Merge pull request #332 from mipt-npm/commandertvis/nd4j

Nd4j based TensorAlgebra implementation

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

@@ -227,7 +227,6 @@ public interface RingND<T, out R : Ring<T>> : Ring<StructureND<T>>, GroupND<T, R
  * Field of [StructureND].
  *
  * @param T the type of the element contained in ND structure.
- * @param N the type of ND structure.
  * @param F the type field of structure elements.
  */
 public interface FieldND<T, out F : Field<T>> : Field<StructureND<T>>, RingND<T, F>, ScaleOperations<StructureND<T>> {

kmath-nd4j/build.gradle.kts

@@ -4,7 +4,7 @@ plugins {
 }
 
 dependencies {
-    api(project(":kmath-core"))
+    api(project(":kmath-tensors"))
     api("org.nd4j:nd4j-api:1.0.0-beta7")
     testImplementation("org.nd4j:nd4j-native:1.0.0-beta7")
     testImplementation("org.nd4j:nd4j-native-platform:1.0.0-beta7")
@@ -15,19 +15,7 @@ readme {
     description = "ND4J NDStructure implementation and according NDAlgebra classes"
     maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
     propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md"))
-
+    feature(id = "nd4jarraystructure") { "NDStructure wrapper for INDArray" }
-    feature(
+    feature(id = "nd4jarrayrings") { "Rings over Nd4jArrayStructure of Int and Long" }
-        id = "nd4jarraystructure",
+    feature(id = "nd4jarrayfields") { "Fields over Nd4jArrayStructure of Float and Double" }
-        description = "NDStructure wrapper for INDArray"
-    )
-
-    feature(
-        id = "nd4jarrayrings",
-        description = "Rings over Nd4jArrayStructure of Int and Long"
-    )
-
-    feature(
-        id = "nd4jarrayfields",
-        description = "Fields over Nd4jArrayStructure of Float and Double"
-    )
 }

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

@@ -6,15 +6,14 @@
 package space.kscience.kmath.nd4j
 
 import org.nd4j.linalg.api.ndarray.INDArray
-import org.nd4j.linalg.api.ops.impl.scalar.Pow
+import org.nd4j.linalg.api.ops.impl.transforms.strict.ACosh
-import org.nd4j.linalg.api.ops.impl.transforms.strict.*
+import org.nd4j.linalg.api.ops.impl.transforms.strict.ASinh
 import org.nd4j.linalg.factory.Nd4j
 import org.nd4j.linalg.ops.transforms.Transforms
 import space.kscience.kmath.misc.PerformancePitfall
 import space.kscience.kmath.misc.UnstableKMathAPI
 import space.kscience.kmath.nd.*
 import space.kscience.kmath.operations.*
-import space.kscience.kmath.structures.*
 
 internal fun AlgebraND<*, *>.checkShape(array: INDArray): INDArray {
     val arrayShape = array.shape().toIntArray()
@@ -29,23 +28,16 @@ internal fun AlgebraND<*, *>.checkShape(array: INDArray): INDArray {
  * @param T the type of ND-structure element.
  * @param C the type of the element context.
  */
-public interface Nd4jArrayAlgebra<T, C : Algebra<T>> : AlgebraND<T, C> {
+public sealed interface Nd4jArrayAlgebra<T, out C : Algebra<T>> : AlgebraND<T, C> {
     /**
-     * Wraps [INDArray] to [N].
+     * Wraps [INDArray] to [Nd4jArrayStructure].
      */
     public fun INDArray.wrap(): Nd4jArrayStructure<T>
 
+    /**
+     * Unwraps to or acquires [INDArray] from [StructureND].
+     */
     public val StructureND<T>.ndArray: INDArray
-        get() = when {
-            !shape.contentEquals(this@Nd4jArrayAlgebra.shape) -> throw ShapeMismatchException(
-                this@Nd4jArrayAlgebra.shape,
-                shape
-            )
-            this is Nd4jArrayStructure -> ndArray //TODO check strides
-            else -> {
-                TODO()
-            }
-        }
 
     public override fun produce(initializer: C.(IntArray) -> T): Nd4jArrayStructure<T> {
         val struct = Nd4j.create(*shape)!!.wrap()
@@ -85,7 +77,7 @@ public interface Nd4jArrayAlgebra<T, C : Algebra<T>> : AlgebraND<T, C> {
  * @param T the type of the element contained in ND structure.
  * @param S the type of space of structure elements.
  */
-public interface Nd4JArrayGroup<T, S : Ring<T>> : GroupND<T, S>, Nd4jArrayAlgebra<T, S> {
+public sealed interface Nd4jArrayGroup<T, out S : Ring<T>> : GroupND<T, S>, Nd4jArrayAlgebra<T, S> {
 
     public override val zero: Nd4jArrayStructure<T>
         get() = Nd4j.zeros(*shape).wrap()
@@ -110,7 +102,7 @@ public interface Nd4JArrayGroup<T, S : Ring<T>> : GroupND<T, S>, Nd4jArrayAlgebr
  * @param R the type of ring of structure elements.
  */
 @OptIn(UnstableKMathAPI::class)
-public interface Nd4jArrayRing<T, R : Ring<T>> : RingND<T, R>, Nd4JArrayGroup<T, R> {
+public sealed interface Nd4jArrayRing<T, out R : Ring<T>> : RingND<T, R>, Nd4jArrayGroup<T, R> {
 
     public override val one: Nd4jArrayStructure<T>
         get() = Nd4j.ones(*shape).wrap()
@@ -135,10 +127,7 @@ public interface Nd4jArrayRing<T, R : Ring<T>> : RingND<T, R>, Nd4JArrayGroup<T,
 
     public companion object {
         private val intNd4jArrayRingCache: ThreadLocal<MutableMap<IntArray, IntNd4jArrayRing>> =
-            ThreadLocal.withInitial { hashMapOf() }
+            ThreadLocal.withInitial(::HashMap)
-
-        private val longNd4jArrayRingCache: ThreadLocal<MutableMap<IntArray, LongNd4jArrayRing>> =
-            ThreadLocal.withInitial { hashMapOf() }
 
         /**
          * Creates an [RingND] for [Int] values or pull it from cache if it was created previously.
@@ -146,20 +135,13 @@ public interface Nd4jArrayRing<T, R : Ring<T>> : RingND<T, R>, Nd4JArrayGroup<T,
         public fun int(vararg shape: Int): Nd4jArrayRing<Int, IntRing> =
             intNd4jArrayRingCache.get().getOrPut(shape) { IntNd4jArrayRing(shape) }
 
-        /**
-         * Creates an [RingND] for [Long] values or pull it from cache if it was created previously.
-         */
-        public fun long(vararg shape: Int): Nd4jArrayRing<Long, LongRing> =
-            longNd4jArrayRingCache.get().getOrPut(shape) { LongNd4jArrayRing(shape) }
-
         /**
          * Creates a most suitable implementation of [RingND] using reified class.
          */
         @Suppress("UNCHECKED_CAST")
-        public inline fun <reified T : Any> auto(vararg shape: Int): Nd4jArrayRing<T, out Ring<T>> = when {
+        public inline fun <reified T : Number> auto(vararg shape: Int): Nd4jArrayRing<T, Ring<T>> = when {
-            T::class == Int::class -> int(*shape) as Nd4jArrayRing<T, out Ring<T>>
+            T::class == Int::class -> int(*shape) as Nd4jArrayRing<T, Ring<T>>
-            T::class == Long::class -> long(*shape) as Nd4jArrayRing<T, out Ring<T>>
+            else -> throw UnsupportedOperationException("This factory method only supports Long type.")
-            else -> throw UnsupportedOperationException("This factory method only supports Int and Long types.")
         }
     }
 }
@@ -168,11 +150,9 @@ public interface Nd4jArrayRing<T, R : Ring<T>> : RingND<T, R>, Nd4JArrayGroup<T,
  * Represents [FieldND] over [Nd4jArrayStructure].
  *
  * @param T the type of the element contained in ND structure.
- * @param N the type of ND structure.
  * @param F the type field of structure elements.
  */
-public interface Nd4jArrayField<T, F : Field<T>> : FieldND<T, F>, Nd4jArrayRing<T, F> {
+public sealed interface Nd4jArrayField<T, out F : Field<T>> : FieldND<T, F>, Nd4jArrayRing<T, F> {
-
     public override fun divide(a: StructureND<T>, b: StructureND<T>): Nd4jArrayStructure<T> =
         a.ndArray.div(b.ndArray).wrap()
 
@@ -180,10 +160,10 @@ public interface Nd4jArrayField<T, F : Field<T>> : FieldND<T, F>, Nd4jArrayRing<
 
     public companion object {
         private val floatNd4jArrayFieldCache: ThreadLocal<MutableMap<IntArray, FloatNd4jArrayField>> =
-            ThreadLocal.withInitial { hashMapOf() }
+            ThreadLocal.withInitial(::HashMap)
 
         private val doubleNd4JArrayFieldCache: ThreadLocal<MutableMap<IntArray, DoubleNd4jArrayField>> =
-            ThreadLocal.withInitial { hashMapOf() }
+            ThreadLocal.withInitial(::HashMap)
 
         /**
          * Creates an [FieldND] for [Float] values or pull it from cache if it was created previously.
@@ -198,26 +178,64 @@ public interface Nd4jArrayField<T, F : Field<T>> : FieldND<T, F>, Nd4jArrayRing<
             doubleNd4JArrayFieldCache.get().getOrPut(shape) { DoubleNd4jArrayField(shape) }
 
         /**
-         * Creates a most suitable implementation of [RingND] using reified class.
+         * Creates a most suitable implementation of [FieldND] using reified class.
          */
         @Suppress("UNCHECKED_CAST")
-        public inline fun <reified T : Any> auto(vararg shape: Int): Nd4jArrayField<T, out Field<T>> = when {
+        public inline fun <reified T : Any> auto(vararg shape: Int): Nd4jArrayField<T, Field<T>> = when {
-            T::class == Float::class -> float(*shape) as Nd4jArrayField<T, out Field<T>>
+            T::class == Float::class -> float(*shape) as Nd4jArrayField<T, Field<T>>
-            T::class == Double::class -> real(*shape) as Nd4jArrayField<T, out Field<T>>
+            T::class == Double::class -> real(*shape) as Nd4jArrayField<T, Field<T>>
             else -> throw UnsupportedOperationException("This factory method only supports Float and Double types.")
         }
     }
 }
 
+/**
+ * Represents intersection of [ExtendedField] and [Field] over [Nd4jArrayStructure].
+ */
+public sealed interface Nd4jArrayExtendedField<T, out F : ExtendedField<T>> : ExtendedField<StructureND<T>>,
+    Nd4jArrayField<T, F> {
+    public override fun sin(arg: StructureND<T>): StructureND<T> = Transforms.sin(arg.ndArray).wrap()
+    public override fun cos(arg: StructureND<T>): StructureND<T> = Transforms.cos(arg.ndArray).wrap()
+    public override fun asin(arg: StructureND<T>): StructureND<T> = Transforms.asin(arg.ndArray).wrap()
+    public override fun acos(arg: StructureND<T>): StructureND<T> = Transforms.acos(arg.ndArray).wrap()
+    public override fun atan(arg: StructureND<T>): StructureND<T> = Transforms.atan(arg.ndArray).wrap()
+
+    public override fun power(arg: StructureND<T>, pow: Number): StructureND<T> =
+        Transforms.pow(arg.ndArray, pow).wrap()
+
+    public override fun exp(arg: StructureND<T>): StructureND<T> = Transforms.exp(arg.ndArray).wrap()
+    public override fun ln(arg: StructureND<T>): StructureND<T> = Transforms.log(arg.ndArray).wrap()
+    public override fun sqrt(arg: StructureND<T>): StructureND<T> = Transforms.sqrt(arg.ndArray).wrap()
+    public override fun sinh(arg: StructureND<T>): StructureND<T> = Transforms.sinh(arg.ndArray).wrap()
+    public override fun cosh(arg: StructureND<T>): StructureND<T> = Transforms.cosh(arg.ndArray).wrap()
+    public override fun tanh(arg: StructureND<T>): StructureND<T> = Transforms.tanh(arg.ndArray).wrap()
+
+    public override fun asinh(arg: StructureND<T>): StructureND<T> =
+        Nd4j.getExecutioner().exec(ASinh(arg.ndArray, arg.ndArray.ulike())).wrap()
+
+    public override fun acosh(arg: StructureND<T>): StructureND<T> =
+        Nd4j.getExecutioner().exec(ACosh(arg.ndArray, arg.ndArray.ulike())).wrap()
+
+    public override fun atanh(arg: StructureND<T>): StructureND<T> = Transforms.atanh(arg.ndArray).wrap()
+}
+
 /**
  * Represents [FieldND] over [Nd4jArrayDoubleStructure].
  */
-public class DoubleNd4jArrayField(public override val shape: IntArray) : Nd4jArrayField<Double, DoubleField>,
+public class DoubleNd4jArrayField(public override val shape: IntArray) : Nd4jArrayExtendedField<Double, DoubleField> {
-    ExtendedField<StructureND<Double>> {
     public override val elementContext: DoubleField get() = DoubleField
 
     public override fun INDArray.wrap(): Nd4jArrayStructure<Double> = checkShape(this).asDoubleStructure()
 
+    @OptIn(PerformancePitfall::class)
+    override val StructureND<Double>.ndArray: INDArray
+        get() = when (this) {
+            is Nd4jArrayStructure<Double> -> checkShape(ndArray)
+            else -> Nd4j.zeros(*shape).also {
+                elements().forEach { (idx, value) -> it.putScalar(idx, value) }
+            }
+        }
+
     override fun scale(a: StructureND<Double>, value: Double): Nd4jArrayStructure<Double> {
         return a.ndArray.mul(value).wrap()
     }
@@ -245,34 +263,25 @@ public class DoubleNd4jArrayField(public override val shape: IntArray) : Nd4jArr
     public override operator fun Double.minus(arg: StructureND<Double>): Nd4jArrayStructure<Double> {
         return arg.ndArray.rsub(this).wrap()
     }
-
-    override fun sin(arg: StructureND<Double>): StructureND<Double> = Transforms.sin(arg.ndArray).wrap()
-
-    override fun cos(arg: StructureND<Double>): StructureND<Double> = Transforms.cos(arg.ndArray).wrap()
-
-    override fun asin(arg: StructureND<Double>): StructureND<Double> = Transforms.asin(arg.ndArray).wrap()
-
-    override fun acos(arg: StructureND<Double>): StructureND<Double> = Transforms.acos(arg.ndArray).wrap()
-
-    override fun atan(arg: StructureND<Double>): StructureND<Double> = Transforms.atan(arg.ndArray).wrap()
-
-    override fun power(arg: StructureND<Double>, pow: Number): StructureND<Double> =
-        Transforms.pow(arg.ndArray,pow).wrap()
-
-    override fun exp(arg: StructureND<Double>): StructureND<Double> = Transforms.exp(arg.ndArray).wrap()
-
-    override fun ln(arg: StructureND<Double>): StructureND<Double> = Transforms.log(arg.ndArray).wrap()
 }
 
 /**
  * Represents [FieldND] over [Nd4jArrayStructure] of [Float].
  */
-public class FloatNd4jArrayField(public override val shape: IntArray) : Nd4jArrayField<Float, FloatField>,
+public class FloatNd4jArrayField(public override val shape: IntArray) : Nd4jArrayExtendedField<Float, FloatField> {
-    ExtendedField<StructureND<Float>> {
     public override val elementContext: FloatField get() = FloatField
 
     public override fun INDArray.wrap(): Nd4jArrayStructure<Float> = checkShape(this).asFloatStructure()
 
+    @OptIn(PerformancePitfall::class)
+    public override val StructureND<Float>.ndArray: INDArray
+        get() = when (this) {
+            is Nd4jArrayStructure<Float> -> checkShape(ndArray)
+            else -> Nd4j.zeros(*shape).also {
+                elements().forEach { (idx, value) -> it.putScalar(idx, value) }
+            }
+        }
+
     override fun scale(a: StructureND<Float>, value: Double): StructureND<Float> =
         a.ndArray.mul(value).wrap()
 
@@ -293,23 +302,6 @@ public class FloatNd4jArrayField(public override val shape: IntArray) : Nd4jArra
 
     public override operator fun Float.minus(arg: StructureND<Float>): Nd4jArrayStructure<Float> =
         arg.ndArray.rsub(this).wrap()
-
-    override fun sin(arg: StructureND<Float>): StructureND<Float> = Sin(arg.ndArray).z().wrap()
-
-    override fun cos(arg: StructureND<Float>): StructureND<Float> = Cos(arg.ndArray).z().wrap()
-
-    override fun asin(arg: StructureND<Float>): StructureND<Float> = ASin(arg.ndArray).z().wrap()
-
-    override fun acos(arg: StructureND<Float>): StructureND<Float> = ACos(arg.ndArray).z().wrap()
-
-    override fun atan(arg: StructureND<Float>): StructureND<Float> = ATan(arg.ndArray).z().wrap()
-
-    override fun power(arg: StructureND<Float>, pow: Number): StructureND<Float> =
-        Pow(arg.ndArray, pow.toDouble()).z().wrap()
-
-    override fun exp(arg: StructureND<Float>): StructureND<Float> = Exp(arg.ndArray).z().wrap()
-
-    override fun ln(arg: StructureND<Float>): StructureND<Float> = Log(arg.ndArray).z().wrap()
 }
 
 /**
@@ -321,6 +313,15 @@ public class IntNd4jArrayRing(public override val shape: IntArray) : Nd4jArrayRi
 
     public override fun INDArray.wrap(): Nd4jArrayStructure<Int> = checkShape(this).asIntStructure()
 
+    @OptIn(PerformancePitfall::class)
+    public override val StructureND<Int>.ndArray: INDArray
+        get() = when (this) {
+            is Nd4jArrayStructure<Int> -> checkShape(ndArray)
+            else -> Nd4j.zeros(*shape).also {
+                elements().forEach { (idx, value) -> it.putScalar(idx, value) }
+            }
+        }
+
     public override operator fun StructureND<Int>.plus(arg: Int): Nd4jArrayStructure<Int> =
         ndArray.add(arg).wrap()
 
@@ -333,25 +334,3 @@ public class IntNd4jArrayRing(public override val shape: IntArray) : Nd4jArrayRi
     public override operator fun Int.minus(arg: StructureND<Int>): Nd4jArrayStructure<Int> =
         arg.ndArray.rsub(this).wrap()
 }
-
-/**
- * Represents [RingND] over [Nd4jArrayStructure] of [Long].
- */
-public class LongNd4jArrayRing(public override val shape: IntArray) : Nd4jArrayRing<Long, LongRing> {
-    public override val elementContext: LongRing
-        get() = LongRing
-
-    public override fun INDArray.wrap(): Nd4jArrayStructure<Long> = checkShape(this).asLongStructure()
-
-    public override operator fun StructureND<Long>.plus(arg: Long): Nd4jArrayStructure<Long> =
-        ndArray.add(arg).wrap()
-
-    public override operator fun StructureND<Long>.minus(arg: Long): Nd4jArrayStructure<Long> =
-        ndArray.sub(arg).wrap()
-
-    public override operator fun StructureND<Long>.times(arg: Long): Nd4jArrayStructure<Long> =
-        ndArray.mul(arg).wrap()
-
-    public override operator fun Long.minus(arg: StructureND<Long>): Nd4jArrayStructure<Long> =
-        arg.ndArray.rsub(this).wrap()
-}

kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayIterator.kt

@@ -48,12 +48,6 @@ private class Nd4jArrayDoubleIterator(iterateOver: INDArray) : Nd4jArrayIterator
 
 internal fun INDArray.realIterator(): Iterator<Pair<IntArray, Double>> = Nd4jArrayDoubleIterator(this)
 
-private class Nd4jArrayLongIterator(iterateOver: INDArray) : Nd4jArrayIteratorBase<Long>(iterateOver) {
-    override fun getSingle(indices: LongArray) = iterateOver.getLong(*indices)
-}
-
-internal fun INDArray.longIterator(): Iterator<Pair<IntArray, Long>> = Nd4jArrayLongIterator(this)
-
 private class Nd4jArrayIntIterator(iterateOver: INDArray) : Nd4jArrayIteratorBase<Int>(iterateOver) {
     override fun getSingle(indices: LongArray) = iterateOver.getInt(*indices.toIntArray())
 }

kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructure.kt

@@ -17,7 +17,8 @@ import space.kscience.kmath.nd.StructureND
  */
 public sealed class Nd4jArrayStructure<T> : MutableStructureND<T> {
     /**
-     * The wrapped [INDArray].
+     * The wrapped [INDArray]. Since KMath uses [Int] indexes, assuming that the size of [INDArray] is less or equal to
+     * [Int.MAX_VALUE].
      */
     public abstract val ndArray: INDArray
 
@@ -25,6 +26,7 @@ public sealed class Nd4jArrayStructure<T> : MutableStructureND<T> {
 
     internal abstract fun elementsIterator(): Iterator<Pair<IntArray, T>>
     internal fun indicesIterator(): Iterator<IntArray> = ndArray.indicesIterator()
+
     @PerformancePitfall
     public override fun elements(): Sequence<Pair<IntArray, T>> = Sequence(::elementsIterator)
 }
@@ -40,17 +42,6 @@ private data class Nd4jArrayIntStructure(override val ndArray: INDArray) : Nd4jA
  */
 public fun INDArray.asIntStructure(): Nd4jArrayStructure<Int> = Nd4jArrayIntStructure(this)
 
-private data class Nd4jArrayLongStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Long>() {
-    override fun elementsIterator(): Iterator<Pair<IntArray, Long>> = ndArray.longIterator()
-    override fun get(index: IntArray): Long = ndArray.getLong(*index.toLongArray())
-    override fun set(index: IntArray, value: Long): Unit = run { ndArray.putScalar(index, value.toDouble()) }
-}
-
-/**
- * Wraps this [INDArray] to [Nd4jArrayStructure].
- */
-public fun INDArray.asLongStructure(): Nd4jArrayStructure<Long> = Nd4jArrayLongStructure(this)
-
 private data class Nd4jArrayDoubleStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Double>() {
     override fun elementsIterator(): Iterator<Pair<IntArray, Double>> = ndArray.realIterator()
     override fun get(index: IntArray): Double = ndArray.getDouble(*index)

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

@@ -0,0 +1,175 @@
+/*
+ * 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.nd4j
+
+import org.nd4j.linalg.api.ndarray.INDArray
+import org.nd4j.linalg.api.ops.impl.summarystats.Variance
+import org.nd4j.linalg.api.ops.impl.transforms.strict.ACosh
+import org.nd4j.linalg.api.ops.impl.transforms.strict.ASinh
+import org.nd4j.linalg.factory.Nd4j
+import org.nd4j.linalg.factory.ops.NDBase
+import org.nd4j.linalg.ops.transforms.Transforms
+import space.kscience.kmath.misc.PerformancePitfall
+import space.kscience.kmath.nd.StructureND
+import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
+import space.kscience.kmath.tensors.api.Tensor
+import space.kscience.kmath.tensors.api.TensorAlgebra
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
+
+/**
+ * ND4J based [TensorAlgebra] implementation.
+ */
+public sealed interface Nd4jTensorAlgebra<T : Number> : AnalyticTensorAlgebra<T> {
+    /**
+     * Wraps [INDArray] to [Nd4jArrayStructure].
+     */
+    public fun INDArray.wrap(): Nd4jArrayStructure<T>
+
+    /**
+     * Unwraps to or acquires [INDArray] from [StructureND].
+     */
+    public val StructureND<T>.ndArray: INDArray
+
+    public override fun T.plus(other: Tensor<T>): Tensor<T> = other.ndArray.add(this).wrap()
+    public override fun Tensor<T>.plus(value: T): Tensor<T> = ndArray.add(value).wrap()
+
+    public override fun Tensor<T>.plus(other: Tensor<T>): Tensor<T> = ndArray.add(other.ndArray).wrap()
+
+    public override fun Tensor<T>.plusAssign(value: T) {
+        ndArray.addi(value)
+    }
+
+    public override fun Tensor<T>.plusAssign(other: Tensor<T>) {
+        ndArray.addi(other.ndArray)
+    }
+
+    public override fun T.minus(other: Tensor<T>): Tensor<T> = other.ndArray.rsub(this).wrap()
+    public override fun Tensor<T>.minus(value: T): Tensor<T> = ndArray.sub(value).wrap()
+    public override fun Tensor<T>.minus(other: Tensor<T>): Tensor<T> = ndArray.sub(other.ndArray).wrap()
+
+    public override fun Tensor<T>.minusAssign(value: T) {
+        ndArray.rsubi(value)
+    }
+
+    public override fun Tensor<T>.minusAssign(other: Tensor<T>) {
+        ndArray.subi(other.ndArray)
+    }
+
+    public override fun T.times(other: Tensor<T>): Tensor<T> = other.ndArray.mul(this).wrap()
+
+    public override fun Tensor<T>.times(value: T): Tensor<T> =
+        ndArray.mul(value).wrap()
+
+    public override fun Tensor<T>.times(other: Tensor<T>): Tensor<T> = ndArray.mul(other.ndArray).wrap()
+
+    public override fun Tensor<T>.timesAssign(value: T) {
+        ndArray.muli(value)
+    }
+
+    public override fun Tensor<T>.timesAssign(other: Tensor<T>) {
+        ndArray.mmuli(other.ndArray)
+    }
+
+    public override fun Tensor<T>.unaryMinus(): Tensor<T> = ndArray.neg().wrap()
+    public override fun Tensor<T>.get(i: Int): Tensor<T> = ndArray.slice(i.toLong()).wrap()
+    public override fun Tensor<T>.transpose(i: Int, j: Int): Tensor<T> = ndArray.swapAxes(i, j).wrap()
+    public override fun Tensor<T>.dot(other: Tensor<T>): Tensor<T> = ndArray.mmul(other.ndArray).wrap()
+
+    public override fun Tensor<T>.min(dim: Int, keepDim: Boolean): Tensor<T> =
+        ndArray.min(keepDim, dim).wrap()
+
+    public override fun Tensor<T>.sum(dim: Int, keepDim: Boolean): Tensor<T> =
+        ndArray.sum(keepDim, dim).wrap()
+
+    public override fun Tensor<T>.max(dim: Int, keepDim: Boolean): Tensor<T> =
+        ndArray.max(keepDim, dim).wrap()
+
+    public override fun Tensor<T>.view(shape: IntArray): Tensor<T> = ndArray.reshape(shape).wrap()
+    public override fun Tensor<T>.viewAs(other: Tensor<T>): Tensor<T> = view(other.shape)
+
+    public override fun Tensor<T>.argMax(dim: Int, keepDim: Boolean): Tensor<T> =
+        ndBase.get().argmax(ndArray, keepDim, dim).wrap()
+
+    public override fun Tensor<T>.mean(dim: Int, keepDim: Boolean): Tensor<T> = ndArray.mean(keepDim, dim).wrap()
+
+    public override fun Tensor<T>.exp(): Tensor<T> = Transforms.exp(ndArray).wrap()
+    public override fun Tensor<T>.ln(): Tensor<T> = Transforms.log(ndArray).wrap()
+    public override fun Tensor<T>.sqrt(): Tensor<T> = Transforms.sqrt(ndArray).wrap()
+    public override fun Tensor<T>.cos(): Tensor<T> = Transforms.cos(ndArray).wrap()
+    public override fun Tensor<T>.acos(): Tensor<T> = Transforms.acos(ndArray).wrap()
+    public override fun Tensor<T>.cosh(): Tensor<T> = Transforms.cosh(ndArray).wrap()
+
+    public override fun Tensor<T>.acosh(): Tensor<T> =
+        Nd4j.getExecutioner().exec(ACosh(ndArray, ndArray.ulike())).wrap()
+
+    public override fun Tensor<T>.sin(): Tensor<T> = Transforms.sin(ndArray).wrap()
+    public override fun Tensor<T>.asin(): Tensor<T> = Transforms.asin(ndArray).wrap()
+    public override fun Tensor<T>.sinh(): Tensor<T> = Transforms.sinh(ndArray).wrap()
+
+    public override fun Tensor<T>.asinh(): Tensor<T> =
+        Nd4j.getExecutioner().exec(ASinh(ndArray, ndArray.ulike())).wrap()
+
+    public override fun Tensor<T>.tan(): Tensor<T> = Transforms.tan(ndArray).wrap()
+    public override fun Tensor<T>.atan(): Tensor<T> = Transforms.atan(ndArray).wrap()
+    public override fun Tensor<T>.tanh(): Tensor<T> = Transforms.tanh(ndArray).wrap()
+    public override fun Tensor<T>.atanh(): Tensor<T> = Transforms.atanh(ndArray).wrap()
+    public override fun Tensor<T>.ceil(): Tensor<T> = Transforms.ceil(ndArray).wrap()
+    public override fun Tensor<T>.floor(): Tensor<T> = Transforms.floor(ndArray).wrap()
+    public override fun Tensor<T>.std(dim: Int, keepDim: Boolean): Tensor<T> = ndArray.std(true, keepDim, dim).wrap()
+    public override fun T.div(other: Tensor<T>): Tensor<T> = other.ndArray.rdiv(this).wrap()
+    public override fun Tensor<T>.div(value: T): Tensor<T> = ndArray.div(value).wrap()
+    public override fun Tensor<T>.div(other: Tensor<T>): Tensor<T> = ndArray.div(other.ndArray).wrap()
+
+    public override fun Tensor<T>.divAssign(value: T) {
+        ndArray.divi(value)
+    }
+
+    public override fun Tensor<T>.divAssign(other: Tensor<T>) {
+        ndArray.divi(other.ndArray)
+    }
+
+    public override fun Tensor<T>.variance(dim: Int, keepDim: Boolean): Tensor<T> =
+        Nd4j.getExecutioner().exec(Variance(ndArray, true, true, dim)).wrap()
+
+    private companion object {
+        private val ndBase: ThreadLocal<NDBase> = ThreadLocal.withInitial(::NDBase)
+    }
+}
+
+/**
+ * [Double] specialization of [Nd4jTensorAlgebra].
+ */
+public object DoubleNd4jTensorAlgebra : Nd4jTensorAlgebra<Double> {
+    public override fun INDArray.wrap(): Nd4jArrayStructure<Double> = asDoubleStructure()
+
+    @OptIn(PerformancePitfall::class)
+    public override val StructureND<Double>.ndArray: INDArray
+        get() = when (this) {
+            is Nd4jArrayStructure<Double> -> ndArray
+            else -> Nd4j.zeros(*shape).also {
+                elements().forEach { (idx, value) -> it.putScalar(idx, value) }
+            }
+        }
+
+    public override fun Tensor<Double>.valueOrNull(): Double? =
+        if (shape contentEquals intArrayOf(1)) ndArray.getDouble(0) else null
+
+    // TODO rewrite
+    @PerformancePitfall
+    public override fun diagonalEmbedding(
+        diagonalEntries: Tensor<Double>,
+        offset: Int,
+        dim1: Int,
+        dim2: Int,
+    ): Tensor<Double> = DoubleTensorAlgebra.diagonalEmbedding(diagonalEntries, offset, dim1, dim2)
+
+    public override fun Tensor<Double>.sum(): Double = ndArray.sumNumber().toDouble()
+    public override fun Tensor<Double>.min(): Double = ndArray.minNumber().toDouble()
+    public override fun Tensor<Double>.max(): Double = ndArray.maxNumber().toDouble()
+    public override fun Tensor<Double>.mean(): Double = ndArray.meanNumber().toDouble()
+    public override fun Tensor<Double>.std(): Double = ndArray.stdNumber().toDouble()
+    public override fun Tensor<Double>.variance(): Double = ndArray.varNumber().toDouble()
+}

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

@@ -5,5 +5,4 @@
 
 package space.kscience.kmath.nd4j
 
-internal fun IntArray.toLongArray(): LongArray = LongArray(size) { this[it].toLong() }
 internal fun LongArray.toIntArray(): IntArray = IntArray(size) { this[it].toInt() }

kmath-nd4j/src/test/kotlin/space/kscience/kmath/nd4j/Nd4jArrayAlgebraTest.kt

@@ -52,7 +52,7 @@ internal class Nd4jArrayAlgebraTest {
 
     @Test
     fun testSin() = DoubleNd4jArrayField(intArrayOf(2, 2)).invoke {
-        val initial = produce { (i, j) -> if (i == j) PI/2 else 0.0 }
+        val initial = produce { (i, j) -> if (i == j) PI / 2 else 0.0 }
         val transformed = sin(initial)
         val expected = produce { (i, j) -> if (i == j) 1.0 else 0.0 }
 

kmath-nd4j/src/test/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructureTest.kt

@@ -72,7 +72,7 @@ internal class Nd4jArrayStructureTest {
     @Test
     fun testSet() {
         val nd = Nd4j.rand(17, 12, 4, 8)!!
-        val struct = nd.asLongStructure()
+        val struct = nd.asIntStructure()
         struct[intArrayOf(1, 2, 3, 4)] = 777
         assertEquals(777, struct[1, 2, 3, 4])
     }

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

@@ -13,7 +13,7 @@ import space.kscience.kmath.operations.Algebra
  *
  * @param T the type of items in the tensors.
  */
-public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
+public interface TensorAlgebra<T> : Algebra<Tensor<T>> {
 
     /**
      * Returns a single tensor value of unit dimension if tensor shape equals to [1].
@@ -27,7 +27,8 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
      *
      * @return the value of a scalar tensor.
      */
-    public fun Tensor<T>.value(): T
+    public fun Tensor<T>.value(): T =
+        valueOrNull() ?: throw IllegalArgumentException("Inconsistent value for tensor of with $shape shape")
 
     /**
      * Each element of the tensor [other] is added to this value.
@@ -60,15 +61,14 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
      *
      * @param value the number to be added to each element of this tensor.
      */
-    public operator fun Tensor<T>.plusAssign(value: T): Unit
+    public operator fun Tensor<T>.plusAssign(value: T)
 
     /**
      * Each element of the tensor [other] is added to each element of this tensor.
      *
      * @param other tensor to be added.
      */
-    public operator fun Tensor<T>.plusAssign(other: Tensor<T>): Unit
+    public operator fun Tensor<T>.plusAssign(other: Tensor<T>)
-
 
     /**
      * Each element of the tensor [other] is subtracted from this value.
@@ -101,14 +101,14 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
      *
      * @param value the number to be subtracted from each element of this tensor.
      */
-    public operator fun Tensor<T>.minusAssign(value: T): Unit
+    public operator fun Tensor<T>.minusAssign(value: T)
 
     /**
      * Each element of the tensor [other] is subtracted from each element of this tensor.
      *
      * @param other tensor to be subtracted.
      */
-    public operator fun Tensor<T>.minusAssign(other: Tensor<T>): Unit
+    public operator fun Tensor<T>.minusAssign(other: Tensor<T>)
 
 
     /**
@@ -142,14 +142,14 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
      *
      * @param value the number to be multiplied by each element of this tensor.
      */
-    public operator fun Tensor<T>.timesAssign(value: T): Unit
+    public operator fun Tensor<T>.timesAssign(value: T)
 
     /**
      * Each element of the tensor [other] is multiplied by each element of this tensor.
      *
      * @param other tensor to be multiplied.
      */
-    public operator fun Tensor<T>.timesAssign(other: Tensor<T>): Unit
+    public operator fun Tensor<T>.timesAssign(other: Tensor<T>)
 
     /**
      * Numerical negative, element-wise.
@@ -217,7 +217,7 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
      * a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after.
      * If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix
      * multiple and removed after.
-     * The non-matrix (i.e. batch) dimensions are broadcasted (and thus must be broadcastable).
+     * The non-matrix (i.e. batch) dimensions are broadcast (and thus must be broadcastable).
      * For example, if `input` is a (j &times; 1 &times; n &times; n) tensor and `other` is a
      * (k &times; n &times; n) tensor, out will be a (j &times; k &times; n &times; n) tensor.
      *
@@ -255,7 +255,7 @@ public interface TensorAlgebra<T>: Algebra<Tensor<T>> {
         diagonalEntries: Tensor<T>,
         offset: Int = 0,
         dim1: Int = -2,
-        dim2: Int = -1
+        dim2: Int = -1,
     ): Tensor<T>
 
     /**

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

@@ -9,20 +9,9 @@ import space.kscience.kmath.nd.as1D
 import space.kscience.kmath.nd.as2D
 import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
 import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
-import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra
 import space.kscience.kmath.tensors.api.Tensor
-import space.kscience.kmath.tensors.core.internal.dotHelper
+import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra
-import space.kscience.kmath.tensors.core.internal.getRandomNormals
 import space.kscience.kmath.tensors.core.internal.*
-import space.kscience.kmath.tensors.core.internal.broadcastOuterTensors
-import space.kscience.kmath.tensors.core.internal.checkBufferShapeConsistency
-import space.kscience.kmath.tensors.core.internal.checkEmptyDoubleBuffer
-import space.kscience.kmath.tensors.core.internal.checkEmptyShape
-import space.kscience.kmath.tensors.core.internal.checkShapesCompatible
-import space.kscience.kmath.tensors.core.internal.checkSquareMatrix
-import space.kscience.kmath.tensors.core.internal.checkTranspose
-import space.kscience.kmath.tensors.core.internal.checkView
-import space.kscience.kmath.tensors.core.internal.minusIndexFrom
 import kotlin.math.*
 
 /**
@@ -38,8 +27,8 @@ public open class DoubleTensorAlgebra :
     override fun Tensor<Double>.valueOrNull(): Double? = if (tensor.shape contentEquals intArrayOf(1))
         tensor.mutableBuffer.array()[tensor.bufferStart] else null
 
-    override fun Tensor<Double>.value(): Double =
+    override fun Tensor<Double>.value(): Double = valueOrNull()
-        valueOrNull() ?: throw IllegalArgumentException("Inconsistent value for tensor of with $shape shape")
+        ?: throw IllegalArgumentException("The tensor shape is $shape, but value method is allowed only for shape [1]")
 
     /**
      * Constructs a tensor with the specified shape and data.
@@ -466,7 +455,7 @@ public open class DoubleTensorAlgebra :
 
     private fun Tensor<Double>.eq(
         other: Tensor<Double>,
-        eqFunction: (Double, Double) -> Boolean
+        eqFunction: (Double, Double) -> Boolean,
     ): Boolean {
         checkShapesCompatible(tensor, other)
         val n = tensor.numElements
@@ -540,7 +529,7 @@ public open class DoubleTensorAlgebra :
     internal fun Tensor<Double>.foldDim(
         foldFunction: (DoubleArray) -> Double,
         dim: Int,
-        keepDim: Boolean
+        keepDim: Boolean,
     ): DoubleTensor {
         check(dim < dimension) { "Dimension $dim out of range $dimension" }
         val resShape = if (keepDim) {
@@ -729,7 +718,7 @@ public open class DoubleTensorAlgebra :
      */
     public fun luPivot(
         luTensor: Tensor<Double>,
-        pivotsTensor: Tensor<Int>
+        pivotsTensor: Tensor<Int>,
     ): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         checkSquareMatrix(luTensor.shape)
         check(