forked from kscience/kmath
Move power to ExtendedFieldOps
This commit is contained in:
parent
5042fda751
commit
a9821772db
@ -11,6 +11,7 @@ import space.kscience.kmath.operations.*
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import space.kscience.kmath.structures.DoubleBuffer
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import kotlin.contracts.InvocationKind
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import kotlin.contracts.contract
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import kotlin.math.pow
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import kotlin.math.pow as kpow
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public class DoubleBufferND(
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@ -165,6 +166,15 @@ public sealed class DoubleFieldOpsND : BufferedFieldOpsND<Double, DoubleField>(D
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override fun atanh(arg: StructureND<Double>): DoubleBufferND =
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mapInline(arg.toBufferND()) { kotlin.math.atanh(it) }
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override fun power(
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arg: StructureND<Double>,
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pow: Number,
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): StructureND<Double> = if (pow is Int) {
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mapInline(arg.toBufferND()) { it.pow(pow) }
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} else {
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mapInline(arg.toBufferND()) { it.pow(pow.toDouble()) }
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}
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public companion object : DoubleFieldOpsND()
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}
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@ -181,7 +191,7 @@ public class DoubleFieldND(override val shape: Shape) :
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it.kpow(pow)
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}
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override fun power(arg: StructureND<Double>, pow: Number): DoubleBufferND = if(pow.isInteger()){
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override fun power(arg: StructureND<Double>, pow: Number): DoubleBufferND = if (pow.isInteger()) {
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power(arg, pow.toInt())
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} else {
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val dpow = pow.toDouble()
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@ -133,6 +133,12 @@ public abstract class DoubleBufferOps : BufferAlgebra<Double, DoubleField>, Exte
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override fun scale(a: Buffer<Double>, value: Double): DoubleBuffer = a.mapInline { it * value }
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override fun power(arg: Buffer<Double>, pow: Number): Buffer<Double> = if (pow is Int) {
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arg.mapInline { it.pow(pow) }
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} else {
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arg.mapInline { it.pow(pow.toDouble()) }
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}
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public companion object : DoubleBufferOps() {
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public inline fun Buffer<Double>.mapInline(block: (Double) -> Double): DoubleBuffer =
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if (this is DoubleBuffer) {
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@ -2,12 +2,13 @@
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* Copyright 2018-2022 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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@file:Suppress("NOTHING_TO_INLINE")
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package space.kscience.kmath.operations
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import space.kscience.kmath.structures.*
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import kotlin.math.pow as kpow
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/**
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* Advanced Number-like semifield that implements basic operations.
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*/
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@ -15,7 +16,8 @@ public interface ExtendedFieldOps<T> :
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FieldOps<T>,
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TrigonometricOperations<T>,
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ExponentialOperations<T>,
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ScaleOperations<T> {
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ScaleOperations<T>,
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PowerOperations<T> {
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override fun tan(arg: T): T = sin(arg) / cos(arg)
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override fun tanh(arg: T): T = sinh(arg) / cosh(arg)
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@ -41,7 +43,7 @@ public interface ExtendedFieldOps<T> :
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/**
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* Advanced Number-like field that implements basic operations.
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*/
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public interface ExtendedField<T> : ExtendedFieldOps<T>, Field<T>, PowerOperations<T>, NumericAlgebra<T> {
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public interface ExtendedField<T> : ExtendedFieldOps<T>, Field<T>, NumericAlgebra<T> {
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override fun sinh(arg: T): T = (exp(arg) - exp(-arg)) / 2.0
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override fun cosh(arg: T): T = (exp(arg) + exp(-arg)) / 2.0
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override fun tanh(arg: T): T = (exp(arg) - exp(-arg)) / (exp(-arg) + exp(arg))
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@ -64,7 +66,7 @@ public interface ExtendedField<T> : ExtendedFieldOps<T>, Field<T>, PowerOperatio
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/**
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* A field for [Double] without boxing. Does not produce appropriate field element.
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*/
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE")
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public object DoubleField : ExtendedField<Double>, Norm<Double, Double>, ScaleOperations<Double> {
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override val bufferFactory: MutableBufferFactory<Double> = MutableBufferFactory(::DoubleBuffer)
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@ -124,7 +126,7 @@ public val Double.Companion.algebra: DoubleField get() = DoubleField
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/**
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* A field for [Float] without boxing. Does not produce appropriate field element.
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*/
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE")
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public object FloatField : ExtendedField<Float>, Norm<Float, Float> {
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override val bufferFactory: MutableBufferFactory<Float> = MutableBufferFactory(::FloatBuffer)
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@ -180,7 +182,7 @@ public val Float.Companion.algebra: FloatField get() = FloatField
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/**
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* A field for [Int] without boxing. Does not produce corresponding ring element.
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*/
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE")
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public object IntRing : Ring<Int>, Norm<Int, Int>, NumericAlgebra<Int> {
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override val bufferFactory: MutableBufferFactory<Int> = MutableBufferFactory(::IntBuffer)
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@ -203,7 +205,7 @@ public val Int.Companion.algebra: IntRing get() = IntRing
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/**
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* A field for [Short] without boxing. Does not produce appropriate ring element.
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*/
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE")
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public object ShortRing : Ring<Short>, Norm<Short, Short>, NumericAlgebra<Short> {
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override val bufferFactory: MutableBufferFactory<Short> = MutableBufferFactory(::ShortBuffer)
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@ -226,7 +228,7 @@ public val Short.Companion.algebra: ShortRing get() = ShortRing
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/**
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* A field for [Byte] without boxing. Does not produce appropriate ring element.
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*/
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE")
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public object ByteRing : Ring<Byte>, Norm<Byte, Byte>, NumericAlgebra<Byte> {
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override val bufferFactory: MutableBufferFactory<Byte> = MutableBufferFactory(::ByteBuffer)
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@ -249,7 +251,7 @@ public val Byte.Companion.algebra: ByteRing get() = ByteRing
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/**
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* A field for [Double] without boxing. Does not produce appropriate ring element.
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*/
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
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@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE")
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public object LongRing : Ring<Long>, Norm<Long, Long>, NumericAlgebra<Long> {
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override val bufferFactory: MutableBufferFactory<Long> = MutableBufferFactory(::LongBuffer)
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@ -138,6 +138,7 @@ public sealed interface Nd4jTensorAlgebra<T : Number, A : Field<T>> : AnalyticTe
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override fun StructureND<T>.atan(): Nd4jArrayStructure<T> = Transforms.atan(ndArray).wrap()
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override fun StructureND<T>.tanh(): Nd4jArrayStructure<T> = Transforms.tanh(ndArray).wrap()
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override fun StructureND<T>.atanh(): Nd4jArrayStructure<T> = Transforms.atanh(ndArray).wrap()
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override fun power(arg: StructureND<T>, pow: Number): StructureND<T> = Transforms.pow(arg.ndArray, pow).wrap()
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override fun StructureND<T>.ceil(): Nd4jArrayStructure<T> = Transforms.ceil(ndArray).wrap()
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override fun StructureND<T>.floor(): Nd4jArrayStructure<T> = Transforms.floor(ndArray).wrap()
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override fun StructureND<T>.std(dim: Int, keepDim: Boolean): Nd4jArrayStructure<T> =
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@ -11,7 +11,6 @@ import space.kscience.kmath.tensors.api.Tensor
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import space.kscience.kmath.tensors.core.internal.array
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import space.kscience.kmath.tensors.core.internal.broadcastTensors
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import space.kscience.kmath.tensors.core.internal.broadcastTo
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import space.kscience.kmath.tensors.core.internal.tensor
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/**
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* Basic linear algebra operations implemented with broadcasting.
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@ -20,7 +19,7 @@ import space.kscience.kmath.tensors.core.internal.tensor
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public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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override fun StructureND<Double>.plus(arg: StructureND<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, arg.tensor)
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val broadcast = broadcastTensors(asDoubleTensor(), arg.asDoubleTensor())
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.indices.linearSize) { i ->
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@ -30,15 +29,15 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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}
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override fun Tensor<Double>.plusAssign(arg: StructureND<Double>) {
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val newOther = broadcastTo(arg.tensor, tensor.shape)
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for (i in 0 until tensor.indices.linearSize) {
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tensor.mutableBuffer.array()[tensor.bufferStart + i] +=
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newOther.mutableBuffer.array()[tensor.bufferStart + i]
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val newOther = broadcastTo(arg.asDoubleTensor(), asDoubleTensor().shape)
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for (i in 0 until asDoubleTensor().indices.linearSize) {
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asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] +=
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newOther.mutableBuffer.array()[asDoubleTensor().bufferStart + i]
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}
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}
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override fun StructureND<Double>.minus(arg: StructureND<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, arg.tensor)
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val broadcast = broadcastTensors(asDoubleTensor(), arg.asDoubleTensor())
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.indices.linearSize) { i ->
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@ -48,15 +47,15 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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}
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override fun Tensor<Double>.minusAssign(arg: StructureND<Double>) {
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val newOther = broadcastTo(arg.tensor, tensor.shape)
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for (i in 0 until tensor.indices.linearSize) {
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tensor.mutableBuffer.array()[tensor.bufferStart + i] -=
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newOther.mutableBuffer.array()[tensor.bufferStart + i]
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val newOther = broadcastTo(arg.asDoubleTensor(), asDoubleTensor().shape)
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for (i in 0 until asDoubleTensor().indices.linearSize) {
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asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] -=
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newOther.mutableBuffer.array()[asDoubleTensor().bufferStart + i]
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}
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}
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override fun StructureND<Double>.times(arg: StructureND<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, arg.tensor)
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val broadcast = broadcastTensors(asDoubleTensor(), arg.asDoubleTensor())
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.indices.linearSize) { i ->
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@ -67,15 +66,15 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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}
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override fun Tensor<Double>.timesAssign(arg: StructureND<Double>) {
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val newOther = broadcastTo(arg.tensor, tensor.shape)
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for (i in 0 until tensor.indices.linearSize) {
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tensor.mutableBuffer.array()[tensor.bufferStart + i] *=
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newOther.mutableBuffer.array()[tensor.bufferStart + i]
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val newOther = broadcastTo(arg.asDoubleTensor(), asDoubleTensor().shape)
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for (i in 0 until asDoubleTensor().indices.linearSize) {
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asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] *=
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newOther.mutableBuffer.array()[asDoubleTensor().bufferStart + i]
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}
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}
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override fun StructureND<Double>.div(arg: StructureND<Double>): DoubleTensor {
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val broadcast = broadcastTensors(tensor, arg.tensor)
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val broadcast = broadcastTensors(asDoubleTensor(), arg.asDoubleTensor())
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val newThis = broadcast[0]
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val newOther = broadcast[1]
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val resBuffer = DoubleArray(newThis.indices.linearSize) { i ->
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@ -86,10 +85,10 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
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}
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override fun Tensor<Double>.divAssign(arg: StructureND<Double>) {
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val newOther = broadcastTo(arg.tensor, tensor.shape)
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for (i in 0 until tensor.indices.linearSize) {
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tensor.mutableBuffer.array()[tensor.bufferStart + i] /=
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newOther.mutableBuffer.array()[tensor.bufferStart + i]
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val newOther = broadcastTo(arg.asDoubleTensor(), asDoubleTensor().shape)
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for (i in 0 until asDoubleTensor().indices.linearSize) {
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asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] /=
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newOther.mutableBuffer.array()[asDoubleTensor().bufferStart + i]
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}
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}
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}
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@ -43,7 +43,7 @@ public open class DoubleTensorAlgebra :
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@PerformancePitfall
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@Suppress("OVERRIDE_BY_INLINE")
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final override inline fun StructureND<Double>.map(transform: DoubleField.(Double) -> Double): DoubleTensor {
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val tensor = this.tensor
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val tensor = this.asDoubleTensor()
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//TODO remove additional copy
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val sourceArray = tensor.copyArray()
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val array = DoubleArray(tensor.numElements) { DoubleField.transform(sourceArray[it]) }
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@ -57,7 +57,7 @@ public open class DoubleTensorAlgebra :
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@PerformancePitfall
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@Suppress("OVERRIDE_BY_INLINE")
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final override inline fun StructureND<Double>.mapIndexed(transform: DoubleField.(index: IntArray, Double) -> Double): DoubleTensor {
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val tensor = this.tensor
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val tensor = this.asDoubleTensor()
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//TODO remove additional copy
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val sourceArray = tensor.copyArray()
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val array = DoubleArray(tensor.numElements) { DoubleField.transform(tensor.indices.index(it), sourceArray[it]) }
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@ -77,9 +77,9 @@ public open class DoubleTensorAlgebra :
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require(left.shape.contentEquals(right.shape)) {
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"The shapes in zip are not equal: left - ${left.shape}, right - ${right.shape}"
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}
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val leftTensor = left.tensor
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val leftTensor = left.asDoubleTensor()
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val leftArray = leftTensor.copyArray()
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val rightTensor = right.tensor
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val rightTensor = right.asDoubleTensor()
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val rightArray = rightTensor.copyArray()
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val array = DoubleArray(leftTensor.numElements) { DoubleField.transform(leftArray[it], rightArray[it]) }
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return DoubleTensor(
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@ -88,8 +88,8 @@ public open class DoubleTensorAlgebra :
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)
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}
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override fun StructureND<Double>.valueOrNull(): Double? = if (tensor.shape contentEquals intArrayOf(1))
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tensor.mutableBuffer.array()[tensor.bufferStart] else null
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override fun StructureND<Double>.valueOrNull(): Double? = if (asDoubleTensor().shape contentEquals intArrayOf(1))
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asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart] else null
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override fun StructureND<Double>.value(): Double = valueOrNull()
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?: throw IllegalArgumentException("The tensor shape is $shape, but value method is allowed only for shape [1]")
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@ -121,10 +121,10 @@ public open class DoubleTensorAlgebra :
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)
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override operator fun Tensor<Double>.get(i: Int): DoubleTensor {
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val lastShape = tensor.shape.drop(1).toIntArray()
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val lastShape = asDoubleTensor().shape.drop(1).toIntArray()
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val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
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val newStart = newShape.reduce(Int::times) * i + tensor.bufferStart
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return DoubleTensor(newShape, tensor.mutableBuffer.array(), newStart)
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val newStart = newShape.reduce(Int::times) * i + asDoubleTensor().bufferStart
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return DoubleTensor(newShape, asDoubleTensor().mutableBuffer.array(), newStart)
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}
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/**
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@ -147,8 +147,8 @@ public open class DoubleTensorAlgebra :
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* @return tensor with the `input` tensor shape and filled with [value].
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*/
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public fun Tensor<Double>.fullLike(value: Double): DoubleTensor {
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val shape = tensor.shape
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val buffer = DoubleArray(tensor.numElements) { value }
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val shape = asDoubleTensor().shape
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val buffer = DoubleArray(asDoubleTensor().numElements) { value }
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return DoubleTensor(shape, buffer)
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}
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@ -165,7 +165,7 @@ public open class DoubleTensorAlgebra :
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*
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* @return tensor filled with the scalar value `0.0`, with the same shape as `input` tensor.
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*/
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public fun StructureND<Double>.zeroesLike(): DoubleTensor = tensor.fullLike(0.0)
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public fun StructureND<Double>.zeroesLike(): DoubleTensor = asDoubleTensor().fullLike(0.0)
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/**
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* Returns a tensor filled with the scalar value `1.0`, with the shape defined by the variable argument [shape].
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@ -180,7 +180,7 @@ public open class DoubleTensorAlgebra :
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*
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* @return tensor filled with the scalar value `1.0`, with the same shape as `input` tensor.
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*/
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public fun Tensor<Double>.onesLike(): DoubleTensor = tensor.fullLike(1.0)
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public fun Tensor<Double>.onesLike(): DoubleTensor = asDoubleTensor().fullLike(1.0)
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/**
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* Returns a 2D tensor with shape ([n], [n]), with ones on the diagonal and zeros elsewhere.
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@ -204,182 +204,182 @@ public open class DoubleTensorAlgebra :
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* @return a copy of the `input` tensor with a copied buffer.
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*/
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public fun StructureND<Double>.copy(): DoubleTensor =
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DoubleTensor(tensor.shape, tensor.mutableBuffer.array().copyOf(), tensor.bufferStart)
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DoubleTensor(asDoubleTensor().shape, asDoubleTensor().mutableBuffer.array().copyOf(), asDoubleTensor().bufferStart)
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override fun Double.plus(arg: StructureND<Double>): DoubleTensor {
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val resBuffer = DoubleArray(arg.tensor.numElements) { i ->
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arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] + this
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val resBuffer = DoubleArray(arg.asDoubleTensor().numElements) { i ->
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arg.asDoubleTensor().mutableBuffer.array()[arg.asDoubleTensor().bufferStart + i] + this
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}
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return DoubleTensor(arg.shape, resBuffer)
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}
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override fun StructureND<Double>.plus(arg: Double): DoubleTensor = arg + tensor
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override fun StructureND<Double>.plus(arg: Double): DoubleTensor = arg + asDoubleTensor()
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override fun StructureND<Double>.plus(arg: StructureND<Double>): DoubleTensor {
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checkShapesCompatible(tensor, arg.tensor)
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val resBuffer = DoubleArray(tensor.numElements) { i ->
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tensor.mutableBuffer.array()[i] + arg.tensor.mutableBuffer.array()[i]
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checkShapesCompatible(asDoubleTensor(), arg.asDoubleTensor())
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val resBuffer = DoubleArray(asDoubleTensor().numElements) { i ->
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asDoubleTensor().mutableBuffer.array()[i] + arg.asDoubleTensor().mutableBuffer.array()[i]
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}
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return DoubleTensor(tensor.shape, resBuffer)
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return DoubleTensor(asDoubleTensor().shape, resBuffer)
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}
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override fun Tensor<Double>.plusAssign(value: Double) {
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for (i in 0 until tensor.numElements) {
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tensor.mutableBuffer.array()[tensor.bufferStart + i] += value
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for (i in 0 until asDoubleTensor().numElements) {
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asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] += value
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}
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}
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override fun Tensor<Double>.plusAssign(arg: StructureND<Double>) {
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checkShapesCompatible(tensor, arg.tensor)
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for (i in 0 until tensor.numElements) {
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tensor.mutableBuffer.array()[tensor.bufferStart + i] +=
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arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
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checkShapesCompatible(asDoubleTensor(), arg.asDoubleTensor())
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for (i in 0 until asDoubleTensor().numElements) {
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] +=
|
||||
arg.asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun Double.minus(arg: StructureND<Double>): DoubleTensor {
|
||||
val resBuffer = DoubleArray(arg.tensor.numElements) { i ->
|
||||
this - arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i]
|
||||
val resBuffer = DoubleArray(arg.asDoubleTensor().numElements) { i ->
|
||||
this - arg.asDoubleTensor().mutableBuffer.array()[arg.asDoubleTensor().bufferStart + i]
|
||||
}
|
||||
return DoubleTensor(arg.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.minus(arg: Double): DoubleTensor {
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] - arg
|
||||
val resBuffer = DoubleArray(asDoubleTensor().numElements) { i ->
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] - arg
|
||||
}
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
return DoubleTensor(asDoubleTensor().shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.minus(arg: StructureND<Double>): DoubleTensor {
|
||||
checkShapesCompatible(tensor, arg)
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[i] - arg.tensor.mutableBuffer.array()[i]
|
||||
checkShapesCompatible(asDoubleTensor(), arg)
|
||||
val resBuffer = DoubleArray(asDoubleTensor().numElements) { i ->
|
||||
asDoubleTensor().mutableBuffer.array()[i] - arg.asDoubleTensor().mutableBuffer.array()[i]
|
||||
}
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
return DoubleTensor(asDoubleTensor().shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun Tensor<Double>.minusAssign(value: Double) {
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] -= value
|
||||
for (i in 0 until asDoubleTensor().numElements) {
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] -= value
|
||||
}
|
||||
}
|
||||
|
||||
override fun Tensor<Double>.minusAssign(arg: StructureND<Double>) {
|
||||
checkShapesCompatible(tensor, arg)
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] -=
|
||||
arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
|
||||
checkShapesCompatible(asDoubleTensor(), arg)
|
||||
for (i in 0 until asDoubleTensor().numElements) {
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] -=
|
||||
arg.asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun Double.times(arg: StructureND<Double>): DoubleTensor {
|
||||
val resBuffer = DoubleArray(arg.tensor.numElements) { i ->
|
||||
arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] * this
|
||||
val resBuffer = DoubleArray(arg.asDoubleTensor().numElements) { i ->
|
||||
arg.asDoubleTensor().mutableBuffer.array()[arg.asDoubleTensor().bufferStart + i] * this
|
||||
}
|
||||
return DoubleTensor(arg.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.times(arg: Double): DoubleTensor = arg * tensor
|
||||
override fun StructureND<Double>.times(arg: Double): DoubleTensor = arg * asDoubleTensor()
|
||||
|
||||
override fun StructureND<Double>.times(arg: StructureND<Double>): DoubleTensor {
|
||||
checkShapesCompatible(tensor, arg)
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] *
|
||||
arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i]
|
||||
checkShapesCompatible(asDoubleTensor(), arg)
|
||||
val resBuffer = DoubleArray(asDoubleTensor().numElements) { i ->
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] *
|
||||
arg.asDoubleTensor().mutableBuffer.array()[arg.asDoubleTensor().bufferStart + i]
|
||||
}
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
return DoubleTensor(asDoubleTensor().shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun Tensor<Double>.timesAssign(value: Double) {
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] *= value
|
||||
for (i in 0 until asDoubleTensor().numElements) {
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] *= value
|
||||
}
|
||||
}
|
||||
|
||||
override fun Tensor<Double>.timesAssign(arg: StructureND<Double>) {
|
||||
checkShapesCompatible(tensor, arg)
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] *=
|
||||
arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
|
||||
checkShapesCompatible(asDoubleTensor(), arg)
|
||||
for (i in 0 until asDoubleTensor().numElements) {
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] *=
|
||||
arg.asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun Double.div(arg: StructureND<Double>): DoubleTensor {
|
||||
val resBuffer = DoubleArray(arg.tensor.numElements) { i ->
|
||||
this / arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i]
|
||||
val resBuffer = DoubleArray(arg.asDoubleTensor().numElements) { i ->
|
||||
this / arg.asDoubleTensor().mutableBuffer.array()[arg.asDoubleTensor().bufferStart + i]
|
||||
}
|
||||
return DoubleTensor(arg.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.div(arg: Double): DoubleTensor {
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] / arg
|
||||
val resBuffer = DoubleArray(asDoubleTensor().numElements) { i ->
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] / arg
|
||||
}
|
||||
return DoubleTensor(shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.div(arg: StructureND<Double>): DoubleTensor {
|
||||
checkShapesCompatible(tensor, arg)
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] /
|
||||
arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i]
|
||||
checkShapesCompatible(asDoubleTensor(), arg)
|
||||
val resBuffer = DoubleArray(asDoubleTensor().numElements) { i ->
|
||||
asDoubleTensor().mutableBuffer.array()[arg.asDoubleTensor().bufferStart + i] /
|
||||
arg.asDoubleTensor().mutableBuffer.array()[arg.asDoubleTensor().bufferStart + i]
|
||||
}
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
return DoubleTensor(asDoubleTensor().shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun Tensor<Double>.divAssign(value: Double) {
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] /= value
|
||||
for (i in 0 until asDoubleTensor().numElements) {
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] /= value
|
||||
}
|
||||
}
|
||||
|
||||
override fun Tensor<Double>.divAssign(arg: StructureND<Double>) {
|
||||
checkShapesCompatible(tensor, arg)
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] /=
|
||||
arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
|
||||
checkShapesCompatible(asDoubleTensor(), arg)
|
||||
for (i in 0 until asDoubleTensor().numElements) {
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i] /=
|
||||
arg.asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.unaryMinus(): DoubleTensor {
|
||||
val resBuffer = DoubleArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i].unaryMinus()
|
||||
val resBuffer = DoubleArray(asDoubleTensor().numElements) { i ->
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + i].unaryMinus()
|
||||
}
|
||||
return DoubleTensor(tensor.shape, resBuffer)
|
||||
return DoubleTensor(asDoubleTensor().shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun Tensor<Double>.transpose(i: Int, j: Int): DoubleTensor {
|
||||
val ii = tensor.minusIndex(i)
|
||||
val jj = tensor.minusIndex(j)
|
||||
checkTranspose(tensor.dimension, ii, jj)
|
||||
val n = tensor.numElements
|
||||
val ii = asDoubleTensor().minusIndex(i)
|
||||
val jj = asDoubleTensor().minusIndex(j)
|
||||
checkTranspose(asDoubleTensor().dimension, ii, jj)
|
||||
val n = asDoubleTensor().numElements
|
||||
val resBuffer = DoubleArray(n)
|
||||
|
||||
val resShape = tensor.shape.copyOf()
|
||||
val resShape = asDoubleTensor().shape.copyOf()
|
||||
resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
|
||||
|
||||
val resTensor = DoubleTensor(resShape, resBuffer)
|
||||
|
||||
for (offset in 0 until n) {
|
||||
val oldMultiIndex = tensor.indices.index(offset)
|
||||
val oldMultiIndex = asDoubleTensor().indices.index(offset)
|
||||
val newMultiIndex = oldMultiIndex.copyOf()
|
||||
newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
|
||||
|
||||
val linearIndex = resTensor.indices.offset(newMultiIndex)
|
||||
resTensor.mutableBuffer.array()[linearIndex] =
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + offset]
|
||||
asDoubleTensor().mutableBuffer.array()[asDoubleTensor().bufferStart + offset]
|
||||
}
|
||||
return resTensor
|
||||
}
|
||||
|
||||
override fun Tensor<Double>.view(shape: IntArray): DoubleTensor {
|
||||
checkView(tensor, shape)
|
||||
return DoubleTensor(shape, tensor.mutableBuffer.array(), tensor.bufferStart)
|
||||
checkView(asDoubleTensor(), shape)
|
||||
return DoubleTensor(shape, asDoubleTensor().mutableBuffer.array(), asDoubleTensor().bufferStart)
|
||||
}
|
||||
|
||||
override fun Tensor<Double>.viewAs(other: StructureND<Double>): DoubleTensor =
|
||||
tensor.view(other.shape)
|
||||
asDoubleTensor().view(other.shape)
|
||||
|
||||
/**
|
||||
* Broadcasting Matrix product of two tensors.
|
||||
@ -412,25 +412,25 @@ public open class DoubleTensorAlgebra :
|
||||
*/
|
||||
@UnstableKMathAPI
|
||||
public infix fun StructureND<Double>.matmul(other: StructureND<Double>): DoubleTensor {
|
||||
if (tensor.shape.size == 1 && other.shape.size == 1) {
|
||||
return DoubleTensor(intArrayOf(1), doubleArrayOf(tensor.times(other).tensor.mutableBuffer.array().sum()))
|
||||
if (asDoubleTensor().shape.size == 1 && other.shape.size == 1) {
|
||||
return DoubleTensor(intArrayOf(1), doubleArrayOf(asDoubleTensor().times(other).asDoubleTensor().mutableBuffer.array().sum()))
|
||||
}
|
||||
|
||||
var newThis = tensor.copy()
|
||||
var newThis = asDoubleTensor().copy()
|
||||
var newOther = other.copy()
|
||||
|
||||
var penultimateDim = false
|
||||
var lastDim = false
|
||||
if (tensor.shape.size == 1) {
|
||||
if (asDoubleTensor().shape.size == 1) {
|
||||
penultimateDim = true
|
||||
newThis = tensor.view(intArrayOf(1) + tensor.shape)
|
||||
newThis = asDoubleTensor().view(intArrayOf(1) + asDoubleTensor().shape)
|
||||
}
|
||||
if (other.shape.size == 1) {
|
||||
lastDim = true
|
||||
newOther = other.tensor.view(other.shape + intArrayOf(1))
|
||||
newOther = other.asDoubleTensor().view(other.shape + intArrayOf(1))
|
||||
}
|
||||
|
||||
val broadcastTensors = broadcastOuterTensors(newThis.tensor, newOther.tensor)
|
||||
val broadcastTensors = broadcastOuterTensors(newThis.asDoubleTensor(), newOther.asDoubleTensor())
|
||||
newThis = broadcastTensors[0]
|
||||
newOther = broadcastTensors[1]
|
||||
|
||||
@ -497,8 +497,8 @@ public open class DoubleTensorAlgebra :
|
||||
diagonalEntries.shape.slice(greaterDim - 1 until n - 1).toIntArray()
|
||||
val resTensor = zeros(resShape)
|
||||
|
||||
for (i in 0 until diagonalEntries.tensor.numElements) {
|
||||
val multiIndex = diagonalEntries.tensor.indices.index(i)
|
||||
for (i in 0 until diagonalEntries.asDoubleTensor().numElements) {
|
||||
val multiIndex = diagonalEntries.asDoubleTensor().indices.index(i)
|
||||
|
||||
var offset1 = 0
|
||||
var offset2 = abs(realOffset)
|
||||
@ -514,7 +514,7 @@ public open class DoubleTensorAlgebra :
|
||||
resTensor[diagonalMultiIndex] = diagonalEntries[multiIndex]
|
||||
}
|
||||
|
||||
return resTensor.tensor
|
||||
return resTensor.asDoubleTensor()
|
||||
}
|
||||
|
||||
/**
|
||||
@ -525,7 +525,7 @@ public open class DoubleTensorAlgebra :
|
||||
* @return true if two tensors have the same shape and elements, false otherwise.
|
||||
*/
|
||||
public fun Tensor<Double>.eq(other: Tensor<Double>, epsilon: Double): Boolean =
|
||||
tensor.eq(other) { x, y -> abs(x - y) < epsilon }
|
||||
asDoubleTensor().eq(other) { x, y -> abs(x - y) < epsilon }
|
||||
|
||||
/**
|
||||
* Compares element-wise two tensors.
|
||||
@ -534,21 +534,21 @@ public open class DoubleTensorAlgebra :
|
||||
* @param other the tensor to compare with `input` tensor.
|
||||
* @return true if two tensors have the same shape and elements, false otherwise.
|
||||
*/
|
||||
public infix fun Tensor<Double>.eq(other: Tensor<Double>): Boolean = tensor.eq(other, 1e-5)
|
||||
public infix fun Tensor<Double>.eq(other: Tensor<Double>): Boolean = asDoubleTensor().eq(other, 1e-5)
|
||||
|
||||
private fun Tensor<Double>.eq(
|
||||
other: Tensor<Double>,
|
||||
eqFunction: (Double, Double) -> Boolean,
|
||||
): Boolean {
|
||||
checkShapesCompatible(tensor, other)
|
||||
val n = tensor.numElements
|
||||
if (n != other.tensor.numElements) {
|
||||
checkShapesCompatible(asDoubleTensor(), other)
|
||||
val n = asDoubleTensor().numElements
|
||||
if (n != other.asDoubleTensor().numElements) {
|
||||
return false
|
||||
}
|
||||
for (i in 0 until n) {
|
||||
if (!eqFunction(
|
||||
tensor.mutableBuffer[tensor.bufferStart + i],
|
||||
other.tensor.mutableBuffer[other.tensor.bufferStart + i]
|
||||
asDoubleTensor().mutableBuffer[asDoubleTensor().bufferStart + i],
|
||||
other.asDoubleTensor().mutableBuffer[other.asDoubleTensor().bufferStart + i]
|
||||
)
|
||||
) {
|
||||
return false
|
||||
@ -578,7 +578,7 @@ public open class DoubleTensorAlgebra :
|
||||
* with `0.0` mean and `1.0` standard deviation.
|
||||
*/
|
||||
public fun Tensor<Double>.randomNormalLike(seed: Long = 0): DoubleTensor =
|
||||
DoubleTensor(tensor.shape, getRandomNormals(tensor.shape.reduce(Int::times), seed))
|
||||
DoubleTensor(asDoubleTensor().shape, getRandomNormals(asDoubleTensor().shape.reduce(Int::times), seed))
|
||||
|
||||
/**
|
||||
* Concatenates a sequence of tensors with equal shapes along the first dimension.
|
||||
@ -592,7 +592,7 @@ public open class DoubleTensorAlgebra :
|
||||
check(tensors.all { it.shape contentEquals shape }) { "Tensors must have same shapes" }
|
||||
val resShape = intArrayOf(tensors.size) + shape
|
||||
val resBuffer = tensors.flatMap {
|
||||
it.tensor.mutableBuffer.array().drop(it.tensor.bufferStart).take(it.tensor.numElements)
|
||||
it.asDoubleTensor().mutableBuffer.array().drop(it.asDoubleTensor().bufferStart).take(it.asDoubleTensor().numElements)
|
||||
}.toDoubleArray()
|
||||
return DoubleTensor(resShape, resBuffer, 0)
|
||||
}
|
||||
@ -606,7 +606,7 @@ public open class DoubleTensorAlgebra :
|
||||
public fun Tensor<Double>.rowsByIndices(indices: IntArray): DoubleTensor = stack(indices.map { this[it] })
|
||||
|
||||
private inline fun StructureND<Double>.fold(foldFunction: (DoubleArray) -> Double): Double =
|
||||
foldFunction(tensor.copyArray())
|
||||
foldFunction(asDoubleTensor().copyArray())
|
||||
|
||||
private inline fun <reified R : Any> StructureND<Double>.foldDim(
|
||||
dim: Int,
|
||||
@ -629,44 +629,44 @@ public open class DoubleTensorAlgebra :
|
||||
val prefix = index.take(dim).toIntArray()
|
||||
val suffix = index.takeLast(dimension - dim - 1).toIntArray()
|
||||
resTensor[index] = foldFunction(DoubleArray(shape[dim]) { i ->
|
||||
tensor[prefix + intArrayOf(i) + suffix]
|
||||
asDoubleTensor()[prefix + intArrayOf(i) + suffix]
|
||||
})
|
||||
}
|
||||
return resTensor
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.sum(): Double = tensor.fold { it.sum() }
|
||||
override fun StructureND<Double>.sum(): Double = asDoubleTensor().fold { it.sum() }
|
||||
|
||||
override fun StructureND<Double>.sum(dim: Int, keepDim: Boolean): DoubleTensor =
|
||||
foldDim(dim, keepDim) { x -> x.sum() }.toDoubleTensor()
|
||||
foldDim(dim, keepDim) { x -> x.sum() }.asDoubleTensor()
|
||||
|
||||
override fun StructureND<Double>.min(): Double = this.fold { it.minOrNull()!! }
|
||||
|
||||
override fun StructureND<Double>.min(dim: Int, keepDim: Boolean): DoubleTensor =
|
||||
foldDim(dim, keepDim) { x -> x.minOrNull()!! }.toDoubleTensor()
|
||||
foldDim(dim, keepDim) { x -> x.minOrNull()!! }.asDoubleTensor()
|
||||
|
||||
override fun StructureND<Double>.max(): Double = this.fold { it.maxOrNull()!! }
|
||||
|
||||
override fun StructureND<Double>.max(dim: Int, keepDim: Boolean): DoubleTensor =
|
||||
foldDim(dim, keepDim) { x -> x.maxOrNull()!! }.toDoubleTensor()
|
||||
foldDim(dim, keepDim) { x -> x.maxOrNull()!! }.asDoubleTensor()
|
||||
|
||||
|
||||
override fun StructureND<Double>.argMax(dim: Int, keepDim: Boolean): IntTensor =
|
||||
foldDim(dim, keepDim) { x ->
|
||||
x.withIndex().maxByOrNull { it.value }?.index!!
|
||||
}.toIntTensor()
|
||||
}.asIntTensor()
|
||||
|
||||
|
||||
override fun StructureND<Double>.mean(): Double = this.fold { it.sum() / tensor.numElements }
|
||||
override fun StructureND<Double>.mean(): Double = this.fold { it.sum() / asDoubleTensor().numElements }
|
||||
|
||||
override fun StructureND<Double>.mean(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(dim, keepDim) { arr ->
|
||||
check(dim < dimension) { "Dimension $dim out of range $dimension" }
|
||||
arr.sum() / shape[dim]
|
||||
}.toDoubleTensor()
|
||||
}.asDoubleTensor()
|
||||
|
||||
override fun StructureND<Double>.std(): Double = fold { arr ->
|
||||
val mean = arr.sum() / tensor.numElements
|
||||
sqrt(arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1))
|
||||
val mean = arr.sum() / asDoubleTensor().numElements
|
||||
sqrt(arr.sumOf { (it - mean) * (it - mean) } / (asDoubleTensor().numElements - 1))
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.std(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
|
||||
@ -676,11 +676,11 @@ public open class DoubleTensorAlgebra :
|
||||
check(dim < dimension) { "Dimension $dim out of range $dimension" }
|
||||
val mean = arr.sum() / shape[dim]
|
||||
sqrt(arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1))
|
||||
}.toDoubleTensor()
|
||||
}.asDoubleTensor()
|
||||
|
||||
override fun StructureND<Double>.variance(): Double = fold { arr ->
|
||||
val mean = arr.sum() / tensor.numElements
|
||||
arr.sumOf { (it - mean) * (it - mean) } / (tensor.numElements - 1)
|
||||
val mean = arr.sum() / asDoubleTensor().numElements
|
||||
arr.sumOf { (it - mean) * (it - mean) } / (asDoubleTensor().numElements - 1)
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.variance(dim: Int, keepDim: Boolean): DoubleTensor = foldDim(
|
||||
@ -690,7 +690,7 @@ public open class DoubleTensorAlgebra :
|
||||
check(dim < dimension) { "Dimension $dim out of range $dimension" }
|
||||
val mean = arr.sum() / shape[dim]
|
||||
arr.sumOf { (it - mean) * (it - mean) } / (shape[dim] - 1)
|
||||
}.toDoubleTensor()
|
||||
}.asDoubleTensor()
|
||||
|
||||
private fun cov(x: DoubleTensor, y: DoubleTensor): Double {
|
||||
val n = x.shape[0]
|
||||
@ -716,45 +716,51 @@ public open class DoubleTensorAlgebra :
|
||||
)
|
||||
for (i in 0 until n) {
|
||||
for (j in 0 until n) {
|
||||
resTensor[intArrayOf(i, j)] = cov(tensors[i].tensor, tensors[j].tensor)
|
||||
resTensor[intArrayOf(i, j)] = cov(tensors[i].asDoubleTensor(), tensors[j].asDoubleTensor())
|
||||
}
|
||||
}
|
||||
return resTensor
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.exp(): DoubleTensor = tensor.map { exp(it) }
|
||||
override fun StructureND<Double>.exp(): DoubleTensor = asDoubleTensor().map { exp(it) }
|
||||
|
||||
override fun StructureND<Double>.ln(): DoubleTensor = tensor.map { ln(it) }
|
||||
override fun StructureND<Double>.ln(): DoubleTensor = asDoubleTensor().map { ln(it) }
|
||||
|
||||
override fun StructureND<Double>.sqrt(): DoubleTensor = tensor.map { sqrt(it) }
|
||||
override fun StructureND<Double>.sqrt(): DoubleTensor = asDoubleTensor().map { sqrt(it) }
|
||||
|
||||
override fun StructureND<Double>.cos(): DoubleTensor = tensor.map { cos(it) }
|
||||
override fun StructureND<Double>.cos(): DoubleTensor = asDoubleTensor().map { cos(it) }
|
||||
|
||||
override fun StructureND<Double>.acos(): DoubleTensor = tensor.map { acos(it) }
|
||||
override fun StructureND<Double>.acos(): DoubleTensor = asDoubleTensor().map { acos(it) }
|
||||
|
||||
override fun StructureND<Double>.cosh(): DoubleTensor = tensor.map { cosh(it) }
|
||||
override fun StructureND<Double>.cosh(): DoubleTensor = asDoubleTensor().map { cosh(it) }
|
||||
|
||||
override fun StructureND<Double>.acosh(): DoubleTensor = tensor.map { acosh(it) }
|
||||
override fun StructureND<Double>.acosh(): DoubleTensor = asDoubleTensor().map { acosh(it) }
|
||||
|
||||
override fun StructureND<Double>.sin(): DoubleTensor = tensor.map { sin(it) }
|
||||
override fun StructureND<Double>.sin(): DoubleTensor = asDoubleTensor().map { sin(it) }
|
||||
|
||||
override fun StructureND<Double>.asin(): DoubleTensor = tensor.map { asin(it) }
|
||||
override fun StructureND<Double>.asin(): DoubleTensor = asDoubleTensor().map { asin(it) }
|
||||
|
||||
override fun StructureND<Double>.sinh(): DoubleTensor = tensor.map { sinh(it) }
|
||||
override fun StructureND<Double>.sinh(): DoubleTensor = asDoubleTensor().map { sinh(it) }
|
||||
|
||||
override fun StructureND<Double>.asinh(): DoubleTensor = tensor.map { asinh(it) }
|
||||
override fun StructureND<Double>.asinh(): DoubleTensor = asDoubleTensor().map { asinh(it) }
|
||||
|
||||
override fun StructureND<Double>.tan(): DoubleTensor = tensor.map { tan(it) }
|
||||
override fun StructureND<Double>.tan(): DoubleTensor = asDoubleTensor().map { tan(it) }
|
||||
|
||||
override fun StructureND<Double>.atan(): DoubleTensor = tensor.map { atan(it) }
|
||||
override fun StructureND<Double>.atan(): DoubleTensor = asDoubleTensor().map { atan(it) }
|
||||
|
||||
override fun StructureND<Double>.tanh(): DoubleTensor = tensor.map { tanh(it) }
|
||||
override fun StructureND<Double>.tanh(): DoubleTensor = asDoubleTensor().map { tanh(it) }
|
||||
|
||||
override fun StructureND<Double>.atanh(): DoubleTensor = tensor.map { atanh(it) }
|
||||
override fun StructureND<Double>.atanh(): DoubleTensor = asDoubleTensor().map { atanh(it) }
|
||||
|
||||
override fun StructureND<Double>.ceil(): DoubleTensor = tensor.map { ceil(it) }
|
||||
override fun power(arg: StructureND<Double>, pow: Number): StructureND<Double> = if (pow is Int) {
|
||||
arg.map { it.pow(pow) }
|
||||
} else {
|
||||
arg.map { it.pow(pow.toDouble()) }
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.floor(): DoubleTensor = tensor.map { floor(it) }
|
||||
override fun StructureND<Double>.ceil(): DoubleTensor = asDoubleTensor().map { ceil(it) }
|
||||
|
||||
override fun StructureND<Double>.floor(): DoubleTensor = asDoubleTensor().map { floor(it) }
|
||||
|
||||
override fun StructureND<Double>.inv(): DoubleTensor = invLU(1e-9)
|
||||
|
||||
@ -770,7 +776,7 @@ public open class DoubleTensorAlgebra :
|
||||
* The `pivots` has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows.
|
||||
*/
|
||||
public fun StructureND<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
|
||||
computeLU(tensor, epsilon)
|
||||
computeLU(asDoubleTensor(), epsilon)
|
||||
?: throw IllegalArgumentException("Tensor contains matrices which are singular at precision $epsilon")
|
||||
|
||||
/**
|
||||
@ -809,7 +815,7 @@ public open class DoubleTensorAlgebra :
|
||||
val pTensor = luTensor.zeroesLike()
|
||||
pTensor
|
||||
.matrixSequence()
|
||||
.zip(pivotsTensor.tensor.vectorSequence())
|
||||
.zip(pivotsTensor.asIntTensor().vectorSequence())
|
||||
.forEach { (p, pivot) -> pivInit(p.as2D(), pivot.as1D(), n) }
|
||||
|
||||
val lTensor = luTensor.zeroesLike()
|
||||
@ -817,7 +823,7 @@ public open class DoubleTensorAlgebra :
|
||||
|
||||
lTensor.matrixSequence()
|
||||
.zip(uTensor.matrixSequence())
|
||||
.zip(luTensor.tensor.matrixSequence())
|
||||
.zip(luTensor.asDoubleTensor().matrixSequence())
|
||||
.forEach { (pairLU, lu) ->
|
||||
val (l, u) = pairLU
|
||||
luPivotHelper(l.as2D(), u.as2D(), lu.as2D(), n)
|
||||
@ -841,12 +847,12 @@ public open class DoubleTensorAlgebra :
|
||||
*/
|
||||
public fun StructureND<Double>.cholesky(epsilon: Double): DoubleTensor {
|
||||
checkSquareMatrix(shape)
|
||||
checkPositiveDefinite(tensor, epsilon)
|
||||
checkPositiveDefinite(asDoubleTensor(), epsilon)
|
||||
|
||||
val n = shape.last()
|
||||
val lTensor = zeroesLike()
|
||||
|
||||
for ((a, l) in tensor.matrixSequence().zip(lTensor.matrixSequence()))
|
||||
for ((a, l) in asDoubleTensor().matrixSequence().zip(lTensor.matrixSequence()))
|
||||
for (i in 0 until n) choleskyHelper(a.as2D(), l.as2D(), n)
|
||||
|
||||
return lTensor
|
||||
@ -858,13 +864,13 @@ public open class DoubleTensorAlgebra :
|
||||
checkSquareMatrix(shape)
|
||||
val qTensor = zeroesLike()
|
||||
val rTensor = zeroesLike()
|
||||
tensor.matrixSequence()
|
||||
asDoubleTensor().matrixSequence()
|
||||
.zip(
|
||||
(qTensor.matrixSequence()
|
||||
.zip(rTensor.matrixSequence()))
|
||||
).forEach { (matrix, qr) ->
|
||||
val (q, r) = qr
|
||||
qrHelper(matrix.asTensor(), q.asTensor(), r.as2D())
|
||||
qrHelper(matrix.toTensor(), q.toTensor(), r.as2D())
|
||||
}
|
||||
|
||||
return qTensor to rTensor
|
||||
@ -887,14 +893,14 @@ public open class DoubleTensorAlgebra :
|
||||
* @return a triple `Triple(U, S, V)`.
|
||||
*/
|
||||
public fun StructureND<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
|
||||
val size = tensor.dimension
|
||||
val commonShape = tensor.shape.sliceArray(0 until size - 2)
|
||||
val (n, m) = tensor.shape.sliceArray(size - 2 until size)
|
||||
val size = asDoubleTensor().dimension
|
||||
val commonShape = asDoubleTensor().shape.sliceArray(0 until size - 2)
|
||||
val (n, m) = asDoubleTensor().shape.sliceArray(size - 2 until size)
|
||||
val uTensor = zeros(commonShape + intArrayOf(min(n, m), n))
|
||||
val sTensor = zeros(commonShape + intArrayOf(min(n, m)))
|
||||
val vTensor = zeros(commonShape + intArrayOf(min(n, m), m))
|
||||
|
||||
val matrices = tensor.matrices
|
||||
val matrices = asDoubleTensor().matrices
|
||||
val uTensors = uTensor.matrices
|
||||
val sTensorVectors = sTensor.vectors
|
||||
val vTensors = vTensor.matrices
|
||||
@ -931,7 +937,7 @@ public open class DoubleTensorAlgebra :
|
||||
* @return a pair `eigenvalues to eigenvectors`.
|
||||
*/
|
||||
public fun StructureND<Double>.symEigSvd(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
|
||||
checkSymmetric(tensor, epsilon)
|
||||
checkSymmetric(asDoubleTensor(), epsilon)
|
||||
|
||||
fun MutableStructure2D<Double>.cleanSym(n: Int) {
|
||||
for (i in 0 until n) {
|
||||
@ -945,7 +951,7 @@ public open class DoubleTensorAlgebra :
|
||||
}
|
||||
}
|
||||
|
||||
val (u, s, v) = tensor.svd(epsilon)
|
||||
val (u, s, v) = asDoubleTensor().svd(epsilon)
|
||||
val shp = s.shape + intArrayOf(1)
|
||||
val utv = u.transpose() matmul v
|
||||
val n = s.shape.last()
|
||||
@ -958,7 +964,7 @@ public open class DoubleTensorAlgebra :
|
||||
}
|
||||
|
||||
public fun StructureND<Double>.symEigJacobi(maxIteration: Int, epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
|
||||
checkSymmetric(tensor, epsilon)
|
||||
checkSymmetric(asDoubleTensor(), epsilon)
|
||||
|
||||
val size = this.dimension
|
||||
val eigenvectors = zeros(this.shape)
|
||||
@ -966,7 +972,7 @@ public open class DoubleTensorAlgebra :
|
||||
|
||||
var eigenvalueStart = 0
|
||||
var eigenvectorStart = 0
|
||||
for (matrix in tensor.matrixSequence()) {
|
||||
for (matrix in asDoubleTensor().matrixSequence()) {
|
||||
val matrix2D = matrix.as2D()
|
||||
val (d, v) = matrix2D.jacobiHelper(maxIteration, epsilon)
|
||||
|
||||
@ -1111,9 +1117,9 @@ public open class DoubleTensorAlgebra :
|
||||
* @return the determinant.
|
||||
*/
|
||||
public fun StructureND<Double>.detLU(epsilon: Double = 1e-9): DoubleTensor {
|
||||
checkSquareMatrix(tensor.shape)
|
||||
val luTensor = tensor.copy()
|
||||
val pivotsTensor = tensor.setUpPivots()
|
||||
checkSquareMatrix(asDoubleTensor().shape)
|
||||
val luTensor = asDoubleTensor().copy()
|
||||
val pivotsTensor = asDoubleTensor().setUpPivots()
|
||||
|
||||
val n = shape.size
|
||||
|
||||
@ -1169,14 +1175,14 @@ public open class DoubleTensorAlgebra :
|
||||
* @return triple of `P`, `L` and `U` tensors.
|
||||
*/
|
||||
public fun StructureND<Double>.lu(epsilon: Double = 1e-9): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
|
||||
val (lu, pivots) = tensor.luFactor(epsilon)
|
||||
val (lu, pivots) = asDoubleTensor().luFactor(epsilon)
|
||||
return luPivot(lu, pivots)
|
||||
}
|
||||
|
||||
override fun StructureND<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
|
||||
}
|
||||
|
||||
public val Double.Companion.tensorAlgebra: DoubleTensorAlgebra.Companion get() = DoubleTensorAlgebra
|
||||
public val DoubleField.tensorAlgebra: DoubleTensorAlgebra.Companion get() = DoubleTensorAlgebra
|
||||
public val Double.Companion.tensorAlgebra: DoubleTensorAlgebra get() = DoubleTensorAlgebra
|
||||
public val DoubleField.tensorAlgebra: DoubleTensorAlgebra get() = DoubleTensorAlgebra
|
||||
|
||||
|
||||
|
||||
@ -39,7 +39,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
@PerformancePitfall
|
||||
@Suppress("OVERRIDE_BY_INLINE")
|
||||
final override inline fun StructureND<Int>.map(transform: IntRing.(Int) -> Int): IntTensor {
|
||||
val tensor = this.tensor
|
||||
val tensor = this.asIntTensor()
|
||||
//TODO remove additional copy
|
||||
val sourceArray = tensor.copyArray()
|
||||
val array = IntArray(tensor.numElements) { IntRing.transform(sourceArray[it]) }
|
||||
@ -53,7 +53,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
@PerformancePitfall
|
||||
@Suppress("OVERRIDE_BY_INLINE")
|
||||
final override inline fun StructureND<Int>.mapIndexed(transform: IntRing.(index: IntArray, Int) -> Int): IntTensor {
|
||||
val tensor = this.tensor
|
||||
val tensor = this.asIntTensor()
|
||||
//TODO remove additional copy
|
||||
val sourceArray = tensor.copyArray()
|
||||
val array = IntArray(tensor.numElements) { IntRing.transform(tensor.indices.index(it), sourceArray[it]) }
|
||||
@ -73,9 +73,9 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
require(left.shape.contentEquals(right.shape)) {
|
||||
"The shapes in zip are not equal: left - ${left.shape}, right - ${right.shape}"
|
||||
}
|
||||
val leftTensor = left.tensor
|
||||
val leftTensor = left.asIntTensor()
|
||||
val leftArray = leftTensor.copyArray()
|
||||
val rightTensor = right.tensor
|
||||
val rightTensor = right.asIntTensor()
|
||||
val rightArray = rightTensor.copyArray()
|
||||
val array = IntArray(leftTensor.numElements) { IntRing.transform(leftArray[it], rightArray[it]) }
|
||||
return IntTensor(
|
||||
@ -84,8 +84,8 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
)
|
||||
}
|
||||
|
||||
override fun StructureND<Int>.valueOrNull(): Int? = if (tensor.shape contentEquals intArrayOf(1))
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart] else null
|
||||
override fun StructureND<Int>.valueOrNull(): Int? = if (asIntTensor().shape contentEquals intArrayOf(1))
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart] else null
|
||||
|
||||
override fun StructureND<Int>.value(): Int = valueOrNull()
|
||||
?: throw IllegalArgumentException("The tensor shape is $shape, but value method is allowed only for shape [1]")
|
||||
@ -119,10 +119,10 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
)
|
||||
|
||||
override operator fun Tensor<Int>.get(i: Int): IntTensor {
|
||||
val lastShape = tensor.shape.drop(1).toIntArray()
|
||||
val lastShape = asIntTensor().shape.drop(1).toIntArray()
|
||||
val newShape = if (lastShape.isNotEmpty()) lastShape else intArrayOf(1)
|
||||
val newStart = newShape.reduce(Int::times) * i + tensor.bufferStart
|
||||
return IntTensor(newShape, tensor.mutableBuffer.array(), newStart)
|
||||
val newStart = newShape.reduce(Int::times) * i + asIntTensor().bufferStart
|
||||
return IntTensor(newShape, asIntTensor().mutableBuffer.array(), newStart)
|
||||
}
|
||||
|
||||
/**
|
||||
@ -145,8 +145,8 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
* @return tensor with the `input` tensor shape and filled with [value].
|
||||
*/
|
||||
public fun Tensor<Int>.fullLike(value: Int): IntTensor {
|
||||
val shape = tensor.shape
|
||||
val buffer = IntArray(tensor.numElements) { value }
|
||||
val shape = asIntTensor().shape
|
||||
val buffer = IntArray(asIntTensor().numElements) { value }
|
||||
return IntTensor(shape, buffer)
|
||||
}
|
||||
|
||||
@ -163,7 +163,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
*
|
||||
* @return tensor filled with the scalar value `0`, with the same shape as `input` tensor.
|
||||
*/
|
||||
public fun StructureND<Int>.zeroesLike(): IntTensor = tensor.fullLike(0)
|
||||
public fun StructureND<Int>.zeroesLike(): IntTensor = asIntTensor().fullLike(0)
|
||||
|
||||
/**
|
||||
* Returns a tensor filled with the scalar value `1`, with the shape defined by the variable argument [shape].
|
||||
@ -178,7 +178,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
*
|
||||
* @return tensor filled with the scalar value `1`, with the same shape as `input` tensor.
|
||||
*/
|
||||
public fun Tensor<Int>.onesLike(): IntTensor = tensor.fullLike(1)
|
||||
public fun Tensor<Int>.onesLike(): IntTensor = asIntTensor().fullLike(1)
|
||||
|
||||
/**
|
||||
* Returns a 2D tensor with shape ([n], [n]), with ones on the diagonal and zeros elsewhere.
|
||||
@ -202,145 +202,145 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
* @return a copy of the `input` tensor with a copied buffer.
|
||||
*/
|
||||
public fun StructureND<Int>.copy(): IntTensor =
|
||||
IntTensor(tensor.shape, tensor.mutableBuffer.array().copyOf(), tensor.bufferStart)
|
||||
IntTensor(asIntTensor().shape, asIntTensor().mutableBuffer.array().copyOf(), asIntTensor().bufferStart)
|
||||
|
||||
override fun Int.plus(arg: StructureND<Int>): IntTensor {
|
||||
val resBuffer = IntArray(arg.tensor.numElements) { i ->
|
||||
arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] + this
|
||||
val resBuffer = IntArray(arg.asIntTensor().numElements) { i ->
|
||||
arg.asIntTensor().mutableBuffer.array()[arg.asIntTensor().bufferStart + i] + this
|
||||
}
|
||||
return IntTensor(arg.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun StructureND<Int>.plus(arg: Int): IntTensor = arg + tensor
|
||||
override fun StructureND<Int>.plus(arg: Int): IntTensor = arg + asIntTensor()
|
||||
|
||||
override fun StructureND<Int>.plus(arg: StructureND<Int>): IntTensor {
|
||||
checkShapesCompatible(tensor, arg.tensor)
|
||||
val resBuffer = IntArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[i] + arg.tensor.mutableBuffer.array()[i]
|
||||
checkShapesCompatible(asIntTensor(), arg.asIntTensor())
|
||||
val resBuffer = IntArray(asIntTensor().numElements) { i ->
|
||||
asIntTensor().mutableBuffer.array()[i] + arg.asIntTensor().mutableBuffer.array()[i]
|
||||
}
|
||||
return IntTensor(tensor.shape, resBuffer)
|
||||
return IntTensor(asIntTensor().shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun Tensor<Int>.plusAssign(value: Int) {
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] += value
|
||||
for (i in 0 until asIntTensor().numElements) {
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i] += value
|
||||
}
|
||||
}
|
||||
|
||||
override fun Tensor<Int>.plusAssign(arg: StructureND<Int>) {
|
||||
checkShapesCompatible(tensor, arg.tensor)
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] +=
|
||||
arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
|
||||
checkShapesCompatible(asIntTensor(), arg.asIntTensor())
|
||||
for (i in 0 until asIntTensor().numElements) {
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i] +=
|
||||
arg.asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun Int.minus(arg: StructureND<Int>): IntTensor {
|
||||
val resBuffer = IntArray(arg.tensor.numElements) { i ->
|
||||
this - arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i]
|
||||
val resBuffer = IntArray(arg.asIntTensor().numElements) { i ->
|
||||
this - arg.asIntTensor().mutableBuffer.array()[arg.asIntTensor().bufferStart + i]
|
||||
}
|
||||
return IntTensor(arg.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun StructureND<Int>.minus(arg: Int): IntTensor {
|
||||
val resBuffer = IntArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] - arg
|
||||
val resBuffer = IntArray(asIntTensor().numElements) { i ->
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i] - arg
|
||||
}
|
||||
return IntTensor(tensor.shape, resBuffer)
|
||||
return IntTensor(asIntTensor().shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun StructureND<Int>.minus(arg: StructureND<Int>): IntTensor {
|
||||
checkShapesCompatible(tensor, arg)
|
||||
val resBuffer = IntArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[i] - arg.tensor.mutableBuffer.array()[i]
|
||||
checkShapesCompatible(asIntTensor(), arg)
|
||||
val resBuffer = IntArray(asIntTensor().numElements) { i ->
|
||||
asIntTensor().mutableBuffer.array()[i] - arg.asIntTensor().mutableBuffer.array()[i]
|
||||
}
|
||||
return IntTensor(tensor.shape, resBuffer)
|
||||
return IntTensor(asIntTensor().shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun Tensor<Int>.minusAssign(value: Int) {
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] -= value
|
||||
for (i in 0 until asIntTensor().numElements) {
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i] -= value
|
||||
}
|
||||
}
|
||||
|
||||
override fun Tensor<Int>.minusAssign(arg: StructureND<Int>) {
|
||||
checkShapesCompatible(tensor, arg)
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] -=
|
||||
arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
|
||||
checkShapesCompatible(asIntTensor(), arg)
|
||||
for (i in 0 until asIntTensor().numElements) {
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i] -=
|
||||
arg.asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun Int.times(arg: StructureND<Int>): IntTensor {
|
||||
val resBuffer = IntArray(arg.tensor.numElements) { i ->
|
||||
arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i] * this
|
||||
val resBuffer = IntArray(arg.asIntTensor().numElements) { i ->
|
||||
arg.asIntTensor().mutableBuffer.array()[arg.asIntTensor().bufferStart + i] * this
|
||||
}
|
||||
return IntTensor(arg.shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun StructureND<Int>.times(arg: Int): IntTensor = arg * tensor
|
||||
override fun StructureND<Int>.times(arg: Int): IntTensor = arg * asIntTensor()
|
||||
|
||||
override fun StructureND<Int>.times(arg: StructureND<Int>): IntTensor {
|
||||
checkShapesCompatible(tensor, arg)
|
||||
val resBuffer = IntArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] *
|
||||
arg.tensor.mutableBuffer.array()[arg.tensor.bufferStart + i]
|
||||
checkShapesCompatible(asIntTensor(), arg)
|
||||
val resBuffer = IntArray(asIntTensor().numElements) { i ->
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i] *
|
||||
arg.asIntTensor().mutableBuffer.array()[arg.asIntTensor().bufferStart + i]
|
||||
}
|
||||
return IntTensor(tensor.shape, resBuffer)
|
||||
return IntTensor(asIntTensor().shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun Tensor<Int>.timesAssign(value: Int) {
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] *= value
|
||||
for (i in 0 until asIntTensor().numElements) {
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i] *= value
|
||||
}
|
||||
}
|
||||
|
||||
override fun Tensor<Int>.timesAssign(arg: StructureND<Int>) {
|
||||
checkShapesCompatible(tensor, arg)
|
||||
for (i in 0 until tensor.numElements) {
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i] *=
|
||||
arg.tensor.mutableBuffer.array()[tensor.bufferStart + i]
|
||||
checkShapesCompatible(asIntTensor(), arg)
|
||||
for (i in 0 until asIntTensor().numElements) {
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i] *=
|
||||
arg.asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i]
|
||||
}
|
||||
}
|
||||
|
||||
override fun StructureND<Int>.unaryMinus(): IntTensor {
|
||||
val resBuffer = IntArray(tensor.numElements) { i ->
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + i].unaryMinus()
|
||||
val resBuffer = IntArray(asIntTensor().numElements) { i ->
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + i].unaryMinus()
|
||||
}
|
||||
return IntTensor(tensor.shape, resBuffer)
|
||||
return IntTensor(asIntTensor().shape, resBuffer)
|
||||
}
|
||||
|
||||
override fun Tensor<Int>.transpose(i: Int, j: Int): IntTensor {
|
||||
val ii = tensor.minusIndex(i)
|
||||
val jj = tensor.minusIndex(j)
|
||||
checkTranspose(tensor.dimension, ii, jj)
|
||||
val n = tensor.numElements
|
||||
val ii = asIntTensor().minusIndex(i)
|
||||
val jj = asIntTensor().minusIndex(j)
|
||||
checkTranspose(asIntTensor().dimension, ii, jj)
|
||||
val n = asIntTensor().numElements
|
||||
val resBuffer = IntArray(n)
|
||||
|
||||
val resShape = tensor.shape.copyOf()
|
||||
val resShape = asIntTensor().shape.copyOf()
|
||||
resShape[ii] = resShape[jj].also { resShape[jj] = resShape[ii] }
|
||||
|
||||
val resTensor = IntTensor(resShape, resBuffer)
|
||||
|
||||
for (offset in 0 until n) {
|
||||
val oldMultiIndex = tensor.indices.index(offset)
|
||||
val oldMultiIndex = asIntTensor().indices.index(offset)
|
||||
val newMultiIndex = oldMultiIndex.copyOf()
|
||||
newMultiIndex[ii] = newMultiIndex[jj].also { newMultiIndex[jj] = newMultiIndex[ii] }
|
||||
|
||||
val linearIndex = resTensor.indices.offset(newMultiIndex)
|
||||
resTensor.mutableBuffer.array()[linearIndex] =
|
||||
tensor.mutableBuffer.array()[tensor.bufferStart + offset]
|
||||
asIntTensor().mutableBuffer.array()[asIntTensor().bufferStart + offset]
|
||||
}
|
||||
return resTensor
|
||||
}
|
||||
|
||||
override fun Tensor<Int>.view(shape: IntArray): IntTensor {
|
||||
checkView(tensor, shape)
|
||||
return IntTensor(shape, tensor.mutableBuffer.array(), tensor.bufferStart)
|
||||
checkView(asIntTensor(), shape)
|
||||
return IntTensor(shape, asIntTensor().mutableBuffer.array(), asIntTensor().bufferStart)
|
||||
}
|
||||
|
||||
override fun Tensor<Int>.viewAs(other: StructureND<Int>): IntTensor =
|
||||
tensor.view(other.shape)
|
||||
asIntTensor().view(other.shape)
|
||||
|
||||
override fun diagonalEmbedding(
|
||||
diagonalEntries: Tensor<Int>,
|
||||
@ -374,8 +374,8 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
diagonalEntries.shape.slice(greaterDim - 1 until n - 1).toIntArray()
|
||||
val resTensor = zeros(resShape)
|
||||
|
||||
for (i in 0 until diagonalEntries.tensor.numElements) {
|
||||
val multiIndex = diagonalEntries.tensor.indices.index(i)
|
||||
for (i in 0 until diagonalEntries.asIntTensor().numElements) {
|
||||
val multiIndex = diagonalEntries.asIntTensor().indices.index(i)
|
||||
|
||||
var offset1 = 0
|
||||
var offset2 = abs(realOffset)
|
||||
@ -391,19 +391,19 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
resTensor[diagonalMultiIndex] = diagonalEntries[multiIndex]
|
||||
}
|
||||
|
||||
return resTensor.tensor
|
||||
return resTensor.asIntTensor()
|
||||
}
|
||||
|
||||
private infix fun Tensor<Int>.eq(
|
||||
other: Tensor<Int>,
|
||||
): Boolean {
|
||||
checkShapesCompatible(tensor, other)
|
||||
val n = tensor.numElements
|
||||
if (n != other.tensor.numElements) {
|
||||
checkShapesCompatible(asIntTensor(), other)
|
||||
val n = asIntTensor().numElements
|
||||
if (n != other.asIntTensor().numElements) {
|
||||
return false
|
||||
}
|
||||
for (i in 0 until n) {
|
||||
if (tensor.mutableBuffer[tensor.bufferStart + i] != other.tensor.mutableBuffer[other.tensor.bufferStart + i]) {
|
||||
if (asIntTensor().mutableBuffer[asIntTensor().bufferStart + i] != other.asIntTensor().mutableBuffer[other.asIntTensor().bufferStart + i]) {
|
||||
return false
|
||||
}
|
||||
}
|
||||
@ -422,7 +422,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
check(tensors.all { it.shape contentEquals shape }) { "Tensors must have same shapes" }
|
||||
val resShape = intArrayOf(tensors.size) + shape
|
||||
val resBuffer = tensors.flatMap {
|
||||
it.tensor.mutableBuffer.array().drop(it.tensor.bufferStart).take(it.tensor.numElements)
|
||||
it.asIntTensor().mutableBuffer.array().drop(it.asIntTensor().bufferStart).take(it.asIntTensor().numElements)
|
||||
}.toIntArray()
|
||||
return IntTensor(resShape, resBuffer, 0)
|
||||
}
|
||||
@ -436,7 +436,7 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
public fun Tensor<Int>.rowsByIndices(indices: IntArray): IntTensor = stack(indices.map { this[it] })
|
||||
|
||||
private inline fun StructureND<Int>.fold(foldFunction: (IntArray) -> Int): Int =
|
||||
foldFunction(tensor.copyArray())
|
||||
foldFunction(asIntTensor().copyArray())
|
||||
|
||||
private inline fun <reified R : Any> StructureND<Int>.foldDim(
|
||||
dim: Int,
|
||||
@ -459,35 +459,35 @@ public open class IntTensorAlgebra : TensorAlgebra<Int, IntRing> {
|
||||
val prefix = index.take(dim).toIntArray()
|
||||
val suffix = index.takeLast(dimension - dim - 1).toIntArray()
|
||||
resTensor[index] = foldFunction(IntArray(shape[dim]) { i ->
|
||||
tensor[prefix + intArrayOf(i) + suffix]
|
||||
asIntTensor()[prefix + intArrayOf(i) + suffix]
|
||||
})
|
||||
}
|
||||
return resTensor
|
||||
}
|
||||
|
||||
override fun StructureND<Int>.sum(): Int = tensor.fold { it.sum() }
|
||||
override fun StructureND<Int>.sum(): Int = asIntTensor().fold { it.sum() }
|
||||
|
||||
override fun StructureND<Int>.sum(dim: Int, keepDim: Boolean): IntTensor =
|
||||
foldDim(dim, keepDim) { x -> x.sum() }.toIntTensor()
|
||||
foldDim(dim, keepDim) { x -> x.sum() }.asIntTensor()
|
||||
|
||||
override fun StructureND<Int>.min(): Int = this.fold { it.minOrNull()!! }
|
||||
|
||||
override fun StructureND<Int>.min(dim: Int, keepDim: Boolean): IntTensor =
|
||||
foldDim(dim, keepDim) { x -> x.minOrNull()!! }.toIntTensor()
|
||||
foldDim(dim, keepDim) { x -> x.minOrNull()!! }.asIntTensor()
|
||||
|
||||
override fun StructureND<Int>.max(): Int = this.fold { it.maxOrNull()!! }
|
||||
|
||||
override fun StructureND<Int>.max(dim: Int, keepDim: Boolean): IntTensor =
|
||||
foldDim(dim, keepDim) { x -> x.maxOrNull()!! }.toIntTensor()
|
||||
foldDim(dim, keepDim) { x -> x.maxOrNull()!! }.asIntTensor()
|
||||
|
||||
|
||||
override fun StructureND<Int>.argMax(dim: Int, keepDim: Boolean): IntTensor =
|
||||
foldDim(dim, keepDim) { x ->
|
||||
x.withIndex().maxByOrNull { it.value }?.index!!
|
||||
}.toIntTensor()
|
||||
}.asIntTensor()
|
||||
}
|
||||
|
||||
public val Int.Companion.tensorAlgebra: IntTensorAlgebra.Companion get() = IntTensorAlgebra
|
||||
public val IntRing.tensorAlgebra: IntTensorAlgebra.Companion get() = IntTensorAlgebra
|
||||
public val Int.Companion.tensorAlgebra: IntTensorAlgebra get() = IntTensorAlgebra
|
||||
public val IntRing.tensorAlgebra: IntTensorAlgebra get() = IntTensorAlgebra
|
||||
|
||||
|
||||
|
||||
@ -58,7 +58,7 @@ internal fun DoubleTensorAlgebra.checkSymmetric(
|
||||
internal fun DoubleTensorAlgebra.checkPositiveDefinite(tensor: DoubleTensor, epsilon: Double = 1e-6) {
|
||||
checkSymmetric(tensor, epsilon)
|
||||
for (mat in tensor.matrixSequence())
|
||||
check(mat.asTensor().detLU().value() > 0.0) {
|
||||
"Tensor contains matrices which are not positive definite ${mat.asTensor().detLU().value()}"
|
||||
check(mat.toTensor().detLU().value() > 0.0) {
|
||||
"Tensor contains matrices which are not positive definite ${mat.toTensor().detLU().value()}"
|
||||
}
|
||||
}
|
||||
@ -13,16 +13,17 @@ import space.kscience.kmath.tensors.core.DoubleTensor
|
||||
import space.kscience.kmath.tensors.core.IntTensor
|
||||
import space.kscience.kmath.tensors.core.TensorLinearStructure
|
||||
|
||||
internal fun BufferedTensor<Int>.asTensor(): IntTensor =
|
||||
internal fun BufferedTensor<Int>.toTensor(): IntTensor =
|
||||
IntTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
|
||||
|
||||
internal fun BufferedTensor<Double>.asTensor(): DoubleTensor =
|
||||
internal fun BufferedTensor<Double>.toTensor(): DoubleTensor =
|
||||
DoubleTensor(this.shape, this.mutableBuffer.array(), this.bufferStart)
|
||||
|
||||
internal fun <T> StructureND<T>.copyToBufferedTensor(): BufferedTensor<T> =
|
||||
BufferedTensor(
|
||||
this.shape,
|
||||
TensorLinearStructure(this.shape).asSequence().map(this::get).toMutableList().asMutableBuffer(), 0
|
||||
TensorLinearStructure(this.shape).asSequence().map(this::get).toMutableList().asMutableBuffer(),
|
||||
0
|
||||
)
|
||||
|
||||
internal fun <T> StructureND<T>.toBufferedTensor(): BufferedTensor<T> = when (this) {
|
||||
@ -34,17 +35,3 @@ internal fun <T> StructureND<T>.toBufferedTensor(): BufferedTensor<T> = when (th
|
||||
}
|
||||
else -> this.copyToBufferedTensor()
|
||||
}
|
||||
|
||||
@PublishedApi
|
||||
internal val StructureND<Double>.tensor: DoubleTensor
|
||||
get() = when (this) {
|
||||
is DoubleTensor -> this
|
||||
else -> this.toBufferedTensor().asTensor()
|
||||
}
|
||||
|
||||
@PublishedApi
|
||||
internal val StructureND<Int>.tensor: IntTensor
|
||||
get() = when (this) {
|
||||
is IntTensor -> this
|
||||
else -> this.toBufferedTensor().asTensor()
|
||||
}
|
||||
|
||||
@ -5,18 +5,26 @@
|
||||
|
||||
package space.kscience.kmath.tensors.core
|
||||
|
||||
import space.kscience.kmath.nd.StructureND
|
||||
import space.kscience.kmath.tensors.api.Tensor
|
||||
import space.kscience.kmath.tensors.core.internal.tensor
|
||||
import space.kscience.kmath.tensors.core.internal.toBufferedTensor
|
||||
import space.kscience.kmath.tensors.core.internal.toTensor
|
||||
|
||||
/**
|
||||
* Casts [Tensor] of [Double] to [DoubleTensor]
|
||||
*/
|
||||
public fun Tensor<Double>.toDoubleTensor(): DoubleTensor = this.tensor
|
||||
public fun StructureND<Double>.asDoubleTensor(): DoubleTensor = when (this) {
|
||||
is DoubleTensor -> this
|
||||
else -> this.toBufferedTensor().toTensor()
|
||||
}
|
||||
|
||||
/**
|
||||
* Casts [Tensor] of [Int] to [IntTensor]
|
||||
*/
|
||||
public fun Tensor<Int>.toIntTensor(): IntTensor = this.tensor
|
||||
public fun StructureND<Int>.asIntTensor(): IntTensor = when (this) {
|
||||
is IntTensor -> this
|
||||
else -> this.toBufferedTensor().toTensor()
|
||||
}
|
||||
|
||||
/**
|
||||
* Returns a copy-protected [DoubleArray] of tensor elements
|
||||
|
||||
@ -14,9 +14,9 @@ import space.kscience.kmath.operations.invoke
|
||||
import space.kscience.kmath.structures.DoubleBuffer
|
||||
import space.kscience.kmath.structures.toDoubleArray
|
||||
import space.kscience.kmath.tensors.core.internal.array
|
||||
import space.kscience.kmath.tensors.core.internal.asTensor
|
||||
import space.kscience.kmath.tensors.core.internal.matrixSequence
|
||||
import space.kscience.kmath.tensors.core.internal.toBufferedTensor
|
||||
import space.kscience.kmath.tensors.core.internal.toTensor
|
||||
import kotlin.test.Test
|
||||
import kotlin.test.assertEquals
|
||||
import kotlin.test.assertTrue
|
||||
@ -56,7 +56,7 @@ internal class TestDoubleTensor {
|
||||
assertEquals(tensor[intArrayOf(0, 1, 0)], 109.56)
|
||||
|
||||
tensor.matrixSequence().forEach {
|
||||
val a = it.asTensor()
|
||||
val a = it.toTensor()
|
||||
val secondRow = a[1].as1D()
|
||||
val secondColumn = a.transpose(0, 1)[1].as1D()
|
||||
assertEquals(secondColumn[0], 77.89)
|
||||
@ -75,10 +75,10 @@ internal class TestDoubleTensor {
|
||||
|
||||
// map to tensors
|
||||
val bufferedTensorArray = ndArray.toBufferedTensor() // strides are flipped so data copied
|
||||
val tensorArray = bufferedTensorArray.asTensor() // data not contiguous so copied again
|
||||
val tensorArray = bufferedTensorArray.toTensor() // data not contiguous so copied again
|
||||
|
||||
val tensorArrayPublic = ndArray.toDoubleTensor() // public API, data copied twice
|
||||
val sharedTensorArray = tensorArrayPublic.toDoubleTensor() // no data copied by matching type
|
||||
val tensorArrayPublic = ndArray.asDoubleTensor() // public API, data copied twice
|
||||
val sharedTensorArray = tensorArrayPublic.asDoubleTensor() // no data copied by matching type
|
||||
|
||||
assertTrue(tensorArray.mutableBuffer.array() contentEquals sharedTensorArray.mutableBuffer.array())
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user