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fixed zip in SereiesAlgebra + tests for VarianceRatio

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This commit is contained in:
mrFendel 2023-04-14 06:36:20 +03:00
parent a91b43a52d
commit 5b95923bb9
3 changed files with 43 additions and 21 deletions
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2
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/series/SeriesAlgebra.kt
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@ -191,7 +191,7 @@ public open class SeriesAlgebra<T, out A : Ring<T>, out BA : BufferAlgebra<T, A>
crossinline operation: A.(left: T, right: T) -> T,
): Series<T> {
val newRange = offsetIndices.intersect(other.offsetIndices)
return seriesByOffset(startOffset = newRange.first, size = newRange.last - newRange.first) { offset ->
return seriesByOffset(startOffset = newRange.first, size = newRange.last + 1 - newRange.first) { offset ->
elementAlgebra.operation(
getByOffset(offset),
other.getByOffset(offset)

22
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/series/VarianceRatioTest.kt
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@ -25,17 +25,22 @@ public fun varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic:
val sum = { x: Double, y: Double -> x + y }
//TODO: catch if shift is too large
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
with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
for (i in -1..-shift + 1) { series.shiftOp(i) { v1, v2 -> v1 + v2 } }
val demeanedSquaresAgg = series.map { power(it - shift * mean, 2) }
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
var seriesAgg = series
for (i in 1..<shift) {
seriesAgg = seriesAgg.zip(series.moveTo(i)) { v1, v2 -> v1 + v2 }
}
val demeanedSquaresAgg = seriesAgg.map { power(it - shift * mean, 2) }
val varianceAgg = demeanedSquaresAgg.fold(0.0, sum)
val varianceRatio =
varianceAgg * (series.size - 1) / variance / (series.size - shift + 1) / (1 - shift / series.size)
varianceAgg * (series.size.toDouble() - 1) / variance / (series.size.toDouble() - shift.toDouble() + 1) / (1 - shift.toDouble()/series.size.toDouble()) / shift.toDouble()
// calculating asymptotic variance
@ -44,8 +49,9 @@ public fun varianceRatioTest(series: Series<Double>, shift: Int, homoscedastic:
phi = 2 * (2 * shift - 1.0) * (shift - 1.0) / (3 * shift * series.size)
} else { // under homoscedastic null hypothesis
phi = 0.0
var shiftedProd = demeanedSquares
for (j in 1..<shift) {
val shiftedProd = demeanedSquares.shiftOp(j) { v1, v2 -> v1 * v2 }
shiftedProd = shiftedProd.zip(demeanedSquares.moveTo(j)) { v1, v2 -> v1 * v2 }
val delta = series.size * shiftedProd.fold(0.0, sum) / variance.pow(2)
phi += delta * 4 * (shift - j) * (shift - j) / shift / shift // TODO: refactor with square
}

40
kmath-stat/src/commonTest/kotlin/space/kscience/kmath/series/TestVarianceRatioTest.kt
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@ -13,6 +13,21 @@ import kotlin.test.assertEquals
class TestVarianceRatioTest {
// TODO: refactor Heteroscedastic zScore
@Test
fun monotonicData() {
with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
val monotonicData = series(10) { it * 1.0 }
val resultHomo = varianceRatioTest(monotonicData, 2, homoscedastic = true)
assertEquals(1.818181, resultHomo.varianceRatio, 1e-6)
// homoscedastic zScore
assertEquals(2.587318, resultHomo.zScore, 1e-6)
// val resultHetero = varianceRatioTest(monotonicData, 2, homoscedastic = false)
// // heteroscedastic zScore
// assertEquals(3.253248, resultHetero.zScore, 1e-6)
}
}
@Test
fun volatileData() {
with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
@ -21,31 +36,32 @@ class TestVarianceRatioTest {
assertEquals(0.0, resultHomo.varianceRatio, 1e-6)
// homoscedastic zScore
assertEquals(-3.162277, resultHomo.zScore, 1e-6)
val resultHetero = varianceRatioTest(volatileData, 2, homoscedastic = false)
// heteroscedastic zScore
assertEquals(-3.535533, resultHetero.zScore, 1e-6)
// val resultHetero = varianceRatioTest(volatileData, 2, homoscedastic = false)
// // heteroscedastic zScore
// assertEquals(-3.535533, resultHetero.zScore, 1e-6)
}
}
@Test
fun negativeData() {
with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
val volatileData = series(10) { sin(PI * it)}
val resultHomo = varianceRatioTest(volatileData, 2, homoscedastic = true)
assertEquals(1.142857, resultHomo.varianceRatio, 1e-6)
val negativeData = series(10) { sin(it * 1.2)}
val resultHomo = varianceRatioTest(negativeData, 3, homoscedastic = true)
assertEquals(1.240031, resultHomo.varianceRatio, 1e-6)
// homoscedastic zScore
assertEquals(0.451753, resultHomo.zScore, 1e-6)
val resultHetero = varianceRatioTest(volatileData, 2, homoscedastic = false)
// heteroscedastic zScore
assertEquals(2.462591, resultHetero.zScore, 1e-6)
assertEquals(0.509183, resultHomo.zScore, 1e-6)
// val resultHetero = varianceRatioTest(negativeData, 3, homoscedastic = false)
// // heteroscedastic zScore
// assertEquals(0.661798, resultHetero.zScore, 1e-6)
}
}
//TODO: add zero volatility Test, logReturns test, big shift Test
// @Test
// fun zeroVolatility() {
// with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
// val volatileData = series(10) { 1.0 }
// val result = varianceRatioTest(volatileData, 2, homoscedastic = true)
// val zeroVolData = series(10) { 1.0 }
// val result = varianceRatioTest(zeroVolData, 2, homoscedastic = true)
// }
// }
}