fix dot bug introduced in the last refactor. Add test for parallel linear algebra.
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@ -9,6 +9,7 @@
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- New Attributes-kt module that could be used as stand-alone. It declares. type-safe attributes containers.
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- New Attributes-kt module that could be used as stand-alone. It declares. type-safe attributes containers.
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- Explicit `mutableStructureND` builders for mutable structures.
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- Explicit `mutableStructureND` builders for mutable structures.
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- `Buffer.asList()` zero-copy transformation.
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- `Buffer.asList()` zero-copy transformation.
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- Wasm support.
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- Parallel implementation of `LinearSpace` for Float64
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- Parallel implementation of `LinearSpace` for Float64
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- Parallel buffer factories
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- Parallel buffer factories
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@ -54,7 +54,7 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
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require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
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require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
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val rows = this@dot.rows.map { it.linearize() }
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val rows = this@dot.rows.map { it.linearize() }
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val columns = other.columns.map { it.linearize() }
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val columns = other.columns.map { it.linearize() }
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val indices = 0 until this.rowNum
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val indices = 0 until this.colNum
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return buildMatrix(rowNum, other.colNum) { i, j ->
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return buildMatrix(rowNum, other.colNum) { i, j ->
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val r = rows[i]
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val r = rows[i]
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val c = columns[j]
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val c = columns[j]
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@ -70,7 +70,7 @@ public object Float64LinearSpace : LinearSpace<Double, Float64Field> {
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override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
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override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
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require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
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require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
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val rows = this@dot.rows.map { it.linearize() }
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val rows = this@dot.rows.map { it.linearize() }
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val indices = 0 until this.rowNum
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val indices = 0 until this.colNum
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return Float64Buffer(rowNum) { i ->
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return Float64Buffer(rowNum) { i ->
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val r = rows[i]
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val r = rows[i]
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var res = 0.0
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var res = 0.0
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@ -28,13 +28,13 @@ class DoubleLUSolverTest {
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}
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}
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@Test
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@Test
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fun testDecomposition() = Double.algebra.linearSpace.run {
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fun testDecomposition() = with(Double.algebra.linearSpace){
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val matrix = matrix(2, 2)(
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val matrix = matrix(2, 2)(
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3.0, 1.0,
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3.0, 1.0,
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2.0, 3.0
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2.0, 3.0
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)
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)
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val lup = lup(matrix)
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val lup = elementAlgebra.lup(matrix)
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//Check determinant
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//Check determinant
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// assertEquals(7.0, lup.determinant)
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// assertEquals(7.0, lup.determinant)
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@ -8,6 +8,7 @@ package space.kscience.kmath.linear
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import space.kscience.kmath.PerformancePitfall
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import space.kscience.kmath.PerformancePitfall
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import space.kscience.kmath.UnstableKMathAPI
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import space.kscience.kmath.UnstableKMathAPI
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import space.kscience.kmath.nd.StructureND
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import space.kscience.kmath.nd.StructureND
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import space.kscience.kmath.operations.Float64Field
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import space.kscience.kmath.operations.algebra
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import space.kscience.kmath.operations.algebra
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import kotlin.test.Test
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import kotlin.test.Test
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import kotlin.test.assertEquals
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import kotlin.test.assertEquals
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@ -58,7 +59,7 @@ class MatrixTest {
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}
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}
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@Test
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@Test
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fun test2DDot() = Double.algebra.linearSpace.run {
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fun test2DDot() = Float64Field.linearSpace {
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val firstMatrix = buildMatrix(2, 3) { i, j -> (i + j).toDouble() }
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val firstMatrix = buildMatrix(2, 3) { i, j -> (i + j).toDouble() }
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val secondMatrix = buildMatrix(3, 2) { i, j -> (i + j).toDouble() }
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val secondMatrix = buildMatrix(3, 2) { i, j -> (i + j).toDouble() }
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@ -70,6 +71,5 @@ class MatrixTest {
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assertEquals(8.0, result[0, 1])
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assertEquals(8.0, result[0, 1])
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assertEquals(8.0, result[1, 0])
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assertEquals(8.0, result[1, 0])
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assertEquals(14.0, result[1, 1])
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assertEquals(14.0, result[1, 1])
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}
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}
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}
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}
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@ -69,7 +69,7 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
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require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
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require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
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val rows = this@dot.rows.map { it.linearize() }
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val rows = this@dot.rows.map { it.linearize() }
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val columns = other.columns.map { it.linearize() }
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val columns = other.columns.map { it.linearize() }
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val indices = 0 until this.rowNum
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val indices = 0 until this.colNum
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return buildMatrix(rowNum, other.colNum) { i, j ->
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return buildMatrix(rowNum, other.colNum) { i, j ->
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val r = rows[i]
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val r = rows[i]
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val c = columns[j]
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val c = columns[j]
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@ -85,7 +85,7 @@ public object Float64ParallelLinearSpace : LinearSpace<Double, Float64Field> {
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override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
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override fun Matrix<Double>.dot(vector: Point<Double>): Float64Buffer {
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require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
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require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
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val rows = this@dot.rows.map { it.linearize() }
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val rows = this@dot.rows.map { it.linearize() }
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val indices = 0 until this.rowNum
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val indices = 0 until this.colNum
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return Float64Buffer(rowNum) { i ->
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return Float64Buffer(rowNum) { i ->
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val r = rows[i]
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val r = rows[i]
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var res = 0.0
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var res = 0.0
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@ -0,0 +1,74 @@
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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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package space.kscience.kmath.linear
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import space.kscience.kmath.PerformancePitfall
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import space.kscience.kmath.UnstableKMathAPI
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import space.kscience.kmath.nd.StructureND
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import space.kscience.kmath.operations.Float64Field
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import kotlin.test.Test
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import kotlin.test.assertEquals
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import kotlin.test.assertTrue
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@UnstableKMathAPI
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@OptIn(PerformancePitfall::class)
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@Suppress("UNUSED_VARIABLE")
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class ParallelMatrixTest {
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@Test
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fun testTranspose() = Float64Field.linearSpace.parallel{
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val matrix = one(3, 3)
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val transposed = matrix.transposed()
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assertTrue { StructureND.contentEquals(matrix, transposed) }
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}
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@Test
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fun testBuilder() = Float64Field.linearSpace.parallel{
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val matrix = matrix(2, 3)(
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1.0, 0.0, 0.0,
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0.0, 1.0, 2.0
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)
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assertEquals(2.0, matrix[1, 2])
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}
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@Test
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fun testMatrixExtension() = Float64Field.linearSpace.parallel{
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val transitionMatrix: Matrix<Double> = VirtualMatrix(type,6, 6) { row, col ->
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when {
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col == 0 -> .50
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row + 1 == col -> .50
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row == 5 && col == 5 -> 1.0
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else -> 0.0
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}
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}
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infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
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var res = this
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repeat(power - 1) {
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res = res dot this@pow
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}
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return res
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}
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val toTenthPower = transitionMatrix pow 10
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}
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@Test
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fun test2DDot() = Float64Field.linearSpace.parallel {
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val firstMatrix = buildMatrix(2, 3) { i, j -> (i + j).toDouble() }
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val secondMatrix = buildMatrix(3, 2) { i, j -> (i + j).toDouble() }
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// val firstMatrix = produce(2, 3) { i, j -> (i + j).toDouble() }
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// val secondMatrix = produce(3, 2) { i, j -> (i + j).toDouble() }
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val result = firstMatrix dot secondMatrix
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assertEquals(2, result.rowNum)
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assertEquals(2, result.colNum)
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assertEquals(8.0, result[0, 1])
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assertEquals(8.0, result[1, 0])
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assertEquals(14.0, result[1, 1])
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}
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}
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