forked from kscience/kmath
refactor BT + docs
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@ -1,116 +1,48 @@
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package space.kscience.kmath.tensors.core
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import space.kscience.kmath.nd.MutableBufferND
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import space.kscience.kmath.structures.*
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import space.kscience.kmath.tensors.api.Tensor
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import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
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public open class BufferedTensor<T>(
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/**
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* [Tensor] implementation provided with [MutableBuffer]
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*/
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public open class BufferedTensor<T> internal constructor(
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override val shape: IntArray,
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internal val mutableBuffer: MutableBuffer<T>,
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internal val bufferStart: Int
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) : Tensor<T> {
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/**
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* [TensorLinearStructure] with the same shape
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*/
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public val linearStructure: TensorLinearStructure
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get() = TensorLinearStructure(shape)
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/**
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* Number of elements in tensor
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*/
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public val numElements: Int
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get() = linearStructure.linearSize
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/**
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* @param index [IntArray] with size equal to tensor dimension
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* @return the element by multidimensional index
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*/
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override fun get(index: IntArray): T = mutableBuffer[bufferStart + linearStructure.offset(index)]
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/**
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* @param index the [IntArray] with size equal to tensor dimension
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* @param value the value to set
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*/
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override fun set(index: IntArray, value: T) {
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mutableBuffer[bufferStart + linearStructure.offset(index)] = value
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}
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/**
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* @return the sequence of pairs multidimensional indices and values
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*/
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override fun elements(): Sequence<Pair<IntArray, T>> = linearStructure.indices().map {
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it to this[it]
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}
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}
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public class IntTensor internal constructor(
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shape: IntArray,
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buffer: IntArray,
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offset: Int = 0
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) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset)
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public class DoubleTensor internal constructor(
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shape: IntArray,
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buffer: DoubleArray,
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offset: Int = 0
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) : BufferedTensor<Double>(shape, DoubleBuffer(buffer), offset) {
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override fun toString(): String = toPrettyString()
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}
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internal fun BufferedTensor<Int>.asTensor(): IntTensor =
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IntTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
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internal fun BufferedTensor<Double>.asTensor(): DoubleTensor =
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DoubleTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
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internal fun <T> Tensor<T>.copyToBufferedTensor(): BufferedTensor<T> =
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BufferedTensor(
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this.shape,
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TensorLinearStructure(this.shape).indices().map(this::get).toMutableList().asMutableBuffer(), 0
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)
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internal fun <T> Tensor<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
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is BufferedTensor<T> -> this
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is MutableBufferND<T> -> if (this.strides.strides contentEquals TensorLinearStructure(this.shape).strides)
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BufferedTensor(this.shape, this.mutableBuffer, 0) else this.copyToBufferedTensor()
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else -> this.copyToBufferedTensor()
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}
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internal val Tensor<Double>.tensor: DoubleTensor
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get() = when (this) {
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is DoubleTensor -> this
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else -> this.toBufferedTensor().asTensor()
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}
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internal val Tensor<Int>.tensor: IntTensor
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get() = when (this) {
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is IntTensor -> this
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else -> this.toBufferedTensor().asTensor()
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}
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public fun Tensor<Double>.toDoubleTensor(): DoubleTensor = this.tensor
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public fun Tensor<Int>.toIntTensor(): IntTensor = this.tensor
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public fun Array<DoubleArray>.toDoubleTensor(): DoubleTensor {
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val n = size
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check(n > 0) { "An empty array cannot be casted to tensor" }
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val m = first().size
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check(m > 0) { "Inner arrays must have at least 1 argument" }
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check(all { size == m }) { "Inner arrays must be the same size" }
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val shape = intArrayOf(n, m)
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val buffer = this.flatMap { arr -> arr.map { it } }.toDoubleArray()
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return DoubleTensor(shape, buffer, 0)
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}
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public fun Array<IntArray>.toIntTensor(): IntTensor {
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val n = size
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check(n > 0) { "An empty array cannot be casted to tensor" }
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val m = first().size
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check(m > 0) { "Inner arrays must have at least 1 argument" }
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check(all { size == m }) { "Inner arrays must be the same size" }
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val shape = intArrayOf(n, m)
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val buffer = this.flatMap { arr -> arr.map { it } }.toIntArray()
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return IntTensor(shape, buffer, 0)
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}
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public fun DoubleTensor.toDoubleArray(): DoubleArray {
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return DoubleArray(numElements) { i ->
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mutableBuffer[bufferStart + i]
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}
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}
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public fun IntTensor.toIntArray(): IntArray {
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return IntArray(numElements) { i ->
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mutableBuffer[bufferStart + i]
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}
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}
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@ -0,0 +1,19 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.tensors.core
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import space.kscience.kmath.structures.DoubleBuffer
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/**
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* Default [BufferedTensor] implementation for [Double] values
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*/
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public class DoubleTensor internal constructor(
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shape: IntArray,
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buffer: DoubleArray,
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offset: Int = 0
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) : BufferedTensor<Double>(shape, DoubleBuffer(buffer), offset) {
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override fun toString(): String = toPrettyString()
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}
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@ -0,0 +1,17 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.tensors.core
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import space.kscience.kmath.structures.IntBuffer
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/**
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* Default [BufferedTensor] implementation for [Int] values
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*/
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public class IntTensor internal constructor(
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shape: IntArray,
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buffer: IntArray,
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offset: Int = 0
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) : BufferedTensor<Int>(shape, IntBuffer(buffer), offset)
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@ -0,0 +1,36 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.tensors.core
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import space.kscience.kmath.tensors.api.Tensor
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/**
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* Casts [Tensor<Double>] to [DoubleTensor]
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*/
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public fun Tensor<Double>.toDoubleTensor(): DoubleTensor = this.tensor
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/**
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* Casts [Tensor<Int>] to [IntTensor]
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*/
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public fun Tensor<Int>.toIntTensor(): IntTensor = this.tensor
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/**
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* @return [DoubleArray] of tensor elements
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*/
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public fun DoubleTensor.toDoubleArray(): DoubleArray {
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return DoubleArray(numElements) { i ->
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mutableBuffer[bufferStart + i]
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}
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}
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/**
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* @return [IntArray] of tensor elements
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*/
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public fun IntTensor.toIntArray(): IntArray {
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return IntArray(numElements) { i ->
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mutableBuffer[bufferStart + i]
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}
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}
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@ -0,0 +1,42 @@
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/*
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* Copyright 2018-2021 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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package space.kscience.kmath.tensors.core
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import space.kscience.kmath.nd.MutableBufferND
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import space.kscience.kmath.structures.asMutableBuffer
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import space.kscience.kmath.tensors.api.Tensor
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import space.kscience.kmath.tensors.core.algebras.TensorLinearStructure
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internal fun BufferedTensor<Int>.asTensor(): IntTensor =
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IntTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
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internal fun BufferedTensor<Double>.asTensor(): DoubleTensor =
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DoubleTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
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internal fun <T> Tensor<T>.copyToBufferedTensor(): BufferedTensor<T> =
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BufferedTensor(
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this.shape,
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TensorLinearStructure(this.shape).indices().map(this::get).toMutableList().asMutableBuffer(), 0
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)
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internal fun <T> Tensor<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
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is BufferedTensor<T> -> this
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is MutableBufferND<T> -> if (this.strides.strides contentEquals TensorLinearStructure(this.shape).strides)
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BufferedTensor(this.shape, this.mutableBuffer, 0) else this.copyToBufferedTensor()
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else -> this.copyToBufferedTensor()
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}
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internal val Tensor<Double>.tensor: DoubleTensor
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get() = when (this) {
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is DoubleTensor -> this
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else -> this.toBufferedTensor().asTensor()
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}
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internal val Tensor<Int>.tensor: IntTensor
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get() = when (this) {
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is IntTensor -> this
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else -> this.toBufferedTensor().asTensor()
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}
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