/*
 * Copyright 2018-2024 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.series

import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.operations.Float64Field.pow
import space.kscience.kmath.operations.fold
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,
)


/**
 * Calculates the Z-statistic and the p-value for the Lo and MacKinlay's Variance Ratio test (1987)
 * under Homoscedastic or Heteroscedstic assumptions
 * with two-sided p-value test
 * 	https://ssrn.com/abstract=346975
 *
 * 	@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" }
    val sum = { x: Double, y: Double -> x + y }


    val mean = series.fold(0.0, sum) / series.size
    val demeanedSquares = series.map { (it - mean).pow(2) }
    val variance = demeanedSquares.fold(0.0, sum)
    if (variance == 0.0) return VarianceRatioTestResult()


    var seriesAgg = series
    for (i in 1..<shift) {
        seriesAgg = seriesAgg.zip(series.moveTo(i)) { v1, v2 -> v1 + v2 }
    }

    val demeanedSquaresAgg = seriesAgg.map { (it - shift * mean).pow(2) }
    val varianceAgg = demeanedSquaresAgg.fold(0.0, sum)

    val varianceRatio =
        varianceAgg * (series.size.toDouble() - 1) / variance / (series.size.toDouble() - shift.toDouble() + 1) / (1 - shift.toDouble() / series.size.toDouble()) / shift.toDouble()


    // calculating asymptotic variance
    val phi = if (homoscedastic) {  // under homoscedastic null hypothesis
        2 * (2 * shift - 1.0) * (shift - 1.0) / (3 * shift * series.size)
    } else { // under heteroscedastic null hypothesis
        var accumulator = 0.0
        for (j in 1..<shift) {
            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)
        }
        accumulator
    }

    val zScore = (varianceRatio - 1) / phi.pow(0.5)
    val pValue = 2 * (1 - NormalDistribution.zSNormalCDF(zScore.absoluteValue))
    return VarianceRatioTestResult(varianceRatio, zScore, pValue)
}





/*
 * Copyright 2018-2024 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.series

import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.operations.Float64Field.pow
import space.kscience.kmath.operations.fold
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,
)


/**
 * Calculates the Z-statistic and the p-value for the Lo and MacKinlay's Variance Ratio test (1987)
 * under Homoscedastic or Heteroscedstic assumptions
 * with two-sided p-value test
 * 	https://ssrn.com/abstract=346975
 *
 * 	@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" }
    val sum = { x: Double, y: Double -> x + y }


    val mean = series.fold(0.0, sum) / series.size
    val demeanedSquares = series.map { (it - mean).pow(2) }
    val variance = demeanedSquares.fold(0.0, sum)
    if (variance == 0.0) return VarianceRatioTestResult()


    var seriesAgg = series
    for (i in 1..<shift) {
        seriesAgg = seriesAgg.zip(series.moveTo(i)) { v1, v2 -> v1 + v2 }
    }

    val demeanedSquaresAgg = seriesAgg.map { (it - shift * mean).pow(2) }
    val varianceAgg = demeanedSquaresAgg.fold(0.0, sum)

    val varianceRatio =
        varianceAgg * (series.size.toDouble() - 1) / variance / (series.size.toDouble() - shift.toDouble() + 1) / (1 - shift.toDouble() / series.size.toDouble()) / shift.toDouble()


    // calculating asymptotic variance
    val phi = if (homoscedastic) {  // under homoscedastic null hypothesis
        2 * (2 * shift - 1.0) * (shift - 1.0) / (3 * shift * series.size)
    } else { // under heteroscedastic null hypothesis
        var accumulator = 0.0
        for (j in 1..<shift) {
            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)
        }
        accumulator
    }

    val zScore = (varianceRatio - 1) / phi.pow(0.5)
    val pValue = 2 * (1 - NormalDistribution.zSNormalCDF(zScore.absoluteValue))
    return VarianceRatioTestResult(varianceRatio, zScore, pValue)
}




