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Remove extra enum class and data class. Fix docstrings and comments readability. Fix code style violations.

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This commit is contained in:
Gleb Minaev 2023-06-16 14:08:32 +03:00
parent 31be2b547b
commit 39244ebc52
2 changed files with 50 additions and 78 deletions
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100
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/series/DynamicTimeWarping.kt
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@ -14,17 +14,9 @@ import kotlin.math.abs
/**
* Offset constants which will be used later. Added them for avoiding "magical numbers" problem.
*/
internal enum class DtwOffset {
LEFT,
BOTTOM,
DIAGONAL
}
/**
* Public class to store result of method. Class contains total penalty cost for series alignment.
* Also, this class contains align matrix (which point of the first series matches to point of the other series).
* Stores a result of [dynamicTimeWarping]. The class contains:
* 1. [Total penalty cost][totalCost] for series alignment.
* 2. [Align matrix][alignMatrix] that describes which point of the first series matches to point of the other series.
*/
public data class DynamicTimeWarpingData(
val totalCost : Double = 0.0,
@ -32,41 +24,21 @@ public data class DynamicTimeWarpingData(
)
/**
* 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.
*/
internal data class PathIndices (var id_x: Int, var id_y: Int) {
fun moveOption (direction: DtwOffset) {
when(direction) {
DtwOffset.BOTTOM -> id_x--
DtwOffset.DIAGONAL -> {
id_x--
id_y--
}
DtwOffset.LEFT -> id_y--
}
}
}
/**
* Final DTW method realization. Returns alignment matrix
* for two series comparing and penalty for this alignment.
* DTW method implementation. Returns alignment matrix for two series comparing and penalty for this alignment.
*/
@OptIn(PerformancePitfall::class)
public fun DoubleFieldOpsND.dynamicTimeWarping(series1 : DoubleBuffer, series2 : DoubleBuffer) : DynamicTimeWarpingData {
var cost = 0.0
var pathLength = 0
// Special matrix of costs alignment for two series.
val costMatrix = structureND(ShapeND(series1.size, series2.size)) {
(row, col) -> abs(series1[row] - series2[col])
// Create a special matrix of costs alignment for the two series.
val costMatrix = structureND(ShapeND(series1.size, series2.size)) { (row, col) ->
abs(series1[row] - series2[col])
}
// Formula: costMatrix[i, j] = euqlideanNorm(series1(i), series2(j)) +
// min(costMatrix[i - 1, j], costMatrix[i, j - 1], costMatrix[i - 1, j - 1]).
for ( (row, col) in costMatrix.indices) {
// Initialise the cost matrix by formulas
// costMatrix[i, j] = euclideanNorm(series1(i), series2(j)) +
// min(costMatrix[i - 1, j], costMatrix[i, j - 1], costMatrix[i - 1, j - 1]).
for ((row, col) in costMatrix.indices) {
costMatrix[row, col] += when {
// There is special cases for i = 0 or j = 0.
row == 0 && col == 0 -> 0.0
row == 0 && col == 0 -> continue
row == 0 -> costMatrix[row, col - 1]
col == 0 -> costMatrix[row - 1, col]
else -> minOf(
@ -76,33 +48,37 @@ public fun DoubleFieldOpsND.dynamicTimeWarping(series1 : DoubleBuffer, series2 :
)
}
}
// alignMatrix contains non-zero values at position where two points from series matches
// Values are penalty for concatenation of current points.
val alignMatrix = structureND(ShapeND(series1.size, series2.size)) {(_, _) -> 0.0}
val indexes = PathIndices(series1.size - 1, series2.size - 1)
val alignMatrix = structureND(ShapeND(series1.size, series2.size)) { _ -> 0.0}
var index1 = series1.size - 1
var index2 = series2.size - 1
var cost = 0.0
var pathLength = 0
with(indexes) {
alignMatrix[id_x, id_y] = costMatrix[id_x, id_y]
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(DtwOffset.LEFT)
}
id_y == 0 || costMatrix[id_x, id_y] == costMatrix[id_x - 1, id_y] + abs(series1[id_x] - series2[id_y]) -> {
moveOption(DtwOffset.BOTTOM)
}
costMatrix[id_x, id_y] == costMatrix[id_x - 1, id_y - 1] + abs(series1[id_x] - series2[id_y]) -> {
moveOption(DtwOffset.DIAGONAL)
}
alignMatrix[index1, index2] = costMatrix[index1, index2]
cost += costMatrix[index1, index2]
pathLength++
while (index1 != 0 || index2 != 0) {
when {
index1 == 0 || costMatrix[index1, index2] == costMatrix[index1, index2 - 1] + abs(series1[index1] - series2[index2]) -> {
index2--
}
index2 == 0 || costMatrix[index1, index2] == costMatrix[index1 - 1, index2] + abs(series1[index1] - series2[index2]) -> {
index1--
}
costMatrix[index1, index2] == costMatrix[index1 - 1, index2 - 1] + abs(series1[index1] - series2[index2]) -> {
index1--
index2--
}
alignMatrix[id_x, id_y] = costMatrix[id_x, id_y]
cost += costMatrix[id_x, id_y]
pathLength++
}
cost /= pathLength
alignMatrix[index1, index2] = costMatrix[index1, index2]
cost += costMatrix[index1, index2]
pathLength++
}
cost /= pathLength
return DynamicTimeWarpingData(cost, alignMatrix)
}

28
kmath-stat/src/commonTest/kotlin/space/kscience/kmath/series/DTWTest.kt
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@ -15,25 +15,21 @@ import kotlin.test.Test
class DTWTest {
@Test
fun someData() : Unit {
with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
val firstSequence: DoubleArray = doubleArrayOf(0.0, 2.0, 3.0, 1.0, 3.0, 0.1, 0.0, 1.0)
val secondSequence: DoubleArray = doubleArrayOf(1.0, 0.0, 3.0, 0.0, 0.0, 3.0, 2.0, 0.0, 2.0)
fun someData() {
val firstSequence: DoubleArray = doubleArrayOf(0.0, 2.0, 3.0, 1.0, 3.0, 0.1, 0.0, 1.0)
val secondSequence: DoubleArray = doubleArrayOf(1.0, 0.0, 3.0, 0.0, 0.0, 3.0, 2.0, 0.0, 2.0)
val seriesOne = firstSequence.asBuffer()
val seriesTwo = secondSequence.asBuffer()
val seriesOne = firstSequence.asBuffer()
val seriesTwo = secondSequence.asBuffer()
val result = DoubleFieldOpsND.dynamicTimeWarping(seriesOne, seriesTwo)
println("Total penalty coefficient: ${result.totalCost}")
print("Alignment: ")
println(result.alignMatrix)
for ((i , j) in result.alignMatrix.indices) {
if (result.alignMatrix[i, j] > 0.0) {
print("[$i, $j] ")
}
val result = DoubleFieldOpsND.dynamicTimeWarping(seriesOne, seriesTwo)
println("Total penalty coefficient: ${result.totalCost}")
print("Alignment: ")
println(result.alignMatrix)
for ((i , j) in result.alignMatrix.indices) {
if (result.alignMatrix[i, j] > 0.0) {
print("[$i, $j] ")
}
}
}
}