Performance fixes
This commit is contained in:
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f8e91c2402
commit
10739e0d04
@ -11,12 +11,11 @@ import kotlinx.benchmark.Scope
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import kotlinx.benchmark.State
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import space.kscience.kmath.commons.linear.CMLinearSpace
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import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
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import space.kscience.kmath.linear.Float64ParallelLinearSpace
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import space.kscience.kmath.linear.invoke
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import space.kscience.kmath.linear.linearSpace
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import space.kscience.kmath.operations.Float64Field
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import space.kscience.kmath.operations.invoke
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import space.kscience.kmath.tensorflow.produceWithTF
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import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
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import space.kscience.kmath.tensors.core.tensorAlgebra
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import kotlin.random.Random
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@ -72,12 +71,12 @@ internal class DotBenchmark {
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}
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@Benchmark
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fun tensorDot(blackhole: Blackhole) = with(Float64Field.tensorAlgebra) {
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fun multikDot(blackhole: Blackhole) = with(multikAlgebra) {
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blackhole.consume(matrix1 dot matrix2)
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}
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@Benchmark
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fun multikDot(blackhole: Blackhole) = with(multikAlgebra) {
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fun tensorDot(blackhole: Blackhole) = with(Float64Field.tensorAlgebra) {
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blackhole.consume(matrix1 dot matrix2)
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}
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@ -87,12 +86,8 @@ internal class DotBenchmark {
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}
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@Benchmark
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fun doubleDot(blackhole: Blackhole) = with(Float64Field.linearSpace) {
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fun parallelDot(blackhole: Blackhole) = with(Float64ParallelLinearSpace) {
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blackhole.consume(matrix1 dot matrix2)
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}
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@Benchmark
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fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
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blackhole.consume(matrix1 dot matrix2)
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}
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}
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@ -15,6 +15,7 @@ import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
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import space.kscience.kmath.linear.invoke
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import space.kscience.kmath.linear.linearSpace
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import space.kscience.kmath.linear.lupSolver
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import space.kscience.kmath.linear.parallel
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import space.kscience.kmath.operations.algebra
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import kotlin.random.Random
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@ -38,16 +39,19 @@ internal class MatrixInverseBenchmark {
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}
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@Benchmark
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fun cmLUPInversion(blackhole: Blackhole) {
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CMLinearSpace {
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blackhole.consume(lupSolver().inverse(matrix))
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}
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fun kmathParallelLupInversion(blackhole: Blackhole) {
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blackhole.consume(Double.algebra.linearSpace.parallel.lupSolver().inverse(matrix))
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}
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@Benchmark
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fun ejmlInverse(blackhole: Blackhole) {
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EjmlLinearSpaceDDRM {
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fun cmLUPInversion(blackhole: Blackhole) = CMLinearSpace {
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blackhole.consume(lupSolver().inverse(matrix))
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}
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@Benchmark
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fun ejmlInverse(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
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blackhole.consume(matrix.toEjml().inverted())
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}
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}
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}
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@ -17,7 +17,7 @@ val benchmarksVersion = spclibs.versions.kotlinx.benchmark.get()
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dependencies {
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api("space.kscience:gradle-tools:$toolsVersion")
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//plugins form benchmarks
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api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:0.4.9")
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api("org.jetbrains.kotlinx:kotlinx-benchmark-plugin:$benchmarksVersion")
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//api("org.jetbrains.kotlin:kotlin-allopen:$kotlinVersion")
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//to be used inside build-script only
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//implementation(spclibs.kotlinx.serialization.json)
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@ -1,443 +0,0 @@
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/*
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* Copyright 2018-2024 KMath contributors.
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* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
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*/
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@file:Suppress("KDocUnresolvedReference")
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package space.kscience.kmath.ejml.codegen
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import org.intellij.lang.annotations.Language
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import java.io.File
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private fun Appendable.appendEjmlVector(type: String, ejmlMatrixType: String) {
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@Language("kotlin") val text = """/**
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* [EjmlVector] specialization for [$type].
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*/
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public class Ejml${type}Vector<out M : $ejmlMatrixType>(override val origin: M) : EjmlVector<$type, M>(origin) {
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init {
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require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" }
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}
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override val type: SafeType<${type}> get() = safeTypeOf()
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override operator fun get(index: Int): $type = origin[0, index]
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}"""
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appendLine(text)
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appendLine()
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}
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private fun Appendable.appendEjmlMatrix(type: String, ejmlMatrixType: String) {
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val text = """/**
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* [EjmlMatrix] specialization for [$type].
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*/
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public class Ejml${type}Matrix<out M : $ejmlMatrixType>(override val origin: M) : EjmlMatrix<$type, M>(origin) {
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override val type: SafeType<${type}> get() = safeTypeOf()
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override operator fun get(i: Int, j: Int): $type = origin[i, j]
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}"""
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appendLine(text)
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appendLine()
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}
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private fun Appendable.appendEjmlLinearSpace(
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type: String,
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kmathAlgebra: String,
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ejmlMatrixParentTypeMatrix: String,
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ejmlMatrixType: String,
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ejmlMatrixDenseType: String,
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ops: String,
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denseOps: String,
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isDense: Boolean,
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) {
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@Language("kotlin") val text = """
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/**
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* [EjmlLinearSpace] implementation based on [CommonOps_$ops], [DecompositionFactory_${ops}] operations and
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* [${ejmlMatrixType}] matrices.
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*/
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public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra}, $ejmlMatrixType>() {
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/**
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* The [${kmathAlgebra}] reference.
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*/
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override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
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override val type: SafeType<${type}> get() = safeTypeOf()
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@Suppress("UNCHECKED_CAST")
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override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
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this is Ejml${type}Matrix<*> && origin is $ejmlMatrixType -> this as Ejml${type}Matrix<${ejmlMatrixType}>
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else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
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}
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@Suppress("UNCHECKED_CAST")
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override fun Point<${type}>.toEjml(): Ejml${type}Vector<${ejmlMatrixType}> = when {
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this is Ejml${type}Vector<*> && origin is $ejmlMatrixType -> this as Ejml${type}Vector<${ejmlMatrixType}>
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else -> Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
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(0 until it.numRows).forEach { row -> it[row, 0] = get(row) }
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})
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}
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override fun buildMatrix(
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rows: Int,
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columns: Int,
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initializer: ${kmathAlgebra}.(i: Int, j: Int) -> ${type},
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): Ejml${type}Matrix<${ejmlMatrixType}> = ${ejmlMatrixType}(rows, columns).also {
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(0 until rows).forEach { row ->
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(0 until columns).forEach { col -> it[row, col] = elementAlgebra.initializer(row, col) }
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}
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}.wrapMatrix()
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override fun buildVector(
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size: Int,
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initializer: ${kmathAlgebra}.(Int) -> ${type},
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): Ejml${type}Vector<${ejmlMatrixType}> = Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
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(0 until it.numRows).forEach { row -> it[row, 0] = elementAlgebra.initializer(row) }
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})
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private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapMatrix() = Ejml${type}Matrix(this)
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private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapVector() = Ejml${type}Vector(this)
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override fun Matrix<${type}>.unaryMinus(): Matrix<${type}> = this * elementAlgebra { -one }
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override fun Matrix<${type}>.dot(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
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val out = ${ejmlMatrixType}(1, 1)
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CommonOps_${ops}.mult(toEjml().origin, other.toEjml().origin, out)
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return out.wrapMatrix()
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}
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override fun Matrix<${type}>.dot(vector: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
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val out = ${ejmlMatrixType}(1, 1)
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CommonOps_${ops}.mult(toEjml().origin, vector.toEjml().origin, out)
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return out.wrapVector()
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}
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override operator fun Matrix<${type}>.minus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
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val out = ${ejmlMatrixType}(1, 1)
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CommonOps_${ops}.add(
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elementAlgebra.one,
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toEjml().origin,
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elementAlgebra { -one },
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other.toEjml().origin,
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out,${
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if (isDense) "" else
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"""
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null,
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null,"""
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}
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)
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return out.wrapMatrix()
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}
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override operator fun Matrix<${type}>.times(value: ${type}): Ejml${type}Matrix<${ejmlMatrixType}> {
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val res = ${ejmlMatrixType}(1, 1)
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CommonOps_${ops}.scale(value, toEjml().origin, res)
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return res.wrapMatrix()
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}
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override fun Point<${type}>.unaryMinus(): Ejml${type}Vector<${ejmlMatrixType}> {
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val res = ${ejmlMatrixType}(1, 1)
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CommonOps_${ops}.changeSign(toEjml().origin, res)
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return res.wrapVector()
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}
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override fun Matrix<${type}>.plus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
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val out = ${ejmlMatrixType}(1, 1)
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CommonOps_${ops}.add(
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elementAlgebra.one,
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toEjml().origin,
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elementAlgebra.one,
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other.toEjml().origin,
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out,${
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if (isDense) "" else
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"""
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null,
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null,"""
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}
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)
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return out.wrapMatrix()
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}
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override fun Point<${type}>.plus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
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val out = ${ejmlMatrixType}(1, 1)
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CommonOps_${ops}.add(
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elementAlgebra.one,
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toEjml().origin,
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elementAlgebra.one,
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other.toEjml().origin,
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out,${
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if (isDense) "" else
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"""
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null,
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null,"""
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}
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)
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return out.wrapVector()
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}
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override fun Point<${type}>.minus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
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val out = ${ejmlMatrixType}(1, 1)
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CommonOps_${ops}.add(
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elementAlgebra.one,
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toEjml().origin,
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elementAlgebra { -one },
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other.toEjml().origin,
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out,${
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if (isDense) "" else
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"""
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null,
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null,"""
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}
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)
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return out.wrapVector()
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}
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override fun ${type}.times(m: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> = m * this
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override fun Point<${type}>.times(value: ${type}): Ejml${type}Vector<${ejmlMatrixType}> {
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val res = ${ejmlMatrixType}(1, 1)
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CommonOps_${ops}.scale(value, toEjml().origin, res)
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return res.wrapVector()
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}
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override fun ${type}.times(v: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> = v * this
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@UnstableKMathAPI
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override fun <F : StructureFeature> computeFeature(structure: Matrix<${type}>, type: KClass<out F>): F? {
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structure.getFeature(type)?.let { return it }
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val origin = structure.toEjml().origin
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return when (type) {
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${
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if (isDense)
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""" InverseMatrixFeature::class -> object : InverseMatrixFeature<${type}> {
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override val inverse: Matrix<${type}> by lazy {
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val res = origin.copy()
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CommonOps_${ops}.invert(res)
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res.wrapMatrix()
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}
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}
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DeterminantFeature::class -> object : DeterminantFeature<${type}> {
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override val determinant: $type by lazy { CommonOps_${ops}.det(origin) }
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}
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SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<${type}> {
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private val svd by lazy {
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DecompositionFactory_${ops}.svd(origin.numRows, origin.numCols, true, true, false)
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.apply { decompose(origin.copy()) }
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}
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override val u: Matrix<${type}> by lazy { svd.getU(null, false).wrapMatrix() }
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override val s: Matrix<${type}> by lazy { svd.getW(null).wrapMatrix() }
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override val v: Matrix<${type}> by lazy { svd.getV(null, false).wrapMatrix() }
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override val singularValues: Point<${type}> by lazy { ${type}Buffer(svd.singularValues) }
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}
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QRDecompositionFeature::class -> object : QRDecompositionFeature<${type}> {
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private val qr by lazy {
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DecompositionFactory_${ops}.qr().apply { decompose(origin.copy()) }
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}
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override val q: Matrix<${type}> by lazy {
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qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
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}
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override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
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}
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CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
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override val l: Matrix<${type}> by lazy {
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val cholesky =
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DecompositionFactory_${ops}.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
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cholesky.getT(null).wrapMatrix().withFeature(LFeature)
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}
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}
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LupDecompositionFeature::class -> object : LupDecompositionFeature<${type}> {
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private val lup by lazy {
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DecompositionFactory_${ops}.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) }
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}
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override val l: Matrix<${type}> by lazy {
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lup.getLower(null).wrapMatrix().withFeature(LFeature)
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}
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override val u: Matrix<${type}> by lazy {
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lup.getUpper(null).wrapMatrix().withFeature(UFeature)
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}
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override val p: Matrix<${type}> by lazy { lup.getRowPivot(null).wrapMatrix() }
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}""" else """ QRDecompositionFeature::class -> object : QRDecompositionFeature<$type> {
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private val qr by lazy {
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DecompositionFactory_${ops}.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
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}
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override val q: Matrix<${type}> by lazy {
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qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
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}
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override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
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}
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CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
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override val l: Matrix<${type}> by lazy {
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val cholesky =
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DecompositionFactory_${ops}.cholesky().apply { decompose(origin.copy()) }
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(cholesky.getT(null) as ${ejmlMatrixParentTypeMatrix}).wrapMatrix().withFeature(LFeature)
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}
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}
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LUDecompositionFeature::class, DeterminantFeature::class, InverseMatrixFeature::class -> object :
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LUDecompositionFeature<${type}>, DeterminantFeature<${type}>, InverseMatrixFeature<${type}> {
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private val lu by lazy {
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DecompositionFactory_${ops}.lu(FillReducing.NONE).apply { decompose(origin.copy()) }
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}
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override val l: Matrix<${type}> by lazy {
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lu.getLower(null).wrapMatrix().withFeature(LFeature)
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}
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override val u: Matrix<${type}> by lazy {
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lu.getUpper(null).wrapMatrix().withFeature(UFeature)
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}
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override val inverse: Matrix<${type}> by lazy {
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var a = origin
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val inverse = ${ejmlMatrixDenseType}(1, 1)
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val solver = LinearSolverFactory_${ops}.lu(FillReducing.NONE)
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if (solver.modifiesA()) a = a.copy()
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val i = CommonOps_${denseOps}.identity(a.numRows)
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solver.solve(i, inverse)
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inverse.wrapMatrix()
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}
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override val determinant: $type by lazy { elementAlgebra.number(lu.computeDeterminant().real) }
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}"""
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}
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else -> null
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}?.let{
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type.cast(it)
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}
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}
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/**
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* Solves for *x* in the following equation: *x = [a] <sup>-1</sup> · [b]*.
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*
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* @param a the base matrix.
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* @param b n by p matrix.
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* @return the solution for *x* that is n by p.
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*/
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public fun solve(a: Matrix<${type}>, b: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
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val res = ${ejmlMatrixType}(1, 1)
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CommonOps_${ops}.solve(${ejmlMatrixType}(a.toEjml().origin), ${ejmlMatrixType}(b.toEjml().origin), res)
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return res.wrapMatrix()
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}
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/**
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* Solves for *x* in the following equation: *x = [a] <sup>-1</sup> · [b]*.
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*
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* @param a the base matrix.
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* @param b n by p vector.
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* @return the solution for *x* that is n by p.
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*/
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public fun solve(a: Matrix<${type}>, b: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
|
||||
val res = ${ejmlMatrixType}(1, 1)
|
||||
CommonOps_${ops}.solve(${ejmlMatrixType}(a.toEjml().origin), ${ejmlMatrixType}(b.toEjml().origin), res)
|
||||
return Ejml${type}Vector(res)
|
||||
}
|
||||
}"""
|
||||
appendLine(text)
|
||||
appendLine()
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Generates routine EJML classes.
|
||||
*/
|
||||
fun ejmlCodegen(outputFile: String): Unit = File(outputFile).run {
|
||||
parentFile.mkdirs()
|
||||
|
||||
writer().use {
|
||||
it.appendLine("/*")
|
||||
it.appendLine(" * Copyright 2018-2024 KMath contributors.")
|
||||
it.appendLine(" * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.")
|
||||
it.appendLine(" */")
|
||||
it.appendLine()
|
||||
it.appendLine("/* This file is generated with buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt */")
|
||||
it.appendLine()
|
||||
it.appendLine("package space.kscience.kmath.ejml")
|
||||
it.appendLine()
|
||||
it.appendLine("""import org.ejml.data.*
|
||||
import org.ejml.dense.row.CommonOps_DDRM
|
||||
import org.ejml.dense.row.CommonOps_FDRM
|
||||
import org.ejml.dense.row.factory.DecompositionFactory_DDRM
|
||||
import org.ejml.dense.row.factory.DecompositionFactory_FDRM
|
||||
import org.ejml.sparse.FillReducing
|
||||
import org.ejml.sparse.csc.CommonOps_DSCC
|
||||
import org.ejml.sparse.csc.CommonOps_FSCC
|
||||
import org.ejml.sparse.csc.factory.DecompositionFactory_DSCC
|
||||
import org.ejml.sparse.csc.factory.DecompositionFactory_FSCC
|
||||
import org.ejml.sparse.csc.factory.LinearSolverFactory_DSCC
|
||||
import org.ejml.sparse.csc.factory.LinearSolverFactory_FSCC
|
||||
import space.kscience.attributes.SafeType
|
||||
import space.kscience.attributes.safeTypeOf
|
||||
import space.kscience.kmath.linear.*
|
||||
import space.kscience.kmath.linear.Matrix
|
||||
import space.kscience.kmath.UnstableKMathAPI
|
||||
import space.kscience.kmath.nd.StructureFeature
|
||||
import space.kscience.kmath.structures.Float64
|
||||
import space.kscience.kmath.structures.Float32
|
||||
import space.kscience.kmath.operations.Float64Field
|
||||
import space.kscience.kmath.operations.Float32Field
|
||||
import space.kscience.kmath.operations.DoubleField
|
||||
import space.kscience.kmath.operations.FloatField
|
||||
import space.kscience.kmath.operations.invoke
|
||||
import space.kscience.kmath.structures.Float64Buffer
|
||||
import space.kscience.kmath.structures.Float32Buffer
|
||||
import space.kscience.kmath.structures.DoubleBuffer
|
||||
import space.kscience.kmath.structures.FloatBuffer
|
||||
import kotlin.reflect.KClass
|
||||
import kotlin.reflect.cast""")
|
||||
it.appendLine()
|
||||
it.appendEjmlVector("Double", "DMatrix")
|
||||
it.appendEjmlVector("Float", "FMatrix")
|
||||
it.appendEjmlMatrix("Double", "DMatrix")
|
||||
it.appendEjmlMatrix("Float", "FMatrix")
|
||||
it.appendEjmlLinearSpace("Double", "Float64Field", "DMatrix", "DMatrixRMaj", "DMatrixRMaj", "DDRM", "DDRM", true)
|
||||
it.appendEjmlLinearSpace("Float", "Float32Field", "FMatrix", "FMatrixRMaj", "FMatrixRMaj", "FDRM", "FDRM", true)
|
||||
|
||||
it.appendEjmlLinearSpace(
|
||||
type = "Double",
|
||||
kmathAlgebra = "Float64Field",
|
||||
ejmlMatrixParentTypeMatrix = "DMatrix",
|
||||
ejmlMatrixType = "DMatrixSparseCSC",
|
||||
ejmlMatrixDenseType = "DMatrixRMaj",
|
||||
ops = "DSCC",
|
||||
denseOps = "DDRM",
|
||||
isDense = false,
|
||||
)
|
||||
|
||||
it.appendEjmlLinearSpace(
|
||||
type = "Float",
|
||||
kmathAlgebra = "Float32Field",
|
||||
ejmlMatrixParentTypeMatrix = "FMatrix",
|
||||
ejmlMatrixType = "FMatrixSparseCSC",
|
||||
ejmlMatrixDenseType = "FMatrixRMaj",
|
||||
ops = "FSCC",
|
||||
denseOps = "FDRM",
|
||||
isDense = false,
|
||||
)
|
||||
}
|
||||
}
|
@ -5,31 +5,26 @@
|
||||
|
||||
package space.kscience.kmath.linear
|
||||
|
||||
import space.kscience.kmath.operations.algebra
|
||||
import kotlin.random.Random
|
||||
import kotlin.time.ExperimentalTime
|
||||
import kotlin.time.measureTime
|
||||
|
||||
@OptIn(ExperimentalTime::class)
|
||||
fun main() {
|
||||
fun main() = with(Float64ParallelLinearSpace) {
|
||||
val random = Random(12224)
|
||||
val dim = 1000
|
||||
|
||||
//creating invertible matrix
|
||||
val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
|
||||
val matrix1 = buildMatrix(dim, dim) { i, j ->
|
||||
if (i <= j) random.nextDouble() else 0.0
|
||||
}
|
||||
val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
|
||||
val matrix2 = buildMatrix(dim, dim) { i, j ->
|
||||
if (i <= j) random.nextDouble() else 0.0
|
||||
}
|
||||
|
||||
val time = measureTime {
|
||||
with(Double.algebra.linearSpace) {
|
||||
repeat(10) {
|
||||
repeat(30) {
|
||||
matrix1 dot matrix2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
println(time)
|
||||
|
||||
|
@ -0,0 +1,27 @@
|
||||
/*
|
||||
* Copyright 2018-2024 KMath contributors.
|
||||
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
|
||||
*/
|
||||
|
||||
package space.kscience.kmath.linear
|
||||
|
||||
import kotlin.random.Random
|
||||
import kotlin.time.measureTime
|
||||
|
||||
fun main(): Unit = with(Float64LinearSpace) {
|
||||
val random = Random(1224)
|
||||
val dim = 500
|
||||
|
||||
//creating invertible matrix
|
||||
val u = buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
|
||||
val l = buildMatrix(dim, dim) { i, j -> if (i >= j) random.nextDouble() else 0.0 }
|
||||
val matrix = l dot u
|
||||
|
||||
val time = measureTime {
|
||||
repeat(20) {
|
||||
lupSolver().inverse(matrix)
|
||||
}
|
||||
}
|
||||
|
||||
println(time)
|
||||
}
|
@ -7,7 +7,7 @@ package space.kscience.kmath.series
|
||||
|
||||
|
||||
import space.kscience.kmath.structures.Buffer
|
||||
import space.kscience.kmath.structures.DoubleBuffer
|
||||
import space.kscience.kmath.structures.Float64Buffer
|
||||
import space.kscience.kmath.structures.asBuffer
|
||||
import space.kscience.kmath.structures.toDoubleArray
|
||||
import space.kscience.plotly.*
|
||||
@ -21,7 +21,7 @@ fun main(): Unit = with(Double.seriesAlgebra()) {
|
||||
|
||||
val arrayOfRandoms = DoubleArray(20) { random.nextDouble() }
|
||||
|
||||
val series1: DoubleBuffer = arrayOfRandoms.asBuffer()
|
||||
val series1: Float64Buffer = arrayOfRandoms.asBuffer()
|
||||
val series2: Series<Double> = series1.moveBy(3)
|
||||
|
||||
val res = series2 - series1
|
||||
@ -42,7 +42,7 @@ fun main(): Unit = with(Double.seriesAlgebra()) {
|
||||
Plotly.plot {
|
||||
series("series1", series1)
|
||||
series("series2", series2)
|
||||
series("dif", res){
|
||||
series("dif", res) {
|
||||
mode = ScatterMode.lines
|
||||
line.color("magenta")
|
||||
}
|
||||
|
2
gradle/wrapper/gradle-wrapper.properties
vendored
2
gradle/wrapper/gradle-wrapper.properties
vendored
@ -1,5 +1,5 @@
|
||||
distributionBase=GRADLE_USER_HOME
|
||||
distributionPath=wrapper/dists
|
||||
distributionUrl=https\://services.gradle.org/distributions/gradle-8.4-bin.zip
|
||||
distributionUrl=https\://services.gradle.org/distributions/gradle-8.6-bin.zip
|
||||
zipStoreBase=GRADLE_USER_HOME
|
||||
zipStorePath=wrapper/dists
|
||||
|
@ -54,11 +54,12 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
|
||||
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
|
||||
val rows = this@dot.rows.map { it.linearize() }
|
||||
val columns = other.columns.map { it.linearize() }
|
||||
val indices = 0 until this.rowNum
|
||||
return buildMatrix(rowNum, other.colNum) { i, j ->
|
||||
val r = rows[i]
|
||||
val c = columns[j]
|
||||
var res = 0.0
|
||||
for (l in r.indices) {
|
||||
for (l in indices) {
|
||||
res += r[l] * c[l]
|
||||
}
|
||||
res
|
||||
@ -69,10 +70,11 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
|
||||
override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
|
||||
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
|
||||
val rows = this@dot.rows.map { it.linearize() }
|
||||
val indices = 0 until this.rowNum
|
||||
return Float64Buffer(rowNum) { i ->
|
||||
val r = rows[i]
|
||||
var res = 0.0
|
||||
for (j in r.indices) {
|
||||
for (j in indices) {
|
||||
res += r[j] * vector[j]
|
||||
}
|
||||
res
|
||||
|
@ -11,6 +11,7 @@ import space.kscience.attributes.Attributes
|
||||
import space.kscience.attributes.PolymorphicAttribute
|
||||
import space.kscience.attributes.safeTypeOf
|
||||
import space.kscience.kmath.UnstableKMathAPI
|
||||
import space.kscience.kmath.nd.*
|
||||
import space.kscience.kmath.operations.*
|
||||
import space.kscience.kmath.structures.*
|
||||
|
||||
@ -150,7 +151,14 @@ public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
|
||||
for (row in col + 1 until m) lu[row, col] /= luDiag
|
||||
}
|
||||
|
||||
return GenericLupDecomposition(this@lup, lu.toStructure2D(), pivot.asBuffer(), even)
|
||||
val shape = ShapeND(rowNum, colNum)
|
||||
|
||||
val structure2D = BufferND(
|
||||
RowStrides(ShapeND(rowNum, colNum)),
|
||||
lu
|
||||
).as2D()
|
||||
|
||||
return GenericLupDecomposition(this@lup, structure2D, pivot.asBuffer(), even)
|
||||
}
|
||||
}
|
||||
|
||||
@ -166,7 +174,7 @@ internal fun <T> LinearSpace<T, Field<T>>.solve(
|
||||
): Matrix<T> {
|
||||
require(matrix.rowNum == lup.l.rowNum) { "Matrix dimension mismatch. Expected ${lup.l.rowNum}, but got ${matrix.colNum}" }
|
||||
|
||||
BufferAccessor2D(matrix.rowNum, matrix.colNum, elementAlgebra.bufferFactory).run {
|
||||
with(BufferAccessor2D(matrix.rowNum, matrix.colNum, elementAlgebra.bufferFactory)) {
|
||||
elementAlgebra {
|
||||
// Apply permutations to b
|
||||
val bp = create { _, _ -> zero }
|
||||
|
@ -77,13 +77,11 @@ private inline fun <T, A : Algebra<T>> BufferAlgebra<T, A>.zipInline(
|
||||
return elementBufferFactory(l.size) { elementAlgebra.block(l[it], r[it]) }
|
||||
}
|
||||
|
||||
public fun <T> BufferAlgebra<T, *>.buffer(size: Int, initializer: (Int) -> T): MutableBuffer<T> {
|
||||
return elementBufferFactory(size, initializer)
|
||||
}
|
||||
public fun <T> BufferAlgebra<T, *>.buffer(size: Int, initializer: (Int) -> T): MutableBuffer<T> =
|
||||
elementBufferFactory(size, initializer)
|
||||
|
||||
public fun <T, A> A.buffer(initializer: (Int) -> T): MutableBuffer<T> where A : BufferAlgebra<T, *>, A : WithSize {
|
||||
return elementBufferFactory(size, initializer)
|
||||
}
|
||||
public fun <T, A> A.buffer(initializer: (Int) -> T): MutableBuffer<T> where A : BufferAlgebra<T, *>, A : WithSize =
|
||||
elementBufferFactory(size, initializer)
|
||||
|
||||
public fun <T, A : TrigonometricOperations<T>> BufferAlgebra<T, A>.sin(arg: Buffer<T>): Buffer<T> =
|
||||
mapInline(arg) { sin(it) }
|
||||
|
@ -42,6 +42,8 @@ public inline fun <reified T> BufferFactory(): BufferFactory<T> = BufferFactory(
|
||||
*/
|
||||
public interface MutableBufferFactory<T> : BufferFactory<T> {
|
||||
override fun invoke(size: Int, builder: (Int) -> T): MutableBuffer<T>
|
||||
|
||||
public companion object
|
||||
}
|
||||
|
||||
/**
|
||||
|
@ -6,12 +6,7 @@
|
||||
package space.kscience.kmath.structures
|
||||
|
||||
import space.kscience.attributes.SafeType
|
||||
import space.kscience.kmath.nd.BufferND
|
||||
import space.kscience.kmath.nd.ShapeND
|
||||
import space.kscience.kmath.nd.Structure2D
|
||||
import space.kscience.kmath.nd.as2D
|
||||
import kotlin.collections.component1
|
||||
import kotlin.collections.component2
|
||||
|
||||
/**
|
||||
* A context that allows to operate on a [MutableBuffer] as on 2d array
|
||||
@ -32,10 +27,10 @@ internal class BufferAccessor2D<T>(
|
||||
|
||||
fun create(mat: Structure2D<T>): MutableBuffer<T> = create { i, j -> mat[i, j] }
|
||||
|
||||
//TODO optimize wrapper
|
||||
fun MutableBuffer<T>.toStructure2D(): Structure2D<T> = BufferND(
|
||||
type, ShapeND(rowNum, colNum)
|
||||
) { (i, j) -> get(i, j) }.as2D()
|
||||
// //TODO optimize wrapper
|
||||
// fun MutableBuffer<T>.toStructure2D(): Structure2D<T> = BufferND(
|
||||
// type, ShapeND(rowNum, colNum)
|
||||
// ) { (i, j) -> get(i, j) }.as2D()
|
||||
|
||||
inner class Row(val buffer: MutableBuffer<T>, val rowIndex: Int) : MutableBuffer<T> {
|
||||
override val type: SafeType<T> get() = buffer.type
|
||||
|
@ -39,6 +39,7 @@ public value class Float64Buffer(public val array: DoubleArray) : PrimitiveBuffe
|
||||
}
|
||||
}
|
||||
|
||||
@Deprecated("Use Float64Buffer instead", ReplaceWith("Float64Buffer"))
|
||||
public typealias DoubleBuffer = Float64Buffer
|
||||
|
||||
/**
|
||||
|
@ -77,7 +77,6 @@ public inline fun <T> MutableBuffer(
|
||||
size: Int,
|
||||
initializer: (Int) -> T,
|
||||
): MutableBuffer<T> = when (type.kType) {
|
||||
typeOf<Boolean>() -> TODO()
|
||||
typeOf<Int8>() -> Int8Buffer(size) { initializer(it) as Int8 } as MutableBuffer<T>
|
||||
typeOf<Int16>() -> MutableBuffer.short(size) { initializer(it) as Int16 } as MutableBuffer<T>
|
||||
typeOf<Int32>() -> MutableBuffer.int(size) { initializer(it) as Int32 } as MutableBuffer<T>
|
||||
|
@ -0,0 +1,125 @@
|
||||
/*
|
||||
* Copyright 2018-2024 KMath contributors.
|
||||
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
|
||||
*/
|
||||
|
||||
package space.kscience.kmath.linear
|
||||
|
||||
import space.kscience.kmath.PerformancePitfall
|
||||
import space.kscience.kmath.nd.*
|
||||
import space.kscience.kmath.operations.Float64BufferOps
|
||||
import space.kscience.kmath.operations.Float64Field
|
||||
import space.kscience.kmath.operations.invoke
|
||||
import space.kscience.kmath.structures.Buffer
|
||||
import space.kscience.kmath.structures.Float64Buffer
|
||||
import space.kscience.kmath.structures.asBuffer
|
||||
import java.util.stream.IntStream
|
||||
|
||||
|
||||
public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
|
||||
|
||||
override val elementAlgebra: Float64Field = Float64Field
|
||||
|
||||
|
||||
override fun buildMatrix(
|
||||
rows: Int,
|
||||
columns: Int,
|
||||
initializer: Float64Field.(i: Int, j: Int) -> Double,
|
||||
): Matrix<Double> {
|
||||
val shape = ShapeND(rows, columns)
|
||||
val indexer = BufferAlgebraND.defaultIndexerBuilder(shape)
|
||||
|
||||
val buffer = IntStream.range(0, indexer.linearSize).parallel().mapToDouble { offset ->
|
||||
val (i, j) = indexer.index(offset)
|
||||
elementAlgebra.initializer(i, j)
|
||||
}.toArray().asBuffer()
|
||||
|
||||
return MutableBufferND(
|
||||
indexer,
|
||||
buffer
|
||||
).as2D()
|
||||
}
|
||||
|
||||
override fun buildVector(size: Int, initializer: Float64Field.(Int) -> Double): Float64Buffer =
|
||||
IntStream.range(0, size).parallel().mapToDouble{ Float64Field.initializer(it) }.toArray().asBuffer()
|
||||
|
||||
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = Floa64FieldOpsND {
|
||||
asND().map { -it }.as2D()
|
||||
}
|
||||
|
||||
override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
|
||||
require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
|
||||
asND().plus(other.asND()).as2D()
|
||||
}
|
||||
|
||||
override fun Matrix<Double>.minus(other: Matrix<Double>): Matrix<Double> = Floa64FieldOpsND {
|
||||
require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
|
||||
asND().minus(other.asND()).as2D()
|
||||
}
|
||||
|
||||
// Create a continuous in-memory representation of this vector for better memory layout handling
|
||||
private fun Buffer<Double>.linearize() = if (this is Float64Buffer) {
|
||||
this.array
|
||||
} else {
|
||||
DoubleArray(size) { get(it) }
|
||||
}
|
||||
|
||||
@OptIn(PerformancePitfall::class)
|
||||
override fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> {
|
||||
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
|
||||
val rows = this@dot.rows.map { it.linearize() }
|
||||
val columns = other.columns.map { it.linearize() }
|
||||
val indices = 0 until this.rowNum
|
||||
return buildMatrix(rowNum, other.colNum) { i, j ->
|
||||
val r = rows[i]
|
||||
val c = columns[j]
|
||||
var res = 0.0
|
||||
for (l in indices) {
|
||||
res += r[l] * c[l]
|
||||
}
|
||||
res
|
||||
}
|
||||
}
|
||||
|
||||
@OptIn(PerformancePitfall::class)
|
||||
override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
|
||||
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
|
||||
val rows = this@dot.rows.map { it.linearize() }
|
||||
val indices = 0 until this.rowNum
|
||||
return Float64Buffer(rowNum) { i ->
|
||||
val r = rows[i]
|
||||
var res = 0.0
|
||||
for (j in indices) {
|
||||
res += r[j] * vector[j]
|
||||
}
|
||||
res
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
override fun Matrix<Double>.times(value: Double): Matrix<Double> = Floa64FieldOpsND {
|
||||
asND().map { it * value }.as2D()
|
||||
}
|
||||
|
||||
public override fun Point<Double>.plus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
|
||||
this@plus + other
|
||||
}
|
||||
|
||||
public override fun Point<Double>.minus(other: Point<Double>): Float64Buffer = Float64BufferOps.run {
|
||||
this@minus - other
|
||||
}
|
||||
|
||||
public override fun Point<Double>.times(value: Double): Float64Buffer = Float64BufferOps.run {
|
||||
scale(this@times, value)
|
||||
}
|
||||
|
||||
public operator fun Point<Double>.div(value: Double): Float64Buffer = Float64BufferOps.run {
|
||||
scale(this@div, 1.0 / value)
|
||||
}
|
||||
|
||||
public override fun Double.times(v: Point<Double>): Float64Buffer = v * this
|
||||
|
||||
|
||||
}
|
||||
|
||||
public val Float64LinearSpace.parallel: Float64ParallelLinearSpace get() = Float64ParallelLinearSpace
|
@ -0,0 +1,58 @@
|
||||
/*
|
||||
* Copyright 2018-2024 KMath contributors.
|
||||
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
|
||||
*/
|
||||
|
||||
package space.kscience.kmath.structures
|
||||
|
||||
import space.kscience.attributes.SafeType
|
||||
import java.util.stream.Collectors
|
||||
import java.util.stream.IntStream
|
||||
import kotlin.reflect.typeOf
|
||||
|
||||
/**
|
||||
* A Java stream-based parallel version of [MutableBuffer].
|
||||
* There is no parallelization for [Int8], [Int16] and [Float32] types.
|
||||
* They are processed sequentially.
|
||||
*/
|
||||
@Suppress("UNCHECKED_CAST")
|
||||
public fun <T> MutableBuffer.Companion.parallel(
|
||||
type: SafeType<T>,
|
||||
size: Int,
|
||||
initializer: (Int) -> T,
|
||||
): MutableBuffer<T> = when (type.kType) {
|
||||
typeOf<Int8>() -> Int8Buffer(size) { initializer(it) as Int8 } as MutableBuffer<T>
|
||||
typeOf<Int16>() -> Int16Buffer(size) { initializer(it) as Int16 } as MutableBuffer<T>
|
||||
typeOf<Int32>() -> IntStream.range(0, size).parallel().map { initializer(it) as Int32 }.toArray()
|
||||
.asBuffer() as MutableBuffer<T>
|
||||
|
||||
typeOf<Int64>() -> IntStream.range(0, size).parallel().mapToLong { initializer(it) as Int64 }.toArray()
|
||||
.asBuffer() as MutableBuffer<T>
|
||||
|
||||
typeOf<Float>() -> Float32Buffer(size) { initializer(it) as Float } as MutableBuffer<T>
|
||||
typeOf<Double>() -> IntStream.range(0, size).parallel().mapToDouble { initializer(it) as Float64 }.toArray()
|
||||
.asBuffer() as MutableBuffer<T>
|
||||
//TODO add unsigned types
|
||||
else -> IntStream.range(0, size).parallel().mapToObj { initializer(it) }.collect(Collectors.toList<T>()).asMutableBuffer(type)
|
||||
}
|
||||
|
||||
public class ParallelBufferFactory<T>(override val type: SafeType<T>) : MutableBufferFactory<T> {
|
||||
override fun invoke(size: Int, builder: (Int) -> T): MutableBuffer<T> {
|
||||
TODO("Not yet implemented")
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* A Java stream-based parallel alternative to [MutableBufferFactory].
|
||||
* See [MutableBuffer.Companion.parallel] for details.
|
||||
*/
|
||||
public fun <T> MutableBufferFactory.Companion.parallel(
|
||||
type: SafeType<T>,
|
||||
): MutableBufferFactory<T> = object : MutableBufferFactory<T> {
|
||||
|
||||
override val type: SafeType<T> = type
|
||||
|
||||
override fun invoke(size: Int, builder: (Int) -> T): MutableBuffer<T> = MutableBuffer.parallel(type, size, builder)
|
||||
|
||||
}
|
@ -59,7 +59,7 @@ public object ValueAndErrorField : Field<ValueAndError> {
|
||||
ValueAndError(a.value * value, a.dispersion * value.pow(2))
|
||||
|
||||
|
||||
private class ValueAndErrorBuffer(val values: DoubleBuffer, val ds: DoubleBuffer) : MutableBuffer<ValueAndError> {
|
||||
private class ValueAndErrorBuffer(val values: Float64Buffer, val ds: Float64Buffer) : MutableBuffer<ValueAndError> {
|
||||
init {
|
||||
require(values.size == ds.size)
|
||||
}
|
||||
|
Loading…
Reference in New Issue
Block a user