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Merge pull request #8 from Quafadas/main
Some basic statistical methods?
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| Original file line number | Diff line number | Diff line change |
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| .bloop | ||
| .bsp | ||
| .js | ||
| .jvm | ||
| .metals | ||
| .native | ||
| .vscode | ||
| target | ||
| project/build.sbt | ||
| project/metals.sbt | ||
| project/project |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1 +1 @@ | ||
| sbt.version=1.8.3 | ||
| sbt.version=1.9.2 |
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| /* | ||
| * Copyright 2023 dragonfly.ai | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| import ai.dragonfly.math.vector.Vec | ||
| import narr.NArray | ||
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| class InstantiateTests extends munit.FunSuite: | ||
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| test(" ways of making vecs ") { | ||
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| //type dim = 5 | ||
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| val v = Vec.fromTuple(1.0, 2.0, 3.0, 4.0, 5.0) | ||
| val v2 = Vec[5](1.0, 2.0, 3.0, 4.0, 5.0) | ||
| val v_fill = Vec.fill[5](1.0) | ||
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| val v_zeros = Vec.zeros[5] | ||
| val v_ones = Vec.ones[5] | ||
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| val v_rand = Vec.random[5]() | ||
| val v_rand_max_min = Vec.random[5](2.0, 0.5) | ||
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| assertEquals(v2.dimension, v2.dimension ) | ||
| assertEquals(v2.dimension, v_fill.dimension ) | ||
| assertEquals(v2.dimension, v_zeros.dimension ) | ||
| assertEquals(v2.dimension, v_rand.dimension ) | ||
| assertEquals(v2.dimension, v_rand.dimension ) | ||
| assertEquals(v2.dimension, v_rand_max_min.dimension ) | ||
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| } | ||
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| end InstantiateTests |
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| /* | ||
| * Copyright 2023 dragonfly.ai | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| import ai.dragonfly.math.vector.Vec | ||
| import narr.NArray | ||
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| class SimpleStatsTests extends munit.FunSuite: | ||
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| test("Some basic properties") { | ||
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| val v = Vec.fromTuple(1.0, 2.0, 3.0, 4.0, 5.0) | ||
| val v2 = Vec.fromTuple(1.0, 2.0, 3.0, 4.0, 5.0) | ||
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| assertEquals(v.dimension, v2.dimension ) | ||
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| // Reference, not value equality! | ||
| assertNotEquals(v, v2 ) | ||
| } | ||
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| test("sample mean") { | ||
| val v = Vec.fromTuple(2.0,4.0,4.0,4.0,5.0,5.0,7.0,9.0) | ||
| assertEquals(v.mean , 5.0) | ||
| } | ||
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| test("sample variance and std") { | ||
| val v = Vec.fromTuple(2.0,4.0,4.0,4.0,5.0,5.0,7.0,9.0) | ||
| assertEqualsDouble(v.variance, 4.571429, 0.00001) | ||
| assertEqualsDouble(v.stdDev, 2.13809, 0.00001) | ||
| } | ||
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| test("sample covariance") { | ||
| // Sample version | ||
| // https://corporatefinanceinstitute.com/resources/data-science/covariance/ | ||
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| val vector1 = Vec.fromTuple(1692.0, 1978.0, 1884.0, 2151.0, 2519.0) | ||
| val vector2 = Vec.fromTuple(68.0, 102.0, 110.0, 112.0, 154.0) | ||
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| val result = vector1.covariance(vector2) | ||
| println(result) | ||
| assertEqualsDouble(result, 9107.3, 0.001) | ||
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| } | ||
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| test("pearson correlation coefficient") { | ||
| // https://www.statisticshowto.com/probability-and-statistics/correlation-coefficient-formula/ | ||
| val v1 = Vec.fromTuple(43.0, 21.0, 25.0, 42.0, 57.0, 59.0) | ||
| val v2 = Vec.fromTuple(99.0, 65.0, 79.0, 75.0, 87.0, 81.0) | ||
| assertEqualsDouble(v1.pearsonCorrelationCoefficient(v2), 0.529809, 0.0001) | ||
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| } | ||
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| test("element rank") { | ||
| val v = Vec.fromTuple(1.0, 5.0, 3.0, 6.0, 1.0, 5.0) | ||
| /* | ||
| 1.0 is the first, but has as tied rank. Take the average - 1.5 | ||
| */ | ||
| assertEquals(v.elementRanks.csv(), Array[Double](1.5,4.5,3.0,6.0,1.5,4.5).mkString(",")) | ||
| } | ||
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| test("spearmans rank") { | ||
| // https://statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide-2.php | ||
| val v1 = Vec.fromTuple(56.0, 75.0, 45.0, 71.0, 62.0, 64.0, 58.0, 80.0, 76.0, 61.0) | ||
| val v2 = Vec.fromTuple(66.0, 70.0, 40.0, 60.0, 65.0, 56.0, 59.0, 77.0, 67.0, 63.0) | ||
| assertEqualsDouble(v1.spearmansRankCorrelation(v2), 0.6727, 0.001 ) | ||
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| // https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient | ||
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| val v3 = Vec[10](86.0, 97.0, 99.0, 100.0, 101.0, 103.0, 106.0, 110.0, 112.0, 113.0) | ||
| val v4 = Vec[10](2, 20.0, 28.0, 27.0, 50.0, 29.0, 7.0, 17.0, 6.0, 12.0) | ||
| assertEqualsDouble(-0.1757575, v3.spearmansRankCorrelation(v4), 0.000001); | ||
| } | ||
| end SimpleStatsTests |
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