Fixing 2D and 1D casts

kmath-core/api/kmath-core.api

@@ -2731,8 +2731,6 @@ public class space/kscience/kmath/tensors/core/BufferedTensor : space/kscience/k
 	public fun <init> ([ILspace/kscience/kmath/structures/MutableBuffer;I)V
 	public fun elements ()Lkotlin/sequences/Sequence;
 	public fun equals (Ljava/lang/Object;)Z
-	public final fun forEachMatrix (Lkotlin/jvm/functions/Function1;)V
-	public final fun forEachVector (Lkotlin/jvm/functions/Function1;)V
 	public fun get ([I)Ljava/lang/Object;
 	public final fun getBuffer ()Lspace/kscience/kmath/structures/MutableBuffer;
 	public fun getDimension ()I
@@ -2740,37 +2738,7 @@ public class space/kscience/kmath/tensors/core/BufferedTensor : space/kscience/k
 	public final fun getNumel ()I
 	public fun getShape ()[I
 	public fun hashCode ()I
-	public final fun matrixSequence ()Lkotlin/sequences/Sequence;
 	public fun set ([ILjava/lang/Object;)V
-	public final fun vectorSequence ()Lkotlin/sequences/Sequence;
-}
-
-public final class space/kscience/kmath/tensors/core/BufferedTensor1D : space/kscience/kmath/tensors/core/BufferedTensor, space/kscience/kmath/nd/MutableStructure1D {
-	public fun copy ()Lspace/kscience/kmath/structures/MutableBuffer;
-	public fun get (I)Ljava/lang/Object;
-	public fun get ([I)Ljava/lang/Object;
-	public fun getDimension ()I
-	public fun getSize ()I
-	public fun iterator ()Ljava/util/Iterator;
-	public fun set (ILjava/lang/Object;)V
-	public fun set ([ILjava/lang/Object;)V
-}
-
-public final class space/kscience/kmath/tensors/core/BufferedTensor2D : space/kscience/kmath/tensors/core/BufferedTensor, space/kscience/kmath/nd/MutableStructure2D {
-	public fun elements ()Lkotlin/sequences/Sequence;
-	public fun get (II)Ljava/lang/Object;
-	public fun get ([I)Ljava/lang/Object;
-	public fun getColNum ()I
-	public fun getColumns ()Ljava/util/List;
-	public fun getRowNum ()I
-	public fun getRows ()Ljava/util/List;
-	public fun getShape ()[I
-	public fun set (IILjava/lang/Object;)V
-}
-
-public final class space/kscience/kmath/tensors/core/BufferedTensorKt {
-	public static final fun as1D (Lspace/kscience/kmath/tensors/core/BufferedTensor;)Lspace/kscience/kmath/tensors/core/BufferedTensor1D;
-	public static final fun as2D (Lspace/kscience/kmath/tensors/core/BufferedTensor;)Lspace/kscience/kmath/tensors/core/BufferedTensor2D;
 }
 
 public final class space/kscience/kmath/tensors/core/DoubleAnalyticTensorAlgebra : space/kscience/kmath/tensors/core/DoubleTensorAlgebra, space/kscience/kmath/tensors/AnalyticTensorAlgebra {
@@ -2876,7 +2844,6 @@ public class space/kscience/kmath/tensors/core/DoubleTensorAlgebra : space/kscie
 	public synthetic fun eq (Lspace/kscience/kmath/nd/MutableStructureND;Lspace/kscience/kmath/nd/MutableStructureND;Ljava/lang/Object;)Z
 	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;)Z
 	public fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;D)Z
-	public final fun eq (Lspace/kscience/kmath/tensors/core/DoubleTensor;Lspace/kscience/kmath/tensors/core/DoubleTensor;Lkotlin/jvm/functions/Function2;)Z
 	public synthetic fun eye (I)Lspace/kscience/kmath/nd/MutableStructureND;
 	public fun eye (I)Lspace/kscience/kmath/tensors/core/DoubleTensor;
 	public final fun fromArray ([I[D)Lspace/kscience/kmath/tensors/core/DoubleTensor;

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

@@ -35,35 +35,34 @@ public open class BufferedTensor<T>(
 
     override fun hashCode(): Int = 0
 
-    public fun vectorSequence(): Sequence<BufferedTensor1D<T>> = sequence {
+    internal fun vectorSequence(): Sequence<BufferedTensor<T>> = sequence {
-        check(shape.size >= 1) { "todo" }
         val n = shape.size
         val vectorOffset = shape[n - 1]
         val vectorShape = intArrayOf(shape.last())
         for (offset in 0 until numel step vectorOffset) {
-            val vector = BufferedTensor<T>(vectorShape, buffer, offset).as1D()
+            val vector = BufferedTensor(vectorShape, buffer, offset)
             yield(vector)
         }
     }
 
-    public fun matrixSequence(): Sequence<BufferedTensor2D<T>> = sequence {
+    internal fun matrixSequence(): Sequence<BufferedTensor<T>> = sequence {
         check(shape.size >= 2) { "todo" }
         val n = shape.size
         val matrixOffset = shape[n - 1] * shape[n - 2]
-        val matrixShape = intArrayOf(shape[n - 2], shape[n - 1]) //todo better way?
+        val matrixShape = intArrayOf(shape[n - 2], shape[n - 1])
         for (offset in 0 until numel step matrixOffset) {
-            val matrix = BufferedTensor<T>(matrixShape, buffer, offset).as2D()
+            val matrix = BufferedTensor(matrixShape, buffer, offset)
             yield(matrix)
         }
     }
 
-    public inline fun forEachVector(vectorAction: (BufferedTensor1D<T>) -> Unit): Unit {
+    internal inline fun forEachVector(vectorAction: (BufferedTensor<T>) -> Unit): Unit {
         for (vector in vectorSequence()) {
             vectorAction(vector)
         }
     }
 
-    public inline fun forEachMatrix(matrixAction: (BufferedTensor2D<T>) -> Unit): Unit {
+    internal inline fun forEachMatrix(matrixAction: (BufferedTensor<T>) -> Unit): Unit {
         for (matrix in matrixSequence()) {
             matrixAction(matrix)
         }
@@ -71,7 +70,6 @@ public open class BufferedTensor<T>(
 
 }
 
-
 public class IntTensor internal constructor(
     shape: IntArray,
     buffer: IntArray,
@@ -112,90 +110,7 @@ public class DoubleTensor internal constructor(
             this(bufferedTensor.shape, bufferedTensor.buffer.array(), bufferedTensor.bufferStart)
 }
 
-
+internal fun BufferedTensor<Int>.asTensor(): IntTensor = IntTensor(this)
-public class BufferedTensor2D<T> internal constructor(
+internal fun BufferedTensor<Long>.asTensor(): LongTensor = LongTensor(this)
-    private val tensor: BufferedTensor<T>,
+internal fun BufferedTensor<Float>.asTensor(): FloatTensor = FloatTensor(this)
-) : BufferedTensor<T>(tensor), MutableStructure2D<T> {
+internal fun BufferedTensor<Double>.asTensor(): DoubleTensor = DoubleTensor(this)
-    init {
-        check(shape.size == 2) {
-            "Shape ${shape.toList()} not compatible with DoubleTensor2D"
-        }
-    }
-
-    override val shape: IntArray
-        get() = tensor.shape
-
-    override val rowNum: Int
-        get() = shape[0]
-    override val colNum: Int
-        get() = shape[1]
-
-    override fun get(i: Int, j: Int): T = tensor[intArrayOf(i, j)]
-
-    override fun get(index: IntArray): T = tensor[index]
-
-    override fun elements(): Sequence<Pair<IntArray, T>> = tensor.elements()
-
-    override fun set(i: Int, j: Int, value: T) {
-        tensor[intArrayOf(i, j)] = value
-    }
-
-    override val rows: List<BufferedTensor1D<T>>
-        get() = List(rowNum) { i ->
-            BufferedTensor1D(
-                BufferedTensor(
-                    shape = intArrayOf(colNum),
-                    buffer = VirtualMutableBuffer(colNum) { j -> get(i, j) },
-                    bufferStart = 0
-                )
-            )
-        }
-
-    override val columns: List<BufferedTensor1D<T>>
-        get() = List(colNum) { j ->
-            BufferedTensor1D(
-                BufferedTensor(
-                    shape = intArrayOf(rowNum),
-                    buffer = VirtualMutableBuffer(rowNum) { i -> get(i, j) },
-                    bufferStart = 0
-                )
-            )
-        }
-}
-
-public class BufferedTensor1D<T> internal constructor(
-    private val tensor: BufferedTensor<T>
-) : BufferedTensor<T>(tensor), MutableStructure1D<T> {
-    init {
-        check(shape.size == 1) {
-            "Shape ${shape.toList()} not compatible with DoubleTensor1D"
-        }
-    }
-
-    override fun get(index: IntArray): T = tensor[index]
-
-    override fun set(index: IntArray, value: T) {
-        tensor[index] = value
-    }
-
-    override val size: Int
-        get() = tensor.linearStructure.size
-
-    override fun get(index: Int): T = tensor[intArrayOf(index)]
-
-    override fun set(index: Int, value: T) {
-        tensor[intArrayOf(index)] = value
-    }
-
-    override fun copy(): MutableBuffer<T> = tensor.buffer.copy()
-
-}
-
-internal fun BufferedTensor<Int>.asIntTensor(): IntTensor = IntTensor(this)
-internal fun BufferedTensor<Long>.asLongTensor(): LongTensor = LongTensor(this)
-internal fun BufferedTensor<Float>.asFloatTensor(): FloatTensor = FloatTensor(this)
-internal fun BufferedTensor<Double>.asDoubleTensor(): DoubleTensor = DoubleTensor(this)
-
-
-public fun <T> BufferedTensor<T>.as2D(): BufferedTensor2D<T> = BufferedTensor2D(this)
-public fun <T> BufferedTensor<T>.as1D(): BufferedTensor1D<T> = BufferedTensor1D(this)

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

@@ -1,5 +1,9 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.nd.MutableStructure1D
+import space.kscience.kmath.nd.MutableStructure2D
+import space.kscience.kmath.nd.as1D
+import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
 import kotlin.math.sqrt
 
@@ -11,6 +15,48 @@ public class DoubleLinearOpsTensorAlgebra :
 
     override fun DoubleTensor.det(): DoubleTensor = detLU()
 
+    private inline fun luHelper(lu: MutableStructure2D<Double>, pivots: MutableStructure1D<Int>, m: Int) {
+        for (row in 0 until m) pivots[row] = row
+
+        for (i in 0 until m) {
+            var maxVal = -1.0
+            var maxInd = i
+
+            for (k in i until m) {
+                val absA = kotlin.math.abs(lu[k, i])
+                if (absA > maxVal) {
+                    maxVal = absA
+                    maxInd = k
+                }
+            }
+
+            //todo check singularity
+
+            if (maxInd != i) {
+
+                val j = pivots[i]
+                pivots[i] = pivots[maxInd]
+                pivots[maxInd] = j
+
+                for (k in 0 until m) {
+                    val tmp = lu[i, k]
+                    lu[i, k] = lu[maxInd, k]
+                    lu[maxInd, k] = tmp
+                }
+
+                pivots[m] += 1
+
+            }
+
+            for (j in i + 1 until m) {
+                lu[j, i] /= lu[i, i]
+                for (k in i + 1 until m) {
+                    lu[j, k] -= lu[j, i] * lu[i, k]
+                }
+            }
+        }
+    }
+
     override fun DoubleTensor.lu(): Pair<DoubleTensor, IntTensor> {
 
         checkSquareMatrix(shape)
@@ -27,90 +73,93 @@ public class DoubleLinearOpsTensorAlgebra :
             IntArray(pivotsShape.reduce(Int::times)) { 0 }
         )
 
-        for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
+        for ((lu, pivots) in luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
-            for (row in 0 until m) pivots[row] = row
+            luHelper(lu.as2D(), pivots.as1D(), m)
-
-            for (i in 0 until m) {
-                var maxVal = -1.0
-                var maxInd = i
-
-                for (k in i until m) {
-                    val absA = kotlin.math.abs(lu[k, i])
-                    if (absA > maxVal) {
-                        maxVal = absA
-                        maxInd = k
-                    }
-                }
-
-                //todo check singularity
-
-                if (maxInd != i) {
-
-                    val j = pivots[i]
-                    pivots[i] = pivots[maxInd]
-                    pivots[maxInd] = j
-
-                    for (k in 0 until m) {
-                        val tmp = lu[i, k]
-                        lu[i, k] = lu[maxInd, k]
-                        lu[maxInd, k] = tmp
-                    }
-
-                    pivots[m] += 1
-
-                }
-
-                for (j in i + 1 until m) {
-                    lu[j, i] /= lu[i, i]
-                    for (k in i + 1 until m) {
-                        lu[j, k] -= lu[j, i] * lu[i, k]
-                    }
-                }
-            }
-        }
-
 
         return Pair(luTensor, pivotsTensor)
 
     }
 
-    override fun luPivot(luTensor: DoubleTensor, pivotsTensor: IntTensor): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
+    private inline fun pivInit(
+        p: MutableStructure2D<Double>,
+        pivot: MutableStructure1D<Int>,
+        n: Int
+    ) {
+        for (i in 0 until n) {
+            p[i, pivot[i]] = 1.0
+        }
+    }
+
+    private inline fun luPivotHelper(
+        l: MutableStructure2D<Double>,
+        u: MutableStructure2D<Double>,
+        lu: MutableStructure2D<Double>,
+        n: Int
+    ) {
+        for (i in 0 until n) {
+            for (j in 0 until n) {
+                if (i == j) {
+                    l[i, j] = 1.0
+                }
+                if (j < i) {
+                    l[i, j] = lu[i, j]
+                }
+                if (j >= i) {
+                    u[i, j] = lu[i, j]
+                }
+            }
+        }
+    }
+
+    override fun luPivot(
+        luTensor: DoubleTensor,
+        pivotsTensor: IntTensor
+    ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         //todo checks
         checkSquareMatrix(luTensor.shape)
-        check(luTensor.shape.dropLast(1).toIntArray() contentEquals pivotsTensor.shape) { "Bed shapes (("} //todo rewrite
+        check(
+            luTensor.shape.dropLast(1).toIntArray() contentEquals pivotsTensor.shape
+        ) { "Bed shapes ((" } //todo rewrite
 
         val n = luTensor.shape.last()
         val pTensor = luTensor.zeroesLike()
-        for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.vectorSequence())){
+        for ((p, pivot) in pTensor.matrixSequence().zip(pivotsTensor.vectorSequence()))
-            for (i in 0 until n){
+            pivInit(p.as2D(), pivot.as1D(), n)
-                p[i, pivot[i]] = 1.0
-            }
-        }
 
         val lTensor = luTensor.zeroesLike()
         val uTensor = luTensor.zeroesLike()
 
-        for ((pairLU, lu) in lTensor.matrixSequence().zip(uTensor.matrixSequence()).zip(luTensor.matrixSequence())){
+        for ((pairLU, lu) in lTensor.matrixSequence().zip(uTensor.matrixSequence())
+            .zip(luTensor.matrixSequence())) {
             val (l, u) = pairLU
-            for (i in 0 until n) {
+            luPivotHelper(l.as2D(), u.as2D(), lu.as2D(), n)
-                for (j in 0 until n) {
-                    if (i == j) {
-                        l[i, j] = 1.0
-                    }
-                    if (j < i) {
-                        l[i, j] = lu[i, j]
-                    }
-                    if (j >= i) {
-                        u[i, j] = lu[i, j]
-                    }
-                }
-            }
         }
 
         return Triple(pTensor, lTensor, uTensor)
 
     }
 
+    private inline fun choleskyHelper(
+        a: MutableStructure2D<Double>,
+        l: MutableStructure2D<Double>,
+        n: Int
+    ) {
+        for (i in 0 until n) {
+            for (j in 0 until i) {
+                var h = a[i, j]
+                for (k in 0 until j) {
+                    h -= l[i, k] * l[j, k]
+                }
+                l[i, j] = h / l[j, j]
+            }
+            var h = a[i, i]
+            for (j in 0 until i) {
+                h -= l[i, j] * l[i, j]
+            }
+            l[i, i] = sqrt(h)
+        }
+    }
+
     override fun DoubleTensor.cholesky(): DoubleTensor {
         // todo checks
         checkSquareMatrix(shape)
@@ -118,22 +167,8 @@ public class DoubleLinearOpsTensorAlgebra :
         val n = shape.last()
         val lTensor = zeroesLike()
 
-        for ((a, l) in this.matrixSequence().zip(lTensor.matrixSequence())) {
+        for ((a, l) in this.matrixSequence().zip(lTensor.matrixSequence()))
-            for (i in 0 until n) {
+            for (i in 0 until n) choleskyHelper(a.as2D(), l.as2D(), n)
-                for (j in 0 until i) {
-                    var h = a[i, j]
-                    for (k in 0 until j) {
-                        h -= l[i, k] * l[j, k]
-                    }
-                    l[i, j] = h / l[j, j]
-                }
-                var h = a[i, i]
-                for (j in 0 until i) {
-                    h -= l[i, j] * l[i, j]
-                }
-                l[i, i] = sqrt(h)
-            }
-        }
 
         return lTensor
     }
@@ -150,9 +185,11 @@ public class DoubleLinearOpsTensorAlgebra :
         TODO("ANDREI")
     }
 
-    private fun luMatrixDet(lu: BufferedTensor2D<Double>, pivots: BufferedTensor1D<Int>): Double {
+    private fun luMatrixDet(luTensor: MutableStructure2D<Double>, pivotsTensor: MutableStructure1D<Int>): Double {
+        val lu = luTensor.as2D()
+        val pivots = pivotsTensor.as1D()
         val m = lu.shape[0]
-        val sign = if((pivots[m] - m) % 2 == 0) 1.0 else -1.0
+        val sign = if ((pivots[m] - m) % 2 == 0) 1.0 else -1.0
         return (0 until m).asSequence().map { lu[it, it] }.fold(sign) { left, right -> left * right }
     }
 
@@ -162,34 +199,34 @@ public class DoubleLinearOpsTensorAlgebra :
 
         val detTensorShape = IntArray(n - 1) { i -> shape[i] }
         detTensorShape[n - 2] = 1
-        val resBuffer =  DoubleArray(detTensorShape.reduce(Int::times)) { 0.0 }
+        val resBuffer = DoubleArray(detTensorShape.reduce(Int::times)) { 0.0 }
 
         val detTensor = DoubleTensor(
             detTensorShape,
             resBuffer
         )
 
-        luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).forEachIndexed { index, (luMatrix, pivots) ->
+        luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).forEachIndexed { index, (lu, pivots) ->
-            resBuffer[index] = luMatrixDet(luMatrix, pivots)
+            resBuffer[index] = luMatrixDet(lu.as2D(), pivots.as1D())
         }
 
         return detTensor
     }
 
     private fun luMatrixInv(
-        lu: BufferedTensor2D<Double>,
+        lu: MutableStructure2D<Double>,
-        pivots: BufferedTensor1D<Int>,
+        pivots: MutableStructure1D<Int>,
-        invMatrix : BufferedTensor2D<Double>
+        invMatrix: MutableStructure2D<Double>
-    ): Unit {
+    ) {
         val m = lu.shape[0]
 
         for (j in 0 until m) {
             for (i in 0 until m) {
-                if (pivots[i] == j){
+                if (pivots[i] == j) {
                     invMatrix[i, j] = 1.0
                 }
 
-                for (k in 0 until i){
+                for (k in 0 until i) {
                     invMatrix[i, j] -= lu[i, k] * invMatrix[k, j]
                 }
             }
@@ -205,13 +242,12 @@ public class DoubleLinearOpsTensorAlgebra :
 
     public fun DoubleTensor.invLU(): DoubleTensor {
         val (luTensor, pivotsTensor) = lu()
-        val n = shape.size
         val invTensor = luTensor.zeroesLike()
 
         val seq = luTensor.matrixSequence().zip(pivotsTensor.vectorSequence()).zip(invTensor.matrixSequence())
         for ((luP, invMatrix) in seq) {
             val (lu, pivots) = luP
-            luMatrixInv(lu, pivots, invMatrix)
+            luMatrixInv(lu.as2D(), pivots.as1D(), invMatrix.as2D())
         }
 
         return invTensor

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

@@ -1,8 +1,7 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.linear.Matrix
 import space.kscience.kmath.nd.MutableStructure2D
-import space.kscience.kmath.nd.Structure2D
+import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
 import kotlin.math.abs
 
@@ -224,6 +223,23 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         return this.view(other.shape)
     }
 
+    private inline fun dotHelper(
+        a: MutableStructure2D<Double>,
+        b: MutableStructure2D<Double>,
+        res: MutableStructure2D<Double>,
+        l: Int, m: Int, n: Int
+    ) {
+        for (i in 0 until l) {
+            for (j in 0 until n) {
+                var curr = 0.0
+                for (k in 0 until m) {
+                    curr += a[i, k] * b[k, j]
+                }
+                res[i, j] = curr
+            }
+        }
+    }
+
     override fun DoubleTensor.dot(other: DoubleTensor): DoubleTensor {
         if (this.shape.size == 1 && other.shape.size == 1) {
             return DoubleTensor(intArrayOf(1), doubleArrayOf(this.times(other).buffer.array().sum()))
@@ -240,7 +256,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         }
         if (other.shape.size == 1) {
             lastDim = true
-            newOther = other.view(other.shape + intArrayOf(1) )
+            newOther = other.view(other.shape + intArrayOf(1))
         }
 
         val broadcastTensors = broadcastOuterTensors(newThis, newOther)
@@ -248,7 +264,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
         newOther = broadcastTensors[1]
 
         val l = newThis.shape[newThis.shape.size - 2]
-        val m1= newThis.shape[newThis.shape.size - 1]
+        val m1 = newThis.shape[newThis.shape.size - 1]
         val m2 = newOther.shape[newOther.shape.size - 2]
         val n = newOther.shape[newOther.shape.size - 1]
         if (m1 != m2) {
@@ -262,21 +278,14 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
         for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
             val (a, b) = ab
-
+            dotHelper(a.as2D(), b.as2D(), res.as2D(), l, m, n)
-            for (i in 0 until l) {
-                for (j in 0 until n) {
-                    var curr = 0.0
-                    for (k in 0 until m) {
-                        curr += a[i, k] * b[k, j]
-                    }
-                   res[i, j] = curr
-                }
-            }
         }
 
         if (penultimateDim) {
-            return resTensor.view(resTensor.shape.dropLast(2).toIntArray() +
+            return resTensor.view(
-                    intArrayOf(resTensor.shape.last()))
+                resTensor.shape.dropLast(2).toIntArray() +
+                        intArrayOf(resTensor.shape.last())
+            )
         }
         if (lastDim) {
             return resTensor.view(resTensor.shape.dropLast(1).toIntArray())
@@ -307,15 +316,11 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double, Dou
 
     public fun DoubleTensor.eq(other: DoubleTensor): Boolean = this.eq(other, 1e-5)
 
-    public fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
+    public fun DoubleTensor.contentEquals(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean =
-        if (!(this.shape contentEquals other.shape)) {
+        this.eq(other, eqFunction)
-            return false
-        }
-        return this.eq(other, eqFunction)
-    }
 
-    public fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
+    private fun DoubleTensor.eq(other: DoubleTensor, eqFunction: (Double, Double) -> Boolean): Boolean {
-        // todo broadcasting checking
+        checkShapesCompatible(this, other)
         val n = this.linearStructure.size
         if (n != other.linearStructure.size) {
             return false

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

@@ -11,14 +11,14 @@ internal inline fun <T, TensorType : TensorStructure<T>,
         "Illegal empty shape provided"
     }
 
-internal inline fun < TensorType : TensorStructure<Double>,
+internal inline fun <TensorType : TensorStructure<Double>,
         TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
         TorchTensorAlgebraType.checkEmptyDoubleBuffer(buffer: DoubleArray): Unit =
     check(buffer.isNotEmpty()) {
         "Illegal empty buffer provided"
     }
 
-internal inline fun < TensorType : TensorStructure<Double>,
+internal inline fun <TensorType : TensorStructure<Double>,
         TorchTensorAlgebraType : TensorAlgebra<Double, TensorType>>
         TorchTensorAlgebraType.checkBufferShapeConsistency(shape: IntArray, buffer: DoubleArray): Unit =
     check(buffer.size == shape.reduce(Int::times)) {
@@ -56,4 +56,4 @@ internal inline fun <T, TensorType : TensorStructure<T>,
     check(shape[n - 1] == shape[n - 2]) {
         "Tensor must be batches of square matrices, but they are ${shape[n - 1]} by ${shape[n - 1]} matrices"
     }
-}
+}

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

@@ -1,5 +1,7 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.nd.as1D
+import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.structures.toDoubleArray
 import kotlin.test.Test
 import kotlin.test.assertEquals