rework structure + fixes
This commit is contained in:
parent
cc11df6174
commit
559e8b24ab
@ -1,4 +1,9 @@
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package space.kscience.kmath.tensors
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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.api
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public interface AnalyticTensorAlgebra<T> :
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public interface AnalyticTensorAlgebra<T> :
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@ -1,4 +1,9 @@
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package space.kscience.kmath.tensors
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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.api
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public interface LinearOpsTensorAlgebra<T> :
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public interface LinearOpsTensorAlgebra<T> :
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@ -1,4 +1,9 @@
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package space.kscience.kmath.tensors
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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.api
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// https://proofwiki.org/wiki/Definition:Algebra_over_Ring
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// https://proofwiki.org/wiki/Definition:Algebra_over_Ring
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public interface TensorAlgebra<T> {
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public interface TensorAlgebra<T> {
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@ -40,7 +45,9 @@ public interface TensorAlgebra<T> {
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//https://pytorch.org/docs/stable/generated/torch.diag_embed.html
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//https://pytorch.org/docs/stable/generated/torch.diag_embed.html
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public fun diagonalEmbedding(
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public fun diagonalEmbedding(
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diagonalEntries: TensorStructure<T>,
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diagonalEntries: TensorStructure<T>,
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offset: Int = 0, dim1: Int = -2, dim2: Int = -1
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offset: Int = 0,
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dim1: Int = -2,
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dim2: Int = -1
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): TensorStructure<T>
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): TensorStructure<T>
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}
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}
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@ -1,8 +1,13 @@
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package space.kscience.kmath.tensors
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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.api
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// https://proofwiki.org/wiki/Definition:Division_Algebra
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// https://proofwiki.org/wiki/Definition:Division_Algebra
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public interface TensorPartialDivisionAlgebra<T> :
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public interface TensorPartialDivisionAlgebra<T> :
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TensorAlgebra<T> {
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TensorAlgebra<T> {
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public operator fun TensorStructure<T>.div(value: T): TensorStructure<T>
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public operator fun TensorStructure<T>.div(value: T): TensorStructure<T>
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public operator fun TensorStructure<T>.div(other: TensorStructure<T>): TensorStructure<T>
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public operator fun TensorStructure<T>.div(other: TensorStructure<T>): TensorStructure<T>
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public operator fun TensorStructure<T>.divAssign(value: T)
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public operator fun TensorStructure<T>.divAssign(value: T)
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package space.kscience.kmath.tensors
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package space.kscience.kmath.tensors.api
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import space.kscience.kmath.nd.MutableStructureND
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import space.kscience.kmath.nd.MutableStructureND
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@ -1,7 +1,8 @@
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package space.kscience.kmath.tensors.core
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package space.kscience.kmath.tensors.core
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import space.kscience.kmath.structures.*
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import space.kscience.kmath.structures.*
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import space.kscience.kmath.tensors.TensorStructure
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import space.kscience.kmath.tensors.api.TensorStructure
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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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public open class BufferedTensor<T>(
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@ -0,0 +1,89 @@
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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.algebras
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import space.kscience.kmath.tensors.api.TensorStructure
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import space.kscience.kmath.tensors.core.*
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import space.kscience.kmath.tensors.core.broadcastTensors
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import space.kscience.kmath.tensors.core.broadcastTo
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public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, other.tensor)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
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newThis.buffer.array()[i] + newOther.buffer.array()[i]
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}
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return DoubleTensor(newThis.shape, resBuffer)
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}
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override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
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val newOther = broadcastTo(other.tensor, tensor.shape)
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for (i in 0 until tensor.linearStructure.size) {
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tensor.buffer.array()[tensor.bufferStart + i] +=
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newOther.buffer.array()[tensor.bufferStart + i]
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}
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}
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override fun TensorStructure<Double>.minus(other: TensorStructure<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, other.tensor)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
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newThis.buffer.array()[i] - newOther.buffer.array()[i]
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}
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return DoubleTensor(newThis.shape, resBuffer)
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}
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override fun TensorStructure<Double>.minusAssign(other: TensorStructure<Double>) {
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val newOther = broadcastTo(other.tensor, tensor.shape)
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for (i in 0 until tensor.linearStructure.size) {
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tensor.buffer.array()[tensor.bufferStart + i] -=
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newOther.buffer.array()[tensor.bufferStart + i]
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}
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}
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override fun TensorStructure<Double>.times(other: TensorStructure<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, other.tensor)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
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newThis.buffer.array()[newThis.bufferStart + i] *
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newOther.buffer.array()[newOther.bufferStart + i]
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}
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return DoubleTensor(newThis.shape, resBuffer)
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}
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override fun TensorStructure<Double>.timesAssign(other: TensorStructure<Double>) {
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val newOther = broadcastTo(other.tensor, tensor.shape)
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for (i in 0 until tensor.linearStructure.size) {
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tensor.buffer.array()[tensor.bufferStart + i] *=
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newOther.buffer.array()[tensor.bufferStart + i]
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}
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}
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override fun TensorStructure<Double>.div(other: TensorStructure<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, other.tensor)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
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newThis.buffer.array()[newOther.bufferStart + i] /
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newOther.buffer.array()[newOther.bufferStart + i]
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}
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return DoubleTensor(newThis.shape, resBuffer)
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}
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override fun TensorStructure<Double>.divAssign(other: TensorStructure<Double>) {
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val newOther = broadcastTo(other.tensor, tensor.shape)
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for (i in 0 until tensor.linearStructure.size) {
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tensor.buffer.array()[tensor.bufferStart + i] /=
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newOther.buffer.array()[tensor.bufferStart + i]
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}
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}
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}
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package space.kscience.kmath.tensors.core
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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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import space.kscience.kmath.tensors.AnalyticTensorAlgebra
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package space.kscience.kmath.tensors.core.algebras
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import space.kscience.kmath.tensors.TensorStructure
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import space.kscience.kmath.tensors.api.AnalyticTensorAlgebra
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import space.kscience.kmath.tensors.api.TensorStructure
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import space.kscience.kmath.tensors.core.DoubleTensor
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import space.kscience.kmath.tensors.core.tensor
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import kotlin.math.*
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import kotlin.math.*
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public class DoubleAnalyticTensorAlgebra:
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public class DoubleAnalyticTensorAlgebra:
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package space.kscience.kmath.tensors.core
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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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import space.kscience.kmath.tensors.LinearOpsTensorAlgebra
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package space.kscience.kmath.tensors.core.algebras
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import space.kscience.kmath.tensors.api.LinearOpsTensorAlgebra
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import space.kscience.kmath.nd.as1D
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import space.kscience.kmath.nd.as1D
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import space.kscience.kmath.nd.as2D
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import space.kscience.kmath.nd.as2D
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import space.kscience.kmath.tensors.TensorStructure
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import space.kscience.kmath.tensors.api.TensorStructure
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import space.kscience.kmath.tensors.core.*
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import space.kscience.kmath.tensors.core.checkSquareMatrix
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import space.kscience.kmath.tensors.core.choleskyHelper
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import space.kscience.kmath.tensors.core.cleanSymHelper
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import space.kscience.kmath.tensors.core.luHelper
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import space.kscience.kmath.tensors.core.luMatrixDet
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import space.kscience.kmath.tensors.core.luMatrixInv
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import space.kscience.kmath.tensors.core.luPivotHelper
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import space.kscience.kmath.tensors.core.pivInit
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import kotlin.math.min
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import kotlin.math.min
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luTensor: TensorStructure<Double>,
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luTensor: TensorStructure<Double>,
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pivotsTensor: TensorStructure<Int>
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pivotsTensor: TensorStructure<Int>
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): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
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): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
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//todo checks
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checkSquareMatrix(luTensor.shape)
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checkSquareMatrix(luTensor.shape)
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check(
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check(
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luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() ||
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luTensor.shape.dropLast(2).toIntArray() contentEquals pivotsTensor.shape.dropLast(1).toIntArray() ||
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luTensor.shape.last() == pivotsTensor.shape.last() - 1
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luTensor.shape.last() == pivotsTensor.shape.last() - 1
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) { "Bad shapes ((" } //todo rewrite
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) { "Inappropriate shapes of input tensors" }
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val n = luTensor.shape.last()
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val n = luTensor.shape.last()
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val pTensor = luTensor.zeroesLike()
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val pTensor = luTensor.zeroesLike()
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for ((matrix, USV) in tensor.matrixSequence()
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for ((matrix, USV) in tensor.matrixSequence()
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.zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
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.zip(resU.matrixSequence().zip(resS.vectorSequence().zip(resV.matrixSequence())))) {
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val size = matrix.shape.reduce { acc, i -> acc * i }
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val matrixSize = matrix.shape.reduce { acc, i -> acc * i }
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val curMatrix = DoubleTensor(
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val curMatrix = DoubleTensor(
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matrix.shape,
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matrix.shape,
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matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + size).toDoubleArray()
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matrix.buffer.array().slice(matrix.bufferStart until matrix.bufferStart + matrixSize).toDoubleArray()
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)
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)
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svdHelper(curMatrix, USV, m, n, epsilon)
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svdHelper(curMatrix, USV, m, n, epsilon)
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}
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}
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package space.kscience.kmath.tensors.core
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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.algebras
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import space.kscience.kmath.nd.as2D
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import space.kscience.kmath.nd.as2D
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import space.kscience.kmath.tensors.TensorPartialDivisionAlgebra
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import space.kscience.kmath.tensors.api.TensorPartialDivisionAlgebra
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import space.kscience.kmath.tensors.TensorStructure
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import space.kscience.kmath.tensors.api.TensorStructure
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import space.kscience.kmath.tensors.core.*
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import space.kscience.kmath.tensors.core.broadcastOuterTensors
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import space.kscience.kmath.tensors.core.checkBufferShapeConsistency
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import space.kscience.kmath.tensors.core.checkEmptyDoubleBuffer
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import space.kscience.kmath.tensors.core.checkEmptyShape
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import space.kscience.kmath.tensors.core.checkShapesCompatible
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import space.kscience.kmath.tensors.core.checkTranspose
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import space.kscience.kmath.tensors.core.checkView
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import space.kscience.kmath.tensors.core.dotHelper
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import space.kscience.kmath.tensors.core.getRandomNormals
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import space.kscience.kmath.tensors.core.minusIndexFrom
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import kotlin.math.abs
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import kotlin.math.abs
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public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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if (m1 != m2) {
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if (m1 != m2) {
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throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
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throw RuntimeException("Tensors dot operation dimension mismatch: ($l, $m1) x ($m2, $n)")
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}
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}
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val m = m1
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val resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
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val resShape = newThis.shape.sliceArray(0..(newThis.shape.size - 2)) + intArrayOf(newOther.shape.last())
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val resSize = resShape.reduce { acc, i -> acc * i }
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val resSize = resShape.reduce { acc, i -> acc * i }
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for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
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for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
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val (a, b) = ab
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val (a, b) = ab
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dotHelper(a.as2D(), b.as2D(), res.as2D(), l, m, n)
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dotHelper(a.as2D(), b.as2D(), res.as2D(), l, m1, n)
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}
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}
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if (penultimateDim) {
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if (penultimateDim) {
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@ -347,7 +362,7 @@ public open class DoubleTensorAlgebra : TensorPartialDivisionAlgebra<Double> {
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return tensor.eq(other) { x, y -> abs(x - y) < delta }
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return tensor.eq(other) { x, y -> abs(x - y) < delta }
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}
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}
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public fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
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public infix fun TensorStructure<Double>.eq(other: TensorStructure<Double>): Boolean = tensor.eq(other, 1e-5)
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private fun TensorStructure<Double>.eq(
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private fun TensorStructure<Double>.eq(
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other: TensorStructure<Double>,
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other: TensorStructure<Double>,
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package space.kscience.kmath.tensors.core
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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.algebras
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import kotlin.math.max
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import kotlin.math.max
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@ -1,89 +1,31 @@
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package space.kscience.kmath.tensors.core
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package space.kscience.kmath.tensors.core
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import space.kscience.kmath.tensors.TensorStructure
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import space.kscience.kmath.tensors.core.algebras.BroadcastDoubleTensorAlgebra
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import kotlin.math.max
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import kotlin.math.max
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public class BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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override fun TensorStructure<Double>.plus(other: TensorStructure<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, other.tensor)
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.linearStructure.size) { i ->
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newThis.buffer.array()[i] + newOther.buffer.array()[i]
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}
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return DoubleTensor(newThis.shape, resBuffer)
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}
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override fun TensorStructure<Double>.plusAssign(other: TensorStructure<Double>) {
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val newOther = broadcastTo(other.tensor, tensor.shape)
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|
||||||
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 =
|
public inline fun <R> BroadcastDoubleTensorAlgebra(block: BroadcastDoubleTensorAlgebra.() -> R): R =
|
||||||
BroadcastDoubleTensorAlgebra().block()
|
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 {
|
internal inline fun broadcastShapes(vararg shapes: IntArray): IntArray {
|
||||||
var totalDim = 0
|
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
|
return resTensor
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -157,25 +82,7 @@ internal inline fun broadcastTensors(vararg tensors: DoubleTensor): List<DoubleT
|
|||||||
val res = ArrayList<DoubleTensor>(0)
|
val res = ArrayList<DoubleTensor>(0)
|
||||||
for (tensor in tensors) {
|
for (tensor in tensors) {
|
||||||
val resTensor = DoubleTensor(totalShape, DoubleArray(n))
|
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)
|
res.add(resTensor)
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -208,7 +115,7 @@ internal inline fun broadcastOuterTensors(vararg tensors: DoubleTensor): List<Do
|
|||||||
for (linearIndex in 0 until n) {
|
for (linearIndex in 0 until n) {
|
||||||
val totalMultiIndex = outerTensor.linearStructure.index(linearIndex)
|
val totalMultiIndex = outerTensor.linearStructure.index(linearIndex)
|
||||||
var curMultiIndex = tensor.shape.sliceArray(0..tensor.shape.size - 3).copyOf()
|
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())
|
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) {
|
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] =
|
resTensor.buffer.array()[resTensor.bufferStart + newLinearIndex] =
|
||||||
newTensor.buffer.array()[newTensor.bufferStart + curLinearIndex]
|
newTensor.buffer.array()[newTensor.bufferStart + curLinearIndex]
|
@ -1,6 +1,8 @@
|
|||||||
package space.kscience.kmath.tensors.core
|
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 =
|
internal inline fun checkEmptyShape(shape: IntArray): Unit =
|
||||||
|
@ -4,6 +4,8 @@ import space.kscience.kmath.nd.MutableStructure1D
|
|||||||
import space.kscience.kmath.nd.MutableStructure2D
|
import space.kscience.kmath.nd.MutableStructure2D
|
||||||
import space.kscience.kmath.nd.as1D
|
import space.kscience.kmath.nd.as1D
|
||||||
import space.kscience.kmath.nd.as2D
|
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.abs
|
||||||
import kotlin.math.min
|
import kotlin.math.min
|
||||||
import kotlin.math.sign
|
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()) {
|
for (vector in vectorSequence()) {
|
||||||
vectorAction(vector)
|
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()) {
|
for (matrix in matrixSequence()) {
|
||||||
matrixAction(matrix)
|
matrixAction(matrix)
|
||||||
}
|
}
|
||||||
@ -284,7 +286,7 @@ internal inline fun DoubleLinearOpsTensorAlgebra.svdHelper(
|
|||||||
matrix: DoubleTensor,
|
matrix: DoubleTensor,
|
||||||
USV: Pair<BufferedTensor<Double>, Pair<BufferedTensor<Double>, BufferedTensor<Double>>>,
|
USV: Pair<BufferedTensor<Double>, Pair<BufferedTensor<Double>, BufferedTensor<Double>>>,
|
||||||
m: Int, n: Int, epsilon: Double
|
m: Int, n: Int, epsilon: Double
|
||||||
): Unit {
|
) {
|
||||||
val res = ArrayList<Triple<Double, DoubleTensor, DoubleTensor>>(0)
|
val res = ArrayList<Triple<Double, DoubleTensor, DoubleTensor>>(0)
|
||||||
val (matrixU, SV) = USV
|
val (matrixU, SV) = USV
|
||||||
val (matrixS, matrixV) = SV
|
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 (i in 0 until n)
|
||||||
for (j in 0 until n) {
|
for (j in 0 until n) {
|
||||||
if (i == j) {
|
if (i == j) {
|
@ -110,7 +110,6 @@ internal inline fun DoubleTensor.toPrettyString(): String = buildString {
|
|||||||
charOffset -=1
|
charOffset -=1
|
||||||
}
|
}
|
||||||
offset += vectorSize
|
offset += vectorSize
|
||||||
// todo refactor
|
|
||||||
if (this@toPrettyString.numElements == offset) {
|
if (this@toPrettyString.numElements == offset) {
|
||||||
break
|
break
|
||||||
}
|
}
|
||||||
|
@ -1,5 +1,6 @@
|
|||||||
package space.kscience.kmath.tensors.core
|
package space.kscience.kmath.tensors.core
|
||||||
|
|
||||||
|
import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
|
||||||
import kotlin.test.Test
|
import kotlin.test.Test
|
||||||
import kotlin.test.assertTrue
|
import kotlin.test.assertTrue
|
||||||
|
|
||||||
|
@ -1,5 +1,6 @@
|
|||||||
package space.kscience.kmath.tensors.core
|
package space.kscience.kmath.tensors.core
|
||||||
|
|
||||||
|
import space.kscience.kmath.tensors.core.algebras.DoubleAnalyticTensorAlgebra
|
||||||
import kotlin.math.abs
|
import kotlin.math.abs
|
||||||
import kotlin.math.exp
|
import kotlin.math.exp
|
||||||
import kotlin.test.Test
|
import kotlin.test.Test
|
||||||
|
@ -1,5 +1,6 @@
|
|||||||
package space.kscience.kmath.tensors.core
|
package space.kscience.kmath.tensors.core
|
||||||
|
|
||||||
|
import space.kscience.kmath.tensors.core.algebras.DoubleLinearOpsTensorAlgebra
|
||||||
import kotlin.math.abs
|
import kotlin.math.abs
|
||||||
import kotlin.test.Test
|
import kotlin.test.Test
|
||||||
import kotlin.test.assertEquals
|
import kotlin.test.assertEquals
|
||||||
@ -125,8 +126,6 @@ class TestDoubleLinearOpsTensorAlgebra {
|
|||||||
|
|
||||||
val (lu, pivots) = tensor.lu()
|
val (lu, pivots) = tensor.lu()
|
||||||
|
|
||||||
// todo check lu
|
|
||||||
|
|
||||||
val (p, l, u) = luPivot(lu, pivots)
|
val (p, l, u) = luPivot(lu, pivots)
|
||||||
|
|
||||||
assertTrue { p.shape contentEquals shape }
|
assertTrue { p.shape contentEquals shape }
|
||||||
|
@ -3,7 +3,7 @@ package space.kscience.kmath.tensors.core
|
|||||||
import space.kscience.kmath.nd.as1D
|
import space.kscience.kmath.nd.as1D
|
||||||
import space.kscience.kmath.nd.as2D
|
import space.kscience.kmath.nd.as2D
|
||||||
import space.kscience.kmath.structures.toDoubleArray
|
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.Test
|
||||||
import kotlin.test.assertEquals
|
import kotlin.test.assertEquals
|
||||||
import kotlin.test.assertTrue
|
import kotlin.test.assertTrue
|
||||||
|
@ -1,7 +1,9 @@
|
|||||||
package space.kscience.kmath.tensors.core
|
package space.kscience.kmath.tensors.core
|
||||||
|
|
||||||
|
|
||||||
|
import space.kscience.kmath.tensors.core.algebras.DoubleTensorAlgebra
|
||||||
import kotlin.test.Test
|
import kotlin.test.Test
|
||||||
|
import kotlin.test.assertFalse
|
||||||
import kotlin.test.assertTrue
|
import kotlin.test.assertTrue
|
||||||
|
|
||||||
class TestDoubleTensorAlgebra {
|
class TestDoubleTensorAlgebra {
|
||||||
@ -133,9 +135,9 @@ class TestDoubleTensorAlgebra {
|
|||||||
assertTrue(diagonal1.buffer.array() contentEquals
|
assertTrue(diagonal1.buffer.array() contentEquals
|
||||||
doubleArrayOf(10.0, 0.0, 0.0, 0.0, 20.0, 0.0, 0.0, 0.0, 30.0))
|
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))
|
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)
|
val diagonal2 = diagonalEmbedding(tensor2, 1, 0, 2)
|
||||||
@ -149,7 +151,15 @@ class TestDoubleTensorAlgebra {
|
|||||||
}
|
}
|
||||||
|
|
||||||
@Test
|
@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))
|
||||||
|
|
||||||
}
|
}
|
||||||
}
|
}
|
Loading…
Reference in New Issue
Block a user