requirements, default parameters, new Test for varianceRatioTest

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/series/VarianceRatioTest.kt

@@ -9,11 +9,10 @@ import space.kscience.kmath.operations.DoubleField.pow
 import space.kscience.kmath.operations.algebra
 import space.kscience.kmath.operations.bufferAlgebra
 import space.kscience.kmath.operations.fold
-import space.kscience.kmath.structures.slice
 
 
 // TODO: add p-value with formula: 2*(1 - cdf(|zScore|))
-public data class VarianceRatioTestResult(val varianceRatio: Double, val zScore: Double)
+public data class VarianceRatioTestResult(val varianceRatio: Double=1.0, val zScore: Double=0.0)
     /**
      * Container class for Variance Ratio Test result:
      * ratio itself, corresponding Z-score, also it's p-value
@@ -27,12 +26,15 @@ public fun varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic:
      * 	https://ssrn.com/abstract=346975
      * **/
 
+    require(shift > 1) {"Shift must be greater than one"}
+    require(shift < series.size) {"Shift must be smaller than sample size"}
     val sum = { x: Double, y: Double -> x + y }
-    //TODO: catch if shift is too large
+
     with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
         val mean = series.fold(0.0, sum) / series.size
         val demeanedSquares = series.map { power(it - mean, 2) }
-        val variance = demeanedSquares.fold(0.0, sum) // TODO: catch if variance is zero
+        val variance = demeanedSquares.fold(0.0, sum)
+        if (variance == 0.0) return VarianceRatioTestResult()
 
 
         var seriesAgg = series
@@ -53,7 +55,7 @@ public fun varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic:
         } else { // under heteroscedastic null hypothesis
             var accumulator = 0.0
             for (j in 1..<shift) {
-                var temp = demeanedSquares
+                val temp = demeanedSquares
                 val delta = series.size * temp.zipWithShift(j) { v1, v2 -> v1 * v2 }.fold(0.0, sum) / variance.pow(2)
                 accumulator += delta * 4 * (shift - j).toDouble().pow(2) / shift.toDouble().pow(2)
             }

kmath-stat/src/commonTest/kotlin/space/kscience/kmath/series/TestVarianceRatioTest.kt

@@ -31,7 +31,7 @@ class TestVarianceRatioTest {
     fun volatileData() {
         with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
             val volatileData = series(10) { sin(PI * it + PI/2) + 1.0}
-            val resultHomo = varianceRatioTest(volatileData, 2, homoscedastic = true)
+            val resultHomo = varianceRatioTest(volatileData, 2)
             assertEquals(0.0, resultHomo.varianceRatio, 1e-6)
             // homoscedastic zScore
             assertEquals(-3.162277, resultHomo.zScore, 1e-6)
@@ -45,7 +45,7 @@ class TestVarianceRatioTest {
     fun negativeData() {
         with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
             val negativeData = series(10) { sin(it * 1.2)}
-            val resultHomo = varianceRatioTest(negativeData, 3, homoscedastic = true)
+            val resultHomo = varianceRatioTest(negativeData, 3)
             assertEquals(1.240031, resultHomo.varianceRatio, 1e-6)
             // homoscedastic zScore
             assertEquals(0.509183, resultHomo.zScore, 1e-6)
@@ -55,12 +55,13 @@ class TestVarianceRatioTest {
         }
     }
 
-    //TODO: add zero volatility Test, logReturns test, big shift Test
+    @Test
-//    @Test
+    fun zeroVolatility() {
-//    fun zeroVolatility() {
+        with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
-//        with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
+            val zeroVolData = series(10) { 0.0 }
-//            val zeroVolData = series(10) { 1.0 }
+            val result = varianceRatioTest(zeroVolData, 4)
-//            val result = varianceRatioTest(zeroVolData, 2, homoscedastic = true)
+            assertEquals(1.0, result.varianceRatio, 1e-6)
-//        }
+            assertEquals(0.0, result.zScore, 1e-6)
-//    }
+        }
+    }
 }