Part 3

examples/src/main/kotlin/space/kscience/kmath/series/emdDemo.kt

@@ -0,0 +1,45 @@
+/*
+ * 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.operations.algebra
+import space.kscience.kmath.operations.bufferAlgebra
+import space.kscience.kmath.structures.*
+import space.kscience.kmath.operations.invoke
+import space.kscience.plotly.*
+import space.kscience.plotly.models.Scatter
+import kotlin.math.sin
+
+private val customAlgebra = (Double.algebra.bufferAlgebra) { SeriesAlgebra(this) { it.toDouble() } }
+
+fun main(): Unit = (customAlgebra) {
+    val signal = DoubleArray(800) {
+        sin(it.toDouble() / 10.0) + 3.5 * sin(it.toDouble() / 60.0)
+    }.asBuffer().moveTo(0)
+    val emd = empiricalModeDecomposition(
+        sConditionThreshold = 1,
+        maxSiftIterations = 15,
+        siftingDelta = 1e-2,
+        nModes = 4
+    ).decompose(signal)
+    println("EMD: ${emd.modes.size} modes extracted, terminated because ${emd.terminatedBecause}")
+
+    fun Plot.series(name: String, buffer: Buffer<Double>, block: Scatter.() -> Unit = {}) {
+        this.scatter {
+            this.name = name
+            this.x.numbers = buffer.labels
+            this.y.doubles = buffer.toDoubleArray()
+            block()
+        }
+    }
+
+    Plotly.plot {
+        series("Signal", signal)
+        emd.modes.forEachIndexed { index, it ->
+            series("Mode ${index+1}", it)
+        }
+    }.makeFile(resourceLocation = ResourceLocation.REMOTE)
+}

examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt

@@ -35,7 +35,7 @@ class StreamDoubleFieldND(override val shape: ShapeND) : FieldND<Double, Float64
     @OptIn(PerformancePitfall::class)
     private val StructureND<Double>.buffer: Float64Buffer
         get() = when {
-            shape != this@StreamDoubleFieldND.shape -> throw ShapeMismatchException(
+            !shape.contentEquals(this@StreamDoubleFieldND.shape) -> throw ShapeMismatchException(
                 this@StreamDoubleFieldND.shape,
                 shape
             )

examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt

@@ -6,6 +6,7 @@
 package space.kscience.kmath.tensors
 
 import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.nd.contentEquals
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.DoubleTensor
 import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
@@ -61,7 +62,7 @@ fun main() {
         // figure out MSE of approximation
         fun mse(yTrue: DoubleTensor, yPred: DoubleTensor): Double {
             require(yTrue.shape.size == 1)
-            require(yTrue.shape == yPred.shape)
+            require(yTrue.shape contentEquals yPred.shape)
 
             val diff = yTrue - yPred
             return sqrt(diff.dot(diff)).value()

examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt

@@ -6,6 +6,7 @@
 package space.kscience.kmath.tensors
 
 import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.nd.contentEquals
 import space.kscience.kmath.operations.asIterable
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.*
@@ -93,7 +94,7 @@ class Dense(
 
 // simple accuracy equal to the proportion of correct answers
 fun accuracy(yPred: DoubleTensor, yTrue: DoubleTensor): Double {
-    check(yPred.shape == yTrue.shape)
+    check(yPred.shape contentEquals yTrue.shape)
     val n = yPred.shape[0]
     var correctCnt = 0
     for (i in 0 until n) {

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferedLinearSpace.kt

@@ -32,12 +32,12 @@ public class BufferedLinearSpace<T, out A : Ring<T>>(
     }
 
     override fun Matrix<T>.plus(other: Matrix<T>): Matrix<T> = ndAlgebra {
-        require(shape == other.shape) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
+        require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
         asND().plus(other.asND()).as2D()
     }
 
     override fun Matrix<T>.minus(other: Matrix<T>): Matrix<T> = ndAlgebra {
-        require(shape == other.shape) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
+        require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
         asND().minus(other.asND()).as2D()
     }
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/Float64LinearSpace.kt

@@ -33,12 +33,12 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
     }
 
     override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
-        require(shape == other.shape) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
+        require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
         asND().plus(other.asND()).as2D()
     }
 
     override fun Matrix<Double>.minus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
-        require(shape == other.shape) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
+        require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
         asND().minus(other.asND()).as2D()
     }
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/AlgebraND.kt

@@ -55,7 +55,7 @@ public interface AlgebraND<T, out C : Algebra<T>> : Algebra<StructureND<T>> {
      */
     @PerformancePitfall("Very slow on remote execution algebras")
     public fun zip(left: StructureND<T>, right: StructureND<T>, transform: C.(T, T) -> T): StructureND<T> {
-        require(left.shape == right.shape) {
+        require(left.shape.contentEquals(right.shape)) {
             "Expected left and right of the same shape, but left - ${left.shape} and right - ${right.shape}"
         }
         return structureND(left.shape) { index ->

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShapeIndices.kt

@@ -106,10 +106,10 @@ public class ColumnStrides(override val shape: ShapeND) : Strides() {
     override fun equals(other: Any?): Boolean {
         if (this === other) return true
         if (other !is ColumnStrides) return false
-        return shape == other.shape
+        return shape.contentEquals(other.shape)
     }
 
-    override fun hashCode(): Int = shape.hashCode()
+    override fun hashCode(): Int = shape.contentHashCode()
 
 
     public companion object
@@ -156,10 +156,10 @@ public class RowStrides(override val shape: ShapeND) : Strides() {
     override fun equals(other: Any?): Boolean {
         if (this === other) return true
         if (other !is RowStrides) return false
-        return shape == other.shape
+        return shape.contentEquals(other.shape)
     }
 
-    override fun hashCode(): Int = shape.hashCode()
+    override fun hashCode(): Int = shape.contentHashCode()
 
     public companion object
 

kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShapeND.kt

@@ -11,30 +11,19 @@ import kotlin.jvm.JvmInline
 /**
  * A read-only ND shape
  */
-public class ShapeND(@PublishedApi internal val array: IntArray) {
+@JvmInline
+public value class ShapeND(@PublishedApi internal val array: IntArray) {
     public val size: Int get() = array.size
     public operator fun get(index: Int): Int = array[index]
     override fun toString(): String = array.contentToString()
-    override fun hashCode(): Int = array.contentHashCode()
-    override fun equals(other: Any?): Boolean = other is ShapeND && array.contentEquals(other.array)
 }
 
 public inline fun ShapeND.forEach(block: (value: Int) -> Unit): Unit = array.forEach(block)
 
 public inline fun ShapeND.forEachIndexed(block: (index: Int, value: Int) -> Unit): Unit = array.forEachIndexed(block)
 
-@Deprecated(
-    message = "ShapeND is made a usual class with correct `equals`. Use it instead of the `contentEquals`.",
-    replaceWith = ReplaceWith("this == other"),
-    level = DeprecationLevel.WARNING,
-)
 public infix fun ShapeND.contentEquals(other: ShapeND): Boolean = array.contentEquals(other.array)
 
-@Deprecated(
-    message = "ShapeND is made a usual class with correct `hashCode`. Use it instead of the `contentHashCode`.",
-    replaceWith = ReplaceWith("this.hashCode()"),
-    level = DeprecationLevel.WARNING,
-)
 public fun ShapeND.contentHashCode(): Int = array.contentHashCode()
 
 public val ShapeND.indices: IntRange get() = array.indices

kmath-core/src/jvmMain/kotlin/space/kscience/kmath/linear/Float64ParallelLinearSpace.kt

@@ -48,12 +48,12 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
     }
 
     override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
-        require(shape == other.shape) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
+        require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
         asND().plus(other.asND()).as2D()
     }
 
     override fun Matrix<Double>.minus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
-        require(shape == other.shape) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
+        require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
         asND().minus(other.asND()).as2D()
     }
 

kmath-multik/src/commonMain/kotlin/space/kscience/kmath/multik/MultikTensorAlgebra.kt

@@ -92,7 +92,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
 
     @OptIn(PerformancePitfall::class)
     override fun zip(left: StructureND<T>, right: StructureND<T>, transform: A.(T, T) -> T): MultikTensor<T> {
-        require(left.shape == right.shape) { "ND array shape mismatch" } //TODO replace by ShapeMismatchException
+        require(left.shape.contentEquals(right.shape)) { "ND array shape mismatch" } //TODO replace by ShapeMismatchException
         val leftArray = left.asMultik().array
         val rightArray = right.asMultik().array
         val data = initMemoryView<T>(leftArray.size, dataType)
@@ -124,7 +124,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
     public fun MutableMultiArray<T, *>.wrap(): MultikTensor<T> = MultikTensor(this.asDNArray())
 
     @OptIn(PerformancePitfall::class)
-    override fun StructureND<T>.valueOrNull(): T? = if (shape == ShapeND(1)) {
+    override fun StructureND<T>.valueOrNull(): T? = if (shape contentEquals ShapeND(1)) {
         get(intArrayOf(0))
     } else null
 
@@ -224,7 +224,7 @@ public abstract class MultikTensorAlgebra<T, A : Ring<T>>(
         }
 
         val mt = asMultik().array
-        return if (ShapeND(mt.shape) == shape) {
+        return if (ShapeND(mt.shape).contentEquals(shape)) {
             mt
         } else {
             @OptIn(UnsafeKMathAPI::class)

kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayAlgebra.kt

@@ -63,7 +63,7 @@ public sealed interface Nd4jArrayAlgebra<T, out C : Algebra<T>> : AlgebraND<T, C
         right: StructureND<T>,
         transform: C.(T, T) -> T,
     ): Nd4jArrayStructure<T> {
-        require(left.shape == right.shape) { "Can't zip tow structures of shape ${left.shape} and ${right.shape}" }
+        require(left.shape.contentEquals(right.shape)) { "Can't zip tow structures of shape ${left.shape} and ${right.shape}" }
         val new = Nd4j.create(*left.shape.asArray()).wrap()
         new.indicesIterator().forEach { idx -> new[idx] = elementAlgebra.transform(left[idx], right[idx]) }
         return new

kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jTensorAlgebra.kt

@@ -49,7 +49,7 @@ public sealed interface Nd4jTensorAlgebra<T : Number, A : Field<T>> : AnalyticTe
 
     @OptIn(PerformancePitfall::class)
     override fun zip(left: StructureND<T>, right: StructureND<T>, transform: A.(T, T) -> T): Nd4jArrayStructure<T> {
-        require(left.shape == right.shape)
+        require(left.shape.contentEquals(right.shape))
         return mutableStructureND(left.shape) { index -> elementAlgebra.transform(left[index], right[index]) }
     }
 
@@ -203,7 +203,7 @@ public object DoubleNd4jTensorAlgebra : Nd4jTensorAlgebra<Double, Float64Field>
         }
 
     override fun StructureND<Double>.valueOrNull(): Double? =
-        if (shape == ShapeND(1)) ndArray.getDouble(0) else null
+        if (shape contentEquals ShapeND(1)) ndArray.getDouble(0) else null
 
     // TODO rewrite
     override fun diagonalEmbedding(

kmath-stat/build.gradle.kts

@@ -14,6 +14,7 @@ kotlin.sourceSets {
         dependencies {
             api(projects.kmathCoroutines)
             //implementation(spclibs.atomicfu)
+            api(project(":kmath-functions"))
         }
     }
 

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

@@ -0,0 +1,288 @@
+/*
+ * 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.interpolation.SplineInterpolator
+import space.kscience.kmath.interpolation.interpolate
+import space.kscience.kmath.operations.*
+import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.structures.last
+
+/**
+ * Empirical mode decomposition of a signal represented as a [Series].
+ *
+ * @param seriesAlgebra context to perform operations in.
+ * @param maxSiftIterations number of iterations in the mode extraction (sifting) process after which
+ * the result is returned even if no other conditions are met.
+ * @param sConditionThreshold one of the possible criteria for sifting process termination:
+ * how many times in a row should a proto-mode satisfy the s-condition (the number of zeros differs from
+ * the number of extrema no more than by 1) to be considered an empirical mode.
+ * @param siftingDelta one of the possible criteria for sifting process termination: if relative difference of
+ * two proto-modes obtained in a sequence is less that this number, the last proto-mode is considered
+ * an empirical mode and returned.
+ * @param nModes how many modes should be extracted at most. The algorithm may return fewer modes if it was not
+ * possible to extract more modes from the signal.
+ */
+public class EmpiricalModeDecomposition<T: Comparable<T>, A: Field<T>, BA, L: T> (
+    private val seriesAlgebra: SeriesAlgebra<T, A, BA, L>,
+    private val sConditionThreshold: Int = 15,
+    private val maxSiftIterations: Int = 20,
+    private val siftingDelta: T,
+    private val nModes: Int = 6
+) where BA: BufferAlgebra<T, A>,  BA: FieldOps<Buffer<T>> {
+
+    /**
+     * Take a signal, construct an upper and a lower envelopes, find the mean value of two,
+     * represented as a series.
+     * @param signal Signal to compute on
+     *
+     * @return mean [Series] or `null`. `null` is returned in case
+     * the signal does not have enough extrema to construct envelopes.
+     */
+    private fun findMean(signal: Series<T>): Series<T>? = (seriesAlgebra) {
+        val interpolator = SplineInterpolator(elementAlgebra)
+        val makeBuffer = elementAlgebra.bufferFactory
+        fun generateEnvelope(extrema: List<Int>, paddedExtremeValues: Buffer<T>): Series<T> {
+            val envelopeFunction = interpolator.interpolate(
+                makeBuffer(extrema.size) { signal.labels[extrema[it]] },
+                paddedExtremeValues
+            )
+            return signal.mapWithLabel { _, label ->
+                // For some reason PolynomialInterpolator is exclusive and the right boundary
+                // TODO Notify interpolator authors
+                envelopeFunction(label) ?: paddedExtremeValues.last()
+                // need to make the interpolator yield values outside boundaries?
+            }
+        }
+        // Extrema padding (experimental) TODO padding needs a dedicated function
+        val maxima = listOf(0) + signal.peaks() + (signal.size - 1)
+        val maxValues = makeBuffer(maxima.size) { signal[maxima[it]] }
+        if (maxValues[0] < maxValues[1]) {
+            maxValues[0] = maxValues[1]
+        }
+        if (maxValues.last() < maxValues[maxValues.size - 2]) {
+            maxValues[maxValues.size - 1] = maxValues[maxValues.size - 2]
+        }
+
+        val minima = listOf(0) + signal.troughs() + (signal.size - 1)
+        val minValues = makeBuffer(minima.size) { signal[minima[it]] }
+        if (minValues[0] > minValues[1]) {
+            minValues[0] = minValues[1]
+        }
+        if (minValues.last() > minValues[minValues.size - 2]) {
+            minValues[minValues.size - 1] = minValues[minValues.size - 2]
+        }
+         return if (maxima.size < 3 || minima.size < 3) null else { // maybe make an early return?
+            val upperEnvelope = generateEnvelope(maxima, maxValues)
+            val lowerEnvelope = generateEnvelope(minima, minValues)
+            return (upperEnvelope + lowerEnvelope).map { it * 0.5 }
+        }
+    }
+
+    /**
+     * Extract a single empirical mode from a signal. This process is called sifting, hence the name.
+     * @param signal Signal to extract a mode from. The first mode is extracted from the initial signal,
+     * subsequent modes are extracted from the residuals between the signal and all previous modes.
+     *
+     * @return [SiftingResult.NotEnoughExtrema] is returned if the signal has too few extrema to extract a mode.
+     * Success of an appropriate type (See [SiftingResult.Success] class) is returned otherwise.
+     */
+    private fun sift(signal: Series<T>): SiftingResult = siftInner(signal, 1, 0)
+
+    /**
+     * Compute a single iteration of the sifting process.
+     */
+    private tailrec fun siftInner(
+        prevMode: Series<T>,
+        iterationNumber: Int,
+        sNumber: Int
+    ): SiftingResult = (seriesAlgebra) {
+        val mean = findMean(prevMode) ?:
+            return if (iterationNumber == 1) SiftingResult.NotEnoughExtrema
+            else SiftingResult.SignalFlattened(prevMode)
+        val mode = prevMode.zip(mean) { p, m -> p - m }
+        val newSNumber = if (sCondition(mode)) sNumber + 1 else sNumber
+        return when {
+            iterationNumber >= maxSiftIterations -> SiftingResult.MaxIterationsReached(mode)
+            sNumber >= sConditionThreshold -> SiftingResult.SNumberReached(mode)
+            relativeDifference(mode, prevMode) < (elementAlgebra) { siftingDelta * mode.size } ->
+                SiftingResult.DeltaReached(mode)
+            else -> siftInner(mode, iterationNumber + 1, newSNumber)
+        }
+    }
+
+    /**
+     * Extract [nModes] empirical modes from a signal represented by a time series.
+     * @param signal Signal to decompose.
+     * @return [EMDecompositionResult] with an appropriate reason for algorithm termination
+     * (see [EMDTerminationReason] for possible reasons).
+     * Modes returned in a list which contains as many modes as it was possible
+     * to extract before triggering one of the termination conditions.
+     */
+     public fun decompose(signal: Series<T>): EMDecompositionResult<T> = (seriesAlgebra) {
+        val modes = mutableListOf<Series<T>>()
+        var residual = signal
+        repeat(nModes) {
+            val nextMode = when(val r = sift(residual)) {
+                SiftingResult.NotEnoughExtrema ->
+                    return EMDecompositionResult(
+                        if (it == 0) EMDTerminationReason.SIGNAL_TOO_FLAT
+                        else EMDTerminationReason.ALL_POSSIBLE_MODES_EXTRACTED,
+                        modes,
+                        residual
+                    )
+                is SiftingResult.Success<*> -> r.result
+            }
+            modes.add(nextMode as Series<T>) // TODO remove unchecked cast
+            residual = residual.zip(nextMode) { l, r -> l - r }
+        }
+        return EMDecompositionResult(EMDTerminationReason.MAX_MODES_REACHED, modes, residual)
+    }
+}
+
+/**
+ * Shortcut to retrieve a decomposition factory from a [SeriesAlgebra] scope.
+ * @receiver scope to perform operations in.
+ * @param maxSiftIterations number of iterations in the mode extraction (sifting) process after which
+ * the result is returned even if no other conditions are met.
+ * @param sConditionThreshold one of the possible criteria for sifting process termination:
+ * how many times in a row should a proto-mode satisfy the s-condition (the number of zeros differs from
+ * the number of extrema no more than by 1) to be considered an empirical mode.
+ * @param siftingDelta one of the possible criteria for sifting process termination: if relative difference of
+ * two proto-modes obtained in a sequence is less that this number, the last proto-mode is considered
+ * an empirical mode and returned.
+ * @param nModes how many modes should be extracted at most. The algorithm may return fewer modes if it was not
+ * possible to extract more modes from the signal.
+ */
+public fun <T: Comparable<T>, L: T, A: Field<T>, BA> SeriesAlgebra<T, A, BA, L>.empiricalModeDecomposition(
+    sConditionThreshold: Int = 15,
+    maxSiftIterations: Int = 20,
+    siftingDelta: T,
+    nModes: Int = 3
+): EmpiricalModeDecomposition<T, A, BA, L>
+where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> = EmpiricalModeDecomposition(
+    seriesAlgebra = this,
+    sConditionThreshold = sConditionThreshold,
+    maxSiftIterations = maxSiftIterations,
+    siftingDelta = siftingDelta,
+    nModes = nModes
+)
+
+/**
+ * Brute force count all zeros in the series.
+ */
+private fun <T: Comparable<T>, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.countZeros(
+    signal: Series<T>
+): Int where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> {
+    require(signal.size >= 2) { "Expected series with at least 2 elements, but got ${signal.size} elements" }
+    data class SignCounter(val prevSign: Int, val zeroCount: Int)
+    fun strictSign(arg: T): Int = if (arg > elementAlgebra.zero) 1 else -1
+
+    return signal.fold(SignCounter(strictSign(signal[0]), 0)) { acc, it ->
+        val currentSign = strictSign(it)
+        if (acc.prevSign != currentSign) SignCounter(currentSign, acc.zeroCount + 1)
+        else SignCounter(currentSign, acc.zeroCount)
+    }.zeroCount
+}
+
+/**
+ * Compute relative difference of two series.
+ */
+private fun <T, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.relativeDifference(
+    current: Series<T>,
+    previous: Series<T>
+): T where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> = (bufferAlgebra) {
+    ((current - previous) * (current - previous))
+        .div(previous * previous)
+        .fold(elementAlgebra.zero) { acc, it -> acc + it}
+}
+
+/**
+ * Brute force count all extrema of a series.
+ */
+private fun <T: Comparable<T>> Series<T>.countExtrema(): Int {
+    require(size >= 3) { "Expected series with at least 3 elements, but got $size elements" }
+    return peaks().size + troughs().size
+}
+
+/**
+ * Check whether the numbers of zeroes and extrema of a series differ by no more than 1.
+ * This is a necessary condition of an empirical mode.
+ */
+private fun <T: Comparable<T>, A: Ring<T>, BA> SeriesAlgebra<T, A, BA, *>.sCondition(
+    signal: Series<T>
+): Boolean where BA: BufferAlgebra<T, A>, BA: FieldOps<Buffer<T>> =
+    (signal.countExtrema() - countZeros(signal)) in -1..1
+
+internal sealed interface SiftingResult {
+
+    /**
+     * Represents a condition when a mode has been successfully
+     * extracted in a sifting process.
+     */
+    open class Success<T>(val result: Series<T>): SiftingResult
+
+    /**
+     * Returned when no termination condition was reached and the proto-mode
+     * has become too flat (with not enough extrema to build envelopes)
+     * after several sifting iterations.
+     */
+    class SignalFlattened<T>(result: Series<T>) : Success<T>(result)
+
+    /**
+     * Returned when sifting process has been terminated due to the
+     * S-number condition being reached.
+     */
+    class SNumberReached<T>(result: Series<T>) : Success<T>(result)
+
+    /**
+     * Returned when sifting process has been terminated due to the
+     * delta condition (Cauchy criterion) being reached.
+     */
+    class DeltaReached<T>(result: Series<T>) : Success<T>(result)
+
+    /**
+     * Returned when sifting process has been terminated after
+     * executing the maximum number of iterations (specified when creating an instance
+     * of [EmpiricalModeDecomposition]).
+     */
+    class MaxIterationsReached<T>(result: Series<T>): Success<T>(result)
+
+    /**
+     * Returned when the submitted signal has not enough extrema to build envelopes,
+     * i.e. when [SignalFlattened] condition has already been reached before the first sifting iteration.
+     */
+    data object NotEnoughExtrema : SiftingResult
+
+}
+
+public enum class EMDTerminationReason {
+    /**
+     * Returned when the signal is too flat, i.e. there are too few extrema
+     * to build envelopes necessary to extract modes.
+     */
+    SIGNAL_TOO_FLAT,
+
+    /**
+     * Returned when there has been extracted as many modes as
+     * specified when creating the instance of [EmpiricalModeDecomposition]
+     */
+    MAX_MODES_REACHED,
+
+    /**
+     * Returned when the algorithm terminated after finding impossible
+     * to extract more modes from the signal and the maximum number
+     * of modes (specified when creating an instance of [EmpiricalModeDecomposition])
+     * has not yet been reached.
+     */
+    ALL_POSSIBLE_MODES_EXTRACTED
+}
+
+public data class EMDecompositionResult<T>(
+    val terminatedBecause: EMDTerminationReason,
+    val modes: List<Series<T>>,
+    val residual: Series<T>
+)

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

@@ -169,7 +169,7 @@ public open class SeriesAlgebra<T, out A : Ring<T>, out BA : BufferAlgebra<T, A>
     public inline fun Buffer<T>.mapWithLabel(crossinline transform: A.(arg: T, label: L) -> T): Series<T> {
         val labels = labels
         val buf = elementAlgebra.bufferFactory(size) {
-            elementAlgebra.transform(getByOffset(it), labels[it])
+            elementAlgebra.transform(get(it), labels[it])
         }
         return buf.moveTo(offsetIndices.first)
     }

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

@@ -0,0 +1,89 @@
+/*
+ * 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
+
+
+public enum class PlateauEdgePolicy  {
+    /**
+     * Midpoints of plateau are returned, edges not belonging to a plateau are ignored.
+     *
+     * A midpoint is the index closest to the average of indices of the left and right edges
+     * of the plateau:
+     *
+     * `val midpoint = ((leftEdge + rightEdge) / 2).toInt`
+     */
+     AVERAGE,
+
+    /**
+     * Both left and right edges are returned.
+     */
+    KEEP_ALL_EDGES,
+
+    /**
+     * Only right edges are returned.
+     */
+    KEEP_RIGHT_EDGES,
+
+    /**
+     * Only left edges are returned.
+     */
+    KEEP_LEFT_EDGES,
+
+    /**
+     * Ignore plateau, only peaks (troughs) with values strictly greater (less)
+     * than values of the adjacent points are returned.
+     */
+    IGNORE
+}
+
+
+public fun <T: Comparable<T>> Series<T>.peaks(
+    plateauEdgePolicy: PlateauEdgePolicy = PlateauEdgePolicy.AVERAGE
+): List<Int> = findPeaks(plateauEdgePolicy, { other -> this > other },  { other -> this >= other })
+
+public fun <T: Comparable<T>> Series<T>.troughs(
+    plateauEdgePolicy: PlateauEdgePolicy = PlateauEdgePolicy.AVERAGE
+): List<Int> = findPeaks(plateauEdgePolicy, { other -> this < other }, { other -> this <= other })
+
+
+private fun <T: Comparable<T>> Series<T>.findPeaks(
+    plateauPolicy: PlateauEdgePolicy = PlateauEdgePolicy.AVERAGE,
+    cmpStrong: T.(T) -> Boolean,
+    cmpWeak: T.(T) -> Boolean
+): List<Int> {
+    require(size >= 3) { "Expected series with at least 3 elements, but got $size elements" }
+    if (plateauPolicy == PlateauEdgePolicy.AVERAGE) return peaksWithPlateau(cmpStrong)
+    fun peakCriterion(left: T, middle: T, right: T): Boolean = when(plateauPolicy) {
+        PlateauEdgePolicy.KEEP_LEFT_EDGES -> middle.cmpStrong(left) && middle.cmpWeak(right)
+        PlateauEdgePolicy.KEEP_RIGHT_EDGES -> middle.cmpWeak(left) && middle.cmpStrong(right)
+        PlateauEdgePolicy.KEEP_ALL_EDGES ->
+            (middle.cmpStrong(left) && middle.cmpWeak(right)) || (middle.cmpWeak(left) && middle.cmpStrong(right))
+        else -> middle.cmpStrong(right) && middle.cmpStrong(left)
+    }
+    val indices = mutableListOf<Int>()
+    for (index in 1 .. size - 2) {
+        val left = this[index - 1]
+        val middle = this[index]
+        val right = this[index + 1]
+        if (peakCriterion(left, middle, right)) indices.add(index)
+    }
+    return indices
+}
+
+
+private fun <T: Comparable<T>> Series<T>.peaksWithPlateau(cmpStrong: T.(T) -> Boolean): List<Int> {
+    val peaks = mutableListOf<Int>()
+    tailrec fun peaksPlateauInner(index: Int) {
+        val nextUnequal = (index + 1 ..< size).firstOrNull { this[it] != this[index] } ?: (size - 1)
+        val newIndex = if (this[index].cmpStrong(this[index - 1]) && this[index].cmpStrong(this[nextUnequal])) {
+            peaks.add((index + nextUnequal) / 2)
+            nextUnequal
+        } else index + 1
+        if (newIndex < size - 1) peaksPlateauInner(newIndex)
+    }
+    peaksPlateauInner(1)
+    return peaks
+}

kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt

@@ -23,6 +23,7 @@ import space.kscience.kmath.UnstableKMathAPI
 import space.kscience.kmath.nd.ShapeND
 import space.kscience.kmath.nd.StructureND
 import space.kscience.kmath.nd.asArray
+import space.kscience.kmath.nd.contentEquals
 import space.kscience.kmath.operations.Ring
 import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.api.TensorAlgebra
@@ -104,7 +105,7 @@ public abstract class TensorFlowAlgebra<T, TT : TNumber, A : Ring<T>> internal c
     protected abstract fun const(value: T): Constant<TT>
 
     @OptIn(PerformancePitfall::class)
-    override fun StructureND<T>.valueOrNull(): T? = if (shape == ShapeND(1))
+    override fun StructureND<T>.valueOrNull(): T? = if (shape contentEquals ShapeND(1))
         get(intArrayOf(0)) else null
 
     /**

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt

@@ -95,7 +95,7 @@ public open class DoubleTensorAlgebra :
 
     override fun StructureND<Double>.valueOrNull(): Double? {
         val dt = asDoubleTensor()
-        return if (dt.shape == ShapeND(1)) dt.source[0] else null
+        return if (dt.shape contentEquals ShapeND(1)) dt.source[0] else null
     }
 
     override fun StructureND<Double>.value(): Double = valueOrNull()

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/IntTensorAlgebra.kt

@@ -89,7 +89,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, Int32Ring> {
 
     override fun StructureND<Int>.valueOrNull(): Int? {
         val dt = asIntTensor()
-        return if (dt.shape == ShapeND(1)) dt.source[0] else null
+        return if (dt.shape contentEquals ShapeND(1)) dt.source[0] else null
     }
 
     override fun StructureND<Int>.value(): Int = valueOrNull()
@@ -387,7 +387,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, Int32Ring> {
     public fun stack(tensors: List<Tensor<Int>>): IntTensor {
         check(tensors.isNotEmpty()) { "List must have at least 1 element" }
         val shape = tensors[0].shape
-        check(tensors.all { it.shape == shape }) { "Tensors must have same shapes" }
+        check(tensors.all { it.shape contentEquals shape }) { "Tensors must have same shapes" }
         val resShape = ShapeND(tensors.size) + shape
 //        val resBuffer: List<Int> = tensors.flatMap {
 //            it.asIntTensor().source.array.drop(it.asIntTensor().bufferStart)

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/checks.kt

@@ -7,6 +7,7 @@ package space.kscience.kmath.tensors.core.internal
 
 import space.kscience.kmath.nd.ShapeND
 import space.kscience.kmath.nd.StructureND
+import space.kscience.kmath.nd.contentEquals
 import space.kscience.kmath.nd.linearSize
 import space.kscience.kmath.tensors.api.Tensor
 import space.kscience.kmath.tensors.core.DoubleTensor
@@ -31,7 +32,7 @@ internal fun checkBufferShapeConsistency(shape: ShapeND, buffer: DoubleArray) =
 
 @PublishedApi
 internal fun <T> checkShapesCompatible(a: StructureND<T>, b: StructureND<T>): Unit =
-    check(a.shape == b.shape) {
+    check(a.shape contentEquals b.shape) {
         "Incompatible shapes ${a.shape} and ${b.shape} "
     }
 

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/tensorOps.kt

@@ -47,7 +47,7 @@ public fun DoubleTensorAlgebra.randomNormalLike(structure: WithShape, seed: Long
 public fun stack(tensors: List<Tensor<Double>>): DoubleTensor {
     check(tensors.isNotEmpty()) { "List must have at least 1 element" }
     val shape = tensors[0].shape
-    check(tensors.all { it.shape == shape }) { "Tensors must have same shapes" }
+    check(tensors.all { it.shape contentEquals shape }) { "Tensors must have same shapes" }
     val resShape = ShapeND(tensors.size) + shape
 //        val resBuffer: List<Double> = tensors.flatMap {
 //            it.asDoubleTensor().source.array.drop(it.asDoubleTensor().bufferStart)
@@ -91,7 +91,7 @@ public fun DoubleTensorAlgebra.luPivot(
 ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
     checkSquareMatrix(luTensor.shape)
     check(
-        luTensor.shape.first(luTensor.shape.size - 2) == pivotsTensor.shape.first(pivotsTensor.shape.size - 1) ||
+        luTensor.shape.first(luTensor.shape.size - 2) contentEquals pivotsTensor.shape.first(pivotsTensor.shape.size - 1) ||
                 luTensor.shape.last() == pivotsTensor.shape.last() - 1
     ) { "Inappropriate shapes of input tensors" }
 

kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestBroadcasting.kt

@@ -6,31 +6,29 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.nd.contentEquals
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.internal.broadcastOuterTensors
 import space.kscience.kmath.tensors.core.internal.broadcastShapes
 import space.kscience.kmath.tensors.core.internal.broadcastTensors
 import space.kscience.kmath.tensors.core.internal.broadcastTo
 import kotlin.test.Test
-import kotlin.test.assertEquals
 import kotlin.test.assertTrue
 
 internal class TestBroadcasting {
 
     @Test
     fun testBroadcastShapes() = DoubleTensorAlgebra {
-        assertEquals(
+        assertTrue(
             broadcastShapes(
                 listOf(ShapeND(2, 3), ShapeND(1, 3), ShapeND(1, 1, 1))
-            ),
-            ShapeND(1, 2, 3)
+            ) contentEquals ShapeND(1, 2, 3)
         )
 
-        assertEquals(
+        assertTrue(
             broadcastShapes(
                 listOf(ShapeND(6, 7), ShapeND(5, 6, 1), ShapeND(7), ShapeND(5, 1, 7))
-            ),
-            ShapeND(5, 6, 7)
+            ) contentEquals ShapeND(5, 6, 7)
         )
     }
 
@@ -40,7 +38,7 @@ internal class TestBroadcasting {
         val tensor2 = fromArray(ShapeND(1, 3), doubleArrayOf(10.0, 20.0, 30.0))
 
         val res = broadcastTo(tensor2, tensor1.shape)
-        assertTrue(res.shape == ShapeND(2, 3))
+        assertTrue(res.shape contentEquals ShapeND(2, 3))
         assertTrue(res.source contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
     }
 
@@ -52,9 +50,9 @@ internal class TestBroadcasting {
 
         val res = broadcastTensors(tensor1, tensor2, tensor3)
 
-        assertEquals(res[0].shape, ShapeND(1, 2, 3))
-        assertEquals(res[1].shape, ShapeND(1, 2, 3))
-        assertEquals(res[2].shape, ShapeND(1, 2, 3))
+        assertTrue(res[0].shape contentEquals ShapeND(1, 2, 3))
+        assertTrue(res[1].shape contentEquals ShapeND(1, 2, 3))
+        assertTrue(res[2].shape contentEquals ShapeND(1, 2, 3))
 
         assertTrue(res[0].source contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         assertTrue(res[1].source contentEquals doubleArrayOf(10.0, 20.0, 30.0, 10.0, 20.0, 30.0))
@@ -69,9 +67,9 @@ internal class TestBroadcasting {
 
         val res = broadcastOuterTensors(tensor1, tensor2, tensor3)
 
-        assertEquals(res[0].shape, ShapeND(1, 2, 3))
-        assertEquals(res[1].shape, ShapeND(1, 1, 3))
-        assertEquals(res[2].shape, ShapeND(1, 1, 1))
+        assertTrue(res[0].shape contentEquals ShapeND(1, 2, 3))
+        assertTrue(res[1].shape contentEquals ShapeND(1, 1, 3))
+        assertTrue(res[2].shape contentEquals ShapeND(1, 1, 1))
 
         assertTrue(res[0].source contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         assertTrue(res[1].source contentEquals doubleArrayOf(10.0, 20.0, 30.0))
@@ -86,9 +84,9 @@ internal class TestBroadcasting {
 
         val res = broadcastOuterTensors(tensor1, tensor2, tensor3)
 
-        assertEquals(res[0].shape, ShapeND(4, 2, 5, 3, 2, 3))
-        assertEquals(res[1].shape, ShapeND(4, 2, 5, 3, 3, 3))
-        assertEquals(res[2].shape, ShapeND(4, 2, 5, 3, 1, 1))
+        assertTrue(res[0].shape contentEquals ShapeND(4, 2, 5, 3, 2, 3))
+        assertTrue(res[1].shape contentEquals ShapeND(4, 2, 5, 3, 3, 3))
+        assertTrue(res[2].shape contentEquals ShapeND(4, 2, 5, 3, 1, 1))
     }
 
     @Test
@@ -101,16 +99,16 @@ internal class TestBroadcasting {
         val tensor31 = tensor3 - tensor1
         val tensor32 = tensor3 - tensor2
 
-        assertEquals(tensor21.shape, ShapeND(2, 3))
+        assertTrue(tensor21.shape contentEquals ShapeND(2, 3))
         assertTrue(tensor21.source contentEquals doubleArrayOf(9.0, 18.0, 27.0, 6.0, 15.0, 24.0))
 
-        assertEquals(tensor31.shape, ShapeND(1, 2, 3))
+        assertTrue(tensor31.shape contentEquals ShapeND(1, 2, 3))
         assertTrue(
             tensor31.source
                     contentEquals doubleArrayOf(499.0, 498.0, 497.0, 496.0, 495.0, 494.0)
         )
 
-        assertEquals(tensor32.shape, ShapeND(1, 1, 3))
+        assertTrue(tensor32.shape contentEquals ShapeND(1, 1, 3))
         assertTrue(tensor32.source contentEquals doubleArrayOf(490.0, 480.0, 470.0))
     }
 

kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleLinearOpsAlgebra.kt

@@ -6,6 +6,7 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.nd.contentEquals
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.tensors.core.internal.svd1d
 import kotlin.math.abs
@@ -112,8 +113,8 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 
         val (q, r) = qr(tensor)
 
-        assertEquals(q.shape, shape)
-        assertEquals(r.shape, shape)
+        assertTrue { q.shape contentEquals shape }
+        assertTrue { r.shape contentEquals shape }
 
         assertTrue((q matmul r).eq(tensor))
 
@@ -132,9 +133,9 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 
         val (p, l, u) = lu(tensor)
 
-        assertEquals(p.shape, shape)
-        assertEquals(l.shape, shape)
-        assertEquals(u.shape, shape)
+        assertTrue { p.shape contentEquals shape }
+        assertTrue { l.shape contentEquals shape }
+        assertTrue { u.shape contentEquals shape }
 
         assertTrue((p matmul tensor).eq(l matmul u))
     }
@@ -156,7 +157,7 @@ internal class TestDoubleLinearOpsTensorAlgebra {
 
         val res = svd1d(tensor2)
 
-        assertEquals(res.shape, ShapeND(2))
+        assertTrue(res.shape contentEquals ShapeND(2))
         assertTrue { abs(abs(res.source[0]) - 0.386) < 0.01 }
         assertTrue { abs(abs(res.source[1]) - 0.922) < 0.01 }
     }

kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt

@@ -6,8 +6,7 @@
 package space.kscience.kmath.tensors.core
 
 
-import space.kscience.kmath.nd.ShapeND
-import space.kscience.kmath.nd.get
+import space.kscience.kmath.nd.*
 import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.testutils.assertBufferEquals
 import kotlin.test.Test
@@ -44,7 +43,7 @@ internal class TestDoubleTensorAlgebra {
         val res = tensor.transposed(0, 0)
 
         assertTrue(res.asDoubleTensor().source contentEquals doubleArrayOf(0.0))
-        assertEquals(res.shape, ShapeND(1))
+        assertTrue(res.shape contentEquals ShapeND(1))
     }
 
     @Test
@@ -53,7 +52,7 @@ internal class TestDoubleTensorAlgebra {
         val res = tensor.transposed(1, 0)
 
         assertTrue(res.asDoubleTensor().source contentEquals doubleArrayOf(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
-        assertEquals(res.shape, ShapeND(2, 3))
+        assertTrue(res.shape contentEquals ShapeND(2, 3))
     }
 
     @Test
@@ -63,9 +62,9 @@ internal class TestDoubleTensorAlgebra {
         val res02 = tensor.transposed(-3, 2)
         val res12 = tensor.transposed()
 
-        assertEquals(res01.shape, ShapeND(2, 1, 3))
-        assertEquals(res02.shape, ShapeND(3, 2, 1))
-        assertEquals(res12.shape, ShapeND(1, 3, 2))
+        assertTrue(res01.shape contentEquals ShapeND(2, 1, 3))
+        assertTrue(res02.shape contentEquals ShapeND(3, 2, 1))
+        assertTrue(res12.shape contentEquals ShapeND(1, 3, 2))
 
         assertTrue(res01.asDoubleTensor().source contentEquals doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         assertTrue(res02.asDoubleTensor().source contentEquals doubleArrayOf(1.0, 4.0, 2.0, 5.0, 3.0, 6.0))
@@ -115,19 +114,19 @@ internal class TestDoubleTensorAlgebra {
 
         val res12 = tensor1.dot(tensor2)
         assertTrue(res12.source contentEquals doubleArrayOf(140.0, 320.0))
-        assertEquals(res12.shape, ShapeND(2))
+        assertTrue(res12.shape contentEquals ShapeND(2))
 
         val res32 = tensor3.matmul(tensor2)
         assertTrue(res32.source contentEquals doubleArrayOf(-140.0))
-        assertEquals(res32.shape, ShapeND(1, 1))
+        assertTrue(res32.shape contentEquals ShapeND(1, 1))
 
         val res22 = tensor2.dot(tensor2)
         assertTrue(res22.source contentEquals doubleArrayOf(1400.0))
-        assertEquals(res22.shape, ShapeND(1))
+        assertTrue(res22.shape contentEquals ShapeND(1))
 
         val res11 = tensor1.dot(tensor11)
         assertTrue(res11.source contentEquals doubleArrayOf(22.0, 28.0, 49.0, 64.0))
-        assertEquals(res11.shape, ShapeND(2, 2))
+        assertTrue(res11.shape contentEquals ShapeND(2, 2))
 
         val res45 = tensor4.matmul(tensor5)
         assertTrue(
@@ -136,7 +135,7 @@ internal class TestDoubleTensorAlgebra {
                 468.0, 501.0, 534.0, 594.0, 636.0, 678.0, 720.0, 771.0, 822.0
             )
         )
-        assertEquals(res45.shape, ShapeND(2, 3, 3))
+        assertTrue(res45.shape contentEquals ShapeND(2, 3, 3))
     }
 
     @Test
@@ -145,35 +144,35 @@ internal class TestDoubleTensorAlgebra {
         val tensor2 = fromArray(ShapeND(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor3 = zeros(ShapeND(2, 3, 4, 5))
 
-        assertEquals(
-            diagonalEmbedding(tensor3, 0, 3, 4).shape,
-            ShapeND(2, 3, 4, 5, 5)
+        assertTrue(
+            diagonalEmbedding(tensor3, 0, 3, 4).shape contentEquals
+                    ShapeND(2, 3, 4, 5, 5)
         )
-        assertEquals(
-            diagonalEmbedding(tensor3, 1, 3, 4).shape,
-            ShapeND(2, 3, 4, 6, 6)
+        assertTrue(
+            diagonalEmbedding(tensor3, 1, 3, 4).shape contentEquals
+                    ShapeND(2, 3, 4, 6, 6)
         )
-        assertEquals(
-            diagonalEmbedding(tensor3, 2, 0, 3).shape,
-            ShapeND(7, 2, 3, 7, 4)
+        assertTrue(
+            diagonalEmbedding(tensor3, 2, 0, 3).shape contentEquals
+                    ShapeND(7, 2, 3, 7, 4)
         )
 
         val diagonal1 = diagonalEmbedding(tensor1, 0, 1, 0)
-        assertEquals(diagonal1.shape, ShapeND(3, 3))
+        assertTrue(diagonal1.shape contentEquals ShapeND(3, 3))
         assertTrue(
             diagonal1.source contentEquals
                     doubleArrayOf(10.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 30.0)
         )
 
         val diagonal1Offset = diagonalEmbedding(tensor1, 1, 1, 0)
-        assertEquals(diagonal1Offset.shape, ShapeND(4, 4))
+        assertTrue(diagonal1Offset.shape contentEquals ShapeND(4, 4))
         assertTrue(
             diagonal1Offset.source contentEquals
                     doubleArrayOf(0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 0.0, 30.0, 0.0)
         )
 
         val diagonal2 = diagonalEmbedding(tensor2, 1, 0, 2)
-        assertEquals(diagonal2.shape, ShapeND(4, 2, 4))
+        assertTrue(diagonal2.shape contentEquals ShapeND(4, 2, 4))
         assertTrue(
             diagonal2.source contentEquals
                     doubleArrayOf(
@@ -203,7 +202,7 @@ internal class TestDoubleTensorAlgebra {
         val l = tensor.getTensor(0).map { it + 1.0 }
         val r = tensor.getTensor(1).map { it - 1.0 }
         val res = l + r
-        assertEquals(ShapeND(5, 5), res.shape)
+        assertTrue { ShapeND(5, 5) contentEquals res.shape }
         assertEquals(2.0, res[4, 4])
     }
 }

kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorFieldOpsND.kt

@@ -67,7 +67,7 @@ public open class ViktorFieldOpsND :
         right: StructureND<Double>,
         transform: Float64Field.(Double, Double) -> Double,
     ): ViktorStructureND {
-        require(left.shape == right.shape)
+        require(left.shape.contentEquals(right.shape))
         return F64Array(*left.shape.asArray()).apply {
             ColumnStrides(left.shape).asSequence().forEach { index ->
                 set(value = Float64Field.transform(left[index], right[index]), indices = index)