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

kmath-stat/src/commonMain/kotlin/space/kscience/kmath/series/DynamicTimeWarping.kt

@@ -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)) +
+
+    // 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]
+    alignMatrix[index1, index2] = costMatrix[index1, index2]
+    cost += costMatrix[index1, index2]
     pathLength++
-        while (id_x != 0 || id_y != 0) {
+    while (index1 != 0 || index2 != 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)
+            index1 == 0 || costMatrix[index1, index2] == costMatrix[index1, index2 - 1] + abs(series1[index1] - series2[index2]) -> {
+                index2--
             }
-                id_y == 0 || costMatrix[id_x, id_y] == costMatrix[id_x - 1, id_y] + abs(series1[id_x] - series2[id_y]) -> {
-                    moveOption(DtwOffset.BOTTOM)
+            index2 == 0 || costMatrix[index1, index2] == costMatrix[index1 - 1, index2] + abs(series1[index1] - series2[index2]) -> {
+                index1--
             }
-                costMatrix[id_x, id_y] == costMatrix[id_x - 1, id_y - 1] + abs(series1[id_x] - series2[id_y]) -> {
-                    moveOption(DtwOffset.DIAGONAL)
+            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]
+        alignMatrix[index1, index2] = costMatrix[index1, index2]
+        cost += costMatrix[index1, index2]
         pathLength++
     }
     cost /= pathLength
-    }
+    
     return DynamicTimeWarpingData(cost, alignMatrix)
 }
 

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

@@ -15,8 +15,7 @@ import kotlin.test.Test
 class DTWTest {
 
     @Test
-    fun someData() : Unit {
-        with(Double.algebra.bufferAlgebra.seriesAlgebra()) {
+    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)
 
@@ -34,6 +33,3 @@ class DTWTest {
         }
     }
 }
-}
-
-