rework structure + fixes

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/AnalyticTensorAlgebra.kt

@@ -1,4 +1,9 @@
-package space.kscience.kmath.tensors
+/*
+ * Copyright 2018-2021 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.tensors.api
 
 
 public interface AnalyticTensorAlgebra<T> :

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt

@@ -1,4 +1,9 @@
-package space.kscience.kmath.tensors
+/*
+ * Copyright 2018-2021 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.tensors.api
 
 
 public interface LinearOpsTensorAlgebra<T> :

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt

@@ -1,4 +1,9 @@
-package space.kscience.kmath.tensors
+/*
+ * Copyright 2018-2021 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.tensors.api
 
 // https://proofwiki.org/wiki/Definition:Algebra_over_Ring
 public interface TensorAlgebra<T> {
@@ -40,7 +45,9 @@ public interface TensorAlgebra<T> {
     //https://pytorch.org/docs/stable/generated/torch.diag_embed.html
     public fun diagonalEmbedding(
         diagonalEntries: TensorStructure<T>,
-        offset: Int = 0, dim1: Int = -2, dim2: Int = -1
+        offset: Int = 0,
+        dim1: Int = -2,
+        dim2: Int = -1
     ): TensorStructure<T>
 
 }

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorPartialDivisionAlgebra.kt

@@ -1,8 +1,13 @@
-package space.kscience.kmath.tensors
+/*
+ * Copyright 2018-2021 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.tensors.api
 
 // https://proofwiki.org/wiki/Definition:Division_Algebra
 public interface TensorPartialDivisionAlgebra<T> :
-        TensorAlgebra<T> {
+    TensorAlgebra<T> {
     public operator fun TensorStructure<T>.div(value: T): TensorStructure<T>
     public operator fun TensorStructure<T>.div(other: TensorStructure<T>): TensorStructure<T>
     public operator fun TensorStructure<T>.divAssign(value: T)

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorStructure.kt

@@ -1,4 +1,4 @@
-package space.kscience.kmath.tensors
+package space.kscience.kmath.tensors.api
 
 import space.kscience.kmath.nd.MutableStructureND
 

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

@@ -1,7 +1,8 @@
 package space.kscience.kmath.tensors.core
 
 import space.kscience.kmath.structures.*
-import space.kscience.kmath.tensors.TensorStructure
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
 
 
 public open class BufferedTensor<T>(

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

@@ -0,0 +1,89 @@
+/*
+ * Copyright 2018-2021 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.tensors.core.algebras
+
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.*
+import space.kscience.kmath.tensors.core.broadcastTensors
+import space.kscience.kmath.tensors.core.broadcastTo
+
+public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
+
+    override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+            newThis.buffer.array()[i] + newOther.buffer.array()[i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] +=
+                newOther.buffer.array()[tensor.bufferStart + i]
+        }
+    }
+
+    override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+            newThis.buffer.array()[i] - newOther.buffer.array()[i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] -=
+                newOther.buffer.array()[tensor.bufferStart + i]
+        }
+    }
+
+    override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+            newThis.buffer.array()[newThis.bufferStart + i] *
+                    newOther.buffer.array()[newOther.bufferStart + i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] *=
+                newOther.buffer.array()[tensor.bufferStart + i]
+        }
+    }
+
+    override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
+        val broadcast = broadcastTensors(tensor, other.tensor)
+        val newThis = broadcast[0]
+        val newOther = broadcast[1]
+        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
+            newThis.buffer.array()[newOther.bufferStart + i] /
+                    newOther.buffer.array()[newOther.bufferStart + i]
+        }
+        return DoubleTensor(newThis.shape, resBuffer)
+    }
+
+    override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
+        val newOther = broadcastTo(other.tensor, tensor.shape)
+        for (i in 0 until tensor.linearStructure.size) {
+            tensor.buffer.array()[tensor.bufferStart + i] /=
+                newOther.buffer.array()[tensor.bufferStart + i]
+        }
+    }
+
+}

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

@@ -1,7 +1,14 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 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.
+ */
 
-import space.kscience.kmath.tensors.AnalyticTensorAlgebra
+package space.kscience.kmath.tensors.core.algebras
-import space.kscience.kmath.tensors.TensorStructure
+
+import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.DoubleTensor
+import space.kscience.kmath.tensors.core.tensor
 import kotlin.math.*
 
 public class DoubleAnalyticTensorAlgebra:

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

@@ -1,9 +1,23 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 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.
+ */
 
-import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
+package space.kscience.kmath.tensors.core.algebras
+
+import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
 import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.tensors.TensorStructure
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.*
+import space.kscience.kmath.tensors.core.checkSquareMatrix
+import space.kscience.kmath.tensors.core.choleskyHelper
+import space.kscience.kmath.tensors.core.cleanSymHelper
+import space.kscience.kmath.tensors.core.luHelper
+import space.kscience.kmath.tensors.core.luMatrixDet
+import space.kscience.kmath.tensors.core.luMatrixInv
+import space.kscience.kmath.tensors.core.luPivotHelper
+import space.kscience.kmath.tensors.core.pivInit
 import kotlin.math.min
 
 
@@ -25,12 +39,11 @@ public class DoubleLinearOpsTensorAlgebra :
         luTensor: TensorStructure<Double>,
         pivotsTensor: TensorStructure<Int>
     ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
-        //todo checks
         checkSquareMatrix(luTensor.shape)
         check(
             luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() ||
                     luTensor.shape.last() == pivotsTensor.shape.last() - 1
-        ) { "Bad shapes ((" } //todo rewrite
+        ) { "Inappropriate shapes of input tensors" }
 
         val n = luTensor.shape.last()
         val pTensor = luTensor.zeroesLike()
@@ -90,10 +103,10 @@ public class DoubleLinearOpsTensorAlgebra :
 
         for ((matrix, USV) in tensor.matrixSequence()
             .zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
-            val size = matrix.shape.reduce { acc, i -> acc * i }
+            val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
             val curMatrix = DoubleTensor(
                 matrix.shape,
-                matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + size).toDoubleArray()
+                matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + matrixSize).toDoubleArray()
             )
             svdHelper(curMatrix, USV, m, n, epsilon)
         }

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

@@ -1,8 +1,24 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 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.tensors.core.algebras
 
 import space.kscience.kmath.nd.as2D
-import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
+import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra
-import space.kscience.kmath.tensors.TensorStructure
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.*
+import space.kscience.kmath.tensors.core.broadcastOuterTensors
+import space.kscience.kmath.tensors.core.checkBufferShapeConsistency
+import space.kscience.kmath.tensors.core.checkEmptyDoubleBuffer
+import space.kscience.kmath.tensors.core.checkEmptyShape
+import space.kscience.kmath.tensors.core.checkShapesCompatible
+import space.kscience.kmath.tensors.core.checkTranspose
+import space.kscience.kmath.tensors.core.checkView
+import space.kscience.kmath.tensors.core.dotHelper
+import space.kscience.kmath.tensors.core.getRandomNormals
+import space.kscience.kmath.tensors.core.minusIndexFrom
 import kotlin.math.abs
 
 public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
@@ -263,7 +279,6 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         if (m1 != m2) {
             throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
         }
-        val m = m1
 
         val resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
         val resSize = resShape.reduce { acc, i -> acc * i }
@@ -271,7 +286,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
 
         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)
+            dotHelper(a.as2D(), b.as2D(), res.as2D(), l, m1, n)
         }
 
         if (penultimateDim) {
@@ -347,7 +362,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
         return tensor.eq(other) { x, y -> abs(x - y) < delta }
     }
 
-    public fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
+    public infix fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
 
     private fun TensorStructure<Double>.eq(
         other: TensorStructure<Double>,

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

@@ -1,4 +1,9 @@
-package space.kscience.kmath.tensors.core
+/*
+ * Copyright 2018-2021 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.tensors.core.algebras
 
 import kotlin.math.max
 

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

@@ -1,89 +1,31 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.tensors.TensorStructure
+import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
 import kotlin.math.max
 
-public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
-
-    override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
-        val broadcast = broadcastTensors(tensor, other.tensor)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[i] + newOther.buffer.array()[i]
-        }
-        return DoubleTensor(newThis.shape, resBuffer)
-    }
-
-    override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
-        val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] +=
-                newOther.buffer.array()[tensor.bufferStart + i]
-        }
-    }
-
-    override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
-        val broadcast = broadcastTensors(tensor, other.tensor)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[i] - newOther.buffer.array()[i]
-        }
-        return DoubleTensor(newThis.shape, resBuffer)
-    }
-
-    override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
-        val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] -=
-                newOther.buffer.array()[tensor.bufferStart + i]
-        }
-    }
-
-    override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
-        val broadcast = broadcastTensors(tensor, other.tensor)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[newThis.bufferStart + i] *
-                    newOther.buffer.array()[newOther.bufferStart + i]
-        }
-        return DoubleTensor(newThis.shape, resBuffer)
-    }
-
-    override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
-        val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] *=
-                newOther.buffer.array()[tensor.bufferStart + i]
-        }
-    }
-
-    override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
-        val broadcast = broadcastTensors(tensor, other.tensor)
-        val newThis = broadcast[0]
-        val newOther = broadcast[1]
-        val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
-            newThis.buffer.array()[newOther.bufferStart + i] /
-                    newOther.buffer.array()[newOther.bufferStart + i]
-        }
-        return DoubleTensor(newThis.shape, resBuffer)
-    }
-
-    override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
-        val newOther = broadcastTo(other.tensor, tensor.shape)
-        for (i in 0 until tensor.linearStructure.size) {
-            tensor.buffer.array()[tensor.bufferStart + i] /=
-                newOther.buffer.array()[tensor.bufferStart + i]
-        }
-    }
-
-}
-
 public inline fun <R> BroadcastDoubleTensorAlgebra(block: BroadcastDoubleTensorAlgebra.() -> R): R =
     BroadcastDoubleTensorAlgebra().block()
 
+internal inline fun multiIndexBroadCasting(tensor: DoubleTensor, resTensor: DoubleTensor, linearSize: Int) {
+    for (linearIndex in 0 until linearSize) {
+        val totalMultiIndex = resTensor.linearStructure.index(linearIndex)
+        val curMultiIndex = tensor.shape.copyOf()
+
+        val offset = totalMultiIndex.size - curMultiIndex.size
+
+        for (i in curMultiIndex.indices) {
+            if (curMultiIndex[i] != 1) {
+                curMultiIndex[i] = totalMultiIndex[i + offset]
+            } else {
+                curMultiIndex[i] = 0
+            }
+        }
+
+        val curLinearIndex = tensor.linearStructure.offset(curMultiIndex)
+        resTensor.buffer.array()[linearIndex] =
+            tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
+    }
+}
 
 internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
     var totalDim = 0
@@ -129,24 +71,7 @@ internal inline fun broadcastTo(tensor: DoubleTensor, newShape: IntArray): Doubl
         }
     }
 
-    for (linearIndex in 0 until n) {
+    multiIndexBroadCasting(tensor, resTensor, n)
-        val totalMultiIndex = resTensor.linearStructure.index(linearIndex)
-        val curMultiIndex = tensor.shape.copyOf()
-
-        val offset = totalMultiIndex.size - curMultiIndex.size
-
-        for (i in curMultiIndex.indices) {
-            if (curMultiIndex[i] != 1) {
-                curMultiIndex[i] = totalMultiIndex[i + offset]
-            } else {
-                curMultiIndex[i] = 0
-            }
-        }
-
-        val curLinearIndex = tensor.linearStructure.offset(curMultiIndex)
-        resTensor.buffer.array()[linearIndex] =
-            tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
-    }
     return resTensor
 }
 
@@ -157,25 +82,7 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
     val res = ArrayList<DoubleTensor>(0)
     for (tensor in tensors) {
         val resTensor = DoubleTensor(totalShape, DoubleArray(n))
-
+        multiIndexBroadCasting(tensor, resTensor, n)
-        for (linearIndex in 0 until n) {
-            val totalMultiIndex = resTensor.linearStructure.index(linearIndex)
-            val curMultiIndex = tensor.shape.copyOf()
-
-            val offset = totalMultiIndex.size - curMultiIndex.size
-
-            for (i in curMultiIndex.indices) {
-                if (curMultiIndex[i] != 1) {
-                    curMultiIndex[i] = totalMultiIndex[i + offset]
-                } else {
-                    curMultiIndex[i] = 0
-                }
-            }
-
-            val curLinearIndex = tensor.linearStructure.offset(curMultiIndex)
-            resTensor.buffer.array()[linearIndex] =
-                tensor.buffer.array()[tensor.bufferStart + curLinearIndex]
-        }
         res.add(resTensor)
     }
 
@@ -208,7 +115,7 @@ internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<Do
         for (linearIndex in 0 until n) {
             val totalMultiIndex = outerTensor.linearStructure.index(linearIndex)
             var curMultiIndex = tensor.shape.sliceArray(0..tensor.shape.size - 3).copyOf()
-            curMultiIndex = IntArray(totalMultiIndex.size - curMultiIndex.size) {1} + curMultiIndex
+            curMultiIndex = IntArray(totalMultiIndex.size - curMultiIndex.size) { 1 } + curMultiIndex
 
             val newTensor = DoubleTensor(curMultiIndex + matrixShape, tensor.buffer.array())
 
@@ -221,10 +128,14 @@ internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<Do
             }
 
             for (i in 0 until matrixSize) {
-                val curLinearIndex = newTensor.linearStructure.offset(curMultiIndex +
+                val curLinearIndex = newTensor.linearStructure.offset(
-                        matrix.linearStructure.index(i))
+                    curMultiIndex +
-                val newLinearIndex = resTensor.linearStructure.offset(totalMultiIndex +
+                            matrix.linearStructure.index(i)
-                        matrix.linearStructure.index(i))
+                )
+                val newLinearIndex = resTensor.linearStructure.offset(
+                    totalMultiIndex +
+                            matrix.linearStructure.index(i)
+                )
 
                 resTensor.buffer.array()[resTensor.bufferStart + newLinearIndex] =
                     newTensor.buffer.array()[newTensor.bufferStart + curLinearIndex]

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

@@ -1,6 +1,8 @@
 package space.kscience.kmath.tensors.core
 
-import space.kscience.kmath.tensors.TensorStructure
+import space.kscience.kmath.tensors.api.TensorStructure
+import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 
 
 internal inline fun checkEmptyShape(shape: IntArray): Unit =

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

@@ -4,6 +4,8 @@ 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.core.algebras.DoubleAnalyticTensorAlgebra
+import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import kotlin.math.abs
 import kotlin.math.min
 import kotlin.math.sign
@@ -31,13 +33,13 @@ internal inline fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTen
     }
 }
 
-internal inline fun <T> BufferedTensor<T>.forEachVector(vectorAction: (BufferedTensor<T>) -> Unit): Unit {
+internal inline fun <T> BufferedTensor<T>.forEachVector(vectorAction: (BufferedTensor<T>) -> Unit) {
     for (vector in vectorSequence()) {
         vectorAction(vector)
     }
 }
 
-internal inline fun <T> BufferedTensor<T>.forEachMatrix(matrixAction: (BufferedTensor<T>) -> Unit): Unit {
+internal inline fun <T> BufferedTensor<T>.forEachMatrix(matrixAction: (BufferedTensor<T>) -> Unit) {
     for (matrix in matrixSequence()) {
         matrixAction(matrix)
     }
@@ -284,7 +286,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
     matrix: DoubleTensor,
     USV: Pair<BufferedTensor<Double>, Pair<BufferedTensor<Double>, BufferedTensor<Double>>>,
     m: Int, n: Int, epsilon: Double
-): Unit {
+) {
     val res = ArrayList<Triple<Double, DoubleTensor, DoubleTensor>>(0)
     val (matrixU, SV) = USV
     val (matrixS, matrixV) = SV
@@ -332,7 +334,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
     }
 }
 
-internal inline fun cleanSymHelper(matrix: MutableStructure2D<Double>, n: Int): Unit {
+internal inline fun cleanSymHelper(matrix: MutableStructure2D<Double>, n: Int) {
     for (i in 0 until n)
         for (j in 0 until n) {
             if (i == j) {

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

@@ -110,7 +110,6 @@ internal inline fun DoubleTensor.toPrettyString(): String = buildString {
             charOffset -=1
         }
         offset += vectorSize
-        // todo refactor
         if (this@toPrettyString.numElements == offset) {
             break
         }

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

@@ -1,5 +1,6 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
 import kotlin.test.assertTrue
 

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

@@ -1,5 +1,6 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
 import kotlin.math.abs
 import kotlin.math.exp
 import kotlin.test.Test

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

@@ -1,5 +1,6 @@
 package space.kscience.kmath.tensors.core
 
+import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
 import kotlin.math.abs
 import kotlin.test.Test
 import kotlin.test.assertEquals
@@ -125,8 +126,6 @@ class TestDoubleLinearOpsTensorAlgebra {
 
         val (lu, pivots) = tensor.lu()
 
-        // todo check lu
-
         val (p, l, u) = luPivot(lu, pivots)
 
         assertTrue { p.shape contentEquals shape }

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

@@ -3,7 +3,7 @@ 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.Ignore
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
 import kotlin.test.assertEquals
 import kotlin.test.assertTrue

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

@@ -1,7 +1,9 @@
 package space.kscience.kmath.tensors.core
 
 
+import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
 import kotlin.test.Test
+import kotlin.test.assertFalse
 import kotlin.test.assertTrue
 
 class TestDoubleTensorAlgebra {
@@ -133,9 +135,9 @@ class TestDoubleTensorAlgebra {
         assertTrue(diagonal1.buffer.array() contentEquals
                 doubleArrayOf(10.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 30.0))
 
-        val diagonal1_offset = diagonalEmbedding(tensor1, 1, 1, 0)
+        val diagonal1Offset = diagonalEmbedding(tensor1, 1, 1, 0)
-        assertTrue(diagonal1_offset.shape contentEquals intArrayOf(4, 4))
+        assertTrue(diagonal1Offset.shape contentEquals intArrayOf(4, 4))
-        assertTrue(diagonal1_offset.buffer.array() contentEquals
+        assertTrue(diagonal1Offset.buffer.array() 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)
@@ -149,7 +151,15 @@ class TestDoubleTensorAlgebra {
     }
 
     @Test
-    fun testContentEqual() = DoubleTensorAlgebra {
+    fun testEq() = DoubleTensorAlgebra {
-        //TODO()
+        val tensor1 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor2 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+        val tensor3 = fromArray(intArrayOf(2, 3), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 5.0))
+        val tensor4 = fromArray(intArrayOf(6, 1), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
+
+        assertTrue(tensor1 eq tensor1)
+        assertTrue(tensor1 eq tensor2)
+        assertFalse(tensor1.eq(tensor3))
+
     }
 }