first DTW method realization

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E––jenY-Poltavchiny 2023-04-22 09:52:14 +03:00
parent 134f265700
commit b8809d1c21

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/*
* 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.series
import space.kscience.kmath.structures.DoubleBuffer
import kotlin.math.abs
public const val LEFT_OFFSET : Int = -1
public const val BOTTOM_OFFSET : Int = 1
public const val DIAGONAL_OFFSET : Int = 0
// TODO: Change container for alignMatrix to kmath special ND structure
public data class DynamicTimeWarpingData(val totalCost : Double = 0.0,
val alignMatrix : Array<BooleanArray> = Array(0) {BooleanArray(0)}) {
override fun equals(other: Any?): Boolean {
if (this === other) return true
if (other == null || this::class != other::class) return false
other as DynamicTimeWarpingData
if (totalCost != other.totalCost) return false
if (!alignMatrix.contentDeepEquals(other.alignMatrix)) return false
return true
}
override fun hashCode(): Int {
var result = totalCost.hashCode()
result = 31 * result + alignMatrix.contentDeepHashCode()
return result
}
}
/**
* costMatrix calculates special matrix of costs alignment for two series.
* Formula: costMatrix[i, j] = euqlidNorm(series1(i), series2(j)) + min(costMatrix[i - 1, j],
* costMatrix[i, j - 1],
* costMatrix[i - 1, j - 1]).
* There is special cases for i = 0 or j = 0.
*/
public fun costMatrix(series1 : DoubleBuffer, series2 : DoubleBuffer) : Array<DoubleArray> {
val dtwMatrix: Array<DoubleArray> = Array(series1.size){ row ->
DoubleArray(series2.size) { col ->
abs(series1[row] - series2[col])
}
}
for (i in dtwMatrix.indices) {
for (j in dtwMatrix[i].indices) {
dtwMatrix[i][j] += when {
i == 0 && j == 0 -> 0.0
i == 0 -> dtwMatrix[i][j-1]
j == 0 -> dtwMatrix[i-1][j]
else -> minOf(
dtwMatrix[i][j-1],
dtwMatrix[i-1][j],
dtwMatrix[i-1][j-1]
)
}
}
}
return dtwMatrix
}
/**
* PathIndices class for better code perceptibility.
* Special fun moveOption represent offset for indices. Arguments of this function
* is flags for bottom, diagonal or left offsets respectively.
*/
public data class PathIndices (var id_x: Int, var id_y: Int) {
public fun moveOption (direction: Int) {
when(direction) {
BOTTOM_OFFSET -> id_x--
DIAGONAL_OFFSET -> {
id_x--
id_y--
}
LEFT_OFFSET -> id_y--
else -> throw Exception("There is no such offset flag!")
}
}
}
/**
* Final DTW method realization. Returns alignment matrix
* for two series comparing and penalty for this alignment.
*/
public fun dynamicTimeWarping(series1 : DoubleBuffer, series2 : DoubleBuffer) : DynamicTimeWarpingData {
var cost = 0.0
var pathLength = 0
val costMatrix = costMatrix(series1, series2)
val alignMatrix : Array<BooleanArray> = Array(costMatrix.size) { BooleanArray(costMatrix.first().size) }
val indexes = PathIndices(alignMatrix.lastIndex, alignMatrix.last().lastIndex)
with(indexes) {
alignMatrix[id_x][id_y] = true
cost += costMatrix[id_x][id_y]
pathLength++
while (id_x != 0 || id_y != 0) {
when {
id_x == 0 || costMatrix[id_x][id_y] == costMatrix[id_x][id_y - 1] + abs(series1[id_x] - series2[id_y]) -> {
moveOption(LEFT_OFFSET)
}
id_y == 0 || costMatrix[id_x][id_y] == costMatrix[id_x - 1][id_y] + abs(series1[id_x] - series2[id_y]) -> {
moveOption(BOTTOM_OFFSET)
}
costMatrix[id_x][id_y] == costMatrix[id_x - 1][id_y - 1] + abs(series1[id_x] - series2[id_y]) -> {
moveOption(DIAGONAL_OFFSET)
}
}
alignMatrix[id_x][id_y] = true
cost += costMatrix[id_x][id_y]
pathLength++
}
cost /= pathLength
}
return DynamicTimeWarpingData(cost, alignMatrix)
}