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API cleanup

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This commit is contained in:
Alexander Nozik 2021-10-02 09:55:52 +03:00
parent 64781a6785
commit 01bbb4bb13
21 changed files with 211 additions and 83 deletions
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2
build.gradle.kts
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@ -19,7 +19,7 @@ allprojects {
}
group = "space.kscience"
version = "0.3.0-dev-14"
version = "0.3.0-dev-15"
}
subprojects {

10
examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
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@ -5,19 +5,19 @@
package space.kscience.kmath.tensors
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
// simple PCA
fun main(): Unit = BroadcastDoubleTensorAlgebra { // work in context with broadcast methods
fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with broadcast methods
val seed = 100500L
// assume x is range from 0 until 10
val x = fromArray(
intArrayOf(10),
(0 until 10).toList().map { it.toDouble() }.toDoubleArray()
DoubleArray(10) { it.toDouble() }
)
// take y dependent on x with noise
@ -62,7 +62,7 @@ fun main(): Unit = BroadcastDoubleTensorAlgebra { // work in context with broad
println("Eigenvector:\n$v")
// reduce dimension of dataset
val datasetReduced = v dot stack(listOf(xScaled, yScaled))
val datasetReduced = v dot stack(listOf(xScaled, yScaled))
println("Reduced data:\n$datasetReduced")
// we can restore original data from reduced data;

6
examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt → examples/src/main/kotlin/space/kscience/kmath/tensors/dataSetNormalization.kt
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@ -5,13 +5,13 @@
package space.kscience.kmath.tensors
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
// Dataset normalization
fun main() = BroadcastDoubleTensorAlgebra { // work in context with broadcast methods
fun main() = Double.tensorAlgebra.withBroadcast { // work in context with broadcast methods
// take dataset of 5-element vectors from normal distribution
val dataset = randomNormal(intArrayOf(100, 5)) * 1.5 // all elements from N(0, 1.5)

6
examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt → examples/src/main/kotlin/space/kscience/kmath/tensors/linearSystemSolvingWithLUP.kt
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@ -5,13 +5,13 @@
package space.kscience.kmath.tensors
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
// solving linear system with LUP decomposition
fun main() = BroadcastDoubleTensorAlgebra {// work in context with linear operations
fun main() = Double.tensorAlgebra.withBroadcast {// work in context with linear operations
// set true value of x
val trueX = fromArray(

0
examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt → examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt
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19
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferedLinearSpace.kt
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@ -8,12 +8,14 @@ package space.kscience.kmath.linear
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.BufferedRingND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.asND
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd.unwrap
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.*
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.VirtualBuffer
import space.kscience.kmath.structures.indices
public class BufferedLinearSpace<T, out A : Ring<T>>(
@ -33,17 +35,17 @@ public class BufferedLinearSpace<T, out A : Ring<T>>(
bufferFactory(size) { elementAlgebra.initializer(it) }
override fun Matrix<T>.unaryMinus(): Matrix<T> = ndRing(rowNum, colNum).run {
unwrap().map { -it }.as2D()
asND().map { -it }.as2D()
}
override fun Matrix<T>.plus(other: Matrix<T>): Matrix<T> = ndRing(rowNum, colNum).run {
require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::plus. Expected $shape but found ${other.shape}" }
unwrap().plus(other.unwrap()).as2D()
asND().plus(other.asND()).as2D()
}
override fun Matrix<T>.minus(other: Matrix<T>): Matrix<T> = ndRing(rowNum, colNum).run {
require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
unwrap().minus(other.unwrap()).as2D()
asND().minus(other.asND()).as2D()
}
private fun Buffer<T>.linearize() = if (this is VirtualBuffer) {
@ -87,13 +89,10 @@ public class BufferedLinearSpace<T, out A : Ring<T>>(
}
override fun Matrix<T>.times(value: T): Matrix<T> = ndRing(rowNum, colNum).run {
unwrap().map { it * value }.as2D()
asND().map { it * value }.as2D()
}
}
public fun <T, A : Ring<T>> A.linearSpace(bufferFactory: BufferFactory<T>): BufferedLinearSpace<T, A> =
BufferedLinearSpace(this, bufferFactory)
public val DoubleField.linearSpace: BufferedLinearSpace<Double, DoubleField>
get() = BufferedLinearSpace(this, ::DoubleBuffer)

109
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/DoubleLinearSpace.kt Normal file
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@ -0,0 +1,109 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.linear
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.DoubleFieldND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.asND
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.indices
public object DoubleLinearSpace : LinearSpace<Double, DoubleField> {
override val elementAlgebra: DoubleField get() = DoubleField
private fun ndRing(
rows: Int,
cols: Int,
): DoubleFieldND = DoubleFieldND(intArrayOf(rows, cols))
override fun buildMatrix(
rows: Int,
columns: Int,
initializer: DoubleField.(i: Int, j: Int) -> Double
): Matrix<Double> = ndRing(rows, columns).produce { (i, j) -> DoubleField.initializer(i, j) }.as2D()
override fun buildVector(size: Int, initializer: DoubleField.(Int) -> Double): DoubleBuffer =
DoubleBuffer(size) { DoubleField.initializer(it) }
override fun Matrix<Double>.unaryMinus(): Matrix<Double> = ndRing(rowNum, colNum).run {
asND().map { -it }.as2D()
}
override fun Matrix<Double>.plus(other: Matrix<Double>): Matrix<Double> = ndRing(rowNum, colNum).run {
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> = ndRing(rowNum, colNum).run {
require(shape.contentEquals(other.shape)) { "Shape mismatch on Matrix::minus. Expected $shape but found ${other.shape}" }
asND().minus(other.asND()).as2D()
}
// Create a continuous in-memory representation of this vector for better memory layout handling
private fun Buffer<Double>.linearize() = if (this is DoubleBuffer) {
this
} else {
DoubleBuffer(size) { get(it) }
}
@OptIn(PerformancePitfall::class)
override fun Matrix<Double>.dot(other: Matrix<Double>): Matrix<Double> {
require(colNum == other.rowNum) { "Matrix dot operation dimension mismatch: ($rowNum, $colNum) x (${other.rowNum}, ${other.colNum})" }
val rows = this@dot.rows.map { it.linearize() }
val columns = other.columns.map { it.linearize() }
return buildMatrix(rowNum, other.colNum) { i, j ->
val r = rows[i]
val c = columns[j]
var res = 0.0
for (l in r.indices) {
res += r[l] * c[l]
}
res
}
}
@OptIn(PerformancePitfall::class)
override fun Matrix<Double>.dot(vector: Point<Double>): DoubleBuffer {
require(colNum == vector.size) { "Matrix dot vector operation dimension mismatch: ($rowNum, $colNum) x (${vector.size})" }
val rows = this@dot.rows.map { it.linearize() }
return DoubleBuffer(rowNum) { i ->
val r = rows[i]
var res = 0.0
for (j in r.indices) {
res += r[j] * vector[j]
}
res
}
}
override fun Matrix<Double>.times(value: Double): Matrix<Double> = ndRing(rowNum, colNum).run {
asND().map { it * value }.as2D()
}
public override fun Point<Double>.plus(other: Point<Double>): DoubleBuffer = DoubleBuffer(size) {
get(it) + other[it]
}
public override fun Point<Double>.minus(other: Point<Double>): DoubleBuffer = DoubleBuffer(size) {
get(it) - other[it]
}
public override fun Point<Double>.times(value: Double): DoubleBuffer = DoubleBuffer(size) { i -> get(i) * value }
public operator fun Point<Double>.div(value: Double): DoubleBuffer = DoubleBuffer(size) { i -> get(i) / value }
public override fun Double.times(v: Point<Double>): DoubleBuffer = v * this
}
public val DoubleField.linearSpace: DoubleLinearSpace get() = DoubleLinearSpace

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure1D.kt
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@ -136,7 +136,7 @@ public fun <T> Buffer<T>.asND(): Structure1D<T> = Buffer1DWrapper(this)
/**
* Expose inner buffer of this [Structure1D] if possible
*/
internal fun <T : Any> Structure1D<T>.unwrap(): Buffer<T> = when {
internal fun <T : Any> Structure1D<T>.asND(): Buffer<T> = when {
this is Buffer1DWrapper<T> -> buffer
this is Structure1DWrapper && structure is BufferND<T> -> structure.buffer
else -> this

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Structure2D.kt
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@ -160,10 +160,10 @@ public fun <T> MutableStructureND<T>.as2D(): MutableStructure2D<T> = this as? Mu
/**
* Expose inner [StructureND] if possible
*/
internal fun <T> Structure2D<T>.unwrap(): StructureND<T> =
internal fun <T> Structure2D<T>.asND(): StructureND<T> =
if (this is Structure2DWrapper) structure
else this
internal fun <T> MutableStructure2D<T>.unwrap(): MutableStructureND<T> =
internal fun <T> MutableStructure2D<T>.asND(): MutableStructureND<T> =
if (this is MutableStructure2DWrapper) structure else this

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/operations/BigInt.kt
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@ -532,7 +532,7 @@ public val BigInt.algebra: BigIntField get() = BigIntField
public inline fun Buffer.Companion.bigInt(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
boxing(size, initializer)
public inline fun BigInt.buffer(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
public inline fun BigInt.Companion.buffer(size: Int, initializer: (Int) -> BigInt): Buffer<BigInt> =
Buffer.boxing(size, initializer)
@Deprecated("Use BigInt::mutableBuffer")
@ -543,4 +543,4 @@ public inline fun BigInt.mutableBuffer(size: Int, initializer: (Int) -> BigInt):
Buffer.boxing(size, initializer)
public fun BigIntField.nd(vararg shape: Int): BufferedRingND<BigInt, BigIntField> =
BufferedRingND(shape, BigIntField, Buffer.Companion::bigInt)
BufferedRingND(shape, BigIntField, BigInt::buffer)

6
kmath-core/src/commonMain/kotlin/space/kscience/kmath/structures/DoubleBuffer.kt
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@ -25,6 +25,12 @@ public value class DoubleBuffer(public val array: DoubleArray) : MutableBuffer<D
override operator fun iterator(): DoubleIterator = array.iterator()
override fun copy(): DoubleBuffer = DoubleBuffer(array.copyOf())
override fun toString(): String = Buffer.toString(this)
public companion object{
public fun zero(size: Int): DoubleBuffer = DoubleArray(size).asBuffer()
}
}
/**

35
kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/DoubleLUSolverTest.kt
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@ -8,6 +8,7 @@ package space.kscience.kmath.linear
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.algebra
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertTrue
@ -21,39 +22,37 @@ fun <T : Any> assertMatrixEquals(expected: StructureND<T>, actual: StructureND<T
class DoubleLUSolverTest {
@Test
fun testInvertOne() {
val matrix = LinearSpace.double.one(2, 2)
val inverted = LinearSpace.double.lupSolver().inverse(matrix)
fun testInvertOne() = Double.algebra.linearSpace.run{
val matrix = one(2, 2)
val inverted = lupSolver().inverse(matrix)
assertMatrixEquals(matrix, inverted)
}
@Test
fun testDecomposition() {
LinearSpace.double.run {
val matrix = matrix(2, 2)(
3.0, 1.0,
2.0, 3.0
)
fun testDecomposition() = Double.algebra.linearSpace.run {
val matrix = matrix(2, 2)(
3.0, 1.0,
2.0, 3.0
)
val lup = lup(matrix)
val lup = lup(matrix)
//Check determinant
assertEquals(7.0, lup.determinant)
//Check determinant
assertEquals(7.0, lup.determinant)
assertMatrixEquals(lup.p dot matrix, lup.l dot lup.u)
}
assertMatrixEquals(lup.p dot matrix, lup.l dot lup.u)
}
@Test
fun testInvert() {
val matrix = LinearSpace.double.matrix(2, 2)(
fun testInvert() = Double.algebra.linearSpace.run{
val matrix = matrix(2, 2)(
3.0, 1.0,
1.0, 3.0
)
val inverted = LinearSpace.double.lupSolver().inverse(matrix)
val inverted = lupSolver().inverse(matrix)
val expected = LinearSpace.double.matrix(2, 2)(
val expected = matrix(2, 2)(
0.375, -0.125,
-0.125, 0.375
)

30
kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt
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@ -9,6 +9,7 @@ import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.operations.algebra
import kotlin.test.Test
import kotlin.test.assertEquals
import kotlin.test.assertTrue
@ -19,15 +20,15 @@ import kotlin.test.assertTrue
class MatrixTest {
@Test
fun testTranspose() {
val matrix = LinearSpace.double.one(3, 3)
fun testTranspose() = Double.algebra.linearSpace.run {
val matrix = one(3, 3)
val transposed = matrix.transpose()
assertTrue { StructureND.contentEquals(matrix, transposed) }
}
@Test
fun testBuilder() {
val matrix = LinearSpace.double.matrix(2, 3)(
fun testBuilder() = Double.algebra.linearSpace.run {
val matrix = matrix(2, 3)(
1.0, 0.0, 0.0,
0.0, 1.0, 2.0
)
@ -36,7 +37,7 @@ class MatrixTest {
}
@Test
fun testMatrixExtension() {
fun testMatrixExtension() = Double.algebra.linearSpace.run {
val transitionMatrix: Matrix<Double> = VirtualMatrix(6, 6) { row, col ->
when {
col == 0 -> .50
@ -49,7 +50,7 @@ class MatrixTest {
infix fun Matrix<Double>.pow(power: Int): Matrix<Double> {
var res = this
repeat(power - 1) {
res = LinearSpace.double.run { res dot this@pow }
res = res dot this@pow
}
return res
}
@ -58,19 +59,18 @@ class MatrixTest {
}
@Test
fun test2DDot() {
fun test2DDot() = Double.algebra.linearSpace.run {
val firstMatrix = StructureND.auto(2, 3) { (i, j) -> (i + j).toDouble() }.as2D()
val secondMatrix = StructureND.auto(3, 2) { (i, j) -> (i + j).toDouble() }.as2D()
LinearSpace.double.run {
// val firstMatrix = produce(2, 3) { i, j -> (i + j).toDouble() }
// val secondMatrix = produce(3, 2) { i, j -> (i + j).toDouble() }
val result = firstMatrix dot secondMatrix
assertEquals(2, result.rowNum)
assertEquals(2, result.colNum)
assertEquals(8.0, result[0, 1])
assertEquals(8.0, result[1, 0])
assertEquals(14.0, result[1, 1])
}
val result = firstMatrix dot secondMatrix
assertEquals(2, result.rowNum)
assertEquals(2, result.colNum)
assertEquals(8.0, result[0, 1])
assertEquals(8.0, result[1, 0])
assertEquals(14.0, result[1, 1])
}
}

10
kmath-core/src/commonTest/kotlin/space/kscience/kmath/structures/NumberNDFieldTest.kt
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@ -5,7 +5,7 @@
package space.kscience.kmath.structures
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.combine
@ -13,6 +13,7 @@ import space.kscience.kmath.nd.get
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Norm
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.operations.invoke
import kotlin.math.abs
import kotlin.math.pow
@ -42,17 +43,18 @@ class NumberNDFieldTest {
}
@Test
fun testGeneration() {
fun testGeneration() = Double.algebra.linearSpace.run {
val array = LinearSpace.double.buildMatrix(3, 3) { i, j ->
val array = buildMatrix(3, 3) { i, j ->
(i * 10 + j).toDouble()
}
for (i in 0..2)
for (i in 0..2) {
for (j in 0..2) {
val expected = (i * 10 + j).toDouble()
assertEquals(expected, array[i, j], "Error at index [$i, $j]")
}
}
}
@Test

8
kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Wrappers.kt
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@ -5,13 +5,11 @@
package space.kscience.kmath.dimensions
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.Point
import space.kscience.kmath.linear.transpose
import space.kscience.kmath.linear.*
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.algebra
import kotlin.jvm.JvmInline
/**
@ -151,7 +149,7 @@ public value class DMatrixContext<T : Any, out A : Ring<T>>(public val context:
context.run { (this@transpose as Matrix<T>).transpose() }.coerce()
public companion object {
public val real: DMatrixContext<Double, DoubleField> = DMatrixContext(LinearSpace.double)
public val real: DMatrixContext<Double, DoubleField> = DMatrixContext(Double.algebra.linearSpace)
}
}

12
kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleMatrixTest.kt
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@ -5,12 +5,12 @@
package space.kscience.kmath.real
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.linear.matrix
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.real.*
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.contentEquals
import kotlin.test.Test
import kotlin.test.assertEquals
@ -59,13 +59,13 @@ internal class DoubleMatrixTest {
}
@Test
fun testMatrixAndDouble() {
fun testMatrixAndDouble() = Double.algebra.linearSpace.run {
val matrix1 = realMatrix(2, 3)(
1.0, 0.0, 3.0,
4.0, 6.0, 2.0
)
val matrix2 = (matrix1 * 2.5 + 1.0 - 2.0) / 2.0
val expectedResult = LinearSpace.double.matrix(2, 3)(
val expectedResult = matrix(2, 3)(
0.75, -0.5, 3.25,
4.5, 7.0, 2.0
)
@ -159,8 +159,8 @@ internal class DoubleMatrixTest {
}
@Test
fun testAllElementOperations() {
val matrix1 = LinearSpace.double.matrix(2, 4)(
fun testAllElementOperations() = Double.algebra.linearSpace.run {
val matrix1 = matrix(2, 4)(
-1.0, 0.0, 3.0, 15.0,
4.0, -6.0, 7.0, -11.0
)

8
kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleVectorTest.kt
View File

@ -5,10 +5,10 @@
package space.kscience.kmath.real
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.asMatrix
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.linear.transpose
import space.kscience.kmath.real.plus
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.DoubleBuffer
import kotlin.test.Test
import kotlin.test.assertEquals
@ -30,12 +30,12 @@ internal class DoubleVectorTest {
}
@Test
fun testDot() {
fun testDot() = Double.algebra.linearSpace.run {
val vector1 = DoubleBuffer(5) { it.toDouble() }
val vector2 = DoubleBuffer(5) { 5 - it.toDouble() }
val matrix1 = vector1.asMatrix()
val matrix2 = vector2.asMatrix().transpose()
val product = LinearSpace.double.run { matrix1 dot matrix2 }
val product = matrix1 dot matrix2
assertEquals(5.0, product[1, 0])
assertEquals(6.0, product[2, 2])
}

3
kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/QowOptimizer.kt
View File

@ -13,6 +13,7 @@ import space.kscience.kmath.linear.*
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.misc.log
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.DoubleL2Norm
@ -32,7 +33,7 @@ public class QowRuns(public val runs: Int) : OptimizationFeature {
@UnstableKMathAPI
public object QowOptimizer : Optimizer<Double, XYFit> {
private val linearSpace: LinearSpace<Double, DoubleField> = LinearSpace.double
private val linearSpace: LinearSpace<Double, DoubleField> = Double.algebra.linearSpace
private val solver: LinearSolver<Double> = linearSpace.lupSolver()
@OptIn(UnstableKMathAPI::class)

11
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/BroadcastDoubleTensorAlgebra.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.tensors.core
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.tensors.api.Tensor
import space.kscience.kmath.tensors.core.internal.array
import space.kscience.kmath.tensors.core.internal.broadcastTensors
@ -90,4 +91,12 @@ public object BroadcastDoubleTensorAlgebra : DoubleTensorAlgebra() {
newOther.mutableBuffer.array()[tensor.bufferStart + i]
}
}
}
}
/**
* Compute a value using broadcast double tensor algebra
*/
@UnstableKMathAPI
public fun <R> DoubleTensorAlgebra.withBroadcast(block: BroadcastDoubleTensorAlgebra.() -> R): R =
BroadcastDoubleTensorAlgebra.block()

2
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
View File

@ -949,4 +949,6 @@ public open class DoubleTensorAlgebra :
override fun Tensor<Double>.lu(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> = lu(1e-9)
}
public val Double.Companion.tensorAlgebra: DoubleTensorAlgebra.Companion get() = DoubleTensorAlgebra

7
settings.gradle.kts
View File

@ -5,11 +5,11 @@ pluginManagement {
gradlePluginPortal()
}
val kotlinVersion = "1.5.30"
val kotlinVersion = "1.6.0-M1"
plugins {
id("org.jetbrains.kotlinx.benchmark") version "0.3.1"
id("ru.mipt.npm.gradle.project") version "0.10.3"
id("ru.mipt.npm.gradle.project") version "0.10.4"
kotlin("multiplatform") version kotlinVersion
kotlin("plugin.allopen") version kotlinVersion
}
@ -17,6 +17,9 @@ pluginManagement {
rootProject.name = "kmath"
enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
enableFeaturePreview("VERSION_CATALOGS")
include(
":kmath-memory",
":kmath-complex",