize a tensor using a filler lambda mapping indices to items.","extraInfo":[]},{"id":"We can use the \"filter\" method as a shortcut for \"stream().filter(..)\".","extraInfo":[]},{"id":"We can use the \"flatMap\" method as a shortcut for \"stream().flatMap(..)\".","extraInfo":[]}], + "ignoredFeatures":[{"id":"Tensor mapping lambdas produce expected tensors.","extraInfo":[]},{"id":"The \"map\" method is a shorter convenience method for mapping to the same type.","extraInfo":[]},{"id":"We can analyse the values of a tensor using various predicate receiving methods","extraInfo":[]}] },{ "className":"ut.tensors.Reshape_Spec", "title":"Tensor Reshaping", @@ -953,11 +953,11 @@ "featureCount":"4", "failures":"0", "errors":"0", - "skipped":"0" , + "skipped":"1" , "successRate":"1.0", "duration":"?", - "executedFeatures":[{"id":"Adding OpenCL device to tensor makes tensor be \"outsourced\" and contain the Device instance as component.","extraInfo":[]},{"id":"Tensors try to migrate themselves to a device that is being added to them as component.","extraInfo":[]},{"id":"The device of a tensor can be accessed via the \"device()\" method.","extraInfo":[]},{"id":"When creating slices of tensors then this should trigger a \"parent - child\" relation noticeable to the device!","extraInfo":[]}], - "ignoredFeatures":[] + "executedFeatures":[{"id":"Tensors try to migrate themselves to a device that is being added to them as component.","extraInfo":[]},{"id":"The device of a tensor can be accessed via the \"device()\" method.","extraInfo":[]},{"id":"When creating slices of tensors then this should trigger a \"parent - child\" relation noticeable to the device!","extraInfo":[]}], + "ignoredFeatures":[{"id":"Adding OpenCL device to tensor makes tensor be \"outsourced\" and contain the Device instance as component.","extraInfo":[]}] },{ "className":"ut.tensors.Tensor_Dot_Product_Spec", "title":"Tensor Dot Products", @@ -1097,11 +1097,11 @@ "featureCount":"4", "failures":"0", "errors":"0", - "skipped":"0" , + "skipped":"1" , "successRate":"1.0", "duration":"?", - "executedFeatures":[{"id":"Inline operations cause illegal state exceptions.","extraInfo":[]},{"id":"Inline operations causes version incrementation.","extraInfo":[]},{"id":"Non-inline operations do not cause version incrementation.","extraInfo":[]},{"id":"Storing a tensor on a device should not change the version of a tensor (Even though its data changed technically).","extraInfo":[]}], - "ignoredFeatures":[] + "executedFeatures":[{"id":"Inline operations cause illegal state exceptions.","extraInfo":[]},{"id":"Inline operations causes version incrementation.","extraInfo":[]},{"id":"Non-inline operations do not cause version incrementation.","extraInfo":[]}], + "ignoredFeatures":[{"id":"Storing a tensor on a device should not change the version of a tensor (Even though its data changed technically).","extraInfo":[]}] },{ "className":"ut.tensors.exceptions.Tensor_Delete_Exception_Spec", "title":"", diff --git a/docs/spock/reports/ut.autograd.AD_And_Computation_Graph_Spec.json b/docs/spock/reports/ut.autograd.AD_And_Computation_Graph_Spec.json index 836b443be..b4aa54478 100644 --- a/docs/spock/reports/ut.autograd.AD_And_Computation_Graph_Spec.json +++ b/docs/spock/reports/ut.autograd.AD_And_Computation_Graph_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.605 seconds" + "duration":"1.288 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"Reshaping produces expected computation graph and also works with reverse mode AD.", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.006 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,7 +37,7 @@ { "id":"Payloads and derivatives are null after garbage collection.", "result":"PASS", - "duration":"0.601 seconds", + "duration":"1.278 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.autograd.Autograd_Explained.json b/docs/spock/reports/ut.autograd.Autograd_Explained.json index 5bd60c40e..ee1605794 100644 --- a/docs/spock/reports/ut.autograd.Autograd_Explained.json +++ b/docs/spock/reports/ut.autograd.Autograd_Explained.json @@ -1,7 +1,7 @@ { "className":"ut.autograd.Autograd_Explained", "title":"Autograd - Automatic Differentiation", - "narrative":"Central to all neural networks in Neureka is the autograd package.\n The autograd package provides automatic differentiation for all default operations on Tensors.\n Neureka is a define-by-run library, which means that your backpropagation is defined by how\n your code is run, and that every single iteration can be different.\n\n The class neureka.Tensor is the central class of the main package.\n If you set its attribute 'rqsGradient' to True, Neureka starts to track all operations on it.\n When you finish the forward pass of your network\n you can call .backward() and have all the gradients computed\n and distributed to the tensors requiring them automatically.\n\n The gradient for a tensor will be accumulated into a child tensor (component) which\n can be accessed via the '.getGradient()' method.\n\n To stop a tensor from tracking history, you can call '.detach()' to detach it from the\n computation history, and to prevent future computation from being tracked.", + "narrative":"Central to all neural networks in Neureka is the autograd package. \n The autograd package provides automatic differentiation for all default operations on Tensors. \n Neureka is a define-by-run library, which means that your backpropagation is defined by how \n your code is run, and that every single iteration can be different. \n\n The class neureka.Tensor is the central class of the main package. \n If you set its attribute 'rqsGradient' to True, Neureka starts to track all operations on it. \n When you finish the forward pass of your network \n you can call .backward() and have all the gradients computed \n and distributed to the tensors requiring them automatically. \n\n The gradient for a tensor will be accumulated into a child tensor (component) which \n can be accessed via the '.getGradient()' method. \n\n To stop a tensor from tracking history, you can call '.detach()' to detach it from the \n computation history, and to prevent future computation from being tracked.", "subjects":["neureka.Tensor","neureka.autograd.GraphNode"], "statistics":{ "runs":"1", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.006 seconds" + "duration":"0.011 seconds" }, "headers":["\n There’s one more class which is very important for autograd implementation : the 'GraphNode class'! \n Tensor and GraphNode instances are interconnected and build up an acyclic graph, \n that encodes a complete history of computation. \n Each tensor has a .getGraphNode() attribute that references the GraphNode \n that has created a given Tensor instance. \n (except for Tensor created by the user or created by a \"detached\" Function instance... ). \n\n ","\n If you want to compute the derivatives, you can call .backward() on a Tensor. \n If the given Tensor is a scalar (i.e. it holds one element and has shape \"(1)\"), you do not need to \n specify any arguments to backward(), however if it has more elements, \n you should specify a gradient argument that is a tensor of matching shape. \n "],"tags":{},"see":[], "features":[ { "id":"Simple automatic differentiation and propagation.", "result":"PASS", - "duration":"0.004 seconds", + "duration":"0.008 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n How can I compute gradients with Neureka automatically?\n "] }, diff --git a/docs/spock/reports/ut.autograd.Autograd_Flags_Explained.json b/docs/spock/reports/ut.autograd.Autograd_Flags_Explained.json index e34d77aee..29f490524 100644 --- a/docs/spock/reports/ut.autograd.Autograd_Flags_Explained.json +++ b/docs/spock/reports/ut.autograd.Autograd_Flags_Explained.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.048 seconds" + "duration":"0.149 seconds" }, "headers":["\n Autograd Advanced - Custom Autograd
        \n
        \n Neureka does not necessarily perform autograd eagerly.
        \n If required then auto-differentiation will occur as one would expect
        \n similarly to the way PyTorch's autograd works.
        \n However for many use cases it might make sense to use different variants
        \n of auto-differentiation.
        \n This specification covers precisely these different autograd modes.
        \n
        \n "],"tags":{},"see":[], "features":[ { - "id":"Advanced backpropagation on all AD-Modes [code: y*y*3, whenRsd: false, whenUse: false, doJIT: false, afterBack: .*1.*4\\.5.*, afterUse: .*1.*4\\.5.*, afterRqd: .*1.*4\\.5.*, afterAll: .*1.*4\\.5.*, #0]", + "id":"Advanced backpropagation on all AD-Modes [0]", "result":"PASS", - "duration":"0.008 seconds", + "duration":"0.030 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n What is JIT-Prop and how does it affect autograd ? Let's take a look !
        \n This run covers the feature having the following settings :
        \n
        \n Neureka.instance().settings().autograd().:
        \n isRetainingPendingErrorForJITProp := false
        \n isApplyingGradientWhenTensorIsUsed := false
        \n isApplyingGradientWhenRequested := false
        \n
        \n ...code producing the result : 'y*y*3'
        \n
        \n is-Retaining-Pending-Error-For-JITProp :
        \n
        \n This flag enables an optimization technique which only propagates error values to
        \n gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them
        \n at divergent differentiation paths within the computation graph.
        \n If the flag is set to true
        \n then error values will accumulate at such junction nodes.
        \n This technique however uses more memory but will
        \n improve performance for some networks substantially.
        \n The technique is termed JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Tensor-Is-Used :
        \n
        \n Gradients will automatically be applied (or JITed) to tensors as soon as
        \n they are being used for calculation (GraphNode instantiation).
        \n This feature works well with JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Requested :
        \n
        \n Gradients will only be applied if requested.
        \n Usually this happens immediately, however
        \n if the flag 'applyGradientWhenTensorIsUsed' is set
        \n to true, then the tensor will only be updated by its
        \n gradient if requested AND the tensor is used fo calculation! (GraphNode instantiation).
        \n
        \n Let's take a look : \n "] }, @@ -59,9 +59,9 @@ }, { - "id":"Advanced backpropagation on all AD-Modes [code: y*y*3, whenRsd: true, whenUse: false, doJIT: false, afterBack: .*1.*4\\.5.*, afterUse: .*1.*4\\.5.*, afterRqd: .*5\\.5.*null.*, afterAll: .*5\\.5.*null.*, #1]", + "id":"Advanced backpropagation on all AD-Modes [1]", "result":"PASS", - "duration":"0.005 seconds", + "duration":"0.014 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n What is JIT-Prop and how does it affect autograd ? Let's take a look !
        \n This run covers the feature having the following settings :
        \n
        \n Neureka.instance().settings().autograd().:
        \n isRetainingPendingErrorForJITProp := false
        \n isApplyingGradientWhenTensorIsUsed := false
        \n isApplyingGradientWhenRequested := true
        \n
        \n ...code producing the result : 'y*y*3'
        \n
        \n is-Retaining-Pending-Error-For-JITProp :
        \n
        \n This flag enables an optimization technique which only propagates error values to
        \n gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them
        \n at divergent differentiation paths within the computation graph.
        \n If the flag is set to true
        \n then error values will accumulate at such junction nodes.
        \n This technique however uses more memory but will
        \n improve performance for some networks substantially.
        \n The technique is termed JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Tensor-Is-Used :
        \n
        \n Gradients will automatically be applied (or JITed) to tensors as soon as
        \n they are being used for calculation (GraphNode instantiation).
        \n This feature works well with JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Requested :
        \n
        \n Gradients will only be applied if requested.
        \n Usually this happens immediately, however
        \n if the flag 'applyGradientWhenTensorIsUsed' is set
        \n to true, then the tensor will only be updated by its
        \n gradient if requested AND the tensor is used fo calculation! (GraphNode instantiation).
        \n
        \n Let's take a look : \n "] }, @@ -104,9 +104,9 @@ }, { - "id":"Advanced backpropagation on all AD-Modes [code: y*y*3, whenRsd: false, whenUse: true, doJIT: false, afterBack: .*1.*4\\.5.*, afterUse: .*5\\.5.*null.*, afterRqd: .*5\\.5.*null.*, afterAll: .*5\\.5.*null.*, #2]", + "id":"Advanced backpropagation on all AD-Modes [2]", "result":"PASS", - "duration":"0.004 seconds", + "duration":"0.014 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n What is JIT-Prop and how does it affect autograd ? Let's take a look !
        \n This run covers the feature having the following settings :
        \n
        \n Neureka.instance().settings().autograd().:
        \n isRetainingPendingErrorForJITProp := false
        \n isApplyingGradientWhenTensorIsUsed := true
        \n isApplyingGradientWhenRequested := false
        \n
        \n ...code producing the result : 'y*y*3'
        \n
        \n is-Retaining-Pending-Error-For-JITProp :
        \n
        \n This flag enables an optimization technique which only propagates error values to
        \n gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them
        \n at divergent differentiation paths within the computation graph.
        \n If the flag is set to true
        \n then error values will accumulate at such junction nodes.
        \n This technique however uses more memory but will
        \n improve performance for some networks substantially.
        \n The technique is termed JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Tensor-Is-Used :
        \n
        \n Gradients will automatically be applied (or JITed) to tensors as soon as
        \n they are being used for calculation (GraphNode instantiation).
        \n This feature works well with JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Requested :
        \n
        \n Gradients will only be applied if requested.
        \n Usually this happens immediately, however
        \n if the flag 'applyGradientWhenTensorIsUsed' is set
        \n to true, then the tensor will only be updated by its
        \n gradient if requested AND the tensor is used fo calculation! (GraphNode instantiation).
        \n
        \n Let's take a look : \n "] }, @@ -149,9 +149,9 @@ }, { - "id":"Advanced backpropagation on all AD-Modes [code: y*y*3, whenRsd: true, whenUse: true, doJIT: false, afterBack: .*1.*4\\.5.*, afterUse: .*1.*4\\.5.*, afterRqd: .*1.*4\\.5.*, afterAll: .*5\\.5.*null.*, #3]", + "id":"Advanced backpropagation on all AD-Modes [3]", "result":"PASS", - "duration":"0.004 seconds", + "duration":"0.017 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n What is JIT-Prop and how does it affect autograd ? Let's take a look !
        \n This run covers the feature having the following settings :
        \n
        \n Neureka.instance().settings().autograd().:
        \n isRetainingPendingErrorForJITProp := false
        \n isApplyingGradientWhenTensorIsUsed := true
        \n isApplyingGradientWhenRequested := true
        \n
        \n ...code producing the result : 'y*y*3'
        \n
        \n is-Retaining-Pending-Error-For-JITProp :
        \n
        \n This flag enables an optimization technique which only propagates error values to
        \n gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them
        \n at divergent differentiation paths within the computation graph.
        \n If the flag is set to true
        \n then error values will accumulate at such junction nodes.
        \n This technique however uses more memory but will
        \n improve performance for some networks substantially.
        \n The technique is termed JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Tensor-Is-Used :
        \n
        \n Gradients will automatically be applied (or JITed) to tensors as soon as
        \n they are being used for calculation (GraphNode instantiation).
        \n This feature works well with JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Requested :
        \n
        \n Gradients will only be applied if requested.
        \n Usually this happens immediately, however
        \n if the flag 'applyGradientWhenTensorIsUsed' is set
        \n to true, then the tensor will only be updated by its
        \n gradient if requested AND the tensor is used fo calculation! (GraphNode instantiation).
        \n
        \n Let's take a look : \n "] }, @@ -194,9 +194,9 @@ }, { - "id":"Advanced backpropagation on all AD-Modes [code: y*y*3, whenRsd: false, whenUse: false, doJIT: true, afterBack: .*1.*null.*, afterUse: .*1.*null.*, afterRqd: .*1.*null.*, afterAll: .*1.*null.*, #4]", + "id":"Advanced backpropagation on all AD-Modes [4]", "result":"PASS", - "duration":"0.005 seconds", + "duration":"0.015 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n What is JIT-Prop and how does it affect autograd ? Let's take a look !
        \n This run covers the feature having the following settings :
        \n
        \n Neureka.instance().settings().autograd().:
        \n isRetainingPendingErrorForJITProp := true
        \n isApplyingGradientWhenTensorIsUsed := false
        \n isApplyingGradientWhenRequested := false
        \n
        \n ...code producing the result : 'y*y*3'
        \n
        \n is-Retaining-Pending-Error-For-JITProp :
        \n
        \n This flag enables an optimization technique which only propagates error values to
        \n gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them
        \n at divergent differentiation paths within the computation graph.
        \n If the flag is set to true
        \n then error values will accumulate at such junction nodes.
        \n This technique however uses more memory but will
        \n improve performance for some networks substantially.
        \n The technique is termed JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Tensor-Is-Used :
        \n
        \n Gradients will automatically be applied (or JITed) to tensors as soon as
        \n they are being used for calculation (GraphNode instantiation).
        \n This feature works well with JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Requested :
        \n
        \n Gradients will only be applied if requested.
        \n Usually this happens immediately, however
        \n if the flag 'applyGradientWhenTensorIsUsed' is set
        \n to true, then the tensor will only be updated by its
        \n gradient if requested AND the tensor is used fo calculation! (GraphNode instantiation).
        \n
        \n Let's take a look : \n "] }, @@ -239,9 +239,9 @@ }, { - "id":"Advanced backpropagation on all AD-Modes [code: y*y*3, whenRsd: true, whenUse: false, doJIT: true, afterBack: .*1.*null.*, afterUse: .*1.*null.*, afterRqd: .*5\\.5.*null.*, afterAll: .*5\\.5.*null.*, #5]", + "id":"Advanced backpropagation on all AD-Modes [5]", "result":"PASS", - "duration":"0.005 seconds", + "duration":"0.013 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n What is JIT-Prop and how does it affect autograd ? Let's take a look !
        \n This run covers the feature having the following settings :
        \n
        \n Neureka.instance().settings().autograd().:
        \n isRetainingPendingErrorForJITProp := true
        \n isApplyingGradientWhenTensorIsUsed := false
        \n isApplyingGradientWhenRequested := true
        \n
        \n ...code producing the result : 'y*y*3'
        \n
        \n is-Retaining-Pending-Error-For-JITProp :
        \n
        \n This flag enables an optimization technique which only propagates error values to
        \n gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them
        \n at divergent differentiation paths within the computation graph.
        \n If the flag is set to true
        \n then error values will accumulate at such junction nodes.
        \n This technique however uses more memory but will
        \n improve performance for some networks substantially.
        \n The technique is termed JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Tensor-Is-Used :
        \n
        \n Gradients will automatically be applied (or JITed) to tensors as soon as
        \n they are being used for calculation (GraphNode instantiation).
        \n This feature works well with JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Requested :
        \n
        \n Gradients will only be applied if requested.
        \n Usually this happens immediately, however
        \n if the flag 'applyGradientWhenTensorIsUsed' is set
        \n to true, then the tensor will only be updated by its
        \n gradient if requested AND the tensor is used fo calculation! (GraphNode instantiation).
        \n
        \n Let's take a look : \n "] }, @@ -284,9 +284,9 @@ }, { - "id":"Advanced backpropagation on all AD-Modes [code: y*y*3, whenRsd: false, whenUse: true, doJIT: true, afterBack: .*1.*null.*, afterUse: .*5\\.5.*null.*, afterRqd: .*5\\.5.*null.*, afterAll: .*5\\.5.*null.*, #6]", + "id":"Advanced backpropagation on all AD-Modes [6]", "result":"PASS", - "duration":"0.004 seconds", + "duration":"0.014 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n What is JIT-Prop and how does it affect autograd ? Let's take a look !
        \n This run covers the feature having the following settings :
        \n
        \n Neureka.instance().settings().autograd().:
        \n isRetainingPendingErrorForJITProp := true
        \n isApplyingGradientWhenTensorIsUsed := true
        \n isApplyingGradientWhenRequested := false
        \n
        \n ...code producing the result : 'y*y*3'
        \n
        \n is-Retaining-Pending-Error-For-JITProp :
        \n
        \n This flag enables an optimization technique which only propagates error values to
        \n gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them
        \n at divergent differentiation paths within the computation graph.
        \n If the flag is set to true
        \n then error values will accumulate at such junction nodes.
        \n This technique however uses more memory but will
        \n improve performance for some networks substantially.
        \n The technique is termed JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Tensor-Is-Used :
        \n
        \n Gradients will automatically be applied (or JITed) to tensors as soon as
        \n they are being used for calculation (GraphNode instantiation).
        \n This feature works well with JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Requested :
        \n
        \n Gradients will only be applied if requested.
        \n Usually this happens immediately, however
        \n if the flag 'applyGradientWhenTensorIsUsed' is set
        \n to true, then the tensor will only be updated by its
        \n gradient if requested AND the tensor is used fo calculation! (GraphNode instantiation).
        \n
        \n Let's take a look : \n "] }, @@ -329,9 +329,9 @@ }, { - "id":"Advanced backpropagation on all AD-Modes [code: y*y*3, whenRsd: true, whenUse: true, doJIT: true, afterBack: .*1.*null.*, afterUse: .*1.*null.*, afterRqd: .*1.*null.*, afterAll: .*5\\.5.*null.*, #7]", + "id":"Advanced backpropagation on all AD-Modes [7]", "result":"PASS", - "duration":"0.003 seconds", + "duration":"0.011 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n What is JIT-Prop and how does it affect autograd ? Let's take a look !
        \n This run covers the feature having the following settings :
        \n
        \n Neureka.instance().settings().autograd().:
        \n isRetainingPendingErrorForJITProp := true
        \n isApplyingGradientWhenTensorIsUsed := true
        \n isApplyingGradientWhenRequested := true
        \n
        \n ...code producing the result : 'y*y*3'
        \n
        \n is-Retaining-Pending-Error-For-JITProp :
        \n
        \n This flag enables an optimization technique which only propagates error values to
        \n gradients if needed by a tensor (the tensor is used again) and otherwise accumulate them
        \n at divergent differentiation paths within the computation graph.
        \n If the flag is set to true
        \n then error values will accumulate at such junction nodes.
        \n This technique however uses more memory but will
        \n improve performance for some networks substantially.
        \n The technique is termed JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Tensor-Is-Used :
        \n
        \n Gradients will automatically be applied (or JITed) to tensors as soon as
        \n they are being used for calculation (GraphNode instantiation).
        \n This feature works well with JIT-Propagation.
        \n
        \n
        \n is-Applying-Gradient-When-Requested :
        \n
        \n Gradients will only be applied if requested.
        \n Usually this happens immediately, however
        \n if the flag 'applyGradientWhenTensorIsUsed' is set
        \n to true, then the tensor will only be updated by its
        \n gradient if requested AND the tensor is used fo calculation! (GraphNode instantiation).
        \n
        \n Let's take a look : \n "] }, @@ -376,7 +376,7 @@ { "id":"We can create a shallow copy of a tensor detached from the computation graph.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.autograd.Autograd_NN_Spec.json b/docs/spock/reports/ut.autograd.Autograd_NN_Spec.json index 9cad1c1b5..6adc9a057 100644 --- a/docs/spock/reports/ut.autograd.Autograd_NN_Spec.json +++ b/docs/spock/reports/ut.autograd.Autograd_NN_Spec.json @@ -1,22 +1,22 @@ { "className":"ut.autograd.Autograd_NN_Spec", "title":"Simple Neural Network autograd integration test", - "narrative":"The integration test below has been implemented by using\n the following code and the result it produces as reference :\n https://medium.com/dair-ai/a-simple-neural-network-from-scratch-with-pytorch-and-google-colab-c7f3830618e0\n\n The following seed has been used to assure reproducibility :\n 'torch.manual_seed(503672689411)'", + "narrative":"The integration test below has been implemented by using\n the following code and the result it produces as reference : \n https://medium.com/dair-ai/a-simple-neural-network-from-scratch-with-pytorch-and-google-colab-c7f3830618e0 \n\n The following seed has been used to assure reproducibility :\n 'torch.manual_seed(503672689411)'", "subjects":["neureka.Tensor","neureka.autograd.GraphNode"], "statistics":{ - "runs":"5", + "runs":"7", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.076 seconds" + "duration":"0.148 seconds" }, "headers":["\n\n "],"tags":{},"see":[], "features":[ { "id":"Autograd works in a simple mat-mul based feed forward neural network.", "result":"PASS", - "duration":"0.012 seconds", + "duration":"0.023 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -35,7 +35,7 @@ { "id":"Autograd works in a simple convolutional dot product based feed forward neural network.", "result":"PASS", - "duration":"0.028 seconds", + "duration":"0.052 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -54,7 +54,7 @@ { "id":"Autograd works in a simple convolutional dot product and float based feed forward neural network.", "result":"PASS", - "duration":"0.023 seconds", + "duration":"0.049 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -71,9 +71,38 @@ }, { - "id":"Autograd work for simple matrix multiplications.", + "id":"Autograd work for simple matrix multiplications. [0]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var a = Tensor.of([2, 3], -1f..4f).setRqsGradient(true).mut.toType(type)","var b = Tensor.of([3, 1], [-4d, -2d, 0d]).setRqsGradient(true).mut.toType(type)"]}, + + {"kind":"when","text":"","code":["var c = a.matMul(b)"]}, + + {"kind":"then","text":"","code":["c.itemType == type"]}, + + {"kind":"and","text":"","code":["a.toString() == \"(2x3):[\" +"," \"-1.0, 0.0, 1.0, \" +"," \"2.0, 3.0, 4.0\" +"," \"]:g:[null]\""]}, + + {"kind":"and","text":"","code":["b.toString() == \"(3x1):[\" +"," \"-4.0, \" +"," \"-2.0, \" +"," \"0.0\" +"," \"]:g:[null]\""]}, + + {"kind":"and","text":"","code":["def cStr = c.toString()","cStr.contains \"(2x1):[4.0, -14.0]\"","cStr.contains \"->d(3x2):[-1.0, 2.0, 0.0, 3.0, 1.0, 4.0]\"","cStr.contains \"->d(1x3):[-4.0, -2.0, 0.0]\""]}, + + {"kind":"when","text":"","code":["c.backward(Tensor.of(c.shape, [-1d, 1d]).mut.toType(type)) // (2x1):[-1, 1]"]}, + + {"kind":"then","text":"","code":["a.toString() == \"(2x3):[-1.0, 0.0, 1.0, 2.0, 3.0, 4.0]:g:[4.0, 2.0, 0.0, -4.0, -2.0, 0.0]\"","b.toString() == \"(3x1):[-4.0, -2.0, 0.0]:g:[3.0, 3.0, 3.0]\""]}, + + {"kind":"where","text":"","code":{"type":["Double","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Autograd work for simple matrix multiplications. [1]", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -100,7 +129,7 @@ }, { - "id":"Autograd works for 2 matrix multiplications in a row.", + "id":"Autograd works for 2 matrix multiplications in a row. [0]", "result":"PASS", "duration":"0.005 seconds", "iterations":{ @@ -126,6 +155,35 @@ {"kind":"where","text":"We test this feature on both the CPU as well as the GPU.","code":{"device":["CPU.get()","Device.get('first gpu')"]}} ], "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Autograd works for 2 matrix multiplications in a row. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We set the experimental \"autoConvertToFloat\" flag to true.","code":["Neureka.get().backend().find(CLBackend).ifPresent({ it.settings.autoConvertToFloat=true })"]}, + + {"kind":"and","text":"\n We create 3 tensors with the shapes (2x3), (3x1) and (2x1) for matrix multiplication.\n All of them ought to be stored on the provided device and \n only the first 2 require gradients, whereas the third one does not.\n We use these tensors to mimic 2 linear forward passes in a neural network.\n ","code":["def a = Tensor.of([2, 3], -1d..4d).setRqsGradient(true).to(device)","def b = Tensor.of([3, 1], [-4d, -2d, 0d]).setRqsGradient(true).to(device)","def x = Tensor.of([[0.5d, 0.5d]]).to(device)"]}, + + {"kind":"expect","text":"Initially none of the tensors requiring gradients have any (see \"g:[null]\").","code":["a.toString() == \"(2x3):[\" +"," \"-1.0, 0.0, 1.0, \" +"," \"2.0, 3.0, 4.0\" +"," \"]:g:[null]\"","b.toString() == \"(3x1):[\" +"," \"-4.0, \" +"," \"-2.0, \" +"," \"0.0\" +"," \"]:g:[null]\"","x.toString() == \"(1x2):[0.5, 0.5]\""]}, + + {"kind":"when","text":"We perform 2 matrix multiplications in a row, using all 3 previously created tensors...","code":["var c = a.matMul(b)","var o = x.matMul(c)"]}, + + {"kind":"then","text":"The results from the two matrix multiplications are as expected.","code":["var cStr = c.toString()","cStr.contains \"(2x1):[4.0, -14.0]\"","cStr.contains \"->d(3x2):[-1.0, 2.0, 0.0, 3.0, 1.0, 4.0]\"","cStr.contains \"->d(1x3):[-4.0, -2.0, 0.0]\"","o.toString() == \"(1x1):[-5.0]; ->d(2x1):[0.5, 0.5]\""]}, + + {"kind":"and","text":"We still expect the first 2 tensors to not yet have any gradients (see \"g:[null]\").","code":["a.toString() == \"(2x3):[\" +"," \"-1.0, 0.0, 1.0, \" +"," \"2.0, 3.0, 4.0\" +"," \"]:g:[null]\"","b.toString() == \"(3x1):[\" +"," \"-4.0, \" +"," \"-2.0, \" +"," \"0.0\" +"," \"]:g:[null]\"","x.toString() == \"(1x2):[0.5, 0.5]\""]}, + + {"kind":"when","text":"We perform back-propagation...","code":["o.backward()"]}, + + {"kind":"then","text":"Contrary to before, the 2 matrices now do have the expected gradients automatically generated by the aut-grad system.","code":["a.toString() == \"(2x3):[-1.0, 0.0, 1.0, 2.0, 3.0, 4.0]:g:[-2.0, -1.0, 0.0, -2.0, -1.0, 0.0]\"","b.toString() == \"(3x1):[-4.0, -2.0, 0.0]:g:[0.5, 1.5, 2.5]\""]}, + + {"kind":"where","text":"We test this feature on both the CPU as well as the GPU.","code":{"device":["CPU.get()","Device.get('first gpu')"]}} + ], + "problems":{"dataValues":[], "errors":[]} } ], diff --git a/docs/spock/reports/ut.autograd.Autograd_Tensor_Spec.json b/docs/spock/reports/ut.autograd.Autograd_Tensor_Spec.json index d28adf068..711218a24 100644 --- a/docs/spock/reports/ut.autograd.Autograd_Tensor_Spec.json +++ b/docs/spock/reports/ut.autograd.Autograd_Tensor_Spec.json @@ -4,19 +4,19 @@ "narrative":"", "subjects":[], "statistics":{ - "runs":"3", + "runs":"4", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.015 seconds" + "duration":"0.052 seconds" }, "headers":["\n

        Autograd Tensor Behavior

        \n

        \n Specified below is the behavior of the autograd system.\n

        \n "],"tags":{},"see":[], "features":[ { "id":"Test basic autograd behaviour. (Not on device)", "result":"PASS", - "duration":"0.003 seconds", + "duration":"0.014 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -55,9 +55,74 @@ }, { - "id":"Second-Test \"x-mul\" autograd behaviour. (Not on device)", + "id":"Second-Test \"x-mul\" autograd behaviour. (Not on device) [0]", "result":"PASS", - "duration":"0.007 seconds", + "duration":"0.014 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Gradient auto apply for tensors in ue is set to false.","code":["Neureka.get().settings().autograd().setIsApplyingGradientWhenTensorIsUsed(false)"]}, + + {"kind":"and","text":"Tensor legacy view is set to true.","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + + {"kind":"when","text":"","code":["def x = Tensor.ofDoubles()"," .withShape(3, 3)"," .andFill("," 1.0, 2.0, 5.0,"," -1.0, 4.0, -2.0,"," -2.0, 3.0, 4.0,"," )","def y = Tensor.of("," Shape.of(2, 2),"," new double[]{"," -1, 3,"," 2, 3,"," }).setRqsGradient(true)"]}, + + {"kind":"then","text":"","code":["then : y.toString().contains(\":g:(null)\")"]}, + + {"kind":"when","text":"","code":["when : def z = Tensor.of(\"I0xi1\", x, y)"]}, + + {"kind":"then","text":"","code":["then : z.toString().contains(\"[2x2]:(15.0, 15.0, 18.0, 8.0)\")"]}, + + {"kind":"when","text":"","code":["when : z = Tensor.of(new Object[]{x, \"x\", y})"]}, + + {"kind":"then","text":"","code":["then : z.toString().contains(\"[2x2]:(15.0, 15.0, 18.0, 8.0)\")"]}, + + {"kind":"when","text":"","code":["when : z.backward(Tensor.of(Shape.of(2, 2), 1))"]}, + + {"kind":"then","text":"","code":["then : y.toString().contains(\"[2x2]:(-1.0, 3.0, 2.0, 3.0):g:(6.0, 9.0, 4.0, 9.0)\")"]}, + + {"kind":"when","text":"","code":["x = Tensor.of(Shape.of(3, 3),"," new double[]{"," 1, 2, 5,"," -1, 4, -2,"," -2, 3, 4,"," }"," ).mut.toType(type)","y = Tensor.of(Shape.of(2, 2),"," new double[]{"," -1, 3,"," 2, 3,"," }).setRqsGradient(true).mut.toType(type)"]}, + + {"kind":"then","text":"","code":["then : y.toString().contains(\":g:(null)\")"]}, + + {"kind":"when","text":"","code":["when : z = Tensor.of(\"I0xi1\", y, x)"]}, + + {"kind":"then","text":"","code":["then : z.toString().contains(\"[2x2]:(15.0, 15.0, 18.0, 8.0)\")"]}, + + {"kind":"and","text":"","code":["and : z.itemType == type"]}, + + {"kind":"when","text":"","code":["when : z = Tensor.of(y, \"x\", x)"]}, + + {"kind":"then","text":"","code":["then : z.toString().contains(\"[2x2]:(15.0, 15.0, 18.0, 8.0)\")"]}, + + {"kind":"and","text":"","code":["and : z.itemType == type"]}, + + {"kind":"when","text":"","code":["when : z.backward(Tensor.of(Shape.of(2, 2), 1d))"]}, + + {"kind":"then","text":"","code":["then : y.toString().contains(\"[2x2]:(-1.0, 3.0, 2.0, 3.0):g:(6.0, 9.0, 4.0, 9.0)\")"]}, + + {"kind":"when","text":"","code":["x = Tensor.of(Shape.of(1), 3d).mut.toType(type)","Tensor b = Tensor.of(Shape.of(1), -5d).mut.toType(type)","Tensor w = Tensor.of(Shape.of(1), -2d).mut.toType(type)","z = Tensor.of(\"I0*i1*i2\", x, b, w)"]}, + + {"kind":"then","text":"","code":["then : z.toString().contains(\"[1]:(30.0)\")"]}, + + {"kind":"and","text":"","code":["and : z.itemType == type"]}, + + {"kind":"when","text":"","code":["x = Tensor.of(Shape.of(1), 4d).setRqsGradient(true).mut.toType(type)","b = Tensor.of(Shape.of(1), 0.5d).mut.toType(type)","w = Tensor.of(Shape.of(1), 0.5d).mut.toType(type)","y = Tensor.of(\"(2**i0**i1**i2**2\", x, b, w)"]}, + + {"kind":"then","text":"","code":["y.toString().contains(\"[1]:(9.24238);\")","y.toString().contains(\" ->d[1]:(4.32078)\")"]}, + + {"kind":"and","text":"","code":["y.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Second-Test \"x-mul\" autograd behaviour. (Not on device) [1]", + "result":"PASS", + "duration":"0.011 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -122,7 +187,7 @@ { "id":"A tensor used as derivative within a computation graph will throw exception when trying to deleting it.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.006 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.autograd.JITProp_Autograd_Tensor_Spec.json b/docs/spock/reports/ut.autograd.JITProp_Autograd_Tensor_Spec.json index 4b811e70e..a4a9a0f4b 100644 --- a/docs/spock/reports/ut.autograd.JITProp_Autograd_Tensor_Spec.json +++ b/docs/spock/reports/ut.autograd.JITProp_Autograd_Tensor_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.121 seconds" + "duration":"0.638 seconds" }, "headers":["\n

        Autograd Tensor Integration Tests

        \n

        \n This specification contains tests which\n cover the autograd behavior of tensors.
        \n The classes involved in governing the tested features are\n the Tensor, GraphNode and Function (& implementations) classes.\n

        \n "],"tags":{},"see":[], "features":[ { "id":"Test pending error optimization", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -39,7 +39,7 @@ { "id":"Test JIT propagation variant one.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -60,7 +60,7 @@ { "id":"Test JIT propagation variant two.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -81,7 +81,7 @@ { "id":"Gradient auto-apply kicks in when used AD uses JIT prop", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.009 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -110,7 +110,7 @@ { "id":"Test no preemptive gradient apply when not requested and auto apply and JIT_prop", "result":"PASS", - "duration":"0.106 seconds", + "duration":"0.598 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -147,7 +147,7 @@ { "id":"Test autograd without JIT and auto apply.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -172,7 +172,7 @@ { "id":"Test in-differential and JIT with auto apply", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -193,7 +193,7 @@ { "id":"Test no JIT prop when forward AD", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.autograd.internal.GraphNode_Instantiation_Exception_Unit_Tests.json b/docs/spock/reports/ut.autograd.internal.GraphNode_Instantiation_Exception_Unit_Tests.json index 47d5d09ec..b16b6ea68 100644 --- a/docs/spock/reports/ut.autograd.internal.GraphNode_Instantiation_Exception_Unit_Tests.json +++ b/docs/spock/reports/ut.autograd.internal.GraphNode_Instantiation_Exception_Unit_Tests.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.071 seconds" + "duration":"0.189 seconds" }, "headers":["\n

        GraphNode Instantiation Tests

        \n

        \n Specified below are strict tests covering the behavior\n of the GraphNode class during instantiation where\n inputs are setup to cause expected exceptions.\n

        \n "],"tags":{},"see":[], "features":[ { "id":"GraphNode instantiation throws exception because tensors of ExecutionCall do not return GraphNode instances.", "result":"PASS", - "duration":"0.048 seconds", + "duration":"0.152 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -35,7 +35,7 @@ { "id":"GraphNode throws an exception when trying to execute an inline operation on inputs with active autograd.", "result":"PASS", - "duration":"0.020 seconds", + "duration":"0.034 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.autograd.internal.GraphNode_Tensor_Exception_Unit_Tests.json b/docs/spock/reports/ut.autograd.internal.GraphNode_Tensor_Exception_Unit_Tests.json index 51789744b..03a1e452b 100644 --- a/docs/spock/reports/ut.autograd.internal.GraphNode_Tensor_Exception_Unit_Tests.json +++ b/docs/spock/reports/ut.autograd.internal.GraphNode_Tensor_Exception_Unit_Tests.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.004 seconds" + "duration":"0.008 seconds" }, "headers":["\n

        GraphNode Tensor Exceptions

        \n

        \n Specified below are strict tests covering the exception behavior\n of the GraphNode class interacting with Tensor instances.\n

        \n "],"tags":{},"see":[], "features":[ { "id":"A tensor cannot be deleted if it is part of a graph and the tensor is used as derivative.", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.006 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.backend.Backend_Extension_Spec.json b/docs/spock/reports/ut.backend.Backend_Extension_Spec.json index 03024bcdd..4e0ed7ae2 100644 --- a/docs/spock/reports/ut.backend.Backend_Extension_Spec.json +++ b/docs/spock/reports/ut.backend.Backend_Extension_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.058 seconds" + "duration":"0.071 seconds" }, "headers":["\n This specification defines the behavior of\n Operation instances and their ability to be extended!
        \n "],"tags":{},"see":[], "features":[ { "id":"Mock operation interacts with FunctionNode (AbstractFunction) instance as expected.", "result":"PASS", - "duration":"0.056 seconds", + "duration":"0.069 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.backend.Backend_MatMul_Extension_Spec.json b/docs/spock/reports/ut.backend.Backend_MatMul_Extension_Spec.json index 41504d5e7..e789bd750 100644 --- a/docs/spock/reports/ut.backend.Backend_MatMul_Extension_Spec.json +++ b/docs/spock/reports/ut.backend.Backend_MatMul_Extension_Spec.json @@ -4,19 +4,19 @@ "narrative":"", "subjects":[], "statistics":{ - "runs":"3", + "runs":"17", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.053 seconds" + "duration":"0.083 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"GEMM matrix multiplication reference implementation can be set as custom OperationType and works as expected.", + "id":"GEMM matrix multiplication reference implementation can be set as custom OperationType and works as expected. [0]", "result":"PASS", - "duration":"0.045 seconds", + "duration":"0.060 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -43,7 +43,53 @@ }, { - "id":"Test context mock for opencl reference implementations.", + "id":"GEMM matrix multiplication reference implementation can be set as custom OperationType and works as expected. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["BackendContext oldContext = Neureka.get().backend()","BackendContext testContext = oldContext.clone()"]}, + + {"kind":"when","text":"","code":["def run = testContext.runner()"]}, + + {"kind":"then","text":"","code":["run { testContext == Neureka.get().backend() }"]}, + + {"kind":"when","text":"","code":["Tensor t1 = Tensor.of([row_sze, com_sze], -3d..8d)","Tensor t2 = Tensor.of([com_sze, col_sze], -7d..4d)"," run {"," Neureka.get().backend()"," .addOperation("," Operation"," .builder()"," .identifier('test_function')"," .operator('test_function')"," .arity(-1)"," .isIndexer(false)"," .isOperator(false)"," .isDifferentiable(true)"," .isInline(false)"," .stringifier("," children -> {"," String expression = String.join(\", \", children);"," if (expression.charAt(0) == '(' && expression.charAt(expression.length() - 1) == ')') {"," return \"test_function\" + expression;"," }"," return \"test_function\" + \"(\" + expression + \")\";"," }"," )"," .build()"," .setAlgorithm("," DeviceAlgorithm.withName(\"my_algorithm\")"," .setIsSuitableFor(call -> SuitabilityPredicate.GOOD )"," .setAutogradModeFor(call -> AutoDiffMode.BACKWARD_ONLY )"," .setExecution("," (outerCaller, outerCall) ->"," Result.of(AbstractDeviceAlgorithm.executeFor("," outerCaller, outerCall,"," call -> AbstractDeviceAlgorithm.executeDeviceAlgorithm( call )"," ))"," .withAutoDiff((ADActionSupplier){ Function f, ExecutionCall> adCall, boolean forward ->"," if (forward) throw new IllegalArgumentException(\"Reshape operation does not support forward-AD!\");"," return ADAction.of((t, error) -> Function.of(f.toString(), false).derive(new Tensor[]{error}, 0));"," })"," )"," .setCallPreparation("," call -> {"," Device device = call.getDevice();"," if ( call.input( 0 ) == null ) // Creating a new tensor:"," {"," Shape shp = Shape.of(call.input( 1 ).getNDConf().shape(0), call.input( 2 ).getNDConf().shape(1))"," Tensor output = Tensor.of(shp, 0.0);"," output.mut.setIsVirtual(false);"," device.store( output );"," call = call.withInputAt( 0, output );"," }"," return call;"," }"," )"," .setImplementationFor("," CPU.class,"," CPUImplementation"," .withArity(3)"," .andImplementation("," (call) -> {"," Tensor drn = call.input(Number.class, 0)"," Tensor src1 = call.input(Number.class, 1)"," Tensor src2 = call.input(Number.class, 2)"," assert src1.shape(1) == src2.shape(0)",""," //for ( int i=0; i a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] + }, + "blocks":[ + {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] + }, + "blocks":[ + {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [3]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] + }, + "blocks":[ + {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] + }, + "blocks":[ + {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [5]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] + }, + "blocks":[ + {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [6]", + "result":"PASS", + "duration":"0.006 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] + }, + "blocks":[ + {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [7]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] + }, + "blocks":[ + {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [8]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] + }, + "blocks":[ + {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [9]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] + }, + "blocks":[ + {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [10]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] + }, + "blocks":[ + {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, + + {"kind":"and","text":"We create the data layout type based on the provided string...","code":["var dataLayout = layout == 'ROW' ? NDConfiguration.Layout.ROW_MAJOR : NDConfiguration.Layout.COLUMN_MAJOR"]}, + + {"kind":"and","text":"After that we convert both matrices to the layout!","code":["a.mut.toLayout( dataLayout )","b.mut.toLayout( dataLayout )"]}, + + {"kind":"expect","text":"This should of cause make report that they indeed have this new layout.","code":["a.NDConf.layout == dataLayout","b.NDConf.layout == dataLayout"]}, + + {"kind":"when","text":"We now perform the matrix multiplication with the 2 matrix tensors...","code":["Tensor c = a.matMul(b)"]}, + + {"kind":"then","text":"The result will have the expected (M x N) shape.","code":["c.shape == [M,N]"]}, + + {"kind":"and","text":"It should have the expected value array.","code":["c.items == expectedC"]}, + + {"kind":"where","text":"We use the following scenario parameters:","code":{"layout":["'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'ROW'","'COL'","'COL'","'COL'","'COL'","'COL'","'COL'"],"type":["Double","Double","Double","Float","Float","Float","Double","Double","Double","Float","Float","Float"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"A":["[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]","[4, 3, 2, 1] as double[]","[-2,1] as double[]","[-2,1] as double[]","[4,3,2,1] as float[]","[-2,1] as float[]","[-2,1] as float[]"],"B":["[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]","[-0.5, 1.5, 1, -2] as double[]","[-1, -1.5] as double[]","[-1, -1.5] as double[]","[-0.5, 1.5, 1, -2] as float[]","[-1, -1.5] as float[]","[-1, -1.5] as float[]"],"expectedC":["[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]","[1, 0, 0, 1 ] as double[]","[ 0.5 ] as double[]","[ 2.0, 3.0, -1.0, -1.5 ] as double[]","[ 1, 0, 0, 1 ] as float[]","[ 0.5 ] as float[]","[ 2.0, 3.0, -1.0, -1.5 ] as float[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The simple CPU matrix multiplication implementation works as expected. [11]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Matrix multiplication is possible between matrices of various dimensions,\n data types as well as data layouts!\n "] }, "blocks":[ {"kind":"given","text":"We instantiate 2 matrices based on the data from the data table at the end of this method.","code":["Tensor a = Tensor.of(type, Shape.of(M,K), A)","Tensor b = Tensor.of(type, Shape.of(K,N), B)"]}, diff --git a/docs/spock/reports/ut.backend.core.Backend_Algorithm_AD_Spec.json b/docs/spock/reports/ut.backend.core.Backend_Algorithm_AD_Spec.json index 395401675..02dee9df6 100644 --- a/docs/spock/reports/ut.backend.core.Backend_Algorithm_AD_Spec.json +++ b/docs/spock/reports/ut.backend.core.Backend_Algorithm_AD_Spec.json @@ -4,19 +4,100 @@ "narrative":"", "subjects":[], "statistics":{ - "runs":"4", + "runs":"37", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.046 seconds" + "duration":"0.091 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"Operator implementations behave as expected.", + "id":"Operator implementations behave as expected. [0]", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"The current Neureka instance is being reset.","code":["Neureka.get().reset()"]}, + + {"kind":"and","text":"A mock ExecutionCall.","code":["var call = ExecutionCall.of().running(Mock(Operation)).on(Mock(Device))"]}, + + {"kind":"expect","text":"The algorithm is not null.","code":["alg != null"]}, + + {"kind":"and","text":"The algorithm is a bi-elementwise algorithm.","code":["alg instanceof BiElementwise"]}, + + {"kind":"and","text":"It has a non empty name and string representation.","code":["!alg.name.isEmpty()","!alg.toString().isEmpty()"]}, + + {"kind":"when","text":"A auto diff mode is being created by every algorithm...","code":["AutoDiffMode mode = alg.autoDiffModeFrom( call )"]}, + + {"kind":"then","text":"The agent is configured to perform forward-AD and it contains the derivative generated by the function!","code":["mode != null"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"alg":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Operator implementations behave as expected. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"The current Neureka instance is being reset.","code":["Neureka.get().reset()"]}, + + {"kind":"and","text":"A mock ExecutionCall.","code":["var call = ExecutionCall.of().running(Mock(Operation)).on(Mock(Device))"]}, + + {"kind":"expect","text":"The algorithm is not null.","code":["alg != null"]}, + + {"kind":"and","text":"The algorithm is a bi-elementwise algorithm.","code":["alg instanceof BiElementwise"]}, + + {"kind":"and","text":"It has a non empty name and string representation.","code":["!alg.name.isEmpty()","!alg.toString().isEmpty()"]}, + + {"kind":"when","text":"A auto diff mode is being created by every algorithm...","code":["AutoDiffMode mode = alg.autoDiffModeFrom( call )"]}, + + {"kind":"then","text":"The agent is configured to perform forward-AD and it contains the derivative generated by the function!","code":["mode != null"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"alg":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Operator implementations behave as expected. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"The current Neureka instance is being reset.","code":["Neureka.get().reset()"]}, + + {"kind":"and","text":"A mock ExecutionCall.","code":["var call = ExecutionCall.of().running(Mock(Operation)).on(Mock(Device))"]}, + + {"kind":"expect","text":"The algorithm is not null.","code":["alg != null"]}, + + {"kind":"and","text":"The algorithm is a bi-elementwise algorithm.","code":["alg instanceof BiElementwise"]}, + + {"kind":"and","text":"It has a non empty name and string representation.","code":["!alg.name.isEmpty()","!alg.toString().isEmpty()"]}, + + {"kind":"when","text":"A auto diff mode is being created by every algorithm...","code":["AutoDiffMode mode = alg.autoDiffModeFrom( call )"]}, + + {"kind":"then","text":"The agent is configured to perform forward-AD and it contains the derivative generated by the function!","code":["mode != null"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"alg":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Operator implementations behave as expected. [3]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -41,7 +122,7 @@ }, { - "id":"Activation implementations behave as expected.", + "id":"Operator implementations behave as expected. [4]", "result":"PASS", "duration":"0", "iterations":{ @@ -54,6 +135,33 @@ {"kind":"expect","text":"The algorithm is not null.","code":["alg != null"]}, + {"kind":"and","text":"The algorithm is a bi-elementwise algorithm.","code":["alg instanceof BiElementwise"]}, + + {"kind":"and","text":"It has a non empty name and string representation.","code":["!alg.name.isEmpty()","!alg.toString().isEmpty()"]}, + + {"kind":"when","text":"A auto diff mode is being created by every algorithm...","code":["AutoDiffMode mode = alg.autoDiffModeFrom( call )"]}, + + {"kind":"then","text":"The agent is configured to perform forward-AD and it contains the derivative generated by the function!","code":["mode != null"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"alg":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations behave as expected. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"The current Neureka instance is being reset.","code":["Neureka.get().reset()"]}, + + {"kind":"and","text":"A mock ExecutionCall.","code":["var call = ExecutionCall.of().running(Mock(Operation)).on(Mock(Device))"]}, + + {"kind":"expect","text":"The algorithm is not null.","code":["alg != null"]}, + {"kind":"and","text":"The algorithm is a activation algorithm.","code":["alg instanceof ElementwiseAlgorithm"]}, {"kind":"and","text":"It has a non empty name and string representation.","code":["!alg.name.isEmpty()","!alg.toString().isEmpty()"]}, @@ -72,7 +180,7 @@ }, { - "id":"Convolution implementations behave as expected.", + "id":"Activation implementations behave as expected. [1]", "result":"PASS", "duration":"0", "iterations":{ @@ -85,7 +193,7 @@ {"kind":"expect","text":"The algorithm is not null.","code":["alg != null"]}, - {"kind":"and","text":"The algorithm is a convolution algorithm.","code":["alg instanceof NDConvolution"]}, + {"kind":"and","text":"The algorithm is a activation algorithm.","code":["alg instanceof ElementwiseAlgorithm"]}, {"kind":"and","text":"It has a non empty name and string representation.","code":["!alg.name.isEmpty()","!alg.toString().isEmpty()"]}, @@ -97,13 +205,912 @@ {"kind":"then","text":"","code":["mode != null"]}, - {"kind":"where","text":"The variable \"imp\" is from a List of Operation implementations of type \"Convolution\".","code":{"alg":[]}} + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Activation\".","code":{"alg":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations behave as expected. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"The current Neureka instance is being reset.","code":["Neureka.get().reset()"]}, + + {"kind":"and","text":"A mock ExecutionCall.","code":["var call = ExecutionCall.of().running(Mock(Operation)).on(Mock(Device))"]}, + + {"kind":"expect","text":"The algorithm is not null.","code":["alg != null"]}, + + {"kind":"and","text":"The algorithm is a activation algorithm.","code":["alg instanceof ElementwiseAlgorithm"]}, + + {"kind":"and","text":"It has a non empty name and string representation.","code":["!alg.name.isEmpty()","!alg.toString().isEmpty()"]}, + + {"kind":"when","text":"","code":["var suitability = alg.isSuitableFor(call)"]}, + + {"kind":"then","text":"","code":["0 <= suitability && suitability <= 1"]}, + + {"kind":"when","text":"","code":["var mode = alg.autoDiffModeFrom(call)"]}, + + {"kind":"then","text":"","code":["mode != null"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Activation\".","code":{"alg":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations behave as expected. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"The current Neureka instance is being reset.","code":["Neureka.get().reset()"]}, + + {"kind":"and","text":"A mock ExecutionCall.","code":["var call = ExecutionCall.of().running(Mock(Operation)).on(Mock(Device))"]}, + + {"kind":"expect","text":"The algorithm is not null.","code":["alg != null"]}, + + {"kind":"and","text":"The algorithm is a activation algorithm.","code":["alg instanceof ElementwiseAlgorithm"]}, + + {"kind":"and","text":"It has a non empty name and string representation.","code":["!alg.name.isEmpty()","!alg.toString().isEmpty()"]}, + + {"kind":"when","text":"","code":["var suitability = alg.isSuitableFor(call)"]}, + + {"kind":"then","text":"","code":["0 <= suitability && suitability <= 1"]}, + + {"kind":"when","text":"","code":["var mode = alg.autoDiffModeFrom(call)"]}, + + {"kind":"then","text":"","code":["mode != null"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Activation\".","code":{"alg":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations behave as expected. 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[15]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"Host- and CL- executor instance are being fetched...","code":["def hostExecutor = imp.getImplementationFor( CPU.class )","def clExecutor = imp.getImplementationFor( OpenCLDevice.class )"]}, + + {"kind":"then","text":"The variables containing the executor instances are not null.","code":["hostExecutor != null","clExecutor != null || !Neureka.get().canAccessOpenCLDevice()"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations have expected Executor instances. [16]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"Host- and CL- executor instance are being fetched...","code":["def hostExecutor = imp.getImplementationFor( CPU.class )","def clExecutor = imp.getImplementationFor( OpenCLDevice.class )"]}, + + {"kind":"then","text":"The variables containing the executor instances are not null.","code":["hostExecutor != null","clExecutor != null || !Neureka.get().canAccessOpenCLDevice()"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations have expected Executor instances. [17]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"Host- and CL- executor instance are being fetched...","code":["def hostExecutor = imp.getImplementationFor( CPU.class )","def clExecutor = imp.getImplementationFor( OpenCLDevice.class )"]}, + + {"kind":"then","text":"The variables containing the executor instances are not null.","code":["hostExecutor != null","clExecutor != null || !Neureka.get().canAccessOpenCLDevice()"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations have expected Executor instances. [18]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"Host- and CL- executor instance are being fetched...","code":["def hostExecutor = imp.getImplementationFor( CPU.class )","def clExecutor = imp.getImplementationFor( OpenCLDevice.class )"]}, + + {"kind":"then","text":"The variables containing the executor instances are not null.","code":["hostExecutor != null","clExecutor != null || !Neureka.get().canAccessOpenCLDevice()"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations have expected Executor instances. [19]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"Host- and CL- executor instance are being fetched...","code":["def hostExecutor = imp.getImplementationFor( CPU.class )","def clExecutor = imp.getImplementationFor( OpenCLDevice.class )"]}, + + {"kind":"then","text":"The variables containing the executor instances are not null.","code":["hostExecutor != null","clExecutor != null || !Neureka.get().canAccessOpenCLDevice()"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations have expected Executor instances. [20]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"Host- and CL- executor instance are being fetched...","code":["def hostExecutor = imp.getImplementationFor( CPU.class )","def clExecutor = imp.getImplementationFor( OpenCLDevice.class )"]}, + + {"kind":"then","text":"The variables containing the executor instances are not null.","code":["hostExecutor != null","clExecutor != null || !Neureka.get().canAccessOpenCLDevice()"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations have expected Executor instances. [21]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"Host- and CL- executor instance are being fetched...","code":["def hostExecutor = imp.getImplementationFor( CPU.class )","def clExecutor = imp.getImplementationFor( OpenCLDevice.class )"]}, + + {"kind":"then","text":"The variables containing the executor instances are not null.","code":["hostExecutor != null","clExecutor != null || !Neureka.get().canAccessOpenCLDevice()"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations have expected Executor instances. [22]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"Host- and CL- executor instance are being fetched...","code":["def hostExecutor = imp.getImplementationFor( CPU.class )","def clExecutor = imp.getImplementationFor( OpenCLDevice.class )"]}, + + {"kind":"then","text":"The variables containing the executor instances are not null.","code":["hostExecutor != null","clExecutor != null || !Neureka.get().canAccessOpenCLDevice()"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations have expected Executor instances. [23]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"Host- and CL- executor instance are being fetched...","code":["def hostExecutor = imp.getImplementationFor( CPU.class )","def clExecutor = imp.getImplementationFor( OpenCLDevice.class )"]}, + + {"kind":"then","text":"The variables containing the executor instances are not null.","code":["hostExecutor != null","clExecutor != null || !Neureka.get().canAccessOpenCLDevice()"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation implementations have expected Executor instances. [24]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"Host- and CL- executor instance are being fetched...","code":["def hostExecutor = imp.getImplementationFor( CPU.class )","def clExecutor = imp.getImplementationFor( OpenCLDevice.class )"]}, + + {"kind":"then","text":"The variables containing the executor instances are not null.","code":["hostExecutor != null","clExecutor != null || !Neureka.get().canAccessOpenCLDevice()"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"HostExecutors of Operator implementations behave as expected. [0]", + "result":"PASS", + "duration":"0.064 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Mock instances to simulate an ExecutionCall instance.","code":["var call = Mock( ExecutionCall )","var device = Mock( CPU )","var tensor = Mock( Tensor )","var mutate = Mock(MutateTensor)","var ndConf = Mock(NDConfiguration)","var hostExecutor = imp.getImplementationFor( CPU.class )","var nativeExecutor = Mock( CPU.JVMExecutor )","var dataObj = Mock(Data)"]}, + + {"kind":"when","text":"Host-executor instance is being called...","code":["hostExecutor.run( call )"]}, + + {"kind":"then","text":"The mock objects are being called as expected.","code":["(1.._) * call.arity() >> 3","(0.._) * tensor.getMut() >> mutate","(0.._) * tensor.mut() >> mutate","(1.._) * call.getDevice() >> device","1 * device.getExecutor() >> nativeExecutor","1 * nativeExecutor.threaded( _, _ )","(0.._) * call.inputs() >> new Tensor[]{ tensor, tensor, tensor }","(0.._) * call.input({it >= 0 && it <= 2}) >> tensor","(0.._) * call.input( Number.class, 0 ) >> tensor","(0.._) * call.input(0) >> tensor","(0.._) * call.input( Number.class, 1 ) >> tensor","(1.._) * tensor.size() >> 0","(0.._) * tensor.itemType >> Double","(0.._) * tensor.getDataAs(double[]) >> new double[0]","(0.._) * mutate.data >> dataObj","(0.._) * dataObj.get >> new double[0]","(0.._) * mutate.getDataAs(double[]) >> new double[0]","(0.._) * mutate.getDataForWriting(double[]) >> new double[0]","(1.._) * tensor.getNDConf() >> ndConf","(1.._) * ndConf.isSimple() >> false"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"HostExecutors of Operator implementations behave as expected. [1]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Mock instances to simulate an ExecutionCall instance.","code":["var call = Mock( ExecutionCall )","var device = Mock( CPU )","var tensor = Mock( Tensor )","var mutate = Mock(MutateTensor)","var ndConf = Mock(NDConfiguration)","var hostExecutor = imp.getImplementationFor( CPU.class )","var nativeExecutor = Mock( CPU.JVMExecutor )","var dataObj = Mock(Data)"]}, + + {"kind":"when","text":"Host-executor instance is being called...","code":["hostExecutor.run( call )"]}, + + {"kind":"then","text":"The mock objects are being called as expected.","code":["(1.._) * call.arity() >> 3","(0.._) * tensor.getMut() >> mutate","(0.._) * tensor.mut() >> mutate","(1.._) * call.getDevice() >> device","1 * device.getExecutor() >> nativeExecutor","1 * nativeExecutor.threaded( _, _ )","(0.._) * call.inputs() >> new Tensor[]{ tensor, tensor, tensor }","(0.._) * call.input({it >= 0 && it <= 2}) >> tensor","(0.._) * call.input( Number.class, 0 ) >> tensor","(0.._) * call.input(0) >> tensor","(0.._) * call.input( Number.class, 1 ) >> tensor","(1.._) * tensor.size() >> 0","(0.._) * tensor.itemType >> Double","(0.._) * tensor.getDataAs(double[]) >> new double[0]","(0.._) * mutate.data >> dataObj","(0.._) * dataObj.get >> new double[0]","(0.._) * mutate.getDataAs(double[]) >> new double[0]","(0.._) * mutate.getDataForWriting(double[]) >> new double[0]","(1.._) * tensor.getNDConf() >> ndConf","(1.._) * ndConf.isSimple() >> false"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"HostExecutors of Operator implementations behave as expected. [2]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Mock instances to simulate an ExecutionCall instance.","code":["var call = Mock( ExecutionCall )","var device = Mock( CPU )","var tensor = Mock( Tensor )","var mutate = Mock(MutateTensor)","var ndConf = Mock(NDConfiguration)","var hostExecutor = imp.getImplementationFor( CPU.class )","var nativeExecutor = Mock( CPU.JVMExecutor )","var dataObj = Mock(Data)"]}, + + {"kind":"when","text":"Host-executor instance is being called...","code":["hostExecutor.run( call )"]}, + + {"kind":"then","text":"The mock objects are being called as expected.","code":["(1.._) * call.arity() >> 3","(0.._) * tensor.getMut() >> mutate","(0.._) * tensor.mut() >> mutate","(1.._) * call.getDevice() >> device","1 * device.getExecutor() >> nativeExecutor","1 * nativeExecutor.threaded( _, _ )","(0.._) * call.inputs() >> new Tensor[]{ tensor, tensor, tensor }","(0.._) * call.input({it >= 0 && it <= 2}) >> tensor","(0.._) * call.input( Number.class, 0 ) >> tensor","(0.._) * call.input(0) >> tensor","(0.._) * call.input( Number.class, 1 ) >> tensor","(1.._) * tensor.size() >> 0","(0.._) * tensor.itemType >> Double","(0.._) * tensor.getDataAs(double[]) >> new double[0]","(0.._) * mutate.data >> dataObj","(0.._) * dataObj.get >> new double[0]","(0.._) * mutate.getDataAs(double[]) >> new double[0]","(0.._) * mutate.getDataForWriting(double[]) >> new double[0]","(1.._) * tensor.getNDConf() >> ndConf","(1.._) * ndConf.isSimple() >> false"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"HostExecutors of Operator implementations behave as expected. [3]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Mock instances to simulate an ExecutionCall instance.","code":["var call = Mock( ExecutionCall )","var device = Mock( CPU )","var tensor = Mock( Tensor )","var mutate = Mock(MutateTensor)","var ndConf = Mock(NDConfiguration)","var hostExecutor = imp.getImplementationFor( CPU.class )","var nativeExecutor = Mock( CPU.JVMExecutor )","var dataObj = Mock(Data)"]}, + + {"kind":"when","text":"Host-executor instance is being called...","code":["hostExecutor.run( call )"]}, + + {"kind":"then","text":"The mock objects are being called as expected.","code":["(1.._) * call.arity() >> 3","(0.._) * tensor.getMut() >> mutate","(0.._) * tensor.mut() >> mutate","(1.._) * call.getDevice() >> device","1 * device.getExecutor() >> nativeExecutor","1 * nativeExecutor.threaded( _, _ )","(0.._) * call.inputs() >> new Tensor[]{ tensor, tensor, tensor }","(0.._) * call.input({it >= 0 && it <= 2}) >> tensor","(0.._) * call.input( Number.class, 0 ) >> tensor","(0.._) * call.input(0) >> tensor","(0.._) * call.input( Number.class, 1 ) >> tensor","(1.._) * tensor.size() >> 0","(0.._) * tensor.itemType >> Double","(0.._) * tensor.getDataAs(double[]) >> new double[0]","(0.._) * mutate.data >> dataObj","(0.._) * dataObj.get >> new double[0]","(0.._) * mutate.getDataAs(double[]) >> new double[0]","(0.._) * mutate.getDataForWriting(double[]) >> new double[0]","(1.._) * tensor.getNDConf() >> ndConf","(1.._) * ndConf.isSimple() >> false"]}, + + {"kind":"where","text":"The variable \"imp\" is from a List of OperationType implementations of type \"Operator\".","code":{"imp":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"HostExecutors of Operator implementations behave as expected. [4]", + "result":"PASS", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.backend.core.Backend_Functional_Algorithm_Spec.json b/docs/spock/reports/ut.backend.core.Backend_Functional_Algorithm_Spec.json index 65a77338f..fad0da873 100644 --- a/docs/spock/reports/ut.backend.core.Backend_Functional_Algorithm_Spec.json +++ b/docs/spock/reports/ut.backend.core.Backend_Functional_Algorithm_Spec.json @@ -4,19 +4,61 @@ "narrative":"", "subjects":[], "statistics":{ - "runs":"3", + "runs":"7", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.011 seconds" + "duration":"0.019 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"A functional algorithm cannot be used if it was not built properly!", + "id":"A functional algorithm cannot be used if it was not built properly! [0]", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a dumb test algorithm.","code":["def algorithm = new TestAlgorithm()"]}, + + {"kind":"when","text":"We call a method on the algorithm...","code":["caller(algorithm)"]}, + + {"kind":"then","text":"This should throw an illegal state exception, simply because we have not built the algorithm properly!","code":["def exception = thrown(IllegalStateException)"]}, + + {"kind":"and","text":"The exception tells us this:","code":["exception.message == \"Trying use an instance of 'TestAlgorithm' with name 'test_name' which was not fully built!\""]}, + + {"kind":"where","text":"We call the following methods:","code":{"caller":["{ Algorithm it -> it.autoDiffModeFrom(null) }","{ Algorithm it -> it.execute(null, null) }","{ Algorithm it -> it.prepare(null) }",""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A functional algorithm cannot be used if it was not built properly! [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a dumb test algorithm.","code":["def algorithm = new TestAlgorithm()"]}, + + {"kind":"when","text":"We call a method on the algorithm...","code":["caller(algorithm)"]}, + + {"kind":"then","text":"This should throw an illegal state exception, simply because we have not built the algorithm properly!","code":["def exception = thrown(IllegalStateException)"]}, + + {"kind":"and","text":"The exception tells us this:","code":["exception.message == \"Trying use an instance of 'TestAlgorithm' with name 'test_name' which was not fully built!\""]}, + + {"kind":"where","text":"We call the following methods:","code":{"caller":["{ Algorithm it -> it.autoDiffModeFrom(null) }","{ Algorithm it -> it.execute(null, null) }","{ Algorithm it -> it.prepare(null) }",""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A functional algorithm cannot be used if it was not built properly! [2]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,7 +79,7 @@ { "id":"A functional algorithm does not accept null as an answer!", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -60,7 +102,34 @@ }, { - "id":"A functional algorithm warns us when modified after it has been built!", + "id":"A functional algorithm warns us when modified after it has been built! [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a dumb test algorithm.","code":["def algorithm = new TestAlgorithm()"]}, + + {"kind":"and","text":"","code":["def oldStream = System.err","System.err = Mock(PrintStream)"]}, + + {"kind":"when","text":"We build it thoroughly...","code":["algorithm"," .setIsSuitableFor(call -> SuitabilityPredicate.EXCELLENT)"," .setAutogradModeFor( call -> AutoDiffMode.BACKWARD_ONLY )"," .setExecution((caller, call ) -> null)"," .setCallPreparation(call -> null)"," .buildFunAlgorithm()"]}, + + {"kind":"then","text":"The algorithm should be usable just fine!","code":["algorithm.isSuitableFor(null) == SuitabilityPredicate.EXCELLENT","algorithm.autoDiffModeFrom(null) == AutoDiffMode.BACKWARD_ONLY","algorithm.execute(null, null) == null","algorithm.prepare(null) == null"]}, + + {"kind":"when","text":"We try to modify the algorithm even if it is already built...","code":["setter(algorithm)"]}, + + {"kind":"then","text":"We will get a warning which wells us that mutating the state of the algorithm is discouraged!","code":["1 * System.err.println("," \"[Test worker] WARN neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm - \" +"," \"Implementation '$type.simpleName' in algorithm '$algorithm' was modified! \" +"," \"Please consider only modifying the standard backend state of Neureka for experimental reasons.\""," )"]}, + + {"kind":"cleanup","text":"","code":["System.err = oldStream"]}, + + {"kind":"where","text":"","code":{"type":["ExecutionPreparation.class","SuitabilityPredicate.class","Execution.class"],"setter":["{ TestAlgorithm it -> it.setCallPreparation( call -> null ) }","{ TestAlgorithm it -> it.setIsSuitableFor( call -> SuitabilityPredicate.NOT_GOOD ) }","{ TestAlgorithm it -> it.setExecution( (caller, call) -> Result.of(AbstractDeviceAlgorithm.executeFor(caller, call, c->null)).withAutoDiff( FallbackAlgorithm::ADAction )) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A functional algorithm warns us when modified after it has been built! [1]", "result":"PASS", "duration":"0.001 seconds", "iterations":{ @@ -84,6 +153,33 @@ {"kind":"where","text":"","code":{"type":["ExecutionPreparation.class","SuitabilityPredicate.class","Execution.class"],"setter":["{ TestAlgorithm it -> it.setCallPreparation( call -> null ) }","{ TestAlgorithm it -> it.setIsSuitableFor( call -> SuitabilityPredicate.NOT_GOOD ) }","{ TestAlgorithm it -> it.setExecution( (caller, call) -> Result.of(AbstractDeviceAlgorithm.executeFor(caller, call, c->null)).withAutoDiff( FallbackAlgorithm::ADAction )) }"]}} ], "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A functional algorithm warns us when modified after it has been built! [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a dumb test algorithm.","code":["def algorithm = new TestAlgorithm()"]}, + + {"kind":"and","text":"","code":["def oldStream = System.err","System.err = Mock(PrintStream)"]}, + + {"kind":"when","text":"We build it thoroughly...","code":["algorithm"," .setIsSuitableFor(call -> SuitabilityPredicate.EXCELLENT)"," .setAutogradModeFor( call -> AutoDiffMode.BACKWARD_ONLY )"," .setExecution((caller, call ) -> null)"," .setCallPreparation(call -> null)"," .buildFunAlgorithm()"]}, + + {"kind":"then","text":"The algorithm should be usable just fine!","code":["algorithm.isSuitableFor(null) == SuitabilityPredicate.EXCELLENT","algorithm.autoDiffModeFrom(null) == AutoDiffMode.BACKWARD_ONLY","algorithm.execute(null, null) == null","algorithm.prepare(null) == null"]}, + + {"kind":"when","text":"We try to modify the algorithm even if it is already built...","code":["setter(algorithm)"]}, + + {"kind":"then","text":"We will get a warning which wells us that mutating the state of the algorithm is discouraged!","code":["1 * System.err.println("," \"[Test worker] WARN neureka.backend.api.template.algorithms.AbstractFunDeviceAlgorithm - \" +"," \"Implementation '$type.simpleName' in algorithm '$algorithm' was modified! \" +"," \"Please consider only modifying the standard backend state of Neureka for experimental reasons.\""," )"]}, + + {"kind":"cleanup","text":"","code":["System.err = oldStream"]}, + + {"kind":"where","text":"","code":{"type":["ExecutionPreparation.class","SuitabilityPredicate.class","Execution.class"],"setter":["{ TestAlgorithm it -> it.setCallPreparation( call -> null ) }","{ TestAlgorithm it -> it.setIsSuitableFor( call -> SuitabilityPredicate.NOT_GOOD ) }","{ TestAlgorithm it -> it.setExecution( (caller, call) -> Result.of(AbstractDeviceAlgorithm.executeFor(caller, call, c->null)).withAutoDiff( FallbackAlgorithm::ADAction )) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} } ], diff --git a/docs/spock/reports/ut.backend.core.Matrix_Multiplication_Spec.json b/docs/spock/reports/ut.backend.core.Matrix_Multiplication_Spec.json index 84001b5de..60ff542b4 100644 --- a/docs/spock/reports/ut.backend.core.Matrix_Multiplication_Spec.json +++ b/docs/spock/reports/ut.backend.core.Matrix_Multiplication_Spec.json @@ -4,19 +4,228 @@ "narrative":"This specification covers library internal matrix multiplication logic,\n specifically the CPU implementation.\n Do not depend on the API used in this specification as it is subject to change!", "subjects":[], "statistics":{ - "runs":"2", + "runs":"13", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"1.154 seconds" + "duration":"10.522 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"The CPU matrix multiplication implementation works as expected.", + "id":"The CPU matrix multiplication implementation works as expected. [0]", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We crete an output array and convert it to the targeted array type.","code":["var C = new double[M*N].asType(type)"]}, + + {"kind":"when","text":"We perform the matrix multiplication.","code":["CPUMatMul.execute(true, A.asType(type), B.asType(type), C, M, K, N)"]}, + + {"kind":"then","text":"The result is as expected.","code":["C == expectedC"]}, + + {"kind":"where","text":"The following data arrays and dimensions can be used for the test.","code":{"type":["float[]","float[]","float[]","double[]","double[]","double[]","int[]","int[]","int[]","long[]","long[]","long[]"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The CPU matrix multiplication implementation works as expected. [11]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -35,7 +244,7 @@ { "id":"The internal matrix multiplication test script runs!", "result":"PASS", - "duration":"1.145 seconds", + "duration":"10.509 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.backend.core.OpenCL_Backend_Spec.json b/docs/spock/reports/ut.backend.core.OpenCL_Backend_Spec.json index 043c6c6be..0da159151 100644 --- a/docs/spock/reports/ut.backend.core.OpenCL_Backend_Spec.json +++ b/docs/spock/reports/ut.backend.core.OpenCL_Backend_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.006 seconds" + "duration":"0.021 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"The OpenCL backend context can load implementations.", "result":"PASS", - "duration":"0.005 seconds", + "duration":"0.019 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.backend.core.Randomization_Spec.json b/docs/spock/reports/ut.backend.core.Randomization_Spec.json index f3b22e315..ef5570160 100644 --- a/docs/spock/reports/ut.backend.core.Randomization_Spec.json +++ b/docs/spock/reports/ut.backend.core.Randomization_Spec.json @@ -4,17 +4,17 @@ "narrative":"", "subjects":["neureka.backend.main.implementations.elementwise.CPURandomization"], "statistics":{ - "runs":"3", + "runs":"10", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.018 seconds" + "duration":"0.039 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"We can make slices of tensors random.", + "id":"We can make slices of tensors random. [0]", "result":"PASS", "duration":"0.005 seconds", "iterations":{ @@ -35,12 +35,159 @@ }, { - "id":"Randomization is in essence the same algorithm as JDKs \"Random\".", + "id":"We can make slices of tensors random. [1]", "result":"PASS", "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"given","text":"","code":["var r = Function.of(\"random(I[0])\")"]}, + + {"kind":"and","text":"","code":["var t = Tensor.of(values)[1..<(values.length-1)] //"]}, + + {"kind":"when","text":"","code":["r.with(Arg.Seed.of(73)).call(t)"]}, + + {"kind":"then","text":"","code":["t.items == expected"]}, + + {"kind":"where","text":"","code":{"values":["[0,0,0,0,0] as int[]","[0,0,0,0,0] as byte[]","[0,0,0,0,0] as short[]","[0,0,0,0,0] as long[]","[0,0,0,0,0] as float[]","[0,0,0,0,0] as double[]","[0,0,0,0,0] as char[]","[0,0,0,0,0] as boolean[]"],"expected":["[1682321148, -442781155, 1450818241] as int[]","[-4, 29, -63] as byte[]","[12028, -19939, -17727] as short[]","[7225514313620035527, -1901730580994377074, 6231216898071663791] as long[]","[1.0493218, 0.96351904, 1.3803823] as float[]","[1.0493217368660326, 0.9635190511757348, 1.3803823236577413] as double[]","[12028, 45597, 47809] as char[]","[false, true, false] as boolean[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can make slices of tensors random. [2]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var r = Function.of(\"random(I[0])\")"]}, + + {"kind":"and","text":"","code":["var t = Tensor.of(values)[1..<(values.length-1)] //"]}, + + {"kind":"when","text":"","code":["r.with(Arg.Seed.of(73)).call(t)"]}, + + {"kind":"then","text":"","code":["t.items == expected"]}, + + {"kind":"where","text":"","code":{"values":["[0,0,0,0,0] as int[]","[0,0,0,0,0] as byte[]","[0,0,0,0,0] as short[]","[0,0,0,0,0] as long[]","[0,0,0,0,0] as float[]","[0,0,0,0,0] as double[]","[0,0,0,0,0] as char[]","[0,0,0,0,0] as boolean[]"],"expected":["[1682321148, -442781155, 1450818241] as int[]","[-4, 29, -63] as byte[]","[12028, -19939, -17727] as short[]","[7225514313620035527, -1901730580994377074, 6231216898071663791] as long[]","[1.0493218, 0.96351904, 1.3803823] as float[]","[1.0493217368660326, 0.9635190511757348, 1.3803823236577413] as double[]","[12028, 45597, 47809] as char[]","[false, true, false] as boolean[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can make slices of tensors random. [3]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var r = Function.of(\"random(I[0])\")"]}, + + {"kind":"and","text":"","code":["var t = Tensor.of(values)[1..<(values.length-1)] //"]}, + + {"kind":"when","text":"","code":["r.with(Arg.Seed.of(73)).call(t)"]}, + + {"kind":"then","text":"","code":["t.items == expected"]}, + + {"kind":"where","text":"","code":{"values":["[0,0,0,0,0] as int[]","[0,0,0,0,0] as byte[]","[0,0,0,0,0] as short[]","[0,0,0,0,0] as long[]","[0,0,0,0,0] as float[]","[0,0,0,0,0] as double[]","[0,0,0,0,0] as char[]","[0,0,0,0,0] as boolean[]"],"expected":["[1682321148, -442781155, 1450818241] as int[]","[-4, 29, -63] as byte[]","[12028, -19939, -17727] as short[]","[7225514313620035527, -1901730580994377074, 6231216898071663791] as long[]","[1.0493218, 0.96351904, 1.3803823] as float[]","[1.0493217368660326, 0.9635190511757348, 1.3803823236577413] as double[]","[12028, 45597, 47809] as char[]","[false, true, false] as boolean[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can make slices of tensors random. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var r = Function.of(\"random(I[0])\")"]}, + + {"kind":"and","text":"","code":["var t = Tensor.of(values)[1..<(values.length-1)] //"]}, + + {"kind":"when","text":"","code":["r.with(Arg.Seed.of(73)).call(t)"]}, + + {"kind":"then","text":"","code":["t.items == expected"]}, + + {"kind":"where","text":"","code":{"values":["[0,0,0,0,0] as int[]","[0,0,0,0,0] as byte[]","[0,0,0,0,0] as short[]","[0,0,0,0,0] as long[]","[0,0,0,0,0] as float[]","[0,0,0,0,0] as double[]","[0,0,0,0,0] as char[]","[0,0,0,0,0] as boolean[]"],"expected":["[1682321148, -442781155, 1450818241] as int[]","[-4, 29, -63] as byte[]","[12028, -19939, -17727] as short[]","[7225514313620035527, -1901730580994377074, 6231216898071663791] as long[]","[1.0493218, 0.96351904, 1.3803823] as float[]","[1.0493217368660326, 0.9635190511757348, 1.3803823236577413] as double[]","[12028, 45597, 47809] as char[]","[false, true, false] as boolean[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can make slices of tensors random. [5]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var r = Function.of(\"random(I[0])\")"]}, + + {"kind":"and","text":"","code":["var t = Tensor.of(values)[1..<(values.length-1)] //"]}, + + {"kind":"when","text":"","code":["r.with(Arg.Seed.of(73)).call(t)"]}, + + {"kind":"then","text":"","code":["t.items == expected"]}, + + {"kind":"where","text":"","code":{"values":["[0,0,0,0,0] as int[]","[0,0,0,0,0] as byte[]","[0,0,0,0,0] as short[]","[0,0,0,0,0] as long[]","[0,0,0,0,0] as float[]","[0,0,0,0,0] as double[]","[0,0,0,0,0] as char[]","[0,0,0,0,0] as boolean[]"],"expected":["[1682321148, -442781155, 1450818241] as int[]","[-4, 29, -63] as byte[]","[12028, -19939, -17727] as short[]","[7225514313620035527, -1901730580994377074, 6231216898071663791] as long[]","[1.0493218, 0.96351904, 1.3803823] as float[]","[1.0493217368660326, 0.9635190511757348, 1.3803823236577413] as double[]","[12028, 45597, 47809] as char[]","[false, true, false] as boolean[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can make slices of tensors random. [6]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var r = Function.of(\"random(I[0])\")"]}, + + {"kind":"and","text":"","code":["var t = Tensor.of(values)[1..<(values.length-1)] //"]}, + + {"kind":"when","text":"","code":["r.with(Arg.Seed.of(73)).call(t)"]}, + + {"kind":"then","text":"","code":["t.items == expected"]}, + + {"kind":"where","text":"","code":{"values":["[0,0,0,0,0] as int[]","[0,0,0,0,0] as byte[]","[0,0,0,0,0] as short[]","[0,0,0,0,0] as long[]","[0,0,0,0,0] as float[]","[0,0,0,0,0] as double[]","[0,0,0,0,0] as char[]","[0,0,0,0,0] as boolean[]"],"expected":["[1682321148, -442781155, 1450818241] as int[]","[-4, 29, -63] as byte[]","[12028, -19939, -17727] as short[]","[7225514313620035527, -1901730580994377074, 6231216898071663791] as long[]","[1.0493218, 0.96351904, 1.3803823] as float[]","[1.0493217368660326, 0.9635190511757348, 1.3803823236577413] as double[]","[12028, 45597, 47809] as char[]","[false, true, false] as boolean[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can make slices of tensors random. [7]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var r = Function.of(\"random(I[0])\")"]}, + + {"kind":"and","text":"","code":["var t = Tensor.of(values)[1..<(values.length-1)] //"]}, + + {"kind":"when","text":"","code":["r.with(Arg.Seed.of(73)).call(t)"]}, + + {"kind":"then","text":"","code":["t.items == expected"]}, + + {"kind":"where","text":"","code":{"values":["[0,0,0,0,0] as int[]","[0,0,0,0,0] as byte[]","[0,0,0,0,0] as short[]","[0,0,0,0,0] as long[]","[0,0,0,0,0] as float[]","[0,0,0,0,0] as double[]","[0,0,0,0,0] as char[]","[0,0,0,0,0] as boolean[]"],"expected":["[1682321148, -442781155, 1450818241] as int[]","[-4, 29, -63] as byte[]","[12028, -19939, -17727] as short[]","[7225514313620035527, -1901730580994377074, 6231216898071663791] as long[]","[1.0493218, 0.96351904, 1.3803823] as float[]","[1.0493217368660326, 0.9635190511757348, 1.3803823236577413] as double[]","[12028, 45597, 47809] as char[]","[false, true, false] as boolean[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Randomization is in essence the same algorithm as JDKs \"Random\".", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"","code":["var random = new Random()","var seed = CPURandomization.initialScramble(666_42_666)","var r2 = [0,0] as double[]"]}, @@ -56,7 +203,7 @@ { "id":"The Randomization class can fill various types of arrays with pseudo random numbers.", "result":"PASS", - "duration":"0.003 seconds", + "duration":"0.009 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.device.CPU_Spec.json b/docs/spock/reports/ut.device.CPU_Spec.json index b308ba9a5..6532a46c5 100644 --- a/docs/spock/reports/ut.device.CPU_Spec.json +++ b/docs/spock/reports/ut.device.CPU_Spec.json @@ -1,7 +1,7 @@ { "className":"ut.device.CPU_Spec", "title":"The CPU device, an API for CPU based execution", - "narrative":"The CPU class, one of many implementations of the Device interface,\n is simply supposed to be an API for dispatching threaded workloads onto the CPU.\n Contrary to other types of device, the CPU will host tensor data by default, simply\n because the tensors will be stored in RAM if no device was specified.", + "narrative":"The CPU class, one of many implementations of the Device interface, \n is simply supposed to be an API for dispatching threaded workloads onto the CPU.\n Contrary to other types of device, the CPU will host tensor data by default, simply\n because the tensors will be stored in RAM if no device was specified.", "subjects":["neureka.devices.host.CPU","neureka.devices.Device"], "statistics":{ "runs":"4", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.704 seconds" + "duration":"5.707 seconds" }, "headers":[" \n

        \n The thread pool of the class neureka.devices.host.CPU executor becomes\n more active when receiving larger workloads which\n benefit from parallelization. \n

        \n "],"tags":{},"see":[], "features":[ { "id":"Thread pool executes given workload in parallel", "result":"PASS", - "duration":"0.504 seconds", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n Warning! This test is flaky simply because it relies on the behaviour of threads\n which may or may not behave as expected. \n "] }, @@ -39,7 +39,7 @@ { "id":"CPU knows the current number of available processor cores!", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -52,7 +52,7 @@ { "id":"The CPU exposes a non null API for executing workloads in parallel.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -67,7 +67,7 @@ { "id":"The CPU device will keep track of the amount of tensors it stores.", "result":"PASS", - "duration":"0.181 seconds", + "duration":"5.697 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.device.Cross_Device_IO_Spec.json b/docs/spock/reports/ut.device.Cross_Device_IO_Spec.json index 68d316820..619846156 100644 --- a/docs/spock/reports/ut.device.Cross_Device_IO_Spec.json +++ b/docs/spock/reports/ut.device.Cross_Device_IO_Spec.json @@ -4,17 +4,98 @@ "narrative":"Tensors should not manage their states\n themselves, simply because the type and location\n of the data is dependent on the device onto which they are stored.\n This specification tests of various device implementations\n enable reading to or writing from the tensors they store.", "subjects":["neureka.devices.Device","neureka.Tensor"], "statistics":{ - "runs":"2", + "runs":"15", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.015 seconds" + "duration":"0.072 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"We can use the access device API to read from a tensor.", + "id":"We can use the access device API to read from a tensor. [0]", + "result":"PASS", + "duration":"0.007 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"expect","text":"The device reports that both tensors have the same data array size.","code":["device.access(t).dataSize == 4","device.access(s).dataSize == 4"]}, + + {"kind":"and","text":"Reading the underlying tensor data from the device will yield the expected result.","code":["device.access(t).readAll(false) == expected","device.access(s).readAll(false) == expected","device.access(t).readAll(true) == expected","device.access(s).readAll(true) == expected"]}, + + {"kind":"and","text":"This is also true for when reading at a particular index.","code":["device.access(t).readArray(arrayType, 1, 1) == [expected[1]]","device.access(s).readArray(arrayType, 1, 1) == [expected[1]]","device.access(t).readArray(arrayType, 2, 1) == [expected[2]]","device.access(s).readArray(arrayType, 2, 1) == [expected[2]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float","Character"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]","['6','a']"],"expected":["[2, 1, 2, 1]","[2,7,8,2]","[6,2,6,6]","[6,2,7,6]","[3.4d, 3.0d, 3.4d, 3.0d]","[5.7f, -1.0f, 5.7f, -1.0f]","[5.7f, -1.0f, 5.7f, -1.0f]","['6','a','6','a']"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to read from a tensor. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"expect","text":"The device reports that both tensors have the same data array size.","code":["device.access(t).dataSize == 4","device.access(s).dataSize == 4"]}, + + {"kind":"and","text":"Reading the underlying tensor data from the device will yield the expected result.","code":["device.access(t).readAll(false) == expected","device.access(s).readAll(false) == expected","device.access(t).readAll(true) == expected","device.access(s).readAll(true) == expected"]}, + + {"kind":"and","text":"This is also true for when reading at a particular index.","code":["device.access(t).readArray(arrayType, 1, 1) == [expected[1]]","device.access(s).readArray(arrayType, 1, 1) == [expected[1]]","device.access(t).readArray(arrayType, 2, 1) == [expected[2]]","device.access(s).readArray(arrayType, 2, 1) == [expected[2]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float","Character"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]","['6','a']"],"expected":["[2, 1, 2, 1]","[2,7,8,2]","[6,2,6,6]","[6,2,7,6]","[3.4d, 3.0d, 3.4d, 3.0d]","[5.7f, -1.0f, 5.7f, -1.0f]","[5.7f, -1.0f, 5.7f, -1.0f]","['6','a','6','a']"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to read from a tensor. [2]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"expect","text":"The device reports that both tensors have the same data array size.","code":["device.access(t).dataSize == 4","device.access(s).dataSize == 4"]}, + + {"kind":"and","text":"Reading the underlying tensor data from the device will yield the expected result.","code":["device.access(t).readAll(false) == expected","device.access(s).readAll(false) == expected","device.access(t).readAll(true) == expected","device.access(s).readAll(true) == expected"]}, + + {"kind":"and","text":"This is also true for when reading at a particular index.","code":["device.access(t).readArray(arrayType, 1, 1) == [expected[1]]","device.access(s).readArray(arrayType, 1, 1) == [expected[1]]","device.access(t).readArray(arrayType, 2, 1) == [expected[2]]","device.access(s).readArray(arrayType, 2, 1) == [expected[2]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float","Character"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]","['6','a']"],"expected":["[2, 1, 2, 1]","[2,7,8,2]","[6,2,6,6]","[6,2,7,6]","[3.4d, 3.0d, 3.4d, 3.0d]","[5.7f, -1.0f, 5.7f, -1.0f]","[5.7f, -1.0f, 5.7f, -1.0f]","['6','a','6','a']"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to read from a tensor. [3]", "result":"PASS", "duration":"0.002 seconds", "iterations":{ @@ -41,7 +122,240 @@ }, { - "id":"We can use the access device API to write to a tensor", + "id":"We can use the access device API to read from a tensor. [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"expect","text":"The device reports that both tensors have the same data array size.","code":["device.access(t).dataSize == 4","device.access(s).dataSize == 4"]}, + + {"kind":"and","text":"Reading the underlying tensor data from the device will yield the expected result.","code":["device.access(t).readAll(false) == expected","device.access(s).readAll(false) == expected","device.access(t).readAll(true) == expected","device.access(s).readAll(true) == expected"]}, + + {"kind":"and","text":"This is also true for when reading at a particular index.","code":["device.access(t).readArray(arrayType, 1, 1) == [expected[1]]","device.access(s).readArray(arrayType, 1, 1) == [expected[1]]","device.access(t).readArray(arrayType, 2, 1) == [expected[2]]","device.access(s).readArray(arrayType, 2, 1) == [expected[2]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float","Character"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]","['6','a']"],"expected":["[2, 1, 2, 1]","[2,7,8,2]","[6,2,6,6]","[6,2,7,6]","[3.4d, 3.0d, 3.4d, 3.0d]","[5.7f, -1.0f, 5.7f, -1.0f]","[5.7f, -1.0f, 5.7f, -1.0f]","['6','a','6','a']"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to read from a tensor. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"expect","text":"The device reports that both tensors have the same data array size.","code":["device.access(t).dataSize == 4","device.access(s).dataSize == 4"]}, + + {"kind":"and","text":"Reading the underlying tensor data from the device will yield the expected result.","code":["device.access(t).readAll(false) == expected","device.access(s).readAll(false) == expected","device.access(t).readAll(true) == expected","device.access(s).readAll(true) == expected"]}, + + {"kind":"and","text":"This is also true for when reading at a particular index.","code":["device.access(t).readArray(arrayType, 1, 1) == [expected[1]]","device.access(s).readArray(arrayType, 1, 1) == [expected[1]]","device.access(t).readArray(arrayType, 2, 1) == [expected[2]]","device.access(s).readArray(arrayType, 2, 1) == [expected[2]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float","Character"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]","['6','a']"],"expected":["[2, 1, 2, 1]","[2,7,8,2]","[6,2,6,6]","[6,2,7,6]","[3.4d, 3.0d, 3.4d, 3.0d]","[5.7f, -1.0f, 5.7f, -1.0f]","[5.7f, -1.0f, 5.7f, -1.0f]","['6','a','6','a']"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to read from a tensor. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"expect","text":"The device reports that both tensors have the same data array size.","code":["device.access(t).dataSize == 4","device.access(s).dataSize == 4"]}, + + {"kind":"and","text":"Reading the underlying tensor data from the device will yield the expected result.","code":["device.access(t).readAll(false) == expected","device.access(s).readAll(false) == expected","device.access(t).readAll(true) == expected","device.access(s).readAll(true) == expected"]}, + + {"kind":"and","text":"This is also true for when reading at a particular index.","code":["device.access(t).readArray(arrayType, 1, 1) == [expected[1]]","device.access(s).readArray(arrayType, 1, 1) == [expected[1]]","device.access(t).readArray(arrayType, 2, 1) == [expected[2]]","device.access(s).readArray(arrayType, 2, 1) == [expected[2]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float","Character"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]","['6','a']"],"expected":["[2, 1, 2, 1]","[2,7,8,2]","[6,2,6,6]","[6,2,7,6]","[3.4d, 3.0d, 3.4d, 3.0d]","[5.7f, -1.0f, 5.7f, -1.0f]","[5.7f, -1.0f, 5.7f, -1.0f]","['6','a','6','a']"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to read from a tensor. [7]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"expect","text":"The device reports that both tensors have the same data array size.","code":["device.access(t).dataSize == 4","device.access(s).dataSize == 4"]}, + + {"kind":"and","text":"Reading the underlying tensor data from the device will yield the expected result.","code":["device.access(t).readAll(false) == expected","device.access(s).readAll(false) == expected","device.access(t).readAll(true) == expected","device.access(s).readAll(true) == expected"]}, + + {"kind":"and","text":"This is also true for when reading at a particular index.","code":["device.access(t).readArray(arrayType, 1, 1) == [expected[1]]","device.access(s).readArray(arrayType, 1, 1) == [expected[1]]","device.access(t).readArray(arrayType, 2, 1) == [expected[2]]","device.access(s).readArray(arrayType, 2, 1) == [expected[2]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float","Character"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]","['6','a']"],"expected":["[2, 1, 2, 1]","[2,7,8,2]","[6,2,6,6]","[6,2,7,6]","[3.4d, 3.0d, 3.4d, 3.0d]","[5.7f, -1.0f, 5.7f, -1.0f]","[5.7f, -1.0f, 5.7f, -1.0f]","['6','a','6','a']"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to write to a tensor [0]", + "result":"PASS", + "duration":"0.008 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"when","text":"We write some test data into different ranges within the 2 tensors.","code":["device.access(t).writeFrom(write, 0).intoRange(0,2)","device.access(s).writeFrom(write, 0).intoRange(2,4)"]}, + + {"kind":"then","text":"Reading the previously written data should yield the expected result.","code":["device.access(t).readArray(arrayType, 0, 2) == [expected[0],expected[1]]","device.access(s).readArray(arrayType, 2, 2) == [expected[2],expected[3]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]"],"write":["[5]","[7]","[1]","[8]","[3]","[4]","[8]"],"expected":["[5, 1, 5, 1]","[7,7,7,2]","[1,2,1,6]","[8,2,8,6]","[3d, 3.0d, 3d, 3.0d]","[4f, -1.0f, 4f, -1.0f]","[8f, -1.0f, 8f, -1.0f]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to write to a tensor [1]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"when","text":"We write some test data into different ranges within the 2 tensors.","code":["device.access(t).writeFrom(write, 0).intoRange(0,2)","device.access(s).writeFrom(write, 0).intoRange(2,4)"]}, + + {"kind":"then","text":"Reading the previously written data should yield the expected result.","code":["device.access(t).readArray(arrayType, 0, 2) == [expected[0],expected[1]]","device.access(s).readArray(arrayType, 2, 2) == [expected[2],expected[3]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]"],"write":["[5]","[7]","[1]","[8]","[3]","[4]","[8]"],"expected":["[5, 1, 5, 1]","[7,7,7,2]","[1,2,1,6]","[8,2,8,6]","[3d, 3.0d, 3d, 3.0d]","[4f, -1.0f, 4f, -1.0f]","[8f, -1.0f, 8f, -1.0f]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to write to a tensor [2]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"when","text":"We write some test data into different ranges within the 2 tensors.","code":["device.access(t).writeFrom(write, 0).intoRange(0,2)","device.access(s).writeFrom(write, 0).intoRange(2,4)"]}, + + {"kind":"then","text":"Reading the previously written data should yield the expected result.","code":["device.access(t).readArray(arrayType, 0, 2) == [expected[0],expected[1]]","device.access(s).readArray(arrayType, 2, 2) == [expected[2],expected[3]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]"],"write":["[5]","[7]","[1]","[8]","[3]","[4]","[8]"],"expected":["[5, 1, 5, 1]","[7,7,7,2]","[1,2,1,6]","[8,2,8,6]","[3d, 3.0d, 3d, 3.0d]","[4f, -1.0f, 4f, -1.0f]","[8f, -1.0f, 8f, -1.0f]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to write to a tensor [3]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"when","text":"We write some test data into different ranges within the 2 tensors.","code":["device.access(t).writeFrom(write, 0).intoRange(0,2)","device.access(s).writeFrom(write, 0).intoRange(2,4)"]}, + + {"kind":"then","text":"Reading the previously written data should yield the expected result.","code":["device.access(t).readArray(arrayType, 0, 2) == [expected[0],expected[1]]","device.access(s).readArray(arrayType, 2, 2) == [expected[2],expected[3]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]"],"write":["[5]","[7]","[1]","[8]","[3]","[4]","[8]"],"expected":["[5, 1, 5, 1]","[7,7,7,2]","[1,2,1,6]","[8,2,8,6]","[3d, 3.0d, 3d, 3.0d]","[4f, -1.0f, 4f, -1.0f]","[8f, -1.0f, 8f, -1.0f]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to write to a tensor [4]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"when","text":"We write some test data into different ranges within the 2 tensors.","code":["device.access(t).writeFrom(write, 0).intoRange(0,2)","device.access(s).writeFrom(write, 0).intoRange(2,4)"]}, + + {"kind":"then","text":"Reading the previously written data should yield the expected result.","code":["device.access(t).readArray(arrayType, 0, 2) == [expected[0],expected[1]]","device.access(s).readArray(arrayType, 2, 2) == [expected[2],expected[3]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]"],"write":["[5]","[7]","[1]","[8]","[3]","[4]","[8]"],"expected":["[5, 1, 5, 1]","[7,7,7,2]","[1,2,1,6]","[8,2,8,6]","[3d, 3.0d, 3d, 3.0d]","[4f, -1.0f, 4f, -1.0f]","[8f, -1.0f, 8f, -1.0f]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to write to a tensor [5]", "result":"PASS", "duration":"0.001 seconds", "iterations":{ @@ -63,6 +377,31 @@ {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]"],"write":["[5]","[7]","[1]","[8]","[3]","[4]","[8]"],"expected":["[5, 1, 5, 1]","[7,7,7,2]","[1,2,1,6]","[8,2,8,6]","[3d, 3.0d, 3d, 3.0d]","[4f, -1.0f, 4f, -1.0f]","[8f, -1.0f, 8f, -1.0f]"]}} ], "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the access device API to write to a tensor [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We fetch the required device instance from its interface.","code":["var device = Device.get(deviceType)"]}, + + {"kind":"and","text":"We fetch the array type of the tested data type!","code":["var arrayType = DataType.of(type).dataArrayType()"]}, + + {"kind":"and","text":"A tensor filled with 4 values which we are going to store on the previously fetched device.","code":["var t = Tensor.of(type).withShape(4).andFill(fill).to(device)"]}, + + {"kind":"and","text":"A slice from the above tensor.","code":["var s = t[1..2]"]}, + + {"kind":"when","text":"We write some test data into different ranges within the 2 tensors.","code":["device.access(t).writeFrom(write, 0).intoRange(0,2)","device.access(s).writeFrom(write, 0).intoRange(2,4)"]}, + + {"kind":"then","text":"Reading the previously written data should yield the expected result.","code":["device.access(t).readArray(arrayType, 0, 2) == [expected[0],expected[1]]","device.access(s).readArray(arrayType, 2, 2) == [expected[2],expected[3]]"]}, + + {"kind":"where","text":"The parameters in the above code can have the following states:","code":{"deviceType":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Integer","Short","Long","Byte","Double","Float","Float"],"fill":["[2, 1]","[2,7,8]","[6,2,6]","[6,2,7]","[3.4, 3]","[5.7,-1]","[5.7,-1]"],"write":["[5]","[7]","[1]","[8]","[3]","[4]","[8]"],"expected":["[5, 1, 5, 1]","[7,7,7,2]","[1,2,1,6]","[8,2,8,6]","[3d, 3.0d, 3d, 3.0d]","[4f, -1.0f, 4f, -1.0f]","[8f, -1.0f, 8f, -1.0f]"]}} + ], + "problems":{"dataValues":[], "errors":[]} } ], diff --git a/docs/spock/reports/ut.device.Cross_Device_Type_Spec.json b/docs/spock/reports/ut.device.Cross_Device_Type_Spec.json index b42332870..0f7cbcc4d 100644 --- a/docs/spock/reports/ut.device.Cross_Device_Type_Spec.json +++ b/docs/spock/reports/ut.device.Cross_Device_Type_Spec.json @@ -1,24 +1,24 @@ { "className":"ut.device.Cross_Device_Type_Spec", "title":"Finding Device Types", - "narrative":"Neureka introduces a the concept of a `Device` which is an interface\n that represents a computational device used for executing tensor / nd-array operations on them.\n The `Device` interface is implemented by various classes which represent\n different types of accelerator hardware such as `CPUs`, `GPUs`, `TPUs`, `FPGAs`, etc.\n These various `Device` types can not be instantiated directly because they model\n the concrete and finite hardware that is available on any given system Neureka is running on.\n This means that they are usually instantiated lazily upon access request or\n upfront by the library backend (usually a backend extension built fo a specific device).\n In order to find these instances embedded in the library backend the `Device` interface\n exposes various static methods which can be used to find a device instance by name or type.", + "narrative":"Neureka introduces a the concept of a `Device` which is an interface\n that represents a computational device used for executing tensor / nd-array operations on them.\n The `Device` interface is implemented by various classes which represent\n different types of accelerator hardware such as `CPUs`, `GPUs`, `TPUs`, `FPGAs`, etc.\n These various `Device` types can not be instantiated directly because they model \n the concrete and finite hardware that is available on any given system Neureka is running on.\n This means that they are usually instantiated lazily upon access request or \n upfront by the library backend (usually a backend extension built fo a specific device).\n In order to find these instances embedded in the library backend the `Device` interface\n exposes various static methods which can be used to find a device instance by name or type.", "subjects":["neureka.devices.Device"], "statistics":{ - "runs":"10", + "runs":"53", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"1", - "duration":"0.834 seconds" + "duration":"6.152 seconds" }, "headers":["\n Specified below is the behaviour of the factory methods in the\n Device interface as well as its various implementations \n which should adhere to a certain set of common behaviours.\n "],"tags":{},"see":[], "features":[ { - "id":"We can find Device implementations or null by passing search keys to the \"get\" method.", + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [0]", "result":"PASS", - "duration":"0.012 seconds", + "duration":"0.004 seconds", "iterations":{ - "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n ","\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n "] + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n "] }, "blocks":[ {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, @@ -33,64 +33,857 @@ }, { - "id":"We can query the backend for devices by specifying both the requested type and a key word.", + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n "] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n "] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n "] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" method are dependent on the current \n system and the available hardware.\n "] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [5]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [6]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [7]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can find Device implementations or null by passing search keys to the \"get\" method. [11]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We pass a query key word to the \"get\" method...","code":["var device = Device.get(query)"]}, + + {"kind":"then","text":"...the result should be a non-null device (if our query key matches something).","code":["device != null"]}, + + {"kind":"and","text":"The resulting Device variable has the expected type (CPU, OpenCLDevice, ...).","code":["device.class == type"]}, + + {"kind":"where","text":"The query key words and the expected device types returned.","code":{"query":["\"cPu\"","\"jVm\"","\"natiVe\"","\"Threaded\"","\"first CPU\"","\"openCl\"","\"first gpu\"","\"nvidia or amd or intel\"","\"rocm or cuda or amd or nvidia\"","\"nvidia amd or intel\"","\"cudarocm gpu\"","\"first\""],"type":["CPU","CPU","CPU","CPU","CPU","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice","OpenCLDevice"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can query the backend for devices by specifying both the requested type and a key word. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" and \"find\" methods are dependent on the current \n system and the available hardware that Neureka encounters.\n "] + }, + "blocks":[ + {"kind":"expect","text":"Querying for a device using a device type and key works as expected.","code":["Device.get(type, key) === expected"]}, + + {"kind":"and","text":"We can use the \"find\" method if we want the result to be wrapped in a nice and safe Optional instance.","code":["!Device.find(type, key).isPresent() && expected == null || Device.find(type, key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following device type, key and expected device instance.","code":{"type":["Device","Device","Device","DummyDevice","DummyDevice","DummyDevice","DummyDevice"],"key":["'cpu'","'jvm'","'processor'","'first'","'any'","'cpu'","'gpu'"],"expected":["CPU.get()","CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can query the backend for devices by specifying both the requested type and a key word. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" and \"find\" methods are dependent on the current \n system and the available hardware that Neureka encounters.\n "] + }, + "blocks":[ + {"kind":"expect","text":"Querying for a device using a device type and key works as expected.","code":["Device.get(type, key) === expected"]}, + + {"kind":"and","text":"We can use the \"find\" method if we want the result to be wrapped in a nice and safe Optional instance.","code":["!Device.find(type, key).isPresent() && expected == null || Device.find(type, key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following device type, key and expected device instance.","code":{"type":["Device","Device","Device","DummyDevice","DummyDevice","DummyDevice","DummyDevice"],"key":["'cpu'","'jvm'","'processor'","'first'","'any'","'cpu'","'gpu'"],"expected":["CPU.get()","CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can query the backend for devices by specifying both the requested type and a key word. [2]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" and \"find\" methods are dependent on the current \n system and the available hardware that Neureka encounters.\n "] + }, + "blocks":[ + {"kind":"expect","text":"Querying for a device using a device type and key works as expected.","code":["Device.get(type, key) === expected"]}, + + {"kind":"and","text":"We can use the \"find\" method if we want the result to be wrapped in a nice and safe Optional instance.","code":["!Device.find(type, key).isPresent() && expected == null || Device.find(type, key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following device type, key and expected device instance.","code":{"type":["Device","Device","Device","DummyDevice","DummyDevice","DummyDevice","DummyDevice"],"key":["'cpu'","'jvm'","'processor'","'first'","'any'","'cpu'","'gpu'"],"expected":["CPU.get()","CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can query the backend for devices by specifying both the requested type and a key word. [3]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" and \"find\" methods are dependent on the current \n system and the available hardware that Neureka encounters.\n "] + }, + "blocks":[ + {"kind":"expect","text":"Querying for a device using a device type and key works as expected.","code":["Device.get(type, key) === expected"]}, + + {"kind":"and","text":"We can use the \"find\" method if we want the result to be wrapped in a nice and safe Optional instance.","code":["!Device.find(type, key).isPresent() && expected == null || Device.find(type, key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following device type, key and expected device instance.","code":{"type":["Device","Device","Device","DummyDevice","DummyDevice","DummyDevice","DummyDevice"],"key":["'cpu'","'jvm'","'processor'","'first'","'any'","'cpu'","'gpu'"],"expected":["CPU.get()","CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can query the backend for devices by specifying both the requested type and a key word. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" and \"find\" methods are dependent on the current \n system and the available hardware that Neureka encounters.\n "] + }, + "blocks":[ + {"kind":"expect","text":"Querying for a device using a device type and key works as expected.","code":["Device.get(type, key) === expected"]}, + + {"kind":"and","text":"We can use the \"find\" method if we want the result to be wrapped in a nice and safe Optional instance.","code":["!Device.find(type, key).isPresent() && expected == null || Device.find(type, key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following device type, key and expected device instance.","code":{"type":["Device","Device","Device","DummyDevice","DummyDevice","DummyDevice","DummyDevice"],"key":["'cpu'","'jvm'","'processor'","'first'","'any'","'cpu'","'gpu'"],"expected":["CPU.get()","CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can query the backend for devices by specifying both the requested type and a key word. [5]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" and \"find\" methods are dependent on the current \n system and the available hardware that Neureka encounters.\n "] + }, + "blocks":[ + {"kind":"expect","text":"Querying for a device using a device type and key works as expected.","code":["Device.get(type, key) === expected"]}, + + {"kind":"and","text":"We can use the \"find\" method if we want the result to be wrapped in a nice and safe Optional instance.","code":["!Device.find(type, key).isPresent() && expected == null || Device.find(type, key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following device type, key and expected device instance.","code":{"type":["Device","Device","Device","DummyDevice","DummyDevice","DummyDevice","DummyDevice"],"key":["'cpu'","'jvm'","'processor'","'first'","'any'","'cpu'","'gpu'"],"expected":["CPU.get()","CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can query the backend for devices by specifying both the requested type and a key word. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that the examples specified in the table below\n may not be the same on every system because the devices\n returned by the \"get\" and \"find\" methods are dependent on the current \n system and the available hardware that Neureka encounters.\n "] + }, + "blocks":[ + {"kind":"expect","text":"Querying for a device using a device type and key works as expected.","code":["Device.get(type, key) === expected"]}, + + {"kind":"and","text":"We can use the \"find\" method if we want the result to be wrapped in a nice and safe Optional instance.","code":["!Device.find(type, key).isPresent() && expected == null || Device.find(type, key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following device type, key and expected device instance.","code":{"type":["Device","Device","Device","DummyDevice","DummyDevice","DummyDevice","DummyDevice"],"key":["'cpu'","'jvm'","'processor'","'first'","'any'","'cpu'","'gpu'"],"expected":["CPU.get()","CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"In total there are 3 different types of methods for finding device instances. [0]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The \"get\" method returns a device instance or null.","code":["Device.get(key) === expected"]}, + + {"kind":"and","text":"The \"any\" method returns a device instance or the \"CPU device\", which is the library default device.","code":["Device.any(key) === expected || Device.any(key) === CPU.get()"]}, + + {"kind":"and","text":"The \"find\" method returns a device instance wrapped in an Optional instance.","code":["!Device.find(key).isPresent() && expected == null || Device.find(key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following search key and expected device instance.","code":{"key":["'cpu'","'central processing'","'e9rt56hqfwe5f0'","'5638135dh90978'","'banana device'","'cupcake'"],"expected":["CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"In total there are 3 different types of methods for finding device instances. [1]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The \"get\" method returns a device instance or null.","code":["Device.get(key) === expected"]}, + + {"kind":"and","text":"The \"any\" method returns a device instance or the \"CPU device\", which is the library default device.","code":["Device.any(key) === expected || Device.any(key) === CPU.get()"]}, + + {"kind":"and","text":"The \"find\" method returns a device instance wrapped in an Optional instance.","code":["!Device.find(key).isPresent() && expected == null || Device.find(key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following search key and expected device instance.","code":{"key":["'cpu'","'central processing'","'e9rt56hqfwe5f0'","'5638135dh90978'","'banana device'","'cupcake'"],"expected":["CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"In total there are 3 different types of methods for finding device instances. [2]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The \"get\" method returns a device instance or null.","code":["Device.get(key) === expected"]}, + + {"kind":"and","text":"The \"any\" method returns a device instance or the \"CPU device\", which is the library default device.","code":["Device.any(key) === expected || Device.any(key) === CPU.get()"]}, + + {"kind":"and","text":"The \"find\" method returns a device instance wrapped in an Optional instance.","code":["!Device.find(key).isPresent() && expected == null || Device.find(key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following search key and expected device instance.","code":{"key":["'cpu'","'central processing'","'e9rt56hqfwe5f0'","'5638135dh90978'","'banana device'","'cupcake'"],"expected":["CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"In total there are 3 different types of methods for finding device instances. [3]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The \"get\" method returns a device instance or null.","code":["Device.get(key) === expected"]}, + + {"kind":"and","text":"The \"any\" method returns a device instance or the \"CPU device\", which is the library default device.","code":["Device.any(key) === expected || Device.any(key) === CPU.get()"]}, + + {"kind":"and","text":"The \"find\" method returns a device instance wrapped in an Optional instance.","code":["!Device.find(key).isPresent() && expected == null || Device.find(key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following search key and expected device instance.","code":{"key":["'cpu'","'central processing'","'e9rt56hqfwe5f0'","'5638135dh90978'","'banana device'","'cupcake'"],"expected":["CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"In total there are 3 different types of methods for finding device instances. [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The \"get\" method returns a device instance or null.","code":["Device.get(key) === expected"]}, + + {"kind":"and","text":"The \"any\" method returns a device instance or the \"CPU device\", which is the library default device.","code":["Device.any(key) === expected || Device.any(key) === CPU.get()"]}, + + {"kind":"and","text":"The \"find\" method returns a device instance wrapped in an Optional instance.","code":["!Device.find(key).isPresent() && expected == null || Device.find(key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following search key and expected device instance.","code":{"key":["'cpu'","'central processing'","'e9rt56hqfwe5f0'","'5638135dh90978'","'banana device'","'cupcake'"],"expected":["CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"In total there are 3 different types of methods for finding device instances. [5]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The \"get\" method returns a device instance or null.","code":["Device.get(key) === expected"]}, + + {"kind":"and","text":"The \"any\" method returns a device instance or the \"CPU device\", which is the library default device.","code":["Device.any(key) === expected || Device.any(key) === CPU.get()"]}, + + {"kind":"and","text":"The \"find\" method returns a device instance wrapped in an Optional instance.","code":["!Device.find(key).isPresent() && expected == null || Device.find(key).get() === expected"]}, + + {"kind":"where","text":"The we can use the following search key and expected device instance.","code":{"key":["'cpu'","'central processing'","'e9rt56hqfwe5f0'","'5638135dh90978'","'banana device'","'cupcake'"],"expected":["CPU.get()","CPU.get()","null","null","null","null"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing a numeric array to a tensor should modify its contents! [0]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Here we demonstrate that a tensor located on a non-CPU `Device` will be\n updated when passing a float or double array, even if it its data is stored\n on the GPU or somewhere else!\n "] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor which we transfer to a device using the \"to\" method...","code":["Tensor t = Tensor.of(Shape.of(3, 2), new double[]{2, 4, -5, 8, 3, -2}).to(device)"]}, + + {"kind":"when","text":"A numeric array is passed to said tensor...","code":["t.mut.setItems(data1)","t.mut.setItems(data2)"]}, + + {"kind":"then","text":"The tensor (as String) contains the expected String.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"The following data is being used :","code":{"device":["Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")"],"data1":["new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}","new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}"],"data2":["new float[0]","new float[]{1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}","new float[0]","new float[]{1, 1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}"],"expected":["\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 6.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\"","\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 1.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing a numeric array to a tensor should modify its contents! [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Here we demonstrate that a tensor located on a non-CPU `Device` will be\n updated when passing a float or double array, even if it its data is stored\n on the GPU or somewhere else!\n "] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor which we transfer to a device using the \"to\" method...","code":["Tensor t = Tensor.of(Shape.of(3, 2), new double[]{2, 4, -5, 8, 3, -2}).to(device)"]}, + + {"kind":"when","text":"A numeric array is passed to said tensor...","code":["t.mut.setItems(data1)","t.mut.setItems(data2)"]}, + + {"kind":"then","text":"The tensor (as String) contains the expected String.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"The following data is being used :","code":{"device":["Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")"],"data1":["new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}","new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}"],"data2":["new float[0]","new float[]{1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}","new float[0]","new float[]{1, 1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}"],"expected":["\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 6.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\"","\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 1.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing a numeric array to a tensor should modify its contents! [2]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Here we demonstrate that a tensor located on a non-CPU `Device` will be\n updated when passing a float or double array, even if it its data is stored\n on the GPU or somewhere else!\n "] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor which we transfer to a device using the \"to\" method...","code":["Tensor t = Tensor.of(Shape.of(3, 2), new double[]{2, 4, -5, 8, 3, -2}).to(device)"]}, + + {"kind":"when","text":"A numeric array is passed to said tensor...","code":["t.mut.setItems(data1)","t.mut.setItems(data2)"]}, + + {"kind":"then","text":"The tensor (as String) contains the expected String.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"The following data is being used :","code":{"device":["Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")"],"data1":["new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}","new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}"],"data2":["new float[0]","new float[]{1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}","new float[0]","new float[]{1, 1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}"],"expected":["\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 6.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\"","\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 1.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing a numeric array to a tensor should modify its contents! [3]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Here we demonstrate that a tensor located on a non-CPU `Device` will be\n updated when passing a float or double array, even if it its data is stored\n on the GPU or somewhere else!\n "] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor which we transfer to a device using the \"to\" method...","code":["Tensor t = Tensor.of(Shape.of(3, 2), new double[]{2, 4, -5, 8, 3, -2}).to(device)"]}, + + {"kind":"when","text":"A numeric array is passed to said tensor...","code":["t.mut.setItems(data1)","t.mut.setItems(data2)"]}, + + {"kind":"then","text":"The tensor (as String) contains the expected String.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"The following data is being used :","code":{"device":["Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")"],"data1":["new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}","new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}"],"data2":["new float[0]","new float[]{1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}","new float[0]","new float[]{1, 1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}"],"expected":["\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 6.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\"","\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 1.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing a numeric array to a tensor should modify its contents! [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Here we demonstrate that a tensor located on a non-CPU `Device` will be\n updated when passing a float or double array, even if it its data is stored\n on the GPU or somewhere else!\n "] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor which we transfer to a device using the \"to\" method...","code":["Tensor t = Tensor.of(Shape.of(3, 2), new double[]{2, 4, -5, 8, 3, -2}).to(device)"]}, + + {"kind":"when","text":"A numeric array is passed to said tensor...","code":["t.mut.setItems(data1)","t.mut.setItems(data2)"]}, + + {"kind":"then","text":"The tensor (as String) contains the expected String.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"The following data is being used :","code":{"device":["Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")"],"data1":["new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}","new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}"],"data2":["new float[0]","new float[]{1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}","new float[0]","new float[]{1, 1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}"],"expected":["\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 6.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\"","\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 1.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing a numeric array to a tensor should modify its contents! [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor which we transfer to a device using the \"to\" method...","code":["Tensor t = Tensor.of(Shape.of(3, 2), new double[]{2, 4, -5, 8, 3, -2}).to(device)"]}, + + {"kind":"when","text":"A numeric array is passed to said tensor...","code":["t.mut.setItems(data1)","t.mut.setItems(data2)"]}, + + {"kind":"then","text":"The tensor (as String) contains the expected String.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"The following data is being used :","code":{"device":["Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")"],"data1":["new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}","new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}"],"data2":["new float[0]","new float[]{1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}","new float[0]","new float[]{1, 1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}"],"expected":["\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 6.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\"","\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 1.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing a numeric array to a tensor should modify its contents! [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor which we transfer to a device using the \"to\" method...","code":["Tensor t = Tensor.of(Shape.of(3, 2), new double[]{2, 4, -5, 8, 3, -2}).to(device)"]}, + + {"kind":"when","text":"A numeric array is passed to said tensor...","code":["t.mut.setItems(data1)","t.mut.setItems(data2)"]}, + + {"kind":"then","text":"The tensor (as String) contains the expected String.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"The following data is being used :","code":{"device":["Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")"],"data1":["new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}","new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}"],"data2":["new float[0]","new float[]{1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}","new float[0]","new float[]{1, 1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}"],"expected":["\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 6.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\"","\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 1.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing a numeric array to a tensor should modify its contents! [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor which we transfer to a device using the \"to\" method...","code":["Tensor t = Tensor.of(Shape.of(3, 2), new double[]{2, 4, -5, 8, 3, -2}).to(device)"]}, + + {"kind":"when","text":"A numeric array is passed to said tensor...","code":["t.mut.setItems(data1)","t.mut.setItems(data2)"]}, + + {"kind":"then","text":"The tensor (as String) contains the expected String.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"The following data is being used :","code":{"device":["Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")"],"data1":["new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}","new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}"],"data2":["new float[0]","new float[]{1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}","new float[0]","new float[]{1, 1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}"],"expected":["\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 6.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\"","\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 1.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing a numeric array to a tensor should modify its contents! [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor which we transfer to a device using the \"to\" method...","code":["Tensor t = Tensor.of(Shape.of(3, 2), new double[]{2, 4, -5, 8, 3, -2}).to(device)"]}, + + {"kind":"when","text":"A numeric array is passed to said tensor...","code":["t.mut.setItems(data1)","t.mut.setItems(data2)"]}, + + {"kind":"then","text":"The tensor (as String) contains the expected String.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"The following data is being used :","code":{"device":["Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")"],"data1":["new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}","new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}"],"data2":["new float[0]","new float[]{1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}","new float[0]","new float[]{1, 1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}"],"expected":["\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 6.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\"","\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 1.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing a numeric array to a tensor should modify its contents! [9]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor which we transfer to a device using the \"to\" method...","code":["Tensor t = Tensor.of(Shape.of(3, 2), new double[]{2, 4, -5, 8, 3, -2}).to(device)"]}, + + {"kind":"when","text":"A numeric array is passed to said tensor...","code":["t.mut.setItems(data1)","t.mut.setItems(data2)"]}, + + {"kind":"then","text":"The tensor (as String) contains the expected String.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"The following data is being used :","code":{"device":["Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"cpu\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")","Device.get(\"openCL\")"],"data1":["new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}","new float[0]","new float[]{2, 3, 4, 5, 6}","new float[]{3, 5, 6}","new double[]{9, 4, 7, -12}","new float[]{22, 24, 35, 80}"],"data2":["new float[0]","new float[]{1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}","new float[0]","new float[]{1, 1, 1, 1, 1}","new float[]{4, 2, 3}","new double[]{-5, -2, 1}","new double[]{-1, -1, -1}"],"expected":["\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 6.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\"","\"(3x2):[2.0, 4.0, -5.0, 8.0, 3.0, -2.0]\"","\"(3x2):[1.0, 1.0, 1.0, 1.0, 1.0, -2.0]\"","\"(3x2):[4.0, 2.0, 3.0, 8.0, 3.0, -2.0]\"","\"(3x2):[-5.0, -2.0, 1.0, -12.0, 3.0, -2.0]\"","\"(3x2):[-1.0, -1.0, -1.0, 80.0, 3.0, -2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Devices expose an API for accessing (reading and writing) the data of a tensor. [0]", + "result":"PASS", + "duration":"0.009 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Because in some environments OpenCL might not be available, the test will be stopped!","code":["if ( device == null ) return"]}, + + {"kind":"when","text":"A 2D tensor is being instantiated by passing the given shape and data...","code":["Tensor t = Tensor.of(Shape.of(shape), data).to(device)"]}, + + {"kind":"then","text":"The tensor values (as List) are as expected.","code":["Arrays.equals(t.getItemsAs(double[].class), DataConverter.get().convert(expected,double[].class))"]}, + + {"kind":"when","text":"The same underlying data is being queried by calling the device...","code":["var result = (0..> device"]}, + + {"kind":"when","text":"The call is being passed to the execution utility method ..","code":["AbstractDeviceAlgorithm.prepareAndExecute( call, AbstractDeviceAlgorithm::executeDeviceAlgorithm )"]}, + + {"kind":"then","text":"...the implementation is being accessed in order to access the mocked lambda...","code":["(1.._) * call.getAlgorithm() >> implementation"]}, + + {"kind":"and","text":"The tensor array is being accessed to check for null. (For exception throwing)","code":["1 * call.inputs() >> new Tensor[]{ Mock(Tensor), null }"]}, + + {"kind":"and","text":"The expected exception is being thrown alongside a descriptive message.","code":["def exception = thrown(IllegalArgumentException)","exception.message == \"Device arguments may not be null!\\n\" +"," \"One or more tensor arguments within the given ExecutionCall instance is null.\""]}, + + {"kind":"where","text":"The following Device instances are being tested :","code":{"device":["CPU.get()","Device.get(\"openCL\")"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Execution calls containing null arguments will cause an exception to be thrown in device instances. [1]", "result":"PASS", - "duration":"0.294 seconds", + "duration":"0.001 seconds", "iterations":{ - "tags":{},"see":[],"extraInfo":["\n Every argument within an ExecutionCall instance has a purpose. Null is not permissible.\n ","\n Every argument within an ExecutionCall instance has a purpose. Null is not permissible.\n "] + "tags":{},"see":[],"extraInfo":[] }, "blocks":[ {"kind":"given","text":"A mocked ExecutionCall with mocked algorithm...","code":["var call = Mock(ExecutionCall)","var implementation = Mock(Algorithm)"]}, @@ -142,7 +960,7 @@ { "id":"Devices store tensors which can also be restored.", "result":"PASS", - "duration":"0.005 seconds", + "duration":"0.029 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n A Device implementation keeps track of the number of tensors it \"owns\",\n which you can see below. This is basically just reference counting.\n\n Also note that in this example we are making some exceptions for the CPU device\n simply because it is the default device on which all tensors are being stored\n if no other device is specified.\n "] }, @@ -171,11 +989,40 @@ }, { - "id":"Devices store slices which can also be restored just like any other tensor.", + "id":"Devices store slices which can also be restored just like any other tensor. [0]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note that in this example we are making some exceptions for the CPU device\n simply because it is the default device on which all tensors are being stored\n if no other device is specified.\n "] + }, + "blocks":[ + {"kind":"given","text":"The given device is available and Neureka is being reset.","code":["if ( device == null ) return"]}, + + {"kind":"and","text":"Two tensors which will be transferred later on...","code":["int initialNumber = device.numberOfStored()","Tensor a = Tensor.of([2, 3], \";)\")","Tensor b = a[1, 0..2]"]}, + + {"kind":"expect","text":"The given device is initially empty.","code":["device.isEmpty() == ( device.numberOfStored() == 0 )","!device.has( a ) || device instanceof CPU","!device.has( b ) || device instanceof CPU"]}, + + {"kind":"when","text":"The the first tensor is being passed to the device...","code":["device.store( a )"]}, + + {"kind":"then","text":"...tensor \"a\" is now on the device.","code":["!device.isEmpty()","device.numberOfStored() == initialNumber + ( device instanceof CPU ? 2 : 1 )","device.has( a )"]}, + + {"kind":"and","text":"","code":["!device.has( b ) || device instanceof CPU"]}, + + {"kind":"when","text":"","code":["device.free( a )"]}, + + {"kind":"then","text":"...the device is empty again.","code":["device.isEmpty() == (( initialNumber == 0 ) && !( device instanceof CPU ))","device.numberOfStored() == initialNumber + ( device instanceof CPU ? 1 : 0 )","!device.has( a ) || device instanceof CPU","!device.has( b ) || device instanceof CPU"]}, + + {"kind":"where","text":"The following Device instances are being tested :","code":{"device":["CPU.get()","Device.get( \"openCL\" )"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Devices store slices which can also be restored just like any other tensor. [1]", "result":"PASS", - "duration":"0.007 seconds", + "duration":"0", "iterations":{ - "tags":{},"see":[],"extraInfo":["\n Note that in this example we are making some exceptions for the CPU device\n simply because it is the default device on which all tensors are being stored\n if no other device is specified.\n ","\n Note that in this example we are making some exceptions for the CPU device\n simply because it is the default device on which all tensors are being stored\n if no other device is specified.\n "] + "tags":{},"see":[],"extraInfo":[] }, "blocks":[ {"kind":"given","text":"The given device is available and Neureka is being reset.","code":["if ( device == null ) return"]}, @@ -200,9 +1047,48 @@ }, { - "id":"A device will keep track of the amount of tensors and data objects it stores.", + "id":"A device will keep track of the amount of tensors and data objects it stores. [0]", + "result":"PASS", + "duration":"5.953 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We note the initial amount of tensors stored on the device.","code":["System.gc()","Sleep.until(5_000, 100, {CPU.get().numberOfStored() == 0})","int initial = device.numberOfStored()","int initialDataObjects = device.numberOfDataObjects()"]}, + + {"kind":"when","text":"We first create a data object...","code":["var data = Data.of( 42, 73, 11, 7 )"]}, + + {"kind":"then","text":"The CPU should not have stored any tensors yet.","code":["device.numberOfStored() == initial"]}, + + {"kind":"when","text":"We create a tensor from the data object...","code":["var t = Tensor.of( Shape.of(2, 2), data ).to(device)"]}, + + {"kind":"then","text":"The device should know about the existence of a new tensor.","code":["device.numberOfStored() == initial + 1"]}, + + {"kind":"and","text":"The number of data objects stored on the device should also be increased.","code":["device.numberOfDataObjects() == initialDataObjects + 1"]}, + + {"kind":"when","text":"We create a new tensor from the first one...","code":["var t2 = t * 2"]}, + + {"kind":"then","text":"The device should know about the existence of a new tensor as well as the data objects.","code":["device.numberOfStored() == initial + 2","device.numberOfDataObjects() == initialDataObjects + 2"]}, + + {"kind":"when","text":"We however create a new reshaped version of the first tensor...","code":["var t3 = t.reshape( 4 )"]}, + + {"kind":"then","text":"The device should also know about the existence of a new tensor, but not a new data object.","code":["device.numberOfStored() == initial + 3","device.numberOfDataObjects() == initialDataObjects + 2"]}, + + {"kind":"when","text":"We delete the references to the tensors, and then give the GC some time to do its job...","code":["t = null","t2 = null","t3 = null","System.gc()","Thread.sleep( 128 )","Sleep.until(1028, {device.numberOfStored() == initial})"]}, + + {"kind":"then","text":"The device should have forgotten about the tensors.","code":["device.numberOfStored() == initial"]}, + + {"kind":"and","text":"The device should have forgotten about the data objects as well.","code":["device.numberOfDataObjects() == initialDataObjects"]}, + + {"kind":"where","text":"The following Device instances are being tested :","code":{"device":["CPU.get()","Device.get( \"openCL\" )"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A device will keep track of the amount of tensors and data objects it stores. [1]", "result":"PASS", - "duration":"0.374 seconds", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -272,11 +1158,42 @@ }, { - "id":"Virtual tensors stay virtual when outsourced.", + "id":"Virtual tensors stay virtual when outsourced. [0]", "result":"PASS", - "duration":"0.006 seconds", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Note: A virtual tensor is a tensor which is not yet fully initialized\n in the sense that the data array is not yet allocated according to the \n tensors size (number of elements they hold).\n This is the case for tensor which are filled homogeneously with a single value,\n like for example an all 0 tensor. \n "] + }, + "blocks":[ + {"kind":"given","text":"We create a homogeneously filled tensor, which is therefor \"virtual\".","code":["var t = Tensor.ofFloats().withShape(4,3).all(-0.54f)"]}, + + {"kind":"and","text":"We also get a device for testing...","code":["var device = Device.get(deviceType)"]}, + + {"kind":"expect","text":"We expect that the tensor is virtual, meaning its underlying data array stores only a single value...","code":["t.isVirtual()"]}, + + {"kind":"when","text":"We send the tensor to the device...","code":["t.to(device)"]}, + + {"kind":"then","text":"This should cause it to be \"outsourced\", (except dor a CPU device of course).","code":["t.isOutsourced() != ( device instanceof CPU )"]}, + + {"kind":"and","text":"...we expect the tensor to stay virtual on the device!","code":["t.isVirtual()"]}, + + {"kind":"when","text":"We restore the device...","code":["device.restore(t)"]}, + + {"kind":"then","text":"The tensor should no longer be outsourced.","code":["!t.isOutsourced()"]}, + + {"kind":"and","text":"It should still be virtual!","code":["t.isVirtual()"]}, + + {"kind":"where","text":"We test on the following devices:","code":{"deviceType":["'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Virtual tensors stay virtual when outsourced. [1]", + "result":"PASS", + "duration":"0.001 seconds", "iterations":{ - "tags":{},"see":[],"extraInfo":["\n Note: A virtual tensor is a tensor which is not yet fully initialized\n in the sense that the data array is not yet allocated according to the \n tensors size (number of elements they hold).\n This is the case for tensor which are filled homogeneously with a single value,\n like for example an all 0 tensor. \n ","\n Note: A virtual tensor is a tensor which is not yet fully initialized\n in the sense that the data array is not yet allocated according to the \n tensors size (number of elements they hold).\n This is the case for tensor which are filled homogeneously with a single value,\n like for example an all 0 tensor. \n "] + "tags":{},"see":[],"extraInfo":[] }, "blocks":[ {"kind":"given","text":"We create a homogeneously filled tensor, which is therefor \"virtual\".","code":["var t = Tensor.ofFloats().withShape(4,3).all(-0.54f)"]}, diff --git a/docs/spock/reports/ut.device.FileDevice_Spec.json b/docs/spock/reports/ut.device.FileDevice_Spec.json index 348c95aa3..740726677 100644 --- a/docs/spock/reports/ut.device.FileDevice_Spec.json +++ b/docs/spock/reports/ut.device.FileDevice_Spec.json @@ -1,22 +1,22 @@ { "className":"ut.device.FileDevice_Spec", "title":"FileDevice, Storing Tensors in Files", - "narrative":"The `FileDevice` class, one of many implementations of the `Device` interface,\n represents a file directory which can store and load tensors as files (`idx`, `jpg`, `png`...).", + "narrative":"The `FileDevice` class, one of many implementations of the `Device` interface, \n represents a file directory which can store and load tensors as files (`idx`, `jpg`, `png`...).", "subjects":["neureka.devices.file.FileDevice","neureka.devices.Device"], "statistics":{ - "runs":"4", + "runs":"12", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.048 seconds" + "duration":"0.161 seconds" }, "headers":["\n This specification covers the behavior of the `FileDevice`\n class, which enables the persistence of tensor data. \n "],"tags":{},"see":[], "features":[ { "id":"A file device stores tensors in idx files by default.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.007 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -43,9 +43,9 @@ }, { - "id":"A file device stores tensors in various file formats.", + "id":"A file device stores tensors in various file formats. [0]", "result":"PASS", - "duration":"0.025 seconds", + "duration":"0.009 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -78,12 +78,152 @@ }, { - "id":"The file device can load known files in a directory.", + "id":"A file device stores tensors in various file formats. [1]", + "result":"PASS", + "duration":"0.062 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new tensor is being created for testing.","code":["var a = Tensor.of( Shape.of(shape), -8d..8d )"]}, + + {"kind":"and","text":"A String representation of the shape.","code":["var shapeStr = String.join('x',(shape as List).collect {String.valueOf(it)})"]}, + + {"kind":"and","text":"A file device instance is being accessed for a given path.","code":["var device = FileDevice.at( path )"]}, + + {"kind":"expect","text":"Initially the device does not store our newly created tensor.","code":["!device.contains(a)"]}, + + {"kind":"when","text":"Tensor \"a\" is being stored in the device...","code":["if ( filename != null ) device.store( a, filename ) else device.store( a )"]}, + + {"kind":"then","text":"The expected file is being created at the given path.","code":["new File( path + '/' + filename ).exists()"," ||","new File( path ).listFiles().any {"," it.name.startsWith('tensor_' + shapeStr + '_f64_') && it.name.endsWith('.idx')","}"]}, + + {"kind":"and","text":"Tensor \"a\" does no longer have a value (stored in RAM).","code":["a.mut.data.getOrNull() == null"]}, + + {"kind":"and","text":"The tensor is now of the expected data-type.","code":["a.dataType == DataType.of( dataTypeClass )"]}, + + {"kind":"and","text":"The device contains a \"FileHandle\" instances of the expected type.","code":["device.fileHandleOf( a ).class == fileHandleClass"]}, + + {"kind":"when","text":"Freeing the tensor...","code":["device.free( a )"]}, + + {"kind":"then","text":"The file will be deleted!","code":["!new File( path + '/' + filename ).exists()","!new File( path ).listFiles().any {it.name.startsWith('tensor_' + shapeStr + '_f64_') }"]}, + + {"kind":"where","text":"The following parameters are being used:","code":{"path":["\"build/test-can\"","\"build/test-can\"","\"build/test-can\"","\"build/test-can\"","\"build/test-can\""],"filename":["\"tensor_2x4x3_.idx\"","\"tensor_2x4x3_.jpg\"","\"tensor_5x3x4_.png\"","null","\"tensor_4x3_.csv\""],"shape":["[2,4,3]","[2,4,3]","[5,3,4]","[2,4,3]","[4,3]"],"fileHandleClass":["IDXHandle.class","JPEGHandle.class","PNGHandle.class","IDXHandle.class","CSVHandle.class"],"dataTypeClass":["F64.class","UI8.class","UI8.class","F64.class","String.class"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A file device stores tensors in various file formats. [2]", "result":"PASS", "duration":"0.006 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"given","text":"A new tensor is being created for testing.","code":["var a = Tensor.of( Shape.of(shape), -8d..8d )"]}, + + {"kind":"and","text":"A String representation of the shape.","code":["var shapeStr = String.join('x',(shape as List).collect {String.valueOf(it)})"]}, + + {"kind":"and","text":"A file device instance is being accessed for a given path.","code":["var device = FileDevice.at( path )"]}, + + {"kind":"expect","text":"Initially the device does not store our newly created tensor.","code":["!device.contains(a)"]}, + + {"kind":"when","text":"Tensor \"a\" is being stored in the device...","code":["if ( filename != null ) device.store( a, filename ) else device.store( a )"]}, + + {"kind":"then","text":"The expected file is being created at the given path.","code":["new File( path + '/' + filename ).exists()"," ||","new File( path ).listFiles().any {"," it.name.startsWith('tensor_' + shapeStr + '_f64_') && it.name.endsWith('.idx')","}"]}, + + {"kind":"and","text":"Tensor \"a\" does no longer have a value (stored in RAM).","code":["a.mut.data.getOrNull() == null"]}, + + {"kind":"and","text":"The tensor is now of the expected data-type.","code":["a.dataType == DataType.of( dataTypeClass )"]}, + + {"kind":"and","text":"The device contains a \"FileHandle\" instances of the expected type.","code":["device.fileHandleOf( a ).class == fileHandleClass"]}, + + {"kind":"when","text":"Freeing the tensor...","code":["device.free( a )"]}, + + {"kind":"then","text":"The file will be deleted!","code":["!new File( path + '/' + filename ).exists()","!new File( path ).listFiles().any {it.name.startsWith('tensor_' + shapeStr + '_f64_') }"]}, + + {"kind":"where","text":"The following parameters are being used:","code":{"path":["\"build/test-can\"","\"build/test-can\"","\"build/test-can\"","\"build/test-can\"","\"build/test-can\""],"filename":["\"tensor_2x4x3_.idx\"","\"tensor_2x4x3_.jpg\"","\"tensor_5x3x4_.png\"","null","\"tensor_4x3_.csv\""],"shape":["[2,4,3]","[2,4,3]","[5,3,4]","[2,4,3]","[4,3]"],"fileHandleClass":["IDXHandle.class","JPEGHandle.class","PNGHandle.class","IDXHandle.class","CSVHandle.class"],"dataTypeClass":["F64.class","UI8.class","UI8.class","F64.class","String.class"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A file device stores tensors in various file formats. [3]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new tensor is being created for testing.","code":["var a = Tensor.of( Shape.of(shape), -8d..8d )"]}, + + {"kind":"and","text":"A String representation of the shape.","code":["var shapeStr = String.join('x',(shape as List).collect {String.valueOf(it)})"]}, + + {"kind":"and","text":"A file device instance is being accessed for a given path.","code":["var device = FileDevice.at( path )"]}, + + {"kind":"expect","text":"Initially the device does not store our newly created tensor.","code":["!device.contains(a)"]}, + + {"kind":"when","text":"Tensor \"a\" is being stored in the device...","code":["if ( filename != null ) device.store( a, filename ) else device.store( a )"]}, + + {"kind":"then","text":"The expected file is being created at the given path.","code":["new File( path + '/' + filename ).exists()"," ||","new File( path ).listFiles().any {"," it.name.startsWith('tensor_' + shapeStr + '_f64_') && it.name.endsWith('.idx')","}"]}, + + {"kind":"and","text":"Tensor \"a\" does no longer have a value (stored in RAM).","code":["a.mut.data.getOrNull() == null"]}, + + {"kind":"and","text":"The tensor is now of the expected data-type.","code":["a.dataType == DataType.of( dataTypeClass )"]}, + + {"kind":"and","text":"The device contains a \"FileHandle\" instances of the expected type.","code":["device.fileHandleOf( a ).class == fileHandleClass"]}, + + {"kind":"when","text":"Freeing the tensor...","code":["device.free( a )"]}, + + {"kind":"then","text":"The file will be deleted!","code":["!new File( path + '/' + filename ).exists()","!new File( path ).listFiles().any {it.name.startsWith('tensor_' + shapeStr + '_f64_') }"]}, + + {"kind":"where","text":"The following parameters are being used:","code":{"path":["\"build/test-can\"","\"build/test-can\"","\"build/test-can\"","\"build/test-can\"","\"build/test-can\""],"filename":["\"tensor_2x4x3_.idx\"","\"tensor_2x4x3_.jpg\"","\"tensor_5x3x4_.png\"","null","\"tensor_4x3_.csv\""],"shape":["[2,4,3]","[2,4,3]","[5,3,4]","[2,4,3]","[4,3]"],"fileHandleClass":["IDXHandle.class","JPEGHandle.class","PNGHandle.class","IDXHandle.class","CSVHandle.class"],"dataTypeClass":["F64.class","UI8.class","UI8.class","F64.class","String.class"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A file device stores tensors in various file formats. [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new tensor is being created for testing.","code":["var a = Tensor.of( Shape.of(shape), -8d..8d )"]}, + + {"kind":"and","text":"A String representation of the shape.","code":["var shapeStr = String.join('x',(shape as List).collect {String.valueOf(it)})"]}, + + {"kind":"and","text":"A file device instance is being accessed for a given path.","code":["var device = FileDevice.at( path )"]}, + + {"kind":"expect","text":"Initially the device does not store our newly created tensor.","code":["!device.contains(a)"]}, + + {"kind":"when","text":"Tensor \"a\" is being stored in the device...","code":["if ( filename != null ) device.store( a, filename ) else device.store( a )"]}, + + {"kind":"then","text":"The expected file is being created at the given path.","code":["new File( path + '/' + filename ).exists()"," ||","new File( path ).listFiles().any {"," it.name.startsWith('tensor_' + shapeStr + '_f64_') && it.name.endsWith('.idx')","}"]}, + + {"kind":"and","text":"Tensor \"a\" does no longer have a value (stored in RAM).","code":["a.mut.data.getOrNull() == null"]}, + + {"kind":"and","text":"The tensor is now of the expected data-type.","code":["a.dataType == DataType.of( dataTypeClass )"]}, + + {"kind":"and","text":"The device contains a \"FileHandle\" instances of the expected type.","code":["device.fileHandleOf( a ).class == fileHandleClass"]}, + + {"kind":"when","text":"Freeing the tensor...","code":["device.free( a )"]}, + + {"kind":"then","text":"The file will be deleted!","code":["!new File( path + '/' + filename ).exists()","!new File( path ).listFiles().any {it.name.startsWith('tensor_' + shapeStr + '_f64_') }"]}, + + {"kind":"where","text":"The following parameters are being used:","code":{"path":["\"build/test-can\"","\"build/test-can\"","\"build/test-can\"","\"build/test-can\"","\"build/test-can\""],"filename":["\"tensor_2x4x3_.idx\"","\"tensor_2x4x3_.jpg\"","\"tensor_5x3x4_.png\"","null","\"tensor_4x3_.csv\""],"shape":["[2,4,3]","[2,4,3]","[5,3,4]","[2,4,3]","[4,3]"],"fileHandleClass":["IDXHandle.class","JPEGHandle.class","PNGHandle.class","IDXHandle.class","CSVHandle.class"],"dataTypeClass":["F64.class","UI8.class","UI8.class","F64.class","String.class"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The file device can load known files in a directory.", + "result":"PASS", + "duration":"0.017 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"A file device instance is being accessed for a simple test path.","code":["def device = FileDevice.at( 'build/resources/test/csv' )"]}, @@ -101,9 +241,141 @@ }, { - "id":"A tensor loaded from a file device can be loaded again.", + "id":"A tensor loaded from a file device can be loaded again. [0]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A file device instance is being accessed for a simple test path.","code":["def device = FileDevice.at( 'build/resources/test/idx2' )"]}, + + {"kind":"and","text":"We create a simple tensor which we want to save.","code":["var t = Tensor.of(shape, data)"]}, + + {"kind":"expect","text":"","code":["device.directory == 'build/resources/test/idx2'","t.getDevice() === CPU.get()","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We save the tensor to the device.","code":["device.store( t, 'my-tensor-file.idx' )"]}, + + {"kind":"then","text":"The device contains the expected tensor.","code":["device.has( t )","t.isOutsourced()","t.getDevice() === device","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We load the tensor from the device.","code":["device.restore( t )"]}, + + {"kind":"then","text":"The tensor is restored correctly.","code":["!device.has( t )","!t.isOutsourced()","t.getDevice() === CPU.get()","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We store the tensor again.","code":["device.store( t, 'my-tensor-file.idx' )"]}, + + {"kind":"then","text":"The tensor is stored correctly.","code":["device.has( t )","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"cleanup","text":"We delete the file again.","code":["new File( 'build/resources/test/idx2/my-tensor-file.idx' ).delete()"]}, + + {"kind":"where","text":"We use the following shapes and data arrays:","code":{"shape":["Shape.of(2, 3)","Shape.of(3, 2)","Shape.of(4)","Shape.of(5)","Shape.of(2)"],"data":["Data.of( 1, 2, 3, 4, 5, 6 )","Data.of( -1, -2, -3, -4, -5, -6 )","Data.of( 1.1, 2.2, 3.3, 4.4 )","Data.of( 0.3f, 0.4f, 0.5f, 0.6f, 0.7f )","Data.of( 42L, -7L )"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A tensor loaded from a file device can be loaded again. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A file device instance is being accessed for a simple test path.","code":["def device = FileDevice.at( 'build/resources/test/idx2' )"]}, + + {"kind":"and","text":"We create a simple tensor which we want to save.","code":["var t = Tensor.of(shape, data)"]}, + + {"kind":"expect","text":"","code":["device.directory == 'build/resources/test/idx2'","t.getDevice() === CPU.get()","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We save the tensor to the device.","code":["device.store( t, 'my-tensor-file.idx' )"]}, + + {"kind":"then","text":"The device contains the expected tensor.","code":["device.has( t )","t.isOutsourced()","t.getDevice() === device","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We load the tensor from the device.","code":["device.restore( t )"]}, + + {"kind":"then","text":"The tensor is restored correctly.","code":["!device.has( t )","!t.isOutsourced()","t.getDevice() === CPU.get()","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We store the tensor again.","code":["device.store( t, 'my-tensor-file.idx' )"]}, + + {"kind":"then","text":"The tensor is stored correctly.","code":["device.has( t )","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"cleanup","text":"We delete the file again.","code":["new File( 'build/resources/test/idx2/my-tensor-file.idx' ).delete()"]}, + + {"kind":"where","text":"We use the following shapes and data arrays:","code":{"shape":["Shape.of(2, 3)","Shape.of(3, 2)","Shape.of(4)","Shape.of(5)","Shape.of(2)"],"data":["Data.of( 1, 2, 3, 4, 5, 6 )","Data.of( -1, -2, -3, -4, -5, -6 )","Data.of( 1.1, 2.2, 3.3, 4.4 )","Data.of( 0.3f, 0.4f, 0.5f, 0.6f, 0.7f )","Data.of( 42L, -7L )"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A tensor loaded from a file device can be loaded again. [2]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A file device instance is being accessed for a simple test path.","code":["def device = FileDevice.at( 'build/resources/test/idx2' )"]}, + + {"kind":"and","text":"We create a simple tensor which we want to save.","code":["var t = Tensor.of(shape, data)"]}, + + {"kind":"expect","text":"","code":["device.directory == 'build/resources/test/idx2'","t.getDevice() === CPU.get()","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We save the tensor to the device.","code":["device.store( t, 'my-tensor-file.idx' )"]}, + + {"kind":"then","text":"The device contains the expected tensor.","code":["device.has( t )","t.isOutsourced()","t.getDevice() === device","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We load the tensor from the device.","code":["device.restore( t )"]}, + + {"kind":"then","text":"The tensor is restored correctly.","code":["!device.has( t )","!t.isOutsourced()","t.getDevice() === CPU.get()","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We store the tensor again.","code":["device.store( t, 'my-tensor-file.idx' )"]}, + + {"kind":"then","text":"The tensor is stored correctly.","code":["device.has( t )","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"cleanup","text":"We delete the file again.","code":["new File( 'build/resources/test/idx2/my-tensor-file.idx' ).delete()"]}, + + {"kind":"where","text":"We use the following shapes and data arrays:","code":{"shape":["Shape.of(2, 3)","Shape.of(3, 2)","Shape.of(4)","Shape.of(5)","Shape.of(2)"],"data":["Data.of( 1, 2, 3, 4, 5, 6 )","Data.of( -1, -2, -3, -4, -5, -6 )","Data.of( 1.1, 2.2, 3.3, 4.4 )","Data.of( 0.3f, 0.4f, 0.5f, 0.6f, 0.7f )","Data.of( 42L, -7L )"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A tensor loaded from a file device can be loaded again. [3]", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A file device instance is being accessed for a simple test path.","code":["def device = FileDevice.at( 'build/resources/test/idx2' )"]}, + + {"kind":"and","text":"We create a simple tensor which we want to save.","code":["var t = Tensor.of(shape, data)"]}, + + {"kind":"expect","text":"","code":["device.directory == 'build/resources/test/idx2'","t.getDevice() === CPU.get()","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We save the tensor to the device.","code":["device.store( t, 'my-tensor-file.idx' )"]}, + + {"kind":"then","text":"The device contains the expected tensor.","code":["device.has( t )","t.isOutsourced()","t.getDevice() === device","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We load the tensor from the device.","code":["device.restore( t )"]}, + + {"kind":"then","text":"The tensor is restored correctly.","code":["!device.has( t )","!t.isOutsourced()","t.getDevice() === CPU.get()","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"when","text":"We store the tensor again.","code":["device.store( t, 'my-tensor-file.idx' )"]}, + + {"kind":"then","text":"The tensor is stored correctly.","code":["device.has( t )","device.loadable.toSet() == [].toSet() // If this fails: consider deleting the build folder!!","device.loaded == []"]}, + + {"kind":"cleanup","text":"We delete the file again.","code":["new File( 'build/resources/test/idx2/my-tensor-file.idx' ).delete()"]}, + + {"kind":"where","text":"We use the following shapes and data arrays:","code":{"shape":["Shape.of(2, 3)","Shape.of(3, 2)","Shape.of(4)","Shape.of(5)","Shape.of(2)"],"data":["Data.of( 1, 2, 3, 4, 5, 6 )","Data.of( -1, -2, -3, -4, -5, -6 )","Data.of( 1.1, 2.2, 3.3, 4.4 )","Data.of( 0.3f, 0.4f, 0.5f, 0.6f, 0.7f )","Data.of( 42L, -7L )"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A tensor loaded from a file device can be loaded again. [4]", + "result":"PASS", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.device.OpenCLDevice_Exception_Spec.json b/docs/spock/reports/ut.device.OpenCLDevice_Exception_Spec.json index 2455c9edf..f9515ccae 100644 --- a/docs/spock/reports/ut.device.OpenCLDevice_Exception_Spec.json +++ b/docs/spock/reports/ut.device.OpenCLDevice_Exception_Spec.json @@ -1,21 +1,21 @@ { "className":"ut.device.OpenCLDevice_Exception_Spec", "title":"OpenCLDevice Exception Handling", - "narrative":"The OpenCLDevice class, one of many implementations of the Device interface,\n represents physical OpenCL devices.\n This specification defines how instances of this class deal with exceptional information.", + "narrative":"The OpenCLDevice class, one of many implementations of the Device interface, \n represents physical OpenCL devices.\n This specification defines how instances of this class deal with exceptional information.", "subjects":[], "statistics":{ - "runs":"4", + "runs":"0", "successRate":"100.0%", "failures":"0", "errors":"0", - "skipped":"0", - "duration":"0.079 seconds" + "skipped":"4", + "duration":"0.006 seconds" }, "headers":["\n

        \n It is important that an OpenCLDevice gives insightful error messages\n when encountering exceptional situations.\n

        \n "],"tags":{},"see":[], "features":[ { "id":"An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.", - "result":"PASS", + "result":"IGNORED", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] @@ -38,8 +38,8 @@ { "id":"Ad hoc compilation produces expected exceptions.", - "result":"PASS", - "duration":"0.039 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -59,8 +59,8 @@ { "id":"Ad hoc compilation produces expected exceptions when duplication is found.", - "result":"PASS", - "duration":"0.036 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -84,7 +84,7 @@ { "id":"Trying to restore a tensor which is not on a device raises exception.", - "result":"PASS", + "result":"IGNORED", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] diff --git a/docs/spock/reports/ut.device.OpenCLDevice_Spec.json b/docs/spock/reports/ut.device.OpenCLDevice_Spec.json index d09cbc0fb..1700a270e 100644 --- a/docs/spock/reports/ut.device.OpenCLDevice_Spec.json +++ b/docs/spock/reports/ut.device.OpenCLDevice_Spec.json @@ -4,19 +4,19 @@ "narrative":"Tensors need devices for execution!\n By default tensors use the `CPU` device, but sometimes we want to\n use something more suitable for large amounts of data and a high degree of parallelization.\n This is were the `OpenCLDevice` comes into play!\n It is a `Device` implementation built on top of the JOCL library, a thin OpenCL API.\n We expect the `OpenCLDevice` to store tensors as well as being able to read and write\n data from and to stored tensors.\n Also, an `OpenCLDevice` should allows us to compile OpenCL kernel code on the fly...", "subjects":[], "statistics":{ - "runs":"8", + "runs":"0", "successRate":"100.0%", "failures":"0", "errors":"0", - "skipped":"0", - "duration":"2.217 seconds" + "skipped":"8", + "duration":"0.006 seconds" }, "headers":["\n Specified below are strict tests covering the behavior\n of the OpenCLDevice when hosting tensors and executing \n operations on them.\n "],"tags":{},"see":[], "features":[ { "id":"An OpenCLDevice loads tensors in a provided lambda temporarily.", - "result":"PASS", - "duration":"0.002 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -40,8 +40,8 @@ { "id":"We can get the items of an outsourced tensor as a primitive array.", - "result":"PASS", - "duration":"0.001 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -65,7 +65,7 @@ { "id":"We can take a look at the underlying data array of an outsourced tensor through the unsafe API.", - "result":"PASS", + "result":"IGNORED", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] @@ -86,8 +86,8 @@ { "id":"Ad hoc compilation produces executable kernel.", - "result":"PASS", - "duration":"0.040 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -111,8 +111,8 @@ { "id":"Ad hoc compilation works for WIP general purpose matrix multiplication.", - "result":"PASS", - "duration":"0.298 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -138,8 +138,8 @@ { "id":"Ad hoc matrix multiplication works for multiple of 16 matrices.", - "result":"PASS", - "duration":"0.188 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -165,8 +165,8 @@ { "id":"Ad hoc compilation works for custom simple row major based matrix multiplication.", - "result":"PASS", - "duration":"0.328 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -192,8 +192,8 @@ { "id":"Ad hoc compilation works for custom column major based tiled matrix multiplication.", - "result":"PASS", - "duration":"1.334 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.device.OpenCL_Spec.json b/docs/spock/reports/ut.device.OpenCL_Spec.json index 5d6428738..ed8302fbd 100644 --- a/docs/spock/reports/ut.device.OpenCL_Spec.json +++ b/docs/spock/reports/ut.device.OpenCL_Spec.json @@ -4,19 +4,19 @@ "narrative":"Neureka models the OpenCL API through various types of classes.\n The most fundamental of these is the `OpenCLDevice` class which\n represents a single device with OpenCL support.\n Besides that, there is also the `OpenCLContext` class which\n represents a OpenCL contexts, platforms and multiple devices on said platforms...", "subjects":["neureka.backend.ocl.CLBackend","neureka.devices.opencl.OpenCLDevice","neureka.devices.opencl.utility.DeviceQuery","neureka.devices.Device"], "statistics":{ - "runs":"5", + "runs":"0", "successRate":"100.0%", "failures":"0", "errors":"0", - "skipped":"0", - "duration":"3.009 seconds" + "skipped":"5", + "duration":"0.001 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"First found OpenCLDevice will have realistic properties inside summary query.", - "result":"PASS", - "duration":"0.001 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -30,7 +30,7 @@ { "id":"First found OpenCLDevice will have realistic numeric properties.", - "result":"PASS", + "result":"IGNORED", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] @@ -45,7 +45,7 @@ { "id":"First found OpenCLDevice will have realistic text properties.", - "result":"PASS", + "result":"IGNORED", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] @@ -60,8 +60,8 @@ { "id":"An OpenCLDevice will throw an exception when trying to add a tensor whose \"data parent\" is not outsourced.", - "result":"PASS", - "duration":"0.020 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -89,8 +89,8 @@ { "id":"A given OpenCL context can be disposed!", - "result":"PASS", - "duration":"2.984 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.device.internal.CLFunctionCompiler_Spec.json b/docs/spock/reports/ut.device.internal.CLFunctionCompiler_Spec.json index acb9de068..bd332708a 100644 --- a/docs/spock/reports/ut.device.internal.CLFunctionCompiler_Spec.json +++ b/docs/spock/reports/ut.device.internal.CLFunctionCompiler_Spec.json @@ -4,19 +4,19 @@ "narrative":"Neureka parses mathematical expressions into an AST representation\n hidden behind the Function interface...\n This feature does not exist without reason, we can use\n this abstract syntax tree to compile to OpenCL kernels\n for optimal execution speed!", "subjects":[], "statistics":{ - "runs":"4", + "runs":"2", "successRate":"100.0%", "failures":"0", "errors":"0", - "skipped":"0", - "duration":"0.170 seconds" + "skipped":"2", + "duration":"0.206 seconds" }, "headers":["\n Specified below are strict tests for covering the ability of \n OpenCL devices to be able produce optimized functions given\n a normal function instance created from a String...\n "],"tags":{},"see":[], "features":[ { "id":"The OpenCLDevice produces a working optimized Function for doubles.", - "result":"PASS", - "duration":"0.046 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -42,8 +42,8 @@ { "id":"The OpenCLDevice produces a working optimized Function for floats.", - "result":"PASS", - "duration":"0.044 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -70,7 +70,7 @@ { "id":"The CLFunctionCompiler produces an operation which properly integrates to the backend.", "result":"PASS", - "duration":"0.056 seconds", + "duration":"0.151 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -129,7 +129,7 @@ { "id":"The CLFunctionCompiler produces the expected \"ad hoc\" kernel.", "result":"PASS", - "duration":"0.021 seconds", + "duration":"0.051 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.device.internal.CPU_Kernel_Spec.json b/docs/spock/reports/ut.device.internal.CPU_Kernel_Spec.json index 3ab554c47..dfdc02d73 100644 --- a/docs/spock/reports/ut.device.internal.CPU_Kernel_Spec.json +++ b/docs/spock/reports/ut.device.internal.CPU_Kernel_Spec.json @@ -4,19 +4,19 @@ "narrative":"", "subjects":["neureka.backend.main.operations.other.internal.CPUReduce"], "statistics":{ - "runs":"2", + "runs":"20", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.027 seconds" + "duration":"0.083 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"The Reduce implementation for the CPU has realistic behaviour", + "id":"The Reduce implementation for the CPU has realistic behaviour [0]", "result":"PASS", - "duration":"0.008 seconds", + "duration":"0.014 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,349 @@ }, { - "id":"The Sum implementation for the CPU has realistic behaviour", + "id":"The Reduce implementation for the CPU has realistic behaviour [1]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.name().hashCode()","var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})","var call = ExecutionCall.of(a).running(Mock(Operation)).on(CPU.get())"]}, + + {"kind":"when","text":"","code":["var index = new CPUReduce(reduceType).run( call )","var result = a.item(index.item() as int)"]}, + + {"kind":"then","text":"","code":["result == expected"]}, + + 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[20]", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.device.internal.OpenCL_Kernel_Unit_Spec.json b/docs/spock/reports/ut.device.internal.OpenCL_Kernel_Unit_Spec.json index 5adc1c2f5..983fd7ad4 100644 --- a/docs/spock/reports/ut.device.internal.OpenCL_Kernel_Unit_Spec.json +++ b/docs/spock/reports/ut.device.internal.OpenCL_Kernel_Unit_Spec.json @@ -4,19 +4,19 @@ "narrative":"", "subjects":["neureka.backend.main.operations.linear.internal.opencl.CLGEMM","neureka.backend.main.operations.linear.internal.opencl.CLReduce"], "statistics":{ - "runs":"5", + "runs":"6", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.012 seconds" + "duration":"0.055 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"The GEMM implementation for the OpenCLDevice has realistic behaviour", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.006 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -31,7 +31,28 @@ }, { - "id":"The Reduce implementation for the OpenCLDevice has realistic behaviour", + "id":"The Reduce implementation for the OpenCLDevice has realistic behaviour [0]", + "result":"PASS", + "duration":"0.011 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var a = Tensor.ofFloats().withShape(19, 7).andWhere({ i, _ -> (1+(7**i)%30)})","var call = Mock(ExecutionCall)","var device = Mock(OpenCLDevice)","var kernel = Mock(KernelCaller)"]}, + + {"kind":"when","text":"","code":["new CLReduce(type).run( call )"]}, + + {"kind":"then","text":"","code":["_ * call.input(0) >> a","(1.._) * call.input(Float, 0) >> a","(1.._) * call.getDevice() >> device","(1.._) * device.maxWorkGroupSize() >> 64","(0.._) * device.hasAdHocKernel(\"fast_${type.name().toLowerCase()}_reduce_RTS64\") >>> [false, true]","(0.._) * device.findAdHocKernel(\"fast_${type.name().toLowerCase()}_reduce_RTS64\") >> Optional.of(kernel)","(0.._) * device.compileAdHocKernel(\"fast_${type.name().toLowerCase()}_reduce_RTS64\", _) >> device","(0.._) * device.findOrCompileAdHocKernel(\"fast_${type.name().toLowerCase()}_reduce_RTS64\", _) >> kernel","(0.._) * device.compileAndGetAdHocKernel(\"fast_${type.name().toLowerCase()}_reduce_RTS64\", _) >> kernel","(0.._) * device.getAdHocKernel(\"fast_${type.name().toLowerCase()}_reduce_RTS64\") >> kernel","(3.._) * kernel.pass(_) >> kernel"]}, + + {"kind":"and","text":"","code":["(0.._) * device.hasAdHocKernel(CLReduce.INDICES_MAPPER_ID) >>> [false, true]","(0.._) * device.compileAdHocKernel(CLReduce.INDICES_MAPPER_ID, _) >> device","(0.._) * device.compileAndGetAdHocKernel(CLReduce.INDICES_MAPPER_ID, _) >> kernel","(0.._) * device.findOrCompileAdHocKernel(CLReduce.INDICES_MAPPER_ID, _) >> kernel","(0.._) * device.getAdHocKernel(CLReduce.INDICES_MAPPER_ID) >> kernel"]}, + + {"kind":"where","text":"","code":{"type":["CLReduce.Type.MIN","CLReduce.Type.MAX"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The Reduce implementation for the OpenCLDevice has realistic behaviour [1]", "result":"PASS", "duration":"0.002 seconds", "iterations":{ @@ -54,7 +75,7 @@ { "id":"The Sum implementation for the OpenCLDevice has realistic behaviour", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.010 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -71,7 +92,7 @@ { "id":"The Sum implementation for the OpenCLDevice has realistic behaviour for when the number of elements is a prime.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.007 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -88,7 +109,7 @@ { "id":"The CLDot implementation for the OpenCLDevice has realistic behaviour", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.011 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.dtype.DataType_Spec.json b/docs/spock/reports/ut.dtype.DataType_Spec.json index 53c1acf54..ebea7b40e 100644 --- a/docs/spock/reports/ut.dtype.DataType_Spec.json +++ b/docs/spock/reports/ut.dtype.DataType_Spec.json @@ -4,17 +4,206 @@ "narrative":"", "subjects":["neureka.dtype.DataType"], "statistics":{ - "runs":"1", + "runs":"10", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.005 seconds" + "duration":"0.010 seconds" }, "headers":["\n This specification tests the \"DataType\" class, which hosts a multiton\n design pattern in order to guarantee uniqueness of instances of the type\n which represent the same class type.
        \n Instances of this class wrap a Class variable which is the type of the data of the tensor.
        \n (The following types are usually used : UI8, I8, UI16, I16, UI32, I64, I32, F32, F64 )\n "],"tags":{},"see":[], "features":[ { - "id":"DataType multi-ton instances behave as expected.", + "id":"DataType multi-ton instances behave as expected. [0]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A \"DataType\" instance representing / wrapping the relevant datatype Class passed to \"instance(...).\"","code":["DataType dt = DataType.of( typeClass )"]}, + + {"kind":"expect","text":"The found instance is not null!","code":["dt != null"]}, + + {"kind":"and","text":"It contains the Class that it represents.","code":["dt.getRepresentativeType() == targetClass"]}, + + {"kind":"and","text":"This class either does or does not implement the \"NumericType\" interface.","code":["dt.typeClassImplements(NumericType.class) == isNumericType"]}, + + {"kind":"where","text":"The following data is being used :","code":{"typeClass":["I16.class","UI8.class","Float.class","Double.class","Short.class","Byte.class","String.class","Date.class","Object.class","Specification.class"],"targetClass":["I16.class","UI8.class","F32.class","F64.class","I16.class","I8.class","String.class","Date.class","Object.class","Specification.class"],"isNumericType":["true","true","true","true","true","true","false","false","false","false"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"DataType multi-ton instances behave as expected. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A \"DataType\" instance representing / wrapping the relevant datatype Class passed to \"instance(...).\"","code":["DataType dt = DataType.of( typeClass )"]}, + + {"kind":"expect","text":"The found instance is not null!","code":["dt != null"]}, + + {"kind":"and","text":"It contains the Class that it represents.","code":["dt.getRepresentativeType() == targetClass"]}, + + {"kind":"and","text":"This class either does or does not implement the \"NumericType\" interface.","code":["dt.typeClassImplements(NumericType.class) == isNumericType"]}, + + {"kind":"where","text":"The following data is being used :","code":{"typeClass":["I16.class","UI8.class","Float.class","Double.class","Short.class","Byte.class","String.class","Date.class","Object.class","Specification.class"],"targetClass":["I16.class","UI8.class","F32.class","F64.class","I16.class","I8.class","String.class","Date.class","Object.class","Specification.class"],"isNumericType":["true","true","true","true","true","true","false","false","false","false"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"DataType multi-ton instances behave as expected. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A \"DataType\" instance representing / wrapping the relevant datatype Class passed to \"instance(...).\"","code":["DataType dt = DataType.of( typeClass )"]}, + + {"kind":"expect","text":"The found instance is not null!","code":["dt != null"]}, + + {"kind":"and","text":"It contains the Class that it represents.","code":["dt.getRepresentativeType() == targetClass"]}, + + {"kind":"and","text":"This class either does or does not implement the \"NumericType\" interface.","code":["dt.typeClassImplements(NumericType.class) == isNumericType"]}, + + {"kind":"where","text":"The following data is being used :","code":{"typeClass":["I16.class","UI8.class","Float.class","Double.class","Short.class","Byte.class","String.class","Date.class","Object.class","Specification.class"],"targetClass":["I16.class","UI8.class","F32.class","F64.class","I16.class","I8.class","String.class","Date.class","Object.class","Specification.class"],"isNumericType":["true","true","true","true","true","true","false","false","false","false"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"DataType multi-ton instances behave as expected. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A \"DataType\" instance representing / wrapping the relevant datatype Class passed to \"instance(...).\"","code":["DataType dt = DataType.of( typeClass )"]}, + + {"kind":"expect","text":"The found instance is not null!","code":["dt != null"]}, + + {"kind":"and","text":"It contains the Class that it represents.","code":["dt.getRepresentativeType() == targetClass"]}, + + {"kind":"and","text":"This class either does or does not implement the \"NumericType\" interface.","code":["dt.typeClassImplements(NumericType.class) == isNumericType"]}, + + {"kind":"where","text":"The following data is being used :","code":{"typeClass":["I16.class","UI8.class","Float.class","Double.class","Short.class","Byte.class","String.class","Date.class","Object.class","Specification.class"],"targetClass":["I16.class","UI8.class","F32.class","F64.class","I16.class","I8.class","String.class","Date.class","Object.class","Specification.class"],"isNumericType":["true","true","true","true","true","true","false","false","false","false"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"DataType multi-ton instances behave as expected. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A \"DataType\" instance representing / wrapping the relevant datatype Class passed to \"instance(...).\"","code":["DataType dt = DataType.of( typeClass )"]}, + + {"kind":"expect","text":"The found instance is not null!","code":["dt != null"]}, + + {"kind":"and","text":"It contains the Class that it represents.","code":["dt.getRepresentativeType() == targetClass"]}, + + {"kind":"and","text":"This class either does or does not implement the \"NumericType\" interface.","code":["dt.typeClassImplements(NumericType.class) == isNumericType"]}, + + {"kind":"where","text":"The following data is being used :","code":{"typeClass":["I16.class","UI8.class","Float.class","Double.class","Short.class","Byte.class","String.class","Date.class","Object.class","Specification.class"],"targetClass":["I16.class","UI8.class","F32.class","F64.class","I16.class","I8.class","String.class","Date.class","Object.class","Specification.class"],"isNumericType":["true","true","true","true","true","true","false","false","false","false"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"DataType multi-ton instances behave as expected. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A \"DataType\" instance representing / wrapping the relevant datatype Class passed to \"instance(...).\"","code":["DataType dt = DataType.of( typeClass )"]}, + + {"kind":"expect","text":"The found instance is not null!","code":["dt != null"]}, + + {"kind":"and","text":"It contains the Class that it represents.","code":["dt.getRepresentativeType() == targetClass"]}, + + {"kind":"and","text":"This class either does or does not implement the \"NumericType\" interface.","code":["dt.typeClassImplements(NumericType.class) == isNumericType"]}, + + {"kind":"where","text":"The following data is being used :","code":{"typeClass":["I16.class","UI8.class","Float.class","Double.class","Short.class","Byte.class","String.class","Date.class","Object.class","Specification.class"],"targetClass":["I16.class","UI8.class","F32.class","F64.class","I16.class","I8.class","String.class","Date.class","Object.class","Specification.class"],"isNumericType":["true","true","true","true","true","true","false","false","false","false"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"DataType multi-ton instances behave as expected. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A \"DataType\" instance representing / wrapping the relevant datatype Class passed to \"instance(...).\"","code":["DataType dt = DataType.of( typeClass )"]}, + + {"kind":"expect","text":"The found instance is not null!","code":["dt != null"]}, + + {"kind":"and","text":"It contains the Class that it represents.","code":["dt.getRepresentativeType() == targetClass"]}, + + {"kind":"and","text":"This class either does or does not implement the \"NumericType\" interface.","code":["dt.typeClassImplements(NumericType.class) == isNumericType"]}, + + {"kind":"where","text":"The following data is being used :","code":{"typeClass":["I16.class","UI8.class","Float.class","Double.class","Short.class","Byte.class","String.class","Date.class","Object.class","Specification.class"],"targetClass":["I16.class","UI8.class","F32.class","F64.class","I16.class","I8.class","String.class","Date.class","Object.class","Specification.class"],"isNumericType":["true","true","true","true","true","true","false","false","false","false"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"DataType multi-ton instances behave as expected. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A \"DataType\" instance representing / wrapping the relevant datatype Class passed to \"instance(...).\"","code":["DataType dt = DataType.of( typeClass )"]}, + + {"kind":"expect","text":"The found instance is not null!","code":["dt != null"]}, + + {"kind":"and","text":"It contains the Class that it represents.","code":["dt.getRepresentativeType() == targetClass"]}, + + {"kind":"and","text":"This class either does or does not implement the \"NumericType\" interface.","code":["dt.typeClassImplements(NumericType.class) == isNumericType"]}, + + {"kind":"where","text":"The following data is being used :","code":{"typeClass":["I16.class","UI8.class","Float.class","Double.class","Short.class","Byte.class","String.class","Date.class","Object.class","Specification.class"],"targetClass":["I16.class","UI8.class","F32.class","F64.class","I16.class","I8.class","String.class","Date.class","Object.class","Specification.class"],"isNumericType":["true","true","true","true","true","true","false","false","false","false"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"DataType multi-ton instances behave as expected. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A \"DataType\" instance representing / wrapping the relevant datatype Class passed to \"instance(...).\"","code":["DataType dt = DataType.of( typeClass )"]}, + + {"kind":"expect","text":"The found instance is not null!","code":["dt != null"]}, + + {"kind":"and","text":"It contains the Class that it represents.","code":["dt.getRepresentativeType() == targetClass"]}, + + {"kind":"and","text":"This class either does or does not implement the \"NumericType\" interface.","code":["dt.typeClassImplements(NumericType.class) == isNumericType"]}, + + {"kind":"where","text":"The following data is being used :","code":{"typeClass":["I16.class","UI8.class","Float.class","Double.class","Short.class","Byte.class","String.class","Date.class","Object.class","Specification.class"],"targetClass":["I16.class","UI8.class","F32.class","F64.class","I16.class","I8.class","String.class","Date.class","Object.class","Specification.class"],"isNumericType":["true","true","true","true","true","true","false","false","false","false"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"DataType multi-ton instances behave as expected. [9]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.dtype.NumericType_Spec.json b/docs/spock/reports/ut.dtype.NumericType_Spec.json index 593e07483..ab2ce5f2c 100644 --- a/docs/spock/reports/ut.dtype.NumericType_Spec.json +++ b/docs/spock/reports/ut.dtype.NumericType_Spec.json @@ -1,20 +1,20 @@ { "className":"ut.dtype.NumericType_Spec", "title":"The NumericType and its implementations model their respective numeric data types.", - "narrative":"This specification covers the behavior of the NumericType interface\n which is responsible for modelling numeric data types which may or may not be native to the JVM.\n These implementations however do not model them in the traditional OO style\n but merely expose useful utility method for converting and representing\n these numeric data types using JVM types.", + "narrative":"This specification covers the behavior of the NumericType interface\n which is responsible for modelling numeric data types which may or may not be native to the JVM. \n These implementations however do not model them in the traditional OO style\n but merely expose useful utility method for converting and representing \n these numeric data types using JVM types.", "subjects":["neureka.dtype.NumericType"], "statistics":{ - "runs":"4", + "runs":"58", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.032 seconds" + "duration":"0.061 seconds" }, "headers":["\n This specification covers implementations\n of the \"interface neureka.dtype.NumericType\" interface.\n Such classes are responsible for\n representing all numeric types including the ones\n which are foreign to the JVM, namely : \n unsigned integer types.\n "],"tags":{},"see":[], "features":[ { - "id":"NumericType implementations return their expected properties.", + "id":"NumericType implementations return their expected properties. [0]", "result":"PASS", "duration":"0", "iterations":{ @@ -35,12 +35,201 @@ }, { - "id":"NumericType implementations behave as expected.", + "id":"NumericType implementations return their expected properties. [1]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"expect","text":"The type instance describes the expected number of bytes.","code":["type.numberOfBytes() == bytes"]}, + + {"kind":"and","text":"It describes the expected JVM target type.","code":["type.targetType() == target"]}, + + {"kind":"and","text":"It also describes the expected array type of said JVM target type.","code":["type.targetArrayType() == array"]}, + + {"kind":"and","text":"The instance knows if it is signed or not.","code":["type.signed() == signed"]}, + + {"kind":"where","text":"The following data is being used: ","code":{"type":["new I8()","new UI8()","new I16()","new UI16()","new I32()","new UI32()","new I64()","new UI64()","new F32()","new F64()"],"bytes":["1","1","2","2","4","4","8","8","4","8"],"target":["Byte.class","Short.class","Short.class","Integer.class","Integer.class","Long.class","Long.class","BigInteger.class","Float.class","Double.class"],"array":["byte[].class","short[].class","short[].class","int[].class","int[].class","long[].class","long[].class","BigInteger[].class","float[].class","double[].class"],"signed":["true","false","true","false","true","false","true","false","true","true"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations return their expected properties. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The type instance describes the expected number of bytes.","code":["type.numberOfBytes() == bytes"]}, + + {"kind":"and","text":"It describes the expected JVM target type.","code":["type.targetType() == target"]}, + + {"kind":"and","text":"It also describes the expected array type of said JVM target type.","code":["type.targetArrayType() == array"]}, + + {"kind":"and","text":"The instance knows if it is signed or not.","code":["type.signed() == signed"]}, + + {"kind":"where","text":"The following data is being used: ","code":{"type":["new I8()","new UI8()","new I16()","new UI16()","new I32()","new UI32()","new I64()","new UI64()","new F32()","new F64()"],"bytes":["1","1","2","2","4","4","8","8","4","8"],"target":["Byte.class","Short.class","Short.class","Integer.class","Integer.class","Long.class","Long.class","BigInteger.class","Float.class","Double.class"],"array":["byte[].class","short[].class","short[].class","int[].class","int[].class","long[].class","long[].class","BigInteger[].class","float[].class","double[].class"],"signed":["true","false","true","false","true","false","true","false","true","true"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations return their expected properties. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The type instance describes the expected number of bytes.","code":["type.numberOfBytes() == bytes"]}, + + {"kind":"and","text":"It describes the expected JVM target type.","code":["type.targetType() == target"]}, + + {"kind":"and","text":"It also describes the expected array type of said JVM target type.","code":["type.targetArrayType() == array"]}, + + {"kind":"and","text":"The instance knows if it is signed or not.","code":["type.signed() == signed"]}, + + {"kind":"where","text":"The following data is being used: ","code":{"type":["new I8()","new UI8()","new I16()","new UI16()","new I32()","new UI32()","new I64()","new UI64()","new F32()","new F64()"],"bytes":["1","1","2","2","4","4","8","8","4","8"],"target":["Byte.class","Short.class","Short.class","Integer.class","Integer.class","Long.class","Long.class","BigInteger.class","Float.class","Double.class"],"array":["byte[].class","short[].class","short[].class","int[].class","int[].class","long[].class","long[].class","BigInteger[].class","float[].class","double[].class"],"signed":["true","false","true","false","true","false","true","false","true","true"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations return their expected properties. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The type instance describes the expected number of bytes.","code":["type.numberOfBytes() == bytes"]}, + + {"kind":"and","text":"It describes the expected JVM target type.","code":["type.targetType() == target"]}, + + {"kind":"and","text":"It also describes the expected array type of said JVM target type.","code":["type.targetArrayType() == array"]}, + + {"kind":"and","text":"The instance knows if it is signed or not.","code":["type.signed() == signed"]}, + + {"kind":"where","text":"The following data is being used: ","code":{"type":["new I8()","new UI8()","new I16()","new UI16()","new I32()","new UI32()","new I64()","new UI64()","new F32()","new F64()"],"bytes":["1","1","2","2","4","4","8","8","4","8"],"target":["Byte.class","Short.class","Short.class","Integer.class","Integer.class","Long.class","Long.class","BigInteger.class","Float.class","Double.class"],"array":["byte[].class","short[].class","short[].class","int[].class","int[].class","long[].class","long[].class","BigInteger[].class","float[].class","double[].class"],"signed":["true","false","true","false","true","false","true","false","true","true"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations return their expected properties. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The type instance describes the expected number of bytes.","code":["type.numberOfBytes() == bytes"]}, + + {"kind":"and","text":"It describes the expected JVM target type.","code":["type.targetType() == target"]}, + + {"kind":"and","text":"It also describes the expected array type of said JVM target type.","code":["type.targetArrayType() == array"]}, + + {"kind":"and","text":"The instance knows if it is signed or not.","code":["type.signed() == signed"]}, + + {"kind":"where","text":"The following data is being used: ","code":{"type":["new I8()","new UI8()","new I16()","new UI16()","new I32()","new UI32()","new I64()","new UI64()","new F32()","new F64()"],"bytes":["1","1","2","2","4","4","8","8","4","8"],"target":["Byte.class","Short.class","Short.class","Integer.class","Integer.class","Long.class","Long.class","BigInteger.class","Float.class","Double.class"],"array":["byte[].class","short[].class","short[].class","int[].class","int[].class","long[].class","long[].class","BigInteger[].class","float[].class","double[].class"],"signed":["true","false","true","false","true","false","true","false","true","true"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations return their expected properties. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The type instance describes the expected number of bytes.","code":["type.numberOfBytes() == bytes"]}, + + {"kind":"and","text":"It describes the expected JVM target type.","code":["type.targetType() == target"]}, + + {"kind":"and","text":"It also describes the expected array type of said JVM target type.","code":["type.targetArrayType() == array"]}, + + {"kind":"and","text":"The instance knows if it is signed or not.","code":["type.signed() == signed"]}, + + {"kind":"where","text":"The following data is being used: ","code":{"type":["new I8()","new UI8()","new I16()","new UI16()","new I32()","new UI32()","new I64()","new UI64()","new F32()","new F64()"],"bytes":["1","1","2","2","4","4","8","8","4","8"],"target":["Byte.class","Short.class","Short.class","Integer.class","Integer.class","Long.class","Long.class","BigInteger.class","Float.class","Double.class"],"array":["byte[].class","short[].class","short[].class","int[].class","int[].class","long[].class","long[].class","BigInteger[].class","float[].class","double[].class"],"signed":["true","false","true","false","true","false","true","false","true","true"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations return their expected properties. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The type instance describes the expected number of bytes.","code":["type.numberOfBytes() == bytes"]}, + + {"kind":"and","text":"It describes the expected JVM target type.","code":["type.targetType() == target"]}, + + {"kind":"and","text":"It also describes the expected array type of said JVM target type.","code":["type.targetArrayType() == array"]}, + + {"kind":"and","text":"The instance knows if it is signed or not.","code":["type.signed() == signed"]}, + + {"kind":"where","text":"The following data is being used: ","code":{"type":["new I8()","new UI8()","new I16()","new UI16()","new I32()","new UI32()","new I64()","new UI64()","new F32()","new F64()"],"bytes":["1","1","2","2","4","4","8","8","4","8"],"target":["Byte.class","Short.class","Short.class","Integer.class","Integer.class","Long.class","Long.class","BigInteger.class","Float.class","Double.class"],"array":["byte[].class","short[].class","short[].class","int[].class","int[].class","long[].class","long[].class","BigInteger[].class","float[].class","double[].class"],"signed":["true","false","true","false","true","false","true","false","true","true"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations return their expected properties. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The type instance describes the expected number of bytes.","code":["type.numberOfBytes() == bytes"]}, + + {"kind":"and","text":"It describes the expected JVM target type.","code":["type.targetType() == target"]}, + + {"kind":"and","text":"It also describes the expected array type of said JVM target type.","code":["type.targetArrayType() == array"]}, + + {"kind":"and","text":"The instance knows if it is signed or not.","code":["type.signed() == signed"]}, + + {"kind":"where","text":"The following data is being used: ","code":{"type":["new I8()","new UI8()","new I16()","new UI16()","new I32()","new UI32()","new I64()","new UI64()","new F32()","new F64()"],"bytes":["1","1","2","2","4","4","8","8","4","8"],"target":["Byte.class","Short.class","Short.class","Integer.class","Integer.class","Long.class","Long.class","BigInteger.class","Float.class","Double.class"],"array":["byte[].class","short[].class","short[].class","int[].class","int[].class","long[].class","long[].class","BigInteger[].class","float[].class","double[].class"],"signed":["true","false","true","false","true","false","true","false","true","true"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations return their expected properties. [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The type instance describes the expected number of bytes.","code":["type.numberOfBytes() == bytes"]}, + + {"kind":"and","text":"It describes the expected JVM target type.","code":["type.targetType() == target"]}, + + {"kind":"and","text":"It also describes the expected array type of said JVM target type.","code":["type.targetArrayType() == array"]}, + + {"kind":"and","text":"The instance knows if it is signed or not.","code":["type.signed() == signed"]}, + + {"kind":"where","text":"The following data is being used: ","code":{"type":["new I8()","new UI8()","new I16()","new UI16()","new I32()","new UI32()","new I64()","new UI64()","new F32()","new F64()"],"bytes":["1","1","2","2","4","4","8","8","4","8"],"target":["Byte.class","Short.class","Short.class","Integer.class","Integer.class","Long.class","Long.class","BigInteger.class","Float.class","Double.class"],"array":["byte[].class","short[].class","short[].class","int[].class","int[].class","long[].class","long[].class","BigInteger[].class","float[].class","double[].class"],"signed":["true","false","true","false","true","false","true","false","true","true"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations behave as expected. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"","code":["def result = type.foreignHolderBytesToTarget( data as byte[] )"]}, @@ -54,30 +243,1031 @@ }, { - "id":"Conversion goes both ways and produces expected numeric values.", + "id":"NumericType implementations behave as expected. [1]", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + {"kind":"given","text":"","code":["def result = type.foreignHolderBytesToTarget( data as byte[] )"]}, - {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + {"kind":"expect","text":"The array of bytes is being converted to a fitting JVM type.","code":["result == converted"]}, - {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + {"kind":"and","text":"The original byte array can be recreated by converting with the inverse...","code":["type.targetToForeignHolderBytes(result) == ( data as byte[] )"]}, - {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + {"kind":"where","text":"The following NumericType instances and bytes are being used :","code":{"type":["new I8()","new UI8()","new I16()","new I16()","new I16()","new I16()","new UI16()","new UI16()","new UI16()","new UI16()","new I32()","new I32()","new UI32()","new UI32()","new I64()","new I64()","new UI64()","new UI64()"],"data":["[-23]","[-23]","[2, 3]","[-16, -53]","[16, -53]","[-1, -1]","[2, 3]","[-16, -53]","[16, -53]","[-1, -1]","[22,-2, 3,-4]","[-22,-2, -3,-4]","[22,-2, 3,-4]","[-22,-2, -3,-4]","[99, 2, 1, 35, 2, 5, 37, 22]","[-99, 2, 1, -35, 2,5,-37,22]","[99, 2, 1, 35, 2, 5, 37, 22]","[-99, 2, 1, -35, 2,5,-37,22]"],"converted":["-23","233","new BigInteger(new byte[]{2, 3}).shortValueExact()","((short)-3893)","4299","-1","new BigInteger(new byte[]{2, 3}).shortValueExact()","(int)(0x10000 + ((short)-3893))","4_299","65_535","385_745_916","-352_387_588","385_745_916","3_942_579_708","7_134_266_009_577_661_718","- 7_133_136_811_068_105_962","7_134_266_009_577_661_718","11_313_607_262_641_445_654"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations behave as expected. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["def result = type.foreignHolderBytesToTarget( data as byte[] )"]}, - {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + {"kind":"expect","text":"The array of bytes is being converted to a fitting JVM type.","code":["result == converted"]}, - {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + {"kind":"and","text":"The original byte array can be recreated by converting with the inverse...","code":["type.targetToForeignHolderBytes(result) == ( data as byte[] )"]}, + + {"kind":"where","text":"The following NumericType instances and bytes are being used :","code":{"type":["new I8()","new UI8()","new I16()","new I16()","new I16()","new I16()","new UI16()","new UI16()","new UI16()","new UI16()","new I32()","new I32()","new UI32()","new UI32()","new I64()","new I64()","new UI64()","new UI64()"],"data":["[-23]","[-23]","[2, 3]","[-16, -53]","[16, -53]","[-1, -1]","[2, 3]","[-16, -53]","[16, -53]","[-1, -1]","[22,-2, 3,-4]","[-22,-2, -3,-4]","[22,-2, 3,-4]","[-22,-2, -3,-4]","[99, 2, 1, 35, 2, 5, 37, 22]","[-99, 2, 1, -35, 2,5,-37,22]","[99, 2, 1, 35, 2, 5, 37, 22]","[-99, 2, 1, -35, 2,5,-37,22]"],"converted":["-23","233","new BigInteger(new byte[]{2, 3}).shortValueExact()","((short)-3893)","4299","-1","new BigInteger(new byte[]{2, 3}).shortValueExact()","(int)(0x10000 + ((short)-3893))","4_299","65_535","385_745_916","-352_387_588","385_745_916","3_942_579_708","7_134_266_009_577_661_718","- 7_133_136_811_068_105_962","7_134_266_009_577_661_718","11_313_607_262_641_445_654"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations behave as expected. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["def result = type.foreignHolderBytesToTarget( data as byte[] )"]}, + + {"kind":"expect","text":"The array of bytes is being converted to a fitting JVM type.","code":["result == converted"]}, + + {"kind":"and","text":"The original byte array can be recreated by converting with the inverse...","code":["type.targetToForeignHolderBytes(result) == ( data as byte[] )"]}, + + {"kind":"where","text":"The following NumericType instances and bytes are being used :","code":{"type":["new I8()","new UI8()","new I16()","new I16()","new I16()","new I16()","new UI16()","new UI16()","new UI16()","new UI16()","new I32()","new I32()","new UI32()","new UI32()","new I64()","new I64()","new UI64()","new UI64()"],"data":["[-23]","[-23]","[2, 3]","[-16, -53]","[16, -53]","[-1, -1]","[2, 3]","[-16, -53]","[16, -53]","[-1, -1]","[22,-2, 3,-4]","[-22,-2, -3,-4]","[22,-2, 3,-4]","[-22,-2, -3,-4]","[99, 2, 1, 35, 2, 5, 37, 22]","[-99, 2, 1, -35, 2,5,-37,22]","[99, 2, 1, 35, 2, 5, 37, 22]","[-99, 2, 1, -35, 2,5,-37,22]"],"converted":["-23","233","new BigInteger(new byte[]{2, 3}).shortValueExact()","((short)-3893)","4299","-1","new BigInteger(new byte[]{2, 3}).shortValueExact()","(int)(0x10000 + ((short)-3893))","4_299","65_535","385_745_916","-352_387_588","385_745_916","3_942_579_708","7_134_266_009_577_661_718","- 7_133_136_811_068_105_962","7_134_266_009_577_661_718","11_313_607_262_641_445_654"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations behave as expected. 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[16]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["def result = type.foreignHolderBytesToTarget( data as byte[] )"]}, + + {"kind":"expect","text":"The array of bytes is being converted to a fitting JVM type.","code":["result == converted"]}, + + {"kind":"and","text":"The original byte array can be recreated by converting with the inverse...","code":["type.targetToForeignHolderBytes(result) == ( data as byte[] )"]}, + + {"kind":"where","text":"The following NumericType instances and bytes are being used :","code":{"type":["new I8()","new UI8()","new I16()","new I16()","new I16()","new I16()","new UI16()","new UI16()","new UI16()","new UI16()","new I32()","new I32()","new UI32()","new UI32()","new I64()","new I64()","new UI64()","new UI64()"],"data":["[-23]","[-23]","[2, 3]","[-16, -53]","[16, -53]","[-1, -1]","[2, 3]","[-16, -53]","[16, -53]","[-1, -1]","[22,-2, 3,-4]","[-22,-2, -3,-4]","[22,-2, 3,-4]","[-22,-2, -3,-4]","[99, 2, 1, 35, 2, 5, 37, 22]","[-99, 2, 1, -35, 2,5,-37,22]","[99, 2, 1, 35, 2, 5, 37, 22]","[-99, 2, 1, -35, 2,5,-37,22]"],"converted":["-23","233","new BigInteger(new byte[]{2, 3}).shortValueExact()","((short)-3893)","4299","-1","new BigInteger(new byte[]{2, 3}).shortValueExact()","(int)(0x10000 + ((short)-3893))","4_299","65_535","385_745_916","-352_387_588","385_745_916","3_942_579_708","7_134_266_009_577_661_718","- 7_133_136_811_068_105_962","7_134_266_009_577_661_718","11_313_607_262_641_445_654"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType implementations behave as expected. [17]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["def result = type.foreignHolderBytesToTarget( data as byte[] )"]}, + + {"kind":"expect","text":"The array of bytes is being converted to a fitting JVM type.","code":["result == converted"]}, + + {"kind":"and","text":"The original byte array can be recreated by converting with the inverse...","code":["type.targetToForeignHolderBytes(result) == ( data as byte[] )"]}, + + {"kind":"where","text":"The following NumericType instances and bytes are being used :","code":{"type":["new I8()","new UI8()","new I16()","new I16()","new I16()","new I16()","new UI16()","new UI16()","new UI16()","new UI16()","new I32()","new I32()","new UI32()","new UI32()","new I64()","new I64()","new UI64()","new UI64()"],"data":["[-23]","[-23]","[2, 3]","[-16, -53]","[16, -53]","[-1, -1]","[2, 3]","[-16, -53]","[16, -53]","[-1, -1]","[22,-2, 3,-4]","[-22,-2, -3,-4]","[22,-2, 3,-4]","[-22,-2, -3,-4]","[99, 2, 1, 35, 2, 5, 37, 22]","[-99, 2, 1, -35, 2,5,-37,22]","[99, 2, 1, 35, 2, 5, 37, 22]","[-99, 2, 1, -35, 2,5,-37,22]"],"converted":["-23","233","new BigInteger(new byte[]{2, 3}).shortValueExact()","((short)-3893)","4299","-1","new BigInteger(new byte[]{2, 3}).shortValueExact()","(int)(0x10000 + ((short)-3893))","4_299","65_535","385_745_916","-352_387_588","385_745_916","3_942_579_708","7_134_266_009_577_661_718","- 7_133_136_811_068_105_962","7_134_266_009_577_661_718","11_313_607_262_641_445_654"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [0]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [3]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [6]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Conversion goes both ways and produces expected numeric values. [11]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We apply a filter in order to guarantee that the right data type is being used.","code":["original = ["," 'UI8' : { o -> o as Byte },"," 'UI16': { o -> o as Short },"," 'UI32': { o -> o as Integer },"," 'UI64': { o -> o as Long },"," 'I8' : { o -> o as Byte },"," 'I16' : { o -> o as Short },"," 'I32' : { o -> o as Integer },"," 'I64' : { o -> o as Long },"," 'F32' : { o -> o as Float },"," 'F64' : { o -> o as Double }","][ num.class.simpleName ](original)"]}, + + {"kind":"and","text":"The convert the raw type (might represent unsigned value) to a JVM compatible target type...","code":["def resultTarget = num.foreignHolderBytesToTarget( rawOriginal )"]}, + + {"kind":"and","text":"Then convert this result to the true byte array of the value...","code":["def backToRaw = num.targetToForeignHolderBytes( resultTarget )"]}, + + {"kind":"then","text":"This produces the expected values which express the following relationships:","code":["resultTarget == target","backToRaw == rawOriginal","num.toTarget( original ) == target","num.convertToHolder(target) == original"]}, + + {"kind":"and","text":"The numeric type instance can perform array conversion.","code":["num.convertToTargetArray( rawOriginal as double[] ) == rawOriginal // Groovy automatically tests values","num.convertToTargetArray( rawOriginal as float[] ) == rawOriginal // ...despite difference types...","num.convertToTargetArray( rawOriginal as int[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as short[] ) == rawOriginal","num.convertToTargetArray( rawOriginal as long[] ) == rawOriginal"]}, + + {"kind":"where","text":"The following \"NumericType\" implementation instances and numeric data is being used: ","code":{"num":["new UI8()","new UI16()","new UI32()","new UI64()","new I8()","new I16()","new I32()","new I64()","new F32()","new F64()","new F32()","new F64()"],"original":["-3","-3","-3","-3","-3","-3","-3","-3","-0.3","-0.3","-5432.39928","-5432.39928"],"rawOriginal":["[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-3]","[255, 253]","[255, 255, 255, 253]","[255, 255, 255, 255, 255, 255, 255, 253]","[-66, -103, -103, -102]","[-65, -45, 51, 51, 51, 51, 51, 51]","[-59, -87, -61, 50]","[-64, -75, 56, 102, 55, 54, -51, -14]"],"target":["255 - 2","65_535 - 2","4_294_967_295 - 2","18_446_744_073_709_551_615 - 2","- 3","- 3","- 3","- 3","- 0.3 as Float","- 0.3 as Double","-5432.39928 as Float","-5432.39928 as Double"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType conversion to holder types yields expected results. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"","code":["def result = num.convertToHolder( from )"]}, + + {"kind":"then","text":"","code":["result == expected"]}, + + {"kind":"and","text":"","code":["result.class == expected.class"]}, + + {"kind":"and","text":"","code":["result.class == holderType"]}, + + {"kind":"and","text":"","code":["num.holderType() == holderType"]}, + + {"kind":"and","text":"","code":["num.holderArrayType() == holderArrayType"]}, + + {"kind":"where","text":"","code":{"num":["new I32()","new I32()","new I32()","new I32()","new I32()","new I32()","new I16()","new I16()","new I16()","new I16()","new I16()","new I16()","new I8()","new I8()","new I8()","new I8()","new I8()","new I8()"],"from":["3 as Byte","8 as Integer","863.834 as Double","2 as Short","9 as Long","23.422 as Float","3 as Byte","8 as Integer","863.834 as Double","2 as Short","9 as Long","23.422 as Float","3 as Byte","8 as Integer","863.834 as Double","2 as Short","9 as Long","23.422 as Float"],"expected":["3 as Integer","8 as Integer","863 as Integer","2 as Integer","9 as Integer","23 as Integer","3 as Short","8 as Short","863 as Short","2 as Short","9 as Short","23 as Short","3 as Byte","8 as Byte","863 as Byte","2 as Byte","9 as Byte","23 as Byte"],"holderType":["Integer.class","Integer.class","Integer.class","Integer.class","Integer.class","Integer.class","Short.class","Short.class","Short.class","Short.class","Short.class","Short.class","Byte.class","Byte.class","Byte.class","Byte.class","Byte.class","Byte.class"],"holderArrayType":["int[].class","int[].class","int[].class","int[].class","int[].class","int[].class","short[].class","short[].class","short[].class","short[].class","short[].class","short[].class","byte[].class","byte[].class","byte[].class","byte[].class","byte[].class","byte[].class"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"NumericType conversion to holder types yields expected results. 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[16]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"","code":["def result = num.convertToHolder( from )"]}, + + {"kind":"then","text":"","code":["result == expected"]}, + + {"kind":"and","text":"","code":["result.class == expected.class"]}, + + {"kind":"and","text":"","code":["result.class == holderType"]}, + + {"kind":"and","text":"","code":["num.holderType() == holderType"]}, + + {"kind":"and","text":"","code":["num.holderArrayType() == holderArrayType"]}, + + {"kind":"where","text":"","code":{"num":["new I32()","new I32()","new I32()","new I32()","new I32()","new I32()","new I16()","new I16()","new I16()","new I16()","new I16()","new I16()","new I8()","new I8()","new I8()","new I8()","new I8()","new I8()"],"from":["3 as Byte","8 as Integer","863.834 as Double","2 as Short","9 as Long","23.422 as Float","3 as Byte","8 as Integer","863.834 as Double","2 as Short","9 as Long","23.422 as Float","3 as Byte","8 as Integer","863.834 as Double","2 as Short","9 as Long","23.422 as Float"],"expected":["3 as Integer","8 as Integer","863 as Integer","2 as Integer","9 as Integer","23 as Integer","3 as Short","8 as Short","863 as Short","2 as Short","9 as Short","23 as Short","3 as Byte","8 as Byte","863 as Byte","2 as Byte","9 as Byte","23 as Byte"],"holderType":["Integer.class","Integer.class","Integer.class","Integer.class","Integer.class","Integer.class","Short.class","Short.class","Short.class","Short.class","Short.class","Short.class","Byte.class","Byte.class","Byte.class","Byte.class","Byte.class","Byte.class"],"holderArrayType":["int[].class","int[].class","int[].class","int[].class","int[].class","int[].class","short[].class","short[].class","short[].class","short[].class","short[].class","short[].class","byte[].class","byte[].class","byte[].class","byte[].class","byte[].class","byte[].class"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"NumericType conversion to holder types yields expected results.", + "id":"NumericType conversion to holder types yields expected results. [17]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.framing.Tensor_Framing_Spec.json b/docs/spock/reports/ut.framing.Tensor_Framing_Spec.json index 1f6e94478..5d4505cfb 100644 --- a/docs/spock/reports/ut.framing.Tensor_Framing_Spec.json +++ b/docs/spock/reports/ut.framing.Tensor_Framing_Spec.json @@ -1,7 +1,7 @@ { "className":"ut.framing.Tensor_Framing_Spec", "title":"Naming Tensors and their Dimensions.", - "narrative":"A powerful concept in the data science as well as machine learning\n world is something usually referred to as \"Data Frames\".\n These are highly flexible 2D data structures\n used to load and store CSV, CRV, etc... files for\n data exploration and further processing.\n Data frames are so powerful because\n their indices are labeled and therefore human readable.\n Neureka's tensors are general purpose data containers\n which may also stored data in 2 dimensions whose\n indices may also be something other than integers.", + "narrative":"A powerful concept in the data science as well as machine learning\n world is something usually referred to as \"Data Frames\".\n These are highly flexible 2D data structures\n used to load and store CSV, CRV, etc... files for \n data exploration and further processing.\n Data frames are so powerful because\n their indices are labeled and therefore human readable.\n Neureka's tensors are general purpose data containers\n which may also stored data in 2 dimensions whose\n indices may also be something other than integers.", "subjects":["neureka.Tensor","neureka.framing.NDFrame"], "statistics":{ "runs":"4", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.014 seconds" + "duration":"0.035 seconds" }, "headers":["\n This specification covers the behavior\n of tensors with respect to specifying aliases for\n indices and then using them for slicing. \n "],"tags":{},"see":[], "features":[ { "id":"We can add labels to tensors through lists or maps passed to the \"label()\" method.", "result":"PASS", - "duration":"0.004 seconds", + "duration":"0.012 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -43,7 +43,7 @@ { "id":"A matrix (rank 2 tensor) can be labeled and their labels can be used to extract slices / subsets.", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.007 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -84,7 +84,7 @@ { "id":"Rank 3 tensors can be labeled and their labels can be used to extract slices / subsets of tensors.", "result":"PASS", - "duration":"0.003 seconds", + "duration":"0.009 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -129,7 +129,7 @@ { "id":"A tensor can be labeled partially.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.introductions.Tensor_NDArray_Spec.json b/docs/spock/reports/ut.introductions.Tensor_NDArray_Spec.json index 4f5af0af0..8ca9667ae 100644 --- a/docs/spock/reports/ut.introductions.Tensor_NDArray_Spec.json +++ b/docs/spock/reports/ut.introductions.Tensor_NDArray_Spec.json @@ -1,7 +1,7 @@ { "className":"ut.introductions.Tensor_NDArray_Spec", "title":"Tensors or Nd-arrays", - "narrative":"*What is the difference?*\n\nIn the world of machine learning we use something called a **'tensor'** to represent data.\nThey might be called **'nd-arrays'** in some other frameworks,\nbut although they are very similar,\nthere are also some important distinctions to be made between these two concepts.\nBoth are at their core merely multidimensional arrays, however,\nthey are different in their typical usage and API.\nnd-arrays are merely used to represent any type of data as a\ncollection of elements in a multidimensional grid,\ntensors on the other hand have additional requirements.\nThey are a type of nd-array which stores numeric data\nas well as expose various mathematical operations for said data.\nIn that sense it is actually merely a more complex kind of number.\nThis concept actually comes from the field of physics,\nwhere it is used to represent a physical quantity.\n\nNeureka models both concepts through the `Tensor` and the `Nda` interfaces.\n`Nda` is an abbreviation of `NdArray`, and `Tensor` is an abbreviation of `Tensor`.\nThe `Tensor` type is a subtype of the `Nda` type, exposing additional methods\nlike for example `plus`, `minus`, `times` and `divide`.\nBoth can be instantiated through static factory methods (and a fluent builder API).", + "narrative":"*What is the difference?*\n\nIn the world of machine learning we use something called a **'tensor'** to represent data.\nThey might be called **'nd-arrays'** in some other frameworks,\nbut although they are very similar, \nthere are also some important distinctions to be made between these two concepts.\nBoth are at their core merely multidimensional arrays, however,\nthey are different in their typical usage and API.\nnd-arrays are merely used to represent any type of data as a \ncollection of elements in a multidimensional grid, \ntensors on the other hand have additional requirements.\nThey are a type of nd-array which stores numeric data \nas well as expose various mathematical operations for said data.\nIn that sense it is actually merely a more complex kind of number.\nThis concept actually comes from the field of physics, \nwhere it is used to represent a physical quantity.\n\nNeureka models both concepts through the `Tensor` and the `Nda` interfaces.\n`Nda` is an abbreviation of `NdArray`, and `Tensor` is an abbreviation of `Tensor`.\nThe `Tensor` type is a subtype of the `Nda` type, exposing additional methods\nlike for example `plus`, `minus`, `times` and `divide`.\nBoth can be instantiated through static factory methods (and a fluent builder API).", "subjects":["neureka.Nda","neureka.Tensor"], "statistics":{ "runs":"2", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.009 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"Tensor is a subtype of NdArray.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"We can use tensors for numeric calculations (but not nd-arrays).", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.math.BackendContext_Spec.json b/docs/spock/reports/ut.math.BackendContext_Spec.json index 6de53af54..7e1126ed3 100644 --- a/docs/spock/reports/ut.math.BackendContext_Spec.json +++ b/docs/spock/reports/ut.math.BackendContext_Spec.json @@ -4,19 +4,19 @@ "narrative":"This specification defines the expected behaviour of the backend context\n which should expose a convenient API to work with.\n This API should allow for tasks to be running on a given context\n which is important for testing and modularity not only\n during library startup but also throughout the runtime.", "subjects":["neureka.backend.api.Operation","neureka.backend.api.BackendContext"], "statistics":{ - "runs":"3", + "runs":"5", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.006 seconds" + "duration":"0.013 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"BackendContext instances can be created by cloning from Singleton instance.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -43,7 +43,7 @@ { "id":"BackendContext instances return Runner instances for easy visiting.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -66,7 +66,65 @@ }, { - "id":"BackendContext instances return Runner instances for easy visiting with return values.", + "id":"BackendContext instances return Runner instances for easy visiting with return values. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"The current thread local BackendContext instance.","code":["def current = Neureka.get().backend()"]}, + + {"kind":"and","text":"A clone is being created by calling \"clone()\" on the given context...","code":["def clone = current.clone()"]}, + + {"kind":"and","text":"We wrap a Runner instance around a wrapper which will test its methods!","code":["def run = runWrapper( clone.runner() )"]}, + + {"kind":"when","text":"Querying the thread local context inside the Runner...","code":["def innerContext = run { Neureka.get().backend() }"]}, + + {"kind":"and","text":"...also outside the Runner lambda...","code":["def outerContext = Neureka.get().backend()"]}, + + {"kind":"then","text":"These two context instances will be different objects!","code":["innerContext != outerContext"]}, + + {"kind":"and","text":"The inner context will in fact be the clone which provided the Runner!","code":["innerContext == clone"]}, + + {"kind":"and","text":"The outer context is as expected simply the current context.","code":["outerContext == current"]}, + + {"kind":"where","text":"The following conceptually identical Runner methods can be used:","code":{"runWrapper":["(BackendContext.Runner runner) -> { (arg) -> runner.call(arg) }","(BackendContext.Runner runner) -> { (arg) -> runner.invoke(arg) }","(BackendContext.Runner runner) -> { (arg) -> runner.runAndGet(arg) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"BackendContext instances return Runner instances for easy visiting with return values. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"The current thread local BackendContext instance.","code":["def current = Neureka.get().backend()"]}, + + {"kind":"and","text":"A clone is being created by calling \"clone()\" on the given context...","code":["def clone = current.clone()"]}, + + {"kind":"and","text":"We wrap a Runner instance around a wrapper which will test its methods!","code":["def run = runWrapper( clone.runner() )"]}, + + {"kind":"when","text":"Querying the thread local context inside the Runner...","code":["def innerContext = run { Neureka.get().backend() }"]}, + + {"kind":"and","text":"...also outside the Runner lambda...","code":["def outerContext = Neureka.get().backend()"]}, + + {"kind":"then","text":"These two context instances will be different objects!","code":["innerContext != outerContext"]}, + + {"kind":"and","text":"The inner context will in fact be the clone which provided the Runner!","code":["innerContext == clone"]}, + + {"kind":"and","text":"The outer context is as expected simply the current context.","code":["outerContext == current"]}, + + {"kind":"where","text":"The following conceptually identical Runner methods can be used:","code":{"runWrapper":["(BackendContext.Runner runner) -> { (arg) -> runner.call(arg) }","(BackendContext.Runner runner) -> { (arg) -> runner.invoke(arg) }","(BackendContext.Runner runner) -> { (arg) -> runner.runAndGet(arg) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"BackendContext instances return Runner instances for easy visiting with return values. [2]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.math.ConCat_Spec.json b/docs/spock/reports/ut.math.ConCat_Spec.json index 10468990f..7b67d942e 100644 --- a/docs/spock/reports/ut.math.ConCat_Spec.json +++ b/docs/spock/reports/ut.math.ConCat_Spec.json @@ -1,22 +1,22 @@ { "className":"ut.math.ConCat_Spec", "title":"Merging Tensors", - "narrative":"Tensors can not only be sliced, but also merged.\n This is most easily achieved through the concatenation operation,\n which stacks 2 tensors alongside a specified axis.\n This specification not only covers how you can concatenate tensors,\n but also how this works alongside autograd and non-numeric tensors.", + "narrative":"Tensors can not only be sliced, but also merged.\n This is most easily achieved through the concatenation operation, \n which stacks 2 tensors alongside a specified axis.\n This specification not only covers how you can concatenate tensors,\n but also how this works alongside autograd and non-numeric tensors.", "subjects":[], "statistics":{ - "runs":"5", + "runs":"7", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.017 seconds" + "duration":"0.060 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can concatenate 2 tensors alongside a specified axis!", "result":"PASS", - "duration":"0.005 seconds", + "duration":"0.011 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -41,7 +41,7 @@ { "id":"We can concatenate 2 float tensors alongside a specified axis!", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.008 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -66,7 +66,7 @@ { "id":"We can concatenate 2 string tensors alongside a specified axis!", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -85,9 +85,36 @@ }, { - "id":"We can concatenate and then back-propagate 2 simple float tensors alongside a specified axis!", + "id":"We can concatenate and then back-propagate 2 simple float tensors alongside a specified axis! [0]", + "result":"PASS", + "duration":"0.008 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create 2 rank 2 tensors, which we want to concatenate, where both require gradients.","code":["var a = Tensor.of(Float, [3, 1], [8, -4, 7]).setRqsGradient(true)","var b = Tensor.of(Float).withShape(3, 1).andFill(5, -1, 2).setRqsGradient(true)"]}, + + {"kind":"and","text":"A function which should perform the concatenation.","code":["var cat = Function.of('concat(I[0], I[1])')"]}, + + {"kind":"when","text":"We call the previously created function alongside the axis alongside we want to concatenate.","code":["var c = cat.with(Arg.Axis.of(1)).call(a, b)"]}, + + {"kind":"then","text":"The resulting tensor should have the expected shape.","code":["c.shape() == [3, 2]"]}, + + {"kind":"when","text":"","code":["var y = c / 2"]}, + + {"kind":"and","text":"","code":["y.backward(Tensor.ofFloats().withShape(3,2).andFill(-1, 2, 0.5, 3, -0.1, 4))"]}, + + {"kind":"then","text":"","code":["a.gradient.get().items == [-0.5, 0.25, -0.05] as float[]","b.gradient.get().items == [1.0, 1.5, 2.0] as float[]"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","Device.get(OpenCLDevice, 'gpu')"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can concatenate and then back-propagate 2 simple float tensors alongside a specified axis! [1]", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -112,9 +139,36 @@ }, { - "id":"We can concatenate and then back-propagate 3 simple float tensors alongside a specified axis!", + "id":"We can concatenate and then back-propagate 3 simple float tensors alongside a specified axis! [0]", + "result":"PASS", + "duration":"0.007 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create 2 rank 2 tensors, which we want to concatenate, where both require gradients.","code":["var a = Tensor.of(Float, [1, 3], [8, -4, 7]).setRqsGradient(true)","var b = Tensor.of(Float).withShape(1, 3).andFill(5, -1, 2).setRqsGradient(true)","var c = Tensor.ofRandom(Float, 1, 3).setRqsGradient(true)"]}, + + {"kind":"and","text":"A function which should perform the concatenation.","code":["var cat = Function.of('concat(I[0], I[1], I[2])')"]}, + + {"kind":"when","text":"We call the previously created function alongside the axis alongside we want to concatenate.","code":["var d = cat.with(Arg.Axis.of(0)).call(a, b, c)"]}, + + {"kind":"then","text":"The resulting tensor should have the expected shape.","code":["d.shape() == [3, 3]"]}, + + {"kind":"when","text":"","code":["var y = d ** 2"]}, + + {"kind":"and","text":"","code":["y.backward(Tensor.ofFloats().withShape(3,3).andFill(-1, 2, 0.5, 3, -0.1, 4))"]}, + + {"kind":"then","text":"","code":["a.gradient.get().items == [-16, -16, 7] as float[]","b.gradient.get().items == [30, 0.2, 16] as float[]","c.gradient.get().items == [0.30829078, -3.1254156, -0.52700233] as float[]"]}, + + {"kind":"where","text":"","code":{"device":["CPU.get()","Device.get(OpenCLDevice, 'gpu')"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can concatenate and then back-propagate 3 simple float tensors alongside a specified axis! [1]", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.math.Function_Exception_Spec.json b/docs/spock/reports/ut.math.Function_Exception_Spec.json index c8c8d3306..7166aaef1 100644 --- a/docs/spock/reports/ut.math.Function_Exception_Spec.json +++ b/docs/spock/reports/ut.math.Function_Exception_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.001 seconds" + "duration":"0.006 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"Function throws exception when not enough inputs provided.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,7 +37,7 @@ { "id":"Function throws exception when arity does not match input number.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.math.Function_Parsing_Spec.json b/docs/spock/reports/ut.math.Function_Parsing_Spec.json index d6d152d14..8cd12533f 100644 --- a/docs/spock/reports/ut.math.Function_Parsing_Spec.json +++ b/docs/spock/reports/ut.math.Function_Parsing_Spec.json @@ -4,17 +4,17 @@ "narrative":"Neureka uses the 'Function' interface as a representation of a\n nested structure of operations.\n This means that a 'Function' is simply an abstract syntax trees made up of other 'Function' implementations\n which are assembled together by a parser receiving a string expression.\n In this specification we ensure that function expressions will be properly parsed into\n 'Function' implementations.", "subjects":["neureka.math.Function","neureka.math.parsing.FunctionParser"], "statistics":{ - "runs":"3", + "runs":"37", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.020 seconds" + "duration":"0.039 seconds" }, "headers":["\n This specification ensures that functions can be created from String expressions\n using factory methods on the interface neureka.math.Function interface.\n The implementation details as to how exactly this leads to an abstract syntax tree\n will not be covered here.\n This is because the parsing procedure is rather complex and the only thing we care about \n is the result. \n
        \n Within a given expression String passed to the parser, function inputs are\n recognized by 'I[j]', 'Ij' or 'ij', where j is the input index.\n Functions accept arrays as their inputs,\n which is why variables must be targeted in such a way.\n There are also many mathematical function like 'sig(..)', 'tanh(..)', 'sin(..)', 'cos(..)' \n and many more which are recognised by the parser. \n Other than that the syntax is rather mundane with respect to traditional\n operations like for example plus '+', minus '-', times '*', ... etc.
        \n "],"tags":{},"see":[], "features":[ { - "id":"Test parsed equations when building Function instances.", + "id":"Test parsed equations when building Function instances. [0]", "result":"PASS", "duration":"0", "iterations":{ @@ -29,7 +29,418 @@ }, { - "id":"Parsed equations throw expected error messages.", + "id":"Test parsed equations when building Function instances. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [5]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [11]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [12]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [13]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [14]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [15]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [16]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [17]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [18]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [19]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [20]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [21]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [22]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [23]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test parsed equations when building Function instances. [24]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will be parsed as expected.","code":["Function.of(equation).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"fast_tanh(i0*i1)\"","\"ig0*(igj)xI[g1]\"","\"sumJs(ij)\"","\"sumJs(1*(4-2/ij))\"","\"quadratic(sftpls(Ij))\"","\"softplus(I[3]**(3/i1)/sumJs(Ij**2)-23+I0/i1)\"","\"1+3+5-23+I0*45/(345-651**I3-6)\"","\"sin(23*i1)-cos(i0**0.3)+tanh(23)\"","\"4 *-2\"","\"fast_gaus(i0*i1)\"","\"2*3/1-2\"","\"3x5xI[4]xI[3]\"","\"[1,0, 5,3, 4]:(tanh(i0xi1))\"","\"[0,2, 1,3, -1](sig(I0))\"","\"I[0]<-I[1]\"","\"quadratic(I[0]) <- (I[1] <- I[2])\"","\"((tanh(i0)\"","'($$(gaus(i0*()'","\"rrlu(i0)\"","\"th(i0)*gaaus(i0+I1)\"","\"dimtrim(I[0])\"","\"add(I[0], 3, 3/I[1])\"","\"multiply(1, 4, -2, I[1])\"","\"divide(I[0], 3*I[1], I[3]-6)\"","\"i0@i1\""],"expected":["\"fast_tanh(I[0] * I[1])\"","\"((Ig[0] * Ig[j]) x Ig[1])\"","\"sumJs(I[j])\"","\"sumJs(1.0 * (4.0 - (2.0 / I[j])))\"","\"quad(softplus(I[j]))\"","\"softplus((((I[3] ** (3.0 / I[1])) / sumJs(I[j] ** 2.0)) - 23.0) + (I[0] / I[1]))\"","\"(1.0 + 3.0 + (5.0 - 23.0) + (I[0] * (45.0 / (345.0 - (651.0 ** I[3]) - 6.0))))\"","\"((sin(23.0 * I[1]) - cos(I[0] ** 0.3)) + tanh(23.0))\"","\"(4.0 * -2.0)\"","\"fast_gaus(I[0] * I[1])\"","\"((2.0 * (3.0 / 1.0)) - 2.0)\"","\"(((3.0 x 5.0) x I[4]) x I[3])\"","\"([1,0,5,3,4]:(tanh(I[0] x I[1])))\"","\"([0,2,1,3,-1]:(sig(I[0])))\"","\"(I[0] <- I[1])\"","\"(quad(I[0]) <- (I[1] <- I[2]))\"","\"tanh(I[0])\"","\"gaus(I[0] * 0.0)\"","\"relu(I[0])\"","\"(tanh(I[0]) * gaus(I[0] + I[1]))\"","\"dimtrim(I[0])\"","\"(I[0] + 3.0 + (3.0 / I[1]))\"","\"(1.0 * 4.0 * -2.0 * I[1])\"","\"(I[0] / (3.0 * I[1]) / (I[3] - 6.0))\"","\"(I[0] @ I[1])\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Parsed equations throw expected error messages. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We try to instantiate a Function by passing an expression String...","code":["Function.of(equation)"]}, + + {"kind":"then","text":"An exception is being thrown that contains the expected message!","code":["def error = thrown(IllegalArgumentException)","assert error.message==expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"softplus(I[0],I[1],I[2])\"","\"sig(I[0],I[1],I[2])\"","\"sumjs(I[0],I[1],I[2])\"","\"prodjs(I[0],I[1],I[2])\""],"expected":["\"The function/operation 'softplus' expects 1 parameters, however 3 where given!\"","\"The function/operation 'sig' expects 1 parameters, however 3 where given!\"","\"The function/operation 'sumJs' expects 1 parameters, however 3 where given!\\nNote: This function is an 'indexer'. Therefore it expects to sum variable 'I[j]' inputs, where 'j' is the index of an iteration.\"","\"The function/operation 'prodJs' expects 1 parameters, however 3 where given!\\nNote: This function is an 'indexer'. Therefore it expects to sum variable 'I[j]' inputs, where 'j' is the index of an iteration.\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Parsed equations throw expected error messages. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We try to instantiate a Function by passing an expression String...","code":["Function.of(equation)"]}, + + {"kind":"then","text":"An exception is being thrown that contains the expected message!","code":["def error = thrown(IllegalArgumentException)","assert error.message==expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"softplus(I[0],I[1],I[2])\"","\"sig(I[0],I[1],I[2])\"","\"sumjs(I[0],I[1],I[2])\"","\"prodjs(I[0],I[1],I[2])\""],"expected":["\"The function/operation 'softplus' expects 1 parameters, however 3 where given!\"","\"The function/operation 'sig' expects 1 parameters, however 3 where given!\"","\"The function/operation 'sumJs' expects 1 parameters, however 3 where given!\\nNote: This function is an 'indexer'. Therefore it expects to sum variable 'I[j]' inputs, where 'j' is the index of an iteration.\"","\"The function/operation 'prodJs' expects 1 parameters, however 3 where given!\\nNote: This function is an 'indexer'. Therefore it expects to sum variable 'I[j]' inputs, where 'j' is the index of an iteration.\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Parsed equations throw expected error messages. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We try to instantiate a Function by passing an expression String...","code":["Function.of(equation)"]}, + + {"kind":"then","text":"An exception is being thrown that contains the expected message!","code":["def error = thrown(IllegalArgumentException)","assert error.message==expected"]}, + + {"kind":"where","text":"The following expressions and expected exception messages are being used :","code":{"equation":["\"softplus(I[0],I[1],I[2])\"","\"sig(I[0],I[1],I[2])\"","\"sumjs(I[0],I[1],I[2])\"","\"prodjs(I[0],I[1],I[2])\""],"expected":["\"The function/operation 'softplus' expects 1 parameters, however 3 where given!\"","\"The function/operation 'sig' expects 1 parameters, however 3 where given!\"","\"The function/operation 'sumJs' expects 1 parameters, however 3 where given!\\nNote: This function is an 'indexer'. Therefore it expects to sum variable 'I[j]' inputs, where 'j' is the index of an iteration.\"","\"The function/operation 'prodJs' expects 1 parameters, however 3 where given!\\nNote: This function is an 'indexer'. Therefore it expects to sum variable 'I[j]' inputs, where 'j' is the index of an iteration.\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Parsed equations throw expected error messages. [3]", "result":"PASS", "duration":"0", "iterations":{ @@ -46,7 +457,82 @@ }, { - "id":"Functions can derive themselves according to the provided index of the input which ought to be derived.", + "id":"Functions can derive themselves according to the provided index of the input which ought to be derived. [0]", + "result":"PASS", + "duration":"0.006 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will produce the expected derivative String.","code":["Function.of(equation).getDerivative( index ).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and derivation indices are being used :","code":{"equation":["\"1 - I[0] * 3\"","\"i0 / 6\"","\"ln( 4 * i0 )\"","\"4**I[0]\"","\"i0 ** 3\"","\"(I[0] * I[1] * I[0]) + 3\"","\"3 ** (i0 / 2)\"","\"(2 * I[0]) / (1 - I[0] * 3)\""],"index":["0","0","0","0","0","0","0","0"],"expected":["\"-3.0\"","\"(1.0 / 6.0)\"","\"(4.0 / (4.0 * I[0]))\"","\"(ln(4.0) * (4.0 ** I[0]))\"","\"(3.0 * (I[0] ** (3.0 - 1.0)))\"","\"((I[1] * I[0]) + (I[0] * I[1]))\"","\"((1.0 / 2.0) * (ln(3.0) * (3.0 ** (I[0] / 2.0))))\"","\"((2.0 / (1.0 - (I[0] * 3.0))) - (((2.0 * I[0]) * -3.0) / ((1.0 - (I[0] * 3.0)) ** 2.0)))\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Functions can derive themselves according to the provided index of the input which ought to be derived. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will produce the expected derivative String.","code":["Function.of(equation).getDerivative( index ).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and derivation indices are being used :","code":{"equation":["\"1 - I[0] * 3\"","\"i0 / 6\"","\"ln( 4 * i0 )\"","\"4**I[0]\"","\"i0 ** 3\"","\"(I[0] * I[1] * I[0]) + 3\"","\"3 ** (i0 / 2)\"","\"(2 * I[0]) / (1 - I[0] * 3)\""],"index":["0","0","0","0","0","0","0","0"],"expected":["\"-3.0\"","\"(1.0 / 6.0)\"","\"(4.0 / (4.0 * I[0]))\"","\"(ln(4.0) * (4.0 ** I[0]))\"","\"(3.0 * (I[0] ** (3.0 - 1.0)))\"","\"((I[1] * I[0]) + (I[0] * I[1]))\"","\"((1.0 / 2.0) * (ln(3.0) * (3.0 ** (I[0] / 2.0))))\"","\"((2.0 / (1.0 - (I[0] * 3.0))) - (((2.0 * I[0]) * -3.0) / ((1.0 - (I[0] * 3.0)) ** 2.0)))\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Functions can derive themselves according to the provided index of the input which ought to be derived. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will produce the expected derivative String.","code":["Function.of(equation).getDerivative( index ).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and derivation indices are being used :","code":{"equation":["\"1 - I[0] * 3\"","\"i0 / 6\"","\"ln( 4 * i0 )\"","\"4**I[0]\"","\"i0 ** 3\"","\"(I[0] * I[1] * I[0]) + 3\"","\"3 ** (i0 / 2)\"","\"(2 * I[0]) / (1 - I[0] * 3)\""],"index":["0","0","0","0","0","0","0","0"],"expected":["\"-3.0\"","\"(1.0 / 6.0)\"","\"(4.0 / (4.0 * I[0]))\"","\"(ln(4.0) * (4.0 ** I[0]))\"","\"(3.0 * (I[0] ** (3.0 - 1.0)))\"","\"((I[1] * I[0]) + (I[0] * I[1]))\"","\"((1.0 / 2.0) * (ln(3.0) * (3.0 ** (I[0] / 2.0))))\"","\"((2.0 / (1.0 - (I[0] * 3.0))) - (((2.0 * I[0]) * -3.0) / ((1.0 - (I[0] * 3.0)) ** 2.0)))\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Functions can derive themselves according to the provided index of the input which ought to be derived. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will produce the expected derivative String.","code":["Function.of(equation).getDerivative( index ).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and derivation indices are being used :","code":{"equation":["\"1 - I[0] * 3\"","\"i0 / 6\"","\"ln( 4 * i0 )\"","\"4**I[0]\"","\"i0 ** 3\"","\"(I[0] * I[1] * I[0]) + 3\"","\"3 ** (i0 / 2)\"","\"(2 * I[0]) / (1 - I[0] * 3)\""],"index":["0","0","0","0","0","0","0","0"],"expected":["\"-3.0\"","\"(1.0 / 6.0)\"","\"(4.0 / (4.0 * I[0]))\"","\"(ln(4.0) * (4.0 ** I[0]))\"","\"(3.0 * (I[0] ** (3.0 - 1.0)))\"","\"((I[1] * I[0]) + (I[0] * I[1]))\"","\"((1.0 / 2.0) * (ln(3.0) * (3.0 ** (I[0] / 2.0))))\"","\"((2.0 / (1.0 - (I[0] * 3.0))) - (((2.0 * I[0]) * -3.0) / ((1.0 - (I[0] * 3.0)) ** 2.0)))\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Functions can derive themselves according to the provided index of the input which ought to be derived. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will produce the expected derivative String.","code":["Function.of(equation).getDerivative( index ).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and derivation indices are being used :","code":{"equation":["\"1 - I[0] * 3\"","\"i0 / 6\"","\"ln( 4 * i0 )\"","\"4**I[0]\"","\"i0 ** 3\"","\"(I[0] * I[1] * I[0]) + 3\"","\"3 ** (i0 / 2)\"","\"(2 * I[0]) / (1 - I[0] * 3)\""],"index":["0","0","0","0","0","0","0","0"],"expected":["\"-3.0\"","\"(1.0 / 6.0)\"","\"(4.0 / (4.0 * I[0]))\"","\"(ln(4.0) * (4.0 ** I[0]))\"","\"(3.0 * (I[0] ** (3.0 - 1.0)))\"","\"((I[1] * I[0]) + (I[0] * I[1]))\"","\"((1.0 / 2.0) * (ln(3.0) * (3.0 ** (I[0] / 2.0))))\"","\"((2.0 / (1.0 - (I[0] * 3.0))) - (((2.0 * I[0]) * -3.0) / ((1.0 - (I[0] * 3.0)) ** 2.0)))\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Functions can derive themselves according to the provided index of the input which ought to be derived. [5]", "result":"PASS", "duration":"0.002 seconds", "iterations":{ @@ -58,6 +544,36 @@ {"kind":"where","text":"The following expressions and derivation indices are being used :","code":{"equation":["\"1 - I[0] * 3\"","\"i0 / 6\"","\"ln( 4 * i0 )\"","\"4**I[0]\"","\"i0 ** 3\"","\"(I[0] * I[1] * I[0]) + 3\"","\"3 ** (i0 / 2)\"","\"(2 * I[0]) / (1 - I[0] * 3)\""],"index":["0","0","0","0","0","0","0","0"],"expected":["\"-3.0\"","\"(1.0 / 6.0)\"","\"(4.0 / (4.0 * I[0]))\"","\"(ln(4.0) * (4.0 ** I[0]))\"","\"(3.0 * (I[0] ** (3.0 - 1.0)))\"","\"((I[1] * I[0]) + (I[0] * I[1]))\"","\"((1.0 / 2.0) * (ln(3.0) * (3.0 ** (I[0] / 2.0))))\"","\"((2.0 / (1.0 - (I[0] * 3.0))) - (((2.0 * I[0]) * -3.0) / ((1.0 - (I[0] * 3.0)) ** 2.0)))\""]}} ], "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Functions can derive themselves according to the provided index of the input which ought to be derived. [6]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will produce the expected derivative String.","code":["Function.of(equation).getDerivative( index ).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and derivation indices are being used :","code":{"equation":["\"1 - I[0] * 3\"","\"i0 / 6\"","\"ln( 4 * i0 )\"","\"4**I[0]\"","\"i0 ** 3\"","\"(I[0] * I[1] * I[0]) + 3\"","\"3 ** (i0 / 2)\"","\"(2 * I[0]) / (1 - I[0] * 3)\""],"index":["0","0","0","0","0","0","0","0"],"expected":["\"-3.0\"","\"(1.0 / 6.0)\"","\"(4.0 / (4.0 * I[0]))\"","\"(ln(4.0) * (4.0 ** I[0]))\"","\"(3.0 * (I[0] ** (3.0 - 1.0)))\"","\"((I[1] * I[0]) + (I[0] * I[1]))\"","\"((1.0 / 2.0) * (ln(3.0) * (3.0 ** (I[0] / 2.0))))\"","\"((2.0 / (1.0 - (I[0] * 3.0))) - (((2.0 * I[0]) * -3.0) / ((1.0 - (I[0] * 3.0)) ** 2.0)))\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Functions can derive themselves according to the provided index of the input which ought to be derived. [7]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"A Function created from a given expression will produce the expected derivative String.","code":["Function.of(equation).getDerivative( index ).toString() == expected"]}, + + {"kind":"where","text":"The following expressions and derivation indices are being used :","code":{"equation":["\"1 - I[0] * 3\"","\"i0 / 6\"","\"ln( 4 * i0 )\"","\"4**I[0]\"","\"i0 ** 3\"","\"(I[0] * I[1] * I[0]) + 3\"","\"3 ** (i0 / 2)\"","\"(2 * I[0]) / (1 - I[0] * 3)\""],"index":["0","0","0","0","0","0","0","0"],"expected":["\"-3.0\"","\"(1.0 / 6.0)\"","\"(4.0 / (4.0 * I[0]))\"","\"(ln(4.0) * (4.0 ** I[0]))\"","\"(3.0 * (I[0] ** (3.0 - 1.0)))\"","\"((I[1] * I[0]) + (I[0] * I[1]))\"","\"((1.0 / 2.0) * (ln(3.0) * (3.0 ** (I[0] / 2.0))))\"","\"((2.0 / (1.0 - (I[0] * 3.0))) - (((2.0 * I[0]) * -3.0) / ((1.0 - (I[0] * 3.0)) ** 2.0)))\""]}} + ], + "problems":{"dataValues":[], "errors":[]} } ], diff --git a/docs/spock/reports/ut.math.Function_Scalar_Spec.json b/docs/spock/reports/ut.math.Function_Scalar_Spec.json index aea751080..8a74eda38 100644 --- a/docs/spock/reports/ut.math.Function_Scalar_Spec.json +++ b/docs/spock/reports/ut.math.Function_Scalar_Spec.json @@ -1,20 +1,20 @@ { "className":"ut.math.Function_Scalar_Spec", "title":"Functions for Scalars", - "narrative":"The Function API and it's implementations\n receive and process arrays of scalars as arguments.\n Functions don't have to be used alongside tensors / nd-arrays,\n they can also compute derivatives based on scalar values.", + "narrative":"The Function API and it's implementations \n receive and process arrays of scalars as arguments.\n Functions don't have to be used alongside tensors / nd-arrays,\n they can also compute derivatives based on scalar values.", "subjects":[], "statistics":{ - "runs":"6", + "runs":"66", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.027 seconds" + "duration":"0.042 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"Function \"1/I[0]\" instance returns expected scalar results.", + "id":"Function \"1/I[0]\" instance returns expected scalar results. [0]", "result":"PASS", "duration":"0", "iterations":{ @@ -31,7 +31,75 @@ }, { - "id":"Function \"I[0]+1/I[0]\" instance returns expected scalar results.", + "id":"Function \"1/I[0]\" instance returns expected scalar results. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Function instance from expression \"1/I[0]\".","code":["Function f = Function.of(\"1/I[0]\", false)"]}, + + {"kind":"expect","text":"The function yields expected scalar results when called.","code":["if (index!=null) assert f.derive( inputs, index ) == expected","else assert f.call( inputs ) == expected"]}, + + {"kind":"where","text":"The following input array, target derivative index and result scalar is used :","code":{"inputs":["[2]","[2]"],"index":["0","null"],"expected":["-0.25","0.5"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function \"I[0]+1/I[0]\" instance returns expected scalar results. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Function instance from expression \"I[0]+1/I[0]\".","code":["Function f = Function.of(\"I[0]+1/I[0]\", false)"]}, + + {"kind":"expect","text":"The function yields expected scalar results when called.","code":["if (index!=null) assert f.derive( inputs, index ) == expected","else assert f.call( inputs ) == expected"]}, + + {"kind":"where","text":"The following input array, target derivative index and result scalar is used :","code":{"inputs":["[2]","[-1]","[-3]","[0.2]"],"index":["null","0","0","0"],"expected":["2.5","0.0","0.8888888888888888","-23.999999999999996"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function \"I[0]+1/I[0]\" instance returns expected scalar results. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Function instance from expression \"I[0]+1/I[0]\".","code":["Function f = Function.of(\"I[0]+1/I[0]\", false)"]}, + + {"kind":"expect","text":"The function yields expected scalar results when called.","code":["if (index!=null) assert f.derive( inputs, index ) == expected","else assert f.call( inputs ) == expected"]}, + + {"kind":"where","text":"The following input array, target derivative index and result scalar is used :","code":{"inputs":["[2]","[-1]","[-3]","[0.2]"],"index":["null","0","0","0"],"expected":["2.5","0.0","0.8888888888888888","-23.999999999999996"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function \"I[0]+1/I[0]\" instance returns expected scalar results. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Function instance from expression \"I[0]+1/I[0]\".","code":["Function f = Function.of(\"I[0]+1/I[0]\", false)"]}, + + {"kind":"expect","text":"The function yields expected scalar results when called.","code":["if (index!=null) assert f.derive( inputs, index ) == expected","else assert f.call( inputs ) == expected"]}, + + {"kind":"where","text":"The following input array, target derivative index and result scalar is used :","code":{"inputs":["[2]","[-1]","[-3]","[0.2]"],"index":["null","0","0","0"],"expected":["2.5","0.0","0.8888888888888888","-23.999999999999996"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function \"I[0]+1/I[0]\" instance returns expected scalar results. [3]", "result":"PASS", "duration":"0", "iterations":{ @@ -48,7 +116,24 @@ }, { - "id":"Function \"(I[0]+1/I[0])**-I[0]\" instance returns expected scalar result.", + "id":"Function \"(I[0]+1/I[0])**-I[0]\" instance returns expected scalar result. [0]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Function instance from expression \"(I[0]+1/I[0])**-I[0]\".","code":["Function f = Function.of(\"(I[0]+1/I[0])**-I[0]\", false)"]}, + + {"kind":"expect","text":"The function yields expected scalar results when called.","code":["if (index!=null) assert f.derive( inputs, index ) == expected","else assert f.call( inputs ) == expected"]}, + + {"kind":"where","text":"The following input array, target derivative index and result scalar is used :","code":{"inputs":["[ 1 ]","[ 0.2 ]"],"index":["null","0"],"expected":["0.5","-0.5217778675999797"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function \"(I[0]+1/I[0])**-I[0]\" instance returns expected scalar result. [1]", "result":"PASS", "duration":"0", "iterations":{ @@ -65,7 +150,109 @@ }, { - "id":"Function \"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\" instance returns expected scalars.", + "id":"Function \"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\" instance returns expected scalars. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Function f = Function.of(\"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\", false)"]}, + + {"kind":"expect","text":"","code":["if ( index != null ) assert f.derive( inputs, index )==expected","else assert f.call( inputs )==expected"]}, + + {"kind":"where","text":"The following input array, target derivative index and result scalar is used :","code":{"inputs":["[ 3 ]","[ 2.5]","[ 0 ]","[ 0 ]","[ 0.5]","[ 1.6]","[ -4 ]"],"index":["null","null","null","0","0","0","0"],"expected":["3.049021713079475","3.507365283517986","0.2","1.1","0.646867884000033","-0.00697440343353687","3.9174193383745917"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function \"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\" instance returns expected scalars. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Function f = Function.of(\"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\", false)"]}, + + {"kind":"expect","text":"","code":["if ( index != null ) assert f.derive( inputs, index )==expected","else assert f.call( inputs )==expected"]}, + + {"kind":"where","text":"The following input array, target derivative index and result scalar is used :","code":{"inputs":["[ 3 ]","[ 2.5]","[ 0 ]","[ 0 ]","[ 0.5]","[ 1.6]","[ -4 ]"],"index":["null","null","null","0","0","0","0"],"expected":["3.049021713079475","3.507365283517986","0.2","1.1","0.646867884000033","-0.00697440343353687","3.9174193383745917"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function \"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\" instance returns expected scalars. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Function f = Function.of(\"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\", false)"]}, + + {"kind":"expect","text":"","code":["if ( index != null ) assert f.derive( inputs, index )==expected","else assert f.call( inputs )==expected"]}, + + {"kind":"where","text":"The following input array, target derivative index and result scalar is used :","code":{"inputs":["[ 3 ]","[ 2.5]","[ 0 ]","[ 0 ]","[ 0.5]","[ 1.6]","[ -4 ]"],"index":["null","null","null","0","0","0","0"],"expected":["3.049021713079475","3.507365283517986","0.2","1.1","0.646867884000033","-0.00697440343353687","3.9174193383745917"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function \"(cos(I[0]*5)/5+I[0])*(1+sin(I[0])/2)\" instance returns expected scalars. 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[43]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new Function instance which is detached! (no autograd support)","code":["Function f = Function.of(equation, false)"]}, + + {"kind":"expect","text":"Calling the function will yield the expected result.","code":["if ( index!=null ) assert f.derive( inputs, index ) == expected","else assert f.call( inputs )==expected"]}, + + {"kind":"where","text":"The following parameters are used :","code":{"equation":["\"6/2*(1+2)\"","\"sumJs(Ij)\"","\"prod(Ij)\"","\"prod(prod(Ij))\"","\"I3/i[1]-I0+2+i2\"","\"i3*i1/(i4-i0-2)-sig(0)+tanh(0)\"","\"(i0*i1)*i2\"","\"softplus(i0*i1)*i2\"","\"prod(ij)\"","\"relu(prod(ij))\"","\"relu(prod(ij))\"","\"quad(prod(ij)+6)\"","\"quad(prod(ij)+6)\"","\"quad(abs(prod(ij))-6)\"","\"quad(abs(prod(ij))-6)\"","\"sumJs(ij)\"","\"sumJs(ij)\"","\"sumJs(ij**1)\"","\"sumJs(ij**1)\"","\"I[1]**2\"","\"I[1]**2\"","\"sumJs(ij**2)\"","\"sumJs(ij**2)\"","\"2**I[1]\"","\"2**I[0]\"","\"2**I[2]\"","\"2**I[1]\"","\"sumJs(2**I[j])\"","\"sumJs(2**I[j])\"","\"I[1]%2\"","\"I[1]%2\"","\"I[2]%2\"","\"I[2]%2\"","\"I[2]%2\"","\"7%I[1]\"","\"7%I[1]\"","\"sum(7%ij)\""],"inputs":["[]","[2, 3.2, 6]","[0.5, 0.5, 100]","[0.5, 0.5, 10]","[5, 4, 3, 12]","[-4, -2, 6, -3, -8]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -5]","[2, 3, -5]","[2, 3, -5]","[2, 4, -5]","[2, 4, -5]","[2, 3, 4]"],"index":["null","null","null","null","null","null","0","0","1","null","1","null","1","null","1","null","1","null","1","null","1","null","1","null","null","null","1","null","1","null","1","null","2","1","null","1","2"],"expected":["9","11.2","25","(2.5 * 2.5 * 2.5)","3","-1.5","-6","-5.985164261060192","-4","-0.12","-0.04","36","-12*-4","36","-12*-4","3","1","3","1","9","6","17","6","8","4","0.25","5.545177444479562","12.25","5.545177444479562","1","1","-1","1","0","3","0 \n\"sum(7%i0)\" [2, 3, 4] null 3\n\"sum(7%ij)\" [2, 3, 4] null 5\n\"sum(7%i0)\" [2, 3, 4] 2 0","0 \n\"sum(i0%3)\" [2, 3, 4] 0 1\n\"sum(ij%3)\" [2, 3, 4] 2 1\n\"sum(ij-3)\" [1, 2, 4] null -2\n\"sum(ij-3)\" [1, 2, 4] 1 1\n\"sum(3-ij)\" [1, 2, 4] null 2\n\"sum(3-ij)\" [1, 2, 4] 1 -1\n\"sum(3-ij-ij)\" [1, 2, 4] 1 -2"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test scalar results of various Function instances. [44]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new Function instance which is detached! (no autograd support)","code":["Function f = Function.of(equation, false)"]}, + + {"kind":"expect","text":"Calling the function will yield the expected result.","code":["if ( index!=null ) assert f.derive( inputs, index ) == expected","else assert f.call( inputs )==expected"]}, + + {"kind":"where","text":"The following parameters are used :","code":{"equation":["\"6/2*(1+2)\"","\"sumJs(Ij)\"","\"prod(Ij)\"","\"prod(prod(Ij))\"","\"I3/i[1]-I0+2+i2\"","\"i3*i1/(i4-i0-2)-sig(0)+tanh(0)\"","\"(i0*i1)*i2\"","\"softplus(i0*i1)*i2\"","\"prod(ij)\"","\"relu(prod(ij))\"","\"relu(prod(ij))\"","\"quad(prod(ij)+6)\"","\"quad(prod(ij)+6)\"","\"quad(abs(prod(ij))-6)\"","\"quad(abs(prod(ij))-6)\"","\"sumJs(ij)\"","\"sumJs(ij)\"","\"sumJs(ij**1)\"","\"sumJs(ij**1)\"","\"I[1]**2\"","\"I[1]**2\"","\"sumJs(ij**2)\"","\"sumJs(ij**2)\"","\"2**I[1]\"","\"2**I[0]\"","\"2**I[2]\"","\"2**I[1]\"","\"sumJs(2**I[j])\"","\"sumJs(2**I[j])\"","\"I[1]%2\"","\"I[1]%2\"","\"I[2]%2\"","\"I[2]%2\"","\"I[2]%2\"","\"7%I[1]\"","\"7%I[1]\"","\"sum(7%ij)\""],"inputs":["[]","[2, 3.2, 6]","[0.5, 0.5, 100]","[0.5, 0.5, 10]","[5, 4, 3, 12]","[-4, -2, 6, -3, -8]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -5]","[2, 3, -5]","[2, 3, -5]","[2, 4, -5]","[2, 4, -5]","[2, 3, 4]"],"index":["null","null","null","null","null","null","0","0","1","null","1","null","1","null","1","null","1","null","1","null","1","null","1","null","null","null","1","null","1","null","1","null","2","1","null","1","2"],"expected":["9","11.2","25","(2.5 * 2.5 * 2.5)","3","-1.5","-6","-5.985164261060192","-4","-0.12","-0.04","36","-12*-4","36","-12*-4","3","1","3","1","9","6","17","6","8","4","0.25","5.545177444479562","12.25","5.545177444479562","1","1","-1","1","0","3","0 \n\"sum(7%i0)\" [2, 3, 4] null 3\n\"sum(7%ij)\" [2, 3, 4] null 5\n\"sum(7%i0)\" [2, 3, 4] 2 0","0 \n\"sum(i0%3)\" [2, 3, 4] 0 1\n\"sum(ij%3)\" [2, 3, 4] 2 1\n\"sum(ij-3)\" [1, 2, 4] null -2\n\"sum(ij-3)\" [1, 2, 4] 1 1\n\"sum(3-ij)\" [1, 2, 4] null 2\n\"sum(3-ij)\" [1, 2, 4] 1 -1\n\"sum(3-ij-ij)\" [1, 2, 4] 1 -2"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test scalar results of various Function instances. [45]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new Function instance which is detached! (no autograd support)","code":["Function f = Function.of(equation, false)"]}, + + {"kind":"expect","text":"Calling the function will yield the expected result.","code":["if ( index!=null ) assert f.derive( inputs, index ) == expected","else assert f.call( inputs )==expected"]}, + + {"kind":"where","text":"The following parameters are used :","code":{"equation":["\"6/2*(1+2)\"","\"sumJs(Ij)\"","\"prod(Ij)\"","\"prod(prod(Ij))\"","\"I3/i[1]-I0+2+i2\"","\"i3*i1/(i4-i0-2)-sig(0)+tanh(0)\"","\"(i0*i1)*i2\"","\"softplus(i0*i1)*i2\"","\"prod(ij)\"","\"relu(prod(ij))\"","\"relu(prod(ij))\"","\"quad(prod(ij)+6)\"","\"quad(prod(ij)+6)\"","\"quad(abs(prod(ij))-6)\"","\"quad(abs(prod(ij))-6)\"","\"sumJs(ij)\"","\"sumJs(ij)\"","\"sumJs(ij**1)\"","\"sumJs(ij**1)\"","\"I[1]**2\"","\"I[1]**2\"","\"sumJs(ij**2)\"","\"sumJs(ij**2)\"","\"2**I[1]\"","\"2**I[0]\"","\"2**I[2]\"","\"2**I[1]\"","\"sumJs(2**I[j])\"","\"sumJs(2**I[j])\"","\"I[1]%2\"","\"I[1]%2\"","\"I[2]%2\"","\"I[2]%2\"","\"I[2]%2\"","\"7%I[1]\"","\"7%I[1]\"","\"sum(7%ij)\""],"inputs":["[]","[2, 3.2, 6]","[0.5, 0.5, 100]","[0.5, 0.5, 10]","[5, 4, 3, 12]","[-4, -2, 6, -3, -8]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -2]","[2, 3, -5]","[2, 3, -5]","[2, 3, -5]","[2, 4, -5]","[2, 4, -5]","[2, 3, 4]"],"index":["null","null","null","null","null","null","0","0","1","null","1","null","1","null","1","null","1","null","1","null","1","null","1","null","null","null","1","null","1","null","1","null","2","1","null","1","2"],"expected":["9","11.2","25","(2.5 * 2.5 * 2.5)","3","-1.5","-6","-5.985164261060192","-4","-0.12","-0.04","36","-12*-4","36","-12*-4","3","1","3","1","9","6","17","6","8","4","0.25","5.545177444479562","12.25","5.545177444479562","1","1","-1","1","0","3","0 \n\"sum(7%i0)\" [2, 3, 4] null 3\n\"sum(7%ij)\" [2, 3, 4] null 5\n\"sum(7%i0)\" [2, 3, 4] 2 0","0 \n\"sum(i0%3)\" [2, 3, 4] 0 1\n\"sum(ij%3)\" [2, 3, 4] 2 1\n\"sum(ij-3)\" [1, 2, 4] null -2\n\"sum(ij-3)\" [1, 2, 4] 1 1\n\"sum(3-ij)\" [1, 2, 4] null 2\n\"sum(3-ij)\" [1, 2, 4] 1 -1\n\"sum(3-ij-ij)\" [1, 2, 4] 1 -2"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test scalar results of various Function instances. [46]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.math.Function_Spec.json b/docs/spock/reports/ut.math.Function_Spec.json index 123974395..7a9509260 100644 --- a/docs/spock/reports/ut.math.Function_Spec.json +++ b/docs/spock/reports/ut.math.Function_Spec.json @@ -4,19 +4,19 @@ "narrative":"This specification tests the default methods on functions\n through a simple dummy implementation of the Function interface.", "subjects":["java.util.function.Function"], "statistics":{ - "runs":"4", + "runs":"40", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.023 seconds" + "duration":"0.046 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"Function implementations ensure that internally created tensors are flagged as \"intermediate\" initially!", + "id":"Function implementations ensure that internally created tensors are flagged as \"intermediate\" initially! [0]", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.007 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -43,12 +43,70 @@ }, { - "id":"Function implementations ensure that outputs which are input members are not flagged as \"intermediate\"!", + "id":"Function implementations ensure that internally created tensors are flagged as \"intermediate\" initially! [1]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"given","text":"","code":["var fun1 = new DummyFunction((Args args, Tensor[] tensors) -> {"," var outputs = [Tensor.of(1d)]"," tensors.length.times { outputs.add(tensors[it]) }"," return outputs[0]"," })"]}, + + {"kind":"and","text":"","code":["var fun2 = new DummyFunction((Args args, Tensor[] tensors) -> {"," var outputs = [Tensor.of(1d)]"," tensors.length.times { outputs.add(tensors[it]) }"," return outputs[0].mut.setIsIntermediate(true)"," })"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(3d)","var b = Tensor.of(-2.5)"]}, + + {"kind":"expect","text":"","code":["!a.isIntermediate()","!b.isIntermediate()"]}, + + {"kind":"when","text":"","code":["caller(a, b, fun1)"]}, + + {"kind":"then","text":"","code":["thrown(IllegalStateException)"]}, + + {"kind":"when","text":"","code":["caller(a, b, fun2)"]}, + + {"kind":"then","text":"","code":["noExceptionThrown()"]}, + + {"kind":"where","text":"","code":{"caller":["{t1, t2, fun -> fun.call(t1, t2)}","{t1, t2, fun -> fun.invoke(t1, t2)}","{t1, t2, fun -> fun.execute(t1, t2)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function implementations ensure that internally created tensors are flagged as \"intermediate\" initially! [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var fun1 = new DummyFunction((Args args, Tensor[] tensors) -> {"," var outputs = [Tensor.of(1d)]"," tensors.length.times { outputs.add(tensors[it]) }"," return outputs[0]"," })"]}, + + {"kind":"and","text":"","code":["var fun2 = new DummyFunction((Args args, Tensor[] tensors) -> {"," var outputs = [Tensor.of(1d)]"," tensors.length.times { outputs.add(tensors[it]) }"," return outputs[0].mut.setIsIntermediate(true)"," })"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(3d)","var b = Tensor.of(-2.5)"]}, + + {"kind":"expect","text":"","code":["!a.isIntermediate()","!b.isIntermediate()"]}, + + {"kind":"when","text":"","code":["caller(a, b, fun1)"]}, + + {"kind":"then","text":"","code":["thrown(IllegalStateException)"]}, + + {"kind":"when","text":"","code":["caller(a, b, fun2)"]}, + + {"kind":"then","text":"","code":["noExceptionThrown()"]}, + + {"kind":"where","text":"","code":{"caller":["{t1, t2, fun -> fun.call(t1, t2)}","{t1, t2, fun -> fun.invoke(t1, t2)}","{t1, t2, fun -> fun.execute(t1, t2)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function implementations ensure that outputs which are input members are not flagged as \"intermediate\"! [0]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"","code":["var fun1 = new DummyFunction((Args args, Tensor[] tensors) -> tensors[0] )"]}, @@ -72,12 +130,70 @@ }, { - "id":"Function implementations will ensure the \"call\" and \"invoke\" does not return tensors flagged as \"intermediate\".", + "id":"Function implementations ensure that outputs which are input members are not flagged as \"intermediate\"! [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var fun1 = new DummyFunction((Args args, Tensor[] tensors) -> tensors[0] )"]}, + + {"kind":"and","text":"","code":["var fun2 = new DummyFunction((Args args, Tensor[] tensors) -> {"," return tensors[0].mut.setIsIntermediate( true ) // This should fail!"," })"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(3.0)","var b = Tensor.of(-2.5)"]}, + + {"kind":"expect","text":"","code":["!a.isIntermediate()","!b.isIntermediate()"]}, + + {"kind":"when","text":"","code":["caller(a, b, fun1)"]}, + + {"kind":"then","text":"","code":["noExceptionThrown()"]}, + + {"kind":"when","text":"","code":["caller(a, b, fun2)"]}, + + {"kind":"then","text":"","code":["thrown(IllegalStateException)"]}, + + {"kind":"where","text":"","code":{"caller":["{t1, t2, fun -> fun.call(t1, t2)}","{t1, t2, fun -> fun.invoke(t1, t2)}","{t1, t2, fun -> fun.execute(t1, t2)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function implementations ensure that outputs which are input members are not flagged as \"intermediate\"! [2]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"given","text":"","code":["var fun1 = new DummyFunction((Args args, Tensor[] tensors) -> tensors[0] )"]}, + + {"kind":"and","text":"","code":["var fun2 = new DummyFunction((Args args, Tensor[] tensors) -> {"," return tensors[0].mut.setIsIntermediate( true ) // This should fail!"," })"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(3.0)","var b = Tensor.of(-2.5)"]}, + + {"kind":"expect","text":"","code":["!a.isIntermediate()","!b.isIntermediate()"]}, + + {"kind":"when","text":"","code":["caller(a, b, fun1)"]}, + + {"kind":"then","text":"","code":["noExceptionThrown()"]}, + + {"kind":"when","text":"","code":["caller(a, b, fun2)"]}, + + {"kind":"then","text":"","code":["thrown(IllegalStateException)"]}, + + {"kind":"where","text":"","code":{"caller":["{t1, t2, fun -> fun.call(t1, t2)}","{t1, t2, fun -> fun.invoke(t1, t2)}","{t1, t2, fun -> fun.execute(t1, t2)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Function implementations will ensure the \"call\" and \"invoke\" does not return tensors flagged as \"intermediate\".", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"","code":["var fun = new DummyFunction((Args args, Tensor[] tensors) -> {"," return Tensor.of(42f).mut.setIsIntermediate(true)"," })"]}, @@ -93,9 +209,553 @@ }, { - "id":"The library context exposes a set of useful functions.", + "id":"The library context exposes a set of useful functions. 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[29]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"","code":["fun.apply(Neureka.get().backend.function).toString() == expected","!fun.apply(Neureka.get().backend.function).isDoingAD()"]}, + + {"kind":"and","text":"","code":["fun.apply(Neureka.get().backend.autogradFunction).toString() == expected","fun.apply(Neureka.get().backend.autogradFunction).isDoingAD()"]}, + + {"kind":"where","text":"","code":{"expected":["'ln(I[0])'","'ln(I[0])'","'gaus(I[0])'","'gaus(I[0])'","'fast_gaus(I[0])'","'fast_gaus(I[0])'","'sig(I[0])'","'sig(I[0])'","'tanh(I[0])'","'tanh(I[0])'","'fast_tanh(I[0])'","'fast_tanh(I[0])'","'softsign(I[0])'","'softsign(I[0])'","'softsign(I[0])'","'quad(I[0])'","'quad(I[0])'","'relu(I[0])'","'relu(I[0])'","'abs(I[0])'","'abs(I[0])'","'sin(I[0])'","'sin(I[0])'","'cos(I[0])'","'cos(I[0])'","'softplus(I[0])'","'softplus(I[0])'","'silu(I[0])'","'silu(I[0])'","'gelu(I[0])'","'gelu(I[0])'","'selu(I[0])'","'selu(I[0])'"],"fun":["{Functions it -> it.ln }","{Functions it -> it.ln() }","{Functions it -> it.gaus }","{Functions it -> it.gaus() }","{Functions it -> it.fastGaus }","{Functions it -> it.fastGaus() }","{Functions it -> it.sigmoid }","{Functions it -> it.sigmoid() }","{Functions it -> it.tanh }","{Functions it -> it.tanh() }","{Functions it -> it.fastTanh }","{Functions it -> it.fastTanh() }","{Functions it -> it.softsign }","{Functions it -> it.softsign() }","{Functions it -> it.softsign }","{Functions it -> it.quad() }","{Functions it -> it.quad }","{Functions it -> it.relu() }","{Functions it -> it.relu }","{Functions it -> it.abs() }","{Functions it -> it.abs }","{Functions it -> it.sin() }","{Functions it -> it.sin }","{Functions it -> it.cos() }","{Functions it -> it.cos }","{Functions it -> it.softplus() }","{Functions it -> it.softplus }","{Functions it -> it.silu() }","{Functions it -> it.silu }","{Functions it -> it.gelu() }","{Functions it -> it.gelu }","{Functions it -> it.selu() }","{Functions it -> it.selu }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The library context exposes a set of useful functions. [30]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"","code":["fun.apply(Neureka.get().backend.function).toString() == expected","!fun.apply(Neureka.get().backend.function).isDoingAD()"]}, + + {"kind":"and","text":"","code":["fun.apply(Neureka.get().backend.autogradFunction).toString() == expected","fun.apply(Neureka.get().backend.autogradFunction).isDoingAD()"]}, + + {"kind":"where","text":"","code":{"expected":["'ln(I[0])'","'ln(I[0])'","'gaus(I[0])'","'gaus(I[0])'","'fast_gaus(I[0])'","'fast_gaus(I[0])'","'sig(I[0])'","'sig(I[0])'","'tanh(I[0])'","'tanh(I[0])'","'fast_tanh(I[0])'","'fast_tanh(I[0])'","'softsign(I[0])'","'softsign(I[0])'","'softsign(I[0])'","'quad(I[0])'","'quad(I[0])'","'relu(I[0])'","'relu(I[0])'","'abs(I[0])'","'abs(I[0])'","'sin(I[0])'","'sin(I[0])'","'cos(I[0])'","'cos(I[0])'","'softplus(I[0])'","'softplus(I[0])'","'silu(I[0])'","'silu(I[0])'","'gelu(I[0])'","'gelu(I[0])'","'selu(I[0])'","'selu(I[0])'"],"fun":["{Functions it -> it.ln }","{Functions it -> it.ln() }","{Functions it -> it.gaus }","{Functions it -> it.gaus() }","{Functions it -> it.fastGaus }","{Functions it -> it.fastGaus() }","{Functions it -> it.sigmoid }","{Functions it -> it.sigmoid() }","{Functions it -> it.tanh }","{Functions it -> it.tanh() }","{Functions it -> it.fastTanh }","{Functions it -> it.fastTanh() }","{Functions it -> it.softsign }","{Functions it -> it.softsign() }","{Functions it -> it.softsign }","{Functions it -> it.quad() }","{Functions it -> it.quad }","{Functions it -> it.relu() }","{Functions it -> it.relu }","{Functions it -> it.abs() }","{Functions it -> it.abs }","{Functions it -> it.sin() }","{Functions it -> it.sin }","{Functions it -> it.cos() }","{Functions it -> it.cos }","{Functions it -> it.softplus() }","{Functions it -> it.softplus }","{Functions it -> it.silu() }","{Functions it -> it.silu }","{Functions it -> it.gelu() }","{Functions it -> it.gelu }","{Functions it -> it.selu() }","{Functions it -> it.selu }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The library context exposes a set of useful functions. [31]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"","code":["fun.apply(Neureka.get().backend.function).toString() == expected","!fun.apply(Neureka.get().backend.function).isDoingAD()"]}, + + {"kind":"and","text":"","code":["fun.apply(Neureka.get().backend.autogradFunction).toString() == expected","fun.apply(Neureka.get().backend.autogradFunction).isDoingAD()"]}, + + {"kind":"where","text":"","code":{"expected":["'ln(I[0])'","'ln(I[0])'","'gaus(I[0])'","'gaus(I[0])'","'fast_gaus(I[0])'","'fast_gaus(I[0])'","'sig(I[0])'","'sig(I[0])'","'tanh(I[0])'","'tanh(I[0])'","'fast_tanh(I[0])'","'fast_tanh(I[0])'","'softsign(I[0])'","'softsign(I[0])'","'softsign(I[0])'","'quad(I[0])'","'quad(I[0])'","'relu(I[0])'","'relu(I[0])'","'abs(I[0])'","'abs(I[0])'","'sin(I[0])'","'sin(I[0])'","'cos(I[0])'","'cos(I[0])'","'softplus(I[0])'","'softplus(I[0])'","'silu(I[0])'","'silu(I[0])'","'gelu(I[0])'","'gelu(I[0])'","'selu(I[0])'","'selu(I[0])'"],"fun":["{Functions it -> it.ln }","{Functions it -> it.ln() }","{Functions it -> it.gaus }","{Functions it -> it.gaus() }","{Functions it -> it.fastGaus }","{Functions it -> it.fastGaus() }","{Functions it -> it.sigmoid }","{Functions it -> it.sigmoid() }","{Functions it -> it.tanh }","{Functions it -> it.tanh() }","{Functions it -> it.fastTanh }","{Functions it -> it.fastTanh() }","{Functions it -> it.softsign }","{Functions it -> it.softsign() }","{Functions it -> it.softsign }","{Functions it -> it.quad() }","{Functions it -> it.quad }","{Functions it -> it.relu() }","{Functions it -> it.relu }","{Functions it -> it.abs() }","{Functions it -> it.abs }","{Functions it -> it.sin() }","{Functions it -> it.sin }","{Functions it -> it.cos() }","{Functions it -> it.cos }","{Functions it -> it.softplus() }","{Functions it -> it.softplus }","{Functions it -> it.silu() }","{Functions it -> it.silu }","{Functions it -> it.gelu() }","{Functions it -> it.gelu }","{Functions it -> it.selu() }","{Functions it -> it.selu }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The library context exposes a set of useful functions. [32]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.math.Tensor_Function_Spec.json b/docs/spock/reports/ut.math.Tensor_Function_Spec.json index c2563cf38..bc5e8b516 100644 --- a/docs/spock/reports/ut.math.Tensor_Function_Spec.json +++ b/docs/spock/reports/ut.math.Tensor_Function_Spec.json @@ -4,19 +4,19 @@ "narrative":"A tensor would be nothing without being able to apply operations on them.\n However, calling operations manually in order to process your\n tensors can be a verbose and error prone task.\n This is where functions come into play.\n Neureka's functions are composed of operations forming an abstract syntax tree.\n Passing tensors to a function will route them trough this tree and apply\n all of the operations on the tensors for you.", "subjects":["neureka.Tensor","neureka.math.Function"], "statistics":{ - "runs":"10", + "runs":"52", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.080 seconds" + "duration":"0.179 seconds" }, "headers":[" \n This specification ensures that tensors supplied\n to functions are executed successfully and produce the expected results.\n "],"tags":{},"see":[], "features":[ { "id":"The tensor API has built-in methods for applying functions.", "result":"PASS", - "duration":"0.004 seconds", + "duration":"0.008 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"The softmax function can be applied to tensors with more than one dimension.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -52,7 +52,7 @@ { "id":"The softmax can be calculated for a particular axis.", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.008 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -77,7 +77,7 @@ { "id":"The softmax can be calculated alongside multiple axes.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.007 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -96,7 +96,7 @@ { "id":"The optimization function for the SGD algorithm produces the expected result", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -117,9 +117,9 @@ }, { - "id":"Tensor results of various Function instances return expected results.", + "id":"Tensor results of various Function instances return expected results. [0]", "result":"PASS", - "duration":"0.019 seconds", + "duration":"0.006 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -142,83 +142,1133 @@ }, { - "id":"Reshaping on 3D tensors works by instantiate a Function instance built from a String.", + "id":"Tensor results of various Function instances return expected results. [1]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)","var f = Function.of(\"[2, 0, 1]:(I[0])\")"]}, + {"kind":"given","text":"We set the experimental \"autoConvertToFloat\" flag to true.","code":["Neureka.get().backend().find(CLBackend).ifPresent({ it.settings.autoConvertToFloat=true })"]}, - {"kind":"when","text":"","code":["when : var t = Tensor.of([3, 4, 2], 1d..5d)"]}, + {"kind":"and","text":"","code":["and : \"A new Function instance created from ${equation}.\"","Function f = Function.of(equation, true) // TODO : test with 'doAD' : false!"]}, - {"kind":"then","text":"","code":["then : t.toString().contains(\"[3x4x2]:(1.0, 2.0, 3.0, 4.0, 5.0, 1.0, 2.0, 3.0, 4.0, 5.0, 1.0, 2.0, 3.0, 4.0, 5.0, 1.0, 2.0, 3.0, 4.0, 5.0, 1.0, 2.0, 3.0, 4.0)\")"]}, + {"kind":"and","text":"","code":["inputs.each {it.to(Device.get(device))}"]}, - {"kind":"when","text":"","code":["when : var r = f(t)"]}, + {"kind":"and","text":"The result is being calculated by invoking the Function instance.","code":["Tensor result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, - {"kind":"then","text":"","code":["r.toString().contains(\"[2x3x4]\")","r.toString().contains(\"[2x3x4]:(1.0, 3.0, 5.0, 2.0, 4.0, 1.0, 3.0, 5.0, 2.0, 4.0, 1.0, 3.0, 2.0, 4.0, 1.0, 3.0, 5.0, 2.0, 4.0, 1.0, 3.0, 5.0, 2.0, 4.0)\")"]} + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0..d(\")"]}, + {"kind":"and","text":"","code":["inputs.each {it.to(Device.get(device))}"]}, - {"kind":"when","text":"","code":["var back = trimmed.backward()"]}, + {"kind":"and","text":"The result is being calculated by invoking the Function instance.","code":["Tensor result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, - {"kind":"then","text":"","code":["back == trimmed"]}, + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, - {"kind":"then","text":"The \"call\" method will not return an intermediate result.","code":["!result1.isIntermediate()"]}, + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0.. result = ( index != null ? f.derive( inputs, index ) : f.call( inputs ) )","List value = result.getItemsAs(double[].class) as List"]}, + + {"kind":"expect","text":"","code":["expect : \"The calculated result ${result} should be (ruffly) equal to expected ${expected}.\"","(0..d(\")"]}, + + {"kind":"when","text":"","code":["var back = trimmed.backward()"]}, + + {"kind":"then","text":"","code":["back == trimmed"]}, + + {"kind":"and","text":"","code":["t.gradient.get().toString() == \"(1x1x3x2x1):[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\""]} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Executed tensors are intermediate tensors.", + "result":"PASS", + "duration":"0.006 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Functions expose different kinds of methods for different kinds of\n purposes, however there is one species of methods with a very important role\n in ensuring memory efficiency.\n These types of methods are the `execute` methods which \n distinguish themselves in that the tensors returned by \n these methods are flagged as \"intermediate\".\n If a tensor is an intermediate one, it becomes eligible \n for deletion when consumed by another function.\n Note that internally every function is usually a composite\n of other functions forming a syntax tree which will process\n input tensors through the execute methods, which causes\n intermediate results to be deleted automatically.\n When executing a function as a user of Neureka\n one should generally avoid using the `execute` method in order to avoid\n accidental deletion of results.\n This is mostly relevant for when designing custom operations.\n "] + }, + "blocks":[ + {"kind":"given","text":"We create a simple function taking one input.","code":["var fun = Function.of('i0 * relu(i0) + 1')"]}, + + {"kind":"and","text":"A vector tensor of 5 float numbers","code":["var t = Tensor.of(1f, -5f, -3f, 2f, 8f)"]}, + + {"kind":"expect","text":"Both the tensor as well as the function were created successfully.","code":["t.itemType == Float","fun.toString() == \"((I[0] * relu(I[0])) + 1.0)\""]}, + + {"kind":"when","text":"We try different kinds of ways of passing the tensor to the function...","code":["var result1 = fun.call(t)","var result2 = fun.invoke(t)","var result3 = fun.execute(t)"]}, + + {"kind":"then","text":"The \"call\" method will not return an intermediate result.","code":["!result1.isIntermediate()"]}, + + {"kind":"and","text":"The functionally identical synonym method \"invoke\" will also yield a non-intermediate result.","code":["!result2.isIntermediate()"]}, + + {"kind":"and","text":"As expected, the tensor of the \"execute\" method is indeed intermediate.","code":["result3.isIntermediate()"]}, + + {"kind":"and","text":"Otherwise all 3 tensors are basically the same.","code":["result1.toString() == \"(5):[2.0, 1.25, 1.09, 5.0, 65.0]\"","result2.toString() == \"(5):[2.0, 1.25, 1.09, 5.0, 65.0]\"","result3.toString() == \"(5):[2.0, 1.25, 1.09, 5.0, 65.0]\""]} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can collect a stream into a tensor.", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"We create a stream of integers.","code":["var stream = Stream.of(1, 2, 3, 4, 5, 6)"]}, diff --git a/docs/spock/reports/ut.miscellaneous.Weired_NN_Spec.json b/docs/spock/reports/ut.miscellaneous.Weired_NN_Spec.json index 68cf2a58b..93e44631a 100644 --- a/docs/spock/reports/ut.miscellaneous.Weired_NN_Spec.json +++ b/docs/spock/reports/ut.miscellaneous.Weired_NN_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.019 seconds" + "duration":"0.036 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"Dot based feed forward and activation produces expected result.", "result":"PASS", - "duration":"0.017 seconds", + "duration":"0.034 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.ndas.Nda_Assign_Spec.json b/docs/spock/reports/ut.ndas.Nda_Assign_Spec.json index 65baab9ad..fe23b2131 100644 --- a/docs/spock/reports/ut.ndas.Nda_Assign_Spec.json +++ b/docs/spock/reports/ut.ndas.Nda_Assign_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.002 seconds" + "duration":"0.007 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can use the \"mut\" API to assign the contents of one nd-array into another one.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"Assignment can be easily achieved through subscription operators.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -52,7 +52,7 @@ { "id":"We can assign one slice into another one.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n Using the 'assign' operation on slices should be handled with care,\n since the operation has side effects on the underlying data array\n which is shared by both the slice and its parent.\n Use the 'copy' operation on slices if you want to avoid this.\n "] }, diff --git a/docs/spock/reports/ut.ndas.Nda_Framing_Spec.json b/docs/spock/reports/ut.ndas.Nda_Framing_Spec.json index 5118d38fc..6d8f0a0ba 100644 --- a/docs/spock/reports/ut.ndas.Nda_Framing_Spec.json +++ b/docs/spock/reports/ut.ndas.Nda_Framing_Spec.json @@ -1,7 +1,7 @@ { "className":"ut.ndas.Nda_Framing_Spec", "title":"Nda framing", - "narrative":"Immutability is a core concept of the Neureka library.\n This means that the Nda API does not expose mutability directly.\n Instead, the API exposes methods that return new instances of Nda\n that are derived from the original instance.\n\n This is also true for labeling operations,\n meaning that the Nda API does not directly expose methods that mutate labels of an Nda\n but instead provides methods that return new instances of Nda\n with different labels.\n\n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!", + "narrative":"Immutability is a core concept of the Neureka library.\n This means that the Nda API does not expose mutability directly.\n Instead, the API exposes methods that return new instances of Nda\n that are derived from the original instance.\n\n This is also true for labeling operations, \n meaning that the Nda API does not directly expose methods that mutate labels of an Nda\n but instead provides methods that return new instances of Nda\n with different labels.\n\n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!", "subjects":["neureka.Nda"], "statistics":{ "runs":"6", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.015 seconds" + "duration":"0.028 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"An Nda can be labeled.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -35,7 +35,7 @@ { "id":"We can label the columns of a rank 2 nd-array.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -52,7 +52,7 @@ { "id":"We can label the columns and rows of a rank 3 nd-array.", "result":"PASS", - "duration":"0", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -69,7 +69,7 @@ { "id":"We can use labels as selectors for slicing.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -90,7 +90,7 @@ { "id":"The slice of a labeled vector is labeled too.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -111,7 +111,7 @@ { "id":"Concatenating 2 labeled nd-arrays will produce a nd-array which is also labeled.", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.007 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.ndas.Nda_Inplace_Framing_Spec.json b/docs/spock/reports/ut.ndas.Nda_Inplace_Framing_Spec.json index e8b6229c8..4e486603d 100644 --- a/docs/spock/reports/ut.ndas.Nda_Inplace_Framing_Spec.json +++ b/docs/spock/reports/ut.ndas.Nda_Inplace_Framing_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.005 seconds" + "duration":"0.023 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can label the columns of a rank 2 nd-array.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"We can label the columns and rows of a rank 3 nd-array.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -50,7 +50,7 @@ { "id":"We can use labels as selectors for slicing.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -71,7 +71,7 @@ { "id":"The slice of a labeled vector is labeled too.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -92,7 +92,7 @@ { "id":"Concatenating 2 labeled nd-arrays will produce a nd-array which is also labeled.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -113,7 +113,7 @@ { "id":"We can concatenate more than 2 nd-arrays.", "result":"PASS", - "duration":"0", + "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.ndas.Nda_Instantiation_Spec.json b/docs/spock/reports/ut.ndas.Nda_Instantiation_Spec.json index 0fa1eadc1..60f15dfb2 100644 --- a/docs/spock/reports/ut.ndas.Nda_Instantiation_Spec.json +++ b/docs/spock/reports/ut.ndas.Nda_Instantiation_Spec.json @@ -4,19 +4,19 @@ "narrative":"In this specification we cover how ND-arrays can be instantiated.", "subjects":["neureka.Nda"], "statistics":{ - "runs":"3", + "runs":"8", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.004 seconds" + "duration":"0.016 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"A vector can be created from an array of values through the \"of\" method.", "result":"PASS", - "duration":"0", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,11 +37,34 @@ }, { - "id":"ND-arrays can be created fluently.", + "id":"ND-arrays can be created fluently. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Nda instance using a fluent builder API.","code":["Nda t = Nda.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type!","code":["t.itemType() == type"]}, + + {"kind":"and","text":"...all items of the array will have the same value, which is the one we passed to the fluent builder.","code":["t.every((Predicate){ it == value })"]}, + + {"kind":"and","text":"The nd-array will have the shape we passed to the builder.","code":["t.shape == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries: 2 * 3 = 6.","code":["t.size == 6"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["42 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 42 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ND-arrays can be created fluently. [1]", "result":"PASS", "duration":"0", "iterations":{ - "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!","This feature is based on a fluent builder API!","This feature is based on a fluent builder API!","This feature is based on a fluent builder API!","This feature is based on a fluent builder API!","This feature is based on a fluent builder API!"] + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] }, "blocks":[ {"kind":"given","text":"We create a new homogeneously filled Nda instance using a fluent builder API.","code":["Nda t = Nda.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, @@ -60,10 +83,102 @@ }, { - "id":"Common types of nd-arrays are best instantiated using type specific convenience methods.", + "id":"ND-arrays can be created fluently. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Nda instance using a fluent builder API.","code":["Nda t = Nda.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type!","code":["t.itemType() == type"]}, + + {"kind":"and","text":"...all items of the array will have the same value, which is the one we passed to the fluent builder.","code":["t.every((Predicate){ it == value })"]}, + + {"kind":"and","text":"The nd-array will have the shape we passed to the builder.","code":["t.shape == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries: 2 * 3 = 6.","code":["t.size == 6"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["42 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 42 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ND-arrays can be created fluently. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Nda instance using a fluent builder API.","code":["Nda t = Nda.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type!","code":["t.itemType() == type"]}, + + {"kind":"and","text":"...all items of the array will have the same value, which is the one we passed to the fluent builder.","code":["t.every((Predicate){ it == value })"]}, + + {"kind":"and","text":"The nd-array will have the shape we passed to the builder.","code":["t.shape == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries: 2 * 3 = 6.","code":["t.size == 6"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["42 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 42 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ND-arrays can be created fluently. [4]", "result":"PASS", "duration":"0", "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Nda instance using a fluent builder API.","code":["Nda t = Nda.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type!","code":["t.itemType() == type"]}, + + {"kind":"and","text":"...all items of the array will have the same value, which is the one we passed to the fluent builder.","code":["t.every((Predicate){ it == value })"]}, + + {"kind":"and","text":"The nd-array will have the shape we passed to the builder.","code":["t.shape == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries: 2 * 3 = 6.","code":["t.size == 6"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["42 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 42 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ND-arrays can be created fluently. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Nda instance using a fluent builder API.","code":["Nda t = Nda.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type!","code":["t.itemType() == type"]}, + + {"kind":"and","text":"...all items of the array will have the same value, which is the one we passed to the fluent builder.","code":["t.every((Predicate){ it == value })"]}, + + {"kind":"and","text":"The nd-array will have the shape we passed to the builder.","code":["t.shape == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries: 2 * 3 = 6.","code":["t.size == 6"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["42 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 42 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Common types of nd-arrays are best instantiated using type specific convenience methods.", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ diff --git a/docs/spock/reports/ut.ndas.Nda_Items_Spec.json b/docs/spock/reports/ut.ndas.Nda_Items_Spec.json index 16307b698..4658d04ba 100644 --- a/docs/spock/reports/ut.ndas.Nda_Items_Spec.json +++ b/docs/spock/reports/ut.ndas.Nda_Items_Spec.json @@ -1,7 +1,7 @@ { "className":"ut.ndas.Nda_Items_Spec", "title":"The Nds Items API", - "narrative":"Nd-arrays are collections of items similar to other\n collection types in Java.\n One useful way to access the items of an nd-array is\n to use the items API.\n\n Using the `at` methods we can access an `Item` object\n which is a wrapper around the item's value and its\n index in the nd-array.\n\n The `Item` object is a simple data class which\n is very similar to the `Optional` class, meaning\n that it can either be empty or contain a value.", + "narrative":"Nd-arrays are collections of items similar to other\n collection types in Java. \n One useful way to access the items of an nd-array is\n to use the items API.\n\n Using the `at` methods we can access an `Item` object\n which is a wrapper around the item's value and its\n index in the nd-array.\n\n The `Item` object is a simple data class which\n is very similar to the `Optional` class, meaning\n that it can either be empty or contain a value.", "subjects":[], "statistics":{ "runs":"6", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.004 seconds" + "duration":"0.010 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can check if items of a tensor is present or not.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -31,7 +31,7 @@ { "id":"We can get the value of an item.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -46,7 +46,7 @@ { "id":"The \"get\" method of an Item object will throw an exception if the item is missing.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n Similar as the `Optional` class, an `Item` object can be empty.\n If we try to get the value of an empty item, an exception will be thrown.\n The reason for this is that we can not be sure that the item is actually\n empty or if it is just not present in the nd-array.\n If you want to get an item's value without throwing an exception \n (but the risk of getting a null value instead) you can use the `orElseNull` method.\n "] }, @@ -82,7 +82,7 @@ { "id":"An item can be converted to an Optional object.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -97,7 +97,7 @@ { "id":"Other than the \"orElse(T)\" method of the Optional class, the same method of an Item will throw an exception if the provided value is null.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n If you want to get an item's value without throwing an exception \n (but the risk of getting a null value instead) you can use the `orElseNull` method.\n The `orElse(T)` method of the `Optional` class will not throw an exception\n if the provided value is null. This is not the case for the `orElse(T)` method\n of an `Item` object.\n "] }, diff --git a/docs/spock/reports/ut.ndas.Nda_Mutation_Spec.json b/docs/spock/reports/ut.ndas.Nda_Mutation_Spec.json index 81a96e1f7..81b974bde 100644 --- a/docs/spock/reports/ut.ndas.Nda_Mutation_Spec.json +++ b/docs/spock/reports/ut.ndas.Nda_Mutation_Spec.json @@ -1,7 +1,7 @@ { "className":"ut.ndas.Nda_Mutation_Spec", "title":"Mutating ND-Arrays", - "narrative":"ND-Arrays should be considered immutable, so we should prefer creating new\n ND-Arrays from existing ones using wither methods.\n However this is not always a good idea as it can be expensive to create new\n ND-Arrays, especially if the ND-Array is very large.\n The ability to mutate ND-Arrays is therefore provided, but only\n accessible via the mutation API exposed by the `getMut()` method.", + "narrative":"ND-Arrays should be considered immutable, so we should prefer creating new \n ND-Arrays from existing ones using wither methods.\n However this is not always a good idea as it can be expensive to create new\n ND-Arrays, especially if the ND-Array is very large.\n The ability to mutate ND-Arrays is therefore provided, but only\n accessible via the mutation API exposed by the `getMut()` method.", "subjects":[], "statistics":{ "runs":"6", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.004 seconds" + "duration":"0.011 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"A simple vector ND-Array can be mutated using the \"setItemAt\" method.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"A ND-Array can be mutated simply using the \"set\" method.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n This method of mutation is best used in Kotlin where it translates\n to the \"set\" operator.\n So it is possible to write code like this: `nda[2, 3] = 42.0`\n "] }, @@ -50,7 +50,7 @@ { "id":"A simple vector ND-Array can be mutated using the \"at(..).set(..)\" methods.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -67,7 +67,7 @@ { "id":"A ND-Array can be mutated using the \"at(..).set(..)\" methods.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -101,7 +101,7 @@ { "id":"We can use the subscription operator to mutate an ND-Array.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.ndas.Nda_Reshape_Spec.json b/docs/spock/reports/ut.ndas.Nda_Reshape_Spec.json index e6491d8f7..35cc90493 100644 --- a/docs/spock/reports/ut.ndas.Nda_Reshape_Spec.json +++ b/docs/spock/reports/ut.ndas.Nda_Reshape_Spec.json @@ -1,7 +1,7 @@ { "className":"ut.ndas.Nda_Reshape_Spec", "title":"Nda Reshaping", - "narrative":"Immutability is a core concept of the Neureka library.\n This means that the Nda API does not expose mutability directly.\n Instead, the API exposes methods that return new instances of Nda\n that are derived from the original instance.\n\n This is also true for reshaping operations,\n meaning that the Nda API does not expose methods that mutate the shape of an Nda\n but instead provides methods that return new instances of Nda\n with a different shape.\n\n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!", + "narrative":"Immutability is a core concept of the Neureka library.\n This means that the Nda API does not expose mutability directly.\n Instead, the API exposes methods that return new instances of Nda\n that are derived from the original instance.\n\n This is also true for reshaping operations, \n meaning that the Nda API does not expose methods that mutate the shape of an Nda\n but instead provides methods that return new instances of Nda\n with a different shape.\n\n Don't be concerned about the performance implications of this,\n because in the vast majority of cases the new instance will be backed by the same data array\n as the original instance!", "subjects":["neureka.Nda"], "statistics":{ "runs":"1", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.001 seconds" + "duration":"0.003 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can create a new Nda instance with a different shape.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.ndim.NDConfiguration_Spec.json b/docs/spock/reports/ut.ndim.NDConfiguration_Spec.json index 1e676db60..1d918ceb0 100644 --- a/docs/spock/reports/ut.ndim.NDConfiguration_Spec.json +++ b/docs/spock/reports/ut.ndim.NDConfiguration_Spec.json @@ -1,22 +1,573 @@ { "className":"ut.ndim.NDConfiguration_Spec", "title":"Making Arrays N-Dimensional", - "narrative":"Under the hood Neureka implements powerful indexing\n abstractions through the `NDConfiguration` interface and its various implementations.\n This allows for the creation of tensors/nd-arrays with arbitrary dimensions,\n the ability to slice them into smaller tensors/nd-arrays with the same underlying data,\n and finally the ability to permute their axes (like transposing them for example).\n\n This specification however only focuses on the behaviour of the `NDConfiguration` interface\n which translates various types of indices.", + "narrative":"Under the hood Neureka implements powerful indexing \n abstractions through the `NDConfiguration` interface and its various implementations.\n This allows for the creation of tensors/nd-arrays with arbitrary dimensions, \n the ability to slice them into smaller tensors/nd-arrays with the same underlying data,\n and finally the ability to permute their axes (like transposing them for example).\n\n This specification however only focuses on the behaviour of the `NDConfiguration` interface\n which translates various types of indices.", "subjects":["neureka.ndim.config.NDConfiguration"], "statistics":{ - "runs":"1", + "runs":"20", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.024 seconds" + "duration":"0.055 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"Various NDConfigurations behave exactly like their general purpose implementation.", + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [0]", "result":"PASS", - "duration":"0.008 seconds", + "duration":"0.013 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [1]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [2]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [3]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [5]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [6]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [7]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [10]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [11]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [12]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [13]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [14]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [15]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [16]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [17]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [18]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A general purpose NDConfiguration implementation as well as a specialized one (provided by the factory method).","code":["var ndc1 = SlicedNDConfiguration.construct(shape, translation, indicesMap, spread, offset)","var ndc2 = NDConfiguration.of(shape, translation, indicesMap, spread, offset)"]}, + + {"kind":"and","text":"2 corresponding iterators:","code":["var i1 = NDIterator.of(ndc1, NDIterator.NonVirtual.FALSE)","var i2 = NDIterator.of(ndc2, NDIterator.NonVirtual.FALSE)"]}, + + {"kind":"expect","text":"","code":["ndc2.getClass() == expected","ndc1.rank() == ndc2.rank()","ndc1.size() == ndc2.size()","ndc1.shape() == ndc2.shape()","ndc1.strides() == ndc2.strides()","ndc1.indicesMap() == ndc2.indicesMap()","ndc1.spread() == ndc2.spread()","ndc1.offset() == ndc2.offset()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indicesOfIndex(it) == ndc2.indicesOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndices(ndc1.indicesOfIndex(it)) == ndc2.indexOfIndices(ndc2.indicesOfIndex(it))","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," ndc1.indexOfIndex(it) == ndc2.indexOfIndex(it)","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," if ( it < ndc1.size()-1 ) { i1.increment(); i2.increment() }"," return matches","})",".every()"]}, + + {"kind":"and","text":"","code":["(0..ndc1.size()-1).collect({"," boolean matches = i1.get() == i2.get()"," i1.decrement(); i2.decrement()"," return matches","})",".every()"]}, + + {"kind":"where","text":"","code":{"shape":["[2,3,8,4]","[2,3,8,4]","[2,3,8,4]","[2,3,8]","[2,3,8]","[2,3,8]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[2,3]","[3]","[42]","[3]","[30]","[2]","[1]","[1]"],"translation":["[96, 32, 4, 1]","[96, 200, 8, 1]","[96, 32, 4, 1]","[24,8,1]","[8,24,7]","[8,24,7]","[3,1]","[1,2]","[1,2]","[81,42]","[1,2]","[3,1]","[3,1]","[1]","[1]","[1]","[8]","[1]","[1]","[1]"],"indicesMap":["[96, 32, 4, 1]","[96, 32, 4, 1]","[96, 92, 4, 1]","[24,8,1]","[1,2,3]","[1,2,3]","[3,1]","[1,2]","[3,1]","[3,99]","[2,1]","[3,1]","[3,1]","[1]","[1]","[2]","[2]","[1]","[1]","[1]"],"spread":["[1,1,1,1]","[1,1,1,1]","[1,4,1,1]","[1, 1, 1]","[1, 1, 1]","[1, 7, 1]","[1, 1]","[1, 1]","[1, 1]","[1, 1]","[7, 2]","[1, 1]","[1, 2]","[1]","[1]","[1]","[1]","[1]","[1]","[1]"],"offset":["[0,0,0,0]","[0,0,0,0]","[0,0,0,0]","[0,0,0]","[0,0,0]","[0,0,0]","[0,0]","[0,0]","[0,0]","[0,0]","[1,8]","[6,0]","[0,0]","[0]","[0]","[0]","[0]","[5]","[0]","[3]"],"expected":["SimpleNDConfiguration","PermutedNDConfiguration","SlicedNDConfiguration","Simple3DConfiguration","Permuted3DConfiguration","Sliced3DConfiguration","Simple2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Permuted2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Sliced2DConfiguration","Simple1DConfiguration","Simple1DConfiguration","Permuted1DConfiguration","Permuted1DConfiguration","Sliced1DConfiguration","Simple0DConfiguration","Sliced0DConfiguration"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various NDConfigurations behave exactly like their general purpose implementation. [19]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.ndim.Nda_Permute_Spec.json b/docs/spock/reports/ut.ndim.Nda_Permute_Spec.json index 8e8845c5b..d40ba5575 100644 --- a/docs/spock/reports/ut.ndim.Nda_Permute_Spec.json +++ b/docs/spock/reports/ut.ndim.Nda_Permute_Spec.json @@ -1,7 +1,7 @@ { "className":"ut.ndim.Nda_Permute_Spec", "title":"Reshaping Nd-Arrays", - "narrative":"Permuting an N-dimensional array means rearranging the dimensions/axes of the N-dimensional array.\n It returns a new nd-array with the same data as the original nd-array,\n but with the specified dimensions rearranged.\n It is very useful for example when you want to\n change the order of dimensions, for example, if you have a nd-array with dimensions (batch_size, channels, height, width),\n you can use permute() to rearrange the dimensions to (batch_size, height, width, channels).\n Another useful application of permute() is transposing a matrix.\n For example, if you have a matrix with dimensions (rows, columns),\n you can use permute() to rearrange the dimensions to (columns, rows).\n\n Permuting is a very cheap operation because it does not copy any data but merely\n creates a new view on the same data with a different access pattern.", + "narrative":"Permuting an N-dimensional array means rearranging the dimensions/axes of the N-dimensional array.\n It returns a new nd-array with the same data as the original nd-array, \n but with the specified dimensions rearranged. \n It is very useful for example when you want to\n change the order of dimensions, for example, if you have a nd-array with dimensions (batch_size, channels, height, width), \n you can use permute() to rearrange the dimensions to (batch_size, height, width, channels).\n Another useful application of permute() is transposing a matrix.\n For example, if you have a matrix with dimensions (rows, columns), \n you can use permute() to rearrange the dimensions to (columns, rows).\n\n Permuting is a very cheap operation because it does not copy any data but merely\n creates a new view on the same data with a different access pattern.", "subjects":["neureka.Nda"], "statistics":{ "runs":"2", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.006 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can use the \"permute\" method to rearrange the dimensions of an nd-array.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n In Neureka `Nda::permute(int...)` rearranges the original nd-array according to the desired \n ordering and returns a new multidimensional rotated nd-array. \n The size of the returned nd-array remains the same as that of the original.\n "] }, @@ -33,7 +33,7 @@ { "id":"We can use the \"transpose\" method to transpose swap 2 dimensions.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n The `transpose` method is a special case of the `permute` method\n which only swaps 2 dimensions (instead of all of them).\n It is based on the algorithm of the `permute` method.\n "] }, diff --git a/docs/spock/reports/ut.ndim.Shape_Spec.json b/docs/spock/reports/ut.ndim.Shape_Spec.json index 0c2d38bc1..c47d2baff 100644 --- a/docs/spock/reports/ut.ndim.Shape_Spec.json +++ b/docs/spock/reports/ut.ndim.Shape_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.019 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"A shape can be created from a list of integers.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -31,7 +31,7 @@ { "id":"A shape can be created from a stream of ints.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -46,7 +46,7 @@ { "id":"A shape can be created from an iterable.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -61,7 +61,7 @@ { "id":"A shape can be mapped to a new shape.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n Note that as a tuple, the shape is immutable so you cannot change its values.\n But as a monad, the shape can be mapped to a new shape\n using the \"map\" method. :)\n "] }, @@ -78,7 +78,7 @@ { "id":"A shape can be sliced.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n This is similar as the \"subList\" method of the java.util.List interface.\n It returns a new shape which is a slice of the original shape\n starting at the given index and ending at the given index.\n "] }, @@ -95,7 +95,7 @@ { "id":"Use the \"any\" or \"every\" method to check if a predicate holds for any or every value of the shape.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n The `Shape` class allows you to check if a condition holds for any or every value of the shape\n in a functional way by simply passing a predicate to the \"any\" or \"every\" method.\n This allows for much more readable code than using a for-loop.\n "] }, @@ -116,7 +116,7 @@ { "id":"You can use the \"count(Predicate)\" method to count the number of values that satisfy a predicate.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.ndim.Tensor_NDConfiguration_Spec.json b/docs/spock/reports/ut.ndim.Tensor_NDConfiguration_Spec.json index 9cff1f6b9..82f5b7ba2 100644 --- a/docs/spock/reports/ut.ndim.Tensor_NDConfiguration_Spec.json +++ b/docs/spock/reports/ut.ndim.Tensor_NDConfiguration_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.011 seconds" }, "headers":["\n For certain situations the \"Tensor\" class should use the correct \n implementations of said interface as configuration for internal index mapping...\n\n "],"tags":{},"see":[], "features":[ { "id":"NDConfiguration instances of tensors have expected state.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -35,7 +35,7 @@ { "id":"NDConfiguration instances of tensors have expected state and behaviour.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.006 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.ndim.Tensor_Permute_Spec.json b/docs/spock/reports/ut.ndim.Tensor_Permute_Spec.json index 7249e2b02..5431ef048 100644 --- a/docs/spock/reports/ut.ndim.Tensor_Permute_Spec.json +++ b/docs/spock/reports/ut.ndim.Tensor_Permute_Spec.json @@ -1,7 +1,7 @@ { "className":"ut.ndim.Tensor_Permute_Spec", "title":"Reshaping Tensors", - "narrative":"Permuting an N-dimensional array means rearranging the dimensions/axes of the N-dimensional array.\n It produces a new tensor with the same data as the original tensor,\n but with the specified dimensions rearranged.\n\n This is very useful for example when you want to\n change the order of dimensions, for example, if you have a tensor with dimensions (batch_size, channels, height, width),\n you can use permute() to rearrange the dimensions to (batch_size, height, width, channels).\n Another useful application of permute() is transposing a matrix.\n For example, if you have a matrix with dimensions (rows, columns),\n you can use permute() to rearrange the dimensions to (columns, rows).\n\n Permuting is a very cheap operation because it does not copy any data but merely\n creates a new view on the same data with a different access pattern.", + "narrative":"Permuting an N-dimensional array means rearranging the dimensions/axes of the N-dimensional array.\n It produces a new tensor with the same data as the original tensor, \n but with the specified dimensions rearranged. \n\n This is very useful for example when you want to\n change the order of dimensions, for example, if you have a tensor with dimensions (batch_size, channels, height, width), \n you can use permute() to rearrange the dimensions to (batch_size, height, width, channels).\n Another useful application of permute() is transposing a matrix.\n For example, if you have a matrix with dimensions (rows, columns), \n you can use permute() to rearrange the dimensions to (columns, rows).\n\n Permuting is a very cheap operation because it does not copy any data but merely\n creates a new view on the same data with a different access pattern.", "subjects":["neureka.Tensor"], "statistics":{ "runs":"2", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.009 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can use the \"permute\" method to rearrange the dimensions of a tensor.", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n In Neureka `Tensor::permute(int...)` rearranges the original tensor according to the desired \n ordering and returns a new multidimensional rotated tensor. \n The size of the returned tensor remains the same as that of the original.\n "] }, @@ -39,7 +39,7 @@ { "id":"When matrices are transpose, they will change their layout type as expected.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.ndim.Tensor_Slice_Permute_Spec.json b/docs/spock/reports/ut.ndim.Tensor_Slice_Permute_Spec.json index f296f46f1..02b5cb151 100644 --- a/docs/spock/reports/ut.ndim.Tensor_Slice_Permute_Spec.json +++ b/docs/spock/reports/ut.ndim.Tensor_Slice_Permute_Spec.json @@ -1,7 +1,7 @@ { "className":"ut.ndim.Tensor_Slice_Permute_Spec", "title":"Permuting Slices of Tensors", - "narrative":"Neureka provides a convenient way to permuting tensors\n even if they are slices of other tensors sharing the same underlying data.\n This is possible because of the under the hood indexing\n abstractions provided by the `NDConfiguration` interface and its various implementations.", + "narrative":"Neureka provides a convenient way to permuting tensors\n even if they are slices of other tensors sharing the same underlying data.\n This is possible because of the under the hood indexing \n abstractions provided by the `NDConfiguration` interface and its various implementations.", "subjects":["neureka.Tensor"], "statistics":{ "runs":"3", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.005 seconds" + "duration":"0.011 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"A slice of a tensor changes as expected when reshaping it.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -45,7 +45,7 @@ { "id":"Two slices of one big tensor perform matrix multiplication flawless.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -66,7 +66,7 @@ { "id":"Reshaping a slice works as expected.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.neureka.Neureka_Spec.json b/docs/spock/reports/ut.neureka.Neureka_Spec.json index 0193f3580..e4f97362d 100644 --- a/docs/spock/reports/ut.neureka.Neureka_Spec.json +++ b/docs/spock/reports/ut.neureka.Neureka_Spec.json @@ -1,22 +1,22 @@ { "className":"ut.neureka.Neureka_Spec", "title":"The Neureka context can be used and configured as expected.", - "narrative":"This specification covers the behavior of the Neureka class which\n exposes a global API for configuring thread local contexts and library settings.\n The purpose of this is to assert that the API exposed by the Neureka class\n is both thread local and configurable.\n This specification also exists to cover standards for the Neureka library in general.", + "narrative":"This specification covers the behavior of the Neureka class which\n exposes a global API for configuring thread local contexts and library settings.\n The purpose of this is to assert that the API exposed by the Neureka class \n is both thread local and configurable.\n This specification also exists to cover standards for the Neureka library in general.", "subjects":["neureka.Neureka"], "statistics":{ - "runs":"6", + "runs":"82", "successRate":"100.0%", "failures":"0", "errors":"0", - "skipped":"0", - "duration":"8.598 seconds" + "skipped":"1", + "duration":"4.898 seconds" }, "headers":["\n This specification defines what types of settings are exposed by\n Neureka as well as more general things like how string representations\n of various library types should look like... \n "],"tags":{},"see":[], "features":[ { "id":"Neureka class instance has expected behaviour.", "result":"PASS", - "duration":"0.049 seconds", + "duration":"0.084 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -27,15 +27,294 @@ {"kind":"then","text":"This setting change applies!","code":["Neureka.get().settings().autograd().isApplyingGradientWhenTensorIsUsed()","!Neureka.get().settings().autograd().isRetainingPendingErrorForJITProp()"]}, - {"kind":"and","text":"The version number is as expected!","code":["Neureka.version()==\"1.0.0\"//version"]} + {"kind":"and","text":"The version number is as expected!","code":["Neureka.version()==\"1.0.1\"//version"]} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Neureka settings class can be locked causing its properties to be immutable.", + "id":"Neureka settings class can be locked causing its properties to be immutable. [0]", "result":"PASS", - "duration":"0.337 seconds", + "duration":"0.057 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Something used to set a property and something to get the property.","code":["def set = { it -> setter(Neureka.get().settings(), it) }","def get = { getter(Neureka.get().settings()) }"]}, + + {"kind":"expect","text":"Initially the property has the expected value.","code":["get() == value"]}, + + {"kind":"when","text":"We lock the settings object...","code":["Neureka.get().settings().setIsLocked(true)"]}, + + {"kind":"and","text":"We try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is not changed!","code":["get() == value"]}, + + {"kind":"when","text":"We unlock the settings object...","code":["Neureka.get().settings().setIsLocked(false)"]}, + + {"kind":"and","text":"Again we try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is changed!","code":["get() != value"]}, + + {"kind":"cleanup","text":"We reset the settings object to its original state.","code":["set(value)"]}, + + {"kind":"where","text":"The properties used are boolean types.","code":{"value":["false","true","false","true","false","false","true","false","false","false"],"getter":["{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsLegacy()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasGradient()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasSlimNumbers()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsScientific()}","{ Neureka.Settings it -> it.ndim().isOnlyUsingDefaultNDConfiguration()}","{ Neureka.Settings it -> it.debug().isKeepingDerivativeTargetPayloads()}","{ Neureka.Settings it -> it.autograd().isPreventingInlineOperations()}","{ Neureka.Settings it -> it.autograd().isRetainingPendingErrorForJITProp()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenTensorIsUsed()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenRequested()}"],"setter":["{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsLegacy(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasGradient(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasSlimNumbers(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsScientific(v)}","{ Neureka.Settings s, v -> s.ndim().setIsOnlyUsingDefaultNDConfiguration(v)}","{ Neureka.Settings s, v -> s.debug().setIsKeepingDerivativeTargetPayloads(v)}","{ Neureka.Settings s, v -> s.autograd().setIsPreventingInlineOperations(v)}","{ Neureka.Settings s, v -> s.autograd().setIsRetainingPendingErrorForJITProp(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenTensorIsUsed(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenRequested(v)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Neureka settings class can be locked causing its properties to be immutable. [1]", + "result":"PASS", + "duration":"0.077 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Something used to set a property and something to get the property.","code":["def set = { it -> setter(Neureka.get().settings(), it) }","def get = { getter(Neureka.get().settings()) }"]}, + + {"kind":"expect","text":"Initially the property has the expected value.","code":["get() == value"]}, + + {"kind":"when","text":"We lock the settings object...","code":["Neureka.get().settings().setIsLocked(true)"]}, + + {"kind":"and","text":"We try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is not changed!","code":["get() == value"]}, + + {"kind":"when","text":"We unlock the settings object...","code":["Neureka.get().settings().setIsLocked(false)"]}, + + {"kind":"and","text":"Again we try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is changed!","code":["get() != value"]}, + + {"kind":"cleanup","text":"We reset the settings object to its original state.","code":["set(value)"]}, + + {"kind":"where","text":"The properties used are boolean types.","code":{"value":["false","true","false","true","false","false","true","false","false","false"],"getter":["{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsLegacy()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasGradient()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasSlimNumbers()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsScientific()}","{ Neureka.Settings it -> it.ndim().isOnlyUsingDefaultNDConfiguration()}","{ Neureka.Settings it -> it.debug().isKeepingDerivativeTargetPayloads()}","{ Neureka.Settings it -> it.autograd().isPreventingInlineOperations()}","{ Neureka.Settings it -> it.autograd().isRetainingPendingErrorForJITProp()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenTensorIsUsed()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenRequested()}"],"setter":["{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsLegacy(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasGradient(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasSlimNumbers(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsScientific(v)}","{ Neureka.Settings s, v -> s.ndim().setIsOnlyUsingDefaultNDConfiguration(v)}","{ Neureka.Settings s, v -> s.debug().setIsKeepingDerivativeTargetPayloads(v)}","{ Neureka.Settings s, v -> s.autograd().setIsPreventingInlineOperations(v)}","{ Neureka.Settings s, v -> s.autograd().setIsRetainingPendingErrorForJITProp(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenTensorIsUsed(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenRequested(v)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Neureka settings class can be locked causing its properties to be immutable. [2]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Something used to set a property and something to get the property.","code":["def set = { it -> setter(Neureka.get().settings(), it) }","def get = { getter(Neureka.get().settings()) }"]}, + + {"kind":"expect","text":"Initially the property has the expected value.","code":["get() == value"]}, + + {"kind":"when","text":"We lock the settings object...","code":["Neureka.get().settings().setIsLocked(true)"]}, + + {"kind":"and","text":"We try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is not changed!","code":["get() == value"]}, + + {"kind":"when","text":"We unlock the settings object...","code":["Neureka.get().settings().setIsLocked(false)"]}, + + {"kind":"and","text":"Again we try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is changed!","code":["get() != value"]}, + + {"kind":"cleanup","text":"We reset the settings object to its original state.","code":["set(value)"]}, + + {"kind":"where","text":"The properties used are boolean types.","code":{"value":["false","true","false","true","false","false","true","false","false","false"],"getter":["{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsLegacy()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasGradient()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasSlimNumbers()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsScientific()}","{ Neureka.Settings it -> it.ndim().isOnlyUsingDefaultNDConfiguration()}","{ Neureka.Settings it -> it.debug().isKeepingDerivativeTargetPayloads()}","{ Neureka.Settings it -> it.autograd().isPreventingInlineOperations()}","{ Neureka.Settings it -> it.autograd().isRetainingPendingErrorForJITProp()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenTensorIsUsed()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenRequested()}"],"setter":["{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsLegacy(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasGradient(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasSlimNumbers(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsScientific(v)}","{ Neureka.Settings s, v -> s.ndim().setIsOnlyUsingDefaultNDConfiguration(v)}","{ Neureka.Settings s, v -> s.debug().setIsKeepingDerivativeTargetPayloads(v)}","{ Neureka.Settings s, v -> s.autograd().setIsPreventingInlineOperations(v)}","{ Neureka.Settings s, v -> s.autograd().setIsRetainingPendingErrorForJITProp(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenTensorIsUsed(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenRequested(v)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Neureka settings class can be locked causing its properties to be immutable. [3]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Something used to set a property and something to get the property.","code":["def set = { it -> setter(Neureka.get().settings(), it) }","def get = { getter(Neureka.get().settings()) }"]}, + + {"kind":"expect","text":"Initially the property has the expected value.","code":["get() == value"]}, + + {"kind":"when","text":"We lock the settings object...","code":["Neureka.get().settings().setIsLocked(true)"]}, + + {"kind":"and","text":"We try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is not changed!","code":["get() == value"]}, + + {"kind":"when","text":"We unlock the settings object...","code":["Neureka.get().settings().setIsLocked(false)"]}, + + {"kind":"and","text":"Again we try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is changed!","code":["get() != value"]}, + + {"kind":"cleanup","text":"We reset the settings object to its original state.","code":["set(value)"]}, + + {"kind":"where","text":"The properties used are boolean types.","code":{"value":["false","true","false","true","false","false","true","false","false","false"],"getter":["{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsLegacy()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasGradient()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasSlimNumbers()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsScientific()}","{ Neureka.Settings it -> it.ndim().isOnlyUsingDefaultNDConfiguration()}","{ Neureka.Settings it -> it.debug().isKeepingDerivativeTargetPayloads()}","{ Neureka.Settings it -> it.autograd().isPreventingInlineOperations()}","{ Neureka.Settings it -> it.autograd().isRetainingPendingErrorForJITProp()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenTensorIsUsed()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenRequested()}"],"setter":["{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsLegacy(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasGradient(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasSlimNumbers(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsScientific(v)}","{ Neureka.Settings s, v -> s.ndim().setIsOnlyUsingDefaultNDConfiguration(v)}","{ Neureka.Settings s, v -> s.debug().setIsKeepingDerivativeTargetPayloads(v)}","{ Neureka.Settings s, v -> s.autograd().setIsPreventingInlineOperations(v)}","{ Neureka.Settings s, v -> s.autograd().setIsRetainingPendingErrorForJITProp(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenTensorIsUsed(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenRequested(v)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Neureka settings class can be locked causing its properties to be immutable. [4]", + "result":"PASS", + "duration":"0.054 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Something used to set a property and something to get the property.","code":["def set = { it -> setter(Neureka.get().settings(), it) }","def get = { getter(Neureka.get().settings()) }"]}, + + {"kind":"expect","text":"Initially the property has the expected value.","code":["get() == value"]}, + + {"kind":"when","text":"We lock the settings object...","code":["Neureka.get().settings().setIsLocked(true)"]}, + + {"kind":"and","text":"We try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is not changed!","code":["get() == value"]}, + + {"kind":"when","text":"We unlock the settings object...","code":["Neureka.get().settings().setIsLocked(false)"]}, + + {"kind":"and","text":"Again we try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is changed!","code":["get() != value"]}, + + {"kind":"cleanup","text":"We reset the settings object to its original state.","code":["set(value)"]}, + + {"kind":"where","text":"The properties used are boolean types.","code":{"value":["false","true","false","true","false","false","true","false","false","false"],"getter":["{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsLegacy()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasGradient()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasSlimNumbers()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsScientific()}","{ Neureka.Settings it -> it.ndim().isOnlyUsingDefaultNDConfiguration()}","{ Neureka.Settings it -> it.debug().isKeepingDerivativeTargetPayloads()}","{ Neureka.Settings it -> it.autograd().isPreventingInlineOperations()}","{ Neureka.Settings it -> it.autograd().isRetainingPendingErrorForJITProp()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenTensorIsUsed()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenRequested()}"],"setter":["{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsLegacy(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasGradient(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasSlimNumbers(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsScientific(v)}","{ Neureka.Settings s, v -> s.ndim().setIsOnlyUsingDefaultNDConfiguration(v)}","{ Neureka.Settings s, v -> s.debug().setIsKeepingDerivativeTargetPayloads(v)}","{ Neureka.Settings s, v -> s.autograd().setIsPreventingInlineOperations(v)}","{ Neureka.Settings s, v -> s.autograd().setIsRetainingPendingErrorForJITProp(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenTensorIsUsed(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenRequested(v)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Neureka settings class can be locked causing its properties to be immutable. [5]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Something used to set a property and something to get the property.","code":["def set = { it -> setter(Neureka.get().settings(), it) }","def get = { getter(Neureka.get().settings()) }"]}, + + {"kind":"expect","text":"Initially the property has the expected value.","code":["get() == value"]}, + + {"kind":"when","text":"We lock the settings object...","code":["Neureka.get().settings().setIsLocked(true)"]}, + + {"kind":"and","text":"We try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is not changed!","code":["get() == value"]}, + + {"kind":"when","text":"We unlock the settings object...","code":["Neureka.get().settings().setIsLocked(false)"]}, + + {"kind":"and","text":"Again we try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is changed!","code":["get() != value"]}, + + {"kind":"cleanup","text":"We reset the settings object to its original state.","code":["set(value)"]}, + + {"kind":"where","text":"The properties used are boolean types.","code":{"value":["false","true","false","true","false","false","true","false","false","false"],"getter":["{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsLegacy()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasGradient()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasSlimNumbers()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsScientific()}","{ Neureka.Settings it -> it.ndim().isOnlyUsingDefaultNDConfiguration()}","{ Neureka.Settings it -> it.debug().isKeepingDerivativeTargetPayloads()}","{ Neureka.Settings it -> it.autograd().isPreventingInlineOperations()}","{ Neureka.Settings it -> it.autograd().isRetainingPendingErrorForJITProp()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenTensorIsUsed()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenRequested()}"],"setter":["{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsLegacy(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasGradient(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasSlimNumbers(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsScientific(v)}","{ Neureka.Settings s, v -> s.ndim().setIsOnlyUsingDefaultNDConfiguration(v)}","{ Neureka.Settings s, v -> s.debug().setIsKeepingDerivativeTargetPayloads(v)}","{ Neureka.Settings s, v -> s.autograd().setIsPreventingInlineOperations(v)}","{ Neureka.Settings s, v -> s.autograd().setIsRetainingPendingErrorForJITProp(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenTensorIsUsed(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenRequested(v)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Neureka settings class can be locked causing its properties to be immutable. [6]", + "result":"PASS", + "duration":"0.054 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Something used to set a property and something to get the property.","code":["def set = { it -> setter(Neureka.get().settings(), it) }","def get = { getter(Neureka.get().settings()) }"]}, + + {"kind":"expect","text":"Initially the property has the expected value.","code":["get() == value"]}, + + {"kind":"when","text":"We lock the settings object...","code":["Neureka.get().settings().setIsLocked(true)"]}, + + {"kind":"and","text":"We try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is not changed!","code":["get() == value"]}, + + {"kind":"when","text":"We unlock the settings object...","code":["Neureka.get().settings().setIsLocked(false)"]}, + + {"kind":"and","text":"Again we try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is changed!","code":["get() != value"]}, + + {"kind":"cleanup","text":"We reset the settings object to its original state.","code":["set(value)"]}, + + {"kind":"where","text":"The properties used are boolean types.","code":{"value":["false","true","false","true","false","false","true","false","false","false"],"getter":["{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsLegacy()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasGradient()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasSlimNumbers()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsScientific()}","{ Neureka.Settings it -> it.ndim().isOnlyUsingDefaultNDConfiguration()}","{ Neureka.Settings it -> it.debug().isKeepingDerivativeTargetPayloads()}","{ Neureka.Settings it -> it.autograd().isPreventingInlineOperations()}","{ Neureka.Settings it -> it.autograd().isRetainingPendingErrorForJITProp()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenTensorIsUsed()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenRequested()}"],"setter":["{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsLegacy(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasGradient(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasSlimNumbers(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsScientific(v)}","{ Neureka.Settings s, v -> s.ndim().setIsOnlyUsingDefaultNDConfiguration(v)}","{ Neureka.Settings s, v -> s.debug().setIsKeepingDerivativeTargetPayloads(v)}","{ Neureka.Settings s, v -> s.autograd().setIsPreventingInlineOperations(v)}","{ Neureka.Settings s, v -> s.autograd().setIsRetainingPendingErrorForJITProp(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenTensorIsUsed(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenRequested(v)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Neureka settings class can be locked causing its properties to be immutable. [7]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Something used to set a property and something to get the property.","code":["def set = { it -> setter(Neureka.get().settings(), it) }","def get = { getter(Neureka.get().settings()) }"]}, + + {"kind":"expect","text":"Initially the property has the expected value.","code":["get() == value"]}, + + {"kind":"when","text":"We lock the settings object...","code":["Neureka.get().settings().setIsLocked(true)"]}, + + {"kind":"and","text":"We try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is not changed!","code":["get() == value"]}, + + {"kind":"when","text":"We unlock the settings object...","code":["Neureka.get().settings().setIsLocked(false)"]}, + + {"kind":"and","text":"Again we try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is changed!","code":["get() != value"]}, + + {"kind":"cleanup","text":"We reset the settings object to its original state.","code":["set(value)"]}, + + {"kind":"where","text":"The properties used are boolean types.","code":{"value":["false","true","false","true","false","false","true","false","false","false"],"getter":["{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsLegacy()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasGradient()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasSlimNumbers()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsScientific()}","{ Neureka.Settings it -> it.ndim().isOnlyUsingDefaultNDConfiguration()}","{ Neureka.Settings it -> it.debug().isKeepingDerivativeTargetPayloads()}","{ Neureka.Settings it -> it.autograd().isPreventingInlineOperations()}","{ Neureka.Settings it -> it.autograd().isRetainingPendingErrorForJITProp()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenTensorIsUsed()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenRequested()}"],"setter":["{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsLegacy(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasGradient(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasSlimNumbers(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsScientific(v)}","{ Neureka.Settings s, v -> s.ndim().setIsOnlyUsingDefaultNDConfiguration(v)}","{ Neureka.Settings s, v -> s.debug().setIsKeepingDerivativeTargetPayloads(v)}","{ Neureka.Settings s, v -> s.autograd().setIsPreventingInlineOperations(v)}","{ Neureka.Settings s, v -> s.autograd().setIsRetainingPendingErrorForJITProp(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenTensorIsUsed(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenRequested(v)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Neureka settings class can be locked causing its properties to be immutable. [8]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Something used to set a property and something to get the property.","code":["def set = { it -> setter(Neureka.get().settings(), it) }","def get = { getter(Neureka.get().settings()) }"]}, + + {"kind":"expect","text":"Initially the property has the expected value.","code":["get() == value"]}, + + {"kind":"when","text":"We lock the settings object...","code":["Neureka.get().settings().setIsLocked(true)"]}, + + {"kind":"and","text":"We try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is not changed!","code":["get() == value"]}, + + {"kind":"when","text":"We unlock the settings object...","code":["Neureka.get().settings().setIsLocked(false)"]}, + + {"kind":"and","text":"Again we try to set the property to another value...","code":["set(!value)"]}, + + {"kind":"then","text":"The property is changed!","code":["get() != value"]}, + + {"kind":"cleanup","text":"We reset the settings object to its original state.","code":["set(value)"]}, + + {"kind":"where","text":"The properties used are boolean types.","code":{"value":["false","true","false","true","false","false","true","false","false","false"],"getter":["{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsLegacy()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasGradient()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getHasSlimNumbers()}","{ Neureka.Settings it -> it.view().getNDPrintSettings().getIsScientific()}","{ Neureka.Settings it -> it.ndim().isOnlyUsingDefaultNDConfiguration()}","{ Neureka.Settings it -> it.debug().isKeepingDerivativeTargetPayloads()}","{ Neureka.Settings it -> it.autograd().isPreventingInlineOperations()}","{ Neureka.Settings it -> it.autograd().isRetainingPendingErrorForJITProp()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenTensorIsUsed()}","{ Neureka.Settings it -> it.autograd().isApplyingGradientWhenRequested()}"],"setter":["{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsLegacy(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasGradient(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setHasSlimNumbers(v)}","{ Neureka.Settings s, v -> s.view().getNDPrintSettings().setIsScientific(v)}","{ Neureka.Settings s, v -> s.ndim().setIsOnlyUsingDefaultNDConfiguration(v)}","{ Neureka.Settings s, v -> s.debug().setIsKeepingDerivativeTargetPayloads(v)}","{ Neureka.Settings s, v -> s.autograd().setIsPreventingInlineOperations(v)}","{ Neureka.Settings s, v -> s.autograd().setIsRetainingPendingErrorForJITProp(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenTensorIsUsed(v)}","{ Neureka.Settings s, v -> s.autograd().setIsApplyingGradientWhenRequested(v)}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Neureka settings class can be locked causing its properties to be immutable. [9]", + "result":"PASS", + "duration":"0.054 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -66,58 +345,1170 @@ { "id":"Every Thread instance has their own Neureka instance.", "result":"PASS", - "duration":"5.725 seconds", + "duration":"0.067 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A map containing entries for Neureka instances.","code":["def map = ['instance 1':null, 'instance 2':null]"]}, + + {"kind":"when","text":"Two newly instantiated tensors store their Neureka instances in the map.","code":["def t1 = new Thread({ map['instance 1'] = Neureka.get() })","def t2 = new Thread({ map['instance 2'] = Neureka.get() })"]}, + + {"kind":"and","text":"The tensors are being started and joined.","code":["t1.start()","t2.start()","t1.join()","t2.join()"]}, + + {"kind":"then","text":"The map entries will no longer be filled with null.","code":["map['instance 1'] != null","map['instance 2'] != null"]}, + + {"kind":"and","text":"The Neureka instances stored in the map will be different objects.","code":["map['instance 1'] != map['instance 2']"]} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various library objects adhere to the same toString formatting convention! [0]", + "result":"PASS", + "duration":"0.059 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The provided object matches the following pattern defining a common standard!","code":["toStringStandard.matcher(neurekaObject.toString()).matches()"]}, + + {"kind":"where","text":"The following objects are being used..","code":{"neurekaObject":["CPU.get()","DataType.of(String)","Relation.newParentToChildren()","new JITProp<>(] as Set)","Neureka.get()","Neureka.get().settings()","Neureka.get().settings().autograd()","Neureka.get().settings().debug()","Neureka.get().settings().dtype()","Neureka.get().settings().ndim()","Neureka.get().settings().view()","Neureka.get().backend().getAutogradFunction()","Neureka.get().backend().getFunction()","Neureka.get().backend()","Neureka.get().backend().getFunctionCache()","ExecutionCall.of(Tensor.of(3d)).running(Neureka.get().backend().getOperation(\"+\")).on(CPU.get())","new CustomDeviceCleaner()","(Tensor.of(2d).setRqsGradient(true)* Tensor.of(-2d)).graphNode.get()","FileDevice.at('.')","NDConfiguration.of((int])2,3,8,4],(int])96, 32, 4, 1],(int])96, 32, 4, 1],(int])1,1,1,1],(int])0,0,0,0])","NDConfiguration.of((int])2,3,8,4],(int])96, 200, 8, 1],(int])96, 32, 4, 1],(int])1,1,1,1],(int])0,0,0,0])","NDConfiguration.of((int])2,3,8,4],(int])96, 32, 4, 1],(int])96, 92, 4, 1],(int])1,4,1,1],(int])0,0,0,0])","NDConfiguration.of((int])2,3,8],(int])24,8,1],(int])24,8,1],(int])1, 1, 1],(int])0,0,0])"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Various library objects adhere to the same toString formatting convention! 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[40]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The provided object matches the following pattern defining a common standard!","code":["toStringStandard.matcher(operation.toString()).matches()"]}, + + {"kind":"and","text":"The same criteria should also be met for every algorithm within the current operation.","code":["operation.getAllAlgorithms().every {"," toStringStandard.matcher(it.toString()).matches()","}"]}, + + {"kind":"where","text":"The following operations are being used..","code":{"operation":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Backend related library objects adhere to the same toString formatting convention! [41]", + "result":"PASS", + "duration":"0.275 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The provided object matches the following pattern defining a common standard!","code":["toStringStandard.matcher(operation.toString()).matches()"]}, + + {"kind":"and","text":"The same criteria should also be met for every algorithm within the current operation.","code":["operation.getAllAlgorithms().every {"," toStringStandard.matcher(it.toString()).matches()","}"]}, + + {"kind":"where","text":"The following operations are being used..","code":{"operation":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Backend related library objects adhere to the same toString formatting convention! [42]", + "result":"PASS", + "duration":"0.061 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The provided object matches the following pattern defining a common standard!","code":["toStringStandard.matcher(operation.toString()).matches()"]}, + + {"kind":"and","text":"The same criteria should also be met for every algorithm within the current operation.","code":["operation.getAllAlgorithms().every {"," toStringStandard.matcher(it.toString()).matches()","}"]}, + + {"kind":"where","text":"The following operations are being used..","code":{"operation":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Backend related library objects adhere to the same toString formatting convention! [43]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The provided object matches the following pattern defining a common standard!","code":["toStringStandard.matcher(operation.toString()).matches()"]}, + + {"kind":"and","text":"The same criteria should also be met for every algorithm within the current operation.","code":["operation.getAllAlgorithms().every {"," toStringStandard.matcher(it.toString()).matches()","}"]}, + + {"kind":"where","text":"The following operations are being used..","code":{"operation":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Backend related library objects adhere to the same toString formatting convention! [44]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The provided object matches the following pattern defining a common standard!","code":["toStringStandard.matcher(operation.toString()).matches()"]}, + + {"kind":"and","text":"The same criteria should also be met for every algorithm within the current operation.","code":["operation.getAllAlgorithms().every {"," toStringStandard.matcher(it.toString()).matches()","}"]}, + + {"kind":"where","text":"The following operations are being used..","code":{"operation":[]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Backend related library objects adhere to the same toString formatting convention! [45]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The provided object matches the following pattern defining a common standard!","code":["toStringStandard.matcher(operation.toString()).matches()"]}, + + {"kind":"and","text":"The same criteria should also be met for every algorithm within the current operation.","code":["operation.getAllAlgorithms().every {"," toStringStandard.matcher(it.toString()).matches()","}"]}, + + {"kind":"where","text":"The following operations are being used..","code":{"operation":[]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Backend related library objects adhere to the same toString formatting convention!", + "id":"Backend related library objects adhere to the same toString formatting convention! [46]", "result":"PASS", - "duration":"1.571 seconds", + "duration":"0.065 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.optimization.ADAM_Spec.json b/docs/spock/reports/ut.optimization.ADAM_Spec.json index 6ce7d8637..1663346ef 100644 --- a/docs/spock/reports/ut.optimization.ADAM_Spec.json +++ b/docs/spock/reports/ut.optimization.ADAM_Spec.json @@ -1,22 +1,22 @@ { "className":"ut.optimization.ADAM_Spec", "title":"", - "narrative":"ADAM is a more powerful alternative to the classical stochastic gradient descent.\n It combines the best properties of the AdaGrad and the RMSProp algorithms, which makes\n it especially well suited for sparse gradients and noisy data.\n Adam is the most popular among the adaptive optimizers\n because its adaptive learning rate working so well with sparse datasets.", + "narrative":"ADAM is a more powerful alternative to the classical stochastic gradient descent. \n It combines the best properties of the AdaGrad and the RMSProp algorithms, which makes \n it especially well suited for sparse gradients and noisy data.\n Adam is the most popular among the adaptive optimizers\n because its adaptive learning rate working so well with sparse datasets.", "subjects":["neureka.optimization.Optimizer"], "statistics":{ - "runs":"3", + "runs":"19", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.050 seconds" + "duration":"0.092 seconds" }, "headers":["\n The code below assumes that for we\n have the following 2 variables setup\n throughout every data table iteration:\n ```\n Tensor w = Tensor.of(0d)\n Optimizer o = Optimizer.ADAM.create(w) \n w.set(o) \n ```\n "],"tags":{},"see":[], "features":[ { - "id":"ADAM optimizes according to expected inputs", + "id":"ADAM optimizes according to expected inputs [0]", "result":"PASS", - "duration":"0.026 seconds", + "duration":"0.010 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,9 +33,231 @@ }, { - "id":"Equations used by ADAM return expected result.", + "id":"ADAM optimizes according to expected inputs [1]", "result":"PASS", - "duration":"0.011 seconds", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.009999999666666677","0.01999","0.02426","0.03034","0.03332","0.03409","0.03738","0.04194","0.04744","0.05112"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ADAM optimizes according to expected inputs [2]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.009999999666666677","0.01999","0.02426","0.03034","0.03332","0.03409","0.03738","0.04194","0.04744","0.05112"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ADAM optimizes according to expected inputs [3]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.009999999666666677","0.01999","0.02426","0.03034","0.03332","0.03409","0.03738","0.04194","0.04744","0.05112"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ADAM optimizes according to expected inputs [4]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.009999999666666677","0.01999","0.02426","0.03034","0.03332","0.03409","0.03738","0.04194","0.04744","0.05112"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ADAM optimizes according to expected inputs [5]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.009999999666666677","0.01999","0.02426","0.03034","0.03332","0.03409","0.03738","0.04194","0.04744","0.05112"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ADAM optimizes according to expected inputs [6]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.009999999666666677","0.01999","0.02426","0.03034","0.03332","0.03409","0.03738","0.04194","0.04744","0.05112"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ADAM optimizes according to expected inputs [7]", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.009999999666666677","0.01999","0.02426","0.03034","0.03332","0.03409","0.03738","0.04194","0.04744","0.05112"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ADAM optimizes according to expected inputs [8]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.009999999666666677","0.01999","0.02426","0.03034","0.03332","0.03409","0.03738","0.04194","0.04744","0.05112"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"ADAM optimizes according to expected inputs [9]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.009999999666666677","0.01999","0.02426","0.03034","0.03332","0.03409","0.03738","0.04194","0.04744","0.05112"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Equations used by ADAM return expected result. [0]", + "result":"PASS", + "duration":"0.010 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create tensors given an equation and array or list of input tensors...","code":["var t1 = Tensor.of( expression, inputs )","var t2 = Tensor.of( expression, inputs as Float[] )","var t3 = Tensor.of( expression, true, inputs.collect(it -> Tensor.of(it) ) )","var t4 = Tensor.of( expression, false, inputs.collect(it -> Tensor.of(it) ) )","var t5 = Tensor.of( expression, false, inputs.collect(it -> Tensor.of(it) ) as Tensor[] )"]}, + + {"kind":"expect","text":"...this produces the expected result String.","code":["t1.toString().contains( expected )","t2.toString().contains( expected.replace(\".29999\", \".30000\") )","t3.toString().contains( expected )","t4.toString().contains( expected )","t5.toString().contains( expected )"]}, + + {"kind":"where","text":"The following expressions, inputs and expected String results are being used :","code":{"expression":["\"( 1 - I[0]) * I[1]\"","\"I[0] * I[1] + (1 - I[2]) * I[3]\"","\"I[0] / ( 1 - I[1] )\"","\"I[0] ** 0.5 + I[1]\"","\"I[0] - I[1] * I[2] /( I[3] ** 0.5 + I[4] )\""],"inputs":["[0.9d, -3d]","[0.9d, 0d, 0.9d, -3d]","[-0.3d, 0.9d]","[9d, 1e-7d]","[0d, 0.01d, -3d, 9d, 1e-7d ]"],"expected":["\"(1):[-0.29999]\"","\"(1):[-0.29999]\"","\"(1):[-3.0]\"","\"(1):[3.0]\"","\"(1):[0.00999]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Equations used by ADAM return expected result. [1]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create tensors given an equation and array or list of input tensors...","code":["var t1 = Tensor.of( expression, inputs )","var t2 = Tensor.of( expression, inputs as Float[] )","var t3 = Tensor.of( expression, true, inputs.collect(it -> Tensor.of(it) ) )","var t4 = Tensor.of( expression, false, inputs.collect(it -> Tensor.of(it) ) )","var t5 = Tensor.of( expression, false, inputs.collect(it -> Tensor.of(it) ) as Tensor[] )"]}, + + {"kind":"expect","text":"...this produces the expected result String.","code":["t1.toString().contains( expected )","t2.toString().contains( expected.replace(\".29999\", \".30000\") )","t3.toString().contains( expected )","t4.toString().contains( expected )","t5.toString().contains( expected )"]}, + + {"kind":"where","text":"The following expressions, inputs and expected String results are being used :","code":{"expression":["\"( 1 - I[0]) * I[1]\"","\"I[0] * I[1] + (1 - I[2]) * I[3]\"","\"I[0] / ( 1 - I[1] )\"","\"I[0] ** 0.5 + I[1]\"","\"I[0] - I[1] * I[2] /( I[3] ** 0.5 + I[4] )\""],"inputs":["[0.9d, -3d]","[0.9d, 0d, 0.9d, -3d]","[-0.3d, 0.9d]","[9d, 1e-7d]","[0d, 0.01d, -3d, 9d, 1e-7d ]"],"expected":["\"(1):[-0.29999]\"","\"(1):[-0.29999]\"","\"(1):[-3.0]\"","\"(1):[3.0]\"","\"(1):[0.00999]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Equations used by ADAM return expected result. [2]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create tensors given an equation and array or list of input tensors...","code":["var t1 = Tensor.of( expression, inputs )","var t2 = Tensor.of( expression, inputs as Float[] )","var t3 = Tensor.of( expression, true, inputs.collect(it -> Tensor.of(it) ) )","var t4 = Tensor.of( expression, false, inputs.collect(it -> Tensor.of(it) ) )","var t5 = Tensor.of( expression, false, inputs.collect(it -> Tensor.of(it) ) as Tensor[] )"]}, + + {"kind":"expect","text":"...this produces the expected result String.","code":["t1.toString().contains( expected )","t2.toString().contains( expected.replace(\".29999\", \".30000\") )","t3.toString().contains( expected )","t4.toString().contains( expected )","t5.toString().contains( expected )"]}, + + {"kind":"where","text":"The following expressions, inputs and expected String results are being used :","code":{"expression":["\"( 1 - I[0]) * I[1]\"","\"I[0] * I[1] + (1 - I[2]) * I[3]\"","\"I[0] / ( 1 - I[1] )\"","\"I[0] ** 0.5 + I[1]\"","\"I[0] - I[1] * I[2] /( I[3] ** 0.5 + I[4] )\""],"inputs":["[0.9d, -3d]","[0.9d, 0d, 0.9d, -3d]","[-0.3d, 0.9d]","[9d, 1e-7d]","[0d, 0.01d, -3d, 9d, 1e-7d ]"],"expected":["\"(1):[-0.29999]\"","\"(1):[-0.29999]\"","\"(1):[-3.0]\"","\"(1):[3.0]\"","\"(1):[0.00999]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Equations used by ADAM return expected result. [3]", + "result":"PASS", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -50,7 +272,75 @@ }, { - "id":"Equations \"I[0]*I[1]+(1-I[2])*I[3]\" and \"(1-I[0])*I[1]\" used within ADAM return expected results.", + "id":"Equations used by ADAM return expected result. [4]", + "result":"PASS", + "duration":"0.008 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create tensors given an equation and array or list of input tensors...","code":["var t1 = Tensor.of( expression, inputs )","var t2 = Tensor.of( expression, inputs as Float[] )","var t3 = Tensor.of( expression, true, inputs.collect(it -> Tensor.of(it) ) )","var t4 = Tensor.of( expression, false, inputs.collect(it -> Tensor.of(it) ) )","var t5 = Tensor.of( expression, false, inputs.collect(it -> Tensor.of(it) ) as Tensor[] )"]}, + + {"kind":"expect","text":"...this produces the expected result String.","code":["t1.toString().contains( expected )","t2.toString().contains( expected.replace(\".29999\", \".30000\") )","t3.toString().contains( expected )","t4.toString().contains( expected )","t5.toString().contains( expected )"]}, + + {"kind":"where","text":"The following expressions, inputs and expected String results are being used :","code":{"expression":["\"( 1 - I[0]) * I[1]\"","\"I[0] * I[1] + (1 - I[2]) * I[3]\"","\"I[0] / ( 1 - I[1] )\"","\"I[0] ** 0.5 + I[1]\"","\"I[0] - I[1] * I[2] /( I[3] ** 0.5 + I[4] )\""],"inputs":["[0.9d, -3d]","[0.9d, 0d, 0.9d, -3d]","[-0.3d, 0.9d]","[9d, 1e-7d]","[0d, 0.01d, -3d, 9d, 1e-7d ]"],"expected":["\"(1):[-0.29999]\"","\"(1):[-0.29999]\"","\"(1):[-3.0]\"","\"(1):[3.0]\"","\"(1):[0.00999]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Equations \"I[0]*I[1]+(1-I[2])*I[3]\" and \"(1-I[0])*I[1]\" used within ADAM return expected results. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["given : Function f = Function.of(expression)"]}, + + {"kind":"expect","text":"","code":["expect : output == f(input)"]}, + + {"kind":"where","text":"The following expressions, inputs and expected String results are being used :","code":{"expression":["\"I[0]*I[1]+(1-I[2])*I[3]\"","\"I[0]*I[1]+(1-I[2])*I[3]\"","\"(1-I[0])*I[1]\"","\"(1-I[0])*I[1]\""],"input":["new double[]{0.9, 0.0, 0.9, -3.0}","new double[]{-0.9, 2.0, 0.2, 1.0}","new double[]{0.9, -3.0}","new double[]{-0.9, 2.0}"],"output":["-0.29999999999999993","-1.0","-0.29999999999999993","3.8"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Equations \"I[0]*I[1]+(1-I[2])*I[3]\" and \"(1-I[0])*I[1]\" used within ADAM return expected results. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["given : Function f = Function.of(expression)"]}, + + {"kind":"expect","text":"","code":["expect : output == f(input)"]}, + + {"kind":"where","text":"The following expressions, inputs and expected String results are being used :","code":{"expression":["\"I[0]*I[1]+(1-I[2])*I[3]\"","\"I[0]*I[1]+(1-I[2])*I[3]\"","\"(1-I[0])*I[1]\"","\"(1-I[0])*I[1]\""],"input":["new double[]{0.9, 0.0, 0.9, -3.0}","new double[]{-0.9, 2.0, 0.2, 1.0}","new double[]{0.9, -3.0}","new double[]{-0.9, 2.0}"],"output":["-0.29999999999999993","-1.0","-0.29999999999999993","3.8"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Equations \"I[0]*I[1]+(1-I[2])*I[3]\" and \"(1-I[0])*I[1]\" used within ADAM return expected results. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["given : Function f = Function.of(expression)"]}, + + {"kind":"expect","text":"","code":["expect : output == f(input)"]}, + + {"kind":"where","text":"The following expressions, inputs and expected String results are being used :","code":{"expression":["\"I[0]*I[1]+(1-I[2])*I[3]\"","\"I[0]*I[1]+(1-I[2])*I[3]\"","\"(1-I[0])*I[1]\"","\"(1-I[0])*I[1]\""],"input":["new double[]{0.9, 0.0, 0.9, -3.0}","new double[]{-0.9, 2.0, 0.2, 1.0}","new double[]{0.9, -3.0}","new double[]{-0.9, 2.0}"],"output":["-0.29999999999999993","-1.0","-0.29999999999999993","3.8"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Equations \"I[0]*I[1]+(1-I[2])*I[3]\" and \"(1-I[0])*I[1]\" used within ADAM return expected results. [3]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.optimization.AdaGrad_Spec.json b/docs/spock/reports/ut.optimization.AdaGrad_Spec.json index 6e35a9693..bf34d4d89 100644 --- a/docs/spock/reports/ut.optimization.AdaGrad_Spec.json +++ b/docs/spock/reports/ut.optimization.AdaGrad_Spec.json @@ -4,19 +4,190 @@ "narrative":"", "subjects":["neureka.optimization.Optimizer"], "statistics":{ - "runs":"1", + "runs":"10", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.013 seconds" + "duration":"0.028 seconds" }, "headers":["\n The code below assumes that for we\n have the following 2 variables setup\n throughout every data table iteration:\n ```\n Tensor w = Tensor.of(0d)\n Optimizer o = Optimizer.AdaGrad.create(w) \n w.set(o) \n ```\n "],"tags":{},"see":[], "features":[ { - "id":"AdaGrad optimizes according to expected inputs", + "id":"AdaGrad optimizes according to expected inputs [0]", "result":"PASS", - "duration":"0.010 seconds", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00999","0.01707","0.01280001","0.01819","0.01481","0.01161","0.0170001","0.02075","0.02426","0.02198"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"AdaGrad optimizes according to expected inputs [1]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00999","0.01707","0.01280001","0.01819","0.01481","0.01161","0.0170001","0.02075","0.02426","0.02198"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"AdaGrad optimizes according to expected inputs [2]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00999","0.01707","0.01280001","0.01819","0.01481","0.01161","0.0170001","0.02075","0.02426","0.02198"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"AdaGrad optimizes according to expected inputs [3]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00999","0.01707","0.01280001","0.01819","0.01481","0.01161","0.0170001","0.02075","0.02426","0.02198"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"AdaGrad optimizes according to expected inputs [4]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00999","0.01707","0.01280001","0.01819","0.01481","0.01161","0.0170001","0.02075","0.02426","0.02198"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"AdaGrad optimizes according to expected inputs [5]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00999","0.01707","0.01280001","0.01819","0.01481","0.01161","0.0170001","0.02075","0.02426","0.02198"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"AdaGrad optimizes according to expected inputs [6]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00999","0.01707","0.01280001","0.01819","0.01481","0.01161","0.0170001","0.02075","0.02426","0.02198"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"AdaGrad optimizes according to expected inputs [7]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00999","0.01707","0.01280001","0.01819","0.01481","0.01161","0.0170001","0.02075","0.02426","0.02198"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"AdaGrad optimizes according to expected inputs [8]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double)gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00999","0.01707","0.01280001","0.01819","0.01481","0.01161","0.0170001","0.02075","0.02426","0.02198"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"AdaGrad optimizes according to expected inputs [9]", + "result":"PASS", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.optimization.Momentum_Spec.json b/docs/spock/reports/ut.optimization.Momentum_Spec.json index d24a66def..3c9684da7 100644 --- a/docs/spock/reports/ut.optimization.Momentum_Spec.json +++ b/docs/spock/reports/ut.optimization.Momentum_Spec.json @@ -1,20 +1,191 @@ { "className":"ut.optimization.Momentum_Spec", "title":"", - "narrative":"Momentum is an extension to the gradient descent optimization\n algorithm that allows the search to build inertia in a direction\n in the search space and overcome the oscillations of noisy\n gradients and coast across flat spots of the search space.", + "narrative":"Momentum is an extension to the gradient descent optimization \n algorithm that allows the search to build inertia in a direction \n in the search space and overcome the oscillations of noisy \n gradients and coast across flat spots of the search space.", "subjects":["neureka.optimization.Optimizer"], "statistics":{ - "runs":"1", + "runs":"10", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.007 seconds" + "duration":"0.018 seconds" }, "headers":["\n The code below assumes that for we\n have the following 2 variables setup\n throughout every data table iteration:\n ```\n Tensor w = Tensor.of(0d)\n Optimizer o = Optimizer.Momentum.create(w) \n w.set(o) \n ```\n "],"tags":{},"see":[], "features":[ { - "id":"Momentum optimizes according to expected inputs", + "id":"Momentum optimizes according to expected inputs [0]", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00299","0.00869","0.01182","0.01764","0.02088","0.02179","0.02661","0.03395","0.04355","0.050200001"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Momentum optimizes according to expected inputs [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00299","0.00869","0.01182","0.01764","0.02088","0.02179","0.02661","0.03395","0.04355","0.050200001"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Momentum optimizes according to expected inputs [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00299","0.00869","0.01182","0.01764","0.02088","0.02179","0.02661","0.03395","0.04355","0.050200001"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Momentum optimizes according to expected inputs [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00299","0.00869","0.01182","0.01764","0.02088","0.02179","0.02661","0.03395","0.04355","0.050200001"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Momentum optimizes according to expected inputs [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00299","0.00869","0.01182","0.01764","0.02088","0.02179","0.02661","0.03395","0.04355","0.050200001"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Momentum optimizes according to expected inputs [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00299","0.00869","0.01182","0.01764","0.02088","0.02179","0.02661","0.03395","0.04355","0.050200001"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Momentum optimizes according to expected inputs [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00299","0.00869","0.01182","0.01764","0.02088","0.02179","0.02661","0.03395","0.04355","0.050200001"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Momentum optimizes according to expected inputs [7]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00299","0.00869","0.01182","0.01764","0.02088","0.02179","0.02661","0.03395","0.04355","0.050200001"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Momentum optimizes according to expected inputs [8]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00299","0.00869","0.01182","0.01764","0.02088","0.02179","0.02661","0.03395","0.04355","0.050200001"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Momentum optimizes according to expected inputs [9]", "result":"PASS", "duration":"0.001 seconds", "iterations":{ diff --git a/docs/spock/reports/ut.optimization.RMSProp_Spec.json b/docs/spock/reports/ut.optimization.RMSProp_Spec.json index fdb7afcc4..0529efe66 100644 --- a/docs/spock/reports/ut.optimization.RMSProp_Spec.json +++ b/docs/spock/reports/ut.optimization.RMSProp_Spec.json @@ -1,22 +1,193 @@ { "className":"ut.optimization.RMSProp_Spec", "title":"", - "narrative":"**Root Mean Squared Propagation**, or RMSProp, is an extension of gradient\n descent and the AdaGrad version of gradient descent that uses a\n decaying average of partial gradients in the adaptation of the\n step size for each parameter.", + "narrative":"**Root Mean Squared Propagation**, or RMSProp, is an extension of gradient \n descent and the AdaGrad version of gradient descent that uses a \n decaying average of partial gradients in the adaptation of the \n step size for each parameter.", "subjects":["neureka.optimization.Optimizer"], "statistics":{ - "runs":"1", + "runs":"10", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.014 seconds" + "duration":"0.028 seconds" }, "headers":["\n The code below assumes that for we\n have the following 2 variables setup\n throughout every data table iteration:\n ```\n Tensor w = Tensor.of(0d)\n Optimizer o = Optimizer.RMSProp.create(w) \n w.set(o) \n ```\n "],"tags":{},"see":[], "features":[ { - "id":"RMSprop optimizes according to expected inputs", + "id":"RMSprop optimizes according to expected inputs [0]", "result":"PASS", - "duration":"0.010 seconds", + "duration":"0.006 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00316","0.00545","0.00402","0.00586","0.00466","0.00349","0.00544","0.00682","0.00815","0.00725"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"RMSprop optimizes according to expected inputs [1]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00316","0.00545","0.00402","0.00586","0.00466","0.00349","0.00544","0.00682","0.00815","0.00725"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"RMSprop optimizes according to expected inputs [2]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00316","0.00545","0.00402","0.00586","0.00466","0.00349","0.00544","0.00682","0.00815","0.00725"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"RMSprop optimizes according to expected inputs [3]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00316","0.00545","0.00402","0.00586","0.00466","0.00349","0.00544","0.00682","0.00815","0.00725"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"RMSprop optimizes according to expected inputs [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00316","0.00545","0.00402","0.00586","0.00466","0.00349","0.00544","0.00682","0.00815","0.00725"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"RMSprop optimizes according to expected inputs [5]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00316","0.00545","0.00402","0.00586","0.00466","0.00349","0.00544","0.00682","0.00815","0.00725"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"RMSprop optimizes according to expected inputs [6]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00316","0.00545","0.00402","0.00586","0.00466","0.00349","0.00544","0.00682","0.00815","0.00725"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"RMSprop optimizes according to expected inputs [7]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00316","0.00545","0.00402","0.00586","0.00466","0.00349","0.00544","0.00682","0.00815","0.00725"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"RMSprop optimizes according to expected inputs [8]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new scalar gradient tensor is being created.","code":["Tensor g = Tensor.of(expectedWeight)"]}, + + {"kind":"and","text":"The following input is being applied to the tensor (and internal optimizer)...","code":["w.set( Tensor.of( (double) gradient ) )","w.applyGradient()"]}, + + {"kind":"expect","text":"The following state emerges:","code":["w.toString().contains(g.toString())","w.shape.hashCode()==g.shape.hashCode()","w.strides().hashCode()==g.strides().hashCode()","w.indicesMap().hashCode()==g.indicesMap().hashCode()","w.spread().hashCode()==g.spread().hashCode()","w.offset().hashCode()==g.offset().hashCode()"]}, + + {"kind":"where","text":"","code":{"gradient":["-3","-3","2","-3","2","2","-4","-3","-3","2"],"expectedWeight":["0.00316","0.00545","0.00402","0.00586","0.00466","0.00349","0.00544","0.00682","0.00815","0.00725"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"RMSprop optimizes according to expected inputs [9]", + "result":"PASS", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Copy_Spec.json b/docs/spock/reports/ut.tensors.Copy_Spec.json index 939738270..6b3123535 100644 --- a/docs/spock/reports/ut.tensors.Copy_Spec.json +++ b/docs/spock/reports/ut.tensors.Copy_Spec.json @@ -4,19 +4,19 @@ "narrative":"In this specification we cover the behaviour of tensors with respect to their copy methods.\n There are to main ways to copy a tensor:
        \n 1. .shallowCopy()
        \n 2. .deepCopy()
        \n
        \n The first method creates a new tensor with the same underlying data array as the original tensor.
        \n The second method on the other hand creates a new tensor with a new data array.
        \n
        \n The first method is the most efficient, but it is not as safe as the second method.
        \n The second method is the most safe, but it is not as efficient.
        \n
        \n Besides these 2 main requirements, there are als some corner cases with respect to\n the components of a tensor (like for example its computation graph) which\n will be covered in this specification as well.", "subjects":["neureka.Tensor"], "statistics":{ - "runs":"5", + "runs":"17", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.013 seconds" + "duration":"0.038 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"A deep copy of a tensor is also a deep copy of the underlying data array.", "result":"PASS", - "duration":"0", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,7 +37,36 @@ }, { - "id":"A shallow copy of a tensor will be flagged as such.", + "id":"A shallow copy of a tensor will be flagged as such. [0]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"expect","text":"The tensor we will use for copying is not flagged as a shallow copy.","code":["!t.isShallowCopy()","t.toString()"]}, + + {"kind":"when","text":"We create a shallow copy of the tensor.","code":["var shallow = t.shallowCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["shallow !== t // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["shallow.shape == t.shape","shallow.mut.data.get() == t.mut.data.get() // The tensors share the same values...","shallow.mut.data.get() === t.mut.data.get() // ...as well as the same array!","shallow.mut.data === t.mut.data // In fact, their data container is the same instance."]}, + + {"kind":"and","text":"","code":["(0.. shallow.at(i) == t.at(i) }) // The values are the same!"]}, + + {"kind":"and","text":"The shallow copy is flagged as such.","code":["shallow.isShallowCopy()"]}, + + {"kind":"and","text":"Because shallow copies are merely \"fully slices\" we expect this flag to be set as well.","code":["shallow.isFullSlice()"]}, + + {"kind":"and","text":"The inverse property is false:","code":["!shallow.isPartialSlice()"]}, + + {"kind":"where","text":"","code":{"t":["Tensor.ofInts().withShape(2, 3).andFill(1, 2, -9, 8, 3, -2)","Tensor.ofBytes().withShape(5).andFill(8, 2, -7, 3, 0)","Tensor.of(1d, 2d, 3d, 4d, 5d, 6d, 7d)2..4]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A shallow copy of a tensor will be flagged as such. [1]", "result":"PASS", "duration":"0", "iterations":{ @@ -66,12 +95,41 @@ }, { - "id":"A deep copy of a slice tensor is also a deep copy of the underlying data array.", + "id":"A shallow copy of a tensor will be flagged as such. [2]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"expect","text":"The tensor we will use for copying is not flagged as a shallow copy.","code":["!t.isShallowCopy()","t.toString()"]}, + + {"kind":"when","text":"We create a shallow copy of the tensor.","code":["var shallow = t.shallowCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["shallow !== t // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["shallow.shape == t.shape","shallow.mut.data.get() == t.mut.data.get() // The tensors share the same values...","shallow.mut.data.get() === t.mut.data.get() // ...as well as the same array!","shallow.mut.data === t.mut.data // In fact, their data container is the same instance."]}, + + {"kind":"and","text":"","code":["(0.. shallow.at(i) == t.at(i) }) // The values are the same!"]}, + + {"kind":"and","text":"The shallow copy is flagged as such.","code":["shallow.isShallowCopy()"]}, + + {"kind":"and","text":"Because shallow copies are merely \"fully slices\" we expect this flag to be set as well.","code":["shallow.isFullSlice()"]}, + + {"kind":"and","text":"The inverse property is false:","code":["!shallow.isPartialSlice()"]}, + + {"kind":"where","text":"","code":{"t":["Tensor.ofInts().withShape(2, 3).andFill(1, 2, -9, 8, 3, -2)","Tensor.ofBytes().withShape(5).andFill(8, 2, -7, 3, 0)","Tensor.of(1d, 2d, 3d, 4d, 5d, 6d, 7d)2..4]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A deep copy of a slice tensor is also a deep copy of the underlying data array.", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"A slice of ints with shape (2, 2) sliced in-place from a tensor of shape (3, 3).","code":["var s = Tensor.ofInts().withShape(3, 3).andFill(1, 2, -9, 8, 3, -2)[0..1, 1..2]"]}, @@ -91,7 +149,30 @@ }, { - "id":"A shallow copy will share the same underlying data as its original tensor.", + "id":"A shallow copy will share the same underlying data as its original tensor. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of ints with shape (2, 3).","code":["var t = Tensor.ofInts().withShape(2, 3).andFill(1, 2, -9, 8, 3, -2)"]}, + + {"kind":"expect","text":"The underlying data array is as expected.","code":["t.mut.data.get() == [1, 2, -9, 8, 3, -2] // It's unsafe because it exposes mutable parts of the tensor!"]}, + + {"kind":"when","text":"We create a shallow copy of the tensor.","code":["var shallow = cloner(t)"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["shallow !== t // It's not the same instance!","shallow.shape == t.shape","shallow.mut.data.get() == t.mut.data.get() // The tensors share the same values!","shallow.mut.data.get() === t.mut.data.get() // The tensors share the exact same data array!"]}, + + {"kind":"and","text":"We verify that they share the same ints through the \"every\" method.","code":["(0.. shallow.at(i) == t.at(i) }) // The values are the same!"]}, + + {"kind":"where","text":"","code":{"cloner":["{ Tensor x -> x.shallowCopy()}","{ Tensor x -> x.shallowClone()}"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A shallow copy will share the same underlying data as its original tensor. [1]", "result":"PASS", "duration":"0", "iterations":{ @@ -114,7 +195,7 @@ }, { - "id":"We can deep copy various types of tensors.", + "id":"We can deep copy various types of tensors. [0]", "result":"PASS", "duration":"0.002 seconds", "iterations":{ @@ -136,6 +217,231 @@ {"kind":"where","text":"We can use the following types and values for the above code.","code":{"type":["Integer","Byte","Short","Long","Float","Double","Boolean","Character","String","Object"],"expected":["[6, 2, 0, -387, 22, 53, -92] as int[]","[-1, 4, 2, -49, 2, -72, 235, 0, 3] as byte[]","[65, -20, -7, -8, -3, -4, -5, -6, -9] as short[]","[0, 5462, -976, -3, -42, -35, -3436, -7, -89] as long[]","[0.5076, -1.0, -2.4, -3.0, -4.0, -5.0, -6.0] as float[]","[4.26434, -4.0, 5.3, -6.6, -7.0, 9.67] as double[]","[true, false, true, false, true, false, true, false, true] as boolean[]","['t', 'e', 's', 't', 'd', 'a', 't', 'a', '!'] as char[]","[\"test\", \"data\", \"!\"] as String[]","[\"What\", 4, 'm' as char, 1] as Object[]"]}} ], "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can deep copy various types of tensors. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A simple vector tensor which we are going to copy.","code":["var t = Tensor.of(type).withShape(expected.length).andFill(expected)"]}, + + {"kind":"and","text":"A slice of the tensor, which we should also be able to deep copy.","code":["var s = t[1..<(expected.length - 1)]"]}, + + {"kind":"when","text":"","code":["var deep = t.deepCopy()","var deepSlice = s.deepCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["deep !== t // It's not the same instance!","deepSlice !== s // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["deep.shape == t.shape","deep.mut.data.get() == t.mut.data.get() // The tensors share the same values!","deep.mut.data.get() !== t.mut.data.get() // ...but they are not the same array!"]}, + + {"kind":"and","text":"Both the copied tensor and its slice have the expected values.","code":["deep.items == expected","deepSlice.items == expected[1..<(expected.length - 1)]"]}, + + {"kind":"where","text":"We can use the following types and values for the above code.","code":{"type":["Integer","Byte","Short","Long","Float","Double","Boolean","Character","String","Object"],"expected":["[6, 2, 0, -387, 22, 53, -92] as int[]","[-1, 4, 2, -49, 2, -72, 235, 0, 3] as byte[]","[65, -20, -7, -8, -3, -4, -5, -6, -9] as short[]","[0, 5462, -976, -3, -42, -35, -3436, -7, -89] as long[]","[0.5076, -1.0, -2.4, -3.0, -4.0, -5.0, -6.0] as float[]","[4.26434, -4.0, 5.3, -6.6, -7.0, 9.67] as double[]","[true, false, true, false, true, false, true, false, true] as boolean[]","['t', 'e', 's', 't', 'd', 'a', 't', 'a', '!'] as char[]","[\"test\", \"data\", \"!\"] as String[]","[\"What\", 4, 'm' as char, 1] as Object[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can deep copy various types of tensors. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A simple vector tensor which we are going to copy.","code":["var t = Tensor.of(type).withShape(expected.length).andFill(expected)"]}, + + {"kind":"and","text":"A slice of the tensor, which we should also be able to deep copy.","code":["var s = t[1..<(expected.length - 1)]"]}, + + {"kind":"when","text":"","code":["var deep = t.deepCopy()","var deepSlice = s.deepCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["deep !== t // It's not the same instance!","deepSlice !== s // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["deep.shape == t.shape","deep.mut.data.get() == t.mut.data.get() // The tensors share the same values!","deep.mut.data.get() !== t.mut.data.get() // ...but they are not the same array!"]}, + + {"kind":"and","text":"Both the copied tensor and its slice have the expected values.","code":["deep.items == expected","deepSlice.items == expected[1..<(expected.length - 1)]"]}, + + {"kind":"where","text":"We can use the following types and values for the above code.","code":{"type":["Integer","Byte","Short","Long","Float","Double","Boolean","Character","String","Object"],"expected":["[6, 2, 0, -387, 22, 53, -92] as int[]","[-1, 4, 2, -49, 2, -72, 235, 0, 3] as byte[]","[65, -20, -7, -8, -3, -4, -5, -6, -9] as short[]","[0, 5462, -976, -3, -42, -35, -3436, -7, -89] as long[]","[0.5076, -1.0, -2.4, -3.0, -4.0, -5.0, -6.0] as float[]","[4.26434, -4.0, 5.3, -6.6, -7.0, 9.67] as double[]","[true, false, true, false, true, false, true, false, true] as boolean[]","['t', 'e', 's', 't', 'd', 'a', 't', 'a', '!'] as char[]","[\"test\", \"data\", \"!\"] as String[]","[\"What\", 4, 'm' as char, 1] as Object[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can deep copy various types of tensors. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A simple vector tensor which we are going to copy.","code":["var t = Tensor.of(type).withShape(expected.length).andFill(expected)"]}, + + {"kind":"and","text":"A slice of the tensor, which we should also be able to deep copy.","code":["var s = t[1..<(expected.length - 1)]"]}, + + {"kind":"when","text":"","code":["var deep = t.deepCopy()","var deepSlice = s.deepCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["deep !== t // It's not the same instance!","deepSlice !== s // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["deep.shape == t.shape","deep.mut.data.get() == t.mut.data.get() // The tensors share the same values!","deep.mut.data.get() !== t.mut.data.get() // ...but they are not the same array!"]}, + + {"kind":"and","text":"Both the copied tensor and its slice have the expected values.","code":["deep.items == expected","deepSlice.items == expected[1..<(expected.length - 1)]"]}, + + {"kind":"where","text":"We can use the following types and values for the above code.","code":{"type":["Integer","Byte","Short","Long","Float","Double","Boolean","Character","String","Object"],"expected":["[6, 2, 0, -387, 22, 53, -92] as int[]","[-1, 4, 2, -49, 2, -72, 235, 0, 3] as byte[]","[65, -20, -7, -8, -3, -4, -5, -6, -9] as short[]","[0, 5462, -976, -3, -42, -35, -3436, -7, -89] as long[]","[0.5076, -1.0, -2.4, -3.0, -4.0, -5.0, -6.0] as float[]","[4.26434, -4.0, 5.3, -6.6, -7.0, 9.67] as double[]","[true, false, true, false, true, false, true, false, true] as boolean[]","['t', 'e', 's', 't', 'd', 'a', 't', 'a', '!'] as char[]","[\"test\", \"data\", \"!\"] as String[]","[\"What\", 4, 'm' as char, 1] as Object[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can deep copy various types of tensors. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A simple vector tensor which we are going to copy.","code":["var t = Tensor.of(type).withShape(expected.length).andFill(expected)"]}, + + {"kind":"and","text":"A slice of the tensor, which we should also be able to deep copy.","code":["var s = t[1..<(expected.length - 1)]"]}, + + {"kind":"when","text":"","code":["var deep = t.deepCopy()","var deepSlice = s.deepCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["deep !== t // It's not the same instance!","deepSlice !== s // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["deep.shape == t.shape","deep.mut.data.get() == t.mut.data.get() // The tensors share the same values!","deep.mut.data.get() !== t.mut.data.get() // ...but they are not the same array!"]}, + + {"kind":"and","text":"Both the copied tensor and its slice have the expected values.","code":["deep.items == expected","deepSlice.items == expected[1..<(expected.length - 1)]"]}, + + {"kind":"where","text":"We can use the following types and values for the above code.","code":{"type":["Integer","Byte","Short","Long","Float","Double","Boolean","Character","String","Object"],"expected":["[6, 2, 0, -387, 22, 53, -92] as int[]","[-1, 4, 2, -49, 2, -72, 235, 0, 3] as byte[]","[65, -20, -7, -8, -3, -4, -5, -6, -9] as short[]","[0, 5462, -976, -3, -42, -35, -3436, -7, -89] as long[]","[0.5076, -1.0, -2.4, -3.0, -4.0, -5.0, -6.0] as float[]","[4.26434, -4.0, 5.3, -6.6, -7.0, 9.67] as double[]","[true, false, true, false, true, false, true, false, true] as boolean[]","['t', 'e', 's', 't', 'd', 'a', 't', 'a', '!'] as char[]","[\"test\", \"data\", \"!\"] as String[]","[\"What\", 4, 'm' as char, 1] as Object[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can deep copy various types of tensors. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A simple vector tensor which we are going to copy.","code":["var t = Tensor.of(type).withShape(expected.length).andFill(expected)"]}, + + {"kind":"and","text":"A slice of the tensor, which we should also be able to deep copy.","code":["var s = t[1..<(expected.length - 1)]"]}, + + {"kind":"when","text":"","code":["var deep = t.deepCopy()","var deepSlice = s.deepCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["deep !== t // It's not the same instance!","deepSlice !== s // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["deep.shape == t.shape","deep.mut.data.get() == t.mut.data.get() // The tensors share the same values!","deep.mut.data.get() !== t.mut.data.get() // ...but they are not the same array!"]}, + + {"kind":"and","text":"Both the copied tensor and its slice have the expected values.","code":["deep.items == expected","deepSlice.items == expected[1..<(expected.length - 1)]"]}, + + {"kind":"where","text":"We can use the following types and values for the above code.","code":{"type":["Integer","Byte","Short","Long","Float","Double","Boolean","Character","String","Object"],"expected":["[6, 2, 0, -387, 22, 53, -92] as int[]","[-1, 4, 2, -49, 2, -72, 235, 0, 3] as byte[]","[65, -20, -7, -8, -3, -4, -5, -6, -9] as short[]","[0, 5462, -976, -3, -42, -35, -3436, -7, -89] as long[]","[0.5076, -1.0, -2.4, -3.0, -4.0, -5.0, -6.0] as float[]","[4.26434, -4.0, 5.3, -6.6, -7.0, 9.67] as double[]","[true, false, true, false, true, false, true, false, true] as boolean[]","['t', 'e', 's', 't', 'd', 'a', 't', 'a', '!'] as char[]","[\"test\", \"data\", \"!\"] as String[]","[\"What\", 4, 'm' as char, 1] as Object[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can deep copy various types of tensors. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A simple vector tensor which we are going to copy.","code":["var t = Tensor.of(type).withShape(expected.length).andFill(expected)"]}, + + {"kind":"and","text":"A slice of the tensor, which we should also be able to deep copy.","code":["var s = t[1..<(expected.length - 1)]"]}, + + {"kind":"when","text":"","code":["var deep = t.deepCopy()","var deepSlice = s.deepCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["deep !== t // It's not the same instance!","deepSlice !== s // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["deep.shape == t.shape","deep.mut.data.get() == t.mut.data.get() // The tensors share the same values!","deep.mut.data.get() !== t.mut.data.get() // ...but they are not the same array!"]}, + + {"kind":"and","text":"Both the copied tensor and its slice have the expected values.","code":["deep.items == expected","deepSlice.items == expected[1..<(expected.length - 1)]"]}, + + {"kind":"where","text":"We can use the following types and values for the above code.","code":{"type":["Integer","Byte","Short","Long","Float","Double","Boolean","Character","String","Object"],"expected":["[6, 2, 0, -387, 22, 53, -92] as int[]","[-1, 4, 2, -49, 2, -72, 235, 0, 3] as byte[]","[65, -20, -7, -8, -3, -4, -5, -6, -9] as short[]","[0, 5462, -976, -3, -42, -35, -3436, -7, -89] as long[]","[0.5076, -1.0, -2.4, -3.0, -4.0, -5.0, -6.0] as float[]","[4.26434, -4.0, 5.3, -6.6, -7.0, 9.67] as double[]","[true, false, true, false, true, false, true, false, true] as boolean[]","['t', 'e', 's', 't', 'd', 'a', 't', 'a', '!'] as char[]","[\"test\", \"data\", \"!\"] as String[]","[\"What\", 4, 'm' as char, 1] as Object[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can deep copy various types of tensors. [7]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A simple vector tensor which we are going to copy.","code":["var t = Tensor.of(type).withShape(expected.length).andFill(expected)"]}, + + {"kind":"and","text":"A slice of the tensor, which we should also be able to deep copy.","code":["var s = t[1..<(expected.length - 1)]"]}, + + {"kind":"when","text":"","code":["var deep = t.deepCopy()","var deepSlice = s.deepCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["deep !== t // It's not the same instance!","deepSlice !== s // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["deep.shape == t.shape","deep.mut.data.get() == t.mut.data.get() // The tensors share the same values!","deep.mut.data.get() !== t.mut.data.get() // ...but they are not the same array!"]}, + + {"kind":"and","text":"Both the copied tensor and its slice have the expected values.","code":["deep.items == expected","deepSlice.items == expected[1..<(expected.length - 1)]"]}, + + {"kind":"where","text":"We can use the following types and values for the above code.","code":{"type":["Integer","Byte","Short","Long","Float","Double","Boolean","Character","String","Object"],"expected":["[6, 2, 0, -387, 22, 53, -92] as int[]","[-1, 4, 2, -49, 2, -72, 235, 0, 3] as byte[]","[65, -20, -7, -8, -3, -4, -5, -6, -9] as short[]","[0, 5462, -976, -3, -42, -35, -3436, -7, -89] as long[]","[0.5076, -1.0, -2.4, -3.0, -4.0, -5.0, -6.0] as float[]","[4.26434, -4.0, 5.3, -6.6, -7.0, 9.67] as double[]","[true, false, true, false, true, false, true, false, true] as boolean[]","['t', 'e', 's', 't', 'd', 'a', 't', 'a', '!'] as char[]","[\"test\", \"data\", \"!\"] as String[]","[\"What\", 4, 'm' as char, 1] as Object[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can deep copy various types of tensors. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A simple vector tensor which we are going to copy.","code":["var t = Tensor.of(type).withShape(expected.length).andFill(expected)"]}, + + {"kind":"and","text":"A slice of the tensor, which we should also be able to deep copy.","code":["var s = t[1..<(expected.length - 1)]"]}, + + {"kind":"when","text":"","code":["var deep = t.deepCopy()","var deepSlice = s.deepCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["deep !== t // It's not the same instance!","deepSlice !== s // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["deep.shape == t.shape","deep.mut.data.get() == t.mut.data.get() // The tensors share the same values!","deep.mut.data.get() !== t.mut.data.get() // ...but they are not the same array!"]}, + + {"kind":"and","text":"Both the copied tensor and its slice have the expected values.","code":["deep.items == expected","deepSlice.items == expected[1..<(expected.length - 1)]"]}, + + {"kind":"where","text":"We can use the following types and values for the above code.","code":{"type":["Integer","Byte","Short","Long","Float","Double","Boolean","Character","String","Object"],"expected":["[6, 2, 0, -387, 22, 53, -92] as int[]","[-1, 4, 2, -49, 2, -72, 235, 0, 3] as byte[]","[65, -20, -7, -8, -3, -4, -5, -6, -9] as short[]","[0, 5462, -976, -3, -42, -35, -3436, -7, -89] as long[]","[0.5076, -1.0, -2.4, -3.0, -4.0, -5.0, -6.0] as float[]","[4.26434, -4.0, 5.3, -6.6, -7.0, 9.67] as double[]","[true, false, true, false, true, false, true, false, true] as boolean[]","['t', 'e', 's', 't', 'd', 'a', 't', 'a', '!'] as char[]","[\"test\", \"data\", \"!\"] as String[]","[\"What\", 4, 'm' as char, 1] as Object[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can deep copy various types of tensors. [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A simple vector tensor which we are going to copy.","code":["var t = Tensor.of(type).withShape(expected.length).andFill(expected)"]}, + + {"kind":"and","text":"A slice of the tensor, which we should also be able to deep copy.","code":["var s = t[1..<(expected.length - 1)]"]}, + + {"kind":"when","text":"","code":["var deep = t.deepCopy()","var deepSlice = s.deepCopy()"]}, + + {"kind":"then","text":"The copy is not the same instance as the original tensor.","code":["deep !== t // It's not the same instance!","deepSlice !== s // It's not the same instance!"]}, + + {"kind":"and","text":"The shape and underlying data array are equal to the original tensor but the data is not identical.","code":["deep.shape == t.shape","deep.mut.data.get() == t.mut.data.get() // The tensors share the same values!","deep.mut.data.get() !== t.mut.data.get() // ...but they are not the same array!"]}, + + {"kind":"and","text":"Both the copied tensor and its slice have the expected values.","code":["deep.items == expected","deepSlice.items == expected[1..<(expected.length - 1)]"]}, + + {"kind":"where","text":"We can use the following types and values for the above code.","code":{"type":["Integer","Byte","Short","Long","Float","Double","Boolean","Character","String","Object"],"expected":["[6, 2, 0, -387, 22, 53, -92] as int[]","[-1, 4, 2, -49, 2, -72, 235, 0, 3] as byte[]","[65, -20, -7, -8, -3, -4, -5, -6, -9] as short[]","[0, 5462, -976, -3, -42, -35, -3436, -7, -89] as long[]","[0.5076, -1.0, -2.4, -3.0, -4.0, -5.0, -6.0] as float[]","[4.26434, -4.0, 5.3, -6.6, -7.0, 9.67] as double[]","[true, false, true, false, true, false, true, false, true] as boolean[]","['t', 'e', 's', 't', 'd', 'a', 't', 'a', '!'] as char[]","[\"test\", \"data\", \"!\"] as String[]","[\"What\", 4, 'm' as char, 1] as Object[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} } ], diff --git a/docs/spock/reports/ut.tensors.DimTrim_Spec.json b/docs/spock/reports/ut.tensors.DimTrim_Spec.json index beeec0944..832d7b048 100644 --- a/docs/spock/reports/ut.tensors.DimTrim_Spec.json +++ b/docs/spock/reports/ut.tensors.DimTrim_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.002 seconds" + "duration":"0.005 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"The \"dimTrim\" operation works on slices too!", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Expression_Based_Tensor_Instantiation_Spec.json b/docs/spock/reports/ut.tensors.Expression_Based_Tensor_Instantiation_Spec.json index a03a5bafe..9ff53b1de 100644 --- a/docs/spock/reports/ut.tensors.Expression_Based_Tensor_Instantiation_Spec.json +++ b/docs/spock/reports/ut.tensors.Expression_Based_Tensor_Instantiation_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.014 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"A tensor can be created from a function as expression.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n The `Tensor.of` method can be used to instantiate a tensor\n using a string expression which defines a function \n followed by an arbitrary number of tensor arguments\n which are used as input for the function.\n "] }, @@ -35,7 +35,7 @@ { "id":"We can instantiate tensors from various simple string expressions.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.009 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Fluent_Tensor_Creation_Spec.json b/docs/spock/reports/ut.tensors.Fluent_Tensor_Creation_Spec.json index 0d2d0e8d9..a48788eaf 100644 --- a/docs/spock/reports/ut.tensors.Fluent_Tensor_Creation_Spec.json +++ b/docs/spock/reports/ut.tensors.Fluent_Tensor_Creation_Spec.json @@ -4,53 +4,819 @@ "narrative":"", "subjects":["neureka.Tensor"], "statistics":{ - "runs":"7", + "runs":"46", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.018 seconds" + "duration":"0.046 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"Tensors can be created fluently.", + "id":"Tensors can be created fluently. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData.length == 1","t.items.size() == 6"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is homogeneously filled it will be a \"virtual tensor\"\n This means that the tensor will not have allocated the memory proportional to the size\n of the tensor!\n ","code":["t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["4 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 4 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors can be created fluently. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData.length == 1","t.items.size() == 6"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is homogeneously filled it will be a \"virtual tensor\"\n This means that the tensor will not have allocated the memory proportional to the size\n of the tensor!\n ","code":["t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["4 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 4 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors can be created fluently. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData.length == 1","t.items.size() == 6"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is homogeneously filled it will be a \"virtual tensor\"\n This means that the tensor will not have allocated the memory proportional to the size\n of the tensor!\n ","code":["t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["4 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 4 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors can be created fluently. [3]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData.length == 1","t.items.size() == 6"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is homogeneously filled it will be a \"virtual tensor\"\n This means that the tensor will not have allocated the memory proportional to the size\n of the tensor!\n ","code":["t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["4 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 4 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors can be created fluently. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData.length == 1","t.items.size() == 6"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is homogeneously filled it will be a \"virtual tensor\"\n This means that the tensor will not have allocated the memory proportional to the size\n of the tensor!\n ","code":["t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["4 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 4 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors can be created fluently. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!"] + }, + "blocks":[ + {"kind":"given","text":"We create a new homogeneously filled Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData.length == 1","t.items.size() == 6"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is homogeneously filled it will be a \"virtual tensor\"\n This means that the tensor will not have allocated the memory proportional to the size\n of the tensor!\n ","code":["t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["4 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 4 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Range based tensors can be created fluently. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a range based Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFillFrom( from ).to( to ).step( step )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Integer.class","Double.class","Double.class","Float.class","Float.class","Byte.class","Long.class"],"from":["-9 as int","-2 as int","2.7 as double","-3 as double","6.4f as float","0f as float","-5 as byte","-65 as long"],"to":["18 as int","4 as int","45.0 as double","3 as double","78.3f as float","1f as float","6 as byte","45 as long"],"step":["2","2","3","0.5","4","0.2f","2","5"],"data":["[-9, -7, -5, -3, -1, 1] as int[]","[-2, 0, 2, 4, -2, 0] as int[]","[2.7, 5.7, 8.7, 11.7, 14.7, 17.7] as double[]","[-3.0, -2.5, -2.0, -1.5, -1.0, -0.5] as double[]","[6.4, 10.4, 14.4, 18.4, 22.4, 26.4] as float[]","[0.0, 0.2, 0.4, 0.6, 0.8, 1.0] as float[]","[-5, -3, -1, 1, 3, 5] as byte[]","[-65, -60, -55, -50, -45, -40] as long[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Range based tensors can be created fluently. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a range based Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFillFrom( from ).to( to ).step( step )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Integer.class","Double.class","Double.class","Float.class","Float.class","Byte.class","Long.class"],"from":["-9 as int","-2 as int","2.7 as double","-3 as double","6.4f as float","0f as float","-5 as byte","-65 as long"],"to":["18 as int","4 as int","45.0 as double","3 as double","78.3f as float","1f as float","6 as byte","45 as long"],"step":["2","2","3","0.5","4","0.2f","2","5"],"data":["[-9, -7, -5, -3, -1, 1] as int[]","[-2, 0, 2, 4, -2, 0] as int[]","[2.7, 5.7, 8.7, 11.7, 14.7, 17.7] as double[]","[-3.0, -2.5, -2.0, -1.5, -1.0, -0.5] as double[]","[6.4, 10.4, 14.4, 18.4, 22.4, 26.4] as float[]","[0.0, 0.2, 0.4, 0.6, 0.8, 1.0] as float[]","[-5, -3, -1, 1, 3, 5] as byte[]","[-65, -60, -55, -50, -45, -40] as long[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Range based tensors can be created fluently. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a range based Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFillFrom( from ).to( to ).step( step )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Integer.class","Double.class","Double.class","Float.class","Float.class","Byte.class","Long.class"],"from":["-9 as int","-2 as int","2.7 as double","-3 as double","6.4f as float","0f as float","-5 as byte","-65 as long"],"to":["18 as int","4 as int","45.0 as double","3 as double","78.3f as float","1f as float","6 as byte","45 as long"],"step":["2","2","3","0.5","4","0.2f","2","5"],"data":["[-9, -7, -5, -3, -1, 1] as int[]","[-2, 0, 2, 4, -2, 0] as int[]","[2.7, 5.7, 8.7, 11.7, 14.7, 17.7] as double[]","[-3.0, -2.5, -2.0, -1.5, -1.0, -0.5] as double[]","[6.4, 10.4, 14.4, 18.4, 22.4, 26.4] as float[]","[0.0, 0.2, 0.4, 0.6, 0.8, 1.0] as float[]","[-5, -3, -1, 1, 3, 5] as byte[]","[-65, -60, -55, -50, -45, -40] as long[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Range based tensors can be created fluently. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a range based Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFillFrom( from ).to( to ).step( step )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Integer.class","Double.class","Double.class","Float.class","Float.class","Byte.class","Long.class"],"from":["-9 as int","-2 as int","2.7 as double","-3 as double","6.4f as float","0f as float","-5 as byte","-65 as long"],"to":["18 as int","4 as int","45.0 as double","3 as double","78.3f as float","1f as float","6 as byte","45 as long"],"step":["2","2","3","0.5","4","0.2f","2","5"],"data":["[-9, -7, -5, -3, -1, 1] as int[]","[-2, 0, 2, 4, -2, 0] as int[]","[2.7, 5.7, 8.7, 11.7, 14.7, 17.7] as double[]","[-3.0, -2.5, -2.0, -1.5, -1.0, -0.5] as double[]","[6.4, 10.4, 14.4, 18.4, 22.4, 26.4] as float[]","[0.0, 0.2, 0.4, 0.6, 0.8, 1.0] as float[]","[-5, -3, -1, 1, 3, 5] as byte[]","[-65, -60, -55, -50, -45, -40] as long[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Range based tensors can be created fluently. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a range based Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFillFrom( from ).to( to ).step( step )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Integer.class","Double.class","Double.class","Float.class","Float.class","Byte.class","Long.class"],"from":["-9 as int","-2 as int","2.7 as double","-3 as double","6.4f as float","0f as float","-5 as byte","-65 as long"],"to":["18 as int","4 as int","45.0 as double","3 as double","78.3f as float","1f as float","6 as byte","45 as long"],"step":["2","2","3","0.5","4","0.2f","2","5"],"data":["[-9, -7, -5, -3, -1, 1] as int[]","[-2, 0, 2, 4, -2, 0] as int[]","[2.7, 5.7, 8.7, 11.7, 14.7, 17.7] as double[]","[-3.0, -2.5, -2.0, -1.5, -1.0, -0.5] as double[]","[6.4, 10.4, 14.4, 18.4, 22.4, 26.4] as float[]","[0.0, 0.2, 0.4, 0.6, 0.8, 1.0] as float[]","[-5, -3, -1, 1, 3, 5] as byte[]","[-65, -60, -55, -50, -45, -40] as long[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Range based tensors can be created fluently. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a range based Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFillFrom( from ).to( to ).step( step )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Integer.class","Double.class","Double.class","Float.class","Float.class","Byte.class","Long.class"],"from":["-9 as int","-2 as int","2.7 as double","-3 as double","6.4f as float","0f as float","-5 as byte","-65 as long"],"to":["18 as int","4 as int","45.0 as double","3 as double","78.3f as float","1f as float","6 as byte","45 as long"],"step":["2","2","3","0.5","4","0.2f","2","5"],"data":["[-9, -7, -5, -3, -1, 1] as int[]","[-2, 0, 2, 4, -2, 0] as int[]","[2.7, 5.7, 8.7, 11.7, 14.7, 17.7] as double[]","[-3.0, -2.5, -2.0, -1.5, -1.0, -0.5] as double[]","[6.4, 10.4, 14.4, 18.4, 22.4, 26.4] as float[]","[0.0, 0.2, 0.4, 0.6, 0.8, 1.0] as float[]","[-5, -3, -1, 1, 3, 5] as byte[]","[-65, -60, -55, -50, -45, -40] as long[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Range based tensors can be created fluently. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a range based Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFillFrom( from ).to( to ).step( step )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Integer.class","Double.class","Double.class","Float.class","Float.class","Byte.class","Long.class"],"from":["-9 as int","-2 as int","2.7 as double","-3 as double","6.4f as float","0f as float","-5 as byte","-65 as long"],"to":["18 as int","4 as int","45.0 as double","3 as double","78.3f as float","1f as float","6 as byte","45 as long"],"step":["2","2","3","0.5","4","0.2f","2","5"],"data":["[-9, -7, -5, -3, -1, 1] as int[]","[-2, 0, 2, 4, -2, 0] as int[]","[2.7, 5.7, 8.7, 11.7, 14.7, 17.7] as double[]","[-3.0, -2.5, -2.0, -1.5, -1.0, -0.5] as double[]","[6.4, 10.4, 14.4, 18.4, 22.4, 26.4] as float[]","[0.0, 0.2, 0.4, 0.6, 0.8, 1.0] as float[]","[-5, -3, -1, 1, 3, 5] as byte[]","[-65, -60, -55, -50, -45, -40] as long[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Range based tensors can be created fluently. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a range based Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFillFrom( from ).to( to ).step( step )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Integer.class","Double.class","Double.class","Float.class","Float.class","Byte.class","Long.class"],"from":["-9 as int","-2 as int","2.7 as double","-3 as double","6.4f as float","0f as float","-5 as byte","-65 as long"],"to":["18 as int","4 as int","45.0 as double","3 as double","78.3f as float","1f as float","6 as byte","45 as long"],"step":["2","2","3","0.5","4","0.2f","2","5"],"data":["[-9, -7, -5, -3, -1, 1] as int[]","[-2, 0, 2, 4, -2, 0] as int[]","[2.7, 5.7, 8.7, 11.7, 14.7, 17.7] as double[]","[-3.0, -2.5, -2.0, -1.5, -1.0, -0.5] as double[]","[6.4, 10.4, 14.4, 18.4, 22.4, 26.4] as float[]","[0.0, 0.2, 0.4, 0.6, 0.8, 1.0] as float[]","[-5, -3, -1, 1, 3, 5] as byte[]","[-65, -60, -55, -50, -45, -40] as long[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Value based tensors can be created fluently. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFill( data )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Boolean.class","Character.class"],"data":["[2, 3, 4] as Integer[]","[-5, 6.5, 7] as Double[]","[6, -1, -2] as Short[]","[22.4, 26.4] as Float[]","[-20, 3, 4, -3] as Byte[]","[23, 199] as Long[]","[true, false] as Boolean[]","['x', 'y'] as Character[]"],"expected":["[2, 3, 4, 2, 3, 4] as int[]","[-5, 6.5, 7, -5, 6.5, 7] as double[]","[6, -1, -2, 6, -1, -2] as short[]","[22.4, 26.4, 22.4, 26.4, 22.4, 26.4] as float[]","[-20, 3, 4, -3, -20, 3] as byte[]","[23, 199, 23, 199, 23, 199] as long[]","[true, false, true, false, true, false] as boolean[]","['x', 'y', 'x', 'y', 'x', 'y'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Value based tensors can be created fluently. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFill( data )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Boolean.class","Character.class"],"data":["[2, 3, 4] as Integer[]","[-5, 6.5, 7] as Double[]","[6, -1, -2] as Short[]","[22.4, 26.4] as Float[]","[-20, 3, 4, -3] as Byte[]","[23, 199] as Long[]","[true, false] as Boolean[]","['x', 'y'] as Character[]"],"expected":["[2, 3, 4, 2, 3, 4] as int[]","[-5, 6.5, 7, -5, 6.5, 7] as double[]","[6, -1, -2, 6, -1, -2] as short[]","[22.4, 26.4, 22.4, 26.4, 22.4, 26.4] as float[]","[-20, 3, 4, -3, -20, 3] as byte[]","[23, 199, 23, 199, 23, 199] as long[]","[true, false, true, false, true, false] as boolean[]","['x', 'y', 'x', 'y', 'x', 'y'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Value based tensors can be created fluently. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFill( data )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Boolean.class","Character.class"],"data":["[2, 3, 4] as Integer[]","[-5, 6.5, 7] as Double[]","[6, -1, -2] as Short[]","[22.4, 26.4] as Float[]","[-20, 3, 4, -3] as Byte[]","[23, 199] as Long[]","[true, false] as Boolean[]","['x', 'y'] as Character[]"],"expected":["[2, 3, 4, 2, 3, 4] as int[]","[-5, 6.5, 7, -5, 6.5, 7] as double[]","[6, -1, -2, 6, -1, -2] as short[]","[22.4, 26.4, 22.4, 26.4, 22.4, 26.4] as float[]","[-20, 3, 4, -3, -20, 3] as byte[]","[23, 199, 23, 199, 23, 199] as long[]","[true, false, true, false, true, false] as boolean[]","['x', 'y', 'x', 'y', 'x', 'y'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Value based tensors can be created fluently. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFill( data )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Boolean.class","Character.class"],"data":["[2, 3, 4] as Integer[]","[-5, 6.5, 7] as Double[]","[6, -1, -2] as Short[]","[22.4, 26.4] as Float[]","[-20, 3, 4, -3] as Byte[]","[23, 199] as Long[]","[true, false] as Boolean[]","['x', 'y'] as Character[]"],"expected":["[2, 3, 4, 2, 3, 4] as int[]","[-5, 6.5, 7, -5, 6.5, 7] as double[]","[6, -1, -2, 6, -1, -2] as short[]","[22.4, 26.4, 22.4, 26.4, 22.4, 26.4] as float[]","[-20, 3, 4, -3, -20, 3] as byte[]","[23, 199, 23, 199, 23, 199] as long[]","[true, false, true, false, true, false] as boolean[]","['x', 'y', 'x', 'y', 'x', 'y'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Value based tensors can be created fluently. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFill( data )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Boolean.class","Character.class"],"data":["[2, 3, 4] as Integer[]","[-5, 6.5, 7] as Double[]","[6, -1, -2] as Short[]","[22.4, 26.4] as Float[]","[-20, 3, 4, -3] as Byte[]","[23, 199] as Long[]","[true, false] as Boolean[]","['x', 'y'] as Character[]"],"expected":["[2, 3, 4, 2, 3, 4] as int[]","[-5, 6.5, 7, -5, 6.5, 7] as double[]","[6, -1, -2, 6, -1, -2] as short[]","[22.4, 26.4, 22.4, 26.4, 22.4, 26.4] as float[]","[-20, 3, 4, -3, -20, 3] as byte[]","[23, 199, 23, 199, 23, 199] as long[]","[true, false, true, false, true, false] as boolean[]","['x', 'y', 'x', 'y', 'x', 'y'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Value based tensors can be created fluently. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFill( data )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Boolean.class","Character.class"],"data":["[2, 3, 4] as Integer[]","[-5, 6.5, 7] as Double[]","[6, -1, -2] as Short[]","[22.4, 26.4] as Float[]","[-20, 3, 4, -3] as Byte[]","[23, 199] as Long[]","[true, false] as Boolean[]","['x', 'y'] as Character[]"],"expected":["[2, 3, 4, 2, 3, 4] as int[]","[-5, 6.5, 7, -5, 6.5, 7] as double[]","[6, -1, -2, 6, -1, -2] as short[]","[22.4, 26.4, 22.4, 26.4, 22.4, 26.4] as float[]","[-20, 3, 4, -3, -20, 3] as byte[]","[23, 199, 23, 199, 23, 199] as long[]","[true, false, true, false, true, false] as boolean[]","['x', 'y', 'x', 'y', 'x', 'y'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Value based tensors can be created fluently. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFill( data )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Boolean.class","Character.class"],"data":["[2, 3, 4] as Integer[]","[-5, 6.5, 7] as Double[]","[6, -1, -2] as Short[]","[22.4, 26.4] as Float[]","[-20, 3, 4, -3] as Byte[]","[23, 199] as Long[]","[true, false] as Boolean[]","['x', 'y'] as Character[]"],"expected":["[2, 3, 4, 2, 3, 4] as int[]","[-5, 6.5, 7, -5, 6.5, 7] as double[]","[6, -1, -2, 6, -1, -2] as short[]","[22.4, 26.4, 22.4, 26.4, 22.4, 26.4] as float[]","[-20, 3, 4, -3, -20, 3] as byte[]","[23, 199, 23, 199, 23, 199] as long[]","[true, false, true, false, true, false] as boolean[]","['x', 'y', 'x', 'y', 'x', 'y'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Value based tensors can be created fluently. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFill( data )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Boolean.class","Character.class"],"data":["[2, 3, 4] as Integer[]","[-5, 6.5, 7] as Double[]","[6, -1, -2] as Short[]","[22.4, 26.4] as Float[]","[-20, 3, 4, -3] as Byte[]","[23, 199] as Long[]","[true, false] as Boolean[]","['x', 'y'] as Character[]"],"expected":["[2, 3, 4, 2, 3, 4] as int[]","[-5, 6.5, 7, -5, 6.5, 7] as double[]","[6, -1, -2, 6, -1, -2] as short[]","[22.4, 26.4, 22.4, 26.4, 22.4, 26.4] as float[]","[-20, 3, 4, -3, -20, 3] as byte[]","[23, 199, 23, 199, 23, 199] as long[]","[true, false, true, false, true, false] as boolean[]","['x', 'y', 'x', 'y', 'x', 'y'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Seed based tensors can be created fluently. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to be populated based on a seed.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andSeed( seed )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"","code":["t.toString().startsWith(\"(3x2):\")"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Short","Float","Byte","Long","Boolean","Character"],"seed":["\"a\"","\"b\"","\"c\"","\"d\"","\"e\"","\"f\"","\"g\"","\"h\""],"expected":["[1431614970, 345625747, -1944974668, -1560046587, -840164727, 1545421892] as int[]","[0.5099337204650233, -0.1940291796851406, 1.4457326764876133, 1.1037197321548482, 0.5318191965243577, 0.19202511115716991] as double[]","[-14216, -20070, 24851, -22296, -9925, 31593] as short[]","[1.4457327, 1.1037197, 0.5318192, 0.19202511, 0.450341, -0.18904476] as float[]","[57, 26, -121, -59, -23, 90] as byte[]","[7366351542344062765, -5751258536495446167, -794811082727408195, 283695574932216990, -455308978758056709, -8426760264878742120] as long[]","[true, false, false, false, false, true] as boolean[]","[-1747130645, -868747698, 1684960924, -1581710323, -1526159736, -842114084] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Seed based tensors can be created fluently. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to be populated based on a seed.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andSeed( seed )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"","code":["t.toString().startsWith(\"(3x2):\")"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Short","Float","Byte","Long","Boolean","Character"],"seed":["\"a\"","\"b\"","\"c\"","\"d\"","\"e\"","\"f\"","\"g\"","\"h\""],"expected":["[1431614970, 345625747, -1944974668, -1560046587, -840164727, 1545421892] as int[]","[0.5099337204650233, -0.1940291796851406, 1.4457326764876133, 1.1037197321548482, 0.5318191965243577, 0.19202511115716991] as double[]","[-14216, -20070, 24851, -22296, -9925, 31593] as short[]","[1.4457327, 1.1037197, 0.5318192, 0.19202511, 0.450341, -0.18904476] as float[]","[57, 26, -121, -59, -23, 90] as byte[]","[7366351542344062765, -5751258536495446167, -794811082727408195, 283695574932216990, -455308978758056709, -8426760264878742120] as long[]","[true, false, false, false, false, true] as boolean[]","[-1747130645, -868747698, 1684960924, -1581710323, -1526159736, -842114084] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Seed based tensors can be created fluently. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to be populated based on a seed.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andSeed( seed )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"","code":["t.toString().startsWith(\"(3x2):\")"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Short","Float","Byte","Long","Boolean","Character"],"seed":["\"a\"","\"b\"","\"c\"","\"d\"","\"e\"","\"f\"","\"g\"","\"h\""],"expected":["[1431614970, 345625747, -1944974668, -1560046587, -840164727, 1545421892] as int[]","[0.5099337204650233, -0.1940291796851406, 1.4457326764876133, 1.1037197321548482, 0.5318191965243577, 0.19202511115716991] as double[]","[-14216, -20070, 24851, -22296, -9925, 31593] as short[]","[1.4457327, 1.1037197, 0.5318192, 0.19202511, 0.450341, -0.18904476] as float[]","[57, 26, -121, -59, -23, 90] as byte[]","[7366351542344062765, -5751258536495446167, -794811082727408195, 283695574932216990, -455308978758056709, -8426760264878742120] as long[]","[true, false, false, false, false, true] as boolean[]","[-1747130645, -868747698, 1684960924, -1581710323, -1526159736, -842114084] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Seed based tensors can be created fluently. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to be populated based on a seed.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andSeed( seed )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"","code":["t.toString().startsWith(\"(3x2):\")"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Short","Float","Byte","Long","Boolean","Character"],"seed":["\"a\"","\"b\"","\"c\"","\"d\"","\"e\"","\"f\"","\"g\"","\"h\""],"expected":["[1431614970, 345625747, -1944974668, -1560046587, -840164727, 1545421892] as int[]","[0.5099337204650233, -0.1940291796851406, 1.4457326764876133, 1.1037197321548482, 0.5318191965243577, 0.19202511115716991] as double[]","[-14216, -20070, 24851, -22296, -9925, 31593] as short[]","[1.4457327, 1.1037197, 0.5318192, 0.19202511, 0.450341, -0.18904476] as float[]","[57, 26, -121, -59, -23, 90] as byte[]","[7366351542344062765, -5751258536495446167, -794811082727408195, 283695574932216990, -455308978758056709, -8426760264878742120] as long[]","[true, false, false, false, false, true] as boolean[]","[-1747130645, -868747698, 1684960924, -1581710323, -1526159736, -842114084] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Seed based tensors can be created fluently. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to be populated based on a seed.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andSeed( seed )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"","code":["t.toString().startsWith(\"(3x2):\")"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Short","Float","Byte","Long","Boolean","Character"],"seed":["\"a\"","\"b\"","\"c\"","\"d\"","\"e\"","\"f\"","\"g\"","\"h\""],"expected":["[1431614970, 345625747, -1944974668, -1560046587, -840164727, 1545421892] as int[]","[0.5099337204650233, -0.1940291796851406, 1.4457326764876133, 1.1037197321548482, 0.5318191965243577, 0.19202511115716991] as double[]","[-14216, -20070, 24851, -22296, -9925, 31593] as short[]","[1.4457327, 1.1037197, 0.5318192, 0.19202511, 0.450341, -0.18904476] as float[]","[57, 26, -121, -59, -23, 90] as byte[]","[7366351542344062765, -5751258536495446167, -794811082727408195, 283695574932216990, -455308978758056709, -8426760264878742120] as long[]","[true, false, false, false, false, true] as boolean[]","[-1747130645, -868747698, 1684960924, -1581710323, -1526159736, -842114084] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Seed based tensors can be created fluently. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to be populated based on a seed.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andSeed( seed )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"","code":["t.toString().startsWith(\"(3x2):\")"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Short","Float","Byte","Long","Boolean","Character"],"seed":["\"a\"","\"b\"","\"c\"","\"d\"","\"e\"","\"f\"","\"g\"","\"h\""],"expected":["[1431614970, 345625747, -1944974668, -1560046587, -840164727, 1545421892] as int[]","[0.5099337204650233, -0.1940291796851406, 1.4457326764876133, 1.1037197321548482, 0.5318191965243577, 0.19202511115716991] as double[]","[-14216, -20070, 24851, -22296, -9925, 31593] as short[]","[1.4457327, 1.1037197, 0.5318192, 0.19202511, 0.450341, -0.18904476] as float[]","[57, 26, -121, -59, -23, 90] as byte[]","[7366351542344062765, -5751258536495446167, -794811082727408195, 283695574932216990, -455308978758056709, -8426760264878742120] as long[]","[true, false, false, false, false, true] as boolean[]","[-1747130645, -868747698, 1684960924, -1581710323, -1526159736, -842114084] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Seed based tensors can be created fluently. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to be populated based on a seed.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andSeed( seed )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"","code":["t.toString().startsWith(\"(3x2):\")"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Short","Float","Byte","Long","Boolean","Character"],"seed":["\"a\"","\"b\"","\"c\"","\"d\"","\"e\"","\"f\"","\"g\"","\"h\""],"expected":["[1431614970, 345625747, -1944974668, -1560046587, -840164727, 1545421892] as int[]","[0.5099337204650233, -0.1940291796851406, 1.4457326764876133, 1.1037197321548482, 0.5318191965243577, 0.19202511115716991] as double[]","[-14216, -20070, 24851, -22296, -9925, 31593] as short[]","[1.4457327, 1.1037197, 0.5318192, 0.19202511, 0.450341, -0.18904476] as float[]","[57, 26, -121, -59, -23, 90] as byte[]","[7366351542344062765, -5751258536495446167, -794811082727408195, 283695574932216990, -455308978758056709, -8426760264878742120] as long[]","[true, false, false, false, false, true] as boolean[]","[-1747130645, -868747698, 1684960924, -1581710323, -1526159736, -842114084] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Seed based tensors can be created fluently. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to be populated based on a seed.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andSeed( seed )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"","code":["t.toString().startsWith(\"(3x2):\")"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Short","Float","Byte","Long","Boolean","Character"],"seed":["\"a\"","\"b\"","\"c\"","\"d\"","\"e\"","\"f\"","\"g\"","\"h\""],"expected":["[1431614970, 345625747, -1944974668, -1560046587, -840164727, 1545421892] as int[]","[0.5099337204650233, -0.1940291796851406, 1.4457326764876133, 1.1037197321548482, 0.5318191965243577, 0.19202511115716991] as double[]","[-14216, -20070, 24851, -22296, -9925, 31593] as short[]","[1.4457327, 1.1037197, 0.5318192, 0.19202511, 0.450341, -0.18904476] as float[]","[57, 26, -121, -59, -23, 90] as byte[]","[7366351542344062765, -5751258536495446167, -794811082727408195, 283695574932216990, -455308978758056709, -8426760264878742120] as long[]","[true, false, false, false, false, true] as boolean[]","[-1747130645, -868747698, 1684960924, -1581710323, -1526159736, -842114084] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Initialization lambda based tensors can be created fluently. [0]", "result":"PASS", "duration":"0.001 seconds", "iterations":{ - "tags":{},"see":[],"extraInfo":["This feature is based on a fluent builder API!","This feature is based on a fluent builder API!","This feature is based on a fluent builder API!","This feature is based on a fluent builder API!","This feature is based on a fluent builder API!","This feature is based on a fluent builder API!"] + "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"We create a new homogeneously filled Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .all( value )"]}, + {"kind":"given","text":"We create a Tensor instance by passing an initialization lambda which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andWhere( initializer )"]}, {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, - {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData.length == 1","t.items.size() == 6"]}, + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, - {"kind":"and","text":"\n Based on the fact that the tensor is homogeneously filled it will be a \"virtual tensor\"\n This means that the tensor will not have allocated the memory proportional to the size\n of the tensor!\n ","code":["t.isVirtual()"]}, + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, - {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Float","Long","Boolean","Character"],"value":["4 as int","4.0 as double","4f as float","42L as Long","false","'°' as char"],"data":["new int[] { 4 }","new double[]{ 4.0 }","new float[] { 4f }","new long[] { 42L }","new boolean[] { false }","new char[] { '°' as char }"]}} + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Character.class","Boolean.class"],"initializer":["{ i, indices -> (i + indices.sum()) }","{ i, indices -> (double) (i + indices.sum()) }","{ i, indices -> (short) (i + indices.sum()) }","{ i, indices -> (float) (i + indices.sum()) }","{ i, indices -> (byte) (i + indices.sum()) }","{ i, indices -> (long) (i + indices.sum()) }","{ i, indices -> (char) (i + indices.sum()) }","{ i, indices -> (boolean)(i % 2 == 0) }"],"expected":["[0, 2, 3, 5, 6, 8] as int[]","[0, 2, 3, 5, 6, 8] as double[]","[0, 2, 3, 5, 6, 8] as short[]","[0, 2, 3, 5, 6, 8] as float[]","[0, 2, 3, 5, 6, 8] as byte[]","[0, 2, 3, 5, 6, 8] as long[]","[0, 2, 3, 5, 6, 8] as char[]","[true, false, true, false, true, false] as boolean[]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Range based tensors can be created fluently.", + "id":"Initialization lambda based tensors can be created fluently. [1]", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"We create a range based Tensor instance using the fluent builder API.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFillFrom( from ).to( to ).step( step )"]}, + {"kind":"given","text":"We create a Tensor instance by passing an initialization lambda which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andWhere( initializer )"]}, {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, - {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, @@ -58,20 +824,20 @@ {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, - {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Integer.class","Double.class","Double.class","Float.class","Float.class","Byte.class","Long.class"],"from":["-9 as int","-2 as int","2.7 as double","-3 as double","6.4f as float","0f as float","-5 as byte","-65 as long"],"to":["18 as int","4 as int","45.0 as double","3 as double","78.3f as float","1f as float","6 as byte","45 as long"],"step":["2","2","3","0.5","4","0.2f","2","5"],"data":["[-9, -7, -5, -3, -1, 1] as int[]","[-2, 0, 2, 4, -2, 0] as int[]","[2.7, 5.7, 8.7, 11.7, 14.7, 17.7] as double[]","[-3.0, -2.5, -2.0, -1.5, -1.0, -0.5] as double[]","[6.4, 10.4, 14.4, 18.4, 22.4, 26.4] as float[]","[0.0, 0.2, 0.4, 0.6, 0.8, 1.0] as float[]","[-5, -3, -1, 1, 3, 5] as byte[]","[-65, -60, -55, -50, -45, -40] as long[]"]}} + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Character.class","Boolean.class"],"initializer":["{ i, indices -> (i + indices.sum()) }","{ i, indices -> (double) (i + indices.sum()) }","{ i, indices -> (short) (i + indices.sum()) }","{ i, indices -> (float) (i + indices.sum()) }","{ i, indices -> (byte) (i + indices.sum()) }","{ i, indices -> (long) (i + indices.sum()) }","{ i, indices -> (char) (i + indices.sum()) }","{ i, indices -> (boolean)(i % 2 == 0) }"],"expected":["[0, 2, 3, 5, 6, 8] as int[]","[0, 2, 3, 5, 6, 8] as double[]","[0, 2, 3, 5, 6, 8] as short[]","[0, 2, 3, 5, 6, 8] as float[]","[0, 2, 3, 5, 6, 8] as byte[]","[0, 2, 3, 5, 6, 8] as long[]","[0, 2, 3, 5, 6, 8] as char[]","[true, false, true, false, true, false] as boolean[]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Value based tensors can be created fluently.", + "id":"Initialization lambda based tensors can be created fluently. [2]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andFill( data )"]}, + {"kind":"given","text":"We create a Tensor instance by passing an initialization lambda which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andWhere( initializer )"]}, {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, @@ -83,20 +849,20 @@ {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, - {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Boolean.class","Character.class"],"data":["[2, 3, 4] as Integer[]","[-5, 6.5, 7] as Double[]","[6, -1, -2] as Short[]","[22.4, 26.4] as Float[]","[-20, 3, 4, -3] as Byte[]","[23, 199] as Long[]","[true, false] as Boolean[]","['x', 'y'] as Character[]"],"expected":["[2, 3, 4, 2, 3, 4] as int[]","[-5, 6.5, 7, -5, 6.5, 7] as double[]","[6, -1, -2, 6, -1, -2] as short[]","[22.4, 26.4, 22.4, 26.4, 22.4, 26.4] as float[]","[-20, 3, 4, -3, -20, 3] as byte[]","[23, 199, 23, 199, 23, 199] as long[]","[true, false, true, false, true, false] as boolean[]","['x', 'y', 'x', 'y', 'x', 'y'] as char[]"]}} + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Character.class","Boolean.class"],"initializer":["{ i, indices -> (i + indices.sum()) }","{ i, indices -> (double) (i + indices.sum()) }","{ i, indices -> (short) (i + indices.sum()) }","{ i, indices -> (float) (i + indices.sum()) }","{ i, indices -> (byte) (i + indices.sum()) }","{ i, indices -> (long) (i + indices.sum()) }","{ i, indices -> (char) (i + indices.sum()) }","{ i, indices -> (boolean)(i % 2 == 0) }"],"expected":["[0, 2, 3, 5, 6, 8] as int[]","[0, 2, 3, 5, 6, 8] as double[]","[0, 2, 3, 5, 6, 8] as short[]","[0, 2, 3, 5, 6, 8] as float[]","[0, 2, 3, 5, 6, 8] as byte[]","[0, 2, 3, 5, 6, 8] as long[]","[0, 2, 3, 5, 6, 8] as char[]","[true, false, true, false, true, false] as boolean[]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Seed based tensors can be created fluently.", + "id":"Initialization lambda based tensors can be created fluently. [3]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"We create a Tensor instance by passing an array of arguments which ought to be populated based on a seed.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andSeed( seed )"]}, + {"kind":"given","text":"We create a Tensor instance by passing an initialization lambda which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andWhere( initializer )"]}, {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, @@ -106,17 +872,90 @@ {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, - {"kind":"and","text":"","code":["t.toString().startsWith(\"(3x2):\")"]}, + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Character.class","Boolean.class"],"initializer":["{ i, indices -> (i + indices.sum()) }","{ i, indices -> (double) (i + indices.sum()) }","{ i, indices -> (short) (i + indices.sum()) }","{ i, indices -> (float) (i + indices.sum()) }","{ i, indices -> (byte) (i + indices.sum()) }","{ i, indices -> (long) (i + indices.sum()) }","{ i, indices -> (char) (i + indices.sum()) }","{ i, indices -> (boolean)(i % 2 == 0) }"],"expected":["[0, 2, 3, 5, 6, 8] as int[]","[0, 2, 3, 5, 6, 8] as double[]","[0, 2, 3, 5, 6, 8] as short[]","[0, 2, 3, 5, 6, 8] as float[]","[0, 2, 3, 5, 6, 8] as byte[]","[0, 2, 3, 5, 6, 8] as long[]","[0, 2, 3, 5, 6, 8] as char[]","[true, false, true, false, true, false] as boolean[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Initialization lambda based tensors can be created fluently. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an initialization lambda which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andWhere( initializer )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, - {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer","Double","Short","Float","Byte","Long","Boolean","Character"],"seed":["\"a\"","\"b\"","\"c\"","\"d\"","\"e\"","\"f\"","\"g\"","\"h\""],"expected":["[1431614970, 345625747, -1944974668, -1560046587, -840164727, 1545421892] as int[]","[0.5099337204650233, -0.1940291796851406, 1.4457326764876133, 1.1037197321548482, 0.5318191965243577, 0.19202511115716991] as double[]","[-14216, -20070, 24851, -22296, -9925, 31593] as short[]","[1.4457327, 1.1037197, 0.5318192, 0.19202511, 0.450341, -0.18904476] as float[]","[57, 26, -121, -59, -23, 90] as byte[]","[7366351542344062765, -5751258536495446167, -794811082727408195, 283695574932216990, -455308978758056709, -8426760264878742120] as long[]","[true, false, false, false, false, true] as boolean[]","[-1747130645, -868747698, 1684960924, -1581710323, -1526159736, -842114084] as char[]"]}} + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Character.class","Boolean.class"],"initializer":["{ i, indices -> (i + indices.sum()) }","{ i, indices -> (double) (i + indices.sum()) }","{ i, indices -> (short) (i + indices.sum()) }","{ i, indices -> (float) (i + indices.sum()) }","{ i, indices -> (byte) (i + indices.sum()) }","{ i, indices -> (long) (i + indices.sum()) }","{ i, indices -> (char) (i + indices.sum()) }","{ i, indices -> (boolean)(i % 2 == 0) }"],"expected":["[0, 2, 3, 5, 6, 8] as int[]","[0, 2, 3, 5, 6, 8] as double[]","[0, 2, 3, 5, 6, 8] as short[]","[0, 2, 3, 5, 6, 8] as float[]","[0, 2, 3, 5, 6, 8] as byte[]","[0, 2, 3, 5, 6, 8] as long[]","[0, 2, 3, 5, 6, 8] as char[]","[true, false, true, false, true, false] as boolean[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Initialization lambda based tensors can be created fluently. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an initialization lambda which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andWhere( initializer )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Character.class","Boolean.class"],"initializer":["{ i, indices -> (i + indices.sum()) }","{ i, indices -> (double) (i + indices.sum()) }","{ i, indices -> (short) (i + indices.sum()) }","{ i, indices -> (float) (i + indices.sum()) }","{ i, indices -> (byte) (i + indices.sum()) }","{ i, indices -> (long) (i + indices.sum()) }","{ i, indices -> (char) (i + indices.sum()) }","{ i, indices -> (boolean)(i % 2 == 0) }"],"expected":["[0, 2, 3, 5, 6, 8] as int[]","[0, 2, 3, 5, 6, 8] as double[]","[0, 2, 3, 5, 6, 8] as short[]","[0, 2, 3, 5, 6, 8] as float[]","[0, 2, 3, 5, 6, 8] as byte[]","[0, 2, 3, 5, 6, 8] as long[]","[0, 2, 3, 5, 6, 8] as char[]","[true, false, true, false, true, false] as boolean[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Initialization lambda based tensors can be created fluently. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a Tensor instance by passing an initialization lambda which ought to iteratively fill the instance.","code":["Tensor t = Tensor.of( type )"," .withShape( 3, 2 )"," .andWhere( initializer )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"and","text":"The tensor will have the shape we passed to the builder.","code":["t.shape() == [3, 2]"]}, + + {"kind":"and","text":"The size of the tensor will be the product of all shape entries!","code":["t.size() == 6"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Short.class","Float.class","Byte.class","Long.class","Character.class","Boolean.class"],"initializer":["{ i, indices -> (i + indices.sum()) }","{ i, indices -> (double) (i + indices.sum()) }","{ i, indices -> (short) (i + indices.sum()) }","{ i, indices -> (float) (i + indices.sum()) }","{ i, indices -> (byte) (i + indices.sum()) }","{ i, indices -> (long) (i + indices.sum()) }","{ i, indices -> (char) (i + indices.sum()) }","{ i, indices -> (boolean)(i % 2 == 0) }"],"expected":["[0, 2, 3, 5, 6, 8] as int[]","[0, 2, 3, 5, 6, 8] as double[]","[0, 2, 3, 5, 6, 8] as short[]","[0, 2, 3, 5, 6, 8] as float[]","[0, 2, 3, 5, 6, 8] as byte[]","[0, 2, 3, 5, 6, 8] as long[]","[0, 2, 3, 5, 6, 8] as char[]","[true, false, true, false, true, false] as boolean[]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Initialization lambda based tensors can be created fluently.", + "id":"Initialization lambda based tensors can be created fluently. [7]", "result":"PASS", "duration":"0", "iterations":{ @@ -141,7 +980,82 @@ }, { - "id":"Vectors can be created fluently.", + "id":"Vectors can be created fluently. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a new `Tensor` instance using the \"vector\" method in the fluent builder API.","code":["Tensor t = Tensor.of( type ).vector( values )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have a one dimensional shape of the same length as the provided data array.","code":["t.shape() == [values.length]"]}, + + {"kind":"and","text":"The size of the tensor will also be as long as the data array!","code":["t.size() == values.length"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Float.class","Long.class"],"values":["[4, 5, -2] as Integer[]","[-1, 7.5] as Double[]","[0.6, -32.7] as Float[]","[1, 3, 2, 4] as Long[]"],"data":["new int[] { 4, 5, -2 }","new double[]{ -1, 7.5 }","new float[] { 0.6, -32.7 }","new long[] { 1, 3, 2, 4 }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Vectors can be created fluently. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a new `Tensor` instance using the \"vector\" method in the fluent builder API.","code":["Tensor t = Tensor.of( type ).vector( values )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have a one dimensional shape of the same length as the provided data array.","code":["t.shape() == [values.length]"]}, + + {"kind":"and","text":"The size of the tensor will also be as long as the data array!","code":["t.size() == values.length"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Float.class","Long.class"],"values":["[4, 5, -2] as Integer[]","[-1, 7.5] as Double[]","[0.6, -32.7] as Float[]","[1, 3, 2, 4] as Long[]"],"data":["new int[] { 4, 5, -2 }","new double[]{ -1, 7.5 }","new float[] { 0.6, -32.7 }","new long[] { 1, 3, 2, 4 }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Vectors can be created fluently. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a new `Tensor` instance using the \"vector\" method in the fluent builder API.","code":["Tensor t = Tensor.of( type ).vector( values )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have a one dimensional shape of the same length as the provided data array.","code":["t.shape() == [values.length]"]}, + + {"kind":"and","text":"The size of the tensor will also be as long as the data array!","code":["t.size() == values.length"]}, + + {"kind":"and","text":"\n Based on the fact that the tensor is not homogeneously filled it will be an \"actual tensor\".\n The opposite of that, a \"virtual tensor\", would mean that a tensor does not have allocated \n memory proportional to the size of the tensor! \n In this case however the tensor should be actual which means that it is not virtual.\n ","code":["!t.isVirtual()"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Float.class","Long.class"],"values":["[4, 5, -2] as Integer[]","[-1, 7.5] as Double[]","[0.6, -32.7] as Float[]","[1, 3, 2, 4] as Long[]"],"data":["new int[] { 4, 5, -2 }","new double[]{ -1, 7.5 }","new float[] { 0.6, -32.7 }","new long[] { 1, 3, 2, 4 }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Vectors can be created fluently. [3]", "result":"PASS", "duration":"0", "iterations":{ @@ -166,7 +1080,76 @@ }, { - "id":"Scalars can be created fluently.", + "id":"Scalars can be created fluently. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a new `Tensor` instance using the \"scalar\" method in the fluent builder API.","code":["Tensor t = Tensor.of( type ).scalar( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have a one dimensional shape of 1.","code":["t.shape() == [1]"]}, + + {"kind":"and","text":"The size of the tensor will also 1!","code":["t.size() == 1"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Float.class","Long.class"],"value":["3 as Integer","5.7 as Double","9.4f as Float","42L as Long"],"data":["new int[] { 3 }","new double[]{ 5.7 }","new float[] { 9.4f }","new long[] { 42L }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalars can be created fluently. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a new `Tensor` instance using the \"scalar\" method in the fluent builder API.","code":["Tensor t = Tensor.of( type ).scalar( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have a one dimensional shape of 1.","code":["t.shape() == [1]"]}, + + {"kind":"and","text":"The size of the tensor will also 1!","code":["t.size() == 1"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Float.class","Long.class"],"value":["3 as Integer","5.7 as Double","9.4f as Float","42L as Long"],"data":["new int[] { 3 }","new double[]{ 5.7 }","new float[] { 9.4f }","new long[] { 42L }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalars can be created fluently. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a new `Tensor` instance using the \"scalar\" method in the fluent builder API.","code":["Tensor t = Tensor.of( type ).scalar( value )"]}, + + {"kind":"expect","text":"This new instance will have the expected data type...","code":["t.dataType == DataType.of(type)"]}, + + {"kind":"and","text":"...also it will contain the expected data.","code":["t.mut.data.get() == data","t.rawData == data"]}, + + {"kind":"and","text":"The tensor will have a one dimensional shape of 1.","code":["t.shape() == [1]"]}, + + {"kind":"and","text":"The size of the tensor will also 1!","code":["t.size() == 1"]}, + + {"kind":"where","text":"The following data is being used to populate the builder API:","code":{"type":["Integer.class","Double.class","Float.class","Long.class"],"value":["3 as Integer","5.7 as Double","9.4f as Float","42L as Long"],"data":["new int[] { 3 }","new double[]{ 5.7 }","new float[] { 9.4f }","new long[] { 42L }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalars can be created fluently. [3]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.tensors.Functional_Nda_Spec.json b/docs/spock/reports/ut.tensors.Functional_Nda_Spec.json index 83c03cc29..215286cae 100644 --- a/docs/spock/reports/ut.tensors.Functional_Nda_Spec.json +++ b/docs/spock/reports/ut.tensors.Functional_Nda_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.272 seconds" + "duration":"0.558 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can initialize an ND-Array using a filler lambda mapping indices to items.", "result":"PASS", - "duration":"0.043 seconds", + "duration":"0.108 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"We can find both min and max items in an ND-array by providing a comparator.", "result":"PASS", - "duration":"0.029 seconds", + "duration":"0.055 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -54,7 +54,7 @@ { "id":"We can analyse the values of a nd-array using various predicate receiving methods", "result":"PASS", - "duration":"0.031 seconds", + "duration":"0.062 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -75,7 +75,7 @@ { "id":"We can use the \"filter\" method as a shortcut for \"stream().filter(..)\".", "result":"PASS", - "duration":"0.027 seconds", + "duration":"0.053 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -90,7 +90,7 @@ { "id":"We can use the \"flatMap\" method as a shortcut for \"stream().flatMap(..)\".", "result":"PASS", - "duration":"0.028 seconds", + "duration":"0.053 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -105,7 +105,7 @@ { "id":"ND-Array mapping lambdas produce expected nd-arrays.", "result":"PASS", - "duration":"0.027 seconds", + "duration":"0.053 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -126,7 +126,7 @@ { "id":"The \"map\" method is a shorter convenience method for mapping to the same type.", "result":"PASS", - "duration":"0.027 seconds", + "duration":"0.053 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -145,7 +145,7 @@ { "id":"We can find both min and max items in a tensor by providing a comparator.", "result":"PASS", - "duration":"0.027 seconds", + "duration":"0.055 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -166,7 +166,7 @@ { "id":"We can collect a stream into a nd-array.", "result":"PASS", - "duration":"0.027 seconds", + "duration":"0.059 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Functional_Tensor_Spec.json b/docs/spock/reports/ut.tensors.Functional_Tensor_Spec.json index 44d7a4eab..311147487 100644 --- a/docs/spock/reports/ut.tensors.Functional_Tensor_Spec.json +++ b/docs/spock/reports/ut.tensors.Functional_Tensor_Spec.json @@ -4,19 +4,19 @@ "narrative":"Tensors expose a powerful API for performing operations on them\n in a functional style.", "subjects":[], "statistics":{ - "runs":"7", + "runs":"4", "successRate":"100.0%", "failures":"0", "errors":"0", - "skipped":"0", - "duration":"0.301 seconds" + "skipped":"3", + "duration":"0.230 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can initialize a tensor using a filler lambda mapping indices to items.", "result":"PASS", - "duration":"0.029 seconds", + "duration":"0.056 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -30,13 +30,13 @@ { "id":"We can analyse the values of a tensor using various predicate receiving methods", - "result":"PASS", - "duration":"0.059 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"We create 2 tensors, where one is a slice of the other.","code":["var a = Tensor.ofInts().withShape(3, 2).andFill(2, 0, 1, 1, 8, 3)","var b = a[1, 0..1]"]}, + {"kind":"given","text":"We create 2 tensors, where one is a slice of the other.","code":["var a = Tensor.ofInts().on(Device.get(device)).withShape(3, 2).andFill(2, 0, 1, 1, 8, 3)","var b = a[1, 0..1]"]}, {"kind":"expect","text":"","code":["!a.every((Predicate){it == 1})","a.any((Predicate){it == 1})","a.any((Predicate){it == 8})","!a.any((Predicate){it == 42})"]}, @@ -54,7 +54,7 @@ { "id":"We can use the \"filter\" method as a shortcut for \"stream().filter(..)\".", "result":"PASS", - "duration":"0.027 seconds", + "duration":"0.056 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -69,7 +69,7 @@ { "id":"We can use the \"flatMap\" method as a shortcut for \"stream().flatMap(..)\".", "result":"PASS", - "duration":"0.030 seconds", + "duration":"0.055 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -83,8 +83,8 @@ { "id":"Tensor mapping lambdas produce expected tensors.", - "result":"PASS", - "duration":"0.059 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -106,8 +106,8 @@ { "id":"The \"map\" method is a shorter convenience method for mapping to the same type.", - "result":"PASS", - "duration":"0.057 seconds", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -128,7 +128,7 @@ { "id":"We can find both min and max items in a tensor by providing a comparator.", "result":"PASS", - "duration":"0.030 seconds", + "duration":"0.058 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Reshape_Spec.json b/docs/spock/reports/ut.tensors.Reshape_Spec.json index e1b0991e4..7faabf24f 100644 --- a/docs/spock/reports/ut.tensors.Reshape_Spec.json +++ b/docs/spock/reports/ut.tensors.Reshape_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.002 seconds" + "duration":"0.013 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can create a new tensor with a different shape.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -35,7 +35,7 @@ { "id":"We can use `-1` in the desired shape if we want the axis size to be determined automatically.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -54,7 +54,7 @@ { "id":"The reshape operation supports autograd!", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n Changing the shape of a tensor is a very common operation in machine learning.\n This is why the reshape operation also supports autograd.\n So for example when you have a tensor `a` with shape `s1` and you reshape it to \n a new tensor `b` with shape `s2` then during backpropagation the error `e_b` of `b`\n with the shape `s2` will be propagated to a new error `e_a` of `a` with the shape `s1`.\n It is basically the reshape operation applied in reverse.\n "] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_As_Container_Spec.json b/docs/spock/reports/ut.tensors.Tensor_As_Container_Spec.json index e19cab974..c5dbb25fe 100644 --- a/docs/spock/reports/ut.tensors.Tensor_As_Container_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_As_Container_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.009 seconds" + "duration":"0.055 seconds" }, "headers":["\n Although you can create a tensor of almost anything, you will\n not be able to execute operations on every kind of tensor... \n "],"tags":{},"see":[], "features":[ { "id":"Plus operator on String tensors works element-wise.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,7 +37,7 @@ { "id":"Tensor operations translate to custom data type \"ComplexNumber\".", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.020 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -54,7 +54,7 @@ { "id":"More tensor operations translate to custom data type \"ComplexNumber\".", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.009 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -69,7 +69,7 @@ { "id":"We can apply predicates on the values of a tensor.", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.014 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_Assign_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Assign_Spec.json index 89cf2e773..516e04c19 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Assign_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Assign_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.011 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We can use the \"mut\" API to assign the contents of one tensor into another one.", "result":"PASS", - "duration":"0", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"Assignment can be easily achieved through subscription operators.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -52,7 +52,7 @@ { "id":"We can assign one slice into another one.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n Note that using the 'assign' operation on slices should be handled with care,\n since the operation has side effects on the underlying data array\n which is shared by both the slice and its parent.\n Use the 'copy' operation on slices if you want to avoid this.\n "] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_Conversion_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Conversion_Spec.json index 9e18f9498..007d11dcb 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Conversion_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Conversion_Spec.json @@ -4,19 +4,19 @@ "narrative":"Here we specify how a tensor can be converted to other data types\n like for example another tensor of a different data type.", "subjects":["neureka.Tensor"], "statistics":{ - "runs":"3", + "runs":"38", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.011 seconds" + "duration":"0.024 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"We turn a tensor into a scalar value or string through the \"as\" operator!", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"Tensors value type can be changed by calling \"toType(...)\".", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] }, @@ -52,11 +52,816 @@ }, { - "id":"We can change the data type of all kinds of tensors.", + "id":"We can change the data type of all kinds of tensors. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [11]", "result":"PASS", "duration":"0", "iterations":{ - "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n ","\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [12]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [13]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [14]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [15]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [16]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [17]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [18]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [19]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [20]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [21]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [22]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [23]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [24]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [25]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [26]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [27]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [28]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [29]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [30]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [31]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [32]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [33]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [34]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] + }, + "blocks":[ + {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, + + {"kind":"when","text":"We change the data type of the tensor using the unsafe \"toType\" method.","code":["var b = a.mut.toType(targetType)"]}, + + {"kind":"then","text":"The returned tensor has the expected data type.","code":["b.itemType == targetType"]}, + + {"kind":"and","text":"The returned tensor has the expected values.","code":["b.rawItems == data.collect({ it.asType(targetType) })"]}, + + {"kind":"and","text":"The returned tensor is in fact the original instance.","code":["a === b"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"sourceType":["Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Long","Long","Long","Long","Long","Long","Integer","Integer","Integer","Integer","Integer","Integer","Short","Short","Short","Short","Short","Short","Byte","Byte","Byte","Byte","Byte","Byte"],"targetType":["Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte","Double","Float","Long","Integer","Short","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can change the data type of all kinds of tensors. [35]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n Warning! The `toType` method mutates the tensor!\n This is especially problematic with respect to generics, \n because if the tensor is still used as a tensor of the old type, \n then the compiler will not be able to detect that the tensor has changed its type.\n This is why we have to use the `unsafe` API exists.\n Only use this method if there are urgent performance requirements and\n you know exactly what you are doing!\n "] }, "blocks":[ {"kind":"given","text":"A simple tensor with a few initial values.","code":["var data = [-3, -12, 42, -42, 12, 3]","var a = Tensor.of(sourceType).withShape(data.size()).andFill(data)"]}, diff --git a/docs/spock/reports/ut.tensors.Tensor_Convolution_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Convolution_Spec.json index 8b8292bc0..5eb636770 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Convolution_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Convolution_Spec.json @@ -4,19 +4,19 @@ "narrative":"This specification shows how convolution can be performed on tensors.\n\n Convolution is a linear operation which is not only important for image processing but also\n a central player in the field of machine learning (especially for computer vision).\n It is used to extract features from images and other typically ~2 dimensional data.\n Other than that it is extremely important in the field of signal processing.", "subjects":[], "statistics":{ - "runs":"10", + "runs":"20", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.639 seconds" + "duration":"1.132 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"We can perform a convolution operation on a 2D tensor.", + "id":"We can perform a convolution operation on a 2D tensor. [0]", "result":"PASS", - "duration":"0.114 seconds", + "duration":"0.056 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,9 +37,76 @@ }, { - "id":"Convolution with tensors of the same shape is equivalent to a dot product.", + "id":"We can perform a convolution operation on a 2D tensor. [1]", "result":"PASS", - "duration":"0.081 seconds", + "duration":"0.049 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor with shape [3, 3] and values [1, 2, 3, ..., 9].","code":["var x ="," Tensor.of(type, [3, 3], ["," 1, 2, 3,"," 4, 5, 6,"," 7, 8, 9"," ])"]}, + + {"kind":"and","text":"A 2D kernel with shape [2, 2] and values [1, 2, 0, -1].","code":["var k ="," Tensor.of(type, [2, 2], ["," 1, 2,"," 0, -1"," ])"]}, + + {"kind":"and","text":"We move both tensors to a device on which we want to execute.","code":["x.to(device)","k.to(device)"]}, + + {"kind":"when","text":"We perform a convolution operation on the tensor with the kernel `k`.","code":["var y = x.conv(k)"]}, + + {"kind":"then","text":"The resulting tensor should have shape [2, 2] and value [0.0, 2.0, 6.0, 8.0].","code":["y.shape == [2, 2]","y.items == [0.0, 2.0, 6.0, 8.0]"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Float"],"device":["'CPU'","'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can perform a convolution operation on a 2D tensor. [2]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor with shape [3, 3] and values [1, 2, 3, ..., 9].","code":["var x ="," Tensor.of(type, [3, 3], ["," 1, 2, 3,"," 4, 5, 6,"," 7, 8, 9"," ])"]}, + + {"kind":"and","text":"A 2D kernel with shape [2, 2] and values [1, 2, 0, -1].","code":["var k ="," Tensor.of(type, [2, 2], ["," 1, 2,"," 0, -1"," ])"]}, + + {"kind":"and","text":"We move both tensors to a device on which we want to execute.","code":["x.to(device)","k.to(device)"]}, + + {"kind":"when","text":"We perform a convolution operation on the tensor with the kernel `k`.","code":["var y = x.conv(k)"]}, + + {"kind":"then","text":"The resulting tensor should have shape [2, 2] and value [0.0, 2.0, 6.0, 8.0].","code":["y.shape == [2, 2]","y.items == [0.0, 2.0, 6.0, 8.0]"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Float"],"device":["'CPU'","'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Convolution with tensors of the same shape is equivalent to a dot product. [0]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor with shape [2, 2] and values [1, 3, 6, -8].","code":["var x ="," Tensor.of(Float, [2, 2], ["," 1, 3,"," 6, -8,"," ])"]}, + + {"kind":"and","text":"A 2D kernel with shape [2, 2] and values [-2, 1, 4, 5].","code":["var k ="," Tensor.of(Float, [2, 2], ["," -2, 1,"," 4, 5"," ])"]}, + + {"kind":"when","text":"We perform a convolution operation on the tensor with the kernel `k`.","code":["var y = x.conv(k)"]}, + + {"kind":"then","text":"The resulting tensor should have shape [1, 1] and value [-15.0].","code":["y.shape == [1, 1]","y.items == [-15.0]"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Float"],"device":["'CPU'","'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Convolution with tensors of the same shape is equivalent to a dot product. [1]", + "result":"PASS", + "duration":"0.050 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -58,9 +125,30 @@ }, { - "id":"Convolution can be performed using non-quadratic matrix tensors.", + "id":"Convolution with tensors of the same shape is equivalent to a dot product. [2]", "result":"PASS", - "duration":"0.078 seconds", + "duration":"0.049 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor with shape [2, 2] and values [1, 3, 6, -8].","code":["var x ="," Tensor.of(Float, [2, 2], ["," 1, 3,"," 6, -8,"," ])"]}, + + {"kind":"and","text":"A 2D kernel with shape [2, 2] and values [-2, 1, 4, 5].","code":["var k ="," Tensor.of(Float, [2, 2], ["," -2, 1,"," 4, 5"," ])"]}, + + {"kind":"when","text":"We perform a convolution operation on the tensor with the kernel `k`.","code":["var y = x.conv(k)"]}, + + {"kind":"then","text":"The resulting tensor should have shape [1, 1] and value [-15.0].","code":["y.shape == [1, 1]","y.items == [-15.0]"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Float"],"device":["'CPU'","'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Convolution can be performed using non-quadratic matrix tensors. [0]", + "result":"PASS", + "duration":"0.050 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -79,9 +167,51 @@ }, { - "id":"Convolution can be performed using tensors with an additional dimension as batch size.", + "id":"Convolution can be performed using non-quadratic matrix tensors. [1]", "result":"PASS", - "duration":"0.080 seconds", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor with shape [2, 3] and values [1, 3, 6, -8, 2, 4].","code":["var x ="," Tensor.of(Float, [2, 3], ["," 1, 3, 6,"," -8, 2, 4,"," ])"]}, + + {"kind":"and","text":"A 2D kernel with shape [1, 2] and values [-2, 1].","code":["var k ="," Tensor.of(Float, [1, 2], ["," -2, 1"," ])"]}, + + {"kind":"when","text":"We perform a convolution operation on the tensor with the kernel `k`.","code":["var y = x.conv(k)"]}, + + {"kind":"then","text":"The resulting tensor should have shape [2, 2] and value [1.0, 0.0, 18.0, 0.0].","code":["y.shape == [2, 2]","y.items == [1.0, 0.0, 18.0, 0.0]"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Float"],"device":["'CPU'","'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Convolution can be performed using non-quadratic matrix tensors. [2]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 2D tensor with shape [2, 3] and values [1, 3, 6, -8, 2, 4].","code":["var x ="," Tensor.of(Float, [2, 3], ["," 1, 3, 6,"," -8, 2, 4,"," ])"]}, + + {"kind":"and","text":"A 2D kernel with shape [1, 2] and values [-2, 1].","code":["var k ="," Tensor.of(Float, [1, 2], ["," -2, 1"," ])"]}, + + {"kind":"when","text":"We perform a convolution operation on the tensor with the kernel `k`.","code":["var y = x.conv(k)"]}, + + {"kind":"then","text":"The resulting tensor should have shape [2, 2] and value [1.0, 0.0, 18.0, 0.0].","code":["y.shape == [2, 2]","y.items == [1.0, 0.0, 18.0, 0.0]"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Float"],"device":["'CPU'","'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Convolution can be performed using tensors with an additional dimension as batch size. [0]", + "result":"PASS", + "duration":"0.052 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -100,11 +230,80 @@ }, { - "id":"The \"x\" (convolution) operator produces expected results (On the CPU).", + "id":"Convolution can be performed using tensors with an additional dimension as batch size. [1]", "result":"PASS", - "duration":"0.054 seconds", + "duration":"0.052 seconds", "iterations":{ - "tags":{},"see":[],"extraInfo":["\n The 'x' operator performs convolution on the provided operands.\n The meaning of the operands is not defined, so one the kernel tensor\n can be the first and second operand. \n ","\n The 'x' operator performs convolution on the provided operands.\n The meaning of the operands is not defined, so one the kernel tensor\n can be the first and second operand. \n "] + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 3D tensor with shape [2, 2, 2] and values [1, 3, 6, -8, 2, 4, 5, 7].","code":["var x ="," Tensor.of(Float, [2, 2, 2], ["," 1, 3,"," 6, -8,"," 2, 4,"," 5, 7"," ])"]}, + + {"kind":"and","text":"A 2D kernel with shape [2, 2] and values [-2, 1, 4, 5].","code":["var k ="," Tensor.of(Float, [1, 2, 2], ["," -2, 1,"," 4, 5"," ])"]}, + + {"kind":"when","text":"We perform a convolution operation on the tensor with the kernel `k`.","code":["var y = x.conv(k)"]}, + + {"kind":"then","text":"The resulting tensor should have shape [2, 1, 1] and value [-15.0, 55.0].","code":["y.shape == [2, 1, 1]","y.items == [-15.0, 55.0]"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Float"],"device":["'CPU'","'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Convolution can be performed using tensors with an additional dimension as batch size. [2]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A 3D tensor with shape [2, 2, 2] and values [1, 3, 6, -8, 2, 4, 5, 7].","code":["var x ="," Tensor.of(Float, [2, 2, 2], ["," 1, 3,"," 6, -8,"," 2, 4,"," 5, 7"," ])"]}, + + {"kind":"and","text":"A 2D kernel with shape [2, 2] and values [-2, 1, 4, 5].","code":["var k ="," Tensor.of(Float, [1, 2, 2], ["," -2, 1,"," 4, 5"," ])"]}, + + {"kind":"when","text":"We perform a convolution operation on the tensor with the kernel `k`.","code":["var y = x.conv(k)"]}, + + {"kind":"then","text":"The resulting tensor should have shape [2, 1, 1] and value [-15.0, 55.0].","code":["y.shape == [2, 1, 1]","y.items == [-15.0, 55.0]"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Float"],"device":["'CPU'","'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The \"x\" (convolution) operator produces expected results (On the CPU). [0]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n The 'x' operator performs convolution on the provided operands.\n The meaning of the operands is not defined, so one the kernel tensor\n can be the first and second operand. \n "] + }, + "blocks":[ + {"kind":"given","text":"Gradient auto apply for tensors in ue is set to false.","code":["Neureka.get().settings().autograd().setIsApplyingGradientWhenTensorIsUsed(false)"]}, + + {"kind":"and","text":"Tensor legacy view is set to true.","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + + {"kind":"and","text":"Two new 3D tensor instances with the shapes: [2x3x1] & [1x3x2].","code":["var x = Tensor.of(Shape.of(2, 3, 1),"," new double[]{"," 3, 2, -1,"," -2, 2, 4"," }"," )"," .mut.toType(type)","var y = Tensor.of(Shape.of(1, 3, 2),"," new double[]{"," 4, -1,"," 3, 2,"," 3, -1"," }"," )"," .mut.toType(type)"]}, + + {"kind":"when","text":"The x-mul result is being instantiated by passing a simple equation to the tensor constructor.","code":["var z = Tensor.of(\"I0xi1\", x, y)"]}, + + {"kind":"then","text":"The result contains the expected String.","code":["z.toString().contains(expected)"]}, + + {"kind":"when","text":"The x-mul result is being instantiated by passing a object array containing equation parameters and syntax.","code":["z = Tensor.of(new Object[]{x, \"x\", y})"]}, + + {"kind":"then","text":"The result contains the expected String.","code":["z.toString().contains(expected)"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float"],"expected":["\"[2x1x2]:(15.0, 2.0, 10.0, 2.0)\"","\"[2x1x2]:(15.0, 2.0, 10.0, 2.0)\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The \"x\" (convolution) operator produces expected results (On the CPU). [1]", + "result":"PASS", + "duration":"0.088 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":["\n The 'x' operator performs convolution on the provided operands.\n The meaning of the operands is not defined, so one the kernel tensor\n can be the first and second operand. \n "] }, "blocks":[ {"kind":"given","text":"Gradient auto apply for tensors in ue is set to false.","code":["Neureka.get().settings().autograd().setIsApplyingGradientWhenTensorIsUsed(false)"]}, @@ -129,7 +328,7 @@ { "id":"Manual convolution produces expected result.", "result":"PASS", - "duration":"0.051 seconds", + "duration":"0.063 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -144,9 +343,30 @@ }, { - "id":"Very simple manual convolution produces expected result.", + "id":"Very simple manual convolution produces expected result. [0]", + "result":"PASS", + "duration":"0.056 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We set the experimental \"autoConvertToFloat\" flag to true.","code":["Neureka.get().backend().find(CLBackend).ifPresent({ it.settings.autoConvertToFloat=true })"]}, + + {"kind":"and","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(false)","Tensor a = Tensor.of([4, 4], 0d..16d).to( device )","Tensor x = a[1..-2,0..-1]","Tensor y = a[0..-3,0..-1]","Tensor z = a[2..-1,0..-1]"]}, + + {"kind":"when","text":"","code":["Tensor rowconvol = x + y + z","Tensor k = rowconvol[0..-1,1..-2]","Tensor v = rowconvol[0..-1,0..-3]","Tensor j = rowconvol[0..-1,2..-1]","Tensor u = a[1..-2,1..-2]","Tensor colconvol = k + v + j - 9 * u","String xAsStr = x.toString()","String yAsStr = y.toString()","String zAsStr = z.toString()","String rcAsStr = rowconvol.toString()","String kAsStr = k.toString()","String vAsStr = v.toString()","String jAsStr = j.toString()","String uAsStr = u.toString()"]}, + + {"kind":"then","text":"","code":["xAsStr.contains(\"(2x4):[4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0]\")","yAsStr.contains(\"(2x4):[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]\")","zAsStr.contains(\"(2x4):[8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0]\")","rcAsStr.contains(\"(2x4):[12.0, 15.0, 18.0, 21.0, 24.0, 27.0, 30.0, 33.0]\")","kAsStr.contains(\"(2x2):[15.0, 18.0, 27.0, 30.0]\")","vAsStr.contains(\"(2x2):[12.0, 15.0, 24.0, 27.0]\")","jAsStr.contains(\"(2x2):[18.0, 21.0, 30.0, 33.0]\")","uAsStr.contains(\"(2x2):[5.0, 6.0, 9.0, 10.0]\")","colconvol.toString().contains(\"(2x2):[0.0, 0.0, 0.0, 0.0]\")"]}, + + {"kind":"where","text":"The following data is being used for tensor instantiation :","code":{"device":["CPU.get()","Device.get(\"openCL\")"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Very simple manual convolution produces expected result. [1]", "result":"PASS", - "duration":"0.066 seconds", + "duration":"0.052 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -167,7 +387,7 @@ { "id":"Autograd works with simple 2D convolution.", "result":"PASS", - "duration":"0.033 seconds", + "duration":"0.061 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -192,7 +412,7 @@ { "id":"Sime convolution works as expected eith autograd.", "result":"PASS", - "duration":"0.034 seconds", + "duration":"0.063 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -217,7 +437,7 @@ { "id":"Tensors have the correct layout after convolution.", "result":"PASS", - "duration":"0.033 seconds", + "duration":"0.057 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_Device_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Device_Spec.json index 79f7484fc..c2ffd6cb4 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Device_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Device_Spec.json @@ -1,21 +1,21 @@ { "className":"ut.tensors.Tensor_Device_Spec", "title":"Tensors on Devices", - "narrative":"This unit test specification covers\n the expected behavior of tensors when interacting\n with instances of implementations of the Device interface.", + "narrative":"This unit test specification covers \n the expected behavior of tensors when interacting\n with instances of implementations of the Device interface.", "subjects":[], "statistics":{ - "runs":"4", + "runs":"3", "successRate":"100.0%", "failures":"0", "errors":"0", - "skipped":"0", - "duration":"0.004 seconds" + "skipped":"1", + "duration":"0.015 seconds" }, "headers":["\n Here you will find out how to store tensors on devices,\n how to move tensors between devices and how to use\n the device specific methods of the tensor class.\n "],"tags":{},"see":[], "features":[ { "id":"Adding OpenCL device to tensor makes tensor be \"outsourced\" and contain the Device instance as component.", - "result":"PASS", + "result":"IGNORED", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] @@ -37,7 +37,7 @@ { "id":"Tensors try to migrate themselves to a device that is being added to them as component.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -58,7 +58,7 @@ { "id":"The device of a tensor can be accessed via the \"device()\" method.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -83,7 +83,7 @@ { "id":"When creating slices of tensors then this should trigger a \"parent - child\" relation noticeable to the device!", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_Dot_Product_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Dot_Product_Spec.json index bbf25e2d0..f5fc05cc6 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Dot_Product_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Dot_Product_Spec.json @@ -4,19 +4,19 @@ "narrative":"A tensor can also be a simple vector, which is a tensor of rank 1.\n This specification demonstrates how to perform dot products on tensors of rank 1.", "subjects":["neureka.Tensor"], "statistics":{ - "runs":"7", + "runs":"21", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.137 seconds" + "duration":"0.037 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"The \"dot\" method calculates the dot product between vectors.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"You can slice a Matrix into vectors and then used them for dot products.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -52,7 +52,7 @@ { "id":"The \"dot\" operation supports autograd.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n The \"dot\" operation supports autograd.\n This means that you can use it to calculate the gradient of a weight tensor.\n This is useful for when you want to build a neural network or some other machine learning model.\n "] }, @@ -71,11 +71,11 @@ }, { - "id":"The dot product operation runs on any device.", + "id":"The dot product operation runs on any device. [0]", "result":"PASS", - "duration":"0.079 seconds", + "duration":"0.002 seconds", "iterations":{ - "tags":{},"see":[],"extraInfo":["\n The dot product operation runs on any device that \n supports OpenCL (meaning that it has OpenCL drivers installed).\n ","\n The dot product operation runs on any device that \n supports OpenCL (meaning that it has OpenCL drivers installed).\n "] + "tags":{},"see":[],"extraInfo":["\n The dot product operation runs on any device that \n supports OpenCL (meaning that it has OpenCL drivers installed).\n "] }, "blocks":[ {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(-1f, -3f, 0f, 4f, 2f).to( device )","var b = Tensor.of( 1f, 2f, 7f, -1f, 3f).to( device )"]}, @@ -90,9 +90,49 @@ }, { - "id":"The dot operation works for virtual tensors as well.", + "id":"The dot product operation runs on any device. [1]", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(-1f, -3f, 0f, 4f, 2f).to( device )","var b = Tensor.of( 1f, 2f, 7f, -1f, 3f).to( device )"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == [ -1f * 1f + -3f * 2f + 0f * 7f + 4f * -1f + 2f * 3f ]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot operation works for virtual tensors as well. [0]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(Shape.of(8), 3f).to(device)","var b = Tensor.of(Shape.of(8), 3f).to(device)"]}, + + {"kind":"expect","text":"the tensors are virtual.","code":["a.isVirtual() // They are scalars in disguise!","b.isVirtual()"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == [ 3f * 3f * 8f ]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot operation works for virtual tensors as well. [1]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -111,9 +151,9 @@ }, { - "id":"The dot operation work even when one tensor is virtual.", + "id":"The dot operation work even when one tensor is virtual. [0]", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -132,9 +172,239 @@ }, { - "id":"The dot product works across different types and devices.", + "id":"The dot operation work even when one tensor is virtual. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(Shape.of(8), 3f).to(device)","var b = Tensor.of(Shape.of(8), Data.of(3f, 4f, -1f)).to(device)"]}, + + {"kind":"expect","text":"the tensors are virtual.","code":["a.isVirtual() // They are scalars in disguise!","!b.isVirtual()"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == [ 57f ]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [2]", "result":"PASS", - "duration":"0.041 seconds", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A pair of vector tensors which we move to the device!","code":["var a = Tensor.of(data1).to(device)","var b = Tensor.of(data2).to(device)"]}, + + {"kind":"when","text":"we calculate the dot product of a and b.","code":["var result = a.dot(b)"]}, + + {"kind":"then","text":"the result is a scalar.","code":["result.shape == Shape.of(1)","result.items == expected"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"data1":["[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]","[ 8d, -4d, -1d ] as double[]","[ 2d, 3d, 4d ] as double[]","[ 1d, -4d ] as double[]","[ 8, -4, -1 ] as int[]","[ 42 ] as int[]","[ 2, 3, 4 ] as long[]","[ 1, -4 ] as long[]","[ 42 ] as long[]","[ 8f, -4f, -1f ] as float[]","[ 42f ] as float[]"],"data2":["[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]","[ 1d, 2d, 4d ] as double[]","[ 0d, 2d, 3d ] as double[]","[ 4d, 2d ] as double[]","[ 1, 2, 4 ] as int[]","[ 56 ] as int[]","[ 0, 2, 3 ] as long[]","[ 4, 2 ] as long[]","[ 56 ] as long[]","[ 1f, 2f, 4f ] as float[]","[ 56f ] as float[]"],"expected":["[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]","[ 8d * 1d + -4d * 2d + -1d * 4d ]","[ 2d * 0d + 3d * 2d + 4d * 3d ]","[ 1d * 4d + -4d * 2d ]","[ 8 * 1 + -4 * 2 + -1 * 4 ]","[ 42 * 56 ]","[ 2 * 0 + 3 * 2 + 4 * 3 ]","[ 1 * 4 + -4 * 2 ]","[ 42 * 56 ]","[ 8f * 1f + -4f * 2f + -1f * 4f ]","[ 42f * 56f ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The dot product works across different types and devices. [11]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_Generics_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Generics_Spec.json index 86addb342..2c5f59f4f 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Generics_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Generics_Spec.json @@ -4,19 +4,19 @@ "narrative":"Tensors do not just store numeric data.\n They can hold anything which can be stuffed into a \"Object[]\" array.\n You could even create a tensor of tensors!", "subjects":[], "statistics":{ - "runs":"3", + "runs":"7", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.004 seconds" + "duration":"0.008 seconds" }, "headers":["\n Here you will find out how to create a tensor of any kind of data. \n "],"tags":{},"see":[], "features":[ { "id":"Anonymous tensor instance has the default datatype class as defined in Neureka settings.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"We can create a tensor of strings.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -50,7 +50,91 @@ }, { - "id":"1D tensors can be created from primitive arrays.", + "id":"1D tensors can be created from primitive arrays. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["def t = Tensor.of(data)"]}, + + {"kind":"expect","text":"","code":["t.rank() == 1"]}, + + {"kind":"and","text":"","code":["t.size() == size"]}, + + {"kind":"and","text":"","code":["t.getItemType() == expected"]}, + + {"kind":"where","text":"","code":{"size":["3","4","2","3","1"],"data":["new float[]{-1f, 3f, 6f}","new int[]{1, -2 , 9, 12}","new byte[]{42 , 73}","new long[]{-16 , 54, 12}","new short[]{26}"],"expected":["Float","Integer","Byte","Long","Short"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"1D tensors can be created from primitive arrays. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["def t = Tensor.of(data)"]}, + + {"kind":"expect","text":"","code":["t.rank() == 1"]}, + + {"kind":"and","text":"","code":["t.size() == size"]}, + + {"kind":"and","text":"","code":["t.getItemType() == expected"]}, + + {"kind":"where","text":"","code":{"size":["3","4","2","3","1"],"data":["new float[]{-1f, 3f, 6f}","new int[]{1, -2 , 9, 12}","new byte[]{42 , 73}","new long[]{-16 , 54, 12}","new short[]{26}"],"expected":["Float","Integer","Byte","Long","Short"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"1D tensors can be created from primitive arrays. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["def t = Tensor.of(data)"]}, + + {"kind":"expect","text":"","code":["t.rank() == 1"]}, + + {"kind":"and","text":"","code":["t.size() == size"]}, + + {"kind":"and","text":"","code":["t.getItemType() == expected"]}, + + {"kind":"where","text":"","code":{"size":["3","4","2","3","1"],"data":["new float[]{-1f, 3f, 6f}","new int[]{1, -2 , 9, 12}","new byte[]{42 , 73}","new long[]{-16 , 54, 12}","new short[]{26}"],"expected":["Float","Integer","Byte","Long","Short"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"1D tensors can be created from primitive arrays. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["def t = Tensor.of(data)"]}, + + {"kind":"expect","text":"","code":["t.rank() == 1"]}, + + {"kind":"and","text":"","code":["t.size() == size"]}, + + {"kind":"and","text":"","code":["t.getItemType() == expected"]}, + + {"kind":"where","text":"","code":{"size":["3","4","2","3","1"],"data":["new float[]{-1f, 3f, 6f}","new int[]{1, -2 , 9, 12}","new byte[]{42 , 73}","new long[]{-16 , 54, 12}","new short[]{26}"],"expected":["Float","Integer","Byte","Long","Short"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"1D tensors can be created from primitive arrays. [4]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.tensors.Tensor_Gradient_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Gradient_Spec.json index 921d4c5b9..64ea29ae0 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Gradient_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Gradient_Spec.json @@ -4,19 +4,19 @@ "narrative":"This specification defines the gradient API on tensors.\n So one ought to be able to check wetter or not a tensor has a gradient attached to it or not.\n In that case one should be able to get this gradient and then work with\n it independently of the original tensor to which it belongs to...", "subjects":[], "statistics":{ - "runs":"3", + "runs":"4", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.008 seconds" }, "headers":["\n

        Tensor Gradient Unit Tests

        \n
        \n Why is there a difference between \"rqsGradient()\" and \"hasGradient()\" ? :\n

        \n

        \n The latter property simply tells if a tensor has another tensor as component.\n This however does not necessitate it to also require gradients via the autograd system.\n This is what the prior property is for. \n

        \n "],"tags":{},"see":[], "features":[ { "id":"Tensors can have gradients but not require them.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -52,7 +52,30 @@ }, { - "id":"Gradient of tensor is being applies regardless of the tensor requiring gradient or not", + "id":"Gradient of tensor is being applies regardless of the tensor requiring gradient or not [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A new simple tensor.","code":["Tensor t = Tensor.of(-3d)"]}, + + {"kind":"and","text":"A second tensor viewed as gradient.","code":["Tensor g = Tensor.of(9d)"]}, + + {"kind":"and","text":"The gradient tensor is added to the prior tensor as component.","code":["t.set( g )"]}, + + {"kind":"when","text":"The request to apply the gradient is being made.","code":["t.applyGradient()"]}, + + {"kind":"then","text":"The tensor changed as expected.","code":["t.toString().contains(expected)"]}, + + {"kind":"where","text":"","code":{"requiresGradient":["true","false"],"expected":["\"(1):[6.0]\"","\"(1):[6.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Gradient of tensor is being applies regardless of the tensor requiring gradient or not [1]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.tensors.Tensor_IO_Spec.json b/docs/spock/reports/ut.tensors.Tensor_IO_Spec.json index f11039777..9e7812a4c 100644 --- a/docs/spock/reports/ut.tensors.Tensor_IO_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_IO_Spec.json @@ -4,119 +4,829 @@ "narrative":"Tensors are complicated data structures with a wide range of different possible states.\n They can host elements of different types residing on many kinds of different devices.\n Here we want to read from and write to the state of a tensor.", "subjects":[], "statistics":{ - "runs":"8", + "runs":"43", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.031 seconds" + "duration":"0.069 seconds" }, "headers":["\n This specification covers some basic behaviour related to\n reading and modifying the data inside a tensor.\n "],"tags":{},"see":[], "features":[ { "id":"Indexing after reshaping works as expected.", "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We are using the legacy view for tensors where bracket types are swapped, just because...","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + + {"kind":"and","text":"A new tensor instance with the shape (4x3).","code":["var t1 = Tensor.of([4, 3], 1d..12d)"]}, + + {"kind":"when","text":"Recording the index behavior before and after a permute operation...","code":["var t1_ioi_1 = t1.indexOfIndices(new int[]{2, 1})","var t1_ioi_2 = t1.indexOfIndices(new int[]{1, 2})","var t1_indices = t1.indicesOfIndex(5)","var t2 = Function.of(\" [ 1, 0 ]:( I[0] ) \")(t1)","var t2_ioi_1 = t2.indexOfIndices(new int[]{1, 2})","var t2_idx = t2.indicesOfIndex(7)","var t1_ioi_3 = t1.indexOfIndices(t1.indicesOfIndex(7)) // Element 7 '8.0' is at index 7!","var t2_ioi_2 = t2.indexOfIndices(t2.indicesOfIndex(7)) // Element 7 '11.0' is at index 10!"]}, + + {"kind":"then","text":"These recorded values are as one would expect.","code":["t1_ioi_1 == 7","t1_ioi_2 == 5","t1_indices[0] == 1","t1_indices[1] == 2","t2_ioi_1 == 7","t2_idx[0] == 1","t2_idx[1] == 3","t1_ioi_3 == 7 // Element 7 '8.0' is at index 7!","t2_ioi_2 == 10 // Element 7 '11.0' is at index 10!","t1.toString().contains(\"[4x3]:(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0)\")","t2.toString().contains(\"[3x4]:(1.0, 4.0, 7.0, 10.0, 2.0, 5.0, 8.0, 11.0, 3.0, 6.0, 9.0, 12.0)\")"]} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensor value type can not be changed by passing float or double arrays to it.", + "result":"PASS", + "duration":"0.006 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We are using the legacy view for tensors where bracket types are swapped, just because...","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + + {"kind":"and","text":"A new tensor instance.","code":["var x = Tensor.of(3d)"]}, + + {"kind":"when","text":"Setting the value of the tensor...","code":["float[] floats = new float[1]","floats[0] = 5","x.mut.setItems(floats)"]}, + + {"kind":"then","text":"...the tensor will change as expected.","code":["!(x.getItems() instanceof float[])","!(x.mut.data.get() instanceof float[])","!(x.rawData instanceof float[])","x.getItemsAs( float[].class )[ 0 ]==5.0f","x.getItemsAs( double[].class )[0]==5.0d"]}, + + {"kind":"when","text":"Doing the same with double array...","code":["double[] doubles = new double[1]","doubles[0] = 4.0","x.mut.setItems(doubles)"]}, + + {"kind":"then","text":"...once again the tensor changes as expected.","code":["x.rawItems instanceof double[]","x.mut.data.get() instanceof double[]","x.rawData instanceof double[]","x.getItemsAs( float[].class )[ 0 ]==4.0f","x.getItemsAs( double[].class )[0]==4.0d","x.isLeave()","!x.isBranch()","!x.isOutsourced()","!x.isVirtual()","!x.isSlice()","!x.isSliceParent()","!x.belongsToGraph()","x.getDevice() !=null","x.getDevice() instanceof CPU","x.rank()==1","!x.rqsGradient()","x.size()==1"]}, + + {"kind":"when","text":"","code":["when : x.mut.toType( Float.class )"]}, + + {"kind":"then","text":"","code":["then : x.rawItems instanceof float[]"]}, + + {"kind":"when","text":"","code":["doubles = new double[1]","doubles[0] = 7.0","x.mut.setItems(doubles)"]}, + + {"kind":"then","text":"","code":["!(x.rawItems instanceof double[])","!(x.mut.data.get() instanceof double[])","!(x.rawData instanceof double[])","x.getItemsAs( float[].class )[ 0 ]==7.0f","x.getItemsAs( double[].class )[0]==7.0d"]} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can re-populate a tensor of shorts from a single scalar value! [11]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + + {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + + {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. [0]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. 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[5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. [6]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can manipulate the underlying data array of a tensor through the mut API. [11]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, + + {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, + + {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, + + {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + + {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + + {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"When we try to manipulate the underlying data array of a virtual tensor then it will become actual. [0]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(1, 1, 1)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"expect","text":"The tensor is virtual because it is filled homogeneously with the same value.","code":["t.isVirtual()","t.items == [1, 1, 1]","t.rawData == [1] // The data array is a single element array."]}, + + {"kind":"when","text":"We access the third item of the tensor and set the value 42.","code":["t.mut.at(2).set(42)"]}, + + {"kind":"then","text":"The tensor is no longer virtual because it now stores 2 different values.","code":["!t.isVirtual()","t.items == [1, 1, 42]","t.rawData == [1, 1, 42]"]}, + + {"kind":"where","text":"We ensure that this works on different devices and with different data types.","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"When we try to manipulate the underlying data array of a virtual tensor then it will become actual. [1]", + "result":"PASS", "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"We are using the legacy view for tensors where bracket types are swapped, just because...","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(1, 1, 1)"]}, + + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, + + {"kind":"expect","text":"The tensor is virtual because it is filled homogeneously with the same value.","code":["t.isVirtual()","t.items == [1, 1, 1]","t.rawData == [1] // The data array is a single element array."]}, + + {"kind":"when","text":"We access the third item of the tensor and set the value 42.","code":["t.mut.at(2).set(42)"]}, + + {"kind":"then","text":"The tensor is no longer virtual because it now stores 2 different values.","code":["!t.isVirtual()","t.items == [1, 1, 42]","t.rawData == [1, 1, 42]"]}, + + {"kind":"where","text":"We ensure that this works on different devices and with different data types.","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"When we try to manipulate the underlying data array of a virtual tensor then it will become actual. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(1, 1, 1)"]}, - {"kind":"and","text":"A new tensor instance with the shape (4x3).","code":["var t1 = Tensor.of([4, 3], 1d..12d)"]}, + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, - {"kind":"when","text":"Recording the index behavior before and after a permute operation...","code":["var t1_ioi_1 = t1.indexOfIndices(new int[]{2, 1})","var t1_ioi_2 = t1.indexOfIndices(new int[]{1, 2})","var t1_indices = t1.indicesOfIndex(5)","var t2 = Function.of(\" [ 1, 0 ]:( I[0] ) \")(t1)","var t2_ioi_1 = t2.indexOfIndices(new int[]{1, 2})","var t2_idx = t2.indicesOfIndex(7)","var t1_ioi_3 = t1.indexOfIndices(t1.indicesOfIndex(7)) // Element 7 '8.0' is at index 7!","var t2_ioi_2 = t2.indexOfIndices(t2.indicesOfIndex(7)) // Element 7 '11.0' is at index 10!"]}, + {"kind":"expect","text":"The tensor is virtual because it is filled homogeneously with the same value.","code":["t.isVirtual()","t.items == [1, 1, 1]","t.rawData == [1] // The data array is a single element array."]}, - {"kind":"then","text":"These recorded values are as one would expect.","code":["t1_ioi_1 == 7","t1_ioi_2 == 5","t1_indices[0] == 1","t1_indices[1] == 2","t2_ioi_1 == 7","t2_idx[0] == 1","t2_idx[1] == 3","t1_ioi_3 == 7 // Element 7 '8.0' is at index 7!","t2_ioi_2 == 10 // Element 7 '11.0' is at index 10!","t1.toString().contains(\"[4x3]:(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0)\")","t2.toString().contains(\"[3x4]:(1.0, 4.0, 7.0, 10.0, 2.0, 5.0, 8.0, 11.0, 3.0, 6.0, 9.0, 12.0)\")"]} + {"kind":"when","text":"We access the third item of the tensor and set the value 42.","code":["t.mut.at(2).set(42)"]}, + + {"kind":"then","text":"The tensor is no longer virtual because it now stores 2 different values.","code":["!t.isVirtual()","t.items == [1, 1, 42]","t.rawData == [1, 1, 42]"]}, + + {"kind":"where","text":"We ensure that this works on different devices and with different data types.","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Float"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Tensor value type can not be changed by passing float or double arrays to it.", + "id":"When we try to manipulate the underlying data array of a virtual tensor then it will become actual. [3]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"We are using the legacy view for tensors where bracket types are swapped, just because...","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, - - {"kind":"and","text":"A new tensor instance.","code":["var x = Tensor.of(3d)"]}, - - {"kind":"when","text":"Setting the value of the tensor...","code":["float[] floats = new float[1]","floats[0] = 5","x.mut.setItems(floats)"]}, - - {"kind":"then","text":"...the tensor will change as expected.","code":["!(x.getItems() instanceof float[])","!(x.mut.data.get() instanceof float[])","!(x.rawData instanceof float[])","x.getItemsAs( float[].class )[ 0 ]==5.0f","x.getItemsAs( double[].class )[0]==5.0d"]}, - - {"kind":"when","text":"Doing the same with double array...","code":["double[] doubles = new double[1]","doubles[0] = 4.0","x.mut.setItems(doubles)"]}, + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(1, 1, 1)"]}, - {"kind":"then","text":"...once again the tensor changes as expected.","code":["x.rawItems instanceof double[]","x.mut.data.get() instanceof double[]","x.rawData instanceof double[]","x.getItemsAs( float[].class )[ 0 ]==4.0f","x.getItemsAs( double[].class )[0]==4.0d","x.isLeave()","!x.isBranch()","!x.isOutsourced()","!x.isVirtual()","!x.isSlice()","!x.isSliceParent()","!x.belongsToGraph()","x.getDevice() !=null","x.getDevice() instanceof CPU","x.rank()==1","!x.rqsGradient()","x.size()==1"]}, + {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, - {"kind":"when","text":"","code":["when : x.mut.toType( Float.class )"]}, + {"kind":"expect","text":"The tensor is virtual because it is filled homogeneously with the same value.","code":["t.isVirtual()","t.items == [1, 1, 1]","t.rawData == [1] // The data array is a single element array."]}, - {"kind":"then","text":"","code":["then : x.rawItems instanceof float[]"]}, + {"kind":"when","text":"We access the third item of the tensor and set the value 42.","code":["t.mut.at(2).set(42)"]}, - {"kind":"when","text":"","code":["doubles = new double[1]","doubles[0] = 7.0","x.mut.setItems(doubles)"]}, + {"kind":"then","text":"The tensor is no longer virtual because it now stores 2 different values.","code":["!t.isVirtual()","t.items == [1, 1, 42]","t.rawData == [1, 1, 42]"]}, - {"kind":"then","text":"","code":["!(x.rawItems instanceof double[])","!(x.mut.data.get() instanceof double[])","!(x.rawData instanceof double[])","x.getItemsAs( float[].class )[ 0 ]==7.0f","x.getItemsAs( double[].class )[0]==7.0d"]} + {"kind":"where","text":"We ensure that this works on different devices and with different data types.","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Float"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"We can re-populate a tensor of shorts from a single scalar value!", + "id":"When we try to manipulate the underlying data array of a virtual tensor then it will become actual. [4]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 666, 73)"]}, + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(1, 1, 1)"]}, {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, - {"kind":"when","text":"We call the \"setValue\" method with a scalar value passed to it...","code":["t.mut.setItems(5)"]}, + {"kind":"expect","text":"The tensor is virtual because it is filled homogeneously with the same value.","code":["t.isVirtual()","t.items == [1, 1, 1]","t.rawData == [1] // The data array is a single element array."]}, - {"kind":"then","text":"The value of the tensor will be an array of 3.","code":["t.items == [5, 5, 5]"]}, + {"kind":"when","text":"We access the third item of the tensor and set the value 42.","code":["t.mut.at(2).set(42)"]}, - {"kind":"and","text":"We now expect the tensor to be virtual, because it stores only a single type of value.","code":["t.isVirtual()"]}, + {"kind":"then","text":"The tensor is no longer virtual because it now stores 2 different values.","code":["!t.isVirtual()","t.items == [1, 1, 42]","t.rawData == [1, 1, 42]"]}, - {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Byte","Integer","Long","Double","Short","Float","Byte","Integer","Long","Double","Short","Float"]}} + {"kind":"where","text":"We ensure that this works on different devices and with different data types.","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Float"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"We can manipulate the underlying data array of a tensor through the mut API.", + "id":"When we try to manipulate the underlying data array of a virtual tensor then it will become actual. [5]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(42, 66, 73)"]}, + {"kind":"given","text":"A tensor of 3 numbers:","code":["var t = Tensor.of(type).vector(1, 1, 1)"]}, {"kind":"and","text":"We store the tensor on the given device, to ensure that it work there as well.","code":["t.to(device)"]}, - {"kind":"when","text":"We create a slice which should internally reference the same data as the slice parent.","code":["var s = t[1]"]}, - - {"kind":"then","text":"The slice has the expected state!","code":["s.isSlice()","s.items == [66]","s.rawData == [42, 66, 73]"]}, - - {"kind":"when","text":"We call the \"setData\" method with a scalar value passed to it...","code":["s.mut.setDataAt(1, -9)"]}, - - {"kind":"then","text":"The change will be reflected in the slice...","code":["s.items == [-9]"]}, + {"kind":"expect","text":"The tensor is virtual because it is filled homogeneously with the same value.","code":["t.isVirtual()","t.items == [1, 1, 1]","t.rawData == [1] // The data array is a single element array."]}, - {"kind":"and","text":"Also in the slice parent!","code":["t.items == [42, -9, 73]"]}, + {"kind":"when","text":"We access the third item of the tensor and set the value 42.","code":["t.mut.at(2).set(42)"]}, - {"kind":"and","text":"Both tensors should have the same data array!","code":["s.rawData == [42, -9, 73]","t.rawData == [42, -9, 73]"]}, + {"kind":"then","text":"The tensor is no longer virtual because it now stores 2 different values.","code":["!t.isVirtual()","t.items == [1, 1, 42]","t.rawData == [1, 1, 42]"]}, - {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Double","Float","Byte","Short","Integer","Long"]}} + {"kind":"where","text":"We ensure that this works on different devices and with different data types.","code":{"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'"],"type":["Double","Float","Byte","Short","Integer","Long","Float"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"When we try to manipulate the underlying data array of a virtual tensor then it will become actual.", + "id":"When we try to manipulate the underlying data array of a virtual tensor then it will become actual. [6]", "result":"PASS", "duration":"0", "iterations":{ @@ -141,7 +851,7 @@ { "id":"A tensor produced by the static \"Tensor.newRandom(shape)\" has expected \"random\" value.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -168,7 +878,7 @@ { "id":"Tensor values can be manipulated", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.009 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -223,7 +933,210 @@ }, { - "id":"The tensor data array can be modified by targeting them with an index.", + "id":"The tensor data array can be modified by targeting them with an index. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"when","text":"","code":["t.mut.setDataAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.getDataAt( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"when","text":"","code":["t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"and","text":"","code":["t.mut.setItemAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.item( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Byte","Short","Long","Integer","Boolean","Character"],"shape":["[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,1]","[2,1]"],"data":["[-42, 24, 9, 3, -34] as float[]","[-42, 24, 9, 3, -34] as double[]","[-42, 24, 9, 3, -34] as byte[]","[-42, 24, 9, 3, -34] as short[]","[-42, 24, 9, 3, -34] as long[]","[-42, 24, 9, 3, -34] as int[]","[false, true, false] as boolean[]","['a', 'b', 'c'] as char[]"],"element":["0.032 as float","0.032 as double","1 as byte","1 as short","1 as long","1 as int","false","'x' as char"],"expected":["[-42.0, 0.032, 9.0, 3.0] as float[]","[-42.0, 0.032, 9.0, 3.0] as double[]","[-42, 1, 9, 3] as byte[]","[-42, 1, 9, 3] as short[]","[-42, 1, 9, 3] as long[]","[-42, 1, 9, 3] as int[]","[false, false] as boolean[]","['a', 'x'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The tensor data array can be modified by targeting them with an index. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"when","text":"","code":["t.mut.setDataAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.getDataAt( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"when","text":"","code":["t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"and","text":"","code":["t.mut.setItemAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.item( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Byte","Short","Long","Integer","Boolean","Character"],"shape":["[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,1]","[2,1]"],"data":["[-42, 24, 9, 3, -34] as float[]","[-42, 24, 9, 3, -34] as double[]","[-42, 24, 9, 3, -34] as byte[]","[-42, 24, 9, 3, -34] as short[]","[-42, 24, 9, 3, -34] as long[]","[-42, 24, 9, 3, -34] as int[]","[false, true, false] as boolean[]","['a', 'b', 'c'] as char[]"],"element":["0.032 as float","0.032 as double","1 as byte","1 as short","1 as long","1 as int","false","'x' as char"],"expected":["[-42.0, 0.032, 9.0, 3.0] as float[]","[-42.0, 0.032, 9.0, 3.0] as double[]","[-42, 1, 9, 3] as byte[]","[-42, 1, 9, 3] as short[]","[-42, 1, 9, 3] as long[]","[-42, 1, 9, 3] as int[]","[false, false] as boolean[]","['a', 'x'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The tensor data array can be modified by targeting them with an index. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"when","text":"","code":["t.mut.setDataAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.getDataAt( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"when","text":"","code":["t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"and","text":"","code":["t.mut.setItemAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.item( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Byte","Short","Long","Integer","Boolean","Character"],"shape":["[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,1]","[2,1]"],"data":["[-42, 24, 9, 3, -34] as float[]","[-42, 24, 9, 3, -34] as double[]","[-42, 24, 9, 3, -34] as byte[]","[-42, 24, 9, 3, -34] as short[]","[-42, 24, 9, 3, -34] as long[]","[-42, 24, 9, 3, -34] as int[]","[false, true, false] as boolean[]","['a', 'b', 'c'] as char[]"],"element":["0.032 as float","0.032 as double","1 as byte","1 as short","1 as long","1 as int","false","'x' as char"],"expected":["[-42.0, 0.032, 9.0, 3.0] as float[]","[-42.0, 0.032, 9.0, 3.0] as double[]","[-42, 1, 9, 3] as byte[]","[-42, 1, 9, 3] as short[]","[-42, 1, 9, 3] as long[]","[-42, 1, 9, 3] as int[]","[false, false] as boolean[]","['a', 'x'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The tensor data array can be modified by targeting them with an index. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"when","text":"","code":["t.mut.setDataAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.getDataAt( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"when","text":"","code":["t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"and","text":"","code":["t.mut.setItemAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.item( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Byte","Short","Long","Integer","Boolean","Character"],"shape":["[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,1]","[2,1]"],"data":["[-42, 24, 9, 3, -34] as float[]","[-42, 24, 9, 3, -34] as double[]","[-42, 24, 9, 3, -34] as byte[]","[-42, 24, 9, 3, -34] as short[]","[-42, 24, 9, 3, -34] as long[]","[-42, 24, 9, 3, -34] as int[]","[false, true, false] as boolean[]","['a', 'b', 'c'] as char[]"],"element":["0.032 as float","0.032 as double","1 as byte","1 as short","1 as long","1 as int","false","'x' as char"],"expected":["[-42.0, 0.032, 9.0, 3.0] as float[]","[-42.0, 0.032, 9.0, 3.0] as double[]","[-42, 1, 9, 3] as byte[]","[-42, 1, 9, 3] as short[]","[-42, 1, 9, 3] as long[]","[-42, 1, 9, 3] as int[]","[false, false] as boolean[]","['a', 'x'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The tensor data array can be modified by targeting them with an index. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"when","text":"","code":["t.mut.setDataAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.getDataAt( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"when","text":"","code":["t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"and","text":"","code":["t.mut.setItemAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.item( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Byte","Short","Long","Integer","Boolean","Character"],"shape":["[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,1]","[2,1]"],"data":["[-42, 24, 9, 3, -34] as float[]","[-42, 24, 9, 3, -34] as double[]","[-42, 24, 9, 3, -34] as byte[]","[-42, 24, 9, 3, -34] as short[]","[-42, 24, 9, 3, -34] as long[]","[-42, 24, 9, 3, -34] as int[]","[false, true, false] as boolean[]","['a', 'b', 'c'] as char[]"],"element":["0.032 as float","0.032 as double","1 as byte","1 as short","1 as long","1 as int","false","'x' as char"],"expected":["[-42.0, 0.032, 9.0, 3.0] as float[]","[-42.0, 0.032, 9.0, 3.0] as double[]","[-42, 1, 9, 3] as byte[]","[-42, 1, 9, 3] as short[]","[-42, 1, 9, 3] as long[]","[-42, 1, 9, 3] as int[]","[false, false] as boolean[]","['a', 'x'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The tensor data array can be modified by targeting them with an index. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"when","text":"","code":["t.mut.setDataAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.getDataAt( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"when","text":"","code":["t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"and","text":"","code":["t.mut.setItemAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.item( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Byte","Short","Long","Integer","Boolean","Character"],"shape":["[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,1]","[2,1]"],"data":["[-42, 24, 9, 3, -34] as float[]","[-42, 24, 9, 3, -34] as double[]","[-42, 24, 9, 3, -34] as byte[]","[-42, 24, 9, 3, -34] as short[]","[-42, 24, 9, 3, -34] as long[]","[-42, 24, 9, 3, -34] as int[]","[false, true, false] as boolean[]","['a', 'b', 'c'] as char[]"],"element":["0.032 as float","0.032 as double","1 as byte","1 as short","1 as long","1 as int","false","'x' as char"],"expected":["[-42.0, 0.032, 9.0, 3.0] as float[]","[-42.0, 0.032, 9.0, 3.0] as double[]","[-42, 1, 9, 3] as byte[]","[-42, 1, 9, 3] as short[]","[-42, 1, 9, 3] as long[]","[-42, 1, 9, 3] as int[]","[false, false] as boolean[]","['a', 'x'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The tensor data array can be modified by targeting them with an index. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"when","text":"","code":["t.mut.setDataAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.getDataAt( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"when","text":"","code":["t = Tensor.of(type).withShape(shape).andFill(data)"]}, + + {"kind":"and","text":"","code":["t.mut.setItemAt( 1, element )"]}, + + {"kind":"then","text":"","code":["t.item( 1 ) == element"]}, + + {"kind":"and","text":"","code":["t.mut.data.get() == expected","t.rawData == expected"]}, + + {"kind":"where","text":"","code":{"type":["Float","Double","Byte","Short","Long","Integer","Boolean","Character"],"shape":["[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,2]","[2,1]","[2,1]"],"data":["[-42, 24, 9, 3, -34] as float[]","[-42, 24, 9, 3, -34] as double[]","[-42, 24, 9, 3, -34] as byte[]","[-42, 24, 9, 3, -34] as short[]","[-42, 24, 9, 3, -34] as long[]","[-42, 24, 9, 3, -34] as int[]","[false, true, false] as boolean[]","['a', 'b', 'c'] as char[]"],"element":["0.032 as float","0.032 as double","1 as byte","1 as short","1 as long","1 as int","false","'x' as char"],"expected":["[-42.0, 0.032, 9.0, 3.0] as float[]","[-42.0, 0.032, 9.0, 3.0] as double[]","[-42, 1, 9, 3] as byte[]","[-42, 1, 9, 3] as short[]","[-42, 1, 9, 3] as long[]","[-42, 1, 9, 3] as int[]","[false, false] as boolean[]","['a', 'x'] as char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The tensor data array can be modified by targeting them with an index. [7]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.tensors.Tensor_Instantiation_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Instantiation_Spec.json index 165f49cc9..0cd1da1b9 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Instantiation_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Instantiation_Spec.json @@ -4,17 +4,63 @@ "narrative":"Tensors are complicated data structures with a wide range of different possible states.\n They can host elements of different types residing on many kinds of different devices.\n Here we want to show how a tensor can be instantiated in different ways.", "subjects":["neureka.Tensor"], "statistics":{ - "runs":"7", + "runs":"25", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.011 seconds" + "duration":"0.027 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"Vector tensors can be instantiated via factory methods.", + "id":"Vector tensors can be instantiated via factory methods. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a vector tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == shape"]}, + + {"kind":"and","text":"The tensor contains the expected items.","code":["t.items == data"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the underlying data is also as expected.","code":["t.rawData == data","t.mut.data.get() == data // This exposes the internal data array"]}, + + {"kind":"where","text":"The following data arrays will lead to the tensor having the expected type and shape.","code":{"data":["new double[]{1.1, 2.2, 3.3}","new float[]{-0.21, 543.3}","new boolean[]{true, false}","new short[]{1, 2, 99, -123}","new long[]{3, 8, 4, 2, 3, 0}","new int[]{66, 1, 4, 42, -40}"],"type":["Double","Float","Boolean","Short","Long","Integer"],"shape":["[ 3 ]","[ 2 ]","[ 2 ]","[ 4 ]","[ 6 ]","[ 5 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Vector tensors can be instantiated via factory methods. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a vector tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == shape"]}, + + {"kind":"and","text":"The tensor contains the expected items.","code":["t.items == data"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the underlying data is also as expected.","code":["t.rawData == data","t.mut.data.get() == data // This exposes the internal data array"]}, + + {"kind":"where","text":"The following data arrays will lead to the tensor having the expected type and shape.","code":{"data":["new double[]{1.1, 2.2, 3.3}","new float[]{-0.21, 543.3}","new boolean[]{true, false}","new short[]{1, 2, 99, -123}","new long[]{3, 8, 4, 2, 3, 0}","new int[]{66, 1, 4, 42, -40}"],"type":["Double","Float","Boolean","Short","Long","Integer"],"shape":["[ 3 ]","[ 2 ]","[ 2 ]","[ 4 ]","[ 6 ]","[ 5 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Vector tensors can be instantiated via factory methods. [2]", "result":"PASS", "duration":"0", "iterations":{ @@ -37,12 +83,81 @@ }, { - "id":"Scalar tensors can be created via static factory methods", + "id":"Vector tensors can be instantiated via factory methods. [3]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"given","text":"We create a vector tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == shape"]}, + + {"kind":"and","text":"The tensor contains the expected items.","code":["t.items == data"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the underlying data is also as expected.","code":["t.rawData == data","t.mut.data.get() == data // This exposes the internal data array"]}, + + {"kind":"where","text":"The following data arrays will lead to the tensor having the expected type and shape.","code":{"data":["new double[]{1.1, 2.2, 3.3}","new float[]{-0.21, 543.3}","new boolean[]{true, false}","new short[]{1, 2, 99, -123}","new long[]{3, 8, 4, 2, 3, 0}","new int[]{66, 1, 4, 42, -40}"],"type":["Double","Float","Boolean","Short","Long","Integer"],"shape":["[ 3 ]","[ 2 ]","[ 2 ]","[ 4 ]","[ 6 ]","[ 5 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Vector tensors can be instantiated via factory methods. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a vector tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == shape"]}, + + {"kind":"and","text":"The tensor contains the expected items.","code":["t.items == data"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the underlying data is also as expected.","code":["t.rawData == data","t.mut.data.get() == data // This exposes the internal data array"]}, + + {"kind":"where","text":"The following data arrays will lead to the tensor having the expected type and shape.","code":{"data":["new double[]{1.1, 2.2, 3.3}","new float[]{-0.21, 543.3}","new boolean[]{true, false}","new short[]{1, 2, 99, -123}","new long[]{3, 8, 4, 2, 3, 0}","new int[]{66, 1, 4, 42, -40}"],"type":["Double","Float","Boolean","Short","Long","Integer"],"shape":["[ 3 ]","[ 2 ]","[ 2 ]","[ 4 ]","[ 6 ]","[ 5 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Vector tensors can be instantiated via factory methods. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a vector tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == shape"]}, + + {"kind":"and","text":"The tensor contains the expected items.","code":["t.items == data"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the underlying data is also as expected.","code":["t.rawData == data","t.mut.data.get() == data // This exposes the internal data array"]}, + + {"kind":"where","text":"The following data arrays will lead to the tensor having the expected type and shape.","code":{"data":["new double[]{1.1, 2.2, 3.3}","new float[]{-0.21, 543.3}","new boolean[]{true, false}","new short[]{1, 2, 99, -123}","new long[]{3, 8, 4, 2, 3, 0}","new int[]{66, 1, 4, 42, -40}"],"type":["Double","Float","Boolean","Short","Long","Integer"],"shape":["[ 3 ]","[ 2 ]","[ 2 ]","[ 4 ]","[ 6 ]","[ 5 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, @@ -62,10 +177,335 @@ }, { - "id":"A matrix tensor can be instantiated using lists for it's shape and values.", + "id":"Scalar tensors can be created via static factory methods [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [4]", "result":"PASS", "duration":"0", "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [11]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [12]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar tensors can be created via static factory methods [13]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We make sure that the data is of the right type (based on the data table):","code":["data = data.asType(type)"]}, + + {"kind":"and","text":"We create a scalar tensor using the \"of\" factory method.","code":["Tensor t = Tensor.of(data)"]}, + + {"kind":"expect","text":"The resulting tensor has the expected item type class.","code":["t.itemType == type"]}, + + {"kind":"and","text":"Also the expected shape.","code":["t.shape() == [ 1 ]"]}, + + {"kind":"and","text":"The tensor has the expected data array.","code":["t.mut.data.get() == [data] // Internal data","t.rawData == [data]"]}, + + {"kind":"and","text":"The tensor is not virtual nor is it a slice... so the item array and data array contain the same values.","code":["t.items == [data]"]}, + + {"kind":"where","text":"","code":{"data":["1.1","-0.21","0.1f","-42.9","true","false","99","-123","3L","8L","1","2","-12","3"],"type":["Double","Double","Float","Float","Boolean","Boolean","Integer","Integer","Long","Long","Short","Short","Byte","Byte"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A matrix tensor can be instantiated using lists for it's shape and values.", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ "tags":{},"see":[],"extraInfo":["\n Note that the following example of tensor instantiation is \n best suited for when Neureka is used in a scripting environment\n like Groovy or Jython which support square bracket list notation.\n In Java code it is recommended to use the fluent API.\n "] }, "blocks":[ @@ -85,7 +525,7 @@ { "id":"A simple 2D vector can be instantiated using lists for it's shape and values.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n Note that the following example of tensor instantiation is \n best suited for when Neureka is used in a scripting environment\n like Groovy or Jython which support square bracket list notation.\n In Java code it is recommended to use the fluent API.\n "] }, @@ -104,7 +544,7 @@ { "id":"Tensors can be instantiated based on arrays for both shapes and values.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -123,7 +563,7 @@ { "id":"Tensors can be instantiated with String seed.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -138,7 +578,7 @@ { "id":"Passing a seed in the form of a String to a tensor produces pseudo random items.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_Interop_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Interop_Spec.json index ff801bf94..367e316fd 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Interop_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Interop_Spec.json @@ -4,19 +4,19 @@ "narrative":"Tensors should have good interoperability with other JDK data structures like images.\n In this specification we define these interoperability requirements.", "subjects":[], "statistics":{ - "runs":"2", + "runs":"8", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.008 seconds" + "duration":"0.035 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"Tensor can be converted to buffered images.", + "id":"Tensor can be converted to buffered images. [0]", "result":"PASS", - "duration":"0.003 seconds", + "duration":"0.024 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -31,7 +31,115 @@ }, { - "id":"Not all tensor can be converted to images.", + "id":"Tensor can be converted to buffered images. [1]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"A tensor of the provided type is converted to a buffered image","code":["var asImage = Tensor.of(type).withShape(shape).andFill(42..73).asImage(image)"]}, + + {"kind":"then","text":"The resulting image has the expected shape","code":["asImage.height == shape[0]","asImage.width == shape[1]"]}, + + {"kind":"where","text":"","code":{"type":["Byte","Integer","Byte","Byte"],"image":["Tensor.ImageType.BGR_3BYTE","Tensor.ImageType.ARGB_1INT","Tensor.ImageType.ABGR_4BYTE","Tensor.ImageType.ABGR_PRE_4BYTE"],"shape":["[3, 5, 3]","[7, 5, 1]","[7, 5, 4]","[7, 5, 4]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensor can be converted to buffered images. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"A tensor of the provided type is converted to a buffered image","code":["var asImage = Tensor.of(type).withShape(shape).andFill(42..73).asImage(image)"]}, + + {"kind":"then","text":"The resulting image has the expected shape","code":["asImage.height == shape[0]","asImage.width == shape[1]"]}, + + {"kind":"where","text":"","code":{"type":["Byte","Integer","Byte","Byte"],"image":["Tensor.ImageType.BGR_3BYTE","Tensor.ImageType.ARGB_1INT","Tensor.ImageType.ABGR_4BYTE","Tensor.ImageType.ABGR_PRE_4BYTE"],"shape":["[3, 5, 3]","[7, 5, 1]","[7, 5, 4]","[7, 5, 4]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensor can be converted to buffered images. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"A tensor of the provided type is converted to a buffered image","code":["var asImage = Tensor.of(type).withShape(shape).andFill(42..73).asImage(image)"]}, + + {"kind":"then","text":"The resulting image has the expected shape","code":["asImage.height == shape[0]","asImage.width == shape[1]"]}, + + {"kind":"where","text":"","code":{"type":["Byte","Integer","Byte","Byte"],"image":["Tensor.ImageType.BGR_3BYTE","Tensor.ImageType.ARGB_1INT","Tensor.ImageType.ABGR_4BYTE","Tensor.ImageType.ABGR_PRE_4BYTE"],"shape":["[3, 5, 3]","[7, 5, 1]","[7, 5, 4]","[7, 5, 4]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Not all tensor can be converted to images. [0]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We try to convert a tensor of the provided type to a buffered image...","code":["Tensor.of(type).withShape(shape).all(-3).asImage(image)"]}, + + {"kind":"then","text":"An exception is thrown!","code":["var exception = thrown(IllegalArgumentException)"]}, + + {"kind":"and","text":"The exception message has a plausible size.","code":["exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["Byte","Integer","String","Byte"],"image":["Tensor.ImageType.BGR_3BYTE","Tensor.ImageType.ARGB_1INT","Tensor.ImageType.ARGB_1INT","Tensor.ImageType.ABGR_4BYTE"],"shape":["[3, 5]","[7, 5, 3]","[7, 5, 1]","[2, 9, 3]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Not all tensor can be converted to images. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We try to convert a tensor of the provided type to a buffered image...","code":["Tensor.of(type).withShape(shape).all(-3).asImage(image)"]}, + + {"kind":"then","text":"An exception is thrown!","code":["var exception = thrown(IllegalArgumentException)"]}, + + {"kind":"and","text":"The exception message has a plausible size.","code":["exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["Byte","Integer","String","Byte"],"image":["Tensor.ImageType.BGR_3BYTE","Tensor.ImageType.ARGB_1INT","Tensor.ImageType.ARGB_1INT","Tensor.ImageType.ABGR_4BYTE"],"shape":["[3, 5]","[7, 5, 3]","[7, 5, 1]","[2, 9, 3]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Not all tensor can be converted to images. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We try to convert a tensor of the provided type to a buffered image...","code":["Tensor.of(type).withShape(shape).all(-3).asImage(image)"]}, + + {"kind":"then","text":"An exception is thrown!","code":["var exception = thrown(IllegalArgumentException)"]}, + + {"kind":"and","text":"The exception message has a plausible size.","code":["exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["Byte","Integer","String","Byte"],"image":["Tensor.ImageType.BGR_3BYTE","Tensor.ImageType.ARGB_1INT","Tensor.ImageType.ARGB_1INT","Tensor.ImageType.ABGR_4BYTE"],"shape":["[3, 5]","[7, 5, 3]","[7, 5, 1]","[2, 9, 3]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Not all tensor can be converted to images. [3]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.tensors.Tensor_Layout_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Layout_Spec.json index 11b859788..c77bd102e 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Layout_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Layout_Spec.json @@ -1,22 +1,22 @@ { "className":"ut.tensors.Tensor_Layout_Spec", "title":"Row or Column Major. Why not both?", - "narrative":"Although Neureka exposes tensors as row major tensors from\n a users point of view, it does in fact support both row major and column major\n based tensor layout under the hood.\n Here we cover how the layout of tensors can be modified\n and we ensure the different tensor types still work as expected...\n (The features in this specification involve mutating tensors, be careful when playing around with this yourself)", + "narrative":"Although Neureka exposes tensors as row major tensors from \n a users point of view, it does in fact support both row major and column major \n based tensor layout under the hood.\n Here we cover how the layout of tensors can be modified\n and we ensure the different tensor types still work as expected...\n (The features in this specification involve mutating tensors, be careful when playing around with this yourself)", "subjects":[], "statistics":{ - "runs":"2", + "runs":"3", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.133 seconds" + "duration":"0.184 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"A new transposed version of a given tensor will be returned by the \"T()\" method.", "result":"PASS", - "duration":"0.029 seconds", + "duration":"0.059 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,9 +37,36 @@ }, { - "id":"Matrix multiplication works for both column and row major matrices across devices.", + "id":"Matrix multiplication works for both column and row major matrices across devices. [0]", "result":"PASS", - "duration":"0.101 seconds", + "duration":"0.066 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We want to view tensors in the \"(shape:[value]\" format so we set the corresponding flag.","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(false)"]}, + + {"kind":"and","text":"","code":["var a = Tensor.ofFloats().withShape(2, 3).andWhere({ it, idx->((7**it)%11-5).floatValue()})","var b = Tensor.ofFloats().withShape(3, 4).andWhere({ it, idx->((5**it)%11-5).floatValue()})","Device.get(device).store(a).store(b)"]}, + + {"kind":"expect","text":"","code":["a.matMul(b).toString({it.hasSlimNumbers = true}) == expectedString"]}, + + {"kind":"when","text":"","code":["a.mut.toLayout(NDConfiguration.Layout.COLUMN_MAJOR)","b.mut.toLayout(NDConfiguration.Layout.COLUMN_MAJOR)"]}, + + {"kind":"then","text":"","code":["a.matMul(b).toString({it.hasSlimNumbers = true}) == expectedString"]}, + + {"kind":"when","text":"","code":["a.mut.toLayout(NDConfiguration.Layout.ROW_MAJOR)","b.mut.toLayout(NDConfiguration.Layout.ROW_MAJOR)"]}, + + {"kind":"then","text":"","code":["a.matMul(b).toString({it.hasSlimNumbers = true}) == expectedString"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'GPU'"],"expectedString":["'(2x4):[24, -8, 8, 0, -1, 28, -14, 7]'","'(2x4):[24, -8, 8, 0, -1, 28, -14, 7]'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Matrix multiplication works for both column and row major matrices across devices. [1]", + "result":"PASS", + "duration":"0.054 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_Operation_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Operation_Spec.json index 4f072759b..9e3fac91f 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Operation_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Operation_Spec.json @@ -1,22 +1,43 @@ { "className":"ut.tensors.Tensor_Operation_Spec", "title":"Running Tensors through operations", - "narrative":"This specification shows how to use the tensor API to run tensors through various operations.\n Operations are triggered either by simply calling methods on tensors or by using\n `Function` objects which are used to define custom operations in the form\n of a syntax tree.", + "narrative":"This specification shows how to use the tensor API to run tensors through various operations.\n Operations are triggered either by simply calling methods on tensors or by using \n `Function` objects which are used to define custom operations in the form \n of a syntax tree.", "subjects":[], "statistics":{ - "runs":"16", + "runs":"164", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"5.029 seconds" + "duration":"10.225 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"The \"dot\" operation reshapes and produces valid \"x\" operation result.", + "id":"The \"dot\" operation reshapes and produces valid \"x\" operation result. [0]", "result":"PASS", - "duration":"0.059 seconds", + "duration":"0.064 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two multi-dimensional tensors.","code":["var a = Tensor.of([1, 4, 4, 1 ], 4f..12f).mut.toType(type)","var b = Tensor.of([1, 3, 5, 2, 1], -5d..3d).mut.toType(type)"]}, + + {"kind":"when","text":"The \"dot\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.convDot(b)"]}, + + {"kind":"then","text":"The result tensor contains the expected shape.","code":["c.toString().contains(\"(4x2x5x2)\")"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The \"dot\" operation reshapes and produces valid \"x\" operation result. [1]", + "result":"PASS", + "duration":"0.057 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -35,9 +56,9 @@ }, { - "id":"The \"matMul\" operation produces the expected result.", + "id":"The \"matMul\" operation produces the expected result. [0]", "result":"PASS", - "duration":"0.368 seconds", + "duration":"0.054 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -56,14 +77,14 @@ }, { - "id":"You can do matrix multiplication using transposed matrices.", + "id":"The \"matMul\" operation produces the expected result. [1]", "result":"PASS", - "duration":"0.386 seconds", + "duration":"0.052 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, @@ -71,20 +92,20 @@ {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"You can do matrix multiplication using transposed matrices as second operand.", + "id":"The \"matMul\" operation produces the expected result. [2]", "result":"PASS", - "duration":"0.497 seconds", + "duration":"0.054 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, @@ -92,281 +113,4296 @@ {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Float","Long","Long","Long","Long","Integer","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","1","2","2","2","1","2","2","2","1","2","2","2","1","2"],"K":["1","2","2","2","1","1","2","2","1","1","2","2","1","1","2","2","1"],"N":["1","1","2","1","2","1","2","1","2","1","2","1","2","1","2","1","2"],"expectedC":["[ 2, 2 ]","[ 4 ]","[ 2.5, -2.0, 0.5, 0.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 2 ]","[ 2.5, -2.0, 0.5, 0.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 2 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 2 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"You can do matrix multiplication using 2 transposed matrices.", + "id":"The \"matMul\" operation produces the expected result. [3]", "result":"PASS", - "duration":"0.190 seconds", + "duration":"0.052 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["println a.toString({it.isMultiline = true})","println b.toString({it.isMultiline = true})","var c = a.matMul(b)","println c.toString({it.isMultiline = true})"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Float"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[3, 3, 3, 3]","[-2, 1]","[-2, 1]","[1, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[0.5, 0.5, 0.5, 0.5]","[-1, -1.5]","[-1, -1.5]","[2]"],"M":["1","2","2","2","1","2","1"],"K":["2","1","2","2","2","1","2"],"N":["1","1","2","2","1","2","1"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 1.0, 0.0, 0.0, 1.0 ]","[ 3.0, 3.0, 3.0, 3.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 4 ]"]}} + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"The \"random\" function/operation populates tensors randomly.", + "id":"The \"matMul\" operation produces the expected result. [4]", "result":"PASS", "duration":"0.053 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(2,4).all(-42)"]}, - - {"kind":"and","text":"","code":["var f = Function.of('random(I[0])')"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"expect","text":"","code":["t.itemType == type"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"when","text":"","code":["var r = f(t)"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"then","text":"","code":["r === t"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"and","text":"","code":["( r.mut.data.get() as float[] ) == [1.0588075, 1.4017555, 1.2537496, -1.3897222, 1.0374786, 0.743316, 1.1692946, 1.3977289] as float[]"]}, + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The \"matMul\" operation produces the expected result. [5]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"when","text":"","code":["r = f.with(Arg.Seed.of(42)).call(t)"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"then","text":"","code":["r === t"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"and","text":"","code":["( r.mut.data.get() as float[] ) == [2.2639139286289724, -0.2763464310754003, 0.3719153742868813, -0.9768504740489802, 0.5154099159307729, 1.1608137295804097, 2.1905023977046336, -0.5449569795660217] as float[]"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"where","text":"","code":{"type":["Double","Float"]}} + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"The values of a randomly populated tensor seems to adhere to a gaussian distribution.", + "id":"The \"matMul\" operation produces the expected result. [6]", "result":"PASS", - "duration":"0.059 seconds", + "duration":"0.052 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(20, 40, 20).all(0)"]}, - - {"kind":"and","text":"","code":["var f = Function.of('random(I[0])')"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"when","text":"","code":["f.with(Arg.Seed.of(-73L)).call(t)","var stats = new Statistics( t.mut.data.get() as double[] )"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"then","text":"","code":["-0.05d < stats.mean && stats.mean < 0.05d"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"and","text":"","code":["0.875d < stats.variance && stats.variance < 1.125d"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"where","text":"","code":{"type":["Double","Float"]}} + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"New method \"asFunction\" of String added at runtime is callable by groovy and also works.", + "id":"The \"matMul\" operation produces the expected result. [7]", "result":"PASS", - "duration":"0.277 seconds", + "duration":"0.053 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"We create two tensors and convert them to a desired type.","code":["var a = Tensor.of([1, 2], [3d, 2d]).mut.toType(type)","var b = Tensor.of([2, 1], [-1f, 4f]).mut.toType(type)"]}, - - {"kind":"and","text":"We prepare bindings for the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, - - {"kind":"expect","text":"The tensors have the type...","code":["a.itemType == type","b.itemType == type"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"when","text":"The groovy code is being evaluated.","code":["var c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Float","Float","Float","Float"],"code":["'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'","'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'"],"expected":["\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\""]}} + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work.", + "id":"The \"matMul\" operation produces the expected result. [8]", "result":"PASS", - "duration":"0.577 seconds", + "duration":"0.055 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"","code":["Neureka.get().settings().view().ndArrays({it.hasSlimNumbers=true})","Tensor a = Tensor.of(5d).mut.toType(type)","Tensor b = Tensor.of(3f).mut.toType(type)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"when","text":"...calling methods on types like Double and Integer that receive `Tensor` instances...","code":["Tensor c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().endsWith(\"[$expected]\")"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Float","Float","Float","Float"],"code":["'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'","'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'"],"expected":["\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\""]}} + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Overloaded operation methods on tensors produce expected results when called.", + "id":"The \"matMul\" operation produces the expected result. [9]", "result":"PASS", - "duration":"0.071 seconds", + "duration":"0.056 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)","Tensor a = Tensor.of(2d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)","Tensor c = Tensor.of(3d).setRqsGradient(true)"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"expect","text":"","code":["( a / a ).toString().contains(\"[1]:(1.0)\")","( c % a ).toString().contains(\"[1]:(1.0)\")","( ( ( b / b ) ** c % a ) * 3 ).toString().contains(\"[1]:(3.0)\")","( a *= b ).toString().contains(\"(-8.0)\")","( a += -c ).toString().contains(\"(-11.0)\")","( a -= c ).toString().contains(\"(-14.0)\")","( a /= Tensor.of(2d) ).toString().contains(\"(-7.0)\")","( a %= c ).toString().contains(\"(-1.0)\")"]} + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Simple slice addition produces expected result.", + "id":"The \"matMul\" operation produces the expected result. [10]", "result":"PASS", - "duration":"0.056 seconds", + "duration":"0.050 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"We set the experimental \"autoConvertToFloat\" flag to true.","code":["Neureka.get().backend().find(CLBackend).ifPresent({ it.settings.autoConvertToFloat=true })"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"and","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(false)","Tensor a = Tensor.of([11, 11], 3d..19d).to( device )","Tensor x = a[1..-2,0..-1]","Tensor y = a[0..-3,0..-1]"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"when","text":"","code":["Tensor t = x + y","String tAsStr = t.toString({it.setRowLimit(50)})"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"then","text":"","code":["tAsStr.contains(\"(9x11):[17.0, 19.0, 21.0, 23.0, 25.0, 27.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, \" +"," \"26.0, 28.0, 30.0, 32.0, 17.0, 19.0, 21.0, 23.0, 25.0, 27.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, \" +"," \"26.0, 28.0, 30.0, 32.0, 17.0, 19.0, 21.0, 23.0, 25.0, 27.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, 26.0, 28.0, 30.0, ... + 49 more]\")"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"where","text":"The following data is being used for tensor instantiation :","code":{"device":["CPU.get()","Device.get(\"openCL\")"]}} + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Auto reshaping and broadcasting works and the result can be back propagated.", + "id":"The \"matMul\" operation produces the expected result. [11]", "result":"PASS", - "duration":"1.155 seconds", + "duration":"0.089 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }","Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, - - {"kind":"and","text":"","code":["String wValue = whichGrad"," ? \"8\" + ( bShape.inject(1, {x,y->x*y}) > 1 ? \", 9\" : \"\" )"," : \"1, 2, 3, 4\""]}, - - {"kind":"and","text":"","code":["def aShape = [2, 2]"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"and","text":"","code":["Tensor a = Tensor.of(aShape, 1d..5d).setRqsGradient(!whichGrad).to(Device.get(device))","Tensor b = Tensor.of(bShape, 8d..9d).setRqsGradient(whichGrad).to(Device.get(device))"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"and","text":"","code":["a.mut.toType(type)","b.mut.toType(type)"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"and","text":"","code":["String wShape = ( whichGrad ? bShape : aShape ).join(\"x\")","Tensor w = ( whichGrad ? b : a )"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"expect","text":"","code":["a.itemType == type || device == 'GPU' // The gpu backend will only be floats!","b.itemType == type || device == 'GPU' // This is because kernels only work on floats..."]}, + {"kind":"where","text":"We use the following data and matrix dimensions!","code":{"type":["Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","2","1","2","2","1","2","2","1","2","2","1"],"N":["2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 1, 0, 0, 1 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices. [0]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"when","text":"","code":["Tensor c = operation.apply(a, b)"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"then","text":"","code":["c.toString({it.hasSlimNumbers = true}).startsWith(\"[2x2]:($cValue)\")","w.toString({it.hasSlimNumbers = true}) == \"[$wShape]:($wValue):g:(null)\""]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"when","text":"","code":["c.backward(Tensor.of([2, 2], [5, -2, 7, 3]).mut.toType(type))"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"then","text":"","code":["w.toString({it.hasSlimNumbers = true}) == \"[$wShape]:($wValue):g:($wGradient)\""]}, + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices. [1]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"when","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(false)"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"then","text":"","code":["c.toString({it.hasSlimNumbers = true}) == \"(2x2):[$cValue]\""]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"where","text":"","code":{"device":["'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'"],"type":["Double","Double","Float","Float","Double","Double","Float","Float","Double","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float"],"whichGrad":["false","false","false","false","false","false","false","false","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true"],"bShape":["[1]","[1]","[1]","[1]","[1]","[1]","[1]","[1]","[2,1]","[2,1]","[1]","[1]","[1]","[1]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]"],"operation":["{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x - y }","{ x, y -> x - y }","{ x, y -> x - y }","{ x, y -> x - y }","{ x, y -> x - y }","{ x, y -> x - y }","{ x, y -> x - y }","{ x, y -> x - y }","{ x, y -> y - x }","{ x, y -> y - x }","{ x, y -> y - x }","{ x, y -> y - x }","{ x, y -> y - x }","{ x, y -> y - x }","{ x, y -> y - x }","{ x, y -> y - x }"],"cValue":["\"9, 10, 11, 12\"","\"9, 10, 11, 12\"","\"9, 10, 11, 12\"","\"9, 10, 11, 12\"","\"8, 16, 24, 32\"","\"8, 16, 24, 32\"","\"8, 16, 24, 32\"","\"8, 16, 24, 32\"","\"9, 10, 12, 13\"","\"9, 10, 12, 13\"","\"9, 10, 11, 12\"","\"9, 10, 11, 12\"","\"9, 10, 11, 12\"","\"9, 10, 11, 12\"","\"9, 11, 11, 13\"","\"9, 11, 11, 13\"","\"9, 11, 11, 13\"","\"9, 11, 11, 13\"","\"9, 11, 11, 13\"","\"9, 11, 11, 13\"","\"9, 11, 11, 13\"","\"9, 11, 11, 13\"","\"-7, -7, -5, -5\"","\"-7, -7, -5, -5\"","\"-7, -7, -5, -5\"","\"-7, -7, -5, -5\"","\"-7, -7, -5, -5\"","\"-7, -7, -5, -5\"","\"-7, -7, -5, -5\"","\"-7, -7, -5, -5\"","\"7, 7, 5, 5\"","\"7, 7, 5, 5\"","\"7, 7, 5, 5\"","\"7, 7, 5, 5\"","\"7, 7, 5, 5\"","\"7, 7, 5, 5\"","\"7, 7, 5, 5\"","\"7, 7, 5, 5\""],"wGradient":["\"5, -2, 7, 3\"","\"5, -2, 7, 3\"","\"5, -2, 7, 3\"","\"5, -2, 7, 3\"","\"40, -16, 56, 24\"","\"40, -16, 56, 24\"","\"40, -16, 56, 24\"","\"40, -16, 56, 24\"","\"3, 10\"","\"3, 10\"","\"13\"","\"13\"","\"13\"","\"13\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"-12, -1\"","\"-12, -1\"","\"-12, -1\"","\"-12, -1\"","\"-12, -1\"","\"-12, -1\"","\"-12, -1\"","\"-12, -1\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"12, 1\"","\"12, 1\""]}} + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Scalar broadcasting works across devices.", + "id":"You can do matrix multiplication using transposed matrices. [2]", "result":"PASS", - "duration":"0.139 seconds", + "duration":"0.053 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"","code":["var a = Tensor.of(type).withShape(3, 2).andFill(-4..4).to(Device.get(device))","var b = Tensor.of(type).withShape(1, 1).andFill(3).to(Device.get(device))"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"expect","text":"","code":["a.itemType == type","b.itemType == type"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"when","text":"","code":["Tensor c = operation.apply(a, b)"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"then","text":"","code":["c.toString() == \"(3x2):[$cValue]\""]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'GPU'","'CPU'","'CPU'"],"type":["Double","Float","Float","Long","Integer"],"operation":["{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }"],"cValue":["\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1, 0, 1, 2, 3, 4\"","\"-1, 0, 1, 2, 3, 4\""]}} + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Operators \"+,*,**\" produce expected results with gradients which can be accessed via a \"Ig[0]\" Function instance", + "id":"You can do matrix multiplication using transposed matrices. [3]", "result":"PASS", - "duration":"0.028 seconds", + "duration":"0.053 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, "blocks":[ - {"kind":"given","text":"Neurekas view is set to legacy and three tensors of which one requires gradients.","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)","Tensor x = Tensor.of(3d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)","Tensor w = Tensor.of(2d)"]}, + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"when","text":"","code":["when : Tensor y = ( (x+b)*w )**2"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"then","text":"","code":["then : y.toString().contains(\"[1]:(4.0); ->d[1]:(-8.0)\")"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"when","text":"","code":["when : y = ((x+b)*w)**2"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"then","text":"","code":["then : y.toString().contains(\"[1]:(4.0); ->d[1]:(-8.0)\")"]}, + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices. [4]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"and","text":"","code":["and : Neureka.get().settings().debug().setIsKeepingDerivativeTargetPayloads(true)"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"when","text":"","code":["y.backward(Tensor.of(1d))"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"and","text":"","code":["Tensor t2 = Tensor.of( \"Ig[0]\", [x] )","Tensor t1 = Tensor.of( \"Ig[0]\", [y] ) // The input does not have a gradient!"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"then","text":"","code":["thrown(IllegalArgumentException)"]}, + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices. [5]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"and","text":"","code":["t2.toString() == \"[1]:(-8.0)\""]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"and","text":"","code":["t2 == x.gradient.get()"]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"and","text":"","code":["and : Neureka.get().settings().debug().setIsKeepingDerivativeTargetPayloads(false)"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, - {"kind":"when","text":"","code":["Tensor[] trs = new Tensor[]{x}"]}, + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices. [6]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, - {"kind":"and","text":"","code":["def fun = Function.of(\"Ig[0]\", false)"]}, + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, - {"kind":"then","text":"","code":["fun(trs).toString() == \"[1]:(-8.0)\""]}, + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, - {"kind":"when","text":"","code":["trs[0] = y"]}, + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices. [7]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices. [8]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices. [9]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices. 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[12]", + "result":"PASS", + "duration":"0.050 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices. [13]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(K, N).andFill(B).mut.toType(type)"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Long","Long","Long","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d, 2d]","[2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["1","2","2","1","2","2","1","2","2","1","2","2","1","2"],"K":["2","1","2","2","1","2","2","1","2","2","1","2","2","1"],"N":["1","1","2","1","2","2","1","2","2","1","2","2","1","2"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 0.0, 2.0, -0.5, 2.5 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 0, 1, 1 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices as second operand. [0]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Float","Long","Long","Long","Long","Integer","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","1","2","2","2","1","2","2","2","1","2","2","2","1","2"],"K":["1","2","2","2","1","1","2","2","1","1","2","2","1","1","2","2","1"],"N":["1","1","2","1","2","1","2","1","2","1","2","1","2","1","2","1","2"],"expectedC":["[ 2, 2 ]","[ 4 ]","[ 2.5, -2.0, 0.5, 0.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 2 ]","[ 2.5, -2.0, 0.5, 0.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 2 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 2 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices as second operand. 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[15]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Float","Long","Long","Long","Long","Integer","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","1","2","2","2","1","2","2","2","1","2","2","2","1","2"],"K":["1","2","2","2","1","1","2","2","1","1","2","2","1","1","2","2","1"],"N":["1","1","2","1","2","1","2","1","2","1","2","1","2","1","2","1","2"],"expectedC":["[ 2, 2 ]","[ 4 ]","[ 2.5, -2.0, 0.5, 0.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 2 ]","[ 2.5, -2.0, 0.5, 0.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 2 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 2 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using transposed matrices as second operand. [16]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(M, K).andFill(A).mut.toType(type)","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["var c = a.matMul(b)"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Float","Float","Float","Float","Long","Long","Long","Long","Integer","Integer","Integer","Integer"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]","[1, 1]","[4, 3, 2, 1]","[-2, 1]","[-2, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]","[2]","[-0.5, 1.5, 1, -2]","[-1, -1.5]","[-1, -1.5]"],"M":["2","1","2","1","2","2","2","1","2","2","2","1","2","2","2","1","2"],"K":["1","2","2","2","1","1","2","2","1","1","2","2","1","1","2","2","1"],"N":["1","1","2","1","2","1","2","1","2","1","2","1","2","1","2","1","2"],"expectedC":["[ 2, 2 ]","[ 4 ]","[ 2.5, -2.0, 0.5, 0.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 2 ]","[ 2.5, -2.0, 0.5, 0.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 2, 2 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]","[ 2, 2 ]","[ 3, -2, 1, 0 ]","[ 1 ]","[ 2, 2, -1, -1 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using 2 transposed matrices. [0]", + "result":"PASS", + "duration":"0.090 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["println a.toString({it.isMultiline = true})","println b.toString({it.isMultiline = true})","var c = a.matMul(b)","println c.toString({it.isMultiline = true})"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Float"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[3, 3, 3, 3]","[-2, 1]","[-2, 1]","[1, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[0.5, 0.5, 0.5, 0.5]","[-1, -1.5]","[-1, -1.5]","[2]"],"M":["1","2","2","2","1","2","1"],"K":["2","1","2","2","2","1","2"],"N":["1","1","2","2","1","2","1"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 1.0, 0.0, 0.0, 1.0 ]","[ 3.0, 3.0, 3.0, 3.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 4 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using 2 transposed matrices. [1]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["println a.toString({it.isMultiline = true})","println b.toString({it.isMultiline = true})","var c = a.matMul(b)","println c.toString({it.isMultiline = true})"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Float"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[3, 3, 3, 3]","[-2, 1]","[-2, 1]","[1, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[0.5, 0.5, 0.5, 0.5]","[-1, -1.5]","[-1, -1.5]","[2]"],"M":["1","2","2","2","1","2","1"],"K":["2","1","2","2","2","1","2"],"N":["1","1","2","2","1","2","1"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 1.0, 0.0, 0.0, 1.0 ]","[ 3.0, 3.0, 3.0, 3.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 4 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using 2 transposed matrices. [2]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["println a.toString({it.isMultiline = true})","println b.toString({it.isMultiline = true})","var c = a.matMul(b)","println c.toString({it.isMultiline = true})"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Float"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[3, 3, 3, 3]","[-2, 1]","[-2, 1]","[1, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[0.5, 0.5, 0.5, 0.5]","[-1, -1.5]","[-1, -1.5]","[2]"],"M":["1","2","2","2","1","2","1"],"K":["2","1","2","2","2","1","2"],"N":["1","1","2","2","1","2","1"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 1.0, 0.0, 0.0, 1.0 ]","[ 3.0, 3.0, 3.0, 3.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 4 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using 2 transposed matrices. [3]", + "result":"PASS", + "duration":"0.054 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["println a.toString({it.isMultiline = true})","println b.toString({it.isMultiline = true})","var c = a.matMul(b)","println c.toString({it.isMultiline = true})"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Float"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[3, 3, 3, 3]","[-2, 1]","[-2, 1]","[1, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[0.5, 0.5, 0.5, 0.5]","[-1, -1.5]","[-1, -1.5]","[2]"],"M":["1","2","2","2","1","2","1"],"K":["2","1","2","2","2","1","2"],"N":["1","1","2","2","1","2","1"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 1.0, 0.0, 0.0, 1.0 ]","[ 3.0, 3.0, 3.0, 3.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 4 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using 2 transposed matrices. [4]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["println a.toString({it.isMultiline = true})","println b.toString({it.isMultiline = true})","var c = a.matMul(b)","println c.toString({it.isMultiline = true})"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Float"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[3, 3, 3, 3]","[-2, 1]","[-2, 1]","[1, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[0.5, 0.5, 0.5, 0.5]","[-1, -1.5]","[-1, -1.5]","[2]"],"M":["1","2","2","2","1","2","1"],"K":["2","1","2","2","2","1","2"],"N":["1","1","2","2","1","2","1"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 1.0, 0.0, 0.0, 1.0 ]","[ 3.0, 3.0, 3.0, 3.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 4 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using 2 transposed matrices. [5]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["println a.toString({it.isMultiline = true})","println b.toString({it.isMultiline = true})","var c = a.matMul(b)","println c.toString({it.isMultiline = true})"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Float"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[3, 3, 3, 3]","[-2, 1]","[-2, 1]","[1, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[0.5, 0.5, 0.5, 0.5]","[-1, -1.5]","[-1, -1.5]","[2]"],"M":["1","2","2","2","1","2","1"],"K":["2","1","2","2","2","1","2"],"N":["1","1","2","2","1","2","1"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 1.0, 0.0, 0.0, 1.0 ]","[ 3.0, 3.0, 3.0, 3.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 4 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"You can do matrix multiplication using 2 transposed matrices. [6]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Two 2-dimensional tensors.","code":["var a = Tensor.of(Double.class).withShape(K, M).andFill(A).mut.toType(type).T()","var b = Tensor.of(Double.class).withShape(N, K).andFill(B).mut.toType(type).T()"]}, + + {"kind":"when","text":"The \"matMul\" method is being called on \"a\" receiving \"b\"...","code":["println a.toString({it.isMultiline = true})","println b.toString({it.isMultiline = true})","var c = a.matMul(b)","println c.toString({it.isMultiline = true})"]}, + + {"kind":"then","text":"The result tensor contains the expected shape and values.","code":["c.shape == [M, N]","c.items == expectedC as List"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Float"],"A":["[1d, 1d]","[1d, 1d]","[4, 3, 2, 1]","[3, 3, 3, 3]","[-2, 1]","[-2, 1]","[1, 1]"],"B":["[2d]","[2d, 2d]","[-0.5, 1.5, 1, -2]","[0.5, 0.5, 0.5, 0.5]","[-1, -1.5]","[-1, -1.5]","[2]"],"M":["1","2","2","2","1","2","1"],"K":["2","1","2","2","2","1","2"],"N":["1","1","2","2","1","2","1"],"expectedC":["[ 4 ]","[ 2, 2 ]","[ 1.0, 0.0, 0.0, 1.0 ]","[ 3.0, 3.0, 3.0, 3.0 ]","[ 0.5 ]","[ 2.0, 3.0, -1.0, -1.5 ]","[ 4 ]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The \"random\" function/operation populates tensors randomly. [0]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(2,4).all(-42)"]}, + + {"kind":"and","text":"","code":["var f = Function.of('random(I[0])')"]}, + + {"kind":"expect","text":"","code":["t.itemType == type"]}, + + {"kind":"when","text":"","code":["var r = f(t)"]}, + + {"kind":"then","text":"","code":["r === t"]}, + + {"kind":"and","text":"","code":["( r.mut.data.get() as float[] ) == [1.0588075, 1.4017555, 1.2537496, -1.3897222, 1.0374786, 0.743316, 1.1692946, 1.3977289] as float[]"]}, + + {"kind":"when","text":"","code":["r = f.with(Arg.Seed.of(42)).call(t)"]}, + + {"kind":"then","text":"","code":["r === t"]}, + + {"kind":"and","text":"","code":["( r.mut.data.get() as float[] ) == [2.2639139286289724, -0.2763464310754003, 0.3719153742868813, -0.9768504740489802, 0.5154099159307729, 1.1608137295804097, 2.1905023977046336, -0.5449569795660217] as float[]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The \"random\" function/operation populates tensors randomly. [1]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(2,4).all(-42)"]}, + + {"kind":"and","text":"","code":["var f = Function.of('random(I[0])')"]}, + + {"kind":"expect","text":"","code":["t.itemType == type"]}, + + {"kind":"when","text":"","code":["var r = f(t)"]}, + + {"kind":"then","text":"","code":["r === t"]}, + + {"kind":"and","text":"","code":["( r.mut.data.get() as float[] ) == [1.0588075, 1.4017555, 1.2537496, -1.3897222, 1.0374786, 0.743316, 1.1692946, 1.3977289] as float[]"]}, + + {"kind":"when","text":"","code":["r = f.with(Arg.Seed.of(42)).call(t)"]}, + + {"kind":"then","text":"","code":["r === t"]}, + + {"kind":"and","text":"","code":["( r.mut.data.get() as float[] ) == [2.2639139286289724, -0.2763464310754003, 0.3719153742868813, -0.9768504740489802, 0.5154099159307729, 1.1608137295804097, 2.1905023977046336, -0.5449569795660217] as float[]"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The values of a randomly populated tensor seems to adhere to a gaussian distribution. [0]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(20, 40, 20).all(0)"]}, + + {"kind":"and","text":"","code":["var f = Function.of('random(I[0])')"]}, + + {"kind":"when","text":"","code":["f.with(Arg.Seed.of(-73L)).call(t)","var stats = new Statistics( t.mut.data.get() as double[] )"]}, + + {"kind":"then","text":"","code":["-0.05d < stats.mean && stats.mean < 0.05d"]}, + + {"kind":"and","text":"","code":["0.875d < stats.variance && stats.variance < 1.125d"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The values of a randomly populated tensor seems to adhere to a gaussian distribution. [1]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var t = Tensor.of(type).withShape(20, 40, 20).all(0)"]}, + + {"kind":"and","text":"","code":["var f = Function.of('random(I[0])')"]}, + + {"kind":"when","text":"","code":["f.with(Arg.Seed.of(-73L)).call(t)","var stats = new Statistics( t.mut.data.get() as double[] )"]}, + + {"kind":"then","text":"","code":["-0.05d < stats.mean && stats.mean < 0.05d"]}, + + {"kind":"and","text":"","code":["0.875d < stats.variance && stats.variance < 1.125d"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New method \"asFunction\" of String added at runtime is callable by groovy and also works. [0]", + "result":"PASS", + "duration":"0.062 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create two tensors and convert them to a desired type.","code":["var a = Tensor.of([1, 2], [3d, 2d]).mut.toType(type)","var b = Tensor.of([2, 1], [-1f, 4f]).mut.toType(type)"]}, + + {"kind":"and","text":"We prepare bindings for the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The tensors have the type...","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["var c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Float","Float","Float","Float"],"code":["'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'","'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'"],"expected":["\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New method \"asFunction\" of String added at runtime is callable by groovy and also works. [1]", + "result":"PASS", + "duration":"0.060 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create two tensors and convert them to a desired type.","code":["var a = Tensor.of([1, 2], [3d, 2d]).mut.toType(type)","var b = Tensor.of([2, 1], [-1f, 4f]).mut.toType(type)"]}, + + {"kind":"and","text":"We prepare bindings for the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The tensors have the type...","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["var c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Float","Float","Float","Float"],"code":["'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'","'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'"],"expected":["\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New method \"asFunction\" of String added at runtime is callable by groovy and also works. [2]", + "result":"PASS", + "duration":"0.057 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create two tensors and convert them to a desired type.","code":["var a = Tensor.of([1, 2], [3d, 2d]).mut.toType(type)","var b = Tensor.of([2, 1], [-1f, 4f]).mut.toType(type)"]}, + + {"kind":"and","text":"We prepare bindings for the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The tensors have the type...","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["var c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Float","Float","Float","Float"],"code":["'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'","'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'"],"expected":["\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New method \"asFunction\" of String added at runtime is callable by groovy and also works. [3]", + "result":"PASS", + "duration":"0.057 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create two tensors and convert them to a desired type.","code":["var a = Tensor.of([1, 2], [3d, 2d]).mut.toType(type)","var b = Tensor.of([2, 1], [-1f, 4f]).mut.toType(type)"]}, + + {"kind":"and","text":"We prepare bindings for the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The tensors have the type...","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["var c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Float","Float","Float","Float"],"code":["'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'","'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'"],"expected":["\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New method \"asFunction\" of String added at runtime is callable by groovy and also works. [4]", + "result":"PASS", + "duration":"0.056 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create two tensors and convert them to a desired type.","code":["var a = Tensor.of([1, 2], [3d, 2d]).mut.toType(type)","var b = Tensor.of([2, 1], [-1f, 4f]).mut.toType(type)"]}, + + {"kind":"and","text":"We prepare bindings for the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The tensors have the type...","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["var c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Float","Float","Float","Float"],"code":["'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'","'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'"],"expected":["\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New method \"asFunction\" of String added at runtime is callable by groovy and also works. [5]", + "result":"PASS", + "duration":"0.058 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create two tensors and convert them to a desired type.","code":["var a = Tensor.of([1, 2], [3d, 2d]).mut.toType(type)","var b = Tensor.of([2, 1], [-1f, 4f]).mut.toType(type)"]}, + + {"kind":"and","text":"We prepare bindings for the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The tensors have the type...","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["var c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Float","Float","Float","Float"],"code":["'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'","'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'"],"expected":["\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New method \"asFunction\" of String added at runtime is callable by groovy and also works. [6]", + "result":"PASS", + "duration":"0.054 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create two tensors and convert them to a desired type.","code":["var a = Tensor.of([1, 2], [3d, 2d]).mut.toType(type)","var b = Tensor.of([2, 1], [-1f, 4f]).mut.toType(type)"]}, + + {"kind":"and","text":"We prepare bindings for the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The tensors have the type...","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["var c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Float","Float","Float","Float"],"code":["'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'","'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'"],"expected":["\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New method \"asFunction\" of String added at runtime is callable by groovy and also works. [7]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create two tensors and convert them to a desired type.","code":["var a = Tensor.of([1, 2], [3d, 2d]).mut.toType(type)","var b = Tensor.of([2, 1], [-1f, 4f]).mut.toType(type)"]}, + + {"kind":"and","text":"We prepare bindings for the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The tensors have the type...","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["var c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)"]}, + + {"kind":"and","text":"","code":["c.itemType == type"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Float","Float","Float","Float"],"code":["'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'","'\"I[0]xI[1]\".asFunction()([a, b])'","'\"I[0]xI[1]\"[a, b]'","'\"i0 x i1\"%[a, b]'","'\"i0\"%a'"],"expected":["\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(2x2):[-3.0, -2.0, 12.0, 8.0]\"","\"(1x2):[3.0, 2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work. [0]", + "result":"PASS", + "duration":"0.059 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().ndArrays({it.hasSlimNumbers=true})","Tensor a = Tensor.of(5d).mut.toType(type)","Tensor b = Tensor.of(3f).mut.toType(type)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"when","text":"...calling methods on types like Double and Integer that receive `Tensor` instances...","code":["Tensor c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().endsWith(\"[$expected]\")"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Float","Float","Float","Float"],"code":["'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'","'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'"],"expected":["\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work. [1]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().ndArrays({it.hasSlimNumbers=true})","Tensor a = Tensor.of(5d).mut.toType(type)","Tensor b = Tensor.of(3f).mut.toType(type)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"when","text":"...calling methods on types like Double and Integer that receive `Tensor` instances...","code":["Tensor c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().endsWith(\"[$expected]\")"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Float","Float","Float","Float"],"code":["'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'","'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'"],"expected":["\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work. 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[14]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().ndArrays({it.hasSlimNumbers=true})","Tensor a = Tensor.of(5d).mut.toType(type)","Tensor b = Tensor.of(3f).mut.toType(type)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"when","text":"...calling methods on types like Double and Integer that receive `Tensor` instances...","code":["Tensor c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().endsWith(\"[$expected]\")"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Float","Float","Float","Float"],"code":["'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'","'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'"],"expected":["\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work. [15]", + "result":"PASS", + "duration":"0.056 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().ndArrays({it.hasSlimNumbers=true})","Tensor a = Tensor.of(5d).mut.toType(type)","Tensor b = Tensor.of(3f).mut.toType(type)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"when","text":"...calling methods on types like Double and Integer that receive `Tensor` instances...","code":["Tensor c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().endsWith(\"[$expected]\")"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Float","Float","Float","Float"],"code":["'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'","'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'"],"expected":["\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work. [16]", + "result":"PASS", + "duration":"0.060 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().ndArrays({it.hasSlimNumbers=true})","Tensor a = Tensor.of(5d).mut.toType(type)","Tensor b = Tensor.of(3f).mut.toType(type)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"when","text":"...calling methods on types like Double and Integer that receive `Tensor` instances...","code":["Tensor c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().endsWith(\"[$expected]\")"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Float","Float","Float","Float"],"code":["'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'","'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'"],"expected":["\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work. [17]", + "result":"PASS", + "duration":"0.061 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().ndArrays({it.hasSlimNumbers=true})","Tensor a = Tensor.of(5d).mut.toType(type)","Tensor b = Tensor.of(3f).mut.toType(type)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"when","text":"...calling methods on types like Double and Integer that receive `Tensor` instances...","code":["Tensor c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().endsWith(\"[$expected]\")"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Float","Float","Float","Float"],"code":["'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'","'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'"],"expected":["\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work. [18]", + "result":"PASS", + "duration":"0.056 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().ndArrays({it.hasSlimNumbers=true})","Tensor a = Tensor.of(5d).mut.toType(type)","Tensor b = Tensor.of(3f).mut.toType(type)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"when","text":"...calling methods on types like Double and Integer that receive `Tensor` instances...","code":["Tensor c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().endsWith(\"[$expected]\")"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Float","Float","Float","Float"],"code":["'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'","'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'"],"expected":["\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"New operator methods added to \"SDK-types\" at runtime are callable by groovy and also work. [19]", + "result":"PASS", + "duration":"0.057 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().ndArrays({it.hasSlimNumbers=true})","Tensor a = Tensor.of(5d).mut.toType(type)","Tensor b = Tensor.of(3f).mut.toType(type)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"when","text":"...calling methods on types like Double and Integer that receive `Tensor` instances...","code":["Tensor c = new GroovyShell(binding).evaluate((code)) as Tensor"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().endsWith(\"[$expected]\")"]}, + + {"kind":"where","text":"","code":{"type":["Double","Double","Double","Double","Double","Double","Double","Double","Double","Double","Float","Float","Float","Float","Float","Float","Float","Float","Float","Float"],"code":["'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'","'(2+a)'","'(2*b)'","'(6/b)'","'(2**b)'","'(4-a)'","'(2.0+a)'","'(2.0*b)'","'(6.0/b)'","'(2.0**b)'","'(4.0-a)'"],"expected":["\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\"","\"7\"","\"6\"","\"2\"","\"8\"","\"-1\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Overloaded operation methods on tensors produce expected results when called.", + "result":"PASS", + "duration":"0.061 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)","Tensor a = Tensor.of(2d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)","Tensor c = Tensor.of(3d).setRqsGradient(true)"]}, + + {"kind":"expect","text":"","code":["( a / a ).toString().contains(\"[1]:(1.0)\")","( c % a ).toString().contains(\"[1]:(1.0)\")","( ( ( b / b ) ** c % a ) * 3 ).toString().contains(\"[1]:(3.0)\")","( a *= b ).toString().contains(\"(-8.0)\")","( a += -c ).toString().contains(\"(-11.0)\")","( a -= c ).toString().contains(\"(-14.0)\")","( a /= Tensor.of(2d) ).toString().contains(\"(-7.0)\")","( a %= c ).toString().contains(\"(-1.0)\")"]} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Simple slice addition produces expected result. [0]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We set the experimental \"autoConvertToFloat\" flag to true.","code":["Neureka.get().backend().find(CLBackend).ifPresent({ it.settings.autoConvertToFloat=true })"]}, + + {"kind":"and","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(false)","Tensor a = Tensor.of([11, 11], 3d..19d).to( device )","Tensor x = a[1..-2,0..-1]","Tensor y = a[0..-3,0..-1]"]}, + + {"kind":"when","text":"","code":["Tensor t = x + y","String tAsStr = t.toString({it.setRowLimit(50)})"]}, + + {"kind":"then","text":"","code":["tAsStr.contains(\"(9x11):[17.0, 19.0, 21.0, 23.0, 25.0, 27.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, \" +"," \"26.0, 28.0, 30.0, 32.0, 17.0, 19.0, 21.0, 23.0, 25.0, 27.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, \" +"," \"26.0, 28.0, 30.0, 32.0, 17.0, 19.0, 21.0, 23.0, 25.0, 27.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, 26.0, 28.0, 30.0, ... + 49 more]\")"]}, + + {"kind":"where","text":"The following data is being used for tensor instantiation :","code":{"device":["CPU.get()","Device.get(\"openCL\")"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Simple slice addition produces expected result. [1]", + "result":"PASS", + "duration":"0.050 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We set the experimental \"autoConvertToFloat\" flag to true.","code":["Neureka.get().backend().find(CLBackend).ifPresent({ it.settings.autoConvertToFloat=true })"]}, + + {"kind":"and","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(false)","Tensor a = Tensor.of([11, 11], 3d..19d).to( device )","Tensor x = a[1..-2,0..-1]","Tensor y = a[0..-3,0..-1]"]}, + + {"kind":"when","text":"","code":["Tensor t = x + y","String tAsStr = t.toString({it.setRowLimit(50)})"]}, + + {"kind":"then","text":"","code":["tAsStr.contains(\"(9x11):[17.0, 19.0, 21.0, 23.0, 25.0, 27.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, \" +"," \"26.0, 28.0, 30.0, 32.0, 17.0, 19.0, 21.0, 23.0, 25.0, 27.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, \" +"," \"26.0, 28.0, 30.0, 32.0, 17.0, 19.0, 21.0, 23.0, 25.0, 27.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, 26.0, 28.0, 30.0, ... + 49 more]\")"]}, + + {"kind":"where","text":"The following data is being used for tensor instantiation :","code":{"device":["CPU.get()","Device.get(\"openCL\")"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Auto reshaping and broadcasting works and the result can be back propagated. [0]", + "result":"PASS", + "duration":"0.057 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }","Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + + {"kind":"and","text":"","code":["String wValue = whichGrad"," ? \"8\" + ( bShape.inject(1, {x,y->x*y}) > 1 ? \", 9\" : \"\" )"," : \"1, 2, 3, 4\""]}, + + {"kind":"and","text":"","code":["def aShape = [2, 2]"]}, + + {"kind":"and","text":"","code":["Tensor a = Tensor.of(aShape, 1d..5d).setRqsGradient(!whichGrad).to(Device.get(device))","Tensor b = Tensor.of(bShape, 8d..9d).setRqsGradient(whichGrad).to(Device.get(device))"]}, + + {"kind":"and","text":"","code":["a.mut.toType(type)","b.mut.toType(type)"]}, + + {"kind":"and","text":"","code":["String wShape = ( whichGrad ? bShape : aShape ).join(\"x\")","Tensor w = ( whichGrad ? b : a )"]}, + + {"kind":"expect","text":"","code":["a.itemType == type || device == 'GPU' // The gpu backend will only be floats!","b.itemType == type || device == 'GPU' // This is because kernels only work on floats..."]}, + + {"kind":"when","text":"","code":["Tensor c = operation.apply(a, b)"]}, + + {"kind":"then","text":"","code":["c.toString({it.hasSlimNumbers = true}).startsWith(\"[2x2]:($cValue)\")","w.toString({it.hasSlimNumbers = true}) == \"[$wShape]:($wValue):g:(null)\""]}, + + {"kind":"when","text":"","code":["c.backward(Tensor.of([2, 2], [5, -2, 7, 3]).mut.toType(type))"]}, + + {"kind":"then","text":"","code":["w.toString({it.hasSlimNumbers = true}) == \"[$wShape]:($wValue):g:($wGradient)\""]}, + + {"kind":"when","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(false)"]}, + + {"kind":"then","text":"","code":["c.toString({it.hasSlimNumbers = true}) == \"(2x2):[$cValue]\""]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'"],"type":["Double","Double","Float","Float","Double","Double","Float","Float","Double","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float"],"whichGrad":["false","false","false","false","false","false","false","false","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true"],"bShape":["[1]","[1]","[1]","[1]","[1]","[1]","[1]","[1]","[2,1]","[2,1]","[1]","[1]","[1]","[1]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]"],"operation":["{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x - 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[36]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }","Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + + {"kind":"and","text":"","code":["String wValue = whichGrad"," ? \"8\" + ( bShape.inject(1, {x,y->x*y}) > 1 ? \", 9\" : \"\" )"," : \"1, 2, 3, 4\""]}, + + {"kind":"and","text":"","code":["def aShape = [2, 2]"]}, + + {"kind":"and","text":"","code":["Tensor a = Tensor.of(aShape, 1d..5d).setRqsGradient(!whichGrad).to(Device.get(device))","Tensor b = Tensor.of(bShape, 8d..9d).setRqsGradient(whichGrad).to(Device.get(device))"]}, + + {"kind":"and","text":"","code":["a.mut.toType(type)","b.mut.toType(type)"]}, + + {"kind":"and","text":"","code":["String wShape = ( whichGrad ? bShape : aShape ).join(\"x\")","Tensor w = ( whichGrad ? b : a )"]}, + + {"kind":"expect","text":"","code":["a.itemType == type || device == 'GPU' // The gpu backend will only be floats!","b.itemType == type || device == 'GPU' // This is because kernels only work on floats..."]}, + + {"kind":"when","text":"","code":["Tensor c = operation.apply(a, b)"]}, + + {"kind":"then","text":"","code":["c.toString({it.hasSlimNumbers = true}).startsWith(\"[2x2]:($cValue)\")","w.toString({it.hasSlimNumbers = true}) == \"[$wShape]:($wValue):g:(null)\""]}, + + {"kind":"when","text":"","code":["c.backward(Tensor.of([2, 2], [5, -2, 7, 3]).mut.toType(type))"]}, + + {"kind":"then","text":"","code":["w.toString({it.hasSlimNumbers = true}) == \"[$wShape]:($wValue):g:($wGradient)\""]}, + + {"kind":"when","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(false)"]}, + + {"kind":"then","text":"","code":["c.toString({it.hasSlimNumbers = true}) == \"(2x2):[$cValue]\""]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'"],"type":["Double","Double","Float","Float","Double","Double","Float","Float","Double","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float"],"whichGrad":["false","false","false","false","false","false","false","false","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true"],"bShape":["[1]","[1]","[1]","[1]","[1]","[1]","[1]","[1]","[2,1]","[2,1]","[1]","[1]","[1]","[1]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]"],"operation":["{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x - 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[37]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = true }","Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)"]}, + + {"kind":"and","text":"","code":["String wValue = whichGrad"," ? \"8\" + ( bShape.inject(1, {x,y->x*y}) > 1 ? \", 9\" : \"\" )"," : \"1, 2, 3, 4\""]}, + + {"kind":"and","text":"","code":["def aShape = [2, 2]"]}, + + {"kind":"and","text":"","code":["Tensor a = Tensor.of(aShape, 1d..5d).setRqsGradient(!whichGrad).to(Device.get(device))","Tensor b = Tensor.of(bShape, 8d..9d).setRqsGradient(whichGrad).to(Device.get(device))"]}, + + {"kind":"and","text":"","code":["a.mut.toType(type)","b.mut.toType(type)"]}, + + {"kind":"and","text":"","code":["String wShape = ( whichGrad ? bShape : aShape ).join(\"x\")","Tensor w = ( whichGrad ? b : a )"]}, + + {"kind":"expect","text":"","code":["a.itemType == type || device == 'GPU' // The gpu backend will only be floats!","b.itemType == type || device == 'GPU' // This is because kernels only work on floats..."]}, + + {"kind":"when","text":"","code":["Tensor c = operation.apply(a, b)"]}, + + {"kind":"then","text":"","code":["c.toString({it.hasSlimNumbers = true}).startsWith(\"[2x2]:($cValue)\")","w.toString({it.hasSlimNumbers = true}) == \"[$wShape]:($wValue):g:(null)\""]}, + + {"kind":"when","text":"","code":["c.backward(Tensor.of([2, 2], [5, -2, 7, 3]).mut.toType(type))"]}, + + {"kind":"then","text":"","code":["w.toString({it.hasSlimNumbers = true}) == \"[$wShape]:($wValue):g:($wGradient)\""]}, + + {"kind":"when","text":"","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(false)"]}, + + {"kind":"then","text":"","code":["c.toString({it.hasSlimNumbers = true}) == \"(2x2):[$cValue]\""]}, + + {"kind":"cleanup","text":"","code":["Neureka.get().backend.find(CLBackend).ifPresent { it.settings.autoConvertToFloat = false }"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'CPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'","'CPU'","'GPU'"],"type":["Double","Double","Float","Float","Double","Double","Float","Float","Double","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float","Double","Double","Float","Float"],"whichGrad":["false","false","false","false","false","false","false","false","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true","true"],"bShape":["[1]","[1]","[1]","[1]","[1]","[1]","[1]","[1]","[2,1]","[2,1]","[1]","[1]","[1]","[1]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]","[1,2]","[1,2]","[1,2]","[1,2]","[2]","[2]","[2]","[2]"],"operation":["{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x * y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x - 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[0]", + "result":"PASS", + "duration":"0.054 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var a = Tensor.of(type).withShape(3, 2).andFill(-4..4).to(Device.get(device))","var b = Tensor.of(type).withShape(1, 1).andFill(3).to(Device.get(device))"]}, + + {"kind":"expect","text":"","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"","code":["Tensor c = operation.apply(a, b)"]}, + + {"kind":"then","text":"","code":["c.toString() == \"(3x2):[$cValue]\""]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'GPU'","'CPU'","'CPU'"],"type":["Double","Float","Float","Long","Integer"],"operation":["{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }"],"cValue":["\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1, 0, 1, 2, 3, 4\"","\"-1, 0, 1, 2, 3, 4\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar broadcasting works across devices. [1]", + "result":"PASS", + "duration":"0.054 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var a = Tensor.of(type).withShape(3, 2).andFill(-4..4).to(Device.get(device))","var b = Tensor.of(type).withShape(1, 1).andFill(3).to(Device.get(device))"]}, + + {"kind":"expect","text":"","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"","code":["Tensor c = operation.apply(a, b)"]}, + + {"kind":"then","text":"","code":["c.toString() == \"(3x2):[$cValue]\""]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'GPU'","'CPU'","'CPU'"],"type":["Double","Float","Float","Long","Integer"],"operation":["{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }"],"cValue":["\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1, 0, 1, 2, 3, 4\"","\"-1, 0, 1, 2, 3, 4\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar broadcasting works across devices. [2]", + "result":"PASS", + "duration":"0.120 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var a = Tensor.of(type).withShape(3, 2).andFill(-4..4).to(Device.get(device))","var b = Tensor.of(type).withShape(1, 1).andFill(3).to(Device.get(device))"]}, + + {"kind":"expect","text":"","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"","code":["Tensor c = operation.apply(a, b)"]}, + + {"kind":"then","text":"","code":["c.toString() == \"(3x2):[$cValue]\""]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'GPU'","'CPU'","'CPU'"],"type":["Double","Float","Float","Long","Integer"],"operation":["{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }"],"cValue":["\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1, 0, 1, 2, 3, 4\"","\"-1, 0, 1, 2, 3, 4\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar broadcasting works across devices. [3]", + "result":"PASS", + "duration":"0.050 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var a = Tensor.of(type).withShape(3, 2).andFill(-4..4).to(Device.get(device))","var b = Tensor.of(type).withShape(1, 1).andFill(3).to(Device.get(device))"]}, + + {"kind":"expect","text":"","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"","code":["Tensor c = operation.apply(a, b)"]}, + + {"kind":"then","text":"","code":["c.toString() == \"(3x2):[$cValue]\""]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'GPU'","'CPU'","'CPU'"],"type":["Double","Float","Float","Long","Integer"],"operation":["{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }"],"cValue":["\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1, 0, 1, 2, 3, 4\"","\"-1, 0, 1, 2, 3, 4\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Scalar broadcasting works across devices. [4]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var a = Tensor.of(type).withShape(3, 2).andFill(-4..4).to(Device.get(device))","var b = Tensor.of(type).withShape(1, 1).andFill(3).to(Device.get(device))"]}, + + {"kind":"expect","text":"","code":["a.itemType == type","b.itemType == type"]}, + + {"kind":"when","text":"","code":["Tensor c = operation.apply(a, b)"]}, + + {"kind":"then","text":"","code":["c.toString() == \"(3x2):[$cValue]\""]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'CPU'","'GPU'","'CPU'","'CPU'"],"type":["Double","Float","Float","Long","Integer"],"operation":["{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }","{ x, y -> x + y }"],"cValue":["\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1.0, 0.0, 1.0, 2.0, 3.0, 4.0\"","\"-1, 0, 1, 2, 3, 4\"","\"-1, 0, 1, 2, 3, 4\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Operators \"+,*,**\" produce expected results with gradients which can be accessed via a \"Ig[0]\" Function instance", + "result":"PASS", + "duration":"0.059 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"Neurekas view is set to legacy and three tensors of which one requires gradients.","code":["Neureka.get().settings().view().getNDPrintSettings().setIsLegacy(true)","Tensor x = Tensor.of(3d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)","Tensor w = Tensor.of(2d)"]}, + + {"kind":"when","text":"","code":["when : Tensor y = ( (x+b)*w )**2"]}, + + {"kind":"then","text":"","code":["then : y.toString().contains(\"[1]:(4.0); ->d[1]:(-8.0)\")"]}, + + {"kind":"when","text":"","code":["when : y = ((x+b)*w)**2"]}, + + {"kind":"then","text":"","code":["then : y.toString().contains(\"[1]:(4.0); ->d[1]:(-8.0)\")"]}, + + {"kind":"and","text":"","code":["and : Neureka.get().settings().debug().setIsKeepingDerivativeTargetPayloads(true)"]}, + + {"kind":"when","text":"","code":["y.backward(Tensor.of(1d))"]}, + + {"kind":"and","text":"","code":["Tensor t2 = Tensor.of( \"Ig[0]\", [x] )","Tensor t1 = Tensor.of( \"Ig[0]\", [y] ) // The input does not have a gradient!"]}, + + {"kind":"then","text":"","code":["thrown(IllegalArgumentException)"]}, + + {"kind":"and","text":"","code":["t2.toString() == \"[1]:(-8.0)\""]}, + + {"kind":"and","text":"","code":["t2 == x.gradient.get()"]}, + + {"kind":"and","text":"","code":["and : Neureka.get().settings().debug().setIsKeepingDerivativeTargetPayloads(false)"]}, + + {"kind":"when","text":"","code":["Tensor[] trs = new Tensor[]{x}"]}, + + {"kind":"and","text":"","code":["def fun = Function.of(\"Ig[0]\", false)"]}, + + {"kind":"then","text":"","code":["fun(trs).toString() == \"[1]:(-8.0)\""]}, + + {"kind":"when","text":"","code":["trs[0] = y"]}, {"kind":"and","text":"","code":["fun = Function.of(\"Ig[0]\", false)"]}, - {"kind":"and","text":"","code":["fun(trs)"]}, + {"kind":"and","text":"","code":["fun(trs)"]}, + + {"kind":"then","text":"","code":["thrown(IllegalArgumentException)"]} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [0]", + "result":"PASS", + "duration":"0.062 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [1]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [2]", + "result":"PASS", + "duration":"0.054 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [3]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [4]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [5]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [6]", + "result":"PASS", + "duration":"0.054 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [7]", + "result":"PASS", + "duration":"0.655 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [8]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [9]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [10]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [11]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [12]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [13]", + "result":"PASS", + "duration":"0.055 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [14]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [15]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [16]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [17]", + "result":"PASS", + "duration":"0.054 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [18]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [19]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [20]", + "result":"PASS", + "duration":"0.057 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, - {"kind":"then","text":"","code":["thrown(IllegalArgumentException)"]} + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [21]", + "result":"PASS", + "duration":"0.053 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [22]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [23]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [24]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [25]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [26]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [27]", + "result":"PASS", + "duration":"0.052 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [28]", + "result":"PASS", + "duration":"0.088 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [29]", + "result":"PASS", + "duration":"0.051 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Activation functions work across types on slices and non sliced tensors. [30]", + "result":"PASS", + "duration":"0.050 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a function based on the provided expression.","code":["var func = Function.of(funExpression)"]}, + + {"kind":"and","text":"We create 2 tensors storing the same values, one sliced and the other a normal tensor.","code":["var t1 = Tensor.of(type).withShape(2, 3).andSeed(\"Tempeh\")","var t2 = Tensor.of(type).withShape(4, 5).all(0)[1..2, 1..3]","t2.mut[0..1, 0..2] = t1"]}, + + {"kind":"expect","text":"The types of both tensors should match what was provided during instantiation.","code":["t1.dataType == DataType.of(type)","t1.itemType == type","t2.dataType == DataType.of(type)","t2.itemType == type"]}, + + {"kind":"when","text":"We apply the function to both tensors...","code":["var result1 = func(t1)","var result2 = func(t2)"]}, + + {"kind":"then","text":"","code":["result1.itemType == type","result2.itemType == type"]}, + + {"kind":"and","text":"The data of the first (non slice) tensor should be as expected.","code":["result1.mut.data.get() == expected instanceof Map ? expected['r1'] : expected"]}, + + {"kind":"and","text":"As well the value of the slice tensor (Its data would be a sparse array).","code":["result2.items == expected instanceof Map ? expected['r2'] : expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float","Integer","Double","Float"],"funExpression":["'tanh(i0)'","'tanh(i0)'","'tanh(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0*i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0-i0)'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(i0)-i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)+i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0)/i0'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'relu(-i0-5)+i0*3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'abs(i0*10)%3'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'gaus(i0)*100%i0'","'random(i0)'","'random(i0)'"],"expected":["[-0.2608431635405718, -0.6400224689534015, -0.15255723053856546, 0.1566537867655921, 0.5489211983894932, -0.17031712209680225] as double[]","[-0.26084316, -0.64002246, -0.15255724, 0.15665378, 0.54892117, -0.17031713] as float[]","[-1, -1, 1, -1, 1, -1] as int[]","[-0.0027019706408068795, -0.008329762613111082, -0.001543641184315801, 0.15861207834235577, 0.6567031992927272, -0.001728424711189524] as double[]","[-0.0027019705, -0.008329763, -0.0015436412, 0.15861207, 0.6567032, -0.0017284247] as float[]","[-7156386, -18495716, 248181051, -13634228, 919305478, -15169971] as int[]","[0.07300645343782339, 0.6938494519078316, 0.023828281059158886, 0.025157791396081604, 0.43125909196130346, 0.029874519822505895] as double[]","[0.07300645, 0.6938495, 0.023828283, 0.025157789, 0.43125907, 0.02987452] as float[]","[988699588, -17870520, 141304729, 1260971300, 210951204, 1018550276] as int[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[]","[0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[]","[0, 0, 0, 0, 0, 0] as int[]","[0.26749509343988104, 0.8246464986979971, 0.1528204772472643, 0.0, 0.0, 0.17111404640776287] as double[]","[0.2674951, 0.82464653, 0.15282048, 0.0, 0.0, 0.17111404] as float[]","[708482220, 1831075919, 0, 1349788550, 0, 1501827147] as int[]","[0.0, 0.0, 0.0, 0.15702595755893223, 0.6501361672998, 0.0] as double[]","[0.0, 0.0, 0.0, 0.15702595174312592, 0.650136142373085, 0.0] as float[]","[0, 0, 245699240, 0, 910112423, 0] as int[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as double[]","[-1.0, -1.0, -1.0, -0.01, -0.01, -1.0] as float[]","[-1, -1, 0, -1, 0, -1] as int[]","[-0.857889221601257, -2.540599021320214, -0.5115487141104245, 0.42425011424364373, 1.9135425658852545, -0.5667989886456676] as double[]","[-0.85788924, -2.540599, -0.51154876, 0.4242501, 1.9135424, -0.566799] as float[]","[-1431277217, 595824021, 742061342, 1568121735, -1546243917, 1260973055] as int[]","[2.7019706408068793, 2.3297626131110825, 1.5436411843158009, 1.5861207834235578, 0.5670319929272729, 1.7284247111895241] as double[]","[2.7019706, 2.3297625, 1.5436412, 1.5861207, 0.56703186, 1.7284248] as float[]","[2, 0, 1, 1, 2, 1] as int[]","[0.011693048642643422, 0.8192993419907051, 0.08721424132459399, 0.1277867634692304, 0.6121424058303924, 0.09210498892686086] as double[]","[0.011690378, 0.81929654, 0.087213635, 0.12778962, 0.6121441, 0.09210494] as float[]","[0, 0, 0, 0, 0, 0] as int[]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.242396559265774, 0.23980663860290638, 0.4667980401594514, 0.0, 0.0, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as double[], 'r1':[2.242396559265774, 0.23980663860290638, 0.4667980401594514, -1.0840395336123059, 0.43090823203242123, 1.0381081218392283] as double[]]","['r2':[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.2423966, 0.23980664, 0.46679804, 0.0, 0.0, -1.0840396, 0.43090823, 1.0381081, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] as float[], 'r1':[2.2423966, 0.23980664, 0.46679804, -1.0840396, 0.43090823, 1.0381081] as float[]]"]}} ], "problems":{"dataValues":[], "errors":[]} }, { - "id":"Activation functions work across types on slices and non sliced tensors.", + "id":"Activation functions work across types on slices and non sliced tensors. [31]", "result":"PASS", - "duration":"0.946 seconds", + "duration":"0.052 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -393,7 +4429,7 @@ { "id":"The transpose operation exposed by the \"T()\" method, supports autograd.", "result":"PASS", - "duration":"0.034 seconds", + "duration":"0.052 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_Slicing_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Slicing_Spec.json index 142179be8..7f8f6bca7 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Slicing_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Slicing_Spec.json @@ -4,17 +4,17 @@ "narrative":"ND-Array data structures can be \"sliced\" in the sense\n that one can create a subset view of the underlying data inside a tensor\n through a new tensor instance...\n This can be a tedious and complicated procedure.\n Therefore a tensor should expose a various user friendly API for slicing which\n are also fit for various languages.\n This specification covers these APIs for tensor slicing.", "subjects":["neureka.Tensor"], "statistics":{ - "runs":"9", + "runs":"14", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.013 seconds" + "duration":"0.036 seconds" }, "headers":["\n This specification covers the behavior of tensors when being sliced\n on multiple different device types using the slice builder API. \n "],"tags":{},"see":[], "features":[ { - "id":"When Slicing only one axis using the SliceBuilder API, the other axes will be sliced implicitly.", + "id":"When Slicing only one axis using the SliceBuilder API, the other axes will be sliced implicitly. [0]", "result":"PASS", "duration":"0.001 seconds", "iterations":{ @@ -43,7 +43,36 @@ }, { - "id":"The \"at\" method and the \"from\" / \"to\" methods can be mixed when slicing a tensor.", + "id":"When Slicing only one axis using the SliceBuilder API, the other axes will be sliced implicitly. [1]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A device could be found.","code":["if ( device == null ) return"]}, + + {"kind":"and","text":"The found device is also supported (Which might not always be the case for the OpenCLDevice).","code":["if ( device instanceof OpenCLDevice && !Neureka.get().canAccessOpenCLDevice() ) return"]}, + + {"kind":"and","text":"A 3 dimensional tensor which will be sliced.","code":["Tensor t = Tensor.of([2, 4, 3], -3d..7d)"]}, + + {"kind":"and","text":"Which will be placed on a given device:","code":["t.to(device)"]}, + + {"kind":"when","text":"Slicing axis 1 of the tensor using the \"from\" & \"to\" methods...","code":["Tensor s = t.slice()"," .axis(1).from(1).to(2)"," .get() // Note: Axis 0 and 2 will be sliced implicitly if not specified!"]}, + + {"kind":"then","text":"This will result in a slice which has 4 axis entries less than the original tensor.","code":["s.shape().sum() == t.shape().sum() - 2"]}, + + {"kind":"and","text":"This new slice will be displayed as follows when printed (with adjusted indent):","code":["s.toString().replace('\\n', '\\n'+\" \"*20) =="," \"\"\"(2x2x3):["," ["," [ 0.0 , 1.0 , 2.0 ],"," [ 3.0 , 4.0 , 5.0 ]"," ],"," ["," [ 1.0 , 2.0 , 3.0 ],"," [ 4.0 , 5.0 , 6.0 ]"," ]"," ]\"\"\""]}, + + {"kind":"and","text":"As already shown by the printed view, the tensor as the expected shape:","code":["s.shape() == [2, 2, 3]"]}, + + {"kind":"where","text":"This works both on the GPU as well as CPU of course.","code":{"device":["Device.get('gpu')","CPU.get()"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The \"at\" method and the \"from\" / \"to\" methods can be mixed when slicing a tensor. [0]", "result":"PASS", "duration":"0", "iterations":{ @@ -72,7 +101,36 @@ }, { - "id":"A tensor can be sliced by passing ranges in the form of primitive arrays.", + "id":"The \"at\" method and the \"from\" / \"to\" methods can be mixed when slicing a tensor. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A device could be found.","code":["if ( device == null ) return"]}, + + {"kind":"and","text":"The found device is also supported (Which might not always be the case for the OpenCLDevice).","code":["if ( device instanceof OpenCLDevice && !Neureka.get().canAccessOpenCLDevice() ) return"]}, + + {"kind":"and","text":"A 3 dimensional tensor which will be sliced.","code":["Tensor t = Tensor.of([3, 3, 4], -11d..3d)"]}, + + {"kind":"and","text":"Which will be placed on a given device:","code":["t.to(device)"]}, + + {"kind":"when","text":"Slicing the tensor using both \"at\", \"from\"/\"to\" and an implicit full ranged slice for axis 1...","code":["Tensor s = t.slice()"," .axis(0).at(1)"," // Note: Axis 1 will be sliced implicitly if not specified!"," .axis(2).from(1).to(2)"," .get()"]}, + + {"kind":"then","text":"This will result in a slice which has 4 axis entries less than the original tensor.","code":["s.shape().sum() == t.shape().sum() - 4"]}, + + {"kind":"and","text":"This new slice will be displayed as follows when printed (with adjusted indent):","code":["s.toString().replace('\\n', '\\n'+\" \"*20) =="," \"\"\"(1x3x2):["," ["," [ 2.0 , 3.0 ],"," [ -9.0 , -8.0 ],"," [ -5.0 , -4.0 ]"," ]"," ]\"\"\""]}, + + {"kind":"and","text":"The \"at\" method sliced a single axis point whereas the \"from\" & \"to\" sliced from 1 to 2.","code":["s.shape() == [1, 3, 2]"]}, + + {"kind":"where","text":"This works both on the GPU as well as CPU of course.","code":{"device":["Device.get('gpu')","CPU.get()"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A tensor can be sliced by passing ranges in the form of primitive arrays. [0]", "result":"PASS", "duration":"0", "iterations":{ @@ -101,7 +159,36 @@ }, { - "id":"A tensor can be sliced by passing ranges in the form of lists (Groovy ranges).", + "id":"A tensor can be sliced by passing ranges in the form of primitive arrays. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A device could be found.","code":["if ( device == null ) return"]}, + + {"kind":"and","text":"The found device is also supported (Which might not always be the case for the OpenCLDevice).","code":["if ( device instanceof OpenCLDevice && !Neureka.get().canAccessOpenCLDevice() ) return"]}, + + {"kind":"and","text":"A 3 dimensional tensor which will be sliced.","code":["Tensor t = Tensor.of([3, 3, 4], -11..3)"]}, + + {"kind":"and","text":"Which will be placed on a given device:","code":["t.to(device)"]}, + + {"kind":"when","text":"Slicing the tensor using primitive int arrays...","code":["var s = t.getAt("," new int[]{1}, // Axis 0"," new int[]{0, 2}, // Axis 1"," new int[]{1, 2} // Axis 2"," )"]}, + + {"kind":"then","text":"This will result in a slice which has 4 axis entries less than the original tensor.","code":["s.shape().sum() == t.shape().sum() - 4"]}, + + {"kind":"and","text":"This new slice will have the expected shape and items:","code":["s.shape == [1, 3, 2]","s.items == [2, 3, -9, -8, -5, -4]"]}, + + {"kind":"and","text":"The the slice will have the following shape","code":["s.shape() == [1, 3, 2]"]}, + + {"kind":"where","text":"This works both on the GPU as well as CPU of course.","code":{"device":["Device.get('gpu')","CPU.get()"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A tensor can be sliced by passing ranges in the form of lists (Groovy ranges). [0]", "result":"PASS", "duration":"0", "iterations":{ @@ -128,9 +215,36 @@ }, { - "id":"The slice builder also supports slicing with custom step sizes.", + "id":"A tensor can be sliced by passing ranges in the form of lists (Groovy ranges). [1]", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A device could be found.","code":["if ( device == null ) return"]}, + + {"kind":"and","text":"The found device is also supported (Which might not always be the case for the OpenCLDevice).","code":["if ( device instanceof OpenCLDevice && !Neureka.get().canAccessOpenCLDevice() ) return"]}, + + {"kind":"and","text":"A 3 dimensional tensor which will be sliced.","code":["var t = Tensor.of([3, 3, 4], -11..3)"]}, + + {"kind":"and","text":"Which will be placed on a given device:","code":["t.to(device)"]}, + + {"kind":"when","text":"Slicing the tensor using lists of integers generated by the Groovy range operator..","code":["var s = t[1, 0..2, 1..2]"]}, + + {"kind":"then","text":"This will result in a slice which has 4 axis entries less than the original tensor.","code":["s.shape().sum() == t.shape().sum() - 4"]}, + + {"kind":"and","text":"This new slice will have the expected shape and items:","code":["s.shape == [1, 3, 2]","s.items == [2, 3, -9, -8, -5, -4]"]}, + + {"kind":"where","text":"This works both on the GPU as well as CPU of course.","code":{"device":["Device.get('gpu')","CPU.get()"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The slice builder also supports slicing with custom step sizes. [0]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -157,12 +271,41 @@ }, { - "id":"Slicing is also a Function with autograd support!", + "id":"The slice builder also supports slicing with custom step sizes. [1]", "result":"PASS", "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"given","text":"A device could be found.","code":["if ( device == null ) return"]}, + + {"kind":"and","text":"The found device is also supported (Which might not always be the case for the OpenCLDevice).","code":["if ( device instanceof OpenCLDevice && !Neureka.get().canAccessOpenCLDevice() ) return"]}, + + {"kind":"and","text":"A 3 dimensional tensor which will be sliced.","code":["Tensor t = Tensor.of([3, 3, 4], -11d..3d)"]}, + + {"kind":"and","text":"Which will be placed on a given device:","code":["t.to(device)"]}, + + {"kind":"when","text":"Slicing the tensor using lists of integers generated by the Groovy range operator..","code":["Tensor s = t.slice()"," .axis(0).at(0)"," .axis(1).at(0)"," .axis(2).from(0).to(3).step(2)"," .get()"]}, + + {"kind":"then","text":"This will result in a slice which has 4 axis entries less than the original tensor.","code":["s.shape().sum() == t.shape().sum() - 6"]}, + + {"kind":"and","text":"This new slice will be displayed as follows when printed:","code":["s.toString() == \"(1x1x2):[\\n\" +"," \" [\\n\" +"," \" [ -11.0, -9.0 ]\\n\" +"," \" ]\\n\" +"," \"]\""]}, + + {"kind":"and","text":"The the slice will have the following shape","code":["s.shape() == [1, 1, 2]"]}, + + {"kind":"where","text":"This works both on the GPU as well as CPU of course.","code":{"device":["Device.get('gpu')","CPU.get()"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Slicing is also a Function with autograd support!", + "result":"PASS", + "duration":"0.006 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"A 2 dimensional tensor requiring gradients.","code":["var t = Tensor.ofBytes().withShape(4, 4).andFill(-1, 7, 3).setRqsGradient(true)"]}, @@ -182,7 +325,7 @@ { "id":"Normal slicing will try to do autograd.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -201,7 +344,7 @@ { "id":"We can avoid autograd when slicing by using the \"detached\" instead of the \"get\" method.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -222,7 +365,7 @@ { "id":"We can slice a scalar tensor from a larger tensor of rank 4.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_State_Spec.json b/docs/spock/reports/ut.tensors.Tensor_State_Spec.json index da37ab732..271ccb116 100644 --- a/docs/spock/reports/ut.tensors.Tensor_State_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_State_Spec.json @@ -1,20 +1,88 @@ { "className":"ut.tensors.Tensor_State_Spec", "title":"The Tensor Initialization and State Specification", - "narrative":"This specification defines the expected states of freshly instantiated\n and initialized tensors.\n After a tensor was created successfully we expect it\n to have certain properties like a shape, rank, type and data array\n among other things.", + "narrative":"This specification defines the expected states of freshly instantiated\n and initialized tensors.\n After a tensor was created successfully we expect it \n to have certain properties like a shape, rank, type and data array\n among other things.", "subjects":[], "statistics":{ - "runs":"7", + "runs":"19", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.016 seconds" + "duration":"0.052 seconds" }, "headers":[" \n Note: This specification is a little older, meaning initially it was not written with \n the intend to be read as living documentation, but rather as a unit test...\n "],"tags":{},"see":[], "features":[ { - "id":"A tensor can be instantiated from a item type class and nested lists.", + "id":"A tensor can be instantiated from a item type class and nested lists. [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We instantiate a tensor using a type and a list of things (or list of list of things, or..).","code":["var t = Tensor.of(type, list)"]}, + + {"kind":"expect","text":"The tensor has the item type, shape and data array!","code":["t.itemType == type","t.shape() == shape","t.mut.data.get() == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Byte","Byte","String"],"list":["[1,2,1]","[5, -4]","[3, 4]","[[3], [4]]","[['3', '4']]"],"shape":["[3]","[2]","[2]","[2, 1]","[1, 2]"],"expected":["[1, 2, 1] as double[]","[5, -4] as float[]","[3, 4] as byte[]","[3, 4] as byte[]","['3', '4'] as String[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A tensor can be instantiated from a item type class and nested lists. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We instantiate a tensor using a type and a list of things (or list of list of things, or..).","code":["var t = Tensor.of(type, list)"]}, + + {"kind":"expect","text":"The tensor has the item type, shape and data array!","code":["t.itemType == type","t.shape() == shape","t.mut.data.get() == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Byte","Byte","String"],"list":["[1,2,1]","[5, -4]","[3, 4]","[[3], [4]]","[['3', '4']]"],"shape":["[3]","[2]","[2]","[2, 1]","[1, 2]"],"expected":["[1, 2, 1] as double[]","[5, -4] as float[]","[3, 4] as byte[]","[3, 4] as byte[]","['3', '4'] as String[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A tensor can be instantiated from a item type class and nested lists. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We instantiate a tensor using a type and a list of things (or list of list of things, or..).","code":["var t = Tensor.of(type, list)"]}, + + {"kind":"expect","text":"The tensor has the item type, shape and data array!","code":["t.itemType == type","t.shape() == shape","t.mut.data.get() == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Byte","Byte","String"],"list":["[1,2,1]","[5, -4]","[3, 4]","[[3], [4]]","[['3', '4']]"],"shape":["[3]","[2]","[2]","[2, 1]","[1, 2]"],"expected":["[1, 2, 1] as double[]","[5, -4] as float[]","[3, 4] as byte[]","[3, 4] as byte[]","['3', '4'] as String[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A tensor can be instantiated from a item type class and nested lists. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We instantiate a tensor using a type and a list of things (or list of list of things, or..).","code":["var t = Tensor.of(type, list)"]}, + + {"kind":"expect","text":"The tensor has the item type, shape and data array!","code":["t.itemType == type","t.shape() == shape","t.mut.data.get() == expected"]}, + + {"kind":"where","text":"","code":{"type":["Double","Float","Byte","Byte","String"],"list":["[1,2,1]","[5, -4]","[3, 4]","[[3], [4]]","[['3', '4']]"],"shape":["[3]","[2]","[2]","[2, 1]","[1, 2]"],"expected":["[1, 2, 1] as double[]","[5, -4] as float[]","[3, 4] as byte[]","[3, 4] as byte[]","['3', '4'] as String[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"A tensor can be instantiated from a item type class and nested lists. [4]", "result":"PASS", "duration":"0", "iterations":{ @@ -33,7 +101,7 @@ { "id":"Tensors as String can be formatted on an entry based level.", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -54,7 +122,7 @@ { "id":"Numeric tensors as String can be formatted on an entry based level.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -69,7 +137,88 @@ }, { - "id":"Tensors as String can be formatted depending on shape.", + "id":"Tensors as String can be formatted depending on shape. [0]", + "result":"PASS", + "duration":"0.010 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We configure a NDPrintSettings object.","code":["def settings ="," Neureka.get()"," .settings()"," .view()"," .getNDPrintSettings()"," .clone()"," .setRowLimit( mode.contains( \"s\" ) ? 3 : 32 )"," .setIsScientific( mode.contains( \"c\" ) )"," .setIsMultiline( mode.contains( \"f\" ) )"," .setHasGradient( mode.contains( \"g\" ) )"," .setCellSize( mode.contains( \"p\" ) ? 6 : mode.contains( \"f\" ) ? 2 : 1 )"," .setHasValue( !(mode.contains( \"shp\" ) || mode.contains(\"shape\")) )"," .setHasRecursiveGraph( mode.contains( \"r\" ) )"," .setHasDerivatives( mode.contains( \"d\" ) )"," .setHasShape( !mode.contains( \"v\" ) )"," .setIsCellBound( mode .contains( \"b\" ) )"," .setPostfix( \"\" )"," .setPrefix( \"\" )"," .setHasSlimNumbers( false )"]}, + + {"kind":"and","text":"Four tensors of various data types:","code":["Tensor t1 = Tensor.of( Float.class, shape, -4f..5f ).set( Tensor.of( shape, -7f..3f ) )","Tensor t2 = Tensor.of( Double.class, shape, -4d..5d ).set( Tensor.of( shape, -7d..3d ) )","Tensor t3 = Tensor.of( Integer.class, shape, -4..5 ).set( Tensor.of( shape, -7..3 ) )","Tensor t4 = Tensor.of( Short.class, shape, (-4 as short)..(5 as short) ).set( Tensor.of( shape, (-7 as short)..(3 as short) ) )","Tensor t5 = Tensor.of( Byte.class, shape, (-4 as byte )..(5 as byte ) ).set( Tensor.of( shape, (-7 as byte)..(3 as byte) ) )"]}, + + {"kind":"expect","text":"The first tensor has the expected internals and produces the correct String representation.","code":["t1.toString(settings) == expected","t1.dataType == DataType.of( Float.class )","t1.mut.data.get() instanceof float[]"]}, + + {"kind":"and","text":"The second tensor has the expected internals and produces the correct String representation.","code":["t2.toString(settings) == expected","t2.dataType == DataType.of( Double.class )","t2.mut.data.get() instanceof double[]"]}, + + {"kind":"and","text":"The third tensor has the expected internals and produces the correct String representation.","code":["t3.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t3.dataType == DataType.of( Integer.class )","t3.mut.data.get() instanceof int[]"]}, + + {"kind":"and","text":"The fourth tensor has the expected internals and produces the correct String representation.","code":["t4.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t4.dataType == DataType.of( Short.class )","t4.mut.data.get() instanceof short[]"]}, + + {"kind":"and","text":"The fifth tensor has the expected internals and produces the correct String representation.","code":["t5.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t5.dataType == DataType.of( Byte.class )","t5.mut.data.get() instanceof byte[]"]}, + + {"kind":"where","text":"The print configurations codes \"mode\", a common shape and expected String representation will be supplied:","code":{"shape":["[2,3]","[2,3]","[3,2]","[2,3,4]","[2,2,3,4]","[2, 70]","[2, 100]","[70, 2]"],"mode":["\"fap\"","\"fa\"","\"fp\"","\"fp\"","\"fp\"","\"f\"","\"f\"","\"f\""],"expected":["\"(2x3):[\\n [ -4.0 , -3.0 , -2.0 ],\\n [ -1.0 , 0.0 , 1.0 ]\\n]\"","\"(2x3):[\\n [ -4.0, -3.0, -2.0 ],\\n [ -1.0, 0.0, 1.0 ]\\n]\"","\"(3x2):[\\n [ -4.0 , -3.0 ],\\n [ -2.0 , -1.0 ],\\n [ 0.0 , 1.0 ]\\n]\"","\"(2x3x4):[\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n]\"","\"(2x2x3x4):[\\n [\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n ],\\n [\\n [\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ],\\n [ -2.0 , -1.0 , 0.0 , 1.0 ]\\n ],\\n [\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ]\\n ]\\n ]\\n]\"","\"(2x70):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(2x100):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(70x2):[\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n ... 38 more ...\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ]\\n]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors as String can be formatted depending on shape. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We configure a NDPrintSettings object.","code":["def settings ="," Neureka.get()"," .settings()"," .view()"," .getNDPrintSettings()"," .clone()"," .setRowLimit( mode.contains( \"s\" ) ? 3 : 32 )"," .setIsScientific( mode.contains( \"c\" ) )"," .setIsMultiline( mode.contains( \"f\" ) )"," .setHasGradient( mode.contains( \"g\" ) )"," .setCellSize( mode.contains( \"p\" ) ? 6 : mode.contains( \"f\" ) ? 2 : 1 )"," .setHasValue( !(mode.contains( \"shp\" ) || mode.contains(\"shape\")) )"," .setHasRecursiveGraph( mode.contains( \"r\" ) )"," .setHasDerivatives( mode.contains( \"d\" ) )"," .setHasShape( !mode.contains( \"v\" ) )"," .setIsCellBound( mode .contains( \"b\" ) )"," .setPostfix( \"\" )"," .setPrefix( \"\" )"," .setHasSlimNumbers( false )"]}, + + {"kind":"and","text":"Four tensors of various data types:","code":["Tensor t1 = Tensor.of( Float.class, shape, -4f..5f ).set( Tensor.of( shape, -7f..3f ) )","Tensor t2 = Tensor.of( Double.class, shape, -4d..5d ).set( Tensor.of( shape, -7d..3d ) )","Tensor t3 = Tensor.of( Integer.class, shape, -4..5 ).set( Tensor.of( shape, -7..3 ) )","Tensor t4 = Tensor.of( Short.class, shape, (-4 as short)..(5 as short) ).set( Tensor.of( shape, (-7 as short)..(3 as short) ) )","Tensor t5 = Tensor.of( Byte.class, shape, (-4 as byte )..(5 as byte ) ).set( Tensor.of( shape, (-7 as byte)..(3 as byte) ) )"]}, + + {"kind":"expect","text":"The first tensor has the expected internals and produces the correct String representation.","code":["t1.toString(settings) == expected","t1.dataType == DataType.of( Float.class )","t1.mut.data.get() instanceof float[]"]}, + + {"kind":"and","text":"The second tensor has the expected internals and produces the correct String representation.","code":["t2.toString(settings) == expected","t2.dataType == DataType.of( Double.class )","t2.mut.data.get() instanceof double[]"]}, + + {"kind":"and","text":"The third tensor has the expected internals and produces the correct String representation.","code":["t3.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t3.dataType == DataType.of( Integer.class )","t3.mut.data.get() instanceof int[]"]}, + + {"kind":"and","text":"The fourth tensor has the expected internals and produces the correct String representation.","code":["t4.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t4.dataType == DataType.of( Short.class )","t4.mut.data.get() instanceof short[]"]}, + + {"kind":"and","text":"The fifth tensor has the expected internals and produces the correct String representation.","code":["t5.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t5.dataType == DataType.of( Byte.class )","t5.mut.data.get() instanceof byte[]"]}, + + {"kind":"where","text":"The print configurations codes \"mode\", a common shape and expected String representation will be supplied:","code":{"shape":["[2,3]","[2,3]","[3,2]","[2,3,4]","[2,2,3,4]","[2, 70]","[2, 100]","[70, 2]"],"mode":["\"fap\"","\"fa\"","\"fp\"","\"fp\"","\"fp\"","\"f\"","\"f\"","\"f\""],"expected":["\"(2x3):[\\n [ -4.0 , -3.0 , -2.0 ],\\n [ -1.0 , 0.0 , 1.0 ]\\n]\"","\"(2x3):[\\n [ -4.0, -3.0, -2.0 ],\\n [ -1.0, 0.0, 1.0 ]\\n]\"","\"(3x2):[\\n [ -4.0 , -3.0 ],\\n [ -2.0 , -1.0 ],\\n [ 0.0 , 1.0 ]\\n]\"","\"(2x3x4):[\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n]\"","\"(2x2x3x4):[\\n [\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n ],\\n [\\n [\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ],\\n [ -2.0 , -1.0 , 0.0 , 1.0 ]\\n ],\\n [\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ]\\n ]\\n ]\\n]\"","\"(2x70):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(2x100):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(70x2):[\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n ... 38 more ...\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ]\\n]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors as String can be formatted depending on shape. [2]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We configure a NDPrintSettings object.","code":["def settings ="," Neureka.get()"," .settings()"," .view()"," .getNDPrintSettings()"," .clone()"," .setRowLimit( mode.contains( \"s\" ) ? 3 : 32 )"," .setIsScientific( mode.contains( \"c\" ) )"," .setIsMultiline( mode.contains( \"f\" ) )"," .setHasGradient( mode.contains( \"g\" ) )"," .setCellSize( mode.contains( \"p\" ) ? 6 : mode.contains( \"f\" ) ? 2 : 1 )"," .setHasValue( !(mode.contains( \"shp\" ) || mode.contains(\"shape\")) )"," .setHasRecursiveGraph( mode.contains( \"r\" ) )"," .setHasDerivatives( mode.contains( \"d\" ) )"," .setHasShape( !mode.contains( \"v\" ) )"," .setIsCellBound( mode .contains( \"b\" ) )"," .setPostfix( \"\" )"," .setPrefix( \"\" )"," .setHasSlimNumbers( false )"]}, + + {"kind":"and","text":"Four tensors of various data types:","code":["Tensor t1 = Tensor.of( Float.class, shape, -4f..5f ).set( Tensor.of( shape, -7f..3f ) )","Tensor t2 = Tensor.of( Double.class, shape, -4d..5d ).set( Tensor.of( shape, -7d..3d ) )","Tensor t3 = Tensor.of( Integer.class, shape, -4..5 ).set( Tensor.of( shape, -7..3 ) )","Tensor t4 = Tensor.of( Short.class, shape, (-4 as short)..(5 as short) ).set( Tensor.of( shape, (-7 as short)..(3 as short) ) )","Tensor t5 = Tensor.of( Byte.class, shape, (-4 as byte )..(5 as byte ) ).set( Tensor.of( shape, (-7 as byte)..(3 as byte) ) )"]}, + + {"kind":"expect","text":"The first tensor has the expected internals and produces the correct String representation.","code":["t1.toString(settings) == expected","t1.dataType == DataType.of( Float.class )","t1.mut.data.get() instanceof float[]"]}, + + {"kind":"and","text":"The second tensor has the expected internals and produces the correct String representation.","code":["t2.toString(settings) == expected","t2.dataType == DataType.of( Double.class )","t2.mut.data.get() instanceof double[]"]}, + + {"kind":"and","text":"The third tensor has the expected internals and produces the correct String representation.","code":["t3.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t3.dataType == DataType.of( Integer.class )","t3.mut.data.get() instanceof int[]"]}, + + {"kind":"and","text":"The fourth tensor has the expected internals and produces the correct String representation.","code":["t4.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t4.dataType == DataType.of( Short.class )","t4.mut.data.get() instanceof short[]"]}, + + {"kind":"and","text":"The fifth tensor has the expected internals and produces the correct String representation.","code":["t5.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t5.dataType == DataType.of( Byte.class )","t5.mut.data.get() instanceof byte[]"]}, + + {"kind":"where","text":"The print configurations codes \"mode\", a common shape and expected String representation will be supplied:","code":{"shape":["[2,3]","[2,3]","[3,2]","[2,3,4]","[2,2,3,4]","[2, 70]","[2, 100]","[70, 2]"],"mode":["\"fap\"","\"fa\"","\"fp\"","\"fp\"","\"fp\"","\"f\"","\"f\"","\"f\""],"expected":["\"(2x3):[\\n [ -4.0 , -3.0 , -2.0 ],\\n [ -1.0 , 0.0 , 1.0 ]\\n]\"","\"(2x3):[\\n [ -4.0, -3.0, -2.0 ],\\n [ -1.0, 0.0, 1.0 ]\\n]\"","\"(3x2):[\\n [ -4.0 , -3.0 ],\\n [ -2.0 , -1.0 ],\\n [ 0.0 , 1.0 ]\\n]\"","\"(2x3x4):[\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n]\"","\"(2x2x3x4):[\\n [\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n ],\\n [\\n [\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ],\\n [ -2.0 , -1.0 , 0.0 , 1.0 ]\\n ],\\n [\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ]\\n ]\\n ]\\n]\"","\"(2x70):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(2x100):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(70x2):[\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n ... 38 more ...\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ]\\n]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors as String can be formatted depending on shape. [3]", "result":"PASS", "duration":"0.002 seconds", "iterations":{ @@ -95,10 +244,118 @@ "problems":{"dataValues":[], "errors":[]} }, + { + "id":"Tensors as String can be formatted depending on shape. [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We configure a NDPrintSettings object.","code":["def settings ="," Neureka.get()"," .settings()"," .view()"," .getNDPrintSettings()"," .clone()"," .setRowLimit( mode.contains( \"s\" ) ? 3 : 32 )"," .setIsScientific( mode.contains( \"c\" ) )"," .setIsMultiline( mode.contains( \"f\" ) )"," .setHasGradient( mode.contains( \"g\" ) )"," .setCellSize( mode.contains( \"p\" ) ? 6 : mode.contains( \"f\" ) ? 2 : 1 )"," .setHasValue( !(mode.contains( \"shp\" ) || mode.contains(\"shape\")) )"," .setHasRecursiveGraph( mode.contains( \"r\" ) )"," .setHasDerivatives( mode.contains( \"d\" ) )"," .setHasShape( !mode.contains( \"v\" ) )"," .setIsCellBound( mode .contains( \"b\" ) )"," .setPostfix( \"\" )"," .setPrefix( \"\" )"," .setHasSlimNumbers( false )"]}, + + {"kind":"and","text":"Four tensors of various data types:","code":["Tensor t1 = Tensor.of( Float.class, shape, -4f..5f ).set( Tensor.of( shape, -7f..3f ) )","Tensor t2 = Tensor.of( Double.class, shape, -4d..5d ).set( Tensor.of( shape, -7d..3d ) )","Tensor t3 = Tensor.of( Integer.class, shape, -4..5 ).set( Tensor.of( shape, -7..3 ) )","Tensor t4 = Tensor.of( Short.class, shape, (-4 as short)..(5 as short) ).set( Tensor.of( shape, (-7 as short)..(3 as short) ) )","Tensor t5 = Tensor.of( Byte.class, shape, (-4 as byte )..(5 as byte ) ).set( Tensor.of( shape, (-7 as byte)..(3 as byte) ) )"]}, + + {"kind":"expect","text":"The first tensor has the expected internals and produces the correct String representation.","code":["t1.toString(settings) == expected","t1.dataType == DataType.of( Float.class )","t1.mut.data.get() instanceof float[]"]}, + + {"kind":"and","text":"The second tensor has the expected internals and produces the correct String representation.","code":["t2.toString(settings) == expected","t2.dataType == DataType.of( Double.class )","t2.mut.data.get() instanceof double[]"]}, + + {"kind":"and","text":"The third tensor has the expected internals and produces the correct String representation.","code":["t3.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t3.dataType == DataType.of( Integer.class )","t3.mut.data.get() instanceof int[]"]}, + + {"kind":"and","text":"The fourth tensor has the expected internals and produces the correct String representation.","code":["t4.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t4.dataType == DataType.of( Short.class )","t4.mut.data.get() instanceof short[]"]}, + + {"kind":"and","text":"The fifth tensor has the expected internals and produces the correct String representation.","code":["t5.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t5.dataType == DataType.of( Byte.class )","t5.mut.data.get() instanceof byte[]"]}, + + {"kind":"where","text":"The print configurations codes \"mode\", a common shape and expected String representation will be supplied:","code":{"shape":["[2,3]","[2,3]","[3,2]","[2,3,4]","[2,2,3,4]","[2, 70]","[2, 100]","[70, 2]"],"mode":["\"fap\"","\"fa\"","\"fp\"","\"fp\"","\"fp\"","\"f\"","\"f\"","\"f\""],"expected":["\"(2x3):[\\n [ -4.0 , -3.0 , -2.0 ],\\n [ -1.0 , 0.0 , 1.0 ]\\n]\"","\"(2x3):[\\n [ -4.0, -3.0, -2.0 ],\\n [ -1.0, 0.0, 1.0 ]\\n]\"","\"(3x2):[\\n [ -4.0 , -3.0 ],\\n [ -2.0 , -1.0 ],\\n [ 0.0 , 1.0 ]\\n]\"","\"(2x3x4):[\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n]\"","\"(2x2x3x4):[\\n [\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n ],\\n [\\n [\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ],\\n [ -2.0 , -1.0 , 0.0 , 1.0 ]\\n ],\\n [\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ]\\n ]\\n ]\\n]\"","\"(2x70):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(2x100):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(70x2):[\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n ... 38 more ...\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ]\\n]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors as String can be formatted depending on shape. [5]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We configure a NDPrintSettings object.","code":["def settings ="," Neureka.get()"," .settings()"," .view()"," .getNDPrintSettings()"," .clone()"," .setRowLimit( mode.contains( \"s\" ) ? 3 : 32 )"," .setIsScientific( mode.contains( \"c\" ) )"," .setIsMultiline( mode.contains( \"f\" ) )"," .setHasGradient( mode.contains( \"g\" ) )"," .setCellSize( mode.contains( \"p\" ) ? 6 : mode.contains( \"f\" ) ? 2 : 1 )"," .setHasValue( !(mode.contains( \"shp\" ) || mode.contains(\"shape\")) )"," .setHasRecursiveGraph( mode.contains( \"r\" ) )"," .setHasDerivatives( mode.contains( \"d\" ) )"," .setHasShape( !mode.contains( \"v\" ) )"," .setIsCellBound( mode .contains( \"b\" ) )"," .setPostfix( \"\" )"," .setPrefix( \"\" )"," .setHasSlimNumbers( false )"]}, + + {"kind":"and","text":"Four tensors of various data types:","code":["Tensor t1 = Tensor.of( Float.class, shape, -4f..5f ).set( Tensor.of( shape, -7f..3f ) )","Tensor t2 = Tensor.of( Double.class, shape, -4d..5d ).set( Tensor.of( shape, -7d..3d ) )","Tensor t3 = Tensor.of( Integer.class, shape, -4..5 ).set( Tensor.of( shape, -7..3 ) )","Tensor t4 = Tensor.of( Short.class, shape, (-4 as short)..(5 as short) ).set( Tensor.of( shape, (-7 as short)..(3 as short) ) )","Tensor t5 = Tensor.of( Byte.class, shape, (-4 as byte )..(5 as byte ) ).set( Tensor.of( shape, (-7 as byte)..(3 as byte) ) )"]}, + + {"kind":"expect","text":"The first tensor has the expected internals and produces the correct String representation.","code":["t1.toString(settings) == expected","t1.dataType == DataType.of( Float.class )","t1.mut.data.get() instanceof float[]"]}, + + {"kind":"and","text":"The second tensor has the expected internals and produces the correct String representation.","code":["t2.toString(settings) == expected","t2.dataType == DataType.of( Double.class )","t2.mut.data.get() instanceof double[]"]}, + + {"kind":"and","text":"The third tensor has the expected internals and produces the correct String representation.","code":["t3.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t3.dataType == DataType.of( Integer.class )","t3.mut.data.get() instanceof int[]"]}, + + {"kind":"and","text":"The fourth tensor has the expected internals and produces the correct String representation.","code":["t4.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t4.dataType == DataType.of( Short.class )","t4.mut.data.get() instanceof short[]"]}, + + {"kind":"and","text":"The fifth tensor has the expected internals and produces the correct String representation.","code":["t5.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t5.dataType == DataType.of( Byte.class )","t5.mut.data.get() instanceof byte[]"]}, + + {"kind":"where","text":"The print configurations codes \"mode\", a common shape and expected String representation will be supplied:","code":{"shape":["[2,3]","[2,3]","[3,2]","[2,3,4]","[2,2,3,4]","[2, 70]","[2, 100]","[70, 2]"],"mode":["\"fap\"","\"fa\"","\"fp\"","\"fp\"","\"fp\"","\"f\"","\"f\"","\"f\""],"expected":["\"(2x3):[\\n [ -4.0 , -3.0 , -2.0 ],\\n [ -1.0 , 0.0 , 1.0 ]\\n]\"","\"(2x3):[\\n [ -4.0, -3.0, -2.0 ],\\n [ -1.0, 0.0, 1.0 ]\\n]\"","\"(3x2):[\\n [ -4.0 , -3.0 ],\\n [ -2.0 , -1.0 ],\\n [ 0.0 , 1.0 ]\\n]\"","\"(2x3x4):[\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n]\"","\"(2x2x3x4):[\\n [\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n ],\\n [\\n [\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ],\\n [ -2.0 , -1.0 , 0.0 , 1.0 ]\\n ],\\n [\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ]\\n ]\\n ]\\n]\"","\"(2x70):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(2x100):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(70x2):[\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n ... 38 more ...\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ]\\n]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors as String can be formatted depending on shape. [6]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We configure a NDPrintSettings object.","code":["def settings ="," Neureka.get()"," .settings()"," .view()"," .getNDPrintSettings()"," .clone()"," .setRowLimit( mode.contains( \"s\" ) ? 3 : 32 )"," .setIsScientific( mode.contains( \"c\" ) )"," .setIsMultiline( mode.contains( \"f\" ) )"," .setHasGradient( mode.contains( \"g\" ) )"," .setCellSize( mode.contains( \"p\" ) ? 6 : mode.contains( \"f\" ) ? 2 : 1 )"," .setHasValue( !(mode.contains( \"shp\" ) || mode.contains(\"shape\")) )"," .setHasRecursiveGraph( mode.contains( \"r\" ) )"," .setHasDerivatives( mode.contains( \"d\" ) )"," .setHasShape( !mode.contains( \"v\" ) )"," .setIsCellBound( mode .contains( \"b\" ) )"," .setPostfix( \"\" )"," .setPrefix( \"\" )"," .setHasSlimNumbers( false )"]}, + + {"kind":"and","text":"Four tensors of various data types:","code":["Tensor t1 = Tensor.of( Float.class, shape, -4f..5f ).set( Tensor.of( shape, -7f..3f ) )","Tensor t2 = Tensor.of( Double.class, shape, -4d..5d ).set( Tensor.of( shape, -7d..3d ) )","Tensor t3 = Tensor.of( Integer.class, shape, -4..5 ).set( Tensor.of( shape, -7..3 ) )","Tensor t4 = Tensor.of( Short.class, shape, (-4 as short)..(5 as short) ).set( Tensor.of( shape, (-7 as short)..(3 as short) ) )","Tensor t5 = Tensor.of( Byte.class, shape, (-4 as byte )..(5 as byte ) ).set( Tensor.of( shape, (-7 as byte)..(3 as byte) ) )"]}, + + {"kind":"expect","text":"The first tensor has the expected internals and produces the correct String representation.","code":["t1.toString(settings) == expected","t1.dataType == DataType.of( Float.class )","t1.mut.data.get() instanceof float[]"]}, + + {"kind":"and","text":"The second tensor has the expected internals and produces the correct String representation.","code":["t2.toString(settings) == expected","t2.dataType == DataType.of( Double.class )","t2.mut.data.get() instanceof double[]"]}, + + {"kind":"and","text":"The third tensor has the expected internals and produces the correct String representation.","code":["t3.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t3.dataType == DataType.of( Integer.class )","t3.mut.data.get() instanceof int[]"]}, + + {"kind":"and","text":"The fourth tensor has the expected internals and produces the correct String representation.","code":["t4.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t4.dataType == DataType.of( Short.class )","t4.mut.data.get() instanceof short[]"]}, + + {"kind":"and","text":"The fifth tensor has the expected internals and produces the correct String representation.","code":["t5.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t5.dataType == DataType.of( Byte.class )","t5.mut.data.get() instanceof byte[]"]}, + + {"kind":"where","text":"The print configurations codes \"mode\", a common shape and expected String representation will be supplied:","code":{"shape":["[2,3]","[2,3]","[3,2]","[2,3,4]","[2,2,3,4]","[2, 70]","[2, 100]","[70, 2]"],"mode":["\"fap\"","\"fa\"","\"fp\"","\"fp\"","\"fp\"","\"f\"","\"f\"","\"f\""],"expected":["\"(2x3):[\\n [ -4.0 , -3.0 , -2.0 ],\\n [ -1.0 , 0.0 , 1.0 ]\\n]\"","\"(2x3):[\\n [ -4.0, -3.0, -2.0 ],\\n [ -1.0, 0.0, 1.0 ]\\n]\"","\"(3x2):[\\n [ -4.0 , -3.0 ],\\n [ -2.0 , -1.0 ],\\n [ 0.0 , 1.0 ]\\n]\"","\"(2x3x4):[\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n]\"","\"(2x2x3x4):[\\n [\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n ],\\n [\\n [\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ],\\n [ -2.0 , -1.0 , 0.0 , 1.0 ]\\n ],\\n [\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ]\\n ]\\n ]\\n]\"","\"(2x70):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(2x100):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(70x2):[\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n ... 38 more ...\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ]\\n]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Tensors as String can be formatted depending on shape. [7]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We configure a NDPrintSettings object.","code":["def settings ="," Neureka.get()"," .settings()"," .view()"," .getNDPrintSettings()"," .clone()"," .setRowLimit( mode.contains( \"s\" ) ? 3 : 32 )"," .setIsScientific( mode.contains( \"c\" ) )"," .setIsMultiline( mode.contains( \"f\" ) )"," .setHasGradient( mode.contains( \"g\" ) )"," .setCellSize( mode.contains( \"p\" ) ? 6 : mode.contains( \"f\" ) ? 2 : 1 )"," .setHasValue( !(mode.contains( \"shp\" ) || mode.contains(\"shape\")) )"," .setHasRecursiveGraph( mode.contains( \"r\" ) )"," .setHasDerivatives( mode.contains( \"d\" ) )"," .setHasShape( !mode.contains( \"v\" ) )"," .setIsCellBound( mode .contains( \"b\" ) )"," .setPostfix( \"\" )"," .setPrefix( \"\" )"," .setHasSlimNumbers( false )"]}, + + {"kind":"and","text":"Four tensors of various data types:","code":["Tensor t1 = Tensor.of( Float.class, shape, -4f..5f ).set( Tensor.of( shape, -7f..3f ) )","Tensor t2 = Tensor.of( Double.class, shape, -4d..5d ).set( Tensor.of( shape, -7d..3d ) )","Tensor t3 = Tensor.of( Integer.class, shape, -4..5 ).set( Tensor.of( shape, -7..3 ) )","Tensor t4 = Tensor.of( Short.class, shape, (-4 as short)..(5 as short) ).set( Tensor.of( shape, (-7 as short)..(3 as short) ) )","Tensor t5 = Tensor.of( Byte.class, shape, (-4 as byte )..(5 as byte ) ).set( Tensor.of( shape, (-7 as byte)..(3 as byte) ) )"]}, + + {"kind":"expect","text":"The first tensor has the expected internals and produces the correct String representation.","code":["t1.toString(settings) == expected","t1.dataType == DataType.of( Float.class )","t1.mut.data.get() instanceof float[]"]}, + + {"kind":"and","text":"The second tensor has the expected internals and produces the correct String representation.","code":["t2.toString(settings) == expected","t2.dataType == DataType.of( Double.class )","t2.mut.data.get() instanceof double[]"]}, + + {"kind":"and","text":"The third tensor has the expected internals and produces the correct String representation.","code":["t3.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t3.dataType == DataType.of( Integer.class )","t3.mut.data.get() instanceof int[]"]}, + + {"kind":"and","text":"The fourth tensor has the expected internals and produces the correct String representation.","code":["t4.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t4.dataType == DataType.of( Short.class )","t4.mut.data.get() instanceof short[]"]}, + + {"kind":"and","text":"The fifth tensor has the expected internals and produces the correct String representation.","code":["t5.toString(settings).replace(' ','') == expected.replace('.0',' ').replace(' ','')","t5.dataType == DataType.of( Byte.class )","t5.mut.data.get() instanceof byte[]"]}, + + {"kind":"where","text":"The print configurations codes \"mode\", a common shape and expected String representation will be supplied:","code":{"shape":["[2,3]","[2,3]","[3,2]","[2,3,4]","[2,2,3,4]","[2, 70]","[2, 100]","[70, 2]"],"mode":["\"fap\"","\"fa\"","\"fp\"","\"fp\"","\"fp\"","\"f\"","\"f\"","\"f\""],"expected":["\"(2x3):[\\n [ -4.0 , -3.0 , -2.0 ],\\n [ -1.0 , 0.0 , 1.0 ]\\n]\"","\"(2x3):[\\n [ -4.0, -3.0, -2.0 ],\\n [ -1.0, 0.0, 1.0 ]\\n]\"","\"(3x2):[\\n [ -4.0 , -3.0 ],\\n [ -2.0 , -1.0 ],\\n [ 0.0 , 1.0 ]\\n]\"","\"(2x3x4):[\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n]\"","\"(2x2x3x4):[\\n [\\n [\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ]\\n ],\\n [\\n [ -2.0 , -1.0 , 0.0 , 1.0 ],\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ]\\n ]\\n ],\\n [\\n [\\n [ 0.0 , 1.0 , 2.0 , 3.0 ],\\n [ 4.0 , 5.0 , -4.0 , -3.0 ],\\n [ -2.0 , -1.0 , 0.0 , 1.0 ]\\n ],\\n [\\n [ 2.0 , 3.0 , 4.0 , 5.0 ],\\n [ -4.0 , -3.0 , -2.0 , -1.0 ],\\n [ 0.0 , 1.0 , 2.0 , 3.0 ]\\n ]\\n ]\\n]\"","\"(2x70):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..38 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(2x100):[\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ],\\n [ -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, ..68 more.., 1.0, 2.0, 3.0, 4.0, 5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0 ]\\n]\"","\"(70x2):[\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n ... 38 more ...\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ],\\n [ -4.0, -3.0 ],\\n [ -2.0, -1.0 ],\\n [ 0.0, 1.0 ],\\n [ 2.0, 3.0 ],\\n [ 4.0, 5.0 ]\\n]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + { "id":"We can create scalar tensors.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -117,7 +374,7 @@ { "id":"Tensor created from shape and datatype has expected state.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -134,7 +391,34 @@ }, { - "id":"The data and the value of a tensor a 2 different things!", + "id":"The data and the value of a tensor a 2 different things! [0]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a simple vector:","code":["var v = Tensor.ofFloats().withShape(3).andFill(-2, 4, 8)"]}, + + {"kind":"and","text":"And then we store it on the device we want to test.","code":["v.to(device)"]}, + + {"kind":"when","text":"We create a slice of the above vector, a scalar...","code":["var s = v.slice().axis(0).at(1).get()"]}, + + {"kind":"then","text":"The slice contains the expected value with respect to the slice parent...","code":["v.at(1).get() == s.at(0).get()"]}, + + {"kind":"and","text":"They both do not share the same value array.","code":["v.items != s.items"]}, + + {"kind":"and","text":"They do however share the same underlying data.","code":["v.mut.data.get() == s.mut.data.get()"]}, + + {"kind":"and","text":"The tensor simply stores the number 4.","code":["s.items == [4f]"]}, + + {"kind":"where","text":"We test the following devices:","code":{"device":["'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The data and the value of a tensor a 2 different things! [1]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.tensors.Tensor_Stats_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Stats_Spec.json index ca394ef51..dc919ea8c 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Stats_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Stats_Spec.json @@ -1,22 +1,22 @@ { "className":"ut.tensors.Tensor_Stats_Spec", "title":"Reducing Tensors", - "narrative":"Various kinds of operations reduce tensors to scalars,\n the most common ones being the min and max operations\n which find the smallest as well as largest number among all\n items of a tensor.\n Neureka exposes various different ways to achieve this,\n all of which are also differential (autograd support).", + "narrative":"Various kinds of operations reduce tensors to scalars,\n the most common ones being the min and max operations \n which find the smallest as well as largest number among all \n items of a tensor.\n Neureka exposes various different ways to achieve this,\n all of which are also differential (autograd support).", "subjects":["neureka.Tensor"], "statistics":{ - "runs":"8", + "runs":"22", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.230 seconds" + "duration":"0.225 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"We can use the max operation as a function", + "id":"We can use the max operation as a function [0]", "result":"PASS", - "duration":"0.081 seconds", + "duration":"0.007 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -39,12 +39,337 @@ }, { - "id":"We can get pre-instantiated min and max functions from the library context.", + "id":"We can use the max operation as a function [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [4]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [9]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [10]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [11]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [12]", "result":"PASS", "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can use the max operation as a function [13]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a min/max function:","code":["var fun = Function.of(reduceType.toLowerCase() + \"(I[0])\")"]}, + + {"kind":"and","text":"A seed, for some variability:","code":["var seed = dataType.getSimpleName().hashCode() + reduceType.hashCode()"]}, + + {"kind":"and","text":"","code":["var a = Tensor.of(dataType)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((seed+31**(i+13))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"when","text":"We apply the function to the tensor:","code":["var result = fun(a)"]}, + + {"kind":"then","text":"The result is correct:","code":["result.items[0] == expected"]}, + + {"kind":"where","text":"","code":{"reduceType":["'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'","'MIN'","'MAX'"],"dataType":["Float","Float","Double","Double","Integer","Integer","Long","Long","Short","Short","Byte","Byte","Float","Float"],"device":["'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'CPU'","'GPU'","'GPU'"],"expected":["-148.0","141.0","-143.0","149.0","-121","148","-146","147","-148","146","-127","124","-148.0","141.0"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can get pre-instantiated min and max functions from the library context.", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"We access the pre-instantiated max function:","code":["var min = Neureka.get().backend.autogradFunction.min"]}, @@ -56,9 +381,30 @@ }, { - "id":"There is no need to use a function, we can use the min() and max() methods on tensors instead.", + "id":"There is no need to use a function, we can use the min() and max() methods on tensors instead. [0]", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.007 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"We create a tensor:","code":["var a = Tensor.of(Float)"," .withShape(19, 7)"," .andWhere({ i, _ -> ((31**(i+42))%301)-151})"]}, + + {"kind":"and","text":"Before applying the function, we copy the tensor to the device:","code":["a.to(device)"]}, + + {"kind":"and","text":"We access the min and max methods:","code":["var min = a.min()","var max = a.max()"]}, + + {"kind":"expect","text":"The results are correct:","code":["min.item(0) == -150.0","max.item(0) == 147.0"]}, + + {"kind":"where","text":"","code":{"device":["'CPU'","'GPU'"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"There is no need to use a function, we can use the min() and max() methods on tensors instead. [1]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -79,7 +425,7 @@ { "id":"Both the min and max operation support autograd (back-propagation).", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.008 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -100,7 +446,7 @@ { "id":"We can use the \"sum\" method to sum the items of a tensor.", "result":"PASS", - "duration":"0.130 seconds", + "duration":"0.172 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -119,7 +465,7 @@ { "id":"The sum operation support autograd (back-propagation).", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -140,7 +486,7 @@ { "id":"A tensor can be summed alongside a specific axis.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -157,7 +503,7 @@ { "id":"Multiple dimensions of a tensor can selectively be summed up.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":["\n Given a tensor with a shape of (x, y, z) and the request to sum up axis 1 and 2,\n then the result will be a tensor with a shape of (x, 1, 1) because the\n sum of all values along the axis 1 and 2 is a single value for each of the two\n first dimensions.\n This operation supports autograd.\n "] }, diff --git a/docs/spock/reports/ut.tensors.Tensor_Version_Spec.json b/docs/spock/reports/ut.tensors.Tensor_Version_Spec.json index b7b89f388..ec6b48714 100644 --- a/docs/spock/reports/ut.tensors.Tensor_Version_Spec.json +++ b/docs/spock/reports/ut.tensors.Tensor_Version_Spec.json @@ -1,22 +1,22 @@ { "className":"ut.tensors.Tensor_Version_Spec", "title":"Tensor (Data Array) Version", - "narrative":"There are two fundamental categories of operations\n which can be applied to tensors :\n Inline operations and Non-Inline operations!\n\n Inline operations are often times problematic because they produce\n side effects by changing passed tensors instead of producing new ones...\n One such bad side effect can easily occur for tensors involved in the\n autograd system, more specifically: the recorded computation graph.\n Inline operations can break the mathematically pureness of the back-propagation\n procedure by for example changing partial derivatives...
        \n In order to prevent said errors from occurring unnoticed tensors\n have versions which will increment when the underlying data of the tensor changes.\n This version will be tracked by the computation graph as well in order to\n match it with the ones stored inside the tensor.\n A mismatch would then yield an exception!\n\n This specification is responsible for defining the behaviour of this\n version number with respect to their wrapping tensors as well as computation graph nodes.", + "narrative":"There are two fundamental categories of operations\n which can be applied to tensors : \n Inline operations and Non-Inline operations! \n\n Inline operations are often times problematic because they produce\n side effects by changing passed tensors instead of producing new ones... \n One such bad side effect can easily occur for tensors involved in the\n autograd system, more specifically: the recorded computation graph. \n Inline operations can break the mathematically pureness of the back-propagation\n procedure by for example changing partial derivatives...
        \n In order to prevent said errors from occurring unnoticed tensors\n have versions which will increment when the underlying data of the tensor changes. \n This version will be tracked by the computation graph as well in order to\n match it with the ones stored inside the tensor. \n A mismatch would then yield an exception! \n\n This specification is responsible for defining the behaviour of this\n version number with respect to their wrapping tensors as well as computation graph nodes.", "subjects":["neureka.Tensor"], "statistics":{ - "runs":"4", + "runs":"22", "successRate":"100.0%", "failures":"0", "errors":"0", - "skipped":"0", - "duration":"0.050 seconds" + "skipped":"1", + "duration":"0.135 seconds" }, "headers":["\n

        \n Here you can find out how the version number of a tensor is\n set and tracked.\n

        \n "],"tags":{},"see":[], "features":[ { - "id":"Inline operations causes version incrementation.", + "id":"Inline operations causes version incrementation. [0]", "result":"PASS", - "duration":"0.013 seconds", + "duration":"0.010 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,9 +37,305 @@ }, { - "id":"Non-inline operations do not cause version incrementation.", + "id":"Inline operations causes version incrementation. [1]", "result":"PASS", - "duration":"0.012 seconds", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"2 tensors a and b.","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( safe_inline )","Tensor a = Tensor.of(4d) + Tensor.of(2d)","Tensor b = Tensor.of(-1d) + Tensor.of(-3d).setRqsGradient(true)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"Initially both tensors have a version number of 0.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The groovy code (performing inline operations) is being evaluated.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected sub-string.","code":["c.toString().contains(expected)","c == a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"","code":{"code":["' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '","' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '"],"safe_inline":["true","true","true","true","false","false","false","false"],"version_of_c":["1","1","1","1","0","0","0","0"],"version_of_a":["1","1","1","1","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Inline operations causes version incrementation. [2]", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"2 tensors a and b.","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( safe_inline )","Tensor a = Tensor.of(4d) + Tensor.of(2d)","Tensor b = Tensor.of(-1d) + Tensor.of(-3d).setRqsGradient(true)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"Initially both tensors have a version number of 0.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The groovy code (performing inline operations) is being evaluated.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected sub-string.","code":["c.toString().contains(expected)","c == a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"","code":{"code":["' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '","' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '"],"safe_inline":["true","true","true","true","false","false","false","false"],"version_of_c":["1","1","1","1","0","0","0","0"],"version_of_a":["1","1","1","1","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Inline operations causes version incrementation. [3]", + "result":"PASS", + "duration":"0.006 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"2 tensors a and b.","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( safe_inline )","Tensor a = Tensor.of(4d) + Tensor.of(2d)","Tensor b = Tensor.of(-1d) + Tensor.of(-3d).setRqsGradient(true)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"Initially both tensors have a version number of 0.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The groovy code (performing inline operations) is being evaluated.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected sub-string.","code":["c.toString().contains(expected)","c == a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"","code":{"code":["' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '","' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '"],"safe_inline":["true","true","true","true","false","false","false","false"],"version_of_c":["1","1","1","1","0","0","0","0"],"version_of_a":["1","1","1","1","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Inline operations causes version incrementation. [4]", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"2 tensors a and b.","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( safe_inline )","Tensor a = Tensor.of(4d) + Tensor.of(2d)","Tensor b = Tensor.of(-1d) + Tensor.of(-3d).setRqsGradient(true)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"Initially both tensors have a version number of 0.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The groovy code (performing inline operations) is being evaluated.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected sub-string.","code":["c.toString().contains(expected)","c == a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"","code":{"code":["' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '","' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '"],"safe_inline":["true","true","true","true","false","false","false","false"],"version_of_c":["1","1","1","1","0","0","0","0"],"version_of_a":["1","1","1","1","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Inline operations causes version incrementation. [5]", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"2 tensors a and b.","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( safe_inline )","Tensor a = Tensor.of(4d) + Tensor.of(2d)","Tensor b = Tensor.of(-1d) + Tensor.of(-3d).setRqsGradient(true)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"Initially both tensors have a version number of 0.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The groovy code (performing inline operations) is being evaluated.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected sub-string.","code":["c.toString().contains(expected)","c == a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"","code":{"code":["' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '","' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '"],"safe_inline":["true","true","true","true","false","false","false","false"],"version_of_c":["1","1","1","1","0","0","0","0"],"version_of_a":["1","1","1","1","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Inline operations causes version incrementation. [6]", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"2 tensors a and b.","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( safe_inline )","Tensor a = Tensor.of(4d) + Tensor.of(2d)","Tensor b = Tensor.of(-1d) + Tensor.of(-3d).setRqsGradient(true)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"Initially both tensors have a version number of 0.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The groovy code (performing inline operations) is being evaluated.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected sub-string.","code":["c.toString().contains(expected)","c == a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"","code":{"code":["' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '","' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '"],"safe_inline":["true","true","true","true","false","false","false","false"],"version_of_c":["1","1","1","1","0","0","0","0"],"version_of_a":["1","1","1","1","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Inline operations causes version incrementation. [7]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"2 tensors a and b.","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( safe_inline )","Tensor a = Tensor.of(4d) + Tensor.of(2d)","Tensor b = Tensor.of(-1d) + Tensor.of(-3d).setRqsGradient(true)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"Initially both tensors have a version number of 0.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The groovy code (performing inline operations) is being evaluated.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected sub-string.","code":["c.toString().contains(expected)","c == a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"","code":{"code":["' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '","' a.mut.plusAssign(b) '","' a.mut.minusAssign(b) '","' a.mut.timesAssign(b) '","' a.mut.divAssign(b) '"],"safe_inline":["true","true","true","true","false","false","false","false"],"version_of_c":["1","1","1","1","0","0","0","0"],"version_of_a":["1","1","1","1","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Non-inline operations do not cause version incrementation. [0]", + "result":"PASS", + "duration":"0.007 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( no_inline )"]}, + + {"kind":"and","text":"Two tensors, one requiring gradients and the other one not.","code":["Tensor a = Tensor.of(6d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)"]}, + + {"kind":"and","text":"A binding for both tensors as preparation for calling the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The versions of both tensors are 0 initially.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The Groovy code is being evaluated inside the Groovy shell.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)","c != a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"The following arguments are being used:","code":{"code":["' a + b '","' a - b '","' a * b '","' a / b '","' a % b '","' a + b '","' a - b '","' a * b '","' a / b '","' a % b '"],"no_inline":["false","false","false","false","false","true","true","true","true","true"],"version_of_c":["0","0","0","0","0","0","0","0","0","0"],"version_of_a":["0","0","0","0","0","0","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Non-inline operations do not cause version incrementation. [1]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( no_inline )"]}, + + {"kind":"and","text":"Two tensors, one requiring gradients and the other one not.","code":["Tensor a = Tensor.of(6d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)"]}, + + {"kind":"and","text":"A binding for both tensors as preparation for calling the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The versions of both tensors are 0 initially.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The Groovy code is being evaluated inside the Groovy shell.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)","c != a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"The following arguments are being used:","code":{"code":["' a + b '","' a - b '","' a * b '","' a / b '","' a % b '","' a + b '","' a - b '","' a * b '","' a / b '","' a % b '"],"no_inline":["false","false","false","false","false","true","true","true","true","true"],"version_of_c":["0","0","0","0","0","0","0","0","0","0"],"version_of_a":["0","0","0","0","0","0","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Non-inline operations do not cause version incrementation. [2]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( no_inline )"]}, + + {"kind":"and","text":"Two tensors, one requiring gradients and the other one not.","code":["Tensor a = Tensor.of(6d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)"]}, + + {"kind":"and","text":"A binding for both tensors as preparation for calling the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The versions of both tensors are 0 initially.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The Groovy code is being evaluated inside the Groovy shell.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)","c != a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"The following arguments are being used:","code":{"code":["' a + b '","' a - b '","' a * b '","' a / b '","' a % b '","' a + b '","' a - b '","' a * b '","' a / b '","' a % b '"],"no_inline":["false","false","false","false","false","true","true","true","true","true"],"version_of_c":["0","0","0","0","0","0","0","0","0","0"],"version_of_a":["0","0","0","0","0","0","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Non-inline operations do not cause version incrementation. [3]", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( no_inline )"]}, + + {"kind":"and","text":"Two tensors, one requiring gradients and the other one not.","code":["Tensor a = Tensor.of(6d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)"]}, + + {"kind":"and","text":"A binding for both tensors as preparation for calling the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The versions of both tensors are 0 initially.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The Groovy code is being evaluated inside the Groovy shell.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)","c != a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"The following arguments are being used:","code":{"code":["' a + b '","' a - b '","' a * b '","' a / b '","' a % b '","' a + b '","' a - b '","' a * b '","' a / b '","' a % b '"],"no_inline":["false","false","false","false","false","true","true","true","true","true"],"version_of_c":["0","0","0","0","0","0","0","0","0","0"],"version_of_a":["0","0","0","0","0","0","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Non-inline operations do not cause version incrementation. [4]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( no_inline )"]}, + + {"kind":"and","text":"Two tensors, one requiring gradients and the other one not.","code":["Tensor a = Tensor.of(6d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)"]}, + + {"kind":"and","text":"A binding for both tensors as preparation for calling the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The versions of both tensors are 0 initially.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The Groovy code is being evaluated inside the Groovy shell.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)","c != a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"The following arguments are being used:","code":{"code":["' a + b '","' a - b '","' a * b '","' a / b '","' a % b '","' a + b '","' a - b '","' a * b '","' a / b '","' a % b '"],"no_inline":["false","false","false","false","false","true","true","true","true","true"],"version_of_c":["0","0","0","0","0","0","0","0","0","0"],"version_of_a":["0","0","0","0","0","0","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Non-inline operations do not cause version incrementation. [5]", + "result":"PASS", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -64,12 +360,120 @@ }, { - "id":"Inline operations cause illegal state exceptions.", + "id":"Non-inline operations do not cause version incrementation. [6]", "result":"PASS", "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( no_inline )"]}, + + {"kind":"and","text":"Two tensors, one requiring gradients and the other one not.","code":["Tensor a = Tensor.of(6d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)"]}, + + {"kind":"and","text":"A binding for both tensors as preparation for calling the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The versions of both tensors are 0 initially.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The Groovy code is being evaluated inside the Groovy shell.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)","c != a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"The following arguments are being used:","code":{"code":["' a + b '","' a - b '","' a * b '","' a / b '","' a % b '","' a + b '","' a - b '","' a * b '","' a / b '","' a % b '"],"no_inline":["false","false","false","false","false","true","true","true","true","true"],"version_of_c":["0","0","0","0","0","0","0","0","0","0"],"version_of_a":["0","0","0","0","0","0","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Non-inline operations do not cause version incrementation. [7]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( no_inline )"]}, + + {"kind":"and","text":"Two tensors, one requiring gradients and the other one not.","code":["Tensor a = Tensor.of(6d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)"]}, + + {"kind":"and","text":"A binding for both tensors as preparation for calling the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The versions of both tensors are 0 initially.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The Groovy code is being evaluated inside the Groovy shell.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)","c != a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"The following arguments are being used:","code":{"code":["' a + b '","' a - b '","' a * b '","' a / b '","' a % b '","' a + b '","' a - b '","' a * b '","' a / b '","' a % b '"],"no_inline":["false","false","false","false","false","true","true","true","true","true"],"version_of_c":["0","0","0","0","0","0","0","0","0","0"],"version_of_a":["0","0","0","0","0","0","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Non-inline operations do not cause version incrementation. [8]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( no_inline )"]}, + + {"kind":"and","text":"Two tensors, one requiring gradients and the other one not.","code":["Tensor a = Tensor.of(6d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)"]}, + + {"kind":"and","text":"A binding for both tensors as preparation for calling the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The versions of both tensors are 0 initially.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The Groovy code is being evaluated inside the Groovy shell.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)","c != a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"The following arguments are being used:","code":{"code":["' a + b '","' a - b '","' a * b '","' a / b '","' a % b '","' a + b '","' a - b '","' a * b '","' a / b '","' a % b '"],"no_inline":["false","false","false","false","false","true","true","true","true","true"],"version_of_c":["0","0","0","0","0","0","0","0","0","0"],"version_of_a":["0","0","0","0","0","0","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Non-inline operations do not cause version incrementation. [9]", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( no_inline )"]}, + + {"kind":"and","text":"Two tensors, one requiring gradients and the other one not.","code":["Tensor a = Tensor.of(6d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)"]}, + + {"kind":"and","text":"A binding for both tensors as preparation for calling the Groovy shell.","code":["Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"The versions of both tensors are 0 initially.","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The Groovy code is being evaluated inside the Groovy shell.","code":["Tensor c = new GroovyShell(binding).evaluate((code))"]}, + + {"kind":"then","text":"The resulting tensor (toString) will contain the expected String.","code":["c.toString().contains(expected)","c != a"]}, + + {"kind":"and","text":"The three tensors have the expected versions.","code":["a.getVersion() == version_of_a","b.getVersion() == version_of_b","c.getVersion() == version_of_c"]}, + + {"kind":"where","text":"The following arguments are being used:","code":{"code":["' a + b '","' a - b '","' a * b '","' a / b '","' a % b '","' a + b '","' a - b '","' a * b '","' a / b '","' a % b '"],"no_inline":["false","false","false","false","false","true","true","true","true","true"],"version_of_c":["0","0","0","0","0","0","0","0","0","0"],"version_of_a":["0","0","0","0","0","0","0","0","0","0"],"version_of_b":["0","0","0","0","0","0","0","0","0","0"],"expected":["\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\"","\"(1):[2.0]\"","\"(1):[10.0]\"","\"(1):[-24.0]\"","\"(1):[-1.5]\"","\"(1):[2.0]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Inline operations cause illegal state exceptions. [0]", + "result":"PASS", + "duration":"0.008 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( true )","Tensor a = Tensor.of(4d) + Tensor.of(2d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, @@ -87,9 +491,78 @@ }, { - "id":"Storing a tensor on a device should not change the version of a tensor (Even though its data changed technically).", + "id":"Inline operations cause illegal state exceptions. [1]", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( true )","Tensor a = Tensor.of(4d) + Tensor.of(2d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["Tensor c = new GroovyShell( binding ).evaluate( code )"]}, + + {"kind":"then","text":"An illegal state exception is being thrown.","code":["def exception = thrown(IllegalStateException)","exception.message == message"]}, + + {"kind":"and","text":"The variable \"c\" is null!","code":["c == null"]}, + + {"kind":"where","text":"","code":{"code":["'a.mut.plusAssign(b) '","'a.mut.minusAssign(b)'","'a.mut.timesAssign(b)'","'a.mut.divAssign(b) '"],"message":["\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\"","\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\"","\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\"","\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Inline operations cause illegal state exceptions. [2]", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( true )","Tensor a = Tensor.of(4d) + Tensor.of(2d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["Tensor c = new GroovyShell( binding ).evaluate( code )"]}, + + {"kind":"then","text":"An illegal state exception is being thrown.","code":["def exception = thrown(IllegalStateException)","exception.message == message"]}, + + {"kind":"and","text":"The variable \"c\" is null!","code":["c == null"]}, + + {"kind":"where","text":"","code":{"code":["'a.mut.plusAssign(b) '","'a.mut.minusAssign(b)'","'a.mut.timesAssign(b)'","'a.mut.divAssign(b) '"],"message":["\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\"","\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\"","\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\"","\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Inline operations cause illegal state exceptions. [3]", + "result":"PASS", + "duration":"0.005 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Neureka.get().settings().autograd().setIsPreventingInlineOperations( true )","Tensor a = Tensor.of(4d) + Tensor.of(2d).setRqsGradient(true)","Tensor b = Tensor.of(-4d)","Binding binding = new Binding()","binding.setVariable('a', a)","binding.setVariable('b', b)"]}, + + {"kind":"expect","text":"","code":["a.getVersion() == 0","b.getVersion() == 0"]}, + + {"kind":"when","text":"The groovy code is being evaluated.","code":["Tensor c = new GroovyShell( binding ).evaluate( code )"]}, + + {"kind":"then","text":"An illegal state exception is being thrown.","code":["def exception = thrown(IllegalStateException)","exception.message == message"]}, + + {"kind":"and","text":"The variable \"c\" is null!","code":["c == null"]}, + + {"kind":"where","text":"","code":{"code":["'a.mut.plusAssign(b) '","'a.mut.minusAssign(b)'","'a.mut.timesAssign(b)'","'a.mut.divAssign(b) '"],"message":["\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\"","\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\"","\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\"","\"Inline operation occurred on tensor which is part of a computation graph node with autograd support!\\nThe following OperationType caused an internal version mismatch: 'left_inline'\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Storing a tensor on a device should not change the version of a tensor (Even though its data changed technically).", + "result":"IGNORED", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.exceptions.Tensor_Delete_Exception_Spec.json b/docs/spock/reports/ut.tensors.exceptions.Tensor_Delete_Exception_Spec.json index 8100599a6..d8636db26 100644 --- a/docs/spock/reports/ut.tensors.exceptions.Tensor_Delete_Exception_Spec.json +++ b/docs/spock/reports/ut.tensors.exceptions.Tensor_Delete_Exception_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.014 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"A deleted tensor will tell you that it has been deleted.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -31,7 +31,7 @@ { "id":"A deleted tensor will throw an exception when accessing its configuration.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -50,7 +50,7 @@ { "id":"A deleted tensor will throw an exception when trying to set its configuration.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -69,7 +69,7 @@ { "id":"A deleted tensor will throw an exception when accessing its data.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -88,7 +88,7 @@ { "id":"A deleted tensor will throw an exception when trying to modify its data.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -107,7 +107,7 @@ { "id":"A deleted tensor will throw an exception when accessing its data type.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -126,7 +126,7 @@ { "id":"A deleted tensor will throw an exception when modifying its data type.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.tensors.exceptions.Tensor_Exception_Spec.json b/docs/spock/reports/ut.tensors.exceptions.Tensor_Exception_Spec.json index ae0e19a17..52ecd657a 100644 --- a/docs/spock/reports/ut.tensors.exceptions.Tensor_Exception_Spec.json +++ b/docs/spock/reports/ut.tensors.exceptions.Tensor_Exception_Spec.json @@ -4,19 +4,19 @@ "narrative":"This specification covers the behavior of the Tensor class in\n exceptional scenarios which are contrary to its intended use.\n The purpose of this is to assert that the Tensor class will provide\n useful feedback to a user to explain that a misuse of its API\n occurred so that the user can correct this misuse.", "subjects":[], "statistics":{ - "runs":"8", + "runs":"25", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.014 seconds" + "duration":"0.047 seconds" }, "headers":["\n

        Tensors Exception Behavior

        \n
        \n

        \n This specification covers the behavior of tensors\n in exceptional situations. \n

        \n "],"tags":{},"see":[], "features":[ { "id":"Trying to inject an empty tensor into another causes fitting exception.", "result":"PASS", - "duration":"0", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -31,7 +31,28 @@ }, { - "id":"Passing null to various methods of the tensor API will throw exceptions.", + "id":"Passing null to various methods of the tensor API will throw exceptions. [0]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [1]", "result":"PASS", "duration":"0", "iterations":{ @@ -52,12 +73,348 @@ }, { - "id":"Passing an invalid object into Tensor constructor causes descriptive exception.", + "id":"Passing null to various methods of the tensor API will throw exceptions. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [8]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [9]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [10]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [11]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [12]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [13]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [14]", "result":"PASS", "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [15]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [16]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing null to various methods of the tensor API will throw exceptions. [17]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["Tensor t = Tensor.of(1, 2, 3)"]}, + + {"kind":"when","text":"","code":["errorCode(t)"]}, + + {"kind":"then","text":"","code":["var exception = thrown(type)"]}, + + {"kind":"and","text":"","code":["exception.message != \"\" && exception.message.length() > 13"]}, + + {"kind":"where","text":"","code":{"type":["IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException","IllegalArgumentException"],"errorCode":["{ Tensor x -> x.times((Tensor)null) }","{ Tensor x -> x.div((Tensor)null) }","{ Tensor x -> x.plus((Tensor)null) }","{ Tensor x -> x.mod((Tensor)null) }","{ Tensor x -> x.mut.timesAssign((Tensor)null) }","{ Tensor x -> x.mut.divAssign((Tensor)null) }","{ Tensor x -> x.mut.plusAssign((Tensor)null) }","{ Tensor x -> x.mut.modAssign((Tensor)null) }","{ Tensor x -> x.mut.minusAssign((Tensor)null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((String[][])null) }","{ Tensor x -> x.mut.labelAxes((Map)null) }","{ Tensor x -> x.mut.label(null) }","{ Tensor x -> x.withLabels((String[][])null) }","{ Tensor x -> x.withLabels(null, (String[])null) }","{ Tensor x -> x.withLabels((String[])null) }","{ Tensor x -> x.withLabels((Map)null) }","{ Tensor x -> x.withLabel(null) }"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Passing an invalid object into Tensor constructor causes descriptive exception.", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, "blocks":[ {"kind":"when","text":"A tensor is being instantiated with a nonsensical parameter.","code":["Tensor.ofRandom(Scanner.class, 2, 4)"]}, @@ -69,7 +426,7 @@ { "id":"Passing an invalid key object into the \"getAt\" method causes a descriptive exception.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -86,7 +443,7 @@ { "id":"Out of dimension bound causes descriptive exception!", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -116,7 +473,7 @@ { "id":"Casting a tensor as something unusual will cuas an exception to be thrown.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -133,7 +490,7 @@ { "id":"Building a tensor with \"null\" as shape argument throws an exception.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.utility.Cleaner_Testing.json b/docs/spock/reports/ut.utility.Cleaner_Testing.json index ddcba964f..dd92a9631 100644 --- a/docs/spock/reports/ut.utility.Cleaner_Testing.json +++ b/docs/spock/reports/ut.utility.Cleaner_Testing.json @@ -1,7 +1,7 @@ { "className":"ut.utility.Cleaner_Testing", "title":"How Neureka Cleans Up", - "narrative":"Under the hood\n Neureka deals whith large arrays of\n data, which are often times\n native data arrays requiring explicit\n memory freeing!\n This freeing of memory can happen at any time\n during the livetime of a nd-array, however\n it should happen at least up until the nd-arra/tensor\n objects representing their referenced data arrays become\n eligible for garbage collection.\n This specification ensures that the custom garbage\n cleaner implementation used by Neureka fulfills this role", + "narrative":"Under the hood \n Neureka deals whith large arrays of\n data, which are often times \n native data arrays requiring explicit\n memory freeing!\n This freeing of memory can happen at any time\n during the livetime of a nd-array, however\n it should happen at least up until the nd-arra/tensor\n objects representing their referenced data arrays become\n eligible for garbage collection.\n This specification ensures that the custom garbage\n cleaner implementation used by Neureka fulfills this role", "subjects":[], "statistics":{ "runs":"1", @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.260 seconds" + "duration":"1.304 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"The DeviceCleaner triggers registered cleaner actions when things are eligible for GC.", "result":"PASS", - "duration":"0.259 seconds", + "duration":"1.302 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -25,11 +25,11 @@ {"kind":"when","text":"","code":["r1 = null","r3 = null","System.gc()"]}, - {"kind":"then","text":"","code":["Sleep.until(700, { refCount == 8 && cleaner._registered == 8 })","r1 == null","r2 != null","r3 == null","r4 != null","r5 != null","r6 != null","r7 != null","r8 != null","r9 != null","r10 != null"]}, + {"kind":"then","text":"","code":["Sleep.until(700, { refCount == 8 && cleaner._toBeCleaned.size() == 8 })","r1 == null","r2 != null","r3 == null","r4 != null","r5 != null","r6 != null","r7 != null","r8 != null","r9 != null","r10 != null"]}, {"kind":"when","text":"","code":["r2 = null","r4 = null","System.gc()"]}, - {"kind":"then","text":"","code":["Sleep.until(750, { refCount == 6 && cleaner._registered == 6 })","r1 == null","r2 == null","r3 == null","r4 == null","r5 != null","r6 != null","r7 != null","r8 != null","r9 != null","r10 != null"]} + {"kind":"then","text":"","code":["Sleep.until(750, { refCount == 6 && cleaner._toBeCleaned.size() == 6 })","r1 == null","r2 == null","r3 == null","r4 == null","r5 != null","r6 != null","r7 != null","r8 != null","r9 != null","r10 != null"]} ], "problems":{"dataValues":[], "errors":[]} } diff --git a/docs/spock/reports/ut.utility.DataConverter_Spec.json b/docs/spock/reports/ut.utility.DataConverter_Spec.json index 91fc62d8e..d0a7d0223 100644 --- a/docs/spock/reports/ut.utility.DataConverter_Spec.json +++ b/docs/spock/reports/ut.utility.DataConverter_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.002 seconds" + "duration":"0.009 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"The DataConverter can convert the given array data.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -31,7 +31,7 @@ { "id":"An array of any type of object may be converted to a array of primitives.", "result":"PASS", - "duration":"0", + "duration":"0.004 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.utility.FileHandle_Spec.json b/docs/spock/reports/ut.utility.FileHandle_Spec.json index 07c23e016..999b5ca80 100644 --- a/docs/spock/reports/ut.utility.FileHandle_Spec.json +++ b/docs/spock/reports/ut.utility.FileHandle_Spec.json @@ -4,19 +4,226 @@ "narrative":"", "subjects":[], "statistics":{ - "runs":"6", + "runs":"24", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.536 seconds" + "duration":"0.787 seconds" }, "headers":["\n This specification covers the expected functionality of\n various \"FileHandle\" implementations.\n Such implementations ought to be able to save tensors to\n a given directory in the file format that they represent.\n Functionalities like : \"store\", \"restore\" and \"free\" must\n behave as expected.\n (For more information take a look a the \"FileHandle\" & \"Storage\" interface)\n "],"tags":{},"see":[], "features":[ { - "id":"Test writing IDX file format.", + "id":"Test writing IDX file format. [0]", "result":"PASS", - "duration":"0.001 seconds", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":[" Neureka.get().settings().view().ndArrays({ NDPrintSettings it ->"," it.setIsScientific( true )"," it.setIsMultiline( false )"," it.setHasGradient( true )"," it.setCellSize( 1 )"," it.setHasValue( true )"," it.setHasRecursiveGraph( false )"," it.setHasDerivatives( false )"," it.setHasShape( true )"," it.setIsCellBound( false )"," it.setPostfix( \"\" )"," it.setPrefix( \"\" )"," it.setHasSlimNumbers( false )","})"]}, + + {"kind":"when","text":"A new IDX file handle for the given filename is being instantiated.","code":["IDXHandle idx = new IDXHandle(tensor, \"build/test-can/\"+filename)"]}, + + {"kind":"then","text":"The file will then exist at the following path: ","code":["new File(\"build/test-can/\"+filename).exists()"]}, + + {"kind":"when","text":"The \"load\" method is being called in order to load the tensor into memory.","code":["Tensor loaded = idx.load()"]}, + + {"kind":"then","text":"The loaded tensor is as expected...","code":["loaded != null","loaded.toString() == expected","loaded.getDataType().getRepresentativeType() == type"]}, + + {"kind":"where","text":"The following paths and file names are being used for testing : ","code":{"tensor":["Tensor.of([2, 4], -2d..4d)","Tensor.of([2, 4], 2d)","Tensor.of(Float, [8], -2f..4f)","Tensor.of([4, 2], 2f)","Tensor.of(Integer, [3], 2..4)","Tensor.of([2, 2], 2)","Tensor.of(Short, [2], 2..4)","Tensor.of([2, 2], 2 as short)","Tensor.of(Byte, [2], 2..4)","Tensor.of([1, 2], 2 as byte)","Tensor.of(Long, [6], -3..4)"],"type":["F64.class","F64.class","F32.class","F32.class","I32.class","I32.class","I16.class","I16.class","I8.class","I8.class","I64.class"],"filename":["\"test.idx3-ubyte\"","\"test2.idx\"","\"test_f32_1.idx\"","\"test_f32_2.idx\"","\"test_i32_1.idx\"","\"test_i32_2.idx\"","\"test_i16_1.idx\"","\"test_i16_2.idx\"","\"test_i8_1.idx\"","\"test_i8_2.idx\"","\"test_i64_1.idx\""],"expected":["\"(2x4):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(2x4):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(8):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(4x2):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(3):[2, 3, 4]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(1x2):[2, 2]\"","\"(6):[-3, -2, -1, 0, 1, 2]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test writing IDX file format. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":[" Neureka.get().settings().view().ndArrays({ NDPrintSettings it ->"," it.setIsScientific( true )"," it.setIsMultiline( false )"," it.setHasGradient( true )"," it.setCellSize( 1 )"," it.setHasValue( true )"," it.setHasRecursiveGraph( false )"," it.setHasDerivatives( false )"," it.setHasShape( true )"," it.setIsCellBound( false )"," it.setPostfix( \"\" )"," it.setPrefix( \"\" )"," it.setHasSlimNumbers( false )","})"]}, + + {"kind":"when","text":"A new IDX file handle for the given filename is being instantiated.","code":["IDXHandle idx = new IDXHandle(tensor, \"build/test-can/\"+filename)"]}, + + {"kind":"then","text":"The file will then exist at the following path: ","code":["new File(\"build/test-can/\"+filename).exists()"]}, + + {"kind":"when","text":"The \"load\" method is being called in order to load the tensor into memory.","code":["Tensor loaded = idx.load()"]}, + + {"kind":"then","text":"The loaded tensor is as expected...","code":["loaded != null","loaded.toString() == expected","loaded.getDataType().getRepresentativeType() == type"]}, + + {"kind":"where","text":"The following paths and file names are being used for testing : ","code":{"tensor":["Tensor.of([2, 4], -2d..4d)","Tensor.of([2, 4], 2d)","Tensor.of(Float, [8], -2f..4f)","Tensor.of([4, 2], 2f)","Tensor.of(Integer, [3], 2..4)","Tensor.of([2, 2], 2)","Tensor.of(Short, [2], 2..4)","Tensor.of([2, 2], 2 as short)","Tensor.of(Byte, [2], 2..4)","Tensor.of([1, 2], 2 as byte)","Tensor.of(Long, [6], -3..4)"],"type":["F64.class","F64.class","F32.class","F32.class","I32.class","I32.class","I16.class","I16.class","I8.class","I8.class","I64.class"],"filename":["\"test.idx3-ubyte\"","\"test2.idx\"","\"test_f32_1.idx\"","\"test_f32_2.idx\"","\"test_i32_1.idx\"","\"test_i32_2.idx\"","\"test_i16_1.idx\"","\"test_i16_2.idx\"","\"test_i8_1.idx\"","\"test_i8_2.idx\"","\"test_i64_1.idx\""],"expected":["\"(2x4):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(2x4):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(8):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(4x2):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(3):[2, 3, 4]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(1x2):[2, 2]\"","\"(6):[-3, -2, -1, 0, 1, 2]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test writing IDX file format. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":[" Neureka.get().settings().view().ndArrays({ NDPrintSettings it ->"," it.setIsScientific( true )"," it.setIsMultiline( false )"," it.setHasGradient( true )"," it.setCellSize( 1 )"," it.setHasValue( true )"," it.setHasRecursiveGraph( false )"," it.setHasDerivatives( false )"," it.setHasShape( true )"," it.setIsCellBound( false )"," it.setPostfix( \"\" )"," it.setPrefix( \"\" )"," it.setHasSlimNumbers( false )","})"]}, + + {"kind":"when","text":"A new IDX file handle for the given filename is being instantiated.","code":["IDXHandle idx = new IDXHandle(tensor, \"build/test-can/\"+filename)"]}, + + {"kind":"then","text":"The file will then exist at the following path: ","code":["new File(\"build/test-can/\"+filename).exists()"]}, + + {"kind":"when","text":"The \"load\" method is being called in order to load the tensor into memory.","code":["Tensor loaded = idx.load()"]}, + + {"kind":"then","text":"The loaded tensor is as expected...","code":["loaded != null","loaded.toString() == expected","loaded.getDataType().getRepresentativeType() == type"]}, + + {"kind":"where","text":"The following paths and file names are being used for testing : ","code":{"tensor":["Tensor.of([2, 4], -2d..4d)","Tensor.of([2, 4], 2d)","Tensor.of(Float, [8], -2f..4f)","Tensor.of([4, 2], 2f)","Tensor.of(Integer, [3], 2..4)","Tensor.of([2, 2], 2)","Tensor.of(Short, [2], 2..4)","Tensor.of([2, 2], 2 as short)","Tensor.of(Byte, [2], 2..4)","Tensor.of([1, 2], 2 as byte)","Tensor.of(Long, [6], -3..4)"],"type":["F64.class","F64.class","F32.class","F32.class","I32.class","I32.class","I16.class","I16.class","I8.class","I8.class","I64.class"],"filename":["\"test.idx3-ubyte\"","\"test2.idx\"","\"test_f32_1.idx\"","\"test_f32_2.idx\"","\"test_i32_1.idx\"","\"test_i32_2.idx\"","\"test_i16_1.idx\"","\"test_i16_2.idx\"","\"test_i8_1.idx\"","\"test_i8_2.idx\"","\"test_i64_1.idx\""],"expected":["\"(2x4):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(2x4):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(8):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(4x2):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(3):[2, 3, 4]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(1x2):[2, 2]\"","\"(6):[-3, -2, -1, 0, 1, 2]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test writing IDX file format. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":[" Neureka.get().settings().view().ndArrays({ NDPrintSettings it ->"," it.setIsScientific( true )"," it.setIsMultiline( false )"," it.setHasGradient( true )"," it.setCellSize( 1 )"," it.setHasValue( true )"," it.setHasRecursiveGraph( false )"," it.setHasDerivatives( false )"," it.setHasShape( true )"," it.setIsCellBound( false )"," it.setPostfix( \"\" )"," it.setPrefix( \"\" )"," it.setHasSlimNumbers( false )","})"]}, + + {"kind":"when","text":"A new IDX file handle for the given filename is being instantiated.","code":["IDXHandle idx = new IDXHandle(tensor, \"build/test-can/\"+filename)"]}, + + {"kind":"then","text":"The file will then exist at the following path: ","code":["new File(\"build/test-can/\"+filename).exists()"]}, + + {"kind":"when","text":"The \"load\" method is being called in order to load the tensor into memory.","code":["Tensor loaded = idx.load()"]}, + + {"kind":"then","text":"The loaded tensor is as expected...","code":["loaded != null","loaded.toString() == expected","loaded.getDataType().getRepresentativeType() == type"]}, + + {"kind":"where","text":"The following paths and file names are being used for testing : ","code":{"tensor":["Tensor.of([2, 4], -2d..4d)","Tensor.of([2, 4], 2d)","Tensor.of(Float, [8], -2f..4f)","Tensor.of([4, 2], 2f)","Tensor.of(Integer, [3], 2..4)","Tensor.of([2, 2], 2)","Tensor.of(Short, [2], 2..4)","Tensor.of([2, 2], 2 as short)","Tensor.of(Byte, [2], 2..4)","Tensor.of([1, 2], 2 as byte)","Tensor.of(Long, [6], -3..4)"],"type":["F64.class","F64.class","F32.class","F32.class","I32.class","I32.class","I16.class","I16.class","I8.class","I8.class","I64.class"],"filename":["\"test.idx3-ubyte\"","\"test2.idx\"","\"test_f32_1.idx\"","\"test_f32_2.idx\"","\"test_i32_1.idx\"","\"test_i32_2.idx\"","\"test_i16_1.idx\"","\"test_i16_2.idx\"","\"test_i8_1.idx\"","\"test_i8_2.idx\"","\"test_i64_1.idx\""],"expected":["\"(2x4):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(2x4):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(8):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(4x2):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(3):[2, 3, 4]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(1x2):[2, 2]\"","\"(6):[-3, -2, -1, 0, 1, 2]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test writing IDX file format. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":[" Neureka.get().settings().view().ndArrays({ NDPrintSettings it ->"," it.setIsScientific( true )"," it.setIsMultiline( false )"," it.setHasGradient( true )"," it.setCellSize( 1 )"," it.setHasValue( true )"," it.setHasRecursiveGraph( false )"," it.setHasDerivatives( false )"," it.setHasShape( true )"," it.setIsCellBound( false )"," it.setPostfix( \"\" )"," it.setPrefix( \"\" )"," it.setHasSlimNumbers( false )","})"]}, + + {"kind":"when","text":"A new IDX file handle for the given filename is being instantiated.","code":["IDXHandle idx = new IDXHandle(tensor, \"build/test-can/\"+filename)"]}, + + {"kind":"then","text":"The file will then exist at the following path: ","code":["new File(\"build/test-can/\"+filename).exists()"]}, + + {"kind":"when","text":"The \"load\" method is being called in order to load the tensor into memory.","code":["Tensor loaded = idx.load()"]}, + + {"kind":"then","text":"The loaded tensor is as expected...","code":["loaded != null","loaded.toString() == expected","loaded.getDataType().getRepresentativeType() == type"]}, + + {"kind":"where","text":"The following paths and file names are being used for testing : ","code":{"tensor":["Tensor.of([2, 4], -2d..4d)","Tensor.of([2, 4], 2d)","Tensor.of(Float, [8], -2f..4f)","Tensor.of([4, 2], 2f)","Tensor.of(Integer, [3], 2..4)","Tensor.of([2, 2], 2)","Tensor.of(Short, [2], 2..4)","Tensor.of([2, 2], 2 as short)","Tensor.of(Byte, [2], 2..4)","Tensor.of([1, 2], 2 as byte)","Tensor.of(Long, [6], -3..4)"],"type":["F64.class","F64.class","F32.class","F32.class","I32.class","I32.class","I16.class","I16.class","I8.class","I8.class","I64.class"],"filename":["\"test.idx3-ubyte\"","\"test2.idx\"","\"test_f32_1.idx\"","\"test_f32_2.idx\"","\"test_i32_1.idx\"","\"test_i32_2.idx\"","\"test_i16_1.idx\"","\"test_i16_2.idx\"","\"test_i8_1.idx\"","\"test_i8_2.idx\"","\"test_i64_1.idx\""],"expected":["\"(2x4):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(2x4):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(8):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(4x2):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(3):[2, 3, 4]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(1x2):[2, 2]\"","\"(6):[-3, -2, -1, 0, 1, 2]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test writing IDX file format. [5]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":[" Neureka.get().settings().view().ndArrays({ NDPrintSettings it ->"," it.setIsScientific( true )"," it.setIsMultiline( false )"," it.setHasGradient( true )"," it.setCellSize( 1 )"," it.setHasValue( true )"," it.setHasRecursiveGraph( false )"," it.setHasDerivatives( false )"," it.setHasShape( true )"," it.setIsCellBound( false )"," it.setPostfix( \"\" )"," it.setPrefix( \"\" )"," it.setHasSlimNumbers( false )","})"]}, + + {"kind":"when","text":"A new IDX file handle for the given filename is being instantiated.","code":["IDXHandle idx = new IDXHandle(tensor, \"build/test-can/\"+filename)"]}, + + {"kind":"then","text":"The file will then exist at the following path: ","code":["new File(\"build/test-can/\"+filename).exists()"]}, + + {"kind":"when","text":"The \"load\" method is being called in order to load the tensor into memory.","code":["Tensor loaded = idx.load()"]}, + + {"kind":"then","text":"The loaded tensor is as expected...","code":["loaded != null","loaded.toString() == expected","loaded.getDataType().getRepresentativeType() == type"]}, + + {"kind":"where","text":"The following paths and file names are being used for testing : ","code":{"tensor":["Tensor.of([2, 4], -2d..4d)","Tensor.of([2, 4], 2d)","Tensor.of(Float, [8], -2f..4f)","Tensor.of([4, 2], 2f)","Tensor.of(Integer, [3], 2..4)","Tensor.of([2, 2], 2)","Tensor.of(Short, [2], 2..4)","Tensor.of([2, 2], 2 as short)","Tensor.of(Byte, [2], 2..4)","Tensor.of([1, 2], 2 as byte)","Tensor.of(Long, [6], -3..4)"],"type":["F64.class","F64.class","F32.class","F32.class","I32.class","I32.class","I16.class","I16.class","I8.class","I8.class","I64.class"],"filename":["\"test.idx3-ubyte\"","\"test2.idx\"","\"test_f32_1.idx\"","\"test_f32_2.idx\"","\"test_i32_1.idx\"","\"test_i32_2.idx\"","\"test_i16_1.idx\"","\"test_i16_2.idx\"","\"test_i8_1.idx\"","\"test_i8_2.idx\"","\"test_i64_1.idx\""],"expected":["\"(2x4):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(2x4):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(8):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(4x2):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(3):[2, 3, 4]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(1x2):[2, 2]\"","\"(6):[-3, -2, -1, 0, 1, 2]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test writing IDX file format. [6]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":[" Neureka.get().settings().view().ndArrays({ NDPrintSettings it ->"," it.setIsScientific( true )"," it.setIsMultiline( false )"," it.setHasGradient( true )"," it.setCellSize( 1 )"," it.setHasValue( true )"," it.setHasRecursiveGraph( false )"," it.setHasDerivatives( false )"," it.setHasShape( true )"," it.setIsCellBound( false )"," it.setPostfix( \"\" )"," it.setPrefix( \"\" )"," it.setHasSlimNumbers( false )","})"]}, + + {"kind":"when","text":"A new IDX file handle for the given filename is being instantiated.","code":["IDXHandle idx = new IDXHandle(tensor, \"build/test-can/\"+filename)"]}, + + {"kind":"then","text":"The file will then exist at the following path: ","code":["new File(\"build/test-can/\"+filename).exists()"]}, + + {"kind":"when","text":"The \"load\" method is being called in order to load the tensor into memory.","code":["Tensor loaded = idx.load()"]}, + + {"kind":"then","text":"The loaded tensor is as expected...","code":["loaded != null","loaded.toString() == expected","loaded.getDataType().getRepresentativeType() == type"]}, + + {"kind":"where","text":"The following paths and file names are being used for testing : ","code":{"tensor":["Tensor.of([2, 4], -2d..4d)","Tensor.of([2, 4], 2d)","Tensor.of(Float, [8], -2f..4f)","Tensor.of([4, 2], 2f)","Tensor.of(Integer, [3], 2..4)","Tensor.of([2, 2], 2)","Tensor.of(Short, [2], 2..4)","Tensor.of([2, 2], 2 as short)","Tensor.of(Byte, [2], 2..4)","Tensor.of([1, 2], 2 as byte)","Tensor.of(Long, [6], -3..4)"],"type":["F64.class","F64.class","F32.class","F32.class","I32.class","I32.class","I16.class","I16.class","I8.class","I8.class","I64.class"],"filename":["\"test.idx3-ubyte\"","\"test2.idx\"","\"test_f32_1.idx\"","\"test_f32_2.idx\"","\"test_i32_1.idx\"","\"test_i32_2.idx\"","\"test_i16_1.idx\"","\"test_i16_2.idx\"","\"test_i8_1.idx\"","\"test_i8_2.idx\"","\"test_i64_1.idx\""],"expected":["\"(2x4):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(2x4):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(8):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(4x2):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(3):[2, 3, 4]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(1x2):[2, 2]\"","\"(6):[-3, -2, -1, 0, 1, 2]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test writing IDX file format. [7]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":[" Neureka.get().settings().view().ndArrays({ NDPrintSettings it ->"," it.setIsScientific( true )"," it.setIsMultiline( false )"," it.setHasGradient( true )"," it.setCellSize( 1 )"," it.setHasValue( true )"," it.setHasRecursiveGraph( false )"," it.setHasDerivatives( false )"," it.setHasShape( true )"," it.setIsCellBound( false )"," it.setPostfix( \"\" )"," it.setPrefix( \"\" )"," it.setHasSlimNumbers( false )","})"]}, + + {"kind":"when","text":"A new IDX file handle for the given filename is being instantiated.","code":["IDXHandle idx = new IDXHandle(tensor, \"build/test-can/\"+filename)"]}, + + {"kind":"then","text":"The file will then exist at the following path: ","code":["new File(\"build/test-can/\"+filename).exists()"]}, + + {"kind":"when","text":"The \"load\" method is being called in order to load the tensor into memory.","code":["Tensor loaded = idx.load()"]}, + + {"kind":"then","text":"The loaded tensor is as expected...","code":["loaded != null","loaded.toString() == expected","loaded.getDataType().getRepresentativeType() == type"]}, + + {"kind":"where","text":"The following paths and file names are being used for testing : ","code":{"tensor":["Tensor.of([2, 4], -2d..4d)","Tensor.of([2, 4], 2d)","Tensor.of(Float, [8], -2f..4f)","Tensor.of([4, 2], 2f)","Tensor.of(Integer, [3], 2..4)","Tensor.of([2, 2], 2)","Tensor.of(Short, [2], 2..4)","Tensor.of([2, 2], 2 as short)","Tensor.of(Byte, [2], 2..4)","Tensor.of([1, 2], 2 as byte)","Tensor.of(Long, [6], -3..4)"],"type":["F64.class","F64.class","F32.class","F32.class","I32.class","I32.class","I16.class","I16.class","I8.class","I8.class","I64.class"],"filename":["\"test.idx3-ubyte\"","\"test2.idx\"","\"test_f32_1.idx\"","\"test_f32_2.idx\"","\"test_i32_1.idx\"","\"test_i32_2.idx\"","\"test_i16_1.idx\"","\"test_i16_2.idx\"","\"test_i8_1.idx\"","\"test_i8_2.idx\"","\"test_i64_1.idx\""],"expected":["\"(2x4):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(2x4):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(8):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(4x2):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(3):[2, 3, 4]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(1x2):[2, 2]\"","\"(6):[-3, -2, -1, 0, 1, 2]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test writing IDX file format. [8]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":[" Neureka.get().settings().view().ndArrays({ NDPrintSettings it ->"," it.setIsScientific( true )"," it.setIsMultiline( false )"," it.setHasGradient( true )"," it.setCellSize( 1 )"," it.setHasValue( true )"," it.setHasRecursiveGraph( false )"," it.setHasDerivatives( false )"," it.setHasShape( true )"," it.setIsCellBound( false )"," it.setPostfix( \"\" )"," it.setPrefix( \"\" )"," it.setHasSlimNumbers( false )","})"]}, + + {"kind":"when","text":"A new IDX file handle for the given filename is being instantiated.","code":["IDXHandle idx = new IDXHandle(tensor, \"build/test-can/\"+filename)"]}, + + {"kind":"then","text":"The file will then exist at the following path: ","code":["new File(\"build/test-can/\"+filename).exists()"]}, + + {"kind":"when","text":"The \"load\" method is being called in order to load the tensor into memory.","code":["Tensor loaded = idx.load()"]}, + + {"kind":"then","text":"The loaded tensor is as expected...","code":["loaded != null","loaded.toString() == expected","loaded.getDataType().getRepresentativeType() == type"]}, + + {"kind":"where","text":"The following paths and file names are being used for testing : ","code":{"tensor":["Tensor.of([2, 4], -2d..4d)","Tensor.of([2, 4], 2d)","Tensor.of(Float, [8], -2f..4f)","Tensor.of([4, 2], 2f)","Tensor.of(Integer, [3], 2..4)","Tensor.of([2, 2], 2)","Tensor.of(Short, [2], 2..4)","Tensor.of([2, 2], 2 as short)","Tensor.of(Byte, [2], 2..4)","Tensor.of([1, 2], 2 as byte)","Tensor.of(Long, [6], -3..4)"],"type":["F64.class","F64.class","F32.class","F32.class","I32.class","I32.class","I16.class","I16.class","I8.class","I8.class","I64.class"],"filename":["\"test.idx3-ubyte\"","\"test2.idx\"","\"test_f32_1.idx\"","\"test_f32_2.idx\"","\"test_i32_1.idx\"","\"test_i32_2.idx\"","\"test_i16_1.idx\"","\"test_i16_2.idx\"","\"test_i8_1.idx\"","\"test_i8_2.idx\"","\"test_i64_1.idx\""],"expected":["\"(2x4):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(2x4):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(8):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(4x2):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(3):[2, 3, 4]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(1x2):[2, 2]\"","\"(6):[-3, -2, -1, 0, 1, 2]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test writing IDX file format. [9]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -37,9 +244,32 @@ }, { - "id":"Test reading IDX file format.", + "id":"Test writing IDX file format. [10]", "result":"PASS", - "duration":"0.006 seconds", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":[" Neureka.get().settings().view().ndArrays({ NDPrintSettings it ->"," it.setIsScientific( true )"," it.setIsMultiline( false )"," it.setHasGradient( true )"," it.setCellSize( 1 )"," it.setHasValue( true )"," it.setHasRecursiveGraph( false )"," it.setHasDerivatives( false )"," it.setHasShape( true )"," it.setIsCellBound( false )"," it.setPostfix( \"\" )"," it.setPrefix( \"\" )"," it.setHasSlimNumbers( false )","})"]}, + + {"kind":"when","text":"A new IDX file handle for the given filename is being instantiated.","code":["IDXHandle idx = new IDXHandle(tensor, \"build/test-can/\"+filename)"]}, + + {"kind":"then","text":"The file will then exist at the following path: ","code":["new File(\"build/test-can/\"+filename).exists()"]}, + + {"kind":"when","text":"The \"load\" method is being called in order to load the tensor into memory.","code":["Tensor loaded = idx.load()"]}, + + {"kind":"then","text":"The loaded tensor is as expected...","code":["loaded != null","loaded.toString() == expected","loaded.getDataType().getRepresentativeType() == type"]}, + + {"kind":"where","text":"The following paths and file names are being used for testing : ","code":{"tensor":["Tensor.of([2, 4], -2d..4d)","Tensor.of([2, 4], 2d)","Tensor.of(Float, [8], -2f..4f)","Tensor.of([4, 2], 2f)","Tensor.of(Integer, [3], 2..4)","Tensor.of([2, 2], 2)","Tensor.of(Short, [2], 2..4)","Tensor.of([2, 2], 2 as short)","Tensor.of(Byte, [2], 2..4)","Tensor.of([1, 2], 2 as byte)","Tensor.of(Long, [6], -3..4)"],"type":["F64.class","F64.class","F32.class","F32.class","I32.class","I32.class","I16.class","I16.class","I8.class","I8.class","I64.class"],"filename":["\"test.idx3-ubyte\"","\"test2.idx\"","\"test_f32_1.idx\"","\"test_f32_2.idx\"","\"test_i32_1.idx\"","\"test_i32_2.idx\"","\"test_i16_1.idx\"","\"test_i16_2.idx\"","\"test_i8_1.idx\"","\"test_i8_2.idx\"","\"test_i64_1.idx\""],"expected":["\"(2x4):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(2x4):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(8):[-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, -2.0]\"","\"(4x2):[2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]\"","\"(3):[2, 3, 4]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(2x2):[2, 2, 2, 2]\"","\"(2):[2, 3]\"","\"(1x2):[2, 2]\"","\"(6):[-3, -2, -1, 0, 1, 2]\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test reading IDX file format. [0]", + "result":"PASS", + "duration":"0.009 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -64,9 +294,63 @@ }, { - "id":"We can load image files as tensors.", + "id":"Test reading IDX file format. [1]", "result":"PASS", - "duration":"0.508 seconds", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A variable for storing a hash :","code":["def hash = \"\""]}, + + {"kind":"when","text":"The given idx file is being loaded by the \"IDXHead\" class into a new tensor...","code":["IDXHandle idx = new IDXHandle( \"build/resources/test/idx/\" + filename )","Tensor loaded = idx.load()"]}, + + {"kind":"and","text":"... this new tensor is then hashed ...","code":["loaded.forEach( e -> hash = ( hash + e ).digest(\"md5\") )"]}, + + {"kind":"then","text":"The hash is as expected.","code":["hash == expected"]}, + + {"kind":"and","text":"The loaded tensor has the expected data type.","code":["loaded.dataType.getRepresentativeType() == I16.class","loaded.dataType == DataType.of( I16.class )"]}, + + {"kind":"and","text":"It contains the correct array type.","code":["loaded.mut.data.get() instanceof short[]"]}, + + {"kind":"and","text":"The \"IDXHead\" instance has the expected state :","code":["idx.valueSize == 28 * 28","idx.valueSize == 28 * 28 * 1 // 1 := ubyte","idx.fileName == filename","idx.location.endsWith( filename )","idx.totalSize == 28 * 28 * 1 + 16","idx.dataType != loaded.dataType","idx.dataType == DataType.of( UI8.class ) // The underlying data is unsigned byte! (Not supported by JVM)"]}, + + {"kind":"where","text":"The following files and the expected hashes of their data were used :","code":{"filename":["\"MNIST-sample-1.idx\"","\"MNIST-sample-2.idx\"","\"MNIST-sample-3.idx\""],"expected":["\"c74e87c7a93605e7a1660ec9e17dcf9f\"","\"4a57297981456a467a302c8738b3ac50\"","\"87eade8bb5659d324030f4e84f6745e7\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Test reading IDX file format. [2]", + "result":"PASS", + "duration":"0.002 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"A variable for storing a hash :","code":["def hash = \"\""]}, + + {"kind":"when","text":"The given idx file is being loaded by the \"IDXHead\" class into a new tensor...","code":["IDXHandle idx = new IDXHandle( \"build/resources/test/idx/\" + filename )","Tensor loaded = idx.load()"]}, + + {"kind":"and","text":"... this new tensor is then hashed ...","code":["loaded.forEach( e -> hash = ( hash + e ).digest(\"md5\") )"]}, + + {"kind":"then","text":"The hash is as expected.","code":["hash == expected"]}, + + {"kind":"and","text":"The loaded tensor has the expected data type.","code":["loaded.dataType.getRepresentativeType() == I16.class","loaded.dataType == DataType.of( I16.class )"]}, + + {"kind":"and","text":"It contains the correct array type.","code":["loaded.mut.data.get() instanceof short[]"]}, + + {"kind":"and","text":"The \"IDXHead\" instance has the expected state :","code":["idx.valueSize == 28 * 28","idx.valueSize == 28 * 28 * 1 // 1 := ubyte","idx.fileName == filename","idx.location.endsWith( filename )","idx.totalSize == 28 * 28 * 1 + 16","idx.dataType != loaded.dataType","idx.dataType == DataType.of( UI8.class ) // The underlying data is unsigned byte! (Not supported by JVM)"]}, + + {"kind":"where","text":"The following files and the expected hashes of their data were used :","code":{"filename":["\"MNIST-sample-1.idx\"","\"MNIST-sample-2.idx\"","\"MNIST-sample-3.idx\""],"expected":["\"c74e87c7a93605e7a1660ec9e17dcf9f\"","\"4a57297981456a467a302c8738b3ac50\"","\"87eade8bb5659d324030f4e84f6745e7\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can load image files as tensors. [0]", + "result":"PASS", + "duration":"0.587 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -85,9 +369,129 @@ }, { - "id":"The FileDevice component \"CSVHead\" can read CSV file formats and load them as tensors.", + "id":"We can load image files as tensors. [1]", "result":"PASS", - "duration":"0.002 seconds", + "duration":"0.014 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var hash = \"\"","var type = filename.split(\"\\\\.\")[1].toLowerCase()"]}, + + {"kind":"when","text":"","code":["FileHandle handle = FileHandle.FACTORY.getLoader(type).load(\"build/resources/test/$type/\" + filename, null)","Tensor loaded = handle.load()","loaded.forEach(e -> hash = ( hash + e ).digest('md5') )"]}, + + {"kind":"then","text":"","code":["loaded != null","!loaded.isVirtual()","loaded.size() == shape.inject( 1, {prod, value -> prod * value} )","loaded.getDataType().getRepresentativeType() == I16.class // Auto convert! (stored as I16)","hash == expected"]}, + + {"kind":"and","text":"","code":["handle.shape == shape","handle.valueSize == shape.inject( 1, {prod, value -> prod * value} )","handle.totalSize == shape.inject( 1, {prod, value -> prod * value} ) //28 * 28 * 1 + 16","handle.location.endsWith( filename )","handle.dataType == DataType.of( UI8.class )","loaded.dataType == DataType.of( I16.class )"]}, + + {"kind":"where","text":"The following jpg files with their expected shape and hash were used.","code":{"filename":["\"small.JPG\"","\"tiny.JPG\"","\"super-tiny.JPG\"","\"tiny.png\""],"shape":["[260, 410, 3]","[10, 46, 3]","[3, 4, 3]","[90, 183, 4]"],"expected":["\"b0e336b03f2ead7297e56b8ca050f34d\"","\"79bf5dd367b5ec05603e395c41dafaa7\"","\"a834038d8ddc53f170fa426c76d45df2\"","\"63bcd21a7580242a1b562bb49cb53e74\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can load image files as tensors. [2]", + "result":"PASS", + "duration":"0.011 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var hash = \"\"","var type = filename.split(\"\\\\.\")[1].toLowerCase()"]}, + + {"kind":"when","text":"","code":["FileHandle handle = FileHandle.FACTORY.getLoader(type).load(\"build/resources/test/$type/\" + filename, null)","Tensor loaded = handle.load()","loaded.forEach(e -> hash = ( hash + e ).digest('md5') )"]}, + + {"kind":"then","text":"","code":["loaded != null","!loaded.isVirtual()","loaded.size() == shape.inject( 1, {prod, value -> prod * value} )","loaded.getDataType().getRepresentativeType() == I16.class // Auto convert! (stored as I16)","hash == expected"]}, + + {"kind":"and","text":"","code":["handle.shape == shape","handle.valueSize == shape.inject( 1, {prod, value -> prod * value} )","handle.totalSize == shape.inject( 1, {prod, value -> prod * value} ) //28 * 28 * 1 + 16","handle.location.endsWith( filename )","handle.dataType == DataType.of( UI8.class )","loaded.dataType == DataType.of( I16.class )"]}, + + {"kind":"where","text":"The following jpg files with their expected shape and hash were used.","code":{"filename":["\"small.JPG\"","\"tiny.JPG\"","\"super-tiny.JPG\"","\"tiny.png\""],"shape":["[260, 410, 3]","[10, 46, 3]","[3, 4, 3]","[90, 183, 4]"],"expected":["\"b0e336b03f2ead7297e56b8ca050f34d\"","\"79bf5dd367b5ec05603e395c41dafaa7\"","\"a834038d8ddc53f170fa426c76d45df2\"","\"63bcd21a7580242a1b562bb49cb53e74\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"We can load image files as tensors. [3]", + "result":"PASS", + "duration":"0.119 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"given","text":"","code":["var hash = \"\"","var type = filename.split(\"\\\\.\")[1].toLowerCase()"]}, + + {"kind":"when","text":"","code":["FileHandle handle = FileHandle.FACTORY.getLoader(type).load(\"build/resources/test/$type/\" + filename, null)","Tensor loaded = handle.load()","loaded.forEach(e -> hash = ( hash + e ).digest('md5') )"]}, + + {"kind":"then","text":"","code":["loaded != null","!loaded.isVirtual()","loaded.size() == shape.inject( 1, {prod, value -> prod * value} )","loaded.getDataType().getRepresentativeType() == I16.class // Auto convert! (stored as I16)","hash == expected"]}, + + {"kind":"and","text":"","code":["handle.shape == shape","handle.valueSize == shape.inject( 1, {prod, value -> prod * value} )","handle.totalSize == shape.inject( 1, {prod, value -> prod * value} ) //28 * 28 * 1 + 16","handle.location.endsWith( filename )","handle.dataType == DataType.of( UI8.class )","loaded.dataType == DataType.of( I16.class )"]}, + + {"kind":"where","text":"The following jpg files with their expected shape and hash were used.","code":{"filename":["\"small.JPG\"","\"tiny.JPG\"","\"super-tiny.JPG\"","\"tiny.png\""],"shape":["[260, 410, 3]","[10, 46, 3]","[3, 4, 3]","[90, 183, 4]"],"expected":["\"b0e336b03f2ead7297e56b8ca050f34d\"","\"79bf5dd367b5ec05603e395c41dafaa7\"","\"a834038d8ddc53f170fa426c76d45df2\"","\"63bcd21a7580242a1b562bb49cb53e74\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The FileDevice component \"CSVHead\" can read CSV file formats and load them as tensors. [0]", + "result":"PASS", + "duration":"0.004 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"","code":["CSVHandle csv = new CSVHandle( \"build/resources/test/csv/\" + filename, params )","Tensor loaded = csv.load()","var hash = loaded.toString().digest('md5')//.forEach( e -> hash = ( hash + e ).digest('md5') )"]}, + + {"kind":"then","text":"","code":["loaded != null","!loaded.isVirtual()","loaded.size() == shape.inject( 1, {prod, value -> prod * value} )","loaded.getDataType().getItemTypeClass() == String.class // Auto convert! (stored as String)","hash == expected"]}, + + {"kind":"and","text":"","code":["csv.shape == shape","csv.valueSize == shape.inject( 1, {prod, value -> prod * value} )","csv.totalSize == byteSize","csv.dataSize == byteSize","csv.location.endsWith( filename )","csv.dataType == DataType.of( String.class )","loaded.dataType == DataType.of( String.class )"]}, + + {"kind":"where","text":"The following jpg files with their expected shape and hash were used.","code":{"filename":["\"biostats.csv\"","\"biostats.csv\"","\"biostats.csv\"","\"biostats.csv\""],"params":["[:]","[firstRowIsLabels:true]","[firstColIsIndex:true]","[firstColIsIndex:true,firstRowIsLabels:true]"],"byteSize":["753","702","639","594"],"shape":["[19, 5]","[18, 5]","[19, 4]","[18, 4]"],"expected":["\"a3dc4ede7814b5d35d20a8c9310cd63c\"","\"baac406a366a51cb6d69e97a90711050\"","\"90c5d3a4b1ea87901879993bb79e9bc1\"","\"61d75f3dccc8d6987e686d151a423310\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The FileDevice component \"CSVHead\" can read CSV file formats and load them as tensors. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"","code":["CSVHandle csv = new CSVHandle( \"build/resources/test/csv/\" + filename, params )","Tensor loaded = csv.load()","var hash = loaded.toString().digest('md5')//.forEach( e -> hash = ( hash + e ).digest('md5') )"]}, + + {"kind":"then","text":"","code":["loaded != null","!loaded.isVirtual()","loaded.size() == shape.inject( 1, {prod, value -> prod * value} )","loaded.getDataType().getItemTypeClass() == String.class // Auto convert! (stored as String)","hash == expected"]}, + + {"kind":"and","text":"","code":["csv.shape == shape","csv.valueSize == shape.inject( 1, {prod, value -> prod * value} )","csv.totalSize == byteSize","csv.dataSize == byteSize","csv.location.endsWith( filename )","csv.dataType == DataType.of( String.class )","loaded.dataType == DataType.of( String.class )"]}, + + {"kind":"where","text":"The following jpg files with their expected shape and hash were used.","code":{"filename":["\"biostats.csv\"","\"biostats.csv\"","\"biostats.csv\"","\"biostats.csv\""],"params":["[:]","[firstRowIsLabels:true]","[firstColIsIndex:true]","[firstColIsIndex:true,firstRowIsLabels:true]"],"byteSize":["753","702","639","594"],"shape":["[19, 5]","[18, 5]","[19, 4]","[18, 4]"],"expected":["\"a3dc4ede7814b5d35d20a8c9310cd63c\"","\"baac406a366a51cb6d69e97a90711050\"","\"90c5d3a4b1ea87901879993bb79e9bc1\"","\"61d75f3dccc8d6987e686d151a423310\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The FileDevice component \"CSVHead\" can read CSV file formats and load them as tensors. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"","code":["CSVHandle csv = new CSVHandle( \"build/resources/test/csv/\" + filename, params )","Tensor loaded = csv.load()","var hash = loaded.toString().digest('md5')//.forEach( e -> hash = ( hash + e ).digest('md5') )"]}, + + {"kind":"then","text":"","code":["loaded != null","!loaded.isVirtual()","loaded.size() == shape.inject( 1, {prod, value -> prod * value} )","loaded.getDataType().getItemTypeClass() == String.class // Auto convert! (stored as String)","hash == expected"]}, + + {"kind":"and","text":"","code":["csv.shape == shape","csv.valueSize == shape.inject( 1, {prod, value -> prod * value} )","csv.totalSize == byteSize","csv.dataSize == byteSize","csv.location.endsWith( filename )","csv.dataType == DataType.of( String.class )","loaded.dataType == DataType.of( String.class )"]}, + + {"kind":"where","text":"The following jpg files with their expected shape and hash were used.","code":{"filename":["\"biostats.csv\"","\"biostats.csv\"","\"biostats.csv\"","\"biostats.csv\""],"params":["[:]","[firstRowIsLabels:true]","[firstColIsIndex:true]","[firstColIsIndex:true,firstRowIsLabels:true]"],"byteSize":["753","702","639","594"],"shape":["[19, 5]","[18, 5]","[19, 4]","[18, 4]"],"expected":["\"a3dc4ede7814b5d35d20a8c9310cd63c\"","\"baac406a366a51cb6d69e97a90711050\"","\"90c5d3a4b1ea87901879993bb79e9bc1\"","\"61d75f3dccc8d6987e686d151a423310\""]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The FileDevice component \"CSVHead\" can read CSV file formats and load them as tensors. [3]", + "result":"PASS", + "duration":"0", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -106,7 +510,7 @@ { "id":"Fully labeled tenors will be stored with their labels included when saving them as CSV.", "result":"PASS", - "duration":"0.004 seconds", + "duration":"0.005 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -129,7 +533,7 @@ { "id":"Partially labeled tenors will be stored with their labels included when saving them as CSV.", "result":"PASS", - "duration":"0", + "duration":"0.003 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.utility.ListReader_Exception_Spec.json b/docs/spock/reports/ut.utility.ListReader_Exception_Spec.json index d098b1e32..2b890cdc4 100644 --- a/docs/spock/reports/ut.utility.ListReader_Exception_Spec.json +++ b/docs/spock/reports/ut.utility.ListReader_Exception_Spec.json @@ -9,14 +9,14 @@ "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.006 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"The ListReader will detect inconsistent types in the provided data.", "result":"PASS", - "duration":"0", + "duration":"0.002 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -33,7 +33,7 @@ { "id":"The ListReader will detect inconsistent degrees of nesting in the provided data.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, diff --git a/docs/spock/reports/ut.utility.ListReader_Spec.json b/docs/spock/reports/ut.utility.ListReader_Spec.json index 81c3a6802..f84fec9d1 100644 --- a/docs/spock/reports/ut.utility.ListReader_Spec.json +++ b/docs/spock/reports/ut.utility.ListReader_Spec.json @@ -4,19 +4,19 @@ "narrative":"This specification covers an internal class which should not be used\n outside this library, namely the ListReader class.\n This class is simply a converter which turns nested lists\n into flat arrays alongside the type of the elements and the shape of this \"tensor\".", "subjects":[], "statistics":{ - "runs":"3", + "runs":"8", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.003 seconds" + "duration":"0.007 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { "id":"The ListReader can interpret nested lists resembling a matrix into a shape list and value list.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -35,7 +35,7 @@ { "id":"The ListReader can interpret nested lists resembling a 3D tensor into a shape list and value list.", "result":"PASS", - "duration":"0", + "duration":"0.001 seconds", "iterations":{ "tags":{},"see":[],"extraInfo":[] }, @@ -52,7 +52,102 @@ }, { - "id":"The ListReader can interpret nested lists into a shape list and value list.", + "id":"The ListReader can interpret nested lists into a shape list and value list. [0]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We use the reader to internally fill 2 lists representing shape and data...","code":["var result = ListReader.read(data, (o)->o)"]}, + + {"kind":"then","text":"The shape list will have the shape of the \"matrix\".","code":["result.shape == expectedShape"]}, + + {"kind":"and","text":"The flattened data is as expected!","code":["result.data == expectedData"]}, + + {"kind":"where","text":"","code":{"data":["[42]","[[43]]","[[-1],[+1]]","[[24, 42]]","[[\"24\", \"42\"]]","[[true],[false]]"],"expectedShape":["[1]","[1, 1]","[2, 1]","[1, 2]","[1, 2]","[2, 1]"],"expectedData":["[42]","[43]","[-1, 1]","[24, 42]","[\"24\", \"42\"]","[true, false]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The ListReader can interpret nested lists into a shape list and value list. [1]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We use the reader to internally fill 2 lists representing shape and data...","code":["var result = ListReader.read(data, (o)->o)"]}, + + {"kind":"then","text":"The shape list will have the shape of the \"matrix\".","code":["result.shape == expectedShape"]}, + + {"kind":"and","text":"The flattened data is as expected!","code":["result.data == expectedData"]}, + + {"kind":"where","text":"","code":{"data":["[42]","[[43]]","[[-1],[+1]]","[[24, 42]]","[[\"24\", \"42\"]]","[[true],[false]]"],"expectedShape":["[1]","[1, 1]","[2, 1]","[1, 2]","[1, 2]","[2, 1]"],"expectedData":["[42]","[43]","[-1, 1]","[24, 42]","[\"24\", \"42\"]","[true, false]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The ListReader can interpret nested lists into a shape list and value list. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We use the reader to internally fill 2 lists representing shape and data...","code":["var result = ListReader.read(data, (o)->o)"]}, + + {"kind":"then","text":"The shape list will have the shape of the \"matrix\".","code":["result.shape == expectedShape"]}, + + {"kind":"and","text":"The flattened data is as expected!","code":["result.data == expectedData"]}, + + {"kind":"where","text":"","code":{"data":["[42]","[[43]]","[[-1],[+1]]","[[24, 42]]","[[\"24\", \"42\"]]","[[true],[false]]"],"expectedShape":["[1]","[1, 1]","[2, 1]","[1, 2]","[1, 2]","[2, 1]"],"expectedData":["[42]","[43]","[-1, 1]","[24, 42]","[\"24\", \"42\"]","[true, false]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The ListReader can interpret nested lists into a shape list and value list. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We use the reader to internally fill 2 lists representing shape and data...","code":["var result = ListReader.read(data, (o)->o)"]}, + + {"kind":"then","text":"The shape list will have the shape of the \"matrix\".","code":["result.shape == expectedShape"]}, + + {"kind":"and","text":"The flattened data is as expected!","code":["result.data == expectedData"]}, + + {"kind":"where","text":"","code":{"data":["[42]","[[43]]","[[-1],[+1]]","[[24, 42]]","[[\"24\", \"42\"]]","[[true],[false]]"],"expectedShape":["[1]","[1, 1]","[2, 1]","[1, 2]","[1, 2]","[2, 1]"],"expectedData":["[42]","[43]","[-1, 1]","[24, 42]","[\"24\", \"42\"]","[true, false]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The ListReader can interpret nested lists into a shape list and value list. [4]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"We use the reader to internally fill 2 lists representing shape and data...","code":["var result = ListReader.read(data, (o)->o)"]}, + + {"kind":"then","text":"The shape list will have the shape of the \"matrix\".","code":["result.shape == expectedShape"]}, + + {"kind":"and","text":"The flattened data is as expected!","code":["result.data == expectedData"]}, + + {"kind":"where","text":"","code":{"data":["[42]","[[43]]","[[-1],[+1]]","[[24, 42]]","[[\"24\", \"42\"]]","[[true],[false]]"],"expectedShape":["[1]","[1, 1]","[2, 1]","[1, 2]","[1, 2]","[2, 1]"],"expectedData":["[42]","[43]","[-1, 1]","[24, 42]","[\"24\", \"42\"]","[true, false]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"The ListReader can interpret nested lists into a shape list and value list. [5]", "result":"PASS", "duration":"0", "iterations":{ diff --git a/docs/spock/reports/ut.utility.Utility_Spec.json b/docs/spock/reports/ut.utility.Utility_Spec.json index 05edddea7..8c53637fc 100644 --- a/docs/spock/reports/ut.utility.Utility_Spec.json +++ b/docs/spock/reports/ut.utility.Utility_Spec.json @@ -4,17 +4,164 @@ "narrative":"", "subjects":[], "statistics":{ - "runs":"1", + "runs":"8", "successRate":"100.0%", "failures":"0", "errors":"0", "skipped":"0", - "duration":"0.006 seconds" + "duration":"0.026 seconds" }, "headers":[],"tags":{},"see":[], "features":[ { - "id":"Object arrays can be converted to primitive arrays.", + "id":"Object arrays can be converted to primitive arrays. [0]", + "result":"PASS", + "duration":"0.003 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"","code":["def result = code(input)"]}, + + {"kind":"then","text":"","code":["result == input"]}, + + {"kind":"and","text":"","code":["result.class != input.class"]}, + + {"kind":"and","text":"","code":["result.class == expectedType"]}, + + {"kind":"where","text":"","code":{"input":["[1, 2, 3] as Float[]","[1, 2, 3] as Double[]","[1, 2, 3] as Integer[]","[1, 2, 3] as Long[]","[1, 2, 3] as Short[]","[1, 2, 3] as Byte[]","[1, 2, 3] as Boolean[]","[1, 2, 3] as Character[]"],"code":["{DataConverter.Utility.objFloatsToPrimFloats(it)}","{DataConverter.Utility.objDoublesToPrimDoubles(it)}","{DataConverter.Utility.objIntsToPrimInts(it)}","{DataConverter.Utility.objLongsToPrimLongs(it)}","{DataConverter.Utility.objShortsToPrimShorts(it)}","{DataConverter.Utility.objBytesToPrimBytes(it)}","{DataConverter.Utility.objBooleansToPrimBooleans(it)}","{DataConverter.Utility.objCharsToPrimChars(it)}"],"expectedType":["float[]","double[]","int[]","long[]","short[]","byte[]","boolean[]","char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Object arrays can be converted to primitive arrays. [1]", + "result":"PASS", + "duration":"0.001 seconds", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"","code":["def result = code(input)"]}, + + {"kind":"then","text":"","code":["result == input"]}, + + {"kind":"and","text":"","code":["result.class != input.class"]}, + + {"kind":"and","text":"","code":["result.class == expectedType"]}, + + {"kind":"where","text":"","code":{"input":["[1, 2, 3] as Float[]","[1, 2, 3] as Double[]","[1, 2, 3] as Integer[]","[1, 2, 3] as Long[]","[1, 2, 3] as Short[]","[1, 2, 3] as Byte[]","[1, 2, 3] as Boolean[]","[1, 2, 3] as Character[]"],"code":["{DataConverter.Utility.objFloatsToPrimFloats(it)}","{DataConverter.Utility.objDoublesToPrimDoubles(it)}","{DataConverter.Utility.objIntsToPrimInts(it)}","{DataConverter.Utility.objLongsToPrimLongs(it)}","{DataConverter.Utility.objShortsToPrimShorts(it)}","{DataConverter.Utility.objBytesToPrimBytes(it)}","{DataConverter.Utility.objBooleansToPrimBooleans(it)}","{DataConverter.Utility.objCharsToPrimChars(it)}"],"expectedType":["float[]","double[]","int[]","long[]","short[]","byte[]","boolean[]","char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Object arrays can be converted to primitive arrays. [2]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"","code":["def result = code(input)"]}, + + {"kind":"then","text":"","code":["result == input"]}, + + {"kind":"and","text":"","code":["result.class != input.class"]}, + + {"kind":"and","text":"","code":["result.class == expectedType"]}, + + {"kind":"where","text":"","code":{"input":["[1, 2, 3] as Float[]","[1, 2, 3] as Double[]","[1, 2, 3] as Integer[]","[1, 2, 3] as Long[]","[1, 2, 3] as Short[]","[1, 2, 3] as Byte[]","[1, 2, 3] as Boolean[]","[1, 2, 3] as Character[]"],"code":["{DataConverter.Utility.objFloatsToPrimFloats(it)}","{DataConverter.Utility.objDoublesToPrimDoubles(it)}","{DataConverter.Utility.objIntsToPrimInts(it)}","{DataConverter.Utility.objLongsToPrimLongs(it)}","{DataConverter.Utility.objShortsToPrimShorts(it)}","{DataConverter.Utility.objBytesToPrimBytes(it)}","{DataConverter.Utility.objBooleansToPrimBooleans(it)}","{DataConverter.Utility.objCharsToPrimChars(it)}"],"expectedType":["float[]","double[]","int[]","long[]","short[]","byte[]","boolean[]","char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Object arrays can be converted to primitive arrays. [3]", + "result":"PASS", + "duration":"0", + "iterations":{ + "tags":{},"see":[],"extraInfo":[] + }, + "blocks":[ + {"kind":"when","text":"","code":["def result = code(input)"]}, + + {"kind":"then","text":"","code":["result == input"]}, + + {"kind":"and","text":"","code":["result.class != input.class"]}, + + {"kind":"and","text":"","code":["result.class == expectedType"]}, + + {"kind":"where","text":"","code":{"input":["[1, 2, 3] as Float[]","[1, 2, 3] as Double[]","[1, 2, 3] as Integer[]","[1, 2, 3] as Long[]","[1, 2, 3] as Short[]","[1, 2, 3] as Byte[]","[1, 2, 3] as Boolean[]","[1, 2, 3] as Character[]"],"code":["{DataConverter.Utility.objFloatsToPrimFloats(it)}","{DataConverter.Utility.objDoublesToPrimDoubles(it)}","{DataConverter.Utility.objIntsToPrimInts(it)}","{DataConverter.Utility.objLongsToPrimLongs(it)}","{DataConverter.Utility.objShortsToPrimShorts(it)}","{DataConverter.Utility.objBytesToPrimBytes(it)}","{DataConverter.Utility.objBooleansToPrimBooleans(it)}","{DataConverter.Utility.objCharsToPrimChars(it)}"],"expectedType":["float[]","double[]","int[]","long[]","short[]","byte[]","boolean[]","char[]"]}} + ], + "problems":{"dataValues":[], "errors":[]} + }, + + { + "id":"Object arrays can be converted to primitive arrays. 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