From 44294a79745ee5e4a1ed468e77e196c25337f9e9 Mon Sep 17 00:00:00 2001 From: c Date: Tue, 3 Oct 2023 11:40:07 -0600 Subject: [PATCH] Removed Demo project. Upgraded App version 0.101, NArr 0.103 Added: - import narr.* to related test classes to prevent linking errors in JS. - ClassTag to Sampleable trait. - sample method to sampleable trait. ran githubWorkflowGenerate --- .github/workflows/ci.yml | 2 +- README.md | 2 - build.sbt | 28 +- demo/shared/src/main/scala/Demo.scala | 56 - .../ai/dragonfly/math/BijectionDemo.scala | 48 - .../ai/dragonfly/math/FactorialDemo.scala | 16 - .../scala/ai/dragonfly/math/GammaDemo.scala | 21 - .../scala/ai/dragonfly/math/LogDemo.scala | 36 - .../ai/dragonfly/math/ProbDistDemo.scala | 57 - .../scala/ai/dragonfly/math/RandomDemo.scala | 5 - .../math/geometry/TetrahedronDemo.scala | 76 - .../math/matrix/DemoEigenDecomposition.scala | 29 - .../math/matrix/DemoLinearRegression.scala | 90 - .../ai/dragonfly/math/matrix/DemoPCA.scala | 202 --- .../math/matrix/DemoVectorInterop.scala | 26 - .../dragonfly/math/matrix/EmpericalData.scala | 1616 ----------------- .../math/matrix/RegressionTest.scala | 78 - .../math/stats/kernel/KernelDemo.scala | 63 - .../probability/distributions/BetaDemo.scala | 9 - .../distributions/BinomialDemo.scala | 13 - .../distributions/DiscreteUniformDemo.scala | 14 - .../distributions/GaussianDemo.scala | 12 - .../distributions/LogNormalDemo.scala | 13 - .../probability/distributions/PERTDemo.scala | 9 - .../distributions/PoissonDemo.scala | 10 - .../distributions/UniformDemo.scala | 11 - .../distributions/stream/BetaDemo.scala | 9 - .../distributions/stream/BinomialDemo.scala | 46 - .../distributions/stream/GaussianDemo.scala | 9 - .../distributions/stream/LogNormalDemo.scala | 9 - .../distributions/stream/PERTDemo.scala | 23 - .../distributions/stream/PoissonDemo.scala | 11 - .../stream/StreamingVectorStatsDemo.scala | 22 - .../dragonfly/math/unicode/UnicodeDemo.scala | 62 - .../dragonfly/math/vector/MixedVecDemo.scala | 44 - .../ai/dragonfly/math/vector/Vec2Demo.scala | 37 - .../ai/dragonfly/math/vector/Vec3Demo.scala | 38 - .../ai/dragonfly/math/vector/Vec4Demo.scala | 65 - .../ai/dragonfly/math/vector/VecNDemo.scala | 70 - .../math/vector/WeightedVecDemo.scala | 21 - old docs/demo.md | 7 - .../ai/dragonfly/math/interval/Interval.scala | 12 +- .../math/stats/UnivariateHistogram.scala | 4 +- .../probability/distributions/Poisson.scala | 9 - .../distributions/Sampleable.scala | 9 +- tests/shared/src/test/scala/Instantiate.scala | 2 +- tests/shared/src/test/scala/SimpleStats.scala | 2 +- .../shared/src/test/scala/VectorSpaces.scala | 1 + tests/shared/src/test/scala/poisson.scala | 7 +- 49 files changed, 28 insertions(+), 3033 deletions(-) delete mode 100644 demo/shared/src/main/scala/Demo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/BijectionDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/FactorialDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/GammaDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/LogDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/ProbDistDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/RandomDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/geometry/TetrahedronDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoEigenDecomposition.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoLinearRegression.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoPCA.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoVectorInterop.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/matrix/EmpericalData.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/matrix/RegressionTest.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/kernel/KernelDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/BetaDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/BinomialDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/DiscreteUniformDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/GaussianDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/LogNormalDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/PERTDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/PoissonDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/UniformDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/BetaDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/BinomialDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/GaussianDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/LogNormalDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/PERTDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/PoissonDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/StreamingVectorStatsDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/unicode/UnicodeDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/vector/MixedVecDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec2Demo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec3Demo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec4Demo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/vector/VecNDemo.scala delete mode 100644 demo/shared/src/main/scala/ai/dragonfly/math/vector/WeightedVecDemo.scala delete mode 100644 old docs/demo.md diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 92acf84..ac342b6 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -205,7 +205,7 @@ jobs: - name: Submit Dependencies uses: scalacenter/sbt-dependency-submission@v2 with: - modules-ignore: slash_3 docs_3 verification_3 jsdocs_sjs1_3 slash-tests_sjs1_3 slash_3 slash_3 demo_3 slash-tests_3 demo_native0.4_3 slash-tests_native0.4_3 demo_sjs1_3 + modules-ignore: slash_3 docs_3 verification_3 jsdocs_sjs1_3 slash-tests_sjs1_3 slash_3 slash_3 slash-tests_3 slash-tests_native0.4_3 configs-ignore: test scala-tool scala-doc-tool test-internal site: diff --git a/README.md b/README.md index 2d52dc6..86a8d20 100644 --- a/README.md +++ b/README.md @@ -48,8 +48,6 @@ libraryDependencies += "ai.dragonfly" %%% "slash" % "" - Linear Regression based on both QR Decomposition and Singular Value Decomposition. - Principal Components Analysis -Try the demo. -    Instead of case classes, traits, or wrappers, this library represents all runtime vector data as native arrays of double precision floating point values. However, it also uses Scala 3 features like opaque types, dependent types, and extension methods to decorate the array primitives with convenient syntax, e.g. overloaded operators like `+ - * / += -= *= /=`, and also, by expressing vector dimensionality as a type parameter, can prevent runtime errors resulting from trying to perform vector operations on vectors of mismatched dimensions at compile time. For example: ```scala diff --git a/build.sbt b/build.sbt index 38bc046..f7ee715 100644 --- a/build.sbt +++ b/build.sbt @@ -10,7 +10,7 @@ import laika.ast.* import laika.markdown.github.GitHubFlavor import laika.parse.code.SyntaxHighlighting -val appVersion:String = "0.1" +val appVersion:String = "0.101" val globalScalaVersion = "3.3.0" ThisBuild / organization := "ai.dragonfly" @@ -45,7 +45,7 @@ lazy val slash = crossProject( .crossType(CrossType.Full) .settings( description := "High performance, low footprint, cross platform, Linear Algebra and Statistics Hacks!", - libraryDependencies += "ai.dragonfly" %%% "narr" % "0.101" + libraryDependencies += "ai.dragonfly" %%% "narr" % "0.103" ) .jvmSettings( libraryDependencies ++= Seq( "org.scala-js" %% "scalajs-stubs" % "1.1.0" ) @@ -65,30 +65,6 @@ lazy val verification = project ) ) -lazy val demo = crossProject( - JSPlatform, - JVMPlatform, - NativePlatform -) - .crossType(CrossType.Full) - .enablePlugins(NoPublishPlugin) - .dependsOn(slash) - .settings( - name := "demo", - Compile / mainClass := Some("Demo"), - libraryDependencies ++= Seq( - "ai.dragonfly" %%% "cliviz" % "0.102", - "ai.dragonfly" %%% "democrossy" % "0.102" - ), - Compile / mainClass := Some("Demo") - ) - .jsSettings( - Compile / fullOptJS / artifactPath := file("./docs/js/main.js"), - scalaJSUseMainModuleInitializer := true - ) - .jvmSettings() - .nativeSettings() - lazy val root = tlCrossRootProject.aggregate(slash, tests).settings(name := "slash") lazy val jsdocs = project diff --git a/demo/shared/src/main/scala/Demo.scala b/demo/shared/src/main/scala/Demo.scala deleted file mode 100644 index 91b1c68..0000000 --- a/demo/shared/src/main/scala/Demo.scala +++ /dev/null @@ -1,56 +0,0 @@ -import ai.dragonfly.democrossy.* - -/** - * Created by clifton on 1/9/17. - */ - -object Demo extends XApp(NativeConsole(style = "padding: 8px; overflow: scroll;")) { - - val allDemos: Array[Demonstration] = Array[Demonstration]( - // Math - //ai.dragonfly.math.LogDemo, - //ai.dragonfly.math.GammaDemo, - //ai.dragonfly.math.FactorialDemo, - // TODO: add Interval demo - ai.dragonfly.math.unicode.UnicodeDemo, - // Geometry: - ai.dragonfly.math.geometry.TetrahedronDemo, - // Vector - // ai.dragonfly.math.vector.SparseVectorDemo, - ai.dragonfly.math.vector.MixedVecDemo, - ai.dragonfly.math.vector.Vec2Demo, - ai.dragonfly.math.vector.Vec3Demo, - ai.dragonfly.math.vector.Vec4Demo, - ai.dragonfly.math.vector.VecNDemo, - ai.dragonfly.math.vector.WeightedVecDemo, - // Matrix - ai.dragonfly.math.matrix.DemoPCA, - ai.dragonfly.math.matrix.DemoLinearRegression, - ai.dragonfly.math.matrix.DemoEigenDecomposition, - ai.dragonfly.math.matrix.DemoVectorInterop, - // Statistics - ai.dragonfly.math.stats.kernel.KernelDemo, - ai.dragonfly.math.stats.probability.distributions.stream.StreamingVectorStatsDemo, - ai.dragonfly.math.stats.probability.distributions.GaussianDemo.demo, - ai.dragonfly.math.stats.probability.distributions.PoissonDemo.demo, - ai.dragonfly.math.stats.probability.distributions.LogNormalDemo.demo, - ai.dragonfly.math.stats.probability.distributions.PERTDemo.demo, - ai.dragonfly.math.stats.probability.distributions.BetaDemo.demo2param, - ai.dragonfly.math.stats.probability.distributions.BetaDemo.demo4param, - ai.dragonfly.math.stats.probability.distributions.BinomialDemo.demo, - ai.dragonfly.math.stats.probability.distributions.UniformDemo.demo, - ai.dragonfly.math.stats.probability.distributions.DiscreteUniformDemo.demo, - ai.dragonfly.math.stats.probability.distributions.stream.GaussianDemo.demo, - ai.dragonfly.math.stats.probability.distributions.stream.PoissonDemo.demo, - ai.dragonfly.math.stats.probability.distributions.stream.LogNormalDemo.demo, - ai.dragonfly.math.stats.probability.distributions.stream.PERTDemo, - ai.dragonfly.math.stats.probability.distributions.stream.BetaDemo.demo, - ai.dragonfly.math.stats.probability.distributions.stream.BinomialDemo.fixedBinomialDemo, - ai.dragonfly.math.stats.probability.distributions.stream.BinomialDemo.openBinomialDemo - ) - - def main(args: Array[String]): Unit = { - for (d <- allDemos) d.demonstrate - } - -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/BijectionDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/BijectionDemo.scala deleted file mode 100644 index eb35e03..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/BijectionDemo.scala +++ /dev/null @@ -1,48 +0,0 @@ -package ai.dragonfly.math - -import ai.dragonfly.math -import ai.dragonfly.democrossy.Demonstration - -object BijectionDemo extends Demonstration { - - object H2O { - - case class Ice(grams:Double) { - def +(s:Ice): Ice = Ice(this.grams + s.grams) - override def toString:String = s"Ice($grams grams)" - } - case class Water(grams:Double) { - def +(s:Water): Water = Water(this.grams + s.grams) - override def toString:String = s"Water($grams grams)" - } - case class Steam(grams:Double) { - def +(s:Steam): Steam = Steam(this.grams + s.grams) - override def toString:String = s"Steam($grams grams)" - } - - given Conversion[Water, Ice] with - def apply(w:Water):Ice = Ice(w.grams) - - given Conversion[Ice, Water] with - def apply(i:Ice):Water = Water(i.grams) - - given Conversion[Water, Steam] with - def apply(w:Water):Steam = Steam(w.grams) - - given Conversion[Steam, Water] with - def apply(s:Steam):Water = Water(s.grams) - - val freezeAndMelt:math.Bijection[Water, Ice] = new math.Bijection[Water, Ice]{} - val precipitateAndEvaporate:math.Bijection[Water, Steam] = new math.Bijection[Water, Steam]{} - - } - import scala.language.implicitConversions - import H2O.* - override def demo():Unit = { - val antarctica:Ice = Ice(24353500000000000.0) - val ocean:Water = Water(1.4E21) - val clouds:Steam = ocean + antarctica; - println(s"Melt Antarctica, $antarctica, and the mass of the ocean increases from $ocean to ${ocean + antarctica}. Boil all of that, and the atmosphere includes $clouds.") - } - override def name:String = "Bijection" -} \ No newline at end of file diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/FactorialDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/FactorialDemo.scala deleted file mode 100644 index 485b7d8..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/FactorialDemo.scala +++ /dev/null @@ -1,16 +0,0 @@ -package ai.dragonfly.math - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.Factorial.! -import scala.language.postfixOps - -object FactorialDemo extends Demonstration { - - override def demo():Unit = { - for (x:Int <- Seq(1, 2, 3, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096)) { - println(s"\t${x}! = ${x!}\n") - } - } - - def name:String = "Factorial" -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/GammaDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/GammaDemo.scala deleted file mode 100644 index 14e3e6d..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/GammaDemo.scala +++ /dev/null @@ -1,21 +0,0 @@ -package ai.dragonfly.math - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math - -object GammaDemo extends Demonstration { - - override def demo():Unit = { - import scala.language.postfixOps - - println("Demonstrate Gamma Function on Integers 1 - 10\n") - for ( i <- 1 until 10 ) { - val i_1:Int = i - 1 - println(s"\tΓ($i):$i_1! => ${math.gamma(i.toDouble)} : ${math.Factorial(i_1)}\n") - } - - } - - override def name:String = "Gamma" - -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/LogDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/LogDemo.scala deleted file mode 100644 index 310f4ef..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/LogDemo.scala +++ /dev/null @@ -1,36 +0,0 @@ -package ai.dragonfly.math - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math -import ai.dragonfly.math.Constant.π - -object LogDemo extends Demonstration { - - override def demo():Unit = { - import scala.language.postfixOps - - println("Demonstrate Log[BASE](x:Double)\n") - - println( s"log[2.0](42.0) = ${log[2.0](42.0)}" ) - var i: Int = 1; while (i > 0) { - println( s"log[2]($i) = ${log[2](i)}" ) - i = i << 1 - } - - println(s"log[3.141592653589793](21) = ${log[3.141592653589793](21)}") - - println("Demonstrate Log(base:Double)\n") - - val logBasePi: Log = Log(π) - i = 0; while (i < 10) { - val x: Double = Math.random() * i - println(s"logBasePi($x) = ${logBasePi(x)}") - i += 1 - } - - - } - - override def name:String = "Gamma" - -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/ProbDistDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/ProbDistDemo.scala deleted file mode 100644 index 96f7eb0..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/ProbDistDemo.scala +++ /dev/null @@ -1,57 +0,0 @@ -package ai.dragonfly.math - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.stats.* -import ai.dragonfly.math.stats.probability.distributions.* -import ai.dragonfly.math.stats.probability.distributions.stream.* - - -case class ProbDistDemo[DOMAIN]( - name:String, - dist:ParametricProbabilityDistribution[DOMAIN], - val histogram: UnivariateHistogram[DOMAIN], - sampleSize:Int = 10000 -) extends Demonstration { - override def demo(): Unit = { - println(s"\nDemonstrating $name:\n\tgenerating $sampleSize random variables from $dist\n") - var i:Int = 0; while (i < sampleSize) { - histogram(dist.random()) - i += 1 - } - println(s"\n$histogram\n") - } -} - - - -case class OnlineProbDistDemo[DOMAIN, PPD <: ParametricProbabilityDistribution[DOMAIN], EPPD <: OnlineUnivariateProbabilityDistributionEstimator[DOMAIN, PPD]]( - name: String, - idealDist: PPD, - streamingDist: EPPD, - sampleSize: Int -)(using `#`: Numeric[DOMAIN]) extends Demonstration { - override def demo():Unit = { - println(s"Estimate $name:\n\tSampling: $idealDist") - val blockSize:Int = sampleSize / 5 - - var i:Int = 1 - val end = sampleSize + 1 - - while (i < end) { - streamingDist.observe(idealDist.random()) - if (i % blockSize == 0) { - println(s"\n\t\testimation after $i samples: ${streamingDist.estimate}") - } - i += 1 - } - println(s"\n\tEstimate: ${streamingDist.estimate}\n\tIdeal Distribution: $idealDist\n") - println(s"\nTest $idealDist.p($idealDist.random())") - i = 0 - while (i < 5) { - val x = idealDist.random() - println(s"\n\tp($x) = ${idealDist.p(x)}") - i += 1 - } - println("\n") - } -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/RandomDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/RandomDemo.scala deleted file mode 100644 index 026f932..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/RandomDemo.scala +++ /dev/null @@ -1,5 +0,0 @@ -package ai.dragonfly.math - -object RandomDemo { - // TODO: Make demonstration for Random. -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/geometry/TetrahedronDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/geometry/TetrahedronDemo.scala deleted file mode 100644 index cfb3c0f..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/geometry/TetrahedronDemo.scala +++ /dev/null @@ -1,76 +0,0 @@ -package ai.dragonfly.math.geometry - - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.vector.* -import Vec.* - - -object TetrahedronDemo extends Demonstration { - -// val `1/6`:Double = 1.0 / 6.0 - - override def demo():Unit = { - val q = 10.0 * Math.cos((Math.PI * 2.0)/3.0) - - val t = Tetrahedron( - Vec[3]( q, q, q), - Vec[3](-q, q, q), - Vec[3]( 0,-q, q), - Vec[3]( 0, 0,-q) - ) - - val sampleCount:Int = 5 - - println("sampling from the interior of the tetrahedron:") - var i:Int = 0; while ( i < sampleCount) { - println(t.random().show) - i += 1 - } - -// -// def drawBoundedSamples(chart:Chart, bounds:Array[Vector2], samples:IndexedSeq[Vector2]):Chart = { -// var v0:Vector2 = bounds.head -// var tail = bounds.tail -// while (tail.nonEmpty) { -// for (v1 <- tail) chart.lineSegment(v0, v1, "Bounds") -// v0 = tail.head -// tail = tail.tail -// } -// chart.lineSegment(bounds.head, bounds.last, "Bounds") -// .scatter("Samples", samples:_*) -// } -// -// val (chartWidth:Int, chartHeight:Int) = (99, 77) -// val pad:Double = Math.abs(q)*0.15 -// val interval = `[]`[Double](Math.floor(Math.min(q, -q) - pad), Math.ceil(Math.max(q, -q) + pad)) -// -// println( -// drawBoundedSamples( -// Chart("Tetrahedron XY Plot", "x", "y", interval, interval, chartWidth, chartHeight), -// t.vertices.map(v => Vector2(v.x, v.y)), -// samples.map(v => Vector2(v.x, v.y)) -// ) -// ).append("\n") -// -// println( -// drawBoundedSamples( -// Chart("Tetrahedron XZ Plot", "x", "z", interval, interval, chartWidth, chartHeight), -// t.vertices.map(v => Vector2(v.x, v.z)), -// samples.map(v => Vector2(v.x, v.z)) -// ) -// ).append("\n") -// -// println( -// drawBoundedSamples( -// Chart("Tetrahedron YZ Plot", "y", "z", interval, interval, chartWidth, chartHeight), -// t.vertices.map(v => Vector2(v.y, v.z)), -// samples.map(v => Vector2(v.y, v.z)) -// ) -// ).append("\n") - - } - - override def name: String = "Tetrahedron" - -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoEigenDecomposition.scala b/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoEigenDecomposition.scala deleted file mode 100644 index 50753a8..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoEigenDecomposition.scala +++ /dev/null @@ -1,29 +0,0 @@ -package ai.dragonfly.math.matrix - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.matrix.decomposition.* -import ai.dragonfly.math.vector.* -import ai.dragonfly.math.vector.Vec.* -import narr.* - -object DemoEigenDecomposition extends Demonstration { - def demo(): Unit = { - val M0: Matrix[4, 4] = Matrix[4, 4]( - NArray[NArray[Double]]( - NArray[Double](1.0, 2.0, 3.0, 4.0), - NArray[Double](0.0, -1.0, 0.0, -3.0), - NArray[Double](4.0, 0.0, -7.0, 0.5), - NArray[Double](0.27, ai.dragonfly.math.Constant.π, 1.1, 0.5), - ) - ) - - println(M0) - println("\n\n") - val eig:Eigen[4] = Eigen[4](M0) - println(eig.realEigenvalues.render()) - println("\n\n") - - } - - def name: String = "DemoEigenValueDecomposition" -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoLinearRegression.scala b/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoLinearRegression.scala deleted file mode 100644 index 5f71e39..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoLinearRegression.scala +++ /dev/null @@ -1,90 +0,0 @@ -package ai.dragonfly.math.matrix - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.interval.Interval.* -import ai.dragonfly.math.matrix.ml.data.StaticSupervisedData -import ai.dragonfly.math.matrix.ml.supervized.regression.{LinearRegressionQR, LinearRegressionSVD, *} -import ai.dragonfly.math.vector.* -import ai.dragonfly.math.vector.Vec.* -import ai.dragonfly.viz.cli.* -import ai.dragonfly.math.interval.* - -object DemoLinearRegression extends Demonstration { - - override def demo(): Unit = { - - println("\n\nLinear Regression Tests: ") - println("\nSynthetic Tests: ") - val slrt: SyntheticLinearRegressionTest[100, 2] = SyntheticLinearRegressionTest[100, 2](Vec[2](2.0, 1.0), 2.0, 1.1) - println(s"Generated Synthetic Test Data: $slrt") - - val interval: Interval[Double] = `[]`[Double](-1.0, 15.0) - - val xPlot: Chart = Chart("X Component", "p.x", "f(p)", slrt.trainingData.domainComponent(0), interval, 100, 40) - val yPlot: Chart = Chart("Y Component", "p.y", "f(p)", slrt.trainingData.domainComponent(1), interval, 100, 40) - - val xY = (0 until slrt.trainingData.sampleSize).map((i: Int) => { - val lv = slrt.trainingData.labeledExample(i); Vec[2](lv.vector(0), lv.y) - }) - val yY = (0 until slrt.trainingData.sampleSize).map((i: Int) => { - val lv = slrt.trainingData.labeledExample(i); Vec[2](lv.vector(1), lv.y) - }) - - xPlot.scatter(" (p.x, f(p))", xY: _*) - yPlot.scatter(" (p.y, f(p))", yY: _*) - - println("\nTest LinearRegressionQR:\n") - - val syntProbLR: LinearRegressionProblem[100, 2] = LinearRegressionProblem[100, 2](slrt.trainingData) - val slrQR = LinearRegressionQR[100, 2].train(syntProbLR) - println(s"\tLinearRegressionQR.train(syntProbLR) => $slrQR\n") - println(s"\tslrt.evaluate(slrQR) => ${slrt.evaluate(slrQR)}\n") - - val p = slrt.trainingData.sampleMean - var yMean: Double = slrQR(p) - - val xSlopeQR = slrQR(p + Vec[2](1.0, 0.0)) - yMean - val ySlopeQR = slrQR(p + Vec[2](0.0, 1.0)) - yMean - - xPlot.line(Vec[2](p(0), yMean), xSlopeQR, "QR (p.x, f'(p))") - yPlot.line(Vec[2](p(1), yMean), ySlopeQR, "QR (p.y, f'(p))") - - println("\n\nTest LinearRegressionSVD:\n") - val slrSVD = LinearRegressionSVD[100, 2].train(syntProbLR) - println(s"\tLinearRegressionSVD.train(syntProbLR) => $slrSVD\n") - println(s"\tslrt.evaluate(slrSVD) => ${slrt.evaluate(slrSVD)}\n") - - yMean = slrSVD(p) - val xSlopeSVD: Double = slrSVD(p + Vec[2](1.0, 0.0)) - yMean - val ySlopeSVD: Double = slrSVD(p + Vec[2](0.0, 1.0)) - yMean - - yMean / slrSVD.a.magnitude - xPlot.line(Vec[2](p(0), yMean), xSlopeSVD, "SVD (p.x, f'(p))") - yPlot.line(Vec[2](p(1), yMean), ySlopeSVD, "SVD (p.y, f'(p))") - - println(xPlot) - println(yPlot) - - println("\nEmperical Tests:\n") - val empericalTrainingData: StaticSupervisedData[100, 8] = new StaticSupervisedData[100, 8](EmpericalData.trainingData_01) - val empericalTestData: StaticSupervisedData[100, 8] = new StaticSupervisedData[100, 8](EmpericalData.testData_01) - val elrt: EmpiricalRegressionTest[100, 8] = EmpiricalRegressionTest[100, 8](empericalTrainingData, empericalTestData) - val emProbLR: LinearRegressionProblem[100, 8] = LinearRegressionProblem[100, 8](empericalTrainingData) - - println(empericalTrainingData.sampleSize) - - println("\nTest LinearRegressionQR:\n") - val elrQR = new LinearRegressionQR[100, 8].train(emProbLR) - println(s"\tLinearRegressionQR.train(emProbLR) => $elrQR\n") - println(s"\tslrt.evaluate(elrQR) => ${elrt.evaluate(elrQR)}\n") - - println("\n\nTest LinearRegressionSVD:\n") - val elrSVD = new LinearRegressionSVD[100, 8].train(emProbLR) - println(s"\tLinearRegressionSVD.train(emProbLR) => $elrSVD\n") - println(s"\tslrt.evaluate(elrSVD) => ${elrt.evaluate(elrSVD)}\n") - - } - - override def name: String = "QR and SVD Linear Regression: " - -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoPCA.scala b/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoPCA.scala deleted file mode 100644 index 593927a..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoPCA.scala +++ /dev/null @@ -1,202 +0,0 @@ -package ai.dragonfly.math.matrix - - -import ai.dragonfly.democrossy.Demonstration -import narr.{NArray, *} -import ai.dragonfly.math.geometry.Line -import ai.dragonfly.math.matrix.ml.data.* -import ai.dragonfly.math.matrix.ml.unsupervised.dimreduction.* -import ai.dragonfly.math.vector.* -import ai.dragonfly.math.vector.Vec.* -import ai.dragonfly.viz.cli.CLImg - -import Console.* - -object DemoPCA extends Demonstration { - - def demo(): Unit = { - - // 2D shapes represented by centered 2D meshes of exactly 9 points each. - val square:Vec[18] = Vec[18]( - -1.000000, 1.000000, // point 1 1 - 0.000000, 1.000000, // point 6 2 - 1.000000, 1.000000, // point 2 3 - 1.000000, 0.000000, // point 7 4 - - 1.000000, -1.000000, // point 4 5 - 0.000000, -1.000000, // point 8 6 - -1.000000, -1.000000, // point 3 7 - -1.000000, 0.000000, // point 5 8 - 0.000000, 0.000000 // point 9 - ) - - val circle:Vec[18] = Vec[18]( - -0.700000, 0.700000, // point 1 1 - 0.000000, 1.000000, // point 6 2 - 0.700000, 0.700000, // point 2 3 - 1.000000, 0.000000, // point 7 4 - 0.700000, -0.700000, // point 4 5 - 0.000000, -1.000000, // point 8 6 - -0.700000, -0.700000, // point 3 7 - -1.000000, 0.000000, // point 5 8 - 0.000000, 0.000000 // point 9 - ) - val almond:Vec[18] = Vec[18]( - -0.488187, 0.800000, // point 1 1 - 0.000000, 1.000000, // point 6 2 - 0.503938, 0.800000, // point 2 3 - 0.600000, 0.500000, // point 7 4 - 0.450000, -0.200000, // point 4 5 - 0.000000, -1.000000, // point 8 6 - -0.450000, -0.200000, // point 3 7 - -0.600000, 0.500000, // point 5 8 - 0.000000, 0.500000 // point 9 - ) - val triangle:Vec[18] = Vec[18]( - -1.000000, 1.000000, // point 1 1 - 0.000000, 1.000000, // point 6 2 - 1.000000, 1.000000, // point 2 3 - 0.500000, 0.000000, // point 7 4 - 0.100000, -0.800000, // point 4 5 - 0.000000, -1.000000, // point 8 6 - -0.100000, -0.800000, // point 3 7 - -0.500000, 0.000000, // point 5 8 - 0.000000, 0.000000 // point 9 - ) - val cross:Vec[18] = Vec[18]( - -0.100000, 0.100000, // point 1 1 - 0.000000, 1.000000, // point 6 2 - 0.100000, 0.100000, // point 2 3 - 1.000000, 0.000000, // point 7 4 - 0.100000, -0.100000, // point 4 5 - 0.000000, -1.000000, // point 8 6 - -0.100000, -0.100000, // point 3 7 - -1.000000, 0.000000, // point 5 8 - 0.000000, 0.000000 // point 9 - ) - val x:Vec[18] = Vec[18]( - -1.000000, 1.000000, // point 1 1 - 0.000000, 0.100000, // point 6 2 - 1.000000, 1.000000, // point 2 3 - 0.100000, 0.000000, // point 7 4 - 1.000000, -1.000000, // point 4 5 - 0.000000, -0.100000, // point 8 6 - -1.000000, -1.000000, // point 3 7 - -0.100000, 0.000000, // point 5 8 - 0.000000, 0.000000 // point 9 - ) - - val vArr:NArray[Vec[18]] = NArray[Vec[18]](square, circle, almond, triangle, cross, x) - - val cimg: CLImg = CLImg(50 * vArr.length, 50) - - println("Sample Shapes:\n") - var i:Int = 0; while (i < vArr.length) { - plotVectorOfShape2D(vArr(i), Vec[2]((i * 50) + 25, 25))(cimg) - i += 1 - } - - println(cimg) - - println(s"$RESET") - - val sud: StaticUnsupervisedData[6, 18] = StaticUnsupervisedData[6, 18](vArr) - val pca = PCA[6, 18](sud) - - println(s"Mean Shape with μ = ${pca.mean.render()}\n") - println(plotVectorOfShape2D(pca.mean, Vec[2](25, 25))()) - - var totalVariance:Double = 0.0 - - for (bp <- pca.basisPairs) { - totalVariance += bp.variance - if (bp.variance > 0.001) { - var i = 0 - val cImg2: CLImg = new CLImg(350, 50) - var s: Double = -3.0 * bp.variance - while (s <= 3.0 * bp.variance) { - plotVectorOfShape2D((bp.basisVector * s) + pca.mean, Vec[2]((i * 50) + 25, 25))(cImg2) - s = s + bp.variance - i = i + 1 - } - println(s"$RESET") - println(s"Singular Shape with σ = ${bp.variance} and Singular Vector: ${bp.basisVector.render()}") - println(s"Shape Variation from -3σ to 3σ (${-3.0 * bp.variance} to ${3.0 * bp.variance}):") - println(cImg2) - println(s"$RESET") - } - } - - val reducer = pca.getReducer[2] - println(s"Dimensionality reduction from ${reducer.domainDimension} to ${reducer.rangeDimension}:") - i = 0; while (i < vArr.length) { - val v:Vec[18] = vArr(i) - val cImg2: CLImg = new CLImg(100, 50) - plotVectorOfShape2D(v, Vec[2](25, 25))(cImg2) - val reducedV = reducer(v) - //plotVectorOfShape2D(reducedV, Vector2(75, 25))(cImg2) - plotVectorOfShape2D(reducer.unapply(reducedV), Vec[2](75, 25))(cImg2) - println(s"${v.render()} -> ${reducedV.show}") - println(cImg2) - println(s"$RESET") - i += 1 - } - - println(s"Dimensionality reduction from ${reducer.domainDimension} to minimum dimension with at least 98% accuracy:") - var cumulativeVariance:Double = 0 - var minDimension:Int = 0 - val itr:Iterator[BasisPair[18]] = pca.basisPairs.iterator - while (cumulativeVariance < 0.98 && itr.hasNext ) { - cumulativeVariance = cumulativeVariance + (itr.next().variance / totalVariance) - minDimension += 1 - } - - println(s"\tFound $minDimension dimensions can account for $cumulativeVariance % of the variance.") - - val vs = VectorSpace(minDimension) - val runTimeDimensionReducer: DimensionalityReducerPCA[18, vs.N] = pca.getReducer[vs.N] - - println(vs.ones.render()) - - i = 0; - while (i < vArr.length) { - val v: Vec[18] = vArr(i) - val cImg2: CLImg = new CLImg(100, 50) - plotVectorOfShape2D(v, Vec[2](25, 25))(cImg2) - val reducedV:Vec[vs.N] = runTimeDimensionReducer(v) - plotVectorOfShape2D(runTimeDimensionReducer.unapply(reducedV), Vec[2](75, 25))(cImg2) - println(s"${v.render()} -> ${reducedV.render()}") - println(cImg2) - println(s"$RESET") - i += 1 - } - } - - - def name: String = "Principle Components Analysis" - - def plotVectorOfShape2D(sv: Vec[18], position: Vec[2])(cimg: CLImg = CLImg(50, 50)): CLImg = { - def transform(x: Double, y: Double): Vec[2] = Vec[2]((15 * x) + position.x, (15 * y) + position.y) - - def segment(i: Int, j: Int): Any = { - Line.trace2D( - transform(sv(i), sv(i + 1)), - transform(sv(j), sv(j + 1)), - (dX: Int, dY: Int) => { - cimg.setPixel(dX, (cimg.height - 1) - dY, 2) - () - } - ) - } - - var i = 0 - while (i + 3 < sv.dimension) { - segment(i, i + 2) - segment(i, sv.dimension - 2) - i = i + 2 - } - segment(0, sv.dimension - 4) - cimg - } - -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoVectorInterop.scala b/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoVectorInterop.scala deleted file mode 100644 index c031bc5..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/DemoVectorInterop.scala +++ /dev/null @@ -1,26 +0,0 @@ -package ai.dragonfly.math.matrix - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.matrix.util.* -import ai.dragonfly.math.vector.* -import ai.dragonfly.math.vector.Vec.* -import narr.* - -import ai.dragonfly.math.Constant.* - -object DemoVectorInterop extends Demonstration { - def demo(): Unit = { - val M0: Matrix[2, 3] = Matrix[2, 3]( - NArray[NArray[Double]]( - NArray[Double](1.0, 1.46557123187676802665, `√2`), - NArray[Double](2.0, π, e), - ) - ) - - val v2: Vec[2] = Vec[2](1.0, 2.0) - - println(s"${v2.show}.times($M0\n) = ${v2.times[3](M0)}") - } - - def name: String = "DemoVectorInterop" -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/EmpericalData.scala b/demo/shared/src/main/scala/ai/dragonfly/math/matrix/EmpericalData.scala deleted file mode 100644 index 0c5b6f6..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/EmpericalData.scala +++ /dev/null @@ -1,1616 +0,0 @@ -package ai.dragonfly.math.matrix - -import ai.dragonfly.math.vector.* -import ai.dragonfly.math.stats.{LabeledVec, SimpleLabeledVector} -import narr.* - -object EmpericalData { - - val trainingData_01: NArray[LabeledVec[8]] = new NArray[LabeledVec[8]](1416) - val testData_01: NArray[LabeledVec[8]] = new NArray[LabeledVec[8]](160) - - def initializeData: Unit = { - // initialize training data: - this.init_0to400 - this.init_401to800 - this.init_801to1200 - this.init_1201toEnd - - // initialize test data: - this.initTestData - } - - def init_0to400: Unit = { - trainingData_01(0) = SimpleLabeledVector[8](0.14649772524284427, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1) = SimpleLabeledVector[8](0.19683337069036058, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(2) = SimpleLabeledVector[8](0.15614790363293235, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(3) = SimpleLabeledVector[8](0.13164384574578333, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(4) = SimpleLabeledVector[8](0.11513581702273953, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(5) = SimpleLabeledVector[8](0.09361617829559475, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(6) = SimpleLabeledVector[8](0.06822644431786952, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(7) = SimpleLabeledVector[8](0.04218258549679398, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(8) = SimpleLabeledVector[8](0.024639461264417094, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(9) = SimpleLabeledVector[8](-0.3242008170964912, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(10) = SimpleLabeledVector[8](-0.2870210542943171, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(11) = SimpleLabeledVector[8](-0.26702217505824927, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(12) = SimpleLabeledVector[8](-0.25536707860100677, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(13) = SimpleLabeledVector[8](-0.23736637490343218, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(14) = SimpleLabeledVector[8](0.16455768734571807, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(15) = SimpleLabeledVector[8](0.13129803994788597, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(16) = SimpleLabeledVector[8](0.13438082617961866, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(17) = SimpleLabeledVector[8](-0.24392503063344215, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(18) = SimpleLabeledVector[8](0.16115412197419277, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(19) = SimpleLabeledVector[8](0.168289587118451, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(20) = SimpleLabeledVector[8](0.1618669953073942, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(21) = SimpleLabeledVector[8](0.15850096917425535, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(22) = SimpleLabeledVector[8](0.15693880153086148, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(23) = SimpleLabeledVector[8](0.15528599031506893, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(24) = SimpleLabeledVector[8](0.15513106697223703, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(25) = SimpleLabeledVector[8](0.1409771128440429, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(26) = SimpleLabeledVector[8](0.14269336068071642, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(27) = SimpleLabeledVector[8](0.11363812318376285, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(28) = SimpleLabeledVector[8](0.09029419681980724, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(29) = SimpleLabeledVector[8](0.41967969877216393, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(30) = SimpleLabeledVector[8](0.662795356324026, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(31) = SimpleLabeledVector[8](2.4110249006488242, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(32) = SimpleLabeledVector[8](0.5811788574334223, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(33) = SimpleLabeledVector[8](1.0747019174837105, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(34) = SimpleLabeledVector[8](1.1414375439252902, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(35) = SimpleLabeledVector[8](1.10227669709448, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(36) = SimpleLabeledVector[8](1.0697209364455522, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(37) = SimpleLabeledVector[8](1.1508123638736625, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(38) = SimpleLabeledVector[8](0.968173071096655, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(39) = SimpleLabeledVector[8](0.7971098111356661, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(40) = SimpleLabeledVector[8](0.7301826759420338, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(41) = SimpleLabeledVector[8](0.7074873036550247, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(42) = SimpleLabeledVector[8](0.5483261501490234, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(43) = SimpleLabeledVector[8](0.6610772477201858, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(44) = SimpleLabeledVector[8](0.7032342296241644, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(45) = SimpleLabeledVector[8](2.129775162905844, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(46) = SimpleLabeledVector[8](1.5191384266325705, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(47) = SimpleLabeledVector[8](3.618409453231154, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(48) = SimpleLabeledVector[8](4.2527282733935445, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(49) = SimpleLabeledVector[8](2.6707722992574485, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(50) = SimpleLabeledVector[8](0.5375107724502044, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(51) = SimpleLabeledVector[8](0.26593927807390383, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(52) = SimpleLabeledVector[8](0.22050235961527925, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(53) = SimpleLabeledVector[8](0.18457348358333728, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(54) = SimpleLabeledVector[8](0.21773372499082783, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(55) = SimpleLabeledVector[8](0.138894662811115, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(56) = SimpleLabeledVector[8](0.11340915487070347, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(57) = SimpleLabeledVector[8](0.4158477250809065, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(58) = SimpleLabeledVector[8](0.47885202155747364, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(59) = SimpleLabeledVector[8](0.6889388986235427, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(60) = SimpleLabeledVector[8](0.6539782871715779, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(61) = SimpleLabeledVector[8](0.7842793315792121, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(62) = SimpleLabeledVector[8](1.2763258852247135, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(63) = SimpleLabeledVector[8](1.7045175622734239, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(64) = SimpleLabeledVector[8](1.2127471490780357, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(65) = SimpleLabeledVector[8](0.9386785516974034, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(66) = SimpleLabeledVector[8](0.8653166634994712, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(67) = SimpleLabeledVector[8](0.6232125282075291, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(68) = SimpleLabeledVector[8](0.5291728734373119, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(69) = SimpleLabeledVector[8](0.28139299710322535, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(70) = SimpleLabeledVector[8](0.13697768338978622, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(71) = SimpleLabeledVector[8](0.3129700491169197, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(72) = SimpleLabeledVector[8](0.2353152131761179, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(73) = SimpleLabeledVector[8](-0.12678543287866567, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(74) = SimpleLabeledVector[8](-0.22108394847303284, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(75) = SimpleLabeledVector[8](-0.12051569604052358, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(76) = SimpleLabeledVector[8](-0.0867597576830264, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(77) = SimpleLabeledVector[8](-0.1982264718612156, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(78) = SimpleLabeledVector[8](-0.27409057450926333, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(79) = SimpleLabeledVector[8](-0.21613019656030896, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(80) = SimpleLabeledVector[8](-0.12496695098383975, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(81) = SimpleLabeledVector[8](-0.15648349532366962, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(82) = SimpleLabeledVector[8](-0.08669666339907098, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(83) = SimpleLabeledVector[8](-0.038939318755970787, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(84) = SimpleLabeledVector[8](-0.07405613026781432, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(85) = SimpleLabeledVector[8](-0.16691673916559518, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(86) = SimpleLabeledVector[8](-0.23127960064902284, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(87) = SimpleLabeledVector[8](-0.2995138199382506, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(88) = SimpleLabeledVector[8](-0.4152346239188322, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(89) = SimpleLabeledVector[8](-0.24527287883413412, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(90) = SimpleLabeledVector[8](-0.17975376788560768, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(91) = SimpleLabeledVector[8](-0.373460801644639, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(92) = SimpleLabeledVector[8](-0.3813241036550456, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(93) = SimpleLabeledVector[8](-0.24858959386124174, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(94) = SimpleLabeledVector[8](-0.24185064331874978, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(95) = SimpleLabeledVector[8](-0.18355694623752228, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(96) = SimpleLabeledVector[8](-0.049995630135913786, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(97) = SimpleLabeledVector[8](-0.15341982750768238, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(98) = SimpleLabeledVector[8](-0.11483835441162775, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(99) = SimpleLabeledVector[8](-0.15271730668397107, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(100) = SimpleLabeledVector[8](-0.09539160310217924, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(101) = SimpleLabeledVector[8](-0.033631771616443415, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(102) = SimpleLabeledVector[8](-0.04734450500612208, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(103) = SimpleLabeledVector[8](0.025617200972898314, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(104) = SimpleLabeledVector[8](-0.019216812154870165, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(105) = SimpleLabeledVector[8](-0.009363825511749892, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(106) = SimpleLabeledVector[8](-0.028001310964016167, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(107) = SimpleLabeledVector[8](-0.09687856508159247, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(108) = SimpleLabeledVector[8](-0.1434504559402018, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(109) = SimpleLabeledVector[8](-0.13657153013126014, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(110) = SimpleLabeledVector[8](-0.16399858316625474, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(111) = SimpleLabeledVector[8](-0.15440833949151933, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(112) = SimpleLabeledVector[8](-0.07166822744482525, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(113) = SimpleLabeledVector[8](-0.1207176926230787, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(114) = SimpleLabeledVector[8](-0.17737075042481082, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(115) = SimpleLabeledVector[8](-0.20286638021531164, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(116) = SimpleLabeledVector[8](-0.19856552768124933, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(117) = SimpleLabeledVector[8](-0.1726403788337729, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(118) = SimpleLabeledVector[8](-0.13773972909079707, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(119) = SimpleLabeledVector[8](-0.26832870540216414, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(120) = SimpleLabeledVector[8](-0.33916833770349486, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(121) = SimpleLabeledVector[8](-0.32097423312437223, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(122) = SimpleLabeledVector[8](-0.2906155771842272, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(123) = SimpleLabeledVector[8](-0.22538469236095923, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(124) = SimpleLabeledVector[8](-0.21799992534177712, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(125) = SimpleLabeledVector[8](-0.3095709038899747, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(126) = SimpleLabeledVector[8](-0.28227690233231795, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(127) = SimpleLabeledVector[8](-0.3415022805369008, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(128) = SimpleLabeledVector[8](-0.40764154292999233, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(129) = SimpleLabeledVector[8](-0.20868563307847088, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(130) = SimpleLabeledVector[8](-0.06832116683410633, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(131) = SimpleLabeledVector[8](0.04599518654428684, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(132) = SimpleLabeledVector[8](0.0017335894268773369, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(133) = SimpleLabeledVector[8](-0.03487487379385735, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(134) = SimpleLabeledVector[8](-0.024775468475143846, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(135) = SimpleLabeledVector[8](-0.01651509944665206, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(136) = SimpleLabeledVector[8](-0.11379036176279334, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(137) = SimpleLabeledVector[8](-0.152565225397401, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(138) = SimpleLabeledVector[8](-0.13916686196246933, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(139) = SimpleLabeledVector[8](-0.18226547247746172, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(140) = SimpleLabeledVector[8](-0.22497630739924057, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(141) = SimpleLabeledVector[8](-0.14954599422297415, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(142) = SimpleLabeledVector[8](-0.14174272036798402, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(143) = SimpleLabeledVector[8](-0.07410763479796983, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(144) = SimpleLabeledVector[8](-0.06688408251725544, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(145) = SimpleLabeledVector[8](-0.11505404407071546, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(146) = SimpleLabeledVector[8](-0.1687486390006775, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(147) = SimpleLabeledVector[8](-0.10297737859388742, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(148) = SimpleLabeledVector[8](-0.10017631940527924, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(149) = SimpleLabeledVector[8](-0.11403830962113203, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(150) = SimpleLabeledVector[8](-0.10520525313580105, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(151) = SimpleLabeledVector[8](-0.03421996301351778, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(152) = SimpleLabeledVector[8](0.17046621624802596, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(153) = SimpleLabeledVector[8](0.2838577698001571, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(154) = SimpleLabeledVector[8](0.1988044324782928, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(155) = SimpleLabeledVector[8](0.3188621323811255, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(156) = SimpleLabeledVector[8](0.42151701549901754, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(157) = SimpleLabeledVector[8](0.47457735335019113, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(158) = SimpleLabeledVector[8](0.35140695374718967, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(159) = SimpleLabeledVector[8](0.44436601004071263, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(160) = SimpleLabeledVector[8](0.46114387843278437, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(161) = SimpleLabeledVector[8](0.41220224273103323, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(162) = SimpleLabeledVector[8](0.4253833941369407, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(163) = SimpleLabeledVector[8](0.4001841097609477, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(164) = SimpleLabeledVector[8](0.4815610625920875, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(165) = SimpleLabeledVector[8](0.2958316960816848, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(166) = SimpleLabeledVector[8](0.2480321836830418, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(167) = SimpleLabeledVector[8](0.41875580479074115, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(168) = SimpleLabeledVector[8](0.5324219406921601, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(169) = SimpleLabeledVector[8](0.40433298070534923, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(170) = SimpleLabeledVector[8](0.3135223700232702, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(171) = SimpleLabeledVector[8](0.3681351775780845, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(172) = SimpleLabeledVector[8](0.3759607674945267, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(173) = SimpleLabeledVector[8](0.2821647377536308, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(174) = SimpleLabeledVector[8](0.27991370541884075, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(175) = SimpleLabeledVector[8](0.19272346130280815, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(176) = SimpleLabeledVector[8](0.10577920540272173, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(177) = SimpleLabeledVector[8](0.07633095826893693, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(178) = SimpleLabeledVector[8](-0.011358288770846619, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(179) = SimpleLabeledVector[8](-0.15035906076946956, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(180) = SimpleLabeledVector[8](-0.14660890676497196, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(181) = SimpleLabeledVector[8](-0.17797444174372096, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(182) = SimpleLabeledVector[8](-0.17295953483235565, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(183) = SimpleLabeledVector[8](-0.23513841158903717, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(184) = SimpleLabeledVector[8](-0.23935736771150892, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(185) = SimpleLabeledVector[8](-0.2391609421451813, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(186) = SimpleLabeledVector[8](-0.2002499890748512, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(187) = SimpleLabeledVector[8](-0.17829096469573918, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(188) = SimpleLabeledVector[8](-0.23075715672784006, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(189) = SimpleLabeledVector[8](-0.28168618608447643, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(190) = SimpleLabeledVector[8](-0.29277564226463765, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(191) = SimpleLabeledVector[8](-0.26640673637068957, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(192) = SimpleLabeledVector[8](-0.24753525688083466, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(193) = SimpleLabeledVector[8](-0.27158724333822487, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(194) = SimpleLabeledVector[8](-0.2903614904173288, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(195) = SimpleLabeledVector[8](-0.18918030034499103, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(196) = SimpleLabeledVector[8](-0.1686072484990006, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(197) = SimpleLabeledVector[8](-0.06330315407761361, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(198) = SimpleLabeledVector[8](-0.023806342011412768, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(199) = SimpleLabeledVector[8](0.037563917855920136, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(200) = SimpleLabeledVector[8](9.439763970468219E-4, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(201) = SimpleLabeledVector[8](-0.030156500606584126, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(202) = SimpleLabeledVector[8](-0.022855416597638928, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(203) = SimpleLabeledVector[8](-0.017035154401714006, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(204) = SimpleLabeledVector[8](-0.05670773593957628, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(205) = SimpleLabeledVector[8](-0.05087935546196047, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(206) = SimpleLabeledVector[8](-0.14330140338222913, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(207) = SimpleLabeledVector[8](-0.1759485622696305, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(208) = SimpleLabeledVector[8](-0.16412858535183067, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(209) = SimpleLabeledVector[8](-0.13400356798859428, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(210) = SimpleLabeledVector[8](-0.12255836735065989, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(211) = SimpleLabeledVector[8](-0.17029516580227352, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(212) = SimpleLabeledVector[8](-0.16641951083650225, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(213) = SimpleLabeledVector[8](-0.16521056887358523, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(214) = SimpleLabeledVector[8](-0.11046325342778647, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(215) = SimpleLabeledVector[8](-0.15597099092480535, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(216) = SimpleLabeledVector[8](-0.14601182077215688, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(217) = SimpleLabeledVector[8](-0.24092664545062853, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(218) = SimpleLabeledVector[8](-0.2659221722805332, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(219) = SimpleLabeledVector[8](-0.24344163909838745, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(220) = SimpleLabeledVector[8](-0.2247227574122229, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(221) = SimpleLabeledVector[8](-0.2077693416738372, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(222) = SimpleLabeledVector[8](-0.2547640997298016, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(223) = SimpleLabeledVector[8](-0.1580412142290747, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(224) = SimpleLabeledVector[8](-0.0780855529908174, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(225) = SimpleLabeledVector[8](0.009854366598770245, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(226) = SimpleLabeledVector[8](0.017554679045856, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(227) = SimpleLabeledVector[8](0.025955601363218048, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(228) = SimpleLabeledVector[8](0.02646991812162713, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(229) = SimpleLabeledVector[8](-0.027665891981850793, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(230) = SimpleLabeledVector[8](-0.02034983244627854, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(231) = SimpleLabeledVector[8](0.05304052782449098, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(232) = SimpleLabeledVector[8](-0.017244430110906882, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(233) = SimpleLabeledVector[8](-0.08290095890658165, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(234) = SimpleLabeledVector[8](-0.07001001560958238, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(235) = SimpleLabeledVector[8](-0.06142202592786875, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(236) = SimpleLabeledVector[8](0.014774460131215231, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(237) = SimpleLabeledVector[8](0.013641525450608916, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(238) = SimpleLabeledVector[8](0.1523764015478702, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(239) = SimpleLabeledVector[8](0.2070122447043237, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(240) = SimpleLabeledVector[8](0.24676161461208587, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(241) = SimpleLabeledVector[8](0.18647867458868173, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(242) = SimpleLabeledVector[8](0.023517642736208846, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(243) = SimpleLabeledVector[8](0.015321173696862284, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(244) = SimpleLabeledVector[8](0.07055092555177818, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(245) = SimpleLabeledVector[8](0.002028917941561493, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(246) = SimpleLabeledVector[8](0.0615906279383127, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(247) = SimpleLabeledVector[8](0.05552395957356051, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(248) = SimpleLabeledVector[8](-0.01278726763103987, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(249) = SimpleLabeledVector[8](0.047514570122486444, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(250) = SimpleLabeledVector[8](-0.018405709126933362, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(251) = SimpleLabeledVector[8](-0.019524691620124067, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(252) = SimpleLabeledVector[8](-0.08123458313229477, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(253) = SimpleLabeledVector[8](-0.14129197184175035, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(254) = SimpleLabeledVector[8](-0.13982741776233581, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(255) = SimpleLabeledVector[8](-0.1995858483260773, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(256) = SimpleLabeledVector[8](-0.19477702544365708, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(257) = SimpleLabeledVector[8](-0.19371683446329027, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(258) = SimpleLabeledVector[8](-0.19328678576832217, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(259) = SimpleLabeledVector[8](-0.19173823249345717, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(260) = SimpleLabeledVector[8](-0.24994632144829612, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(261) = SimpleLabeledVector[8](-0.3009636145085983, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(262) = SimpleLabeledVector[8](-0.20890248425834237, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(263) = SimpleLabeledVector[8](-0.24807817915138397, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(264) = SimpleLabeledVector[8](-0.2289639550749374, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(265) = SimpleLabeledVector[8](-0.21291420491376373, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(266) = SimpleLabeledVector[8](-0.2733019598791386, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(267) = SimpleLabeledVector[8](-0.3314196253769781, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(268) = SimpleLabeledVector[8](-0.30197660801516574, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(269) = SimpleLabeledVector[8](-0.2666912945264044, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(270) = SimpleLabeledVector[8](-0.08453689152020669, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(271) = SimpleLabeledVector[8](-0.15155947356258087, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(272) = SimpleLabeledVector[8](-0.13701780099909916, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(273) = SimpleLabeledVector[8](-0.2144098893257938, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(274) = SimpleLabeledVector[8](-0.1996191868819973, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(275) = SimpleLabeledVector[8](-0.08962597747353311, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(276) = SimpleLabeledVector[8](-0.17216658424894982, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(277) = SimpleLabeledVector[8](-0.16535165656790332, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(278) = SimpleLabeledVector[8](-0.06103916473983154, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(279) = SimpleLabeledVector[8](-0.04367133630480567, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(280) = SimpleLabeledVector[8](-0.0316999262549894, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(281) = SimpleLabeledVector[8](0.07856746502447871, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(282) = SimpleLabeledVector[8](-0.012708623498329182, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(283) = SimpleLabeledVector[8](0.00703463780432683, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(284) = SimpleLabeledVector[8](0.138217135553348, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(285) = SimpleLabeledVector[8](0.1477874706542424, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(286) = SimpleLabeledVector[8](0.1523390673462694, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(287) = SimpleLabeledVector[8](0.03437302777743403, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(288) = SimpleLabeledVector[8](-0.08226363324254103, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(289) = SimpleLabeledVector[8](-0.16896407814390305, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(290) = SimpleLabeledVector[8](-0.158635392982571, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(291) = SimpleLabeledVector[8](-0.15062549205984618, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(292) = SimpleLabeledVector[8](-0.2680269569303487, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(293) = SimpleLabeledVector[8](-0.2987946780676218, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(294) = SimpleLabeledVector[8](-0.29749005886638735, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(295) = SimpleLabeledVector[8](-0.2964075001445503, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(296) = SimpleLabeledVector[8](-0.2956344223923484, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(297) = SimpleLabeledVector[8](-0.29534316374448766, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(298) = SimpleLabeledVector[8](-0.29359401881525055, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(299) = SimpleLabeledVector[8](-0.20665269378047196, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(300) = SimpleLabeledVector[8](-0.19253758632337367, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(301) = SimpleLabeledVector[8](-0.17120320856212595, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(302) = SimpleLabeledVector[8](-0.03476782320272438, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(303) = SimpleLabeledVector[8](-0.0273408728528695, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(304) = SimpleLabeledVector[8](0.1162921566479473, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(305) = SimpleLabeledVector[8](0.12176845536655109, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(306) = SimpleLabeledVector[8](5.804314968831304E-4, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(307) = SimpleLabeledVector[8](0.01331316464320574, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(308) = SimpleLabeledVector[8](0.15636886512795098, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(309) = SimpleLabeledVector[8](0.14498650172209196, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(310) = SimpleLabeledVector[8](0.1400844527096262, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(311) = SimpleLabeledVector[8](0.13049456739163456, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(312) = SimpleLabeledVector[8](0.11865035008804016, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(313) = SimpleLabeledVector[8](-0.01753092089482844, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(314) = SimpleLabeledVector[8](-0.018413769792780227, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(315) = SimpleLabeledVector[8](-0.02102932599447584, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(316) = SimpleLabeledVector[8](-0.019973698575195816, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(317) = SimpleLabeledVector[8](-0.017093974406594697, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(318) = SimpleLabeledVector[8](-0.014808673832805602, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(319) = SimpleLabeledVector[8](0.10823275335498982, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(320) = SimpleLabeledVector[8](0.08076853169855663, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(321) = SimpleLabeledVector[8](-0.05036137731622509, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(322) = SimpleLabeledVector[8](-0.04536011386311181, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(323) = SimpleLabeledVector[8](-0.15398809157362442, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(324) = SimpleLabeledVector[8](-0.1396253569646646, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(325) = SimpleLabeledVector[8](-0.13230358469293543, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(326) = SimpleLabeledVector[8](-0.1238992202536944, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(327) = SimpleLabeledVector[8](-0.09855942475440208, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(328) = SimpleLabeledVector[8](0.03721605397208153, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(329) = SimpleLabeledVector[8](-0.09528735010292709, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(330) = SimpleLabeledVector[8](0.03801494089002939, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(331) = SimpleLabeledVector[8](0.03683393745309192, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(332) = SimpleLabeledVector[8](0.03699278245321093, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(333) = SimpleLabeledVector[8](-0.09223449432246403, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(334) = SimpleLabeledVector[8](-0.08817563604311329, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(335) = SimpleLabeledVector[8](-0.0850305470928206, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(336) = SimpleLabeledVector[8](-0.08178153647331403, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(337) = SimpleLabeledVector[8](-0.07831774343013247, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(338) = SimpleLabeledVector[8](-0.07585016766628318, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(339) = SimpleLabeledVector[8](-0.0729672307384596, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(340) = SimpleLabeledVector[8](-0.05718151661880426, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(341) = SimpleLabeledVector[8](-0.04805499210573333, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(342) = SimpleLabeledVector[8](-0.04295063832669317, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(343) = SimpleLabeledVector[8](-0.0283427689800573, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(344) = SimpleLabeledVector[8](-0.028904579069920368, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(345) = SimpleLabeledVector[8](0.09219288508227569, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(346) = SimpleLabeledVector[8](0.07732732230155465, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(347) = SimpleLabeledVector[8](0.06124959336690231, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(348) = SimpleLabeledVector[8](0.05533468327383362, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(349) = SimpleLabeledVector[8](0.05608929064650795, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(350) = SimpleLabeledVector[8](0.05283913249528951, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(351) = SimpleLabeledVector[8](-0.07910593561477294, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(352) = SimpleLabeledVector[8](-0.07671688746073736, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(353) = SimpleLabeledVector[8](-0.18036749839920144, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(354) = SimpleLabeledVector[8](-0.17088922222298547, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(355) = SimpleLabeledVector[8](-0.16610964394569352, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(356) = SimpleLabeledVector[8](-0.2920823407123567, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(357) = SimpleLabeledVector[8](-0.24935516231141422, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(358) = SimpleLabeledVector[8](-0.39396687856673773, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(359) = SimpleLabeledVector[8](-0.38237219152544527, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(360) = SimpleLabeledVector[8](-0.5222933400627907, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(361) = SimpleLabeledVector[8](-0.47571756673043947, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(362) = SimpleLabeledVector[8](-0.4379816324814898, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(363) = SimpleLabeledVector[8](-0.21943733193394446, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(364) = SimpleLabeledVector[8](-0.18813856442496715, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(365) = SimpleLabeledVector[8](-0.16418910095809727, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(366) = SimpleLabeledVector[8](-0.15021964748258282, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(367) = SimpleLabeledVector[8](-0.12088885807332878, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(368) = SimpleLabeledVector[8](-0.11274082759518213, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(369) = SimpleLabeledVector[8](-0.09507098155469851, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(370) = SimpleLabeledVector[8](-0.2569879635523537, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(371) = SimpleLabeledVector[8](-0.22972705371847318, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(372) = SimpleLabeledVector[8](-0.21863843855108317, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(373) = SimpleLabeledVector[8](-0.20994633161120715, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(374) = SimpleLabeledVector[8](-0.1802031067085565, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(375) = SimpleLabeledVector[8](-0.1519335077178743, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(376) = SimpleLabeledVector[8](-0.12058673910731946, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(377) = SimpleLabeledVector[8](-0.11064818749493845, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(378) = SimpleLabeledVector[8](-0.11866808538360848, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(379) = SimpleLabeledVector[8](0.17435526447635397, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(380) = SimpleLabeledVector[8](0.17718205105068083, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(381) = SimpleLabeledVector[8](0.17508115044296127, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(382) = SimpleLabeledVector[8](0.1588172981336129, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(383) = SimpleLabeledVector[8](0.16117748440797597, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(384) = SimpleLabeledVector[8](0.4430964753581994, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(385) = SimpleLabeledVector[8](0.12854123120634817, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(386) = SimpleLabeledVector[8](0.12927427966344546, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(387) = SimpleLabeledVector[8](0.1313243512356246, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(388) = SimpleLabeledVector[8](0.11433641306905043, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(389) = SimpleLabeledVector[8](0.08674084401066513, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(390) = SimpleLabeledVector[8](0.07301573382175437, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(391) = SimpleLabeledVector[8](0.06747936417490127, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(392) = SimpleLabeledVector[8](0.05809415414718298, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(393) = SimpleLabeledVector[8](0.036781489919376534, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(394) = SimpleLabeledVector[8](0.029326639390146125, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(395) = SimpleLabeledVector[8](0.2792443385110107, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(396) = SimpleLabeledVector[8](0.2559816389275845, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(397) = SimpleLabeledVector[8](0.22070754369744114, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(398) = SimpleLabeledVector[8](0.18630985502552264, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(399) = SimpleLabeledVector[8](0.16710226384673, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(400) = SimpleLabeledVector[8](-0.09319125957959903, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - } - - def init_401to800: Unit = { - trainingData_01(401) = SimpleLabeledVector[8](-0.09645388944079213, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(402) = SimpleLabeledVector[8](-0.09434181844834837, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(403) = SimpleLabeledVector[8](-0.09568598739342593, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(404) = SimpleLabeledVector[8](-0.08415577384871246, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(405) = SimpleLabeledVector[8](-0.08568118672947748, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(406) = SimpleLabeledVector[8](-0.08455861865870114, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(407) = SimpleLabeledVector[8](0.14116467778327166, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(408) = SimpleLabeledVector[8](-0.09503772931791546, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(409) = SimpleLabeledVector[8](-0.09165374063642427, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(410) = SimpleLabeledVector[8](-0.09062842820422544, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(411) = SimpleLabeledVector[8](-0.08925295789506556, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(412) = SimpleLabeledVector[8](-0.09029496724329944, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(413) = SimpleLabeledVector[8](-0.09128406513341147, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(414) = SimpleLabeledVector[8](-0.08630147356157564, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(415) = SimpleLabeledVector[8](-0.07834592495699155, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(416) = SimpleLabeledVector[8](-0.06596584907231651, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(417) = SimpleLabeledVector[8](-0.006634953266916896, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(418) = SimpleLabeledVector[8](-0.007075555817079743, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(419) = SimpleLabeledVector[8](-0.0076138609389321855, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(420) = SimpleLabeledVector[8](-0.007969943760688441, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(421) = SimpleLabeledVector[8](-0.008717571525265573, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(422) = SimpleLabeledVector[8](-0.00905107318615328, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(423) = SimpleLabeledVector[8](-0.009290388077768027, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(424) = SimpleLabeledVector[8](-0.009883994249910216, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(425) = SimpleLabeledVector[8](1.2143064331837663E-15, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(426) = SimpleLabeledVector[8](6.938893903907233E-16, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(427) = SimpleLabeledVector[8](3.469446951953615E-16, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(428) = SimpleLabeledVector[8](-6.938893903907223E-16, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(429) = SimpleLabeledVector[8](-1.9081958235744843E-15, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(430) = SimpleLabeledVector[8](6.938893903907233E-16, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(431) = SimpleLabeledVector[8](-6.938893903907223E-16, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(432) = SimpleLabeledVector[8](6.938893903907233E-16, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(433) = SimpleLabeledVector[8](-6.938893903907223E-16, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(434) = SimpleLabeledVector[8](0.24999999999999983, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(435) = SimpleLabeledVector[8](0.22399136614942827, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(436) = SimpleLabeledVector[8](0.21557770251983074, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(437) = SimpleLabeledVector[8](0.1983951600599779, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(438) = SimpleLabeledVector[8](0.1900843466248023, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(439) = SimpleLabeledVector[8](0.17392423435298832, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(440) = SimpleLabeledVector[8](-0.08657031833969733, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(441) = SimpleLabeledVector[8](-0.08375258658538337, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(442) = SimpleLabeledVector[8](-0.08281623780318695, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(443) = SimpleLabeledVector[8](-0.08469711592660806, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(444) = SimpleLabeledVector[8](-0.08520353380358262, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(445) = SimpleLabeledVector[8](-0.08992119567125795, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(446) = SimpleLabeledVector[8](-0.09534896156207147, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(447) = SimpleLabeledVector[8](-0.09515576239629657, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(448) = SimpleLabeledVector[8](-0.09614360559229317, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(449) = SimpleLabeledVector[8](-0.09580039948415234, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(450) = SimpleLabeledVector[8](-0.0944334536597286, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(451) = SimpleLabeledVector[8](0.13250825163492583, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(452) = SimpleLabeledVector[8](0.12196092849381023, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(453) = SimpleLabeledVector[8](0.11398910455299721, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(454) = SimpleLabeledVector[8](0.0844324155460194, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(455) = SimpleLabeledVector[8](0.08467405096373179, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(456) = SimpleLabeledVector[8](0.08304761571150167, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(457) = SimpleLabeledVector[8](0.08562100234129613, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(458) = SimpleLabeledVector[8](0.0797888159535262, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(459) = SimpleLabeledVector[8](0.07850405940644489, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(460) = SimpleLabeledVector[8](0.08989544011577366, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(461) = SimpleLabeledVector[8](0.07651841142081245, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(462) = SimpleLabeledVector[8](0.06468473116378863, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(463) = SimpleLabeledVector[8](0.05435715616557752, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(464) = SimpleLabeledVector[8](0.25535285953605386, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(465) = SimpleLabeledVector[8](0.20933921517727974, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(466) = SimpleLabeledVector[8](0.17686734721915204, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(467) = SimpleLabeledVector[8](0.15064202705997615, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(468) = SimpleLabeledVector[8](0.1257298165434665, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(469) = SimpleLabeledVector[8](0.2960270248934479, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(470) = SimpleLabeledVector[8](0.2455810921008634, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(471) = SimpleLabeledVector[8](0.18990972121502708, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(472) = SimpleLabeledVector[8](0.34339534226688895, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(473) = SimpleLabeledVector[8](0.43679150321189886, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(474) = SimpleLabeledVector[8](0.32884281573289215, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(475) = SimpleLabeledVector[8](0.2938336513049686, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(476) = SimpleLabeledVector[8](0.26950193337903816, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(477) = SimpleLabeledVector[8](0.25007262857240836, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(478) = SimpleLabeledVector[8](0.23486477191906474, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(479) = SimpleLabeledVector[8](0.08224791233436804, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(480) = SimpleLabeledVector[8](0.20448299278905224, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(481) = SimpleLabeledVector[8](0.05327743351518621, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(482) = SimpleLabeledVector[8](0.037547297979590985, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(483) = SimpleLabeledVector[8](0.025234138249743396, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(484) = SimpleLabeledVector[8](0.011844097473446002, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(485) = SimpleLabeledVector[8](-0.004184787945120062, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(486) = SimpleLabeledVector[8](-0.017855292490338015, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(487) = SimpleLabeledVector[8](-0.02732914721787276, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(488) = SimpleLabeledVector[8](-0.034012712130157594, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(489) = SimpleLabeledVector[8](-0.03783722454073417, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(490) = SimpleLabeledVector[8](-0.04605881037000135, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(491) = SimpleLabeledVector[8](-0.16952452026926534, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(492) = SimpleLabeledVector[8](-0.05106245718845208, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(493) = SimpleLabeledVector[8](-0.17405143914067145, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(494) = SimpleLabeledVector[8](-0.14956584620362376, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(495) = SimpleLabeledVector[8](-0.1294550943982752, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(496) = SimpleLabeledVector[8](-0.11757047236301796, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(497) = SimpleLabeledVector[8](-0.11060400225030938, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(498) = SimpleLabeledVector[8](-0.1042569157028758, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(499) = SimpleLabeledVector[8](-0.0927721331619702, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(500) = SimpleLabeledVector[8](-0.08858478110681682, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(501) = SimpleLabeledVector[8](-0.06691985289913377, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(502) = SimpleLabeledVector[8](-0.05875709935834799, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(503) = SimpleLabeledVector[8](-0.052490178947664695, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(504) = SimpleLabeledVector[8](-0.0500572039633016, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(505) = SimpleLabeledVector[8](-0.1761033877478494, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(506) = SimpleLabeledVector[8](-0.16787416206350464, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(507) = SimpleLabeledVector[8](-0.1585002750492438, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(508) = SimpleLabeledVector[8](-0.0010441869899934038, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(509) = SimpleLabeledVector[8](0.008475375206089477, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(510) = SimpleLabeledVector[8](0.007721869623765848, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(511) = SimpleLabeledVector[8](0.1573669643539871, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(512) = SimpleLabeledVector[8](0.2860704078756559, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(513) = SimpleLabeledVector[8](0.24629895206676325, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(514) = SimpleLabeledVector[8](0.07164106437407303, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(515) = SimpleLabeledVector[8](0.05911844437122809, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(516) = SimpleLabeledVector[8](0.1878676203137767, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(517) = SimpleLabeledVector[8](0.04598066506722954, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(518) = SimpleLabeledVector[8](0.042566873827556674, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(519) = SimpleLabeledVector[8](0.04003244560753632, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(520) = SimpleLabeledVector[8](0.03665652788731899, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(521) = SimpleLabeledVector[8](0.03477628006893032, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(522) = SimpleLabeledVector[8](0.025826895073561645, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(523) = SimpleLabeledVector[8](0.15176349907854988, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(524) = SimpleLabeledVector[8](0.14334865115071804, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(525) = SimpleLabeledVector[8](0.13496422719744247, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(526) = SimpleLabeledVector[8](0.12466911335904804, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(527) = SimpleLabeledVector[8](0.10758813918184666, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(528) = SimpleLabeledVector[8](0.09513548439443892, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(529) = SimpleLabeledVector[8](0.061440155067443734, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(530) = SimpleLabeledVector[8](-0.06761241762349177, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(531) = SimpleLabeledVector[8](-0.061086875286376026, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(532) = SimpleLabeledVector[8](-0.06300220931608087, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(533) = SimpleLabeledVector[8](-0.06480355862919956, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(534) = SimpleLabeledVector[8](-0.05919046264916056, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(535) = SimpleLabeledVector[8](0.05712067523837486, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(536) = SimpleLabeledVector[8](0.052796220408174575, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(537) = SimpleLabeledVector[8](0.04495359122594034, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(538) = SimpleLabeledVector[8](0.040713047742316016, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(539) = SimpleLabeledVector[8](0.034774779538946095, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(540) = SimpleLabeledVector[8](0.03280983844651298, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(541) = SimpleLabeledVector[8](0.028268253040691492, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(542) = SimpleLabeledVector[8](0.02970964888731603, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(543) = SimpleLabeledVector[8](0.03216429276937517, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(544) = SimpleLabeledVector[8](0.03291305868591417, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(545) = SimpleLabeledVector[8](0.14518519313955883, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(546) = SimpleLabeledVector[8](0.13324861449239656, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(547) = SimpleLabeledVector[8](0.12907740398450004, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(548) = SimpleLabeledVector[8](0.10984780042428775, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(549) = SimpleLabeledVector[8](-0.020788214386482845, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(550) = SimpleLabeledVector[8](-0.021203499059987887, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(551) = SimpleLabeledVector[8](-0.020993887391184147, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(552) = SimpleLabeledVector[8](-0.133371547235804, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(553) = SimpleLabeledVector[8](-0.1276874532088655, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(554) = SimpleLabeledVector[8](-0.12510923522398879, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(555) = SimpleLabeledVector[8](-0.120665639022946, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(556) = SimpleLabeledVector[8](-0.11931878757852818, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(557) = SimpleLabeledVector[8](-0.11518609555535905, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(558) = SimpleLabeledVector[8](-0.22224410234631956, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(559) = SimpleLabeledVector[8](-0.21085670313197924, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(560) = SimpleLabeledVector[8](-0.1976107740697216, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(561) = SimpleLabeledVector[8](-0.30733369286348367, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(562) = SimpleLabeledVector[8](-0.2774304268090805, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(563) = SimpleLabeledVector[8](-0.3830164431726287, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(564) = SimpleLabeledVector[8](-0.21605226450155385, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(565) = SimpleLabeledVector[8](-0.18820226187658998, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(566) = SimpleLabeledVector[8](-0.16996616963410024, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(567) = SimpleLabeledVector[8](-0.15149606227312112, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(568) = SimpleLabeledVector[8](-0.13374378641730147, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(569) = SimpleLabeledVector[8](-0.1236635784169689, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(570) = SimpleLabeledVector[8](-0.1081712180597786, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(571) = SimpleLabeledVector[8](-0.09659327661107216, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(572) = SimpleLabeledVector[8](-0.08733062543594795, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(573) = SimpleLabeledVector[8](-0.0785086619610684, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(574) = SimpleLabeledVector[8](-0.07184172676608176, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(575) = SimpleLabeledVector[8](-0.04889864787464014, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(576) = SimpleLabeledVector[8](-0.03100500406353392, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(577) = SimpleLabeledVector[8](-0.014383145040715124, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(578) = SimpleLabeledVector[8](0.001183896841853005, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(579) = SimpleLabeledVector[8](0.009289083200893009, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(580) = SimpleLabeledVector[8](0.016590736441560835, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(581) = SimpleLabeledVector[8](0.01764167071425271, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(582) = SimpleLabeledVector[8](-0.14143445785520206, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(583) = SimpleLabeledVector[8](-0.13431309362233645, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(584) = SimpleLabeledVector[8](-0.11935404780618852, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(585) = SimpleLabeledVector[8](-0.11010951816115268, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(586) = SimpleLabeledVector[8](-0.1041864018289353, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(587) = SimpleLabeledVector[8](-0.09568375061153908, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(588) = SimpleLabeledVector[8](-0.08612821218446397, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(589) = SimpleLabeledVector[8](-0.0782307597738863, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(590) = SimpleLabeledVector[8](-0.06746359390684932, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(591) = SimpleLabeledVector[8](-0.05768276902060368, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(592) = SimpleLabeledVector[8](-0.05211226070911044, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(593) = SimpleLabeledVector[8](-0.045447628900328135, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(594) = SimpleLabeledVector[8](-0.03987858292444928, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(595) = SimpleLabeledVector[8](-0.028957001177570572, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(596) = SimpleLabeledVector[8](-0.02481170375972966, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(597) = SimpleLabeledVector[8](-0.011803255475897492, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(598) = SimpleLabeledVector[8](-0.008210677398208808, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(599) = SimpleLabeledVector[8](-5.55111512312578E-16, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(600) = SimpleLabeledVector[8](-2.7755575615628835E-15, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(601) = SimpleLabeledVector[8](-0.20000000000000193, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(602) = SimpleLabeledVector[8](0.021867292223288443, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(603) = SimpleLabeledVector[8](0.023078562293522915, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(604) = SimpleLabeledVector[8](-0.17987291899655733, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(605) = SimpleLabeledVector[8](0.043449617341195186, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(606) = SimpleLabeledVector[8](0.0439641213646615, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(607) = SimpleLabeledVector[8](-0.1630496982601358, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(608) = SimpleLabeledVector[8](-0.1538634392450747, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(609) = SimpleLabeledVector[8](0.07612984758667384, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(610) = SimpleLabeledVector[8](0.07541171073888119, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(611) = SimpleLabeledVector[8](0.07576006866375071, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(612) = SimpleLabeledVector[8](0.07466180012412219, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(613) = SimpleLabeledVector[8](0.07250458675621914, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(614) = SimpleLabeledVector[8](0.0735022777080215, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(615) = SimpleLabeledVector[8](0.07457385925262637, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(616) = SimpleLabeledVector[8](0.0740207938153611, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(617) = SimpleLabeledVector[8](0.07897295981328031, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(618) = SimpleLabeledVector[8](0.07718671093907747, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(619) = SimpleLabeledVector[8](0.07070114602689541, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(620) = SimpleLabeledVector[8](0.0645487654865174, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(621) = SimpleLabeledVector[8](0.03795545840743249, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(622) = SimpleLabeledVector[8](0.03680009216626895, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(623) = SimpleLabeledVector[8](0.02821142934880952, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(624) = SimpleLabeledVector[8](-0.184051528187815, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(625) = SimpleLabeledVector[8](-0.1596557940467168, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(626) = SimpleLabeledVector[8](-0.14136211547396058, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(627) = SimpleLabeledVector[8](-0.1434114166763312, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(628) = SimpleLabeledVector[8](-0.1387324536856645, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(629) = SimpleLabeledVector[8](-0.1324791717804988, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(630) = SimpleLabeledVector[8](-0.12382781941118595, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(631) = SimpleLabeledVector[8](-0.11823903539841814, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(632) = SimpleLabeledVector[8](-0.1146498134829739, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(633) = SimpleLabeledVector[8](-0.10970971387852682, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(634) = SimpleLabeledVector[8](-0.10467954833554768, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(635) = SimpleLabeledVector[8](-0.10094301642429136, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(636) = SimpleLabeledVector[8](-0.09825184246209956, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(637) = SimpleLabeledVector[8](-0.08779206898348253, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(638) = SimpleLabeledVector[8](-0.07673099396856593, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(639) = SimpleLabeledVector[8](-0.058998522931959244, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(640) = SimpleLabeledVector[8](-0.03939351165094288, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(641) = SimpleLabeledVector[8](-0.032619185440572164, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(642) = SimpleLabeledVector[8](-0.025621300485598495, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(643) = SimpleLabeledVector[8](-0.013561443624390494, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(644) = SimpleLabeledVector[8](-5.8980598183211094E-15, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(645) = SimpleLabeledVector[8](-1.040834085586083E-15, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(646) = SimpleLabeledVector[8](-3.2959746043559224E-15, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(647) = SimpleLabeledVector[8](-1.040834085586083E-15, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(648) = SimpleLabeledVector[8](-3.2959746043559224E-15, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(649) = SimpleLabeledVector[8](-2.428612866367524E-15, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(650) = SimpleLabeledVector[8](-1.7347234759768068E-16, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(651) = SimpleLabeledVector[8](1.2143064331837663E-15, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(652) = SimpleLabeledVector[8](3.469446951953626E-15, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(653) = SimpleLabeledVector[8](3.469446951953626E-15, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(654) = SimpleLabeledVector[8](1.5612511283791288E-15, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(655) = SimpleLabeledVector[8](3.469446951953626E-15, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(656) = SimpleLabeledVector[8](1.5612511283791288E-15, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(657) = SimpleLabeledVector[8](2.428612866367536E-15, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(658) = SimpleLabeledVector[8](2.428612866367536E-15, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(659) = SimpleLabeledVector[8](3.469446951953615E-16, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(660) = SimpleLabeledVector[8](-1.5612511283791238E-15, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(661) = SimpleLabeledVector[8](-1.5612511283791238E-15, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(662) = SimpleLabeledVector[8](1.2143064331837663E-15, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(663) = SimpleLabeledVector[8](-2.428612866367524E-15, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(664) = SimpleLabeledVector[8](-1.040834085586083E-15, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(665) = SimpleLabeledVector[8](-2.428612866367524E-15, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(666) = SimpleLabeledVector[8](-1.7347234759768068E-16, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(667) = SimpleLabeledVector[8](-5.8980598183211094E-15, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(668) = SimpleLabeledVector[8](-1.9081958235744843E-15, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(669) = SimpleLabeledVector[8](-3.2959746043559224E-15, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(670) = SimpleLabeledVector[8](-3.2959746043559224E-15, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(671) = SimpleLabeledVector[8](-2.7755575615628835E-15, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(672) = SimpleLabeledVector[8](-1.7347234759768068E-16, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(673) = SimpleLabeledVector[8](2.081668171172173E-15, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(674) = SimpleLabeledVector[8](2.081668171172173E-15, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(675) = SimpleLabeledVector[8](1.2143064331837663E-15, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(676) = SimpleLabeledVector[8](1.2143064331837663E-15, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(677) = SimpleLabeledVector[8](-1.040834085586083E-15, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(678) = SimpleLabeledVector[8](2.428612866367536E-15, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(679) = SimpleLabeledVector[8](2.081668171172173E-15, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(680) = SimpleLabeledVector[8](-0.25000000000000083, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(681) = SimpleLabeledVector[8](0.02472349672813715, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(682) = SimpleLabeledVector[8](0.022976082065241665, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(683) = SimpleLabeledVector[8](0.022907129997465407, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(684) = SimpleLabeledVector[8](0.022391473336590643, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(685) = SimpleLabeledVector[8](0.02174980150212254, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(686) = SimpleLabeledVector[8](0.024547376592654383, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(687) = SimpleLabeledVector[8](0.024313491867650285, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(688) = SimpleLabeledVector[8](0.02422502305188077, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(689) = SimpleLabeledVector[8](0.024194756112743413, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(690) = SimpleLabeledVector[8](0.024438381815979397, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(691) = SimpleLabeledVector[8](0.025698749249486584, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(692) = SimpleLabeledVector[8](0.025945940677369914, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(693) = SimpleLabeledVector[8](0.026818869743381206, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(694) = SimpleLabeledVector[8](0.027248817872006687, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(695) = SimpleLabeledVector[8](0.02759612286106143, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(696) = SimpleLabeledVector[8](0.027836043893009952, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(697) = SimpleLabeledVector[8](0.03117676024163691, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(698) = SimpleLabeledVector[8](0.030720334824951262, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(699) = SimpleLabeledVector[8](-0.22668487745707663, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(700) = SimpleLabeledVector[8](-0.23579153866865116, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(701) = SimpleLabeledVector[8](-0.22304846641387058, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(702) = SimpleLabeledVector[8](-0.20327049863829008, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(703) = SimpleLabeledVector[8](-0.19641106368669084, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(704) = SimpleLabeledVector[8](-0.18656740672650313, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(705) = SimpleLabeledVector[8](-0.1755698771792061, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(706) = SimpleLabeledVector[8](-0.16812999958162173, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(707) = SimpleLabeledVector[8](-0.14616987515706242, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(708) = SimpleLabeledVector[8](-0.13373998281485708, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(709) = SimpleLabeledVector[8](-0.1254720009913315, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(710) = SimpleLabeledVector[8](-0.10786133099736706, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(711) = SimpleLabeledVector[8](-0.0960232198200161, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(712) = SimpleLabeledVector[8](-0.08622071476001632, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(713) = SimpleLabeledVector[8](-0.07253950534632198, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(714) = SimpleLabeledVector[8](-0.05933207961248325, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(715) = SimpleLabeledVector[8](-0.049589435017710584, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(716) = SimpleLabeledVector[8](-0.02735129428990259, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(717) = SimpleLabeledVector[8](-0.016880509002418685, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(718) = SimpleLabeledVector[8](0.3262372870944557, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(719) = SimpleLabeledVector[8](0.3157730063318584, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(720) = SimpleLabeledVector[8](0.2921290336898682, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(721) = SimpleLabeledVector[8](0.25889170396588085, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(722) = SimpleLabeledVector[8](0.2185430724709012, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(723) = SimpleLabeledVector[8](0.20521773435497495, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(724) = SimpleLabeledVector[8](0.18426116866515083, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(725) = SimpleLabeledVector[8](0.17897622324191562, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(726) = SimpleLabeledVector[8](0.14032156503757443, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(727) = SimpleLabeledVector[8](0.11311117811085368, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(728) = SimpleLabeledVector[8](0.10407222151589818, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(729) = SimpleLabeledVector[8](0.04746263203460475, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(730) = SimpleLabeledVector[8](0.040116687282401434, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(731) = SimpleLabeledVector[8](0.29259001306671445, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(732) = SimpleLabeledVector[8](0.25653474334703924, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(733) = SimpleLabeledVector[8](-0.009617548767819187, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(734) = SimpleLabeledVector[8](-0.022348964383479646, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(735) = SimpleLabeledVector[8](-0.031054978290874887, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(736) = SimpleLabeledVector[8](-0.03325348735671103, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(737) = SimpleLabeledVector[8](0.20819484620553771, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(738) = SimpleLabeledVector[8](0.1861945039833851, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(739) = SimpleLabeledVector[8](0.3770756153945312, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(740) = SimpleLabeledVector[8](0.10523042832360363, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(741) = SimpleLabeledVector[8](0.09402034329806035, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(742) = SimpleLabeledVector[8](0.3029733370476915, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(743) = SimpleLabeledVector[8](0.2914040945019868, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(744) = SimpleLabeledVector[8](0.26380434911451545, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(745) = SimpleLabeledVector[8](0.20101890689310598, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(746) = SimpleLabeledVector[8](0.1819301185379186, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(747) = SimpleLabeledVector[8](0.14467976139758632, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(748) = SimpleLabeledVector[8](0.11218765445865478, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(749) = SimpleLabeledVector[8](0.10105741690448078, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(750) = SimpleLabeledVector[8](0.27276484823763175, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(751) = SimpleLabeledVector[8](0.24096640884423587, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(752) = SimpleLabeledVector[8](0.20678263858350773, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(753) = SimpleLabeledVector[8](0.18990637922368858, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(754) = SimpleLabeledVector[8](3.3186125440597396E-4, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(755) = SimpleLabeledVector[8](-0.007654293383694234, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(756) = SimpleLabeledVector[8](-0.027002145214561044, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(757) = SimpleLabeledVector[8](-0.027319057204361393, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(758) = SimpleLabeledVector[8](-0.036415010544486086, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(759) = SimpleLabeledVector[8](-0.04352624100209436, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(760) = SimpleLabeledVector[8](-0.04412324656208537, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(761) = SimpleLabeledVector[8](-0.0445541406772036, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(762) = SimpleLabeledVector[8](-0.04452881471290198, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(763) = SimpleLabeledVector[8](-0.20410671323607504, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(764) = SimpleLabeledVector[8](-0.1842169317416003, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(765) = SimpleLabeledVector[8](-0.17660022675864506, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(766) = SimpleLabeledVector[8](-0.17180252520915842, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(767) = SimpleLabeledVector[8](-0.17089677129187347, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(768) = SimpleLabeledVector[8](-0.1628864306747636, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(769) = SimpleLabeledVector[8](-0.15750127126041924, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(770) = SimpleLabeledVector[8](-0.15491368198535163, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(771) = SimpleLabeledVector[8](-0.1376313760830631, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(772) = SimpleLabeledVector[8](-0.11707609572354474, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(773) = SimpleLabeledVector[8](-0.10596229160420507, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(774) = SimpleLabeledVector[8](-0.24574591766669915, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(775) = SimpleLabeledVector[8](-0.217961715781016, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(776) = SimpleLabeledVector[8](-0.20206001119181974, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(777) = SimpleLabeledVector[8](-0.38511433665631595, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(778) = SimpleLabeledVector[8](-0.3271582463121031, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(779) = SimpleLabeledVector[8](-0.27912046692133785, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(780) = SimpleLabeledVector[8](-0.27095982782278305, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(781) = SimpleLabeledVector[8](-0.4993305005560314, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(782) = SimpleLabeledVector[8](-0.19887398752487062, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(783) = SimpleLabeledVector[8](-0.16468126239175307, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(784) = SimpleLabeledVector[8](-0.13692286560374708, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(785) = SimpleLabeledVector[8](-0.11687544342862988, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(786) = SimpleLabeledVector[8](-0.112087427648885, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(787) = SimpleLabeledVector[8](-0.09057816599280671, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(788) = SimpleLabeledVector[8](-0.07483644444238752, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(789) = SimpleLabeledVector[8](-0.0665223792102343, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(790) = SimpleLabeledVector[8](-0.05452850773758154, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(791) = SimpleLabeledVector[8](-0.04115472004522128, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(792) = SimpleLabeledVector[8](-0.03402755336369249, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(793) = SimpleLabeledVector[8](-0.027735462555817117, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(794) = SimpleLabeledVector[8](-0.017812556502077522, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(795) = SimpleLabeledVector[8](0.003736623855941259, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(796) = SimpleLabeledVector[8](0.011240824387062998, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(797) = SimpleLabeledVector[8](0.02477497154545338, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(798) = SimpleLabeledVector[8](0.027640786649181407, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(799) = SimpleLabeledVector[8](0.0322223331303061, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(800) = SimpleLabeledVector[8](0.33333333333333587, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - } - - def init_801to1200: Unit = { - trainingData_01(801) = SimpleLabeledVector[8](0.30485889481378475, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(802) = SimpleLabeledVector[8](0.29350075624620275, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(803) = SimpleLabeledVector[8](-0.038955805566373854, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(804) = SimpleLabeledVector[8](-0.042300791287316925, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(805) = SimpleLabeledVector[8](-0.04299714833687725, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(806) = SimpleLabeledVector[8](-0.04412978615611952, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(807) = SimpleLabeledVector[8](-0.045110199325696994, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(808) = SimpleLabeledVector[8](-0.04814197353567082, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(809) = SimpleLabeledVector[8](-0.05092543963505874, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(810) = SimpleLabeledVector[8](-0.04888531380173822, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(811) = SimpleLabeledVector[8](0.2657597977393675, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(812) = SimpleLabeledVector[8](0.25096862470066783, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(813) = SimpleLabeledVector[8](0.24129080633307684, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(814) = SimpleLabeledVector[8](-0.08756048781932813, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(815) = SimpleLabeledVector[8](0.20012860098111487, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(816) = SimpleLabeledVector[8](0.16897330043521658, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(817) = SimpleLabeledVector[8](0.138950629706026, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(818) = SimpleLabeledVector[8](0.12715347709234812, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(819) = SimpleLabeledVector[8](0.38853635526563296, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(820) = SimpleLabeledVector[8](0.3363348825548701, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(821) = SimpleLabeledVector[8](0.3193224134083701, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(822) = SimpleLabeledVector[8](0.2763920667918583, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(823) = SimpleLabeledVector[8](0.21944254897047172, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(824) = SimpleLabeledVector[8](0.18756555586468307, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(825) = SimpleLabeledVector[8](0.15345455291373308, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(826) = SimpleLabeledVector[8](0.11865077972558136, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(827) = SimpleLabeledVector[8](0.1102553865369098, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(828) = SimpleLabeledVector[8](0.08700231038808656, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(829) = SimpleLabeledVector[8](0.07733904530858782, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(830) = SimpleLabeledVector[8](0.07213112221427519, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(831) = SimpleLabeledVector[8](0.06218416900834685, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(832) = SimpleLabeledVector[8](0.03607200314869409, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(833) = SimpleLabeledVector[8](0.02631548244312228, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(834) = SimpleLabeledVector[8](0.01428657989363909, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(835) = SimpleLabeledVector[8](0.007759220445503079, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(836) = SimpleLabeledVector[8](3.469446951953626E-15, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(837) = SimpleLabeledVector[8](0.20000000000000623, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(838) = SimpleLabeledVector[8](0.1887817536649346, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(839) = SimpleLabeledVector[8](-0.0157525733464987, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(840) = SimpleLabeledVector[8](-0.015420159857925867, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(841) = SimpleLabeledVector[8](-0.015647824819883322, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(842) = SimpleLabeledVector[8](-0.015227483202705165, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(843) = SimpleLabeledVector[8](0.18224208300023176, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(844) = SimpleLabeledVector[8](0.1501522322777269, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(845) = SimpleLabeledVector[8](0.32070849496323556, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(846) = SimpleLabeledVector[8](0.43286667913045523, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(847) = SimpleLabeledVector[8](0.31815543600804586, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(848) = SimpleLabeledVector[8](0.2758448197789771, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(849) = SimpleLabeledVector[8](0.24492929525824828, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(850) = SimpleLabeledVector[8](0.3649048208999422, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(851) = SimpleLabeledVector[8](0.32778656088729863, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(852) = SimpleLabeledVector[8](0.3033652269986779, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(853) = SimpleLabeledVector[8](0.28773791678647287, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(854) = SimpleLabeledVector[8](0.26605673194389223, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(855) = SimpleLabeledVector[8](0.11163013994764673, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(856) = SimpleLabeledVector[8](0.09962429073895346, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(857) = SimpleLabeledVector[8](0.09068514809239481, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(858) = SimpleLabeledVector[8](0.06751245606694223, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(859) = SimpleLabeledVector[8](0.055775220433168904, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(860) = SimpleLabeledVector[8](0.052055561552094065, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(861) = SimpleLabeledVector[8](0.03765157232810127, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(862) = SimpleLabeledVector[8](0.028630727340020628, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(863) = SimpleLabeledVector[8](-4.3330004251403213E-4, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(864) = SimpleLabeledVector[8](-0.013337435766658946, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(865) = SimpleLabeledVector[8](-0.015168332385371427, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(866) = SimpleLabeledVector[8](-0.01572728242059476, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(867) = SimpleLabeledVector[8](-0.016231232573716912, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(868) = SimpleLabeledVector[8](-0.008180296252504632, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(869) = SimpleLabeledVector[8](-0.0021581775486075334, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(870) = SimpleLabeledVector[8](0.0022816752817034895, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(871) = SimpleLabeledVector[8](0.009222045614884208, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(872) = SimpleLabeledVector[8](0.011960085498307328, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(873) = SimpleLabeledVector[8](0.13821832393178535, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(874) = SimpleLabeledVector[8](0.11900728289266098, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(875) = SimpleLabeledVector[8](0.2236780089516998, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(876) = SimpleLabeledVector[8](0.18964625702390803, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(877) = SimpleLabeledVector[8](0.17336289845028158, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(878) = SimpleLabeledVector[8](0.1601874926973203, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(879) = SimpleLabeledVector[8](0.24294020693695975, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(880) = SimpleLabeledVector[8](0.4284930251150219, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(881) = SimpleLabeledVector[8](0.23243976539260022, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(882) = SimpleLabeledVector[8](0.20723238927580195, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(883) = SimpleLabeledVector[8](0.18725458327214906, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(884) = SimpleLabeledVector[8](0.17400838539894115, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(885) = SimpleLabeledVector[8](0.25930634333798724, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(886) = SimpleLabeledVector[8](0.23131747264842456, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(887) = SimpleLabeledVector[8](0.21101875495127254, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(888) = SimpleLabeledVector[8](0.1926037642453878, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(889) = SimpleLabeledVector[8](0.08616484023656128, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(890) = SimpleLabeledVector[8](0.08081215649312018, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(891) = SimpleLabeledVector[8](0.07217900250705718, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(892) = SimpleLabeledVector[8](0.05534306783344851, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(893) = SimpleLabeledVector[8](0.047639972831528495, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(894) = SimpleLabeledVector[8](0.11842008979217097, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(895) = SimpleLabeledVector[8](0.013497461927901865, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(896) = SimpleLabeledVector[8](0.16760701635167796, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(897) = SimpleLabeledVector[8](0.2288252245498371, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(898) = SimpleLabeledVector[8](0.1206873402605068, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(899) = SimpleLabeledVector[8](0.09882462932793211, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(900) = SimpleLabeledVector[8](0.08092243763937425, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(901) = SimpleLabeledVector[8](0.07802364711687956, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(902) = SimpleLabeledVector[8](0.06952961361340672, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(903) = SimpleLabeledVector[8](0.06060230381385205, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(904) = SimpleLabeledVector[8](0.05287933014554799, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(905) = SimpleLabeledVector[8](0.04448738449293602, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(906) = SimpleLabeledVector[8](0.04003970133620417, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(907) = SimpleLabeledVector[8](0.03512237256150532, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(908) = SimpleLabeledVector[8](0.032022576178400534, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(909) = SimpleLabeledVector[8](0.02753446798033121, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(910) = SimpleLabeledVector[8](0.022054859453562427, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(911) = SimpleLabeledVector[8](0.0884152058082928, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(912) = SimpleLabeledVector[8](0.008937664043555425, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(913) = SimpleLabeledVector[8](-0.07221546418821967, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(914) = SimpleLabeledVector[8](-0.007822558809076733, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(915) = SimpleLabeledVector[8](5.4767410493230945E-5, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(916) = SimpleLabeledVector[8](5.880396853740078E-5, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(917) = SimpleLabeledVector[8](-0.06850803114352615, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(918) = SimpleLabeledVector[8](-0.0629893452787186, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(919) = SimpleLabeledVector[8](-0.12862689796181553, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(920) = SimpleLabeledVector[8](-0.116192024039597, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(921) = SimpleLabeledVector[8](-0.10658297897204523, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(922) = SimpleLabeledVector[8](-0.09706059323159662, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(923) = SimpleLabeledVector[8](-0.16452523411148184, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(924) = SimpleLabeledVector[8](-0.15973306512789914, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(925) = SimpleLabeledVector[8](-0.15251149111863685, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(926) = SimpleLabeledVector[8](-0.14737882572662658, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(927) = SimpleLabeledVector[8](-0.06417987283022518, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(928) = SimpleLabeledVector[8](-0.05561311621290145, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(929) = SimpleLabeledVector[8](-0.05159120902444317, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(930) = SimpleLabeledVector[8](-0.04595530809120541, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(931) = SimpleLabeledVector[8](-0.12262310943101015, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(932) = SimpleLabeledVector[8](-0.19025940220701823, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(933) = SimpleLabeledVector[8](-0.26373477058816897, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(934) = SimpleLabeledVector[8](-0.2522491512866754, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(935) = SimpleLabeledVector[8](-0.24448042410443196, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(936) = SimpleLabeledVector[8](-0.23323450513478325, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(937) = SimpleLabeledVector[8](-0.2242367863960222, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(938) = SimpleLabeledVector[8](-0.13067398483805873, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(939) = SimpleLabeledVector[8](-0.11712446192006916, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(940) = SimpleLabeledVector[8](-0.09937933402129626, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(941) = SimpleLabeledVector[8](-0.08792466766959657, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(942) = SimpleLabeledVector[8](-0.07687452290381211, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(943) = SimpleLabeledVector[8](-0.060160592158329945, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(944) = SimpleLabeledVector[8](-0.1387546027415056, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(945) = SimpleLabeledVector[8](-0.13190944691804302, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(946) = SimpleLabeledVector[8](-0.12345801114599753, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(947) = SimpleLabeledVector[8](-0.1165199713935023, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(948) = SimpleLabeledVector[8](-0.11132134202192537, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(949) = SimpleLabeledVector[8](-0.2016043146181787, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(950) = SimpleLabeledVector[8](-0.18175240756798985, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(951) = SimpleLabeledVector[8](-0.16299244990503128, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(952) = SimpleLabeledVector[8](-0.1408654922022927, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(953) = SimpleLabeledVector[8](-0.011985659693210452, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(954) = SimpleLabeledVector[8](0.10639844623351649, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(955) = SimpleLabeledVector[8](0.1008394815906923, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(956) = SimpleLabeledVector[8](0.09360089282629507, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(957) = SimpleLabeledVector[8](0.08403470651116196, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(958) = SimpleLabeledVector[8](0.07946031534658851, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(959) = SimpleLabeledVector[8](0.07492295415611558, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(960) = SimpleLabeledVector[8](-0.03076048436561146, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(961) = SimpleLabeledVector[8](-0.026499868612014244, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(962) = SimpleLabeledVector[8](0.08577781269144072, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(963) = SimpleLabeledVector[8](0.08633122820044081, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(964) = SimpleLabeledVector[8](-0.02205219284934227, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(965) = SimpleLabeledVector[8](-0.018984308932907343, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(966) = SimpleLabeledVector[8](-0.019418489818229082, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(967) = SimpleLabeledVector[8](0.08943391574051038, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(968) = SimpleLabeledVector[8](0.19242490587414515, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(969) = SimpleLabeledVector[8](0.17705881272233906, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(970) = SimpleLabeledVector[8](0.14138906571506904, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(971) = SimpleLabeledVector[8](0.12401775625451127, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(972) = SimpleLabeledVector[8](0.10998486520753624, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(973) = SimpleLabeledVector[8](-0.0012574532267335843, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(974) = SimpleLabeledVector[8](0.08830048354477052, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(975) = SimpleLabeledVector[8](0.0858649932249022, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(976) = SimpleLabeledVector[8](0.08375048829781938, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(977) = SimpleLabeledVector[8](-0.018807154778495892, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(978) = SimpleLabeledVector[8](0.07369904263354383, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(979) = SimpleLabeledVector[8](0.06569597381849322, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(980) = SimpleLabeledVector[8](0.050554577272456894, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(981) = SimpleLabeledVector[8](0.04318826723282104, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(982) = SimpleLabeledVector[8](-0.053978791728966115, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(983) = SimpleLabeledVector[8](-0.052548214548302545, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(984) = SimpleLabeledVector[8](-0.05969482289361029, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(985) = SimpleLabeledVector[8](-0.15684708275722645, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(986) = SimpleLabeledVector[8](-0.15193151532269822, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(987) = SimpleLabeledVector[8](-0.046505097585243876, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(988) = SimpleLabeledVector[8](-0.13475139144781095, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(989) = SimpleLabeledVector[8](-0.1277589665985084, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(990) = SimpleLabeledVector[8](-0.02380964365756276, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(991) = SimpleLabeledVector[8](0.07854975661944448, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(992) = SimpleLabeledVector[8](0.07161368042843891, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(993) = SimpleLabeledVector[8](0.06907394546731213, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(994) = SimpleLabeledVector[8](0.06655401364715459, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(995) = SimpleLabeledVector[8](-0.030155474564891965, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(996) = SimpleLabeledVector[8](0.0762625560479182, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(997) = SimpleLabeledVector[8](-0.020519627963466812, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(998) = SimpleLabeledVector[8](-0.016875135375932125, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(999) = SimpleLabeledVector[8](-0.013999334134790407, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1000) = SimpleLabeledVector[8](0.08452354411656783, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1001) = SimpleLabeledVector[8](0.07421599331113668, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1002) = SimpleLabeledVector[8](0.06717042060314789, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1003) = SimpleLabeledVector[8](0.0625219155365228, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1004) = SimpleLabeledVector[8](0.2330509926505254, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1005) = SimpleLabeledVector[8](0.26234620352796245, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1006) = SimpleLabeledVector[8](0.27167120505276415, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1007) = SimpleLabeledVector[8](0.28893210544866677, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1008) = SimpleLabeledVector[8](0.029074691202165735, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1009) = SimpleLabeledVector[8](0.023748312649521555, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1010) = SimpleLabeledVector[8](0.16437138837579923, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1011) = SimpleLabeledVector[8](0.14700684777487993, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1012) = SimpleLabeledVector[8](0.21073457293704367, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1013) = SimpleLabeledVector[8](0.12296959333647586, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1014) = SimpleLabeledVector[8](0.11189810787717568, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1015) = SimpleLabeledVector[8](0.10468756418948767, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1016) = SimpleLabeledVector[8](0.09618770462411158, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1017) = SimpleLabeledVector[8](0.08998685091601043, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1018) = SimpleLabeledVector[8](0.08409187277354815, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1019) = SimpleLabeledVector[8](0.011024480892803914, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1020) = SimpleLabeledVector[8](0.005629734955680187, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1021) = SimpleLabeledVector[8](-0.06803027198385073, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1022) = SimpleLabeledVector[8](-0.07004078394328578, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1023) = SimpleLabeledVector[8](-0.07181245711548145, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1024) = SimpleLabeledVector[8](-0.08417023907285137, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1025) = SimpleLabeledVector[8](-0.02832564841699602, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1026) = SimpleLabeledVector[8](-0.0391631965249555, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1027) = SimpleLabeledVector[8](0.0357115756522981, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1028) = SimpleLabeledVector[8](0.10927408229620748, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1029) = SimpleLabeledVector[8](0.025026269159131264, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1030) = SimpleLabeledVector[8](0.08351225398362708, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1031) = SimpleLabeledVector[8](0.018589146694007786, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1032) = SimpleLabeledVector[8](-0.0449794582141005, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1033) = SimpleLabeledVector[8](0.028220405266978995, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1034) = SimpleLabeledVector[8](-0.09950028284581665, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1035) = SimpleLabeledVector[8](-0.08909950823833546, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1036) = SimpleLabeledVector[8](0.04934389528157557, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1037) = SimpleLabeledVector[8](0.0485485415359131, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1038) = SimpleLabeledVector[8](0.04645078233934743, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1039) = SimpleLabeledVector[8](0.10946482343694232, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1040) = SimpleLabeledVector[8](0.2272995142357155, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1041) = SimpleLabeledVector[8](0.12021987708264589, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1042) = SimpleLabeledVector[8](0.1041421332134462, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1043) = SimpleLabeledVector[8](0.09478617476135406, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1044) = SimpleLabeledVector[8](0.08648072506872932, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1045) = SimpleLabeledVector[8](0.1361285411871867, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1046) = SimpleLabeledVector[8](0.29637013633220144, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1047) = SimpleLabeledVector[8](0.36392528663494167, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1048) = SimpleLabeledVector[8](0.6473001710883967, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1049) = SimpleLabeledVector[8](0.6121907953834776, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1050) = SimpleLabeledVector[8](0.4460517525044061, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1051) = SimpleLabeledVector[8](0.37005159921124625, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1052) = SimpleLabeledVector[8](0.12168530218037599, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1053) = SimpleLabeledVector[8](0.23046895360230335, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1054) = SimpleLabeledVector[8](0.2071890418329668, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1055) = SimpleLabeledVector[8](0.26308179083658867, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1056) = SimpleLabeledVector[8](0.33345836309906424, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1057) = SimpleLabeledVector[8](0.4114725511870601, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1058) = SimpleLabeledVector[8](0.39995150466112517, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1059) = SimpleLabeledVector[8](0.33071518080753914, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1060) = SimpleLabeledVector[8](0.2564176569508167, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1061) = SimpleLabeledVector[8](0.16222706918260987, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1062) = SimpleLabeledVector[8](0.07727267907823442, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1063) = SimpleLabeledVector[8](0.16439685878717447, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1064) = SimpleLabeledVector[8](0.14855609508600592, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1065) = SimpleLabeledVector[8](0.12732601196627122, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1066) = SimpleLabeledVector[8](0.07250580815359599, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1067) = SimpleLabeledVector[8](-0.005214548002185768, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1068) = SimpleLabeledVector[8](0.05023807878163111, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1069) = SimpleLabeledVector[8](0.07814283699260037, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1070) = SimpleLabeledVector[8](0.09818809063035541, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1071) = SimpleLabeledVector[8](0.14852391240868776, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1072) = SimpleLabeledVector[8](0.2315681566885195, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1073) = SimpleLabeledVector[8](0.2228333557576616, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1074) = SimpleLabeledVector[8](0.2030503272089219, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1075) = SimpleLabeledVector[8](0.40790640583493337, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1076) = SimpleLabeledVector[8](0.5528015757849326, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1077) = SimpleLabeledVector[8](0.596178601934127, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1078) = SimpleLabeledVector[8](0.5850637994225247, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1079) = SimpleLabeledVector[8](0.5362685919814811, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1080) = SimpleLabeledVector[8](0.7981184448533769, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1081) = SimpleLabeledVector[8](0.6731876948035668, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1082) = SimpleLabeledVector[8](0.563062009808264, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1083) = SimpleLabeledVector[8](0.4475805731014168, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1084) = SimpleLabeledVector[8](0.3468516726760778, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1085) = SimpleLabeledVector[8](0.3841518266811852, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1086) = SimpleLabeledVector[8](0.3568423196146786, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1087) = SimpleLabeledVector[8](0.4038459244950526, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1088) = SimpleLabeledVector[8](0.36641799801339364, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1089) = SimpleLabeledVector[8](0.5603038201320613, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1090) = SimpleLabeledVector[8](0.50637441639505, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1091) = SimpleLabeledVector[8](0.3253111752799454, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1092) = SimpleLabeledVector[8](0.21417305842960388, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1093) = SimpleLabeledVector[8](0.222234039091778, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1094) = SimpleLabeledVector[8](0.30376869424242375, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1095) = SimpleLabeledVector[8](0.34094468113030624, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1096) = SimpleLabeledVector[8](0.4948056043804617, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1097) = SimpleLabeledVector[8](0.2996051835573369, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1098) = SimpleLabeledVector[8](0.28892346728521934, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1099) = SimpleLabeledVector[8](0.19806365672019538, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1100) = SimpleLabeledVector[8](0.1895642571420073, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1101) = SimpleLabeledVector[8](0.04281484443759255, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1102) = SimpleLabeledVector[8](0.04387752256550742, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1103) = SimpleLabeledVector[8](0.037430467786702865, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1104) = SimpleLabeledVector[8](0.031063619130105063, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1105) = SimpleLabeledVector[8](0.043348134762312536, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1106) = SimpleLabeledVector[8](0.03607189553480601, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1107) = SimpleLabeledVector[8](-0.05550189045237212, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1108) = SimpleLabeledVector[8](-0.0415028632700474, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1109) = SimpleLabeledVector[8](-0.05348385168570962, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1110) = SimpleLabeledVector[8](-0.08849681320258053, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1111) = SimpleLabeledVector[8](0.015823312702979604, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1112) = SimpleLabeledVector[8](0.0992926426921951, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1113) = SimpleLabeledVector[8](0.14232718006091008, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1114) = SimpleLabeledVector[8](0.04833359549897394, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1115) = SimpleLabeledVector[8](-0.003905706344373324, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1116) = SimpleLabeledVector[8](-0.041668172712620875, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1117) = SimpleLabeledVector[8](-0.052165729747471606, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1118) = SimpleLabeledVector[8](-0.06591778555820542, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1119) = SimpleLabeledVector[8](0.012920346046960374, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1120) = SimpleLabeledVector[8](-3.704401959321479E-4, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1121) = SimpleLabeledVector[8](-0.0020873691752255003, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1122) = SimpleLabeledVector[8](0.0048332146348098425, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1123) = SimpleLabeledVector[8](0.011345506357572156, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1124) = SimpleLabeledVector[8](-0.007060209011086496, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1125) = SimpleLabeledVector[8](-0.007957961459413273, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1126) = SimpleLabeledVector[8](-0.001243806474562982, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1127) = SimpleLabeledVector[8](0.001265451266483513, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1128) = SimpleLabeledVector[8](-0.029808556945879756, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1129) = SimpleLabeledVector[8](-0.024436438875341783, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1130) = SimpleLabeledVector[8](0.026854762227470276, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1131) = SimpleLabeledVector[8](-0.021672127706223815, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1132) = SimpleLabeledVector[8](-0.02335478213363184, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1133) = SimpleLabeledVector[8](0.009408693364230064, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1134) = SimpleLabeledVector[8](0.026005622265608135, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1135) = SimpleLabeledVector[8](0.05549229743887969, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1136) = SimpleLabeledVector[8](0.10360938089799683, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1137) = SimpleLabeledVector[8](0.14152994816723058, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1138) = SimpleLabeledVector[8](0.18909691981996915, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1139) = SimpleLabeledVector[8](0.16101254068924725, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1140) = SimpleLabeledVector[8](0.1119072261390616, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1141) = SimpleLabeledVector[8](0.08776252378810911, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1142) = SimpleLabeledVector[8](-0.057230064365435485, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1143) = SimpleLabeledVector[8](-0.04487524743658424, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1144) = SimpleLabeledVector[8](-0.08309334714145668, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1145) = SimpleLabeledVector[8](-0.03198127232222792, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1146) = SimpleLabeledVector[8](-0.07131639453206923, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1147) = SimpleLabeledVector[8](-0.16970647628792565, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1148) = SimpleLabeledVector[8](-0.101814681742013, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1149) = SimpleLabeledVector[8](-0.13936074534132714, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1150) = SimpleLabeledVector[8](-0.049074754727971036, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1151) = SimpleLabeledVector[8](0.023020872281864797, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1152) = SimpleLabeledVector[8](0.0378954417420378, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1153) = SimpleLabeledVector[8](0.05853230504353703, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1154) = SimpleLabeledVector[8](0.05419041762391232, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1155) = SimpleLabeledVector[8](0.05195226178364098, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1156) = SimpleLabeledVector[8](0.04205548754738769, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1157) = SimpleLabeledVector[8](0.05607611816510686, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1158) = SimpleLabeledVector[8](0.02432819918726669, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1159) = SimpleLabeledVector[8](0.10585262069176177, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1160) = SimpleLabeledVector[8](0.24541855409628763, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1161) = SimpleLabeledVector[8](0.26586641080535217, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1162) = SimpleLabeledVector[8](0.2170262029599886, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1163) = SimpleLabeledVector[8](0.30758238187365716, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1164) = SimpleLabeledVector[8](0.24825825624221037, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1165) = SimpleLabeledVector[8](0.2182240765989934, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1166) = SimpleLabeledVector[8](0.20991791988310682, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1167) = SimpleLabeledVector[8](0.27753652779908045, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1168) = SimpleLabeledVector[8](0.41254679269049827, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1169) = SimpleLabeledVector[8](0.372453574909164, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1170) = SimpleLabeledVector[8](0.22697908470250042, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1171) = SimpleLabeledVector[8](0.17728997576431443, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1172) = SimpleLabeledVector[8](-0.08784702053176822, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1173) = SimpleLabeledVector[8](-0.03438944960078056, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1174) = SimpleLabeledVector[8](-0.15007701301612145, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1175) = SimpleLabeledVector[8](-0.29051912502816835, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1176) = SimpleLabeledVector[8](-0.14829711464327225, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1177) = SimpleLabeledVector[8](-0.09869411063277035, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1178) = SimpleLabeledVector[8](-0.0010736181499364358, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1179) = SimpleLabeledVector[8](-0.018695077702847118, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1180) = SimpleLabeledVector[8](-0.11043594973724014, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1181) = SimpleLabeledVector[8](-0.1784186790802539, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1182) = SimpleLabeledVector[8](-0.08855481274988565, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1183) = SimpleLabeledVector[8](-0.04489408541526623, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1184) = SimpleLabeledVector[8](0.00272346791386986, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1185) = SimpleLabeledVector[8](0.060135332253889257, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1186) = SimpleLabeledVector[8](-0.008469919405859653, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1187) = SimpleLabeledVector[8](0.006970634332816815, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1188) = SimpleLabeledVector[8](0.01848345897149686, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1189) = SimpleLabeledVector[8](-0.048964540795585564, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1190) = SimpleLabeledVector[8](-0.005708799538332477, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1191) = SimpleLabeledVector[8](-0.008060619373790575, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1192) = SimpleLabeledVector[8](-0.050392701179612406, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1193) = SimpleLabeledVector[8](-0.08646244628178422, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1194) = SimpleLabeledVector[8](-0.04803137199864194, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1195) = SimpleLabeledVector[8](-0.024607428979258376, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1196) = SimpleLabeledVector[8](-0.05785381304683519, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1197) = SimpleLabeledVector[8](-0.06915941576599507, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1198) = SimpleLabeledVector[8](-0.10893194083334368, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1199) = SimpleLabeledVector[8](-0.13784174821363063, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1200) = SimpleLabeledVector[8](-0.19001654593312894, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - } - - def init_1201toEnd: Unit = { - trainingData_01(1201) = SimpleLabeledVector[8](-0.2767855853172973, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1202) = SimpleLabeledVector[8](-0.1712258294089953, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1203) = SimpleLabeledVector[8](-0.09205508521867484, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1204) = SimpleLabeledVector[8](-0.09157571699273875, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1205) = SimpleLabeledVector[8](-0.12574833553084608, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1206) = SimpleLabeledVector[8](-0.08057566014886014, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1207) = SimpleLabeledVector[8](-0.04697842044132997, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1208) = SimpleLabeledVector[8](7.454923657744908E-4, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1209) = SimpleLabeledVector[8](-0.017348243423983042, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1210) = SimpleLabeledVector[8](-0.006372315029145643, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1211) = SimpleLabeledVector[8](0.0587668323022053, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1212) = SimpleLabeledVector[8](0.08409076532232004, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1213) = SimpleLabeledVector[8](0.06745975506214812, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1214) = SimpleLabeledVector[8](0.08375397678865779, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1215) = SimpleLabeledVector[8](0.09115000585483486, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1216) = SimpleLabeledVector[8](0.05040392047889628, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1217) = SimpleLabeledVector[8](0.03126189635584511, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1218) = SimpleLabeledVector[8](0.043587848650636536, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1219) = SimpleLabeledVector[8](0.046599601313134564, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1220) = SimpleLabeledVector[8](0.04439107773108339, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1221) = SimpleLabeledVector[8](0.0059944407156167475, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1222) = SimpleLabeledVector[8](-0.06151834336033468, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1223) = SimpleLabeledVector[8](-0.07693518178715422, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1224) = SimpleLabeledVector[8](-0.0954849051056411, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1225) = SimpleLabeledVector[8](-0.11372367253594239, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1226) = SimpleLabeledVector[8](-0.0963916918031606, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1227) = SimpleLabeledVector[8](-0.13194937068662935, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1228) = SimpleLabeledVector[8](-0.1865173484765259, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1229) = SimpleLabeledVector[8](-0.09090836912750591, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1230) = SimpleLabeledVector[8](-0.053551792288508156, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1231) = SimpleLabeledVector[8](-0.04803537286602838, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1232) = SimpleLabeledVector[8](-0.01031715309549812, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1233) = SimpleLabeledVector[8](-0.0054561091158179895, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1234) = SimpleLabeledVector[8](0.08098017763125819, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1235) = SimpleLabeledVector[8](0.02500529701136562, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1236) = SimpleLabeledVector[8](0.05373577867432796, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1237) = SimpleLabeledVector[8](0.07022061657510584, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1238) = SimpleLabeledVector[8](0.10236428749920953, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1239) = SimpleLabeledVector[8](0.0737531154935659, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1240) = SimpleLabeledVector[8](0.082089379377228, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1241) = SimpleLabeledVector[8](0.10308149116094555, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1242) = SimpleLabeledVector[8](0.10639560003682524, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1243) = SimpleLabeledVector[8](0.11720299233048223, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1244) = SimpleLabeledVector[8](0.14338925170469233, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1245) = SimpleLabeledVector[8](0.14210659311875393, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1246) = SimpleLabeledVector[8](0.24542340597132514, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1247) = SimpleLabeledVector[8](0.35911510920420164, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1248) = SimpleLabeledVector[8](0.3911880463361938, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1249) = SimpleLabeledVector[8](0.2856911168781202, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1250) = SimpleLabeledVector[8](0.19033352712710938, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1251) = SimpleLabeledVector[8](0.16902357020861175, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1252) = SimpleLabeledVector[8](0.2591823547619959, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1253) = SimpleLabeledVector[8](0.3479460274319173, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1254) = SimpleLabeledVector[8](0.28331507735218714, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1255) = SimpleLabeledVector[8](0.3184245850968284, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1256) = SimpleLabeledVector[8](0.38444333241792245, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1257) = SimpleLabeledVector[8](0.27887032501328923, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1258) = SimpleLabeledVector[8](0.1928784166244531, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1259) = SimpleLabeledVector[8](0.12311641040387504, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1260) = SimpleLabeledVector[8](0.1811315803476331, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1261) = SimpleLabeledVector[8](0.2079433243887438, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1262) = SimpleLabeledVector[8](0.19526080774066085, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1263) = SimpleLabeledVector[8](0.14438896962973993, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1264) = SimpleLabeledVector[8](0.14892869069915804, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1265) = SimpleLabeledVector[8](0.19854479025421493, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1266) = SimpleLabeledVector[8](0.1651079452666534, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1267) = SimpleLabeledVector[8](0.11354086474835487, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1268) = SimpleLabeledVector[8](0.0830414701728335, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1269) = SimpleLabeledVector[8](-0.05942098573516078, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1270) = SimpleLabeledVector[8](-0.09568292770773469, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1271) = SimpleLabeledVector[8](-0.05468877491422746, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1272) = SimpleLabeledVector[8](-0.10328406051373859, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1273) = SimpleLabeledVector[8](-0.02925454757305907, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1274) = SimpleLabeledVector[8](-0.11146897430621426, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1275) = SimpleLabeledVector[8](-0.10958935824025348, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1276) = SimpleLabeledVector[8](-0.067010207610853, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1277) = SimpleLabeledVector[8](-0.07528528142819739, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1278) = SimpleLabeledVector[8](-0.1013916492005024, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1279) = SimpleLabeledVector[8](-0.09688507924951746, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1280) = SimpleLabeledVector[8](-0.11182521641351661, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1281) = SimpleLabeledVector[8](-0.18734630530937368, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1282) = SimpleLabeledVector[8](-0.19050976831597627, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1283) = SimpleLabeledVector[8](-0.15038731087961615, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1284) = SimpleLabeledVector[8](-0.09862395974749652, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1285) = SimpleLabeledVector[8](-0.09238184903346872, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1286) = SimpleLabeledVector[8](-0.02893651867625368, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1287) = SimpleLabeledVector[8](-0.06804874425344254, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1288) = SimpleLabeledVector[8](-0.07796138824457935, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1289) = SimpleLabeledVector[8](-0.12107221754635308, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1290) = SimpleLabeledVector[8](-0.10369642491428462, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1291) = SimpleLabeledVector[8](-0.02274381913385726, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1292) = SimpleLabeledVector[8](-0.0042323203524358855, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1293) = SimpleLabeledVector[8](-0.021357374824902395, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1294) = SimpleLabeledVector[8](-0.0050900370481686006, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1295) = SimpleLabeledVector[8](-0.006118090399428336, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1296) = SimpleLabeledVector[8](0.027860522312275662, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1297) = SimpleLabeledVector[8](0.01777266376712093, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1298) = SimpleLabeledVector[8](-0.003053085782937309, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1299) = SimpleLabeledVector[8](-0.03581131427835751, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1300) = SimpleLabeledVector[8](-0.03768089462933707, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1301) = SimpleLabeledVector[8](-0.01144989845057614, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1302) = SimpleLabeledVector[8](-0.005486753196154888, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1303) = SimpleLabeledVector[8](3.1941713227919753E-4, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1304) = SimpleLabeledVector[8](-0.019722500016706924, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1305) = SimpleLabeledVector[8](-0.021799021465633756, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1306) = SimpleLabeledVector[8](-0.029693970301928083, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1307) = SimpleLabeledVector[8](-0.012457282129006134, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1308) = SimpleLabeledVector[8](-0.01921093301135237, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1309) = SimpleLabeledVector[8](-0.010170204624482733, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1310) = SimpleLabeledVector[8](-0.018504045685097458, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1311) = SimpleLabeledVector[8](-0.025885670203703484, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1312) = SimpleLabeledVector[8](-0.01429498867108707, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1313) = SimpleLabeledVector[8](-0.004122830746714814, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1314) = SimpleLabeledVector[8](-0.08579285478426055, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1315) = SimpleLabeledVector[8](-0.07868972088965004, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1316) = SimpleLabeledVector[8](-0.06524392272657964, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1317) = SimpleLabeledVector[8](-0.07096894819402708, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1318) = SimpleLabeledVector[8](-0.06728956584850963, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1319) = SimpleLabeledVector[8](-0.05882517213880199, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1320) = SimpleLabeledVector[8](-0.007795669051275211, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1321) = SimpleLabeledVector[8](-0.03501472604848363, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1322) = SimpleLabeledVector[8](-0.041517939358279576, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1323) = SimpleLabeledVector[8](-0.03691041772209017, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1324) = SimpleLabeledVector[8](0.011769809680262743, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1325) = SimpleLabeledVector[8](0.10472243004633629, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1326) = SimpleLabeledVector[8](0.04293982418810912, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1327) = SimpleLabeledVector[8](0.006619213854183477, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1328) = SimpleLabeledVector[8](-0.015238035213866569, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1329) = SimpleLabeledVector[8](-0.012966439328232809, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1330) = SimpleLabeledVector[8](-0.016856936639096612, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1331) = SimpleLabeledVector[8](-0.011089738625802172, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1332) = SimpleLabeledVector[8](-0.07742599738375473, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1333) = SimpleLabeledVector[8](-0.0996352964516485, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1334) = SimpleLabeledVector[8](-0.11830248700888081, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1335) = SimpleLabeledVector[8](-0.10395845480016985, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1336) = SimpleLabeledVector[8](-0.06641611580859184, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1337) = SimpleLabeledVector[8](-0.0802894325885562, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1338) = SimpleLabeledVector[8](-0.11444056763078549, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1339) = SimpleLabeledVector[8](-0.20691275111592028, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1340) = SimpleLabeledVector[8](-0.19294638091180435, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1341) = SimpleLabeledVector[8](-0.21414962119523318, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1342) = SimpleLabeledVector[8](-0.15593979390790175, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1343) = SimpleLabeledVector[8](-0.12904995440810535, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1344) = SimpleLabeledVector[8](-0.052041321394861055, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1345) = SimpleLabeledVector[8](-0.06960443509564349, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1346) = SimpleLabeledVector[8](-0.20425689337362754, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1347) = SimpleLabeledVector[8](-0.1874430454746994, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1348) = SimpleLabeledVector[8](-0.19435479327947314, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1349) = SimpleLabeledVector[8](-0.11954417747685905, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1350) = SimpleLabeledVector[8](-0.13511107806877715, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1351) = SimpleLabeledVector[8](-0.15132880521788622, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1352) = SimpleLabeledVector[8](-0.17742255748060715, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1353) = SimpleLabeledVector[8](-0.16561694440354954, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1354) = SimpleLabeledVector[8](-0.10012960651018246, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1355) = SimpleLabeledVector[8](-0.16057025728157542, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1356) = SimpleLabeledVector[8](-0.1641872659413204, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1357) = SimpleLabeledVector[8](-0.13987064106538433, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1358) = SimpleLabeledVector[8](-0.10536722942311613, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1359) = SimpleLabeledVector[8](-0.1374490054475691, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1360) = SimpleLabeledVector[8](-0.11922643906410339, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1361) = SimpleLabeledVector[8](-0.08594149469938414, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1362) = SimpleLabeledVector[8](-0.11468341883996357, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1363) = SimpleLabeledVector[8](-0.08513409642718571, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1364) = SimpleLabeledVector[8](-0.025897855408798603, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1365) = SimpleLabeledVector[8](0.03187845040611601, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1366) = SimpleLabeledVector[8](0.07948409907124722, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1367) = SimpleLabeledVector[8](0.08137011293081138, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1368) = SimpleLabeledVector[8](0.16678965315657387, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1369) = SimpleLabeledVector[8](0.03445950107567592, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1370) = SimpleLabeledVector[8](0.004161423137463569, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1371) = SimpleLabeledVector[8](-0.0055018015221155875, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1372) = SimpleLabeledVector[8](0.021446106501140292, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1373) = SimpleLabeledVector[8](-9.459582634803383E-4, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1374) = SimpleLabeledVector[8](-0.019456187409894268, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1375) = SimpleLabeledVector[8](-0.05241409533783852, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1376) = SimpleLabeledVector[8](-0.08221481298325882, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1377) = SimpleLabeledVector[8](-0.08918177436425558, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1378) = SimpleLabeledVector[8](-0.09963944742709957, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1379) = SimpleLabeledVector[8](-0.09525686918948276, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1380) = SimpleLabeledVector[8](-0.12630154576302935, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1381) = SimpleLabeledVector[8](-0.08935571041427506, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1382) = SimpleLabeledVector[8](-0.012271936191666685, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1383) = SimpleLabeledVector[8](0.02179236827007229, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1384) = SimpleLabeledVector[8](-2.5772405978601834E-4, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1385) = SimpleLabeledVector[8](0.018400763203391016, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1386) = SimpleLabeledVector[8](0.09917611310282667, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1387) = SimpleLabeledVector[8](0.19391103894995237, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1388) = SimpleLabeledVector[8](0.16825766137693746, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1389) = SimpleLabeledVector[8](0.06403727185778807, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1390) = SimpleLabeledVector[8](0.005173858457263726, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1391) = SimpleLabeledVector[8](-0.10019852522434675, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1392) = SimpleLabeledVector[8](-0.19493480218789655, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1393) = SimpleLabeledVector[8](-0.1399901075183889, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1394) = SimpleLabeledVector[8](-0.19455957308866423, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1395) = SimpleLabeledVector[8](-0.1621892523037071, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1396) = SimpleLabeledVector[8](-0.1151732347443529, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1397) = SimpleLabeledVector[8](-0.14891398316649573, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1398) = SimpleLabeledVector[8](-0.14338042636966927, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1399) = SimpleLabeledVector[8](-0.1229689815809411, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1400) = SimpleLabeledVector[8](-0.12943028616935207, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1401) = SimpleLabeledVector[8](-0.14449065872500924, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1402) = SimpleLabeledVector[8](-0.11776036110807758, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1403) = SimpleLabeledVector[8](-0.11522933196181852, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1404) = SimpleLabeledVector[8](-0.1278902623348098, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1405) = SimpleLabeledVector[8](-0.08927405445595146, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1406) = SimpleLabeledVector[8](-0.03454592887886219, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1407) = SimpleLabeledVector[8](-0.04103903431619889, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1408) = SimpleLabeledVector[8](0.008465543412050603, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1409) = SimpleLabeledVector[8](0.049300665255783374, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - trainingData_01(1410) = SimpleLabeledVector[8](0.06122983601803879, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1411) = SimpleLabeledVector[8](0.06751268697914992, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1412) = SimpleLabeledVector[8](0.03303656430935641, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1413) = SimpleLabeledVector[8](-0.022256767119860292, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1414) = SimpleLabeledVector[8](-0.034087324465218595, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - trainingData_01(1415) = SimpleLabeledVector[8](-0.04159012195510739, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - } - - - def initTestData: Unit = { - testData_01(0) = SimpleLabeledVector[8](-0.3144129671697874, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(1) = SimpleLabeledVector[8](-0.2995203854735745, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(2) = SimpleLabeledVector[8](0.15604068111195227, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(3) = SimpleLabeledVector[8](0.1624968476219781, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(4) = SimpleLabeledVector[8](0.07663657784748745, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(5) = SimpleLabeledVector[8](0.46467074361696376, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(6) = SimpleLabeledVector[8](0.6622407474436021, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(7) = SimpleLabeledVector[8](3.8814054612912443, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(8) = SimpleLabeledVector[8](0.7285769287363584, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(9) = SimpleLabeledVector[8](0.1090244187569218, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(10) = SimpleLabeledVector[8](0.44463683322036496, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(11) = SimpleLabeledVector[8](0.22155472412515673, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(12) = SimpleLabeledVector[8](-0.08080577714664075, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(13) = SimpleLabeledVector[8](-0.2628903941849283, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(14) = SimpleLabeledVector[8](-0.09482343421037097, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(15) = SimpleLabeledVector[8](-0.18825653569870401, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(16) = SimpleLabeledVector[8](-0.3786889999171123, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(17) = SimpleLabeledVector[8](-0.20150474114919747, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(18) = SimpleLabeledVector[8](-0.15804639226339526, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(19) = SimpleLabeledVector[8](0.2703310622412375, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(20) = SimpleLabeledVector[8](-0.01743800752444722, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(21) = SimpleLabeledVector[8](-0.17938752677935957, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(22) = SimpleLabeledVector[8](-0.20748591686783321, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(23) = SimpleLabeledVector[8](-0.04853836811721103, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(24) = SimpleLabeledVector[8](-0.25159239872177586, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(25) = SimpleLabeledVector[8](-0.2208273340654381, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(26) = SimpleLabeledVector[8](-0.23977451435326658, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(27) = SimpleLabeledVector[8](0.012697279091230978, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(28) = SimpleLabeledVector[8](-0.05269244583410061, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(29) = SimpleLabeledVector[8](0.1210756995802451, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(30) = SimpleLabeledVector[8](-0.09319849248134873, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(31) = SimpleLabeledVector[8](0.03465380412207174, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(32) = SimpleLabeledVector[8](0.10979651760437101, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(33) = SimpleLabeledVector[8](0.06544422993175393, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(34) = SimpleLabeledVector[8](0.06386007204464426, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(35) = SimpleLabeledVector[8](-0.1973188570515229, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(36) = SimpleLabeledVector[8](-0.17532814575749026, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(37) = SimpleLabeledVector[8](-0.26077133124052543, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(38) = SimpleLabeledVector[8](-0.13824093383322833, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(39) = SimpleLabeledVector[8](-0.28446305883210593, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(40) = SimpleLabeledVector[8](-0.13863053007989087, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(41) = SimpleLabeledVector[8](-0.12782084167488694, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(42) = SimpleLabeledVector[8](-0.12097674365919127, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(43) = SimpleLabeledVector[8](0.04982382830192813, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(44) = SimpleLabeledVector[8](0.04412961088295193, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(45) = SimpleLabeledVector[8](-0.08530207725314236, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(46) = SimpleLabeledVector[8](-0.09127539332231148, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(47) = SimpleLabeledVector[8](-0.09368143227340824, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(48) = SimpleLabeledVector[8](-0.0516710706173648, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(49) = SimpleLabeledVector[8](-0.009626448941857116, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(50) = SimpleLabeledVector[8](-0.08449185201291924, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(51) = SimpleLabeledVector[8](0.263652466613877, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(52) = SimpleLabeledVector[8](0.21704020661225717, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(53) = SimpleLabeledVector[8](0.1972941794808283, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(54) = SimpleLabeledVector[8](-0.04692862426905305, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(55) = SimpleLabeledVector[8](-0.07374095994337077, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(56) = SimpleLabeledVector[8](-0.04395085871954363, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(57) = SimpleLabeledVector[8](0.029759931310897086, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(58) = SimpleLabeledVector[8](0.07804345584358366, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(59) = SimpleLabeledVector[8](-0.0668424152648263, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(60) = SimpleLabeledVector[8](0.06432776353230607, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(61) = SimpleLabeledVector[8](0.02790838526242214, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(62) = SimpleLabeledVector[8](0.028558394047574046, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(63) = SimpleLabeledVector[8](-0.020544283966574352, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(64) = SimpleLabeledVector[8](-0.13274969927004923, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(65) = SimpleLabeledVector[8](-0.12293438631062686, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(66) = SimpleLabeledVector[8](-0.12164846148801697, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(67) = SimpleLabeledVector[8](-0.20198509329081543, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(68) = SimpleLabeledVector[8](-0.2901430224004534, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(69) = SimpleLabeledVector[8](-0.06516970364715001, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(70) = SimpleLabeledVector[8](-0.1512855419628376, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(71) = SimpleLabeledVector[8](-0.15241311863828733, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(72) = SimpleLabeledVector[8](-0.14456459380235168, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(73) = SimpleLabeledVector[8](-0.016365315365585692, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(74) = SimpleLabeledVector[8](-0.17226819640060625, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(75) = SimpleLabeledVector[8](0.07571762469640084, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(76) = SimpleLabeledVector[8](0.03727081546154451, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(77) = SimpleLabeledVector[8](-0.1485272249583415, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(78) = SimpleLabeledVector[8](-2.7755575615628835E-15, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(79) = SimpleLabeledVector[8](6.938893903907233E-16, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(80) = SimpleLabeledVector[8](-1.040834085586083E-15, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(81) = SimpleLabeledVector[8](-2.428612866367524E-15, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(82) = SimpleLabeledVector[8](1.2143064331837663E-15, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(83) = SimpleLabeledVector[8](0.025014099106889245, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(84) = SimpleLabeledVector[8](0.026336954514196223, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(85) = SimpleLabeledVector[8](-0.1556637698335547, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(86) = SimpleLabeledVector[8](0.19351850270968873, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(87) = SimpleLabeledVector[8](0.16813332257057198, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(88) = SimpleLabeledVector[8](0.34244769998638613, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(89) = SimpleLabeledVector[8](0.27512595111868576, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(90) = SimpleLabeledVector[8](0.23812713650689488, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(91) = SimpleLabeledVector[8](0.13014143088917426, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(92) = SimpleLabeledVector[8](0.004356106636047194, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(93) = SimpleLabeledVector[8](-0.2672359501564799, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(94) = SimpleLabeledVector[8](-0.21180955883814245, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(95) = SimpleLabeledVector[8](0.03308657977705839, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(96) = SimpleLabeledVector[8](0.03529870426086125, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(97) = SimpleLabeledVector[8](0.21770523146641949, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(98) = SimpleLabeledVector[8](0.1739229742141121, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(99) = SimpleLabeledVector[8](0.12703914703271926, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(100) = SimpleLabeledVector[8](0.05272879450819005, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(101) = SimpleLabeledVector[8](0.042336307975109434, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(102) = SimpleLabeledVector[8](2.081668171172173E-15, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(103) = SimpleLabeledVector[8](-0.009666473743894373, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(104) = SimpleLabeledVector[8](-0.009575179278504055, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(105) = SimpleLabeledVector[8](-0.08625421838974982, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(106) = SimpleLabeledVector[8](0.04446360237120966, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(107) = SimpleLabeledVector[8](0.01167672011588452, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(108) = SimpleLabeledVector[8](-0.01631421601014503, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(109) = SimpleLabeledVector[8](0.0650069030212242, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(110) = SimpleLabeledVector[8](0.004260838707811159, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(111) = SimpleLabeledVector[8](-0.0017006453721835624, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(112) = SimpleLabeledVector[8](-0.003867441209011083, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(113) = SimpleLabeledVector[8](-0.007288739112691943, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(114) = SimpleLabeledVector[8](-0.07137166869302997, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(115) = SimpleLabeledVector[8](0.16808108349409595, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(116) = SimpleLabeledVector[8](0.07970173508452605, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(117) = SimpleLabeledVector[8](-0.05760398564964089, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(118) = SimpleLabeledVector[8](-0.04982196696496857, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(119) = SimpleLabeledVector[8](-0.02931609248374652, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(120) = SimpleLabeledVector[8](0.07155375235666034, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(121) = SimpleLabeledVector[8](0.37986940277062664, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(122) = SimpleLabeledVector[8](0.02188194603641543, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(123) = SimpleLabeledVector[8](0.4108490868320232, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(124) = SimpleLabeledVector[8](0.2391340148588829, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(125) = SimpleLabeledVector[8](0.06316003017081982, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(126) = SimpleLabeledVector[8](0.838455217436003, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(127) = SimpleLabeledVector[8](0.07176198606732281, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(128) = SimpleLabeledVector[8](-0.005615353832945616, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(129) = SimpleLabeledVector[8](0.03262910058341197, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(130) = SimpleLabeledVector[8](-0.07099887640664528, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(131) = SimpleLabeledVector[8](0.25212550251671695, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(132) = SimpleLabeledVector[8](-0.10130303321751738, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(133) = SimpleLabeledVector[8](0.03157861156937092, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(134) = SimpleLabeledVector[8](-6.404772607067795E-4, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(135) = SimpleLabeledVector[8](-0.030516456026368488, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(136) = SimpleLabeledVector[8](-0.11468273342837514, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(137) = SimpleLabeledVector[8](8.237382184441743E-4, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(138) = SimpleLabeledVector[8](0.05148466802375684, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(139) = SimpleLabeledVector[8](0.043718751198818906, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(140) = SimpleLabeledVector[8](0.15674132979698774, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(141) = SimpleLabeledVector[8](0.22322038613178835, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(142) = SimpleLabeledVector[8](0.34245011030763883, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(143) = SimpleLabeledVector[8](0.32226549417172873, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(144) = SimpleLabeledVector[8](0.16752375036454395, NArray[Double](0.7142857142857143, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(145) = SimpleLabeledVector[8](0.10266596579536763, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(146) = SimpleLabeledVector[8](-0.023170923892991906, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(147) = SimpleLabeledVector[8](-0.11784048747707776, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(148) = SimpleLabeledVector[8](-0.052764139664050795, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(149) = SimpleLabeledVector[8](0.03084053901122121, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(150) = SimpleLabeledVector[8](0.028962814708930522, NArray[Double](0.14285714285714285, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(151) = SimpleLabeledVector[8](-0.054902172548864134, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(152) = SimpleLabeledVector[8](-0.024198162800307236, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(153) = SimpleLabeledVector[8](-0.12249489452476219, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(154) = SimpleLabeledVector[8](-0.17855167504160757, NArray[Double](-0.7142857142857143, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(155) = SimpleLabeledVector[8](-0.1480412403018322, NArray[Double](-0.42857142857142855, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(156) = SimpleLabeledVector[8](-0.15913399689007426, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(157) = SimpleLabeledVector[8](0.0921669510094773, NArray[Double](0.42857142857142855, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - testData_01(158) = SimpleLabeledVector[8](0.08907265208736621, NArray[Double](1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0).asInstanceOf[Vec[8]]) - testData_01(159) = SimpleLabeledVector[8](-0.003352411138139781, NArray[Double](-0.14285714285714285, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0).asInstanceOf[Vec[8]]) - } - - initializeData -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/RegressionTest.scala b/demo/shared/src/main/scala/ai/dragonfly/math/matrix/RegressionTest.scala deleted file mode 100644 index 65721cc..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/matrix/RegressionTest.scala +++ /dev/null @@ -1,78 +0,0 @@ -package ai.dragonfly.math.matrix - -import ai.dragonfly.math.* -import ai.dragonfly.math.Random.* -import ai.dragonfly.math.matrix.ml.data.* -import ai.dragonfly.math.matrix.ml.supervized.regression.* -import ai.dragonfly.math.stats.* -import ai.dragonfly.math.vector.* -import ai.dragonfly.math.vector.Vec.* -import narr.* - -trait LinearRegressionTest[M <: Int, N <: Int] { - def trainingData:SupervisedData[M, N] - def testData:SupervisedData[M, N] - def evaluate(model: LinearRegressionModel[N]):LinearRegressionTestScore -} - -case class SyntheticLinearRegressionTest[M <: Int, N <: Int](trueCoefficients: Vec[N], bias: Double, noise:Double = 1.0)(using ValueOf[M], ValueOf[N]) extends LinearRegressionTest[M, N] { - val sampleSize:Int = valueOf[M] - val maxNorm:Double = trueCoefficients.dimension * trueCoefficients.magnitude //Math.min(2.0 * dimension, sampleSize) - - var syntheticError: Double = 0.0 - - override val trainingData:SupervisedData[M, N] = { - val td: NArray[LabeledVec[N]] = new NArray[LabeledVec[N]](sampleSize) - - var i:Int = 0; while (i < td.length) { - val xi: Vec[N] = defaultRandom.nextVec[N](maxNorm) - val yi: Double = f(xi) - - val yi_noisy = yi + (noise * (defaultRandom.between(-0.5, 0.5))) - - td(i) = SimpleLabeledVector[N](yi_noisy, xi) - val err = yi_noisy - yi - - syntheticError = syntheticError + err * err - i += 1 - } - new StaticSupervisedData[M, N](td) - } - - syntheticError = Math.sqrt(syntheticError / trainingData.sampleSize) - - private def f(xi:Vec[N]):Double = (xi dot trueCoefficients) + bias - - override def evaluate(model: LinearRegressionModel[N]):LinearRegressionTestScore = { - var observedError:Double = 0.0 - var i:Int = 0; while (i < testData.sampleSize) { - val lv = testData.labeledExample(i) - val err = model(lv.x) - f(lv.x) -// println(s"\ty = ${f(lv.x)} y' = ${model(lv.x)} error = $err : $lv") - observedError = observedError + (err * err) - i += 1 - } - observedError = Math.sqrt( observedError / testData.sampleSize ) - LinearRegressionTestScore(model.standardError, observedError) - } - - override def testData: SupervisedData[M, N] = trainingData -} - - -case class EmpiricalRegressionTest[M <: Int, N <: Int](override val trainingData:SupervisedData[M, N], override val testData:SupervisedData[M, N]) extends LinearRegressionTest[M, N] { - override def evaluate(model: LinearRegressionModel[N]):LinearRegressionTestScore = { - var observedError:Double = 0.0 - var i:Int = 0; while (i < testData.sampleSize) { - val lv = testData.labeledExample(i) - val err = model(lv.x) - lv.y -// println(s"\ty = ${lv.y} y' = ${model(lv.x)} error = $err : $lv") - observedError = observedError + (err * err) - i += 1 - } - observedError = Math.sqrt(observedError / testData.sampleSize) - LinearRegressionTestScore(model.standardError, observedError) - } -} - -case class LinearRegressionTestScore(standardError:Double, testError:Double) {} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/kernel/KernelDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/kernel/KernelDemo.scala deleted file mode 100644 index 88f0c42..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/kernel/KernelDemo.scala +++ /dev/null @@ -1,63 +0,0 @@ -package ai.dragonfly.math.stats.kernel - -/** - * Created by clifton on 5/16/15. - */ - -import ai.dragonfly.math.* -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.stats.DenseHistogramOfContinuousDistribution -import ai.dragonfly.math.stats.probability.distributions.Gaussian -import narr.* - -object KernelDemo extends Demonstration { - override def demo():Unit = { - val exclusionRadius:Double = 12.0 - - var t: Double = 0.0 - val step:Double = 0.1 - val totalSteps:Double = exclusionRadius / step - - type N = 1 - - val gk:GaussianKernel[N] = GaussianKernel[N](exclusionRadius, new Gaussian(0.0, 16.0)) - val ek:EpanechnikovKernel[N] = EpanechnikovKernel[N](exclusionRadius) - val uk:UniformKernel[N] = UniformKernel[N](exclusionRadius) - val dk:DiscreteKernel[N] = DiscreteKernel[N]( - exclusionRadius, - NArray.tabulate[Double](squareInPlace(totalSteps).toInt)((i:Int) => { - val t2:Double = squareInPlace(i * step) - 0.5 * (gk.weight(t2) + ek.weight(t2)) - }) - ) - - - val gkh = new DenseHistogramOfContinuousDistribution(10, 0.0, 12.0) - val ekh = new DenseHistogramOfContinuousDistribution(10, 0.0, 12.0) - val ukh = new DenseHistogramOfContinuousDistribution(10, 0.0, 12.0) - val dkh = new DenseHistogramOfContinuousDistribution(10, 0.0, 12.0) - - while (t < exclusionRadius) { - - val t2: Double = squareInPlace(t) - - gkh( t, gk.weight(t2) ) - ekh( t, ek.weight(t2) ) - ukh( t, uk.weight(t2) ) - dkh( t, dk.weight(t2) ) - - t = t + 0.1 - } - - println("Gaussian Kernel ") - println(gkh.toString) - println("Epanechnikov Kernel ") - println(ekh.toString) - println("Uniform Kernel ") - println(ukh.toString) - println("Discrete Kernel ") - println(dkh.toString) - } - - override def name: String = "Kernel" -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/BetaDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/BetaDemo.scala deleted file mode 100644 index dc12cd2..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/BetaDemo.scala +++ /dev/null @@ -1,9 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions - -import ai.dragonfly.math.* -import stats.* - -object BetaDemo { - val demo2param = ProbDistDemo("Beta", Beta(0.5, 5.0), DenseHistogramOfContinuousDistribution(9, 0, 1)) - val demo4param = ProbDistDemo("Beta", Beta(2.0, 1.0, 33, 42), DenseHistogramOfContinuousDistribution(15, 33, 42)) -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/BinomialDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/BinomialDemo.scala deleted file mode 100644 index 59e8540..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/BinomialDemo.scala +++ /dev/null @@ -1,13 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions - -import ai.dragonfly.math.* -import stats.* -import ai.dragonfly.math.interval.* - - -import scala.language.postfixOps - -object BinomialDemo { - val demo = ProbDistDemo("Binomial", Binomial(21, 0.42), DenseHistogramOfDiscreteDistribution(11, 0, 21)) - lazy val domain:Domain[Long] = Domain.ℕ_Long -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/DiscreteUniformDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/DiscreteUniformDemo.scala deleted file mode 100644 index 2165336..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/DiscreteUniformDemo.scala +++ /dev/null @@ -1,14 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions - -import ai.dragonfly.math.* -import stats.* - -import scala.language.postfixOps - -object DiscreteUniformDemo { - val demo = ProbDistDemo( - "Discrete Uniform", - DiscreteUniform(5, 15), - DenseHistogramOfDiscreteDistribution(7, 5, 15) - ) -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/GaussianDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/GaussianDemo.scala deleted file mode 100644 index b8ad574..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/GaussianDemo.scala +++ /dev/null @@ -1,12 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions - -import ai.dragonfly.math.* -import stats.* - - - -object GaussianDemo { - val g10_42:Gaussian = Gaussian(10.0, 42.0) - val σ6:Long = Math.ceil(g10_42.σ * 6).toLong - val demo = ProbDistDemo( "Gaussian", g10_42, DenseHistogramOfContinuousDistribution(13, g10_42.μ - σ6, g10_42.μ + σ6 )) -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/LogNormalDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/LogNormalDemo.scala deleted file mode 100644 index aa26374..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/LogNormalDemo.scala +++ /dev/null @@ -1,13 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions - -import ai.dragonfly.math.* -import stats.* - -// https://en.wikipedia.org/wiki/Log-normal_distribution#Generation_and_parameters - -object LogNormalDemo { - - val ln15_5:LogNormal = LogNormal(15.0, 5.0) - val demo = ProbDistDemo("LogNormal", ln15_5, DenseHistogramOfContinuousDistribution(21, 1, ln15_5.μ + (6.0*ln15_5.σ))) - -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/PERTDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/PERTDemo.scala deleted file mode 100644 index 0dfced3..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/PERTDemo.scala +++ /dev/null @@ -1,9 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions - - -import ai.dragonfly.math.{ProbDistDemo} -import ai.dragonfly.math.stats.* - -object PERTDemo { - val demo = ProbDistDemo("PERT", PERT(5.0, 6.0, 11.0), DenseHistogramOfContinuousDistribution(11, 5.0, 11.0)) -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/PoissonDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/PoissonDemo.scala deleted file mode 100644 index bf71be5..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/PoissonDemo.scala +++ /dev/null @@ -1,10 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions - -import ai.dragonfly.math.* -import stats.* - -object PoissonDemo { - val p15:Poisson = Poisson(15) - val σ6:Long = Math.ceil(p15.σ * 6).toLong - val demo = ProbDistDemo("Poisson", p15, DenseHistogramOfDiscreteDistribution(15, p15.λ.toLong - σ6, p15.λ.toLong + σ6)) -} \ No newline at end of file diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/UniformDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/UniformDemo.scala deleted file mode 100644 index faef824..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/UniformDemo.scala +++ /dev/null @@ -1,11 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions - -import ai.dragonfly.math.* - -import stats.* - -import scala.language.postfixOps - -object UniformDemo { - val demo = ProbDistDemo("Uniform", Uniform(5.0, 11.0), DenseHistogramOfContinuousDistribution(7, 5, 11)) -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/BetaDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/BetaDemo.scala deleted file mode 100644 index 4ca3c4c..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/BetaDemo.scala +++ /dev/null @@ -1,9 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions.stream - -import ai.dragonfly.math.* -import ai.dragonfly.math.stats.* -import probability.distributions - -object BetaDemo { - val demo = OnlineProbDistDemo[Double, distributions.Beta, Beta]("Streaming Beta", distributions.Beta(3.0, 0.75, 42.0, 69.0), Beta(), 10000) -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/BinomialDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/BinomialDemo.scala deleted file mode 100644 index 5e489c9..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/BinomialDemo.scala +++ /dev/null @@ -1,46 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions.stream - -import ai.dragonfly.math.stats.probability.distributions -import ai.dragonfly.math.* -import ai.dragonfly.democrossy.Demonstration - - -import scala.language.postfixOps -import scala.language.implicitConversions - -object BinomialDemo { - - val fixedBinomialDemo = OnlineProbDistDemo[Long, distributions.Binomial, FixedBinomial]("Streaming FixedBinomial", distributions.Binomial(42L, 0.69), FixedBinomial(42L), 1000) - - val openBinomialDemo = new Demonstration { - override def name: String = "Streaming Binomial" - - def si:Double = 1 + ((Math.random() - 0.5) / 8.0) - - override def demo(): Unit = { - val sampleSize = 1000 - val idealDist = distributions.Binomial(69, 0.42) - val streamingDist = Binomial() - println(s"Estimate $name:\n\tSampling: $idealDist") - val blockSize:Int = sampleSize / 5 - val end = sampleSize + 1 - for (i <- 1 until end) { - val ki = idealDist.random() * si - val ni = idealDist.n * si - streamingDist.observe(ki.toLong, ni.toLong) - if (i % blockSize == 0) { - println(s"\n\t\testimation after $i samples: ${streamingDist.estimate}") - } - } - println(s"\n\tEstimate: ${streamingDist.estimate}\n\tIdeal Distribution: $idealDist\n") - println(s"\nTest $idealDist.p($idealDist.random())") - - for (i <- 0 until 5) { - val x = idealDist.random() - println(s"\n\tp($x) = ${idealDist.p(x)}") - } - println("\n") - } - - } -} \ No newline at end of file diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/GaussianDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/GaussianDemo.scala deleted file mode 100644 index b64ef33..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/GaussianDemo.scala +++ /dev/null @@ -1,9 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions.stream - -import ai.dragonfly.math.OnlineProbDistDemo -import ai.dragonfly.math.stats.* -import probability.distributions - -object GaussianDemo { - val demo: OnlineProbDistDemo[Double, distributions.Gaussian, Gaussian] = OnlineProbDistDemo[Double, distributions.Gaussian, Gaussian]("Streaming Gaussian", distributions.Gaussian(42.0, 7.0), Gaussian(), 1000) -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/LogNormalDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/LogNormalDemo.scala deleted file mode 100644 index 4d5908f..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/LogNormalDemo.scala +++ /dev/null @@ -1,9 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions.stream - -import ai.dragonfly.math.* -import ai.dragonfly.math.stats.probability.distributions - - -object LogNormalDemo { - val demo = OnlineProbDistDemo[Double, distributions.LogNormal, LogNormal]("Streaming LogNormal", distributions.LogNormal(69, 21), LogNormal(), 1000) -} \ No newline at end of file diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/PERTDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/PERTDemo.scala deleted file mode 100644 index 03f9621..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/PERTDemo.scala +++ /dev/null @@ -1,23 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions.stream - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.* -import ai.dragonfly.math.stats.probability.distributions -import ai.dragonfly.math.stats.probability.distributions.stream - - -object PERTDemo extends Demonstration { - val d:Demonstration = OnlineProbDistDemo[Double, distributions.PERT, stream.PERT]( - "Streaming PERT", - distributions.PERT(21, 42.0, 69.0), - /* I understand the superiority of stream.Beta over stream.PERT, but I have reasons! */ - try { new PERT } catch { case UseBetaDistributionInstead(pert) => pert }, - 1000 - ) - override def demo(): Unit = { - println(stream.PERT.doNotUse) - d.demo() - } - - override def name: String = "stream.PERT" -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/PoissonDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/PoissonDemo.scala deleted file mode 100644 index d3b4703..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/PoissonDemo.scala +++ /dev/null @@ -1,11 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions.stream - -import ai.dragonfly.math.stats.probability.distributions -import ai.dragonfly.math.* - -import scala.language.postfixOps -import scala.language.implicitConversions - -object PoissonDemo { - val demo = OnlineProbDistDemo[Long, distributions.Poisson, Poisson]("Streaming Poisson", distributions.Poisson(69), Poisson(), 10000) -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/StreamingVectorStatsDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/StreamingVectorStatsDemo.scala deleted file mode 100644 index 2bb50de..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/stream/StreamingVectorStatsDemo.scala +++ /dev/null @@ -1,22 +0,0 @@ -package ai.dragonfly.math.stats.probability.distributions.stream - -import ai.dragonfly.math.Random.* -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.vector.* -import Vec.* -import narr.* - -/** - * Created by clifton on 1/10/17. - */ - - -object StreamingVectorStatsDemo extends Demonstration { - override def demo():Unit = { - val svs:StreamingVectorStats[4] = new StreamingVectorStats[4] - for (i <- 0 until 10000) svs(defaultRandom.nextVec[4](1000)) - println(svs) - } - - override def name: String = "StreamingVectorStats" -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/unicode/UnicodeDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/unicode/UnicodeDemo.scala deleted file mode 100644 index e382da7..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/unicode/UnicodeDemo.scala +++ /dev/null @@ -1,62 +0,0 @@ -package ai.dragonfly.math.unicode - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.unicode.* - -object UnicodeDemo extends Demonstration { - import Console.* - override def demo():Unit = { - - println(s"Write ${GREEN}Byte$RESET in superscript and subscript:") - println(s"\texalt(Byte.MinValue) => exalt(${Byte.MinValue}) => ${exalt(Byte.MinValue)}") - println(s"\texalt(Byte.MaxValue) => exalt(${Byte.MaxValue}) => ${exalt(Byte.MaxValue)}") - - println(s"\tabase(Byte.MinValue) => abase(${Byte.MinValue}) => ${abase(Byte.MinValue)}") - println(s"\tabase(Byte.MaxValue) => abase(${Byte.MaxValue}) => ${abase(Byte.MaxValue)}") - - - println(s"Write ${GREEN}Short$RESET in superscript and subscript:") - println(s"\texalt(Short.MinValue) => exalt(${Short.MinValue}) => ${exalt(Short.MinValue)}") - println(s"\texalt(Short.MaxValue) => exalt(${Short.MaxValue}) => ${exalt(Short.MaxValue)}") - - println(s"\tabase(Short.MinValue) => abase(${Short.MinValue}) => ${abase(Short.MinValue)}") - println(s"\tabase(Short.MaxValue) => abase(${Short.MaxValue}) => ${abase(Short.MaxValue)}") - - - println(s"Write ${GREEN}Int$RESET in superscript and subscript:") - println(s"\texalt(Int.MinValue) => exalt(${Int.MinValue}) => ${exalt(Int.MinValue)}") - println(s"\texalt(Int.MaxValue) => exalt(${Int.MaxValue}) => ${exalt(Int.MaxValue)}") - - println(s"\tabase(Int.MinValue) => abase(${Int.MinValue}) => ${abase(Int.MinValue)}") - println(s"\tabase(Int.MaxValue) => abase(${Int.MaxValue}) => ${abase(Int.MaxValue)}") - - - println(s"Write ${GREEN}Long$RESET in superscript and subscript:") - println(s"\texalt(Long.MinValue) => exalt(${Long.MinValue}) => ${exalt(Long.MinValue)}") - println(s"\texalt(Long.MaxValue) => exalt(${Long.MaxValue}) => ${exalt(Long.MaxValue)}") - - println(s"\tabase(Long.MinValue) => abase(${Long.MinValue}) => ${abase(Long.MinValue)}") - println(s"\tabase(Long.MaxValue) => abase(${Long.MaxValue}) => ${abase(Long.MaxValue)}") - - - println(s"Write ${GREEN}Float$RESET in superscript and subscript:") - println(s"\texalt(Float.MinValue) => exalt(${Float.MinValue}) => ${exalt(Float.MinValue)}") - println(s"\texalt(Float.MinPositiveValue) => exalt(${Float.MinPositiveValue}) => ${exalt(Float.MinPositiveValue)}") - println(s"\texalt(Float.MaxValue) => exalt(${Float.MaxValue}) => ${exalt(Float.MaxValue)}") - - println(s"\tabase(Float.MinValue) => abase(${Float.MinValue}) => ${abase(Float.MinValue)}") - println(s"\tabase(Float.MinPositiveValue) => abase(${Float.MinPositiveValue}) => ${abase(Float.MinPositiveValue)}") - println(s"\tabase(Float.MaxValue) => abase(${Float.MaxValue}) => ${abase(Float.MaxValue)}") - - - println(s"Write ${GREEN}Double$RESET in superscript and subscript:") - println(s"\texalt(Double.MinValue) => exalt(${Double.MinValue}) => ${exalt(Double.MinValue)}") - println(s"\texalt(Double.MinPositiveValue) => exalt(${Double.MinPositiveValue}) => ${exalt(Double.MinPositiveValue)}") - println(s"\texalt(Double.MaxValue) => exalt(${Double.MaxValue}) => ${exalt(Double.MaxValue)}") - - println(s"\tabase(Double.MinValue) => abase(${Double.MinValue}) => ${abase(Double.MinValue)}") - println(s"\tabase(Double.MinPositiveValue) => abase(${Double.MinPositiveValue}) => ${abase(Double.MinPositiveValue)}") - println(s"\tabase(Double.MaxValue) => abase(${Double.MaxValue}) => ${abase(Double.MaxValue)}") - } - override def name:String = "Unicode" -} \ No newline at end of file diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/vector/MixedVecDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/vector/MixedVecDemo.scala deleted file mode 100644 index 1a6ee5c..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/vector/MixedVecDemo.scala +++ /dev/null @@ -1,44 +0,0 @@ -package ai.dragonfly.math.vector - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.* -import ai.dragonfly.math.Random.* -import ai.dragonfly.math.vector.Vec.* -import narr.* - -import scala.language.postfixOps - -object MixedVecDemo extends Demonstration { - - val r = defaultRandom - - override def demo():Unit = { - val v2:Vec[2] = r.nextVec[2]() - val v3:Vec[3] = r.nextVec[3]() - val v4:Vec[4] = r.nextVec[4]() - - println(s"${v2.show}.x : ${v2.x}") - println(s"${v3.show}.x : ${v3.x}") - println(s"${v4.show}.x : ${v4.x}") - - println(s"${v2.show}.y : ${v2.y}") - println(s"${v3.show}.y : ${v3.y}") - println(s"${v4.show}.y : ${v4.y}") - - println(s"${v2.show}.z : Compiler Error!") - println(s"${v3.show}.z : ${v3.z}") - println(s"${v4.show}.z : ${v4.z}") - - println(s"${v2.show}.w : Compiler Error!") - println(s"${v3.show}.w : Compiler Error!") - println(s"${v4.show}.w : ${v4.w}") - - val t: ( - Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double, Double - ) = (1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0) - println(s"${Vec.fromTuple(t).show}") - - } - - override def name: String = "MixedVecDemo" -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec2Demo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec2Demo.scala deleted file mode 100644 index 8560f8c..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec2Demo.scala +++ /dev/null @@ -1,37 +0,0 @@ -package ai.dragonfly.math.vector - -import Console.* - -import narr.* - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.* -import Constant.π - -import Vec.* -/** - * Created by clifton on 1/10/17. - */ - -object Vec2Demo extends Demonstration { - - override def demo():Unit = { - - val radians:NArray[Double] = NArray[Double](π/8, π/7, π/6, π/5, π/4, π/3, π/2, π) - - var di:Int = 0 - while (di < radians.length) { - val vr = Vec[2](1, 0) - val v = vr.copy - val theta = radians(di) - vr.rotate(theta) - println(s"${v.show}.rotate($GREEN$theta$RESET) -> ${vr.show}") - println(s"${v.show}.angleFrom(${vr.show}) -> $GREEN${v.angleFrom(vr)}$RESET\n") - di = di + 1 - } - - } - - override def name: String = "Vec[2]" - -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec3Demo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec3Demo.scala deleted file mode 100644 index 9529850..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec3Demo.scala +++ /dev/null @@ -1,38 +0,0 @@ -package ai.dragonfly.math.vector - -import ai.dragonfly.democrossy.Demonstration -import narr.* -import Vec.* - -/** - * Created by clifton on 1/10/17. - */ - -object Vec3Demo extends Demonstration { - - override def demo():Unit = { - val i = Vec[3](1, 0, 0) - val j = Vec[3](0, 1, 0) - val k = Vec[3](0, 0, 1) - - println(s"i3 X j3 -> ${(i ⨯ j).show}\n") - println(s"j3 X i3 -> ${(j ⨯ i).show}\n") - - println(s"i3 X k3 -> ${(i ⨯ k).show}\n") - println(s"k3 X i3 -> ${(k ⨯ i).show}\n") - - println(s"j3 X k3 -> ${(j ⨯ k).show}\n") - println(s"k3 X j3 -> ${(k ⨯ j).show}\n") - - println(s"i3 dot j3 -> ${i dot j}\n") - println(s"j3 dot i3 -> ${j dot i}\n") - - println(s"i3 dot k3 -> ${i dot k}\n") - println(s"k3 dot i3 -> ${k dot i}\n") - - println(s"j3 dot k3 -> ${j dot k}\n") - println(s"k3 dot j3 -> ${k dot j}\n") - } - - override def name: String = "Vec[3]" -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec4Demo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec4Demo.scala deleted file mode 100644 index dfece0a..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/vector/Vec4Demo.scala +++ /dev/null @@ -1,65 +0,0 @@ -package ai.dragonfly.math.vector - -import ai.dragonfly.math.* -import ai.dragonfly.democrossy.Demonstration - -import scala.language.postfixOps -import ai.dragonfly.math.squareInPlace -import Vec.* -import narr.* - -object Vec4Demo extends Demonstration { - - override def demo():Unit = { - val i = Vec[4](1, 0, 0, 0) - val j = Vec[4](0, 1, 0, 0) - val k = Vec[4](0, 0, 1, 0) - val l = Vec[4](0, 0, 0, 1) - - println(s"i4 dot j4 -> ${i dot j}\n") - println(s"j4 dot i4 -> ${j dot i}\n") - - println(s"i4 dot k4 -> ${i dot k}\n") - println(s"k4 dot i4 -> ${k dot i}\n") - - println(s"j4 dot k4 -> ${j dot k}\n") - println(s"k4 dot j4 -> ${k dot j}\n") - - println(s"i4 dot l4 -> ${i dot l}\n") - println(s"l4 dot i4 -> ${l dot i}\n") - - println(s"j4 dot l4 -> ${j dot l}\n") - println(s"l4 dot j4 -> ${l dot j}\n") - - println(s"k4 dot l4 -> ${k dot l}\n") - println(s"l4 dot k4 -> ${l dot k}\n") - - println(s"Unary Minus: -i = ${ (-i).show }\n") - - val v0:Vec[4] = Vec[4](0.5, 0.0, 1.0, 0.75) - println("val v₀ = Vec[4](0.5, 0.0, 1.0, 0.75)") - print("\n\tv₀:"); println(v0.show) - print("\nv₀.scale(3):\n\t"); print(v0.scale(3.0).show); println(" /* in place operation */") - val v1:Vec[4] = Vec[4](5, 6, 7, 8) - println("\nv₁ = "); println(v1.show) - print("\nv₁.add(v1) = "); print(v1.add(v1).show); println(" /* in place operation */") - val v2:Vec[4] = Vec[4](0.25, 0.25, 0.25, 0.25) - print("\nv₂ = "); println(v2.show) - print("\nv₂.dot(v₀) = "); println(v2.dot(v0)) - print("\nv₂ = "); println(v2.show) - print("\nv₂.subtract(v₀) = "); print(v2.subtract(v0).show); println(" /* in place operation */") - - for (i <- 0 until 10) { - val vT:Vec[4] = Vec.random[4](10.0) - print("\nval vT = Vec[4].random() = "); print(vT.show) - print("\n\t∥"); print(vT.show); print("∥ = "); println(vT.norm) - print("\n\tvT.normalize = "); println(vT.normalize().show); println(" /* in place operation */") - print("\n\t∥"); print(vT.show); print("∥ = "); println(vT.norm) - print("\n\t"); print(vT.show); print(" * 2.0 = "); println((vT * 2).show); println(" /* Copy operation */") - print("\n\tvT remains unnaffected: "); println(vT.show) - } - println("\n") - } - - override def name: String = "Vec[4]" -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/vector/VecNDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/vector/VecNDemo.scala deleted file mode 100644 index f7cdc23..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/vector/VecNDemo.scala +++ /dev/null @@ -1,70 +0,0 @@ -package ai.dragonfly.math.vector - -import ai.dragonfly.democrossy.Demonstration -import ai.dragonfly.math.* -import ai.dragonfly.math.vector.Vec.* -import ai.dragonfly.math.Random -import Random.* - -import narr.* - -import scala.language.postfixOps - -object VecNDemo extends Demonstration { - - val r = defaultRandom - override def demo():Unit = { - val v42a: Vec[42] = r.nextVec[42]() - val v42b: Vec[42] = r.nextVec[42]() - print("Random Vec[42] : ") - println(v42a.render()) - print("In CSV format v42a.csv() : ") - println(v42a.csv()) - print("In TSV format v42a.tsv() : ") - println(v42a.tsv()) - print("(v42a - v42b).render() : ") - println((v42a - v42b).render()) - println("\n") - - val v42c = Vec[42]( - 0, 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, 13, - 14, 15, 16, 17, 18, 19, 20, - 21, 22, 23, 24, 25, 26, 27, - 28, 29, 30, 31, 32, 33, 34, - 35, 36, 37, 38, 39, 40, 41 - ) - println(v42c.render()) - - - // Vectors with lengths defined at runtime. - // method 1: - val l0:Int = r.nextInt (100) - type N0 = l0.type - val rtv0: Vec[N0] = r.nextVec[N0]() - println(rtv0.render()) - - // method 2: - val l1: Int = r.nextInt(100) - val rtv1: Vec[l1.type] = r.nextVec[l1.type]() - println(rtv1.render()) - -/* - // Unfortunately this doesn't work and throws a compiler error even though l1 == l2 is true: - val l1: Int = r.nextInt(100) - val l2: Int = 0 + l1 - val rtv1: Vec[l1.type] = r.nextVec[l1.type]() - val rtv2: Vec[l2.type] = r.nextVec[l2.type]() - println((rtv1 + rtv2).render()) - - // [error] 57 | println((rtv1 + rtv2).render()) - // [error] | ^^^^ - // [error] | Found: (rtv2 : ai.dragonfly.math.vector.Vec[(l2 : Int)]) - // [error] | Required: ai.dragonfly.math.vector.Vec[(l1 : Int)] - // However you can do this: - println((rtv1 + rtv2.asInstanceOf[Vec[l1.type]]).render()) -*/ - } - - override def name: String = "Vec[N]" -} diff --git a/demo/shared/src/main/scala/ai/dragonfly/math/vector/WeightedVecDemo.scala b/demo/shared/src/main/scala/ai/dragonfly/math/vector/WeightedVecDemo.scala deleted file mode 100644 index 56dfb4e..0000000 --- a/demo/shared/src/main/scala/ai/dragonfly/math/vector/WeightedVecDemo.scala +++ /dev/null @@ -1,21 +0,0 @@ -package ai.dragonfly.math.vector - -import ai.dragonfly.democrossy.Demonstration - -import Vec.* - -object WeightedVecDemo extends Demonstration { - - override def demo():Unit = { - val wv0 = WeightedVec[3](Vec[3](1.1, 2.5, 0.1), 0.5) - println(s"\tWeightedVec: $wv0\n") - println(s"\tWeightedVec.weighted: ${wv0.weighted}\n") - println(s"\tWeightedVec.weight: ${wv0.weight}\n") - println(s"\tWeightedVec.addWeight(0.25): ${wv0.addWeight(0.25)}\n") - println(s"\tWeightedVec: $wv0\n") - println(s"\tWeightedVec.weighted: ${wv0.weighted}\n") - println(s"\tWeightedVec.weight: ${wv0.weight}\n") - } - - override def name: String = "WeightedVec[3]" -} diff --git a/old docs/demo.md b/old docs/demo.md deleted file mode 100644 index 9e32930..0000000 --- a/old docs/demo.md +++ /dev/null @@ -1,7 +0,0 @@ -# Vector -## About this Demo: -   The Console Output below exercises various features of the vector library:
- -
- - diff --git a/slash/shared/src/main/scala/ai/dragonfly/math/interval/Interval.scala b/slash/shared/src/main/scala/ai/dragonfly/math/interval/Interval.scala index be39566..d6488bb 100644 --- a/slash/shared/src/main/scala/ai/dragonfly/math/interval/Interval.scala +++ b/slash/shared/src/main/scala/ai/dragonfly/math/interval/Interval.scala @@ -18,6 +18,8 @@ package ai.dragonfly.math.interval import ai.dragonfly.math.stats.probability.distributions.Sampleable +import scala.reflect.ClassTag + object Interval { import ai.dragonfly.math.Random.* private inline def between(i:Interval[Int], r:scala.util.Random) = r.between(i.min, i.MAX) @@ -83,14 +85,14 @@ object Interval { val LEFT_CLOSED:Int = 0x1 << 1 val CLOSED:Int = RIGHT_CLOSED | LEFT_CLOSED - def `[]`[DOMAIN](min:DOMAIN, MAX:DOMAIN)(using `#`: Numeric[DOMAIN]):Interval[DOMAIN] = new Interval[DOMAIN](CLOSED, min, MAX) - def `(]`[DOMAIN](min:DOMAIN, MAX:DOMAIN)(using `#`: Numeric[DOMAIN]):Interval[DOMAIN] = new Interval[DOMAIN](RIGHT_CLOSED, min, MAX) - def `[)`[DOMAIN](min:DOMAIN, MAX:DOMAIN)(using `#`: Numeric[DOMAIN]):Interval[DOMAIN] = new Interval[DOMAIN](LEFT_CLOSED, min, MAX) - def `()`[DOMAIN](min:DOMAIN, MAX:DOMAIN)(using `#`: Numeric[DOMAIN]):Interval[DOMAIN] = new Interval[DOMAIN](OPEN, min, MAX) + def `[]`[DOMAIN:ClassTag](min:DOMAIN, MAX:DOMAIN)(using `#`: Numeric[DOMAIN]):Interval[DOMAIN] = new Interval[DOMAIN](CLOSED, min, MAX) + def `(]`[DOMAIN:ClassTag](min:DOMAIN, MAX:DOMAIN)(using `#`: Numeric[DOMAIN]):Interval[DOMAIN] = new Interval[DOMAIN](RIGHT_CLOSED, min, MAX) + def `[)`[DOMAIN:ClassTag](min:DOMAIN, MAX:DOMAIN)(using `#`: Numeric[DOMAIN]):Interval[DOMAIN] = new Interval[DOMAIN](LEFT_CLOSED, min, MAX) + def `()`[DOMAIN:ClassTag](min:DOMAIN, MAX:DOMAIN)(using `#`: Numeric[DOMAIN]):Interval[DOMAIN] = new Interval[DOMAIN](OPEN, min, MAX) } -case class Interval[DOMAIN](code:Int, min:DOMAIN, MAX:DOMAIN)(using `#`: Numeric[DOMAIN]) extends Sampleable[DOMAIN] { +case class Interval[DOMAIN:ClassTag](code:Int, min:DOMAIN, MAX:DOMAIN)(using `#`: Numeric[DOMAIN]) extends Sampleable[DOMAIN] { import `#`.* lazy val norm:DOMAIN = `#`.minus(MAX, min) diff --git a/slash/shared/src/main/scala/ai/dragonfly/math/stats/UnivariateHistogram.scala b/slash/shared/src/main/scala/ai/dragonfly/math/stats/UnivariateHistogram.scala index 0ca5440..219c160 100644 --- a/slash/shared/src/main/scala/ai/dragonfly/math/stats/UnivariateHistogram.scala +++ b/slash/shared/src/main/scala/ai/dragonfly/math/stats/UnivariateHistogram.scala @@ -240,7 +240,7 @@ class DenseHistogramOfContinuousDistribution(override val size: Int, override va //} object UnivariateGenerativeModel { - def apply[T](hist: UnivariateHistogram[T]): UnivariateGenerativeModel[T] = { + def apply[T:ClassTag](hist: UnivariateHistogram[T]): UnivariateGenerativeModel[T] = { var cumulative: immutable.TreeMap[Double, Int] = immutable.TreeMap[Double, Int]() var total:Double = 0.0 @@ -261,7 +261,7 @@ object UnivariateGenerativeModel { } } -class UnivariateGenerativeModel[T]( +class UnivariateGenerativeModel[T:ClassTag]( private val hist:UnivariateHistogram[T], private val cumulative: immutable.TreeMap[Double, Int] ) extends Sampleable[T] { diff --git a/slash/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/Poisson.scala b/slash/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/Poisson.scala index d54a186..4ab7c6b 100644 --- a/slash/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/Poisson.scala +++ b/slash/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/Poisson.scala @@ -19,7 +19,6 @@ package ai.dragonfly.math.stats.probability.distributions import ai.dragonfly.math.* import stats.* import interval.* -import ai.dragonfly.math.vector.Vec object Poisson { val domain:Domain[Long] = Domain.ℕ_Long @@ -55,14 +54,6 @@ case class Poisson(λ:Double) extends ParametricProbabilityDistribution[Long] { k - 1 } - inline def sample[N<:Int](n: Int)(inline r:scala.util.Random): Vec[N] = { - val v = Vec.zeros[n.type] - for (i <- 0 until n) { - v(i) = random(r).toDouble - } - v.asInstanceOf[Vec[N]] - } - //Poissonλ = μ = σ² = $λ, σ = √λ = $σ, n = $s0) override def toString: String = s"Poisson(λ = μ = σ² = $λ, √λ = $σ)" } diff --git a/slash/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/Sampleable.scala b/slash/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/Sampleable.scala index cc6edeb..3135518 100644 --- a/slash/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/Sampleable.scala +++ b/slash/shared/src/main/scala/ai/dragonfly/math/stats/probability/distributions/Sampleable.scala @@ -16,7 +16,14 @@ package ai.dragonfly.math.stats.probability.distributions +import narr.* -trait Sampleable[DOMAIN] { +import scala.reflect.ClassTag + + +trait Sampleable[DOMAIN:ClassTag] { def random(r:scala.util.Random = ai.dragonfly.math.Random.defaultRandom): DOMAIN + + inline def sample(n: Int, r:scala.util.Random = ai.dragonfly.math.Random.defaultRandom): NArray[DOMAIN] = NArray.tabulate(n)(_ => random(r)) + } diff --git a/tests/shared/src/test/scala/Instantiate.scala b/tests/shared/src/test/scala/Instantiate.scala index 3df075c..ff5d178 100644 --- a/tests/shared/src/test/scala/Instantiate.scala +++ b/tests/shared/src/test/scala/Instantiate.scala @@ -15,7 +15,7 @@ */ import ai.dragonfly.math.vector.Vec -import narr.NArray +import narr.* class Instantiate extends munit.FunSuite: diff --git a/tests/shared/src/test/scala/SimpleStats.scala b/tests/shared/src/test/scala/SimpleStats.scala index bc38708..6f89221 100644 --- a/tests/shared/src/test/scala/SimpleStats.scala +++ b/tests/shared/src/test/scala/SimpleStats.scala @@ -15,7 +15,7 @@ */ import ai.dragonfly.math.vector.Vec -import narr.NArray +import narr.* class SimpleStats extends munit.FunSuite: diff --git a/tests/shared/src/test/scala/VectorSpaces.scala b/tests/shared/src/test/scala/VectorSpaces.scala index 904b4a3..e91cc0e 100644 --- a/tests/shared/src/test/scala/VectorSpaces.scala +++ b/tests/shared/src/test/scala/VectorSpaces.scala @@ -14,6 +14,7 @@ * limitations under the License. */ +import narr.* import ai.dragonfly.math.* import ai.dragonfly.math.vector.* import ai.dragonfly.math.Constant.π diff --git a/tests/shared/src/test/scala/poisson.scala b/tests/shared/src/test/scala/poisson.scala index 8d61de2..97df155 100644 --- a/tests/shared/src/test/scala/poisson.scala +++ b/tests/shared/src/test/scala/poisson.scala @@ -15,6 +15,8 @@ */ import ai.dragonfly.math.stats.probability.distributions.Poisson +import narr.* +import ai.dragonfly.math.vector.* class PoissonTests extends munit.FunSuite: test("Po[1]") { @@ -29,7 +31,7 @@ class PoissonTests extends munit.FunSuite: } - test("dispersion"){ + test("dispersion") { val mean = 10.0 @@ -39,7 +41,8 @@ class PoissonTests extends munit.FunSuite: assertEqualsDouble(dist1.`σ²`, mean, 0.00001) val rand = ai.dragonfly.math.Random.defaultRandom - val sample = dist1.sample[100000](100000)(rand) + val poissonSample:NArray[Long] = dist1.sample(100000, rand) + val sample:Vec[100000] = Vec.tabulate[100000]( (i:Int) => poissonSample(i).toDouble ) assertEqualsDouble( sample.mean, mean, 0.2) assertEqualsDouble( sample.variance, mean, 0.2)