forked from kscience/kmath
Minor optimization for RealNDAlgebra
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4c256a9f14
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d0c9d97706
@ -28,7 +28,7 @@ internal class ExpressionsInterpretersBenchmark {
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@Benchmark
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fun mstExpression() {
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val expr = algebra.mstInField {
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symbol("x") * number(2.0) + number(2.0) / symbol("x") - number(16.0)
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symbol("x") * 2.0 + 2.0 / symbol("x") - 16.0
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}
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invokeAndSum(expr)
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@ -37,7 +37,7 @@ internal class ExpressionsInterpretersBenchmark {
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@Benchmark
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fun asmExpression() {
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val expr = algebra.mstInField {
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symbol("x") * number(2.0) + number(2.0) / symbol("x") - number(16.0)
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symbol("x") * 2.0 + 2.0 / symbol("x") - 16.0
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}.compile()
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invokeAndSum(expr)
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@ -2,9 +2,8 @@ package kscience.kmath.benchmarks
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import kotlinx.benchmark.Benchmark
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import kscience.kmath.commons.linear.CMMatrixContext
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import kscience.kmath.commons.linear.toCM
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import kscience.kmath.ejml.EjmlMatrixContext
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import kscience.kmath.ejml.toEjml
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import kscience.kmath.linear.BufferMatrixContext
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import kscience.kmath.linear.RealMatrixContext
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import kscience.kmath.linear.real
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@ -26,11 +25,11 @@ class DotBenchmark {
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val matrix1 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
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val matrix2 = Matrix.real(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
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val cmMatrix1 = matrix1.toCM()
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val cmMatrix2 = matrix2.toCM()
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val cmMatrix1 = CMMatrixContext { matrix1.toCM() }
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val cmMatrix2 = CMMatrixContext { matrix2.toCM() }
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val ejmlMatrix1 = matrix1.toEjml()
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val ejmlMatrix2 = matrix2.toEjml()
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val ejmlMatrix1 = EjmlMatrixContext { matrix1.toEjml() }
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val ejmlMatrix2 = EjmlMatrixContext { matrix2.toEjml() }
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}
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@Benchmark
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@ -49,9 +48,10 @@ class DotBenchmark {
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@Benchmark
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fun ejmlMultiplicationwithConversion() {
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EjmlMatrixContext {
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val ejmlMatrix1 = matrix1.toEjml()
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val ejmlMatrix2 = matrix2.toEjml()
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EjmlMatrixContext {
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ejmlMatrix1 dot ejmlMatrix2
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}
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}
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@ -1,25 +0,0 @@
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package kscience.kmath.benchmarks
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import kscience.kmath.structures.NDField
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import org.openjdk.jmh.annotations.Benchmark
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import org.openjdk.jmh.annotations.Scope
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import org.openjdk.jmh.annotations.State
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import org.openjdk.jmh.infra.Blackhole
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import kotlin.random.Random
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@State(Scope.Benchmark)
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class LargeNDBenchmark {
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val arraySize = 10000
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val RANDOM = Random(222)
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val src1 = DoubleArray(arraySize) { RANDOM.nextDouble() }
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val src2 = DoubleArray(arraySize) { RANDOM.nextDouble() }
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val field = NDField.real(arraySize)
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val kmathArray1 = field.produce { (a) -> src1[a] }
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val kmathArray2 = field.produce { (a) -> src2[a] }
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@Benchmark
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fun test10000(bh: Blackhole) {
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bh.consume(field.add(kmathArray1, kmathArray2))
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}
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}
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@ -5,10 +5,8 @@ import kotlinx.benchmark.Benchmark
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import kscience.kmath.commons.linear.CMMatrixContext
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import kscience.kmath.commons.linear.CMMatrixContext.dot
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import kscience.kmath.commons.linear.inverse
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import kscience.kmath.commons.linear.toCM
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import kscience.kmath.ejml.EjmlMatrixContext
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import kscience.kmath.ejml.inverse
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import kscience.kmath.ejml.toEjml
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import kscience.kmath.operations.invoke
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import kscience.kmath.structures.Matrix
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import org.openjdk.jmh.annotations.Scope
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@ -35,16 +33,14 @@ class LinearAlgebraBenchmark {
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@Benchmark
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fun cmLUPInversion() {
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CMMatrixContext {
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val cm = matrix.toCM() //avoid overhead on conversion
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inverse(cm)
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inverse(matrix)
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}
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}
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@Benchmark
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fun ejmlInverse() {
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EjmlMatrixContext {
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val km = matrix.toEjml() //avoid overhead on conversion
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inverse(km)
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inverse(matrix)
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}
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}
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}
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@ -95,8 +95,9 @@ public open class FunctionalExpressionRing<T, A : Ring<T>>(
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super<FunctionalExpressionSpace>.binaryOperationFunction(operation)
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}
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public open class FunctionalExpressionField<T, A : Field<T>>(algebra: A) :
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FunctionalExpressionRing<T, A>(algebra), Field<Expression<T>> {
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public open class FunctionalExpressionField<T, A : Field<T>>(
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algebra: A,
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) : FunctionalExpressionRing<T, A>(algebra), Field<Expression<T>> {
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/**
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* Builds an Expression of division an expression by another one.
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*/
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@ -224,6 +224,7 @@ public inline fun <reified T : Comparable<T>, F : Field<T>> GenericMatrixContext
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): Matrix<T> = solveWithLUP(matrix, one(matrix.rowNum, matrix.colNum), bufferFactory, checkSingular)
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@OptIn(UnstableKMathAPI::class)
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public fun RealMatrixContext.solveWithLUP(a: Matrix<Double>, b: Matrix<Double>): Matrix<Double> {
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// Use existing decomposition if it is provided by matrix
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val bufferFactory: MutableBufferFactory<Double> = MutableBuffer.Companion::real
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@ -3,6 +3,7 @@ package kscience.kmath.operations
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import kscience.kmath.memory.MemoryReader
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import kscience.kmath.memory.MemorySpec
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import kscience.kmath.memory.MemoryWriter
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import kscience.kmath.misc.UnstableKMathAPI
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import kscience.kmath.structures.Buffer
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import kscience.kmath.structures.MemoryBuffer
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import kscience.kmath.structures.MutableBuffer
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@ -41,6 +42,7 @@ private val PI_DIV_2 = Complex(PI / 2, 0)
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/**
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* A field of [Complex].
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*/
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@OptIn(UnstableKMathAPI::class)
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public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, RingWithNumbers<Complex> {
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override val zero: Complex = 0.0.toComplex()
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override val one: Complex = 1.0.toComplex()
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@ -24,35 +24,31 @@ public class RealNDField(override val shape: IntArray) :
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return produce { d }
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}
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private inline fun buildBuffer(size: Int, crossinline initializer: (Int) -> Double): Buffer<Double> =
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RealBuffer(DoubleArray(size) { initializer(it) })
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/**
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* Inline transform an NDStructure to
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*/
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override fun map(
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@Suppress("OVERRIDE_BY_INLINE")
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override inline fun map(
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arg: NDBuffer<Double>,
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transform: RealField.(Double) -> Double
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transform: RealField.(Double) -> Double,
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): RealNDElement {
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check(arg)
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val array = buildBuffer(arg.strides.linearSize) { offset -> RealField.transform(arg.buffer[offset]) }
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val array = RealBuffer(arg.strides.linearSize) { offset -> RealField.transform(arg.buffer[offset]) }
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return BufferedNDFieldElement(this, array)
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}
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override fun produce(initializer: RealField.(IntArray) -> Double): RealNDElement {
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val array = buildBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
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@Suppress("OVERRIDE_BY_INLINE")
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override inline fun produce(initializer: RealField.(IntArray) -> Double): RealNDElement {
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val array = RealBuffer(strides.linearSize) { offset -> elementContext.initializer(strides.index(offset)) }
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return BufferedNDFieldElement(this, array)
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}
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override fun mapIndexed(
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@Suppress("OVERRIDE_BY_INLINE")
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override inline fun mapIndexed(
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arg: NDBuffer<Double>,
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transform: RealField.(index: IntArray, Double) -> Double
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transform: RealField.(index: IntArray, Double) -> Double,
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): RealNDElement {
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check(arg)
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return BufferedNDFieldElement(
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this,
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buildBuffer(arg.strides.linearSize) { offset ->
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RealBuffer(arg.strides.linearSize) { offset ->
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elementContext.transform(
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arg.strides.index(offset),
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arg.buffer[offset]
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@ -60,16 +56,17 @@ public class RealNDField(override val shape: IntArray) :
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})
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}
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override fun combine(
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@Suppress("OVERRIDE_BY_INLINE")
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override inline fun combine(
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a: NDBuffer<Double>,
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b: NDBuffer<Double>,
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transform: RealField.(Double, Double) -> Double
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transform: RealField.(Double, Double) -> Double,
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): RealNDElement {
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check(a, b)
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return BufferedNDFieldElement(
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this,
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buildBuffer(strides.linearSize) { offset -> elementContext.transform(a.buffer[offset], b.buffer[offset]) }
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)
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val buffer = RealBuffer(strides.linearSize) { offset ->
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elementContext.transform(a.buffer[offset], b.buffer[offset])
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}
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return BufferedNDFieldElement(this, buffer)
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}
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override fun NDBuffer<Double>.toElement(): FieldElement<NDBuffer<Double>, *, out BufferedNDField<Double, RealField>> =
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@ -7,6 +7,7 @@ import kscience.kmath.structures.as2D
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import kotlin.test.Test
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import kotlin.test.assertEquals
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@Suppress("UNUSED_VARIABLE")
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class MatrixTest {
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@Test
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fun testTranspose() {
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@ -8,6 +8,7 @@ import kotlin.math.pow
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import kotlin.test.Test
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import kotlin.test.assertEquals
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@Suppress("UNUSED_VARIABLE")
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class NumberNDFieldTest {
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val array1: RealNDElement = real2D(3, 3) { i, j -> (i + j).toDouble() }
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val array2: RealNDElement = real2D(3, 3) { i, j -> (i - j).toDouble() }
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@ -6,6 +6,7 @@ import kscience.kmath.dimensions.DMatrixContext
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import kscience.kmath.dimensions.one
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import kotlin.test.Test
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@Suppress("UNUSED_VARIABLE")
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internal class DMatrixContextTest {
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@Test
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fun testDimensionSafeMatrix() {
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