Variance test post-merge cleanup

.space.kts

@@ -1,3 +1,3 @@
-job("Build") {
-    gradlew("openjdk:11", "build")
+job("Build and run tests") {
+    gradlew("amazoncorretto:17-alpine", "build")
 }

build.gradle.kts

@@ -15,7 +15,7 @@ allprojects {
     }
 
     group = "space.kscience"
-    version = "0.3.1-dev-RC"
+    version = "0.3.1"
 }
 
 subprojects {

kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/_generated.kt

@@ -19,9 +19,9 @@ import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC
 import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC
 import org.ejml.sparse.csc.factory.LinearSolverFactory_DSCC
 import org.ejml.sparse.csc.factory.LinearSolverFactory_FSCC
+import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.linear.*
 import space.kscience.kmath.linear.Matrix
-import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.nd.StructureFeature
 import space.kscience.kmath.operations.DoubleField
 import space.kscience.kmath.operations.FloatField

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/NormalDistribution.kt

@@ -6,6 +6,7 @@
 package space.kscience.kmath.distributions
 
 import space.kscience.kmath.chains.Chain
+import space.kscience.kmath.operations.DoubleField.pow
 import space.kscience.kmath.random.RandomGenerator
 import space.kscience.kmath.samplers.GaussianSampler
 import space.kscience.kmath.samplers.InternalErf
@@ -34,8 +35,23 @@ public class NormalDistribution(public val sampler: GaussianSampler) : Distribut
         }
     }
 
-    private companion object {
+    public companion object {
         private val SQRT2 = sqrt(2.0)
+
+        /**
+         * Zelen & Severo approximation for the standard normal CDF.
+         * The error upper boundary by 7.5 * 10e-8.
+         */
+        public fun zSNormalCDF(x: Double): Double {
+            val t = 1 / (1 + 0.2316419 * abs(x))
+            val sum = 0.319381530 * t -
+                    0.356563782 * t.pow(2) +
+                    1.781477937 * t.pow(3) -
+                    1.821255978 * t.pow(4) +
+                    1.330274429 * t.pow(5)
+            val temp = sum * exp(-abs(x).pow(2) / 2) / (2 * PI).pow(0.5)
+            return if (x >= 0) 1 - temp else temp
+        }
     }
 }
 

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/distributions/zSNormalCdf.kt

@@ -1,24 +0,0 @@
-/*
- * Copyright 2018-2023 KMath contributors.
- * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
- */
-
-package space.kscience.kmath.distributions
-
-import space.kscience.kmath.operations.DoubleField.pow
-import kotlin.math.PI
-import kotlin.math.absoluteValue
-import kotlin.math.exp
-
-
-/**
- * Zelen & Severo approximation for the standard normal CDF.
- * The error is bounded by 7.5 * 10e-8.
- * */
-public fun zSNormalCDF(x: Double): Double {
-
-    val t = 1 / (1 + 0.2316419 * x.absoluteValue)
-    val summ = 0.319381530*t - 0.356563782*t.pow(2) + 1.781477937*t.pow(3) - 1.821255978*t.pow(4) + 1.330274429*t.pow(5)
-    val temp = summ * exp(-x.absoluteValue.pow(2) / 2) / (2 * PI).pow(0.5)
-    return if (x >= 0)  1 - temp else temp
-}

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/series/VarianceRatioTest.kt

@@ -5,7 +5,7 @@
 
 package space.kscience.kmath.series
 
-import space.kscience.kmath.distributions.zSNormalCDF
+import space.kscience.kmath.distributions.NormalDistribution
 import space.kscience.kmath.operations.DoubleField.pow
 import space.kscience.kmath.operations.fold
 import kotlin.math.absoluteValue
@@ -14,8 +14,12 @@ import kotlin.math.absoluteValue
 /**
  * Container class for Variance Ratio Test result:
  * ratio itself, corresponding Z-score, also it's p-value
- * **/
-public data class VarianceRatioTestResult(val varianceRatio: Double=1.0, val zScore: Double=0.0, val pValue: Double=0.5)
+ */
+public data class VarianceRatioTestResult(
+    val varianceRatio: Double = 1.0,
+    val zScore: Double = 0.0,
+    val pValue: Double = 0.5,
+)
 
 
 /**
@@ -23,8 +27,14 @@ public data class VarianceRatioTestResult(val varianceRatio: Double=1.0, val zSc
  * under Homoscedastic or Heteroscedstic assumptions
  * with two-sided p-value test
  * 	https://ssrn.com/abstract=346975
- * **/
-public fun SeriesAlgebra<Double, *, *, *>.varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic: Boolean=true): VarianceRatioTestResult {
+ *
+ * 	@author https://github.com/mrFendel
+ */
+public fun SeriesAlgebra<Double, *, *, *>.varianceRatioTest(
+    series: Series<Double>,
+    shift: Int,
+    homoscedastic: Boolean = true,
+): VarianceRatioTestResult {
 
     require(shift > 1) { "Shift must be greater than one" }
     require(shift < series.size) { "Shift must be smaller than sample size" }
@@ -63,7 +73,7 @@ public fun SeriesAlgebra<Double, *, *, *>.varianceRatioTest(series: Series<Doubl
     }
 
     val zScore = (varianceRatio - 1) / phi.pow(0.5)
-    val pValue = 2*(1 - zSNormalCDF(zScore.absoluteValue))
+    val pValue = 2 * (1 - NormalDistribution.zSNormalCDF(zScore.absoluteValue))
     return VarianceRatioTestResult(varianceRatio, zScore, pValue)
 }