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Finishing fixes

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This commit is contained in:
Alexander Nozik 2024-02-17 21:32:26 +03:00
parent 7d88fb0166
commit f8e91c2402
69 changed files with 548 additions and 449 deletions
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2
CHANGELOG.md
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@ -20,9 +20,11 @@
- Kmath-memory is moved on top of core.
### Deprecated
- ND4J engine
### Removed
- `asPolynomial` function due to scope pollution
- Codegend for ejml (450 lines of codegen for 1000 lines of code is too much)
### Fixed
- Median statistics

2
benchmarks/src/jsMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
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@ -99,7 +99,7 @@ class ExpressionsInterpretersBenchmark {
private val estree = node.estreeCompileToExpression(Float64Field)
private val raw = Expression<Double> { args ->
val x = args[x]!!
val x = args.getValue(x)
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}
}

10
examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt
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@ -13,10 +13,7 @@ import space.kscience.kmath.expressions.autodiff
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.operations.toList
import space.kscience.kmath.optimization.FunctionOptimizationTarget
import space.kscience.kmath.optimization.optimizeWith
import space.kscience.kmath.optimization.result
import space.kscience.kmath.optimization.resultValue
import space.kscience.kmath.optimization.*
import space.kscience.kmath.random.RandomGenerator
import space.kscience.kmath.real.DoubleVector
import space.kscience.kmath.real.map
@ -80,8 +77,9 @@ suspend fun main() {
val result = chi2.optimizeWith(
CMOptimizer,
mapOf(a to 1.5, b to 0.9, c to 1.0),
FunctionOptimizationTarget.MINIMIZE
)
){
FunctionOptimizationTarget(OptimizationDirection.MINIMIZE)
}
//display a page with plot and numerical results
val page = Plotly.page {

3
examples/src/main/kotlin/space/kscience/kmath/fit/qowFit.kt
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@ -7,6 +7,7 @@ package space.kscience.kmath.fit
import kotlinx.html.br
import kotlinx.html.h3
import space.kscience.attributes.Attributes
import space.kscience.kmath.data.XYErrorColumnarData
import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.expressions.Symbol
@ -64,7 +65,7 @@ suspend fun main() {
QowOptimizer,
Double.autodiff,
mapOf(a to 0.9, b to 1.2, c to 2.0, e to 1.0, d to 1.0, e to 0.0),
OptimizationParameters(a, b, c, d)
attributes = Attributes(OptimizationParameters, listOf(a, b, c, d))
) { arg ->
//bind variables to autodiff context
val a by binding

5
examples/src/main/kotlin/space/kscience/kmath/functions/interpolate.kt
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@ -8,7 +8,6 @@ package space.kscience.kmath.functions
import space.kscience.kmath.interpolation.SplineInterpolator
import space.kscience.kmath.interpolation.interpolatePolynomials
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64Buffer
import space.kscience.plotly.Plotly
import space.kscience.plotly.UnstablePlotlyAPI
import space.kscience.plotly.makeFile
@ -24,9 +23,7 @@ fun main() {
x to sin(x)
}
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(
Float64Field, ::Float64Buffer
).interpolatePolynomials(data)
val polynomial: PiecewisePolynomial<Double> = SplineInterpolator(Float64Field).interpolatePolynomials(data)
val function = polynomial.asFunction(Float64Field, 0.0)

4
examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt
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@ -8,7 +8,7 @@
package space.kscience.kmath.structures
import space.kscience.kmath.complex.*
import space.kscience.kmath.linear.transpose
import space.kscience.kmath.linear.transposed
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.nd.as2D
import space.kscience.kmath.nd.ndAlgebra
@ -60,7 +60,7 @@ fun complexExample() {
val sum = matrix + x + 1.0
//Represent the sum as 2d-structure and transpose
sum.as2D().transpose()
sum.as2D().transposed()
}
}
}

1
examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt
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@ -7,6 +7,7 @@ package space.kscience.kmath.structures
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.NumbersAddOps

6
examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt
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@ -6,20 +6,18 @@
package space.kscience.kmath.structures
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.mapToBuffer
import kotlin.system.measureTimeMillis
private inline fun <T, reified R : Any> BufferND<T>.mapToBufferND(
bufferFactory: BufferFactory<R> = BufferFactory.auto(),
bufferFactory: BufferFactory<R> = BufferFactory(),
crossinline block: (T) -> R,
): BufferND<R> = BufferND(indices, buffer.mapToBuffer(bufferFactory, block))
@Suppress("UNUSED_VARIABLE")
fun main() {
val n = 6000
val structure = StructureND.buffered(ShapeND(n, n), Buffer.Companion::auto) { 1.0 }
val structure = BufferND(n, n) { 1.0 }
structure.mapToBufferND { it + 1 } // warm-up
val time1 = measureTimeMillis { val res = structure.mapToBufferND { it + 1 } }
println("Structure mapping finished in $time1 millis")

2
examples/src/main/kotlin/space/kscience/kmath/structures/buffers.kt
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@ -13,7 +13,7 @@ import space.kscience.kmath.operations.withSize
inline fun <reified R : Any> MutableBuffer.Companion.same(
n: Int,
value: R
): MutableBuffer<R> = auto(n) { value }
): MutableBuffer<R> = MutableBuffer(n) { value }
fun main() {

63
kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt
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@ -7,16 +7,20 @@ package space.kscience.kmath.commons.linear
import org.apache.commons.math3.linear.*
import org.apache.commons.math3.linear.LUDecomposition
import org.apache.commons.math3.linear.SingularValueDecomposition
import space.kscience.attributes.SafeType
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.linear.*
import space.kscience.kmath.linear.CholeskyDecomposition
import space.kscience.kmath.linear.QRDecomposition
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.StructureAttribute
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.Float64Buffer
import kotlin.reflect.cast
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.IntBuffer
import space.kscience.kmath.structures.asBuffer
public class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
override val type: SafeType<Double> get() = DoubleField.type
@ -109,45 +113,44 @@ public object CMLinearSpace : LinearSpace<Double, Float64Field> {
val origin = structure.toCM().origin
return when (attribute) {
IsDiagonal -> if (origin is DiagonalMatrix) IsDiagonal else null
val raw: Any? = when (attribute) {
IsDiagonal -> if (origin is DiagonalMatrix) Unit else null
Determinant -> LUDecomposition(origin).determinant
LUP -> GenericLupDecomposition {
private val lup by lazy { LUDecomposition(origin) }
override val determinant: Double by lazy { lup.determinant }
override val l: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(lup.l).withAttribute(LowerTriangular) }
override val u: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(lup.u).withAttribute(UpperTriangular) }
override val p: Matrix<Double> by lazy { CMMatrix(lup.p) }
LUP -> object : LupDecomposition<Float64> {
val lup by lazy { LUDecomposition(origin) }
override val pivot: IntBuffer get() = lup.pivot.asBuffer()
override val l: Matrix<Float64> get() = lup.l.wrap()
override val u: Matrix<Float64> get() = lup.u.wrap()
}
CholeskyDecompositionAttribute -> object : CholeskyDecompositionAttribute<Double> {
override val l: Matrix<Double> by lazy<Matrix<Double>> {
val cholesky = CholeskyDecomposition(origin)
CMMatrix(cholesky.l).withAttribute(LowerTriangular)
}
Cholesky -> object : CholeskyDecomposition<Float64> {
val cmCholesky by lazy { org.apache.commons.math3.linear.CholeskyDecomposition(origin) }
override val l: Matrix<Double> get() = cmCholesky.l.wrap()
}
QRDecompositionAttribute -> object : QRDecompositionAttribute<Double> {
private val qr by lazy { QRDecomposition(origin) }
override val q: Matrix<Double> by lazy<Matrix<Double>> {
CMMatrix(qr.q).withAttribute(
OrthogonalAttribute
)
}
override val r: Matrix<Double> by lazy<Matrix<Double>> { CMMatrix(qr.r).withAttribute(UpperTriangular) }
QR -> object : QRDecomposition<Float64> {
val cmQr by lazy { org.apache.commons.math3.linear.QRDecomposition(origin) }
override val q: Matrix<Float64> get() = cmQr.q.wrap().withAttribute(OrthogonalAttribute)
override val r: Matrix<Float64> get() = cmQr.r.wrap().withAttribute(UpperTriangular)
}
SVDAttribute -> object : SVDAttribute<Double> {
private val sv by lazy { SingularValueDecomposition(origin) }
override val u: Matrix<Double> by lazy { CMMatrix(sv.u) }
override val s: Matrix<Double> by lazy { CMMatrix(sv.s) }
override val v: Matrix<Double> by lazy { CMMatrix(sv.v) }
override val singularValues: Point<Double> by lazy { Float64Buffer(sv.singularValues) }
SVD -> object : space.kscience.kmath.linear.SingularValueDecomposition<Float64> {
val cmSvd by lazy { SingularValueDecomposition(origin) }
override val u: Matrix<Float64> get() = cmSvd.u.wrap()
override val s: Matrix<Float64> get() = cmSvd.s.wrap()
override val v: Matrix<Float64> get() = cmSvd.v.wrap()
override val singularValues: Point<Float64> get() = cmSvd.singularValues.asBuffer()
}
else -> null
}?.let(type::cast)
}
@Suppress("UNCHECKED_CAST")
return raw as V?
}
}
public operator fun CMMatrix.plus(other: CMMatrix): CMMatrix = CMMatrix(origin.add(other.origin))

31
kmath-commons/src/jvmMain/kotlin/space/kscience/kmath/commons/optimization/CMOptimizer.kt
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@ -3,6 +3,7 @@
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@file:OptIn(UnstableKMathAPI::class)
package space.kscience.kmath.commons.optimization
import org.apache.commons.math3.optim.*
@ -20,7 +21,6 @@ import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.expressions.SymbolIndexer
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.withSymbols
import space.kscience.kmath.misc.log
import space.kscience.kmath.optimization.*
import kotlin.collections.set
import kotlin.reflect.KClass
@ -28,7 +28,7 @@ import kotlin.reflect.KClass
public operator fun PointValuePair.component1(): DoubleArray = point
public operator fun PointValuePair.component2(): Double = value
public object CMOptimizerEngine: OptimizationAttribute<() -> MultivariateOptimizer>
public object CMOptimizerEngine : OptimizationAttribute<() -> MultivariateOptimizer>
/**
* Specify a Commons-maths optimization engine
@ -37,7 +37,7 @@ public fun AttributesBuilder<FunctionOptimization<Double>>.cmEngine(optimizerBui
set(CMOptimizerEngine, optimizerBuilder)
}
public object CMOptimizerData: SetAttribute<SymbolIndexer.() -> OptimizationData>
public object CMOptimizerData : SetAttribute<SymbolIndexer.() -> OptimizationData>
/**
* Specify Commons-maths optimization data.
@ -118,21 +118,24 @@ public object CMOptimizer : Optimizer<Double, FunctionOptimization<Double>> {
val logger = problem.attributes[OptimizationLog]
for (feature in problem.attributes) {
when (feature) {
is CMOptimizerData -> feature.data.forEach { dataBuilder ->
addOptimizationData(dataBuilder())
}
is FunctionOptimizationTarget -> when (feature) {
FunctionOptimizationTarget.MAXIMIZE -> addOptimizationData(GoalType.MAXIMIZE)
FunctionOptimizationTarget.MINIMIZE -> addOptimizationData(GoalType.MINIMIZE)
}
else -> logger?.log { "The feature $feature is unused in optimization" }
problem.attributes[CMOptimizerData]?.let { builders: Set<SymbolIndexer.() -> OptimizationData> ->
builders.forEach { dataBuilder ->
addOptimizationData(dataBuilder())
}
}
problem.attributes[FunctionOptimizationTarget]?.let { direction: OptimizationDirection ->
when (direction) {
OptimizationDirection.MAXIMIZE -> addOptimizationData(GoalType.MAXIMIZE)
OptimizationDirection.MINIMIZE -> addOptimizationData(GoalType.MINIMIZE)
}
}
val (point, value) = cmOptimizer.optimize(*optimizationData.values.toTypedArray())
return problem.withAttributes(OptimizationResult(point.toMap()), OptimizationValue(value))
return problem.withAttributes {
result(point.toMap())
value(value)
}
}
}
}

6
kmath-commons/src/jvmTest/kotlin/space/kscience/kmath/commons/integration/IntegrationTest.kt
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@ -7,6 +7,8 @@ package space.kscience.kmath.commons.integration
import org.junit.jupiter.api.Test
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.integration.IntegrandAbsoluteAccuracy
import space.kscience.kmath.integration.IntegrandRelativeAccuracy
import space.kscience.kmath.integration.integrate
import space.kscience.kmath.integration.value
import space.kscience.kmath.operations.Float64Field.sin
@ -27,8 +29,8 @@ internal class IntegrationTest {
@Test
fun customSimpson() {
val res = CMIntegrator.simpson().integrate(0.0..PI, {
targetRelativeAccuracy = 1e-4
targetAbsoluteAccuracy = 1e-4
IntegrandRelativeAccuracy(1e-4)
IntegrandAbsoluteAccuracy(1e-4)
}, function).value
assertTrue { abs(res - 2) < 1e-3 }
assertTrue { abs(res - 2) > 1e-12 }

5
kmath-commons/src/jvmTest/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt
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@ -73,8 +73,9 @@ internal class OptimizeTest {
val result: FunctionOptimization<Double> = chi2.optimizeWith(
CMOptimizer,
mapOf(a to 1.5, b to 0.9, c to 1.0),
FunctionOptimizationTarget.MINIMIZE
)
){
FunctionOptimizationTarget(OptimizationDirection.MINIMIZE)
}
println(result)
println("Chi2/dof = ${result.resultValue / (x.size - 3)}")
}

7
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Expression.kt
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@ -7,6 +7,7 @@ package space.kscience.kmath.expressions
import space.kscience.attributes.SafeType
import space.kscience.attributes.WithType
import space.kscience.attributes.safeTypeOf
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.Algebra
import space.kscience.kmath.operations.DoubleField
@ -30,12 +31,18 @@ public interface Expression<T> : WithType<T> {
public operator fun invoke(arguments: Map<Symbol, T>): T
}
/**
* Create an expression from a functional block.
*/
public fun <T> Expression(type: SafeType<T>, block: (Map<Symbol, T>) -> T): Expression<T> = object : Expression<T> {
override fun invoke(arguments: Map<Symbol, T>): T = block(arguments)
override val type: SafeType<T> = type
}
public inline fun <reified T> Expression(noinline block: (Map<Symbol, T>) -> T): Expression<T> =
Expression(safeTypeOf<T>(), block)
/**
* Specialization of [Expression] for [Double] allowing better performance because of using array.
*/

24
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt
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@ -15,14 +15,19 @@ import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.*
public interface LupDecomposition<T> {
public val linearSpace: LinearSpace<T, Field<T>>
public val elementAlgebra: Field<T> get() = linearSpace.elementAlgebra
public val pivot: IntBuffer
public val l: Matrix<T>
public val u: Matrix<T>
}
/**
* Create a pivot matrix from pivot vector using provided [LinearSpace]
*/
public fun <T> LupDecomposition<T>.pivotMatrix(linearSpace: LinearSpace<T, Ring<T>>): Matrix<T> =
VirtualMatrix(linearSpace.type, l.rowNum, l.colNum) { row, column ->
if (column == pivot[row]) linearSpace.elementAlgebra.one else linearSpace.elementAlgebra.zero
}
/**
* Matrices with this feature support LU factorization with partial pivoting: *[p] &middot; a = [l] &middot; [u]* where
* *a* is the owning matrix.
@ -31,12 +36,14 @@ public interface LupDecomposition<T> {
* @param lu combined L and U matrix
*/
public class GenericLupDecomposition<T>(
override val linearSpace: LinearSpace<T, Field<T>>,
public val linearSpace: LinearSpace<T, Field<T>>,
private val lu: Matrix<T>,
override val pivot: IntBuffer,
private val even: Boolean,
) : LupDecomposition<T> {
private val elementAlgebra get() = linearSpace.elementAlgebra
override val l: Matrix<T>
get() = VirtualMatrix(lu.type, lu.rowNum, lu.colNum, attributes = Attributes(LowerTriangular)) { i, j ->
when {
@ -51,11 +58,6 @@ public class GenericLupDecomposition<T>(
if (j >= i) lu[i, j] else elementAlgebra.zero
}
public val pivotMatrix: Matrix<T>
get() = VirtualMatrix(linearSpace.type, l.rowNum, l.colNum) { row, column ->
if (column == pivot[row]) elementAlgebra.one else elementAlgebra.zero
}
public val determinant: T by lazy {
elementAlgebra { (0 until l.shape[0]).fold(if (even) one else -one) { value, i -> value * lu[i, i] } }
}
@ -79,7 +81,7 @@ internal fun <T : Comparable<T>> LinearSpace<T, Ring<T>>.abs(value: T): T =
public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
matrix: Matrix<T>,
checkSingular: (T) -> Boolean,
): LupDecomposition<T> = elementAlgebra {
): GenericLupDecomposition<T> = elementAlgebra {
require(matrix.rowNum == matrix.colNum) { "LU decomposition supports only square matrices" }
val m = matrix.colNum
val pivot = IntArray(matrix.rowNum)
@ -156,7 +158,7 @@ public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
public fun LinearSpace<Double, Float64Field>.lup(
matrix: Matrix<Double>,
singularityThreshold: Double = 1e-11,
): LupDecomposition<Double> = lup(matrix) { it < singularityThreshold }
): GenericLupDecomposition<Double> = lup(matrix) { it < singularityThreshold }
internal fun <T> LinearSpace<T, Field<T>>.solve(
lup: LupDecomposition<T>,

3
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/Transposed.kt
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@ -25,5 +25,4 @@ public class TransposedMatrix<T>(public val origin: Matrix<T>) : Matrix<T> {
/**
* Create a virtual transposed matrix without copying anything. `A.transpose().transpose() === A`
*/
public val <T> Matrix<T>.transposed: Matrix<T>
get() = (this as? TransposedMatrix<T>)?.origin ?: TransposedMatrix(this)
public fun <T> Matrix<T>.transposed(): Matrix<T> = (this as? TransposedMatrix<T>)?.origin ?: TransposedMatrix(this)

7
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/Float64FieldND.kt
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@ -5,9 +5,11 @@
package space.kscience.kmath.nd
import space.kscience.attributes.SafeType
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.*
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.Float64Buffer
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
@ -20,7 +22,10 @@ import kotlin.math.pow as kpow
public class Float64BufferND(
indexes: ShapeIndexer,
override val buffer: Float64Buffer,
) : MutableBufferND<Double>(indexes, buffer), MutableStructureNDOfDouble{
) : MutableBufferND<Double>(indexes, buffer), MutableStructureNDOfDouble {
override val type: SafeType<Float64> get() = Float64Field.type
override fun getDouble(index: IntArray): Double = buffer[indices.offset(index)]
override fun setDouble(index: IntArray, value: Double) {

10
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/primitiveStructureND.kt
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@ -5,9 +5,15 @@
package space.kscience.kmath.nd
import space.kscience.attributes.SafeType
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.Int32Field
import space.kscience.kmath.structures.Float64
public interface StructureNDOfDouble : StructureND<Double> {
override val type: SafeType<Float64> get() = Float64Field.type
/**
* Guaranteed non-blocking access to content
*/
@ -22,6 +28,7 @@ public fun StructureND<Double>.getDouble(index: IntArray): Double =
if (this is StructureNDOfDouble) getDouble(index) else get(index)
public interface MutableStructureNDOfDouble : StructureNDOfDouble, MutableStructureND<Double> {
/**
* Guaranteed non-blocking access to content
*/
@ -34,6 +41,9 @@ public fun MutableStructureND<Double>.getDouble(index: IntArray): Double =
public interface StructureNDOfInt : StructureND<Int> {
override val type: SafeType<Int> get() = Int32Field.type
/**
* Guaranteed non-blocking access to content
*/

2
kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/DoubleLUSolverTest.kt
View File

@ -40,7 +40,7 @@ class DoubleLUSolverTest {
//Check determinant
assertEquals(7.0, lup.determinant)
assertMatrixEquals(lup.pivotMatrix dot matrix, lup.l dot lup.u)
assertMatrixEquals(lup.pivotMatrix(this) dot matrix, lup.l dot lup.u)
}
@Test

2
kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/MatrixTest.kt
View File

@ -21,7 +21,7 @@ class MatrixTest {
@Test
fun testTranspose() = Double.algebra.linearSpace.run {
val matrix = one(3, 3)
val transposed = matrix.transposed
val transposed = matrix.transposed()
assertTrue { StructureND.contentEquals(matrix, transposed) }
}

1
kmath-coroutines/build.gradle.kts
View File

@ -6,6 +6,7 @@ kscience {
jvm()
js()
native()
wasm()
dependencies {
api(project(":kmath-core"))

5
kmath-coroutines/src/jvmTest/kotlin/space/kscience/kmath/streaming/RingBufferTest.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.streaming
import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.operations.asSequence
import kotlin.test.Test
import kotlin.test.assertEquals
@ -14,7 +15,7 @@ import kotlin.test.assertEquals
internal class RingBufferTest {
@Test
fun push() {
val buffer = RingBuffer.build(20, Double.NaN)
val buffer = RingBuffer(20, Double.NaN)
runBlocking {
for (i in 1..30) {
buffer.push(i.toDouble())
@ -30,7 +31,7 @@ internal class RingBufferTest {
while (true) emit(i++)
}
val windowed = flow.windowed(10)
val windowed = flow.windowed(10, Int32Ring)
runBlocking {
@Suppress("UNUSED_VARIABLE") val first = windowed.take(1).single()

1
kmath-dimensions/build.gradle.kts
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@ -6,6 +6,7 @@ kscience{
jvm()
js()
native()
wasm()
dependencies{
api(projects.kmathCore)

2
kmath-dimensions/src/commonMain/kotlin/space/kscience/kmath/dimensions/Wrappers.kt
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@ -153,7 +153,7 @@ public value class DMatrixContext<T : Any, out A : Ring<T>>(public val context:
context.run { this@unaryMinus.unaryMinus() }.coerce()
public inline fun <reified R : Dimension, reified C : Dimension> DMatrix<T, C, R>.transposed(): DMatrix<T, R, C> =
context.run { (this@transposed as Matrix<T>).transposed }.coerce()
context.run { (this@transposed as Matrix<T>).transposed() }.coerce()
public companion object {
public val real: DMatrixContext<Double, Float64Field> = DMatrixContext(Double.algebra.linearSpace)

23
kmath-dimensions/src/wasmJsMain/kotlin/space/kscience/kmath/dimensions/Dimension.wasmJs.kt Normal file
View File

@ -0,0 +1,23 @@
/*
* 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.dimensions
import kotlin.reflect.KClass
private val dimensionMap: MutableMap<Int, Dimension> = hashMapOf(1 to D1, 2 to D2, 3 to D3)
@Suppress("UNCHECKED_CAST")
public actual fun <D : Dimension> Dimension.Companion.resolve(type: KClass<D>): D = dimensionMap
.entries
.map(MutableMap.MutableEntry<Int, Dimension>::value)
.find { it::class == type } as? D
?: error("Can't resolve dimension $type")
public actual fun Dimension.Companion.of(dim: Int): Dimension = dimensionMap.getOrPut(dim) {
object : Dimension {
override val dim: Int get() = dim
}
}

28
kmath-ejml/build.gradle.kts
View File

@ -1,15 +1,15 @@
import space.kscience.kmath.ejml.codegen.ejmlCodegen
plugins {
id("space.kscience.gradle.jvm")
}
val ejmlVerision = "0.43.1"
dependencies {
api("org.ejml:ejml-ddense:0.41")
api("org.ejml:ejml-fdense:0.41")
api("org.ejml:ejml-dsparse:0.41")
api("org.ejml:ejml-fsparse:0.41")
api(project(":kmath-core"))
api("org.ejml:ejml-ddense:$ejmlVerision")
api("org.ejml:ejml-fdense:$ejmlVerision")
api("org.ejml:ejml-dsparse:$ejmlVerision")
api("org.ejml:ejml-fsparse:$ejmlVerision")
api(projects.kmathCore)
}
readme {
@ -32,10 +32,10 @@ readme {
) { "LinearSpace implementations." }
}
kotlin.sourceSets.main {
val codegen by tasks.creating {
ejmlCodegen(kotlin.srcDirs.first().absolutePath + "/space/kscience/kmath/ejml/_generated.kt")
}
kotlin.srcDirs(files().builtBy(codegen))
}
//kotlin.sourceSets.main {
// val codegen by tasks.creating {
// ejmlCodegen(kotlin.srcDirs.first().absolutePath + "/space/kscience/kmath/ejml/_generated.kt")
// }
//
// kotlin.srcDirs(files().builtBy(codegen))
//}

354
kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/_generated.kt → kmath-ejml/src/main/kotlin/space/kscience/kmath/ejml/implementations.kt
View File

@ -17,21 +17,18 @@ 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.UnstableKMathAPI
import space.kscience.kmath.linear.*
import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.nd.StructureFeature
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.nd.StructureAttribute
import space.kscience.kmath.operations.Float32Field
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.DoubleBuffer
import space.kscience.kmath.structures.FloatBuffer
import kotlin.reflect.KClass
import kotlin.reflect.cast
import space.kscience.kmath.structures.Float32
import space.kscience.kmath.structures.IntBuffer
import space.kscience.kmath.structures.asBuffer
/**
* [EjmlVector] specialization for [Double].
@ -78,7 +75,6 @@ public class EjmlFloatMatrix<out M : FMatrix>(override val origin: M) : EjmlMatr
}
/**
* [EjmlLinearSpace] implementation based on [CommonOps_DDRM], [DecompositionFactory_DDRM] operations and
* [DMatrixRMaj] matrices.
@ -143,10 +139,10 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra.one,
toEjml().origin,
elementAlgebra { -one },
other.toEjml().origin,
other.toEjml().origin,
out,
)
@ -167,12 +163,12 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixRMaj> {
val out = DMatrixRMaj(1, 1)
CommonOps_DDRM.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra.one,
other.toEjml().origin,
other.toEjml().origin,
out,
)
@ -217,77 +213,65 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
override fun Double.times(v: Point<Double>): EjmlDoubleVector<DMatrixRMaj> = v * this
@UnstableKMathAPI
override fun <F : StructureFeature> computeFeature(structure: Matrix<Double>, type: KClass<out F>): F? {
structure.getFeature(type)?.let { return it }
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
val origin = structure.toEjml().origin
return when (type) {
InverseMatrixFeature::class -> object : InverseMatrixFeature<Double> {
override val inverse: Matrix<Double> by lazy {
val res = origin.copy()
CommonOps_DDRM.invert(res)
res.wrapMatrix()
}
val raw: Any? = when (attribute) {
Inverted -> {
val res = origin.copy()
CommonOps_DDRM.invert(res)
res.wrapMatrix()
}
DeterminantFeature::class -> object : DeterminantFeature<Double> {
override val determinant: Double by lazy { CommonOps_DDRM.det(origin) }
}
SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Double> {
private val svd by lazy {
DecompositionFactory_DDRM.svd(origin.numRows, origin.numCols, true, true, false)
Determinant -> CommonOps_DDRM.det(origin)
SVD -> object : SingularValueDecomposition<Double> {
val ejmlSvd by lazy {
DecompositionFactory_DDRM
.svd(origin.numRows, origin.numCols, true, true, false)
.apply { decompose(origin.copy()) }
}
override val u: Matrix<Double> get() = ejmlSvd.getU(null, false).wrapMatrix()
override val s: Matrix<Double> get() = ejmlSvd.getW(null).wrapMatrix()
override val v: Matrix<Double> get() = ejmlSvd.getV(null, false).wrapMatrix()
override val singularValues: Point<Double> get() = ejmlSvd.singularValues.asBuffer()
override val u: Matrix<Double> by lazy { svd.getU(null, false).wrapMatrix() }
override val s: Matrix<Double> by lazy { svd.getW(null).wrapMatrix() }
override val v: Matrix<Double> by lazy { svd.getV(null, false).wrapMatrix() }
override val singularValues: Point<Double> by lazy { DoubleBuffer(svd.singularValues) }
}
QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
private val qr by lazy {
DecompositionFactory_DDRM.qr().apply { decompose(origin.copy()) }
}
override val q: Matrix<Double> by lazy {
qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
}
override val r: Matrix<Double> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
QR -> object : QRDecomposition<Double> {
val ejmlQr by lazy { DecompositionFactory_DDRM.qr().apply { decompose(origin.copy()) } }
override val q: Matrix<Double> get() = ejmlQr.getQ(null, false).wrapMatrix()
override val r: Matrix<Double> get() = ejmlQr.getR(null, false).wrapMatrix()
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
Cholesky -> object : CholeskyDecomposition<Double> {
override val l: Matrix<Double> by lazy {
val cholesky =
DecompositionFactory_DDRM.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
cholesky.getT(null).wrapMatrix().withFeature(LFeature)
cholesky.getT(null).wrapMatrix().withAttribute(LowerTriangular)
}
}
LupDecompositionFeature::class -> object : LupDecompositionFeature<Double> {
LUP -> object : LupDecomposition<Double> {
private val lup by lazy {
DecompositionFactory_DDRM.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) }
}
override val l: Matrix<Double> by lazy {
lup.getLower(null).wrapMatrix().withFeature(LFeature)
}
override val l: Matrix<Double>
get() = lup.getLower(null).wrapMatrix().withAttribute(LowerTriangular)
override val u: Matrix<Double> by lazy {
lup.getUpper(null).wrapMatrix().withFeature(UFeature)
}
override val p: Matrix<Double> by lazy { lup.getRowPivot(null).wrapMatrix() }
override val u: Matrix<Double>
get() = lup.getUpper(null).wrapMatrix().withAttribute(UpperTriangular)
override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
}
else -> null
}?.let{
type.cast(it)
}
@Suppress("UNCHECKED_CAST")
return raw as V?
}
/**
@ -318,7 +302,6 @@ public object EjmlLinearSpaceDDRM : EjmlLinearSpace<Double, Float64Field, DMatri
}
/**
* [EjmlLinearSpace] implementation based on [CommonOps_FDRM], [DecompositionFactory_FDRM] operations and
* [FMatrixRMaj] matrices.
@ -383,10 +366,10 @@ public object EjmlLinearSpaceFDRM : EjmlLinearSpace<Float, Float32Field, FMatrix
val out = FMatrixRMaj(1, 1)
CommonOps_FDRM.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra.one,
toEjml().origin,
elementAlgebra { -one },
other.toEjml().origin,
other.toEjml().origin,
out,
)
@ -407,12 +390,12 @@ public object EjmlLinearSpaceFDRM : EjmlLinearSpace<Float, Float32Field, FMatrix
override fun Matrix<Float>.plus(other: Matrix<Float>): EjmlFloatMatrix<FMatrixRMaj> {
val out = FMatrixRMaj(1, 1)
CommonOps_FDRM.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra.one,
other.toEjml().origin,
other.toEjml().origin,
out,
)
@ -457,77 +440,65 @@ public object EjmlLinearSpaceFDRM : EjmlLinearSpace<Float, Float32Field, FMatrix
override fun Float.times(v: Point<Float>): EjmlFloatVector<FMatrixRMaj> = v * this
@UnstableKMathAPI
override fun <F : StructureFeature> computeFeature(structure: Matrix<Float>, type: KClass<out F>): F? {
structure.getFeature(type)?.let { return it }
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float32>, attribute: A): V? {
val origin = structure.toEjml().origin
return when (type) {
InverseMatrixFeature::class -> object : InverseMatrixFeature<Float> {
override val inverse: Matrix<Float> by lazy {
val res = origin.copy()
CommonOps_FDRM.invert(res)
res.wrapMatrix()
}
val raw: Any? = when (attribute) {
Inverted -> {
val res = origin.copy()
CommonOps_FDRM.invert(res)
res.wrapMatrix()
}
DeterminantFeature::class -> object : DeterminantFeature<Float> {
override val determinant: Float by lazy { CommonOps_FDRM.det(origin) }
}
SingularValueDecompositionFeature::class -> object : SingularValueDecompositionFeature<Float> {
private val svd by lazy {
DecompositionFactory_FDRM.svd(origin.numRows, origin.numCols, true, true, false)
Determinant -> CommonOps_FDRM.det(origin)
SVD -> object : SingularValueDecomposition<Float32> {
val ejmlSvd by lazy {
DecompositionFactory_FDRM
.svd(origin.numRows, origin.numCols, true, true, false)
.apply { decompose(origin.copy()) }
}
override val u: Matrix<Float32> get() = ejmlSvd.getU(null, false).wrapMatrix()
override val s: Matrix<Float32> get() = ejmlSvd.getW(null).wrapMatrix()
override val v: Matrix<Float32> get() = ejmlSvd.getV(null, false).wrapMatrix()
override val singularValues: Point<Float32> get() = ejmlSvd.singularValues.asBuffer()
override val u: Matrix<Float> by lazy { svd.getU(null, false).wrapMatrix() }
override val s: Matrix<Float> by lazy { svd.getW(null).wrapMatrix() }
override val v: Matrix<Float> by lazy { svd.getV(null, false).wrapMatrix() }
override val singularValues: Point<Float> by lazy { FloatBuffer(svd.singularValues) }
}
QRDecompositionFeature::class -> object : QRDecompositionFeature<Float> {
private val qr by lazy {
DecompositionFactory_FDRM.qr().apply { decompose(origin.copy()) }
}
override val q: Matrix<Float> by lazy {
qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
}
override val r: Matrix<Float> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
QR -> object : QRDecomposition<Float32> {
val ejmlQr by lazy { DecompositionFactory_FDRM.qr().apply { decompose(origin.copy()) } }
override val q: Matrix<Float32> get() = ejmlQr.getQ(null, false).wrapMatrix()
override val r: Matrix<Float32> get() = ejmlQr.getR(null, false).wrapMatrix()
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Float> {
override val l: Matrix<Float> by lazy {
Cholesky -> object : CholeskyDecomposition<Float32> {
override val l: Matrix<Float32> by lazy {
val cholesky =
DecompositionFactory_FDRM.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
cholesky.getT(null).wrapMatrix().withFeature(LFeature)
cholesky.getT(null).wrapMatrix().withAttribute(LowerTriangular)
}
}
LupDecompositionFeature::class -> object : LupDecompositionFeature<Float> {
LUP -> object : LupDecomposition<Float32> {
private val lup by lazy {
DecompositionFactory_FDRM.lu(origin.numRows, origin.numCols).apply { decompose(origin.copy()) }
}
override val l: Matrix<Float> by lazy {
lup.getLower(null).wrapMatrix().withFeature(LFeature)
}
override val l: Matrix<Float32>
get() = lup.getLower(null).wrapMatrix().withAttribute(LowerTriangular)
override val u: Matrix<Float> by lazy {
lup.getUpper(null).wrapMatrix().withFeature(UFeature)
}
override val p: Matrix<Float> by lazy { lup.getRowPivot(null).wrapMatrix() }
override val u: Matrix<Float32>
get() = lup.getUpper(null).wrapMatrix().withAttribute(UpperTriangular)
override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
}
else -> null
}?.let{
type.cast(it)
}
@Suppress("UNCHECKED_CAST")
return raw as V?
}
/**
@ -558,7 +529,6 @@ public object EjmlLinearSpaceFDRM : EjmlLinearSpace<Float, Float32Field, FMatrix
}
/**
* [EjmlLinearSpace] implementation based on [CommonOps_DSCC], [DecompositionFactory_DSCC] operations and
* [DMatrixSparseCSC] matrices.
@ -623,12 +593,12 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra.one,
toEjml().origin,
elementAlgebra { -one },
other.toEjml().origin,
other.toEjml().origin,
out,
null,
null,
null,
)
@ -649,14 +619,14 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
override fun Matrix<Double>.plus(other: Matrix<Double>): EjmlDoubleMatrix<DMatrixSparseCSC> {
val out = DMatrixSparseCSC(1, 1)
CommonOps_DSCC.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra.one,
other.toEjml().origin,
other.toEjml().origin,
out,
null,
null,
null,
)
@ -672,7 +642,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
elementAlgebra.one,
other.toEjml().origin,
out,
null,
null,
null,
)
@ -688,7 +658,7 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
elementAlgebra { -one },
other.toEjml().origin,
out,
null,
null,
null,
)
@ -705,64 +675,52 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
override fun Double.times(v: Point<Double>): EjmlDoubleVector<DMatrixSparseCSC> = v * this
@UnstableKMathAPI
override fun <F : StructureFeature> computeFeature(structure: Matrix<Double>, type: KClass<out F>): F? {
structure.getFeature(type)?.let { return it }
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Double>, attribute: A): V? {
val origin = structure.toEjml().origin
return when (type) {
QRDecompositionFeature::class -> object : QRDecompositionFeature<Double> {
private val qr by lazy {
DecompositionFactory_DSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
}
override val q: Matrix<Double> by lazy {
qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
}
override val r: Matrix<Double> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
val raw: Any? = when (attribute) {
Inverted -> {
val res = DMatrixRMaj(origin.numRows,origin.numCols)
CommonOps_DSCC.invert(origin,res)
res.wrapMatrix()
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Double> {
Determinant -> CommonOps_DSCC.det(origin)
QR -> object : QRDecomposition<Double> {
val ejmlQr by lazy { DecompositionFactory_DSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) } }
override val q: Matrix<Double> get() = ejmlQr.getQ(null, false).wrapMatrix()
override val r: Matrix<Double> get() = ejmlQr.getR(null, false).wrapMatrix()
}
Cholesky -> object : CholeskyDecomposition<Double> {
override val l: Matrix<Double> by lazy {
val cholesky =
DecompositionFactory_DSCC.cholesky().apply { decompose(origin.copy()) }
(cholesky.getT(null) as DMatrix).wrapMatrix().withFeature(LFeature)
(cholesky.getT(null) as DMatrix).wrapMatrix().withAttribute(LowerTriangular)
}
}
LUDecompositionFeature::class, DeterminantFeature::class, InverseMatrixFeature::class -> object :
LUDecompositionFeature<Double>, DeterminantFeature<Double>, InverseMatrixFeature<Double> {
private val lu by lazy {
LUP -> object : LupDecomposition<Double> {
private val lup by lazy {
DecompositionFactory_DSCC.lu(FillReducing.NONE).apply { decompose(origin.copy()) }
}
override val l: Matrix<Double> by lazy {
lu.getLower(null).wrapMatrix().withFeature(LFeature)
}
override val l: Matrix<Double>
get() = lup.getLower(null).wrapMatrix().withAttribute(LowerTriangular)
override val u: Matrix<Double> by lazy {
lu.getUpper(null).wrapMatrix().withFeature(UFeature)
}
override val inverse: Matrix<Double> by lazy {
var a = origin
val inverse = DMatrixRMaj(1, 1)
val solver = LinearSolverFactory_DSCC.lu(FillReducing.NONE)
if (solver.modifiesA()) a = a.copy()
val i = CommonOps_DDRM.identity(a.numRows)
solver.solve(i, inverse)
inverse.wrapMatrix()
}
override val determinant: Double by lazy { elementAlgebra.number(lu.computeDeterminant().real) }
override val u: Matrix<Double>
get() = lup.getUpper(null).wrapMatrix().withAttribute(UpperTriangular)
override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
}
else -> null
}?.let{
type.cast(it)
}
@Suppress("UNCHECKED_CAST")
return raw as V?
}
/**
@ -793,7 +751,6 @@ public object EjmlLinearSpaceDSCC : EjmlLinearSpace<Double, Float64Field, DMatri
}
/**
* [EjmlLinearSpace] implementation based on [CommonOps_FSCC], [DecompositionFactory_FSCC] operations and
* [FMatrixSparseCSC] matrices.
@ -858,12 +815,12 @@ public object EjmlLinearSpaceFSCC : EjmlLinearSpace<Float, Float32Field, FMatrix
val out = FMatrixSparseCSC(1, 1)
CommonOps_FSCC.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra.one,
toEjml().origin,
elementAlgebra { -one },
other.toEjml().origin,
other.toEjml().origin,
out,
null,
null,
null,
)
@ -884,14 +841,14 @@ public object EjmlLinearSpaceFSCC : EjmlLinearSpace<Float, Float32Field, FMatrix
override fun Matrix<Float>.plus(other: Matrix<Float>): EjmlFloatMatrix<FMatrixSparseCSC> {
val out = FMatrixSparseCSC(1, 1)
CommonOps_FSCC.add(
elementAlgebra.one,
toEjml().origin,
elementAlgebra.one,
other.toEjml().origin,
other.toEjml().origin,
out,
null,
null,
null,
)
@ -907,7 +864,7 @@ public object EjmlLinearSpaceFSCC : EjmlLinearSpace<Float, Float32Field, FMatrix
elementAlgebra.one,
other.toEjml().origin,
out,
null,
null,
null,
)
@ -923,7 +880,7 @@ public object EjmlLinearSpaceFSCC : EjmlLinearSpace<Float, Float32Field, FMatrix
elementAlgebra { -one },
other.toEjml().origin,
out,
null,
null,
null,
)
@ -939,65 +896,52 @@ public object EjmlLinearSpaceFSCC : EjmlLinearSpace<Float, Float32Field, FMatrix
}
override fun Float.times(v: Point<Float>): EjmlFloatVector<FMatrixSparseCSC> = v * this
@UnstableKMathAPI
override fun <F : StructureFeature> computeFeature(structure: Matrix<Float>, type: KClass<out F>): F? {
structure.getFeature(type)?.let { return it }
override fun <V, A : StructureAttribute<V>> computeAttribute(structure: Structure2D<Float32>, attribute: A): V? {
val origin = structure.toEjml().origin
return when (type) {
QRDecompositionFeature::class -> object : QRDecompositionFeature<Float> {
private val qr by lazy {
DecompositionFactory_FSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) }
}
override val q: Matrix<Float> by lazy {
qr.getQ(null, false).wrapMatrix().withFeature(OrthogonalFeature)
}
override val r: Matrix<Float> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
val raw: Any? = when (attribute) {
Inverted -> {
val res = FMatrixRMaj(origin.numRows,origin.numCols)
CommonOps_FSCC.invert(origin,res)
res.wrapMatrix()
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<Float> {
override val l: Matrix<Float> by lazy {
Determinant -> CommonOps_FSCC.det(origin)
QR -> object : QRDecomposition<Float32> {
val ejmlQr by lazy { DecompositionFactory_FSCC.qr(FillReducing.NONE).apply { decompose(origin.copy()) } }
override val q: Matrix<Float32> get() = ejmlQr.getQ(null, false).wrapMatrix()
override val r: Matrix<Float32> get() = ejmlQr.getR(null, false).wrapMatrix()
}
Cholesky -> object : CholeskyDecomposition<Float32> {
override val l: Matrix<Float32> by lazy {
val cholesky =
DecompositionFactory_FSCC.cholesky().apply { decompose(origin.copy()) }
(cholesky.getT(null) as FMatrix).wrapMatrix().withFeature(LFeature)
(cholesky.getT(null) as FMatrix).wrapMatrix().withAttribute(LowerTriangular)
}
}
LUDecompositionFeature::class, DeterminantFeature::class, InverseMatrixFeature::class -> object :
LUDecompositionFeature<Float>, DeterminantFeature<Float>, InverseMatrixFeature<Float> {
private val lu by lazy {
LUP -> object : LupDecomposition<Float32> {
private val lup by lazy {
DecompositionFactory_FSCC.lu(FillReducing.NONE).apply { decompose(origin.copy()) }
}
override val l: Matrix<Float> by lazy {
lu.getLower(null).wrapMatrix().withFeature(LFeature)
}
override val l: Matrix<Float32>
get() = lup.getLower(null).wrapMatrix().withAttribute(LowerTriangular)
override val u: Matrix<Float> by lazy {
lu.getUpper(null).wrapMatrix().withFeature(UFeature)
}
override val inverse: Matrix<Float> by lazy {
var a = origin
val inverse = FMatrixRMaj(1, 1)
val solver = LinearSolverFactory_FSCC.lu(FillReducing.NONE)
if (solver.modifiesA()) a = a.copy()
val i = CommonOps_FDRM.identity(a.numRows)
solver.solve(i, inverse)
inverse.wrapMatrix()
}
override val determinant: Float by lazy { elementAlgebra.number(lu.computeDeterminant().real) }
override val u: Matrix<Float32>
get() = lup.getUpper(null).wrapMatrix().withAttribute(UpperTriangular)
override val pivot: IntBuffer get() = lup.getRowPivotV(null).asBuffer()
}
else -> null
}?.let{
type.cast(it)
}
@Suppress("UNCHECKED_CAST")
return raw as V?
}
/**

10
kmath-ejml/src/test/kotlin/space/kscience/kmath/ejml/EjmlMatrixTest.kt
View File

@ -58,19 +58,19 @@ internal class EjmlMatrixTest {
@OptIn(UnstableKMathAPI::class)
@Test
fun features() {
fun features() = EjmlLinearSpaceDDRM {
val m = randomMatrix
val w = EjmlDoubleMatrix(m)
val det: Determinant<Double> = EjmlLinearSpaceDDRM.attributeForOrNull(w) ?: fail()
assertEquals(CommonOps_DDRM.det(m), det.determinant)
val lup: LupDecompositionAttribute<Double> = EjmlLinearSpaceDDRM.attributeForOrNull(w) ?: fail()
val det: Double = w.getOrComputeAttribute(Determinant) ?: fail()
assertEquals(CommonOps_DDRM.det(m), det)
val lup: LupDecomposition<Double> = w.getOrComputeAttribute(LUP) ?: fail()
val ludecompositionF64 = DecompositionFactory_DDRM.lu(m.numRows, m.numCols)
.also { it.decompose(m.copy()) }
assertMatrixEquals(EjmlDoubleMatrix(ludecompositionF64.getLower(null)), lup.l)
assertMatrixEquals(EjmlDoubleMatrix(ludecompositionF64.getUpper(null)), lup.u)
assertMatrixEquals(EjmlDoubleMatrix(ludecompositionF64.getRowPivot(null)), lup.p)
assertMatrixEquals(EjmlDoubleMatrix(ludecompositionF64.getRowPivot(null)), lup.pivotMatrix(this))
}
@Test

1
kmath-for-real/build.gradle.kts
View File

@ -6,6 +6,7 @@ kscience {
jvm()
js()
native()
wasm()
dependencies {
api(projects.kmathCore)

4
kmath-for-real/src/commonTest/kotlin/space/kscience/kmath/real/DoubleVectorTest.kt
View File

@ -7,7 +7,7 @@ package space.kscience.kmath.real
import space.kscience.kmath.linear.asMatrix
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.linear.transpose
import space.kscience.kmath.linear.transposed
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.structures.Float64Buffer
import kotlin.test.Test
@ -34,7 +34,7 @@ internal class DoubleVectorTest {
val vector1 = Float64Buffer(5) { it.toDouble() }
val vector2 = Float64Buffer(5) { 5 - it.toDouble() }
val matrix1 = vector1.asMatrix()
val matrix2 = vector2.asMatrix().transpose()
val matrix2 = vector2.asMatrix().transposed()
val product = matrix1 dot matrix2
assertEquals(5.0, product[1, 0])
assertEquals(6.0, product[2, 2])

1
kmath-functions/build.gradle.kts
View File

@ -6,7 +6,6 @@ kscience{
jvm()
js()
native()
wasm()
dependencies {

2
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/integration/GaussIntegrator.kt
View File

@ -92,7 +92,7 @@ public inline fun <reified T : Any> GaussIntegrator<T>.integrate(
range: ClosedRange<Double>,
order: Int = 10,
intervals: Int = 10,
attributesBuilder: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit,
attributesBuilder: AttributesBuilder<UnivariateIntegrand<T>>.() -> Unit = {},
noinline function: (Double) -> T,
): UnivariateIntegrand<T> {
require(range.endInclusive > range.start) { "The range upper bound should be higher than lower bound" }

2
kmath-functions/src/commonMain/kotlin/space/kscience/kmath/interpolation/SplineInterpolator.kt
View File

@ -23,7 +23,7 @@ import space.kscience.kmath.structures.MutableBufferFactory
*/
public class SplineInterpolator<T : Comparable<T>>(
override val algebra: Field<T>,
public val bufferFactory: MutableBufferFactory<T>,
public val bufferFactory: MutableBufferFactory<T> = algebra.bufferFactory,
) : PolynomialInterpolator<T> {
//TODO possibly optimize zeroed buffers

7
kmath-functions/src/commonTest/kotlin/space/kscience/kmath/functions/testUtils/IntModulo.kt
View File

@ -9,6 +9,7 @@ package space.kscience.kmath.functions.testUtils
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.structures.MutableBufferFactory
class IntModulo {
@ -109,15 +110,17 @@ class IntModulo {
}
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE")
class IntModuloRing : Ring<IntModulo>, ScaleOperations<IntModulo> {
class IntModuloRing(modulus: Int) : Ring<IntModulo>, ScaleOperations<IntModulo> {
val modulus: Int
constructor(modulus: Int) {
init {
require(modulus != 0) { "modulus can not be zero" }
this.modulus = if (modulus < 0) -modulus else modulus
}
override val bufferFactory: MutableBufferFactory<IntModulo> = MutableBufferFactory()
override inline val zero: IntModulo get() = IntModulo(0, modulus, toCheckInput = false)
override inline val one: IntModulo get() = IntModulo(1, modulus, toCheckInput = false)

2
kmath-functions/src/commonTest/kotlin/space/kscience/kmath/functions/testUtils/Rational.kt
View File

@ -10,6 +10,7 @@ package space.kscience.kmath.functions.testUtils
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.operations.Field
import space.kscience.kmath.operations.NumbersAddOps
import space.kscience.kmath.structures.MutableBufferFactory
@Suppress("NAME_SHADOWING")
class Rational {
@ -159,6 +160,7 @@ class Rational {
@Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE")
@OptIn(UnstableKMathAPI::class)
object RationalField : Field<Rational>, NumbersAddOps<Rational> {
override val bufferFactory: MutableBufferFactory<Rational> = MutableBufferFactory()
override inline val zero: Rational get() = Rational.ZERO
override inline val one: Rational get() = Rational.ONE

10
kmath-functions/src/commonTest/kotlin/space/kscience/kmath/integration/SplineIntegralTest.kt
View File

@ -18,15 +18,15 @@ import kotlin.test.assertEquals
class SplineIntegralTest {
@Test
fun integratePolynomial(){
fun integratePolynomial() {
val polynomial = Polynomial(1.0, 2.0, 3.0)
val integral = polynomial.integrate(Float64Field,1.0..2.0)
val integral = polynomial.integrate(Float64Field, 1.0..2.0)
assertEquals(11.0, integral, 0.001)
}
@Test
fun gaussSin() {
val res = Float64Field.splineIntegrator.integrate(0.0..2 * PI, IntegrandMaxCalls(5)) { x ->
val res = Float64Field.splineIntegrator.integrate(0.0..2 * PI, { IntegrandMaxCalls(5) }) { x ->
sin(x)
}
assertEquals(0.0, res.value, 1e-2)
@ -34,8 +34,8 @@ class SplineIntegralTest {
@Test
fun gaussUniform() {
val res = Float64Field.splineIntegrator.integrate(35.0..100.0, IntegrandMaxCalls(20)) { x ->
if(x in 30.0..50.0){
val res = Float64Field.splineIntegrator.integrate(35.0..100.0, { IntegrandMaxCalls(20) }) { x ->
if (x in 30.0..50.0) {
1.0
} else {
0.0

12
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Line.kt
View File

@ -7,8 +7,8 @@ package space.kscience.kmath.geometry
import kotlinx.serialization.SerialName
import kotlinx.serialization.Serializable
import space.kscience.kmath.geometry.euclidean2d.DoubleVector2D
import space.kscience.kmath.geometry.euclidean3d.DoubleVector3D
import space.kscience.kmath.geometry.euclidean2d.Float64Vector2D
import space.kscience.kmath.geometry.euclidean3d.Float64Vector3D
/**
* A line formed by [start] vector of start and a [direction] vector. Direction vector is not necessarily normalized,
@ -25,8 +25,8 @@ private data class LineImpl<out V : Any>(override val start: V, override val dir
public fun <V : Any> Line(base: V, direction: V): Line<V> = LineImpl(base, direction)
public typealias Line2D = Line<DoubleVector2D>
public typealias Line3D = Line<DoubleVector3D>
public typealias Line2D = Line<Float64Vector2D>
public typealias Line3D = Line<Float64Vector3D>
/**
* A directed line segment between [begin] and [end]
@ -49,5 +49,5 @@ public fun <V : Any> LineSegment<V>.line(algebra: GeometrySpace<V, *>): Line<V>
Line(begin, end - begin)
}
public typealias LineSegment2D = LineSegment<DoubleVector2D>
public typealias LineSegment3D = LineSegment<DoubleVector3D>
public typealias LineSegment2D = LineSegment<Float64Vector2D>
public typealias LineSegment3D = LineSegment<Float64Vector3D>

4
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/Vector3D.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.geometry
import space.kscience.attributes.SafeType
import space.kscience.kmath.linear.Point
import space.kscience.kmath.structures.Buffer
@ -33,6 +34,9 @@ public fun <T> Buffer<T>.asVector3D(): Vector3D<T> = object : Vector3D<T> {
require(this@asVector3D.size == 3) { "Buffer of size 3 is required for Vector3D" }
}
override val type: SafeType<T> = this@asVector3D.type
override val x: T get() = this@asVector3D[0]
override val y: T get() = this@asVector3D[1]
override val z: T get() = this@asVector3D[2]

34
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/angles.kt
View File

@ -11,6 +11,8 @@ import kotlinx.serialization.builtins.serializer
import kotlinx.serialization.descriptors.SerialDescriptor
import kotlinx.serialization.encoding.Decoder
import kotlinx.serialization.encoding.Encoder
import space.kscience.kmath.operations.Group
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.jvm.JvmInline
import kotlin.math.PI
import kotlin.math.floor
@ -28,11 +30,19 @@ public sealed interface Angle : Comparable<Angle> {
public operator fun div(other: Angle): Double
public operator fun unaryMinus(): Angle
public companion object {
public val zero: Radians = Radians(0.0)
public companion object: Group<Angle> {
override val zero: Radians = Radians(0.0)
public val pi: Radians = Radians(PI)
public val piTimes2: Radians = Radians(PI * 2)
public val piDiv2: Radians = Radians(PI / 2)
override fun add(left: Angle, right: Angle): Angle = left + right
@Suppress("EXTENSION_SHADOWED_BY_MEMBER")
override fun Angle.unaryMinus(): Angle = -this
override val bufferFactory: MutableBufferFactory<Angle> = MutableBufferFactory()
}
}
@ -43,7 +53,7 @@ public object AngleSerializer : KSerializer<Angle> {
override fun deserialize(decoder: Decoder): Angle = decoder.decodeDouble().degrees
override fun serialize(encoder: Encoder, value: Angle) {
encoder.encodeDouble(value.degrees)
encoder.encodeDouble(value.toDegrees().value)
}
}
@ -56,16 +66,16 @@ public value class Radians(public val value: Double) : Angle {
override fun toRadians(): Radians = this
override fun toDegrees(): Degrees = Degrees(value * 180 / PI)
public override fun plus(other: Angle): Radians = Radians(value + other.radians)
public override fun minus(other: Angle): Radians = Radians(value - other.radians)
public override fun plus(other: Angle): Radians = Radians(value + other.toRadians().value)
public override fun minus(other: Angle): Radians = Radians(value - other.toRadians().value)
public override fun times(other: Number): Radians = Radians(value * other.toDouble())
public override fun div(other: Number): Radians = Radians(value / other.toDouble())
override fun div(other: Angle): Double = value / other.radians
override fun div(other: Angle): Double = value / other.toRadians().value
public override fun unaryMinus(): Radians = Radians(-value)
override fun compareTo(other: Angle): Int = value.compareTo(other.radians)
override fun compareTo(other: Angle): Int = value.compareTo(other.toRadians().value)
}
public fun sin(angle: Angle): Double = kotlin.math.sin(angle.toRadians().value)
@ -85,16 +95,16 @@ public value class Degrees(public val value: Double) : Angle {
override fun toRadians(): Radians = Radians(value * PI / 180)
override fun toDegrees(): Degrees = this
public override fun plus(other: Angle): Degrees = Degrees(value + other.degrees)
public override fun minus(other: Angle): Degrees = Degrees(value - other.degrees)
public override fun plus(other: Angle): Degrees = Degrees(value + other.toDegrees().value)
public override fun minus(other: Angle): Degrees = Degrees(value - other.toDegrees().value)
public override fun times(other: Number): Degrees = Degrees(value * other.toDouble())
public override fun div(other: Number): Degrees = Degrees(value / other.toDouble())
override fun div(other: Angle): Double = value / other.degrees
override fun div(other: Angle): Double = value / other.toDegrees().value
public override fun unaryMinus(): Degrees = Degrees(-value)
override fun compareTo(other: Angle): Int = value.compareTo(other.degrees)
override fun compareTo(other: Angle): Int = value.compareTo(other.toDegrees().value)
}
public val Number.degrees: Degrees get() = Degrees(toDouble())
@ -106,6 +116,6 @@ public val Angle.degrees: Double get() = toDegrees().value
* Normalized angle 2 PI range symmetric around [center]. By default, uses (0, 2PI) range.
*/
public fun Angle.normalized(center: Angle = Angle.pi): Angle =
this - Angle.piTimes2 * floor((radians + PI - center.radians) / PI / 2)
this - Angle.piTimes2 * floor((toRadians().value + PI - center.toRadians().value) / PI / 2)
public fun abs(angle: Angle): Angle = if (angle < Angle.zero) -angle else angle

9
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean2d/Float32Space2D.kt
View File

@ -11,19 +11,22 @@ import kotlinx.serialization.Serializable
import kotlinx.serialization.descriptors.SerialDescriptor
import kotlinx.serialization.encoding.Decoder
import kotlinx.serialization.encoding.Encoder
import space.kscience.attributes.SafeType
import space.kscience.kmath.geometry.GeometrySpace
import space.kscience.kmath.geometry.Vector2D
import space.kscience.kmath.operations.Float32Field
import space.kscience.kmath.structures.Float32
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.math.pow
import kotlin.math.sqrt
@Serializable(Float32Space2D.VectorSerializer::class)
public interface Float32Vector2D : Vector2D<Float>
public interface Float32Vector2D : Vector2D<Float32>{
override val type: SafeType<Float32> get() = Float32Field.type
}
public object Float32Space2D : GeometrySpace<Float32Vector2D, Float32> {
@Serializable
@SerialName("Float32Vector2D")
private data class Vector2DImpl(
@ -72,6 +75,8 @@ public object Float32Space2D : GeometrySpace<Float32Vector2D, Float32> {
public val yAxis: Float32Vector2D = vector(0.0, 1.0)
override val defaultPrecision: Float32 = 1e-3f
override val bufferFactory: MutableBufferFactory<Float32Vector2D> = MutableBufferFactory()
}
public fun Float32Vector2D(x: Number, y: Number): Float32Vector2D = Float32Space2D.vector(x, y)

49
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean2d/Float64Space2D.kt
View File

@ -11,66 +11,77 @@ import kotlinx.serialization.Serializable
import kotlinx.serialization.descriptors.SerialDescriptor
import kotlinx.serialization.encoding.Decoder
import kotlinx.serialization.encoding.Encoder
import space.kscience.attributes.SafeType
import space.kscience.kmath.geometry.GeometrySpace
import space.kscience.kmath.geometry.Vector2D
import space.kscience.kmath.linear.Float64LinearSpace
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.math.pow
import kotlin.math.sqrt
public typealias DoubleVector2D = Vector2D<Double>
public typealias Float64Vector2D = Vector2D<Double>
@Serializable(Float64Space2D.VectorSerializer::class)
public interface Float64Vector2D : Vector2D<Float64> {
override val type: SafeType<Float64> get() = Float64Field.type
}
@Deprecated("Use Float64Vector2D", ReplaceWith("Float64Vector2D"))
public typealias DoubleVector2D = Float64Vector2D
public val Vector2D<Double>.r: Double get() = Float64Space2D.norm(this)
/**
* 2D Euclidean space
*/
public object Float64Space2D : GeometrySpace<DoubleVector2D>, ScaleOperations<DoubleVector2D> {
public object Float64Space2D : GeometrySpace<Float64Vector2D, Float64>, ScaleOperations<Float64Vector2D> {
@Serializable
@SerialName("Float64Vector2D")
private data class Vector2DImpl(
override val x: Double,
override val y: Double,
) : DoubleVector2D
) : Float64Vector2D
public object VectorSerializer : KSerializer<DoubleVector2D> {
public object VectorSerializer : KSerializer<Float64Vector2D> {
private val proxySerializer = Vector2DImpl.serializer()
override val descriptor: SerialDescriptor get() = proxySerializer.descriptor
override fun deserialize(decoder: Decoder): DoubleVector2D = decoder.decodeSerializableValue(proxySerializer)
override fun deserialize(decoder: Decoder): Float64Vector2D = decoder.decodeSerializableValue(proxySerializer)
override fun serialize(encoder: Encoder, value: DoubleVector2D) {
override fun serialize(encoder: Encoder, value: Float64Vector2D) {
val vector = value as? Vector2DImpl ?: Vector2DImpl(value.x, value.y)
encoder.encodeSerializableValue(proxySerializer, vector)
}
}
public fun vector(x: Number, y: Number): DoubleVector2D = Vector2DImpl(x.toDouble(), y.toDouble())
public fun vector(x: Number, y: Number): Float64Vector2D = Vector2DImpl(x.toDouble(), y.toDouble())
override val zero: DoubleVector2D by lazy { vector(0.0, 0.0) }
override val zero: Float64Vector2D by lazy { vector(0.0, 0.0) }
override fun norm(arg: DoubleVector2D): Double = sqrt(arg.x.pow(2) + arg.y.pow(2))
override fun norm(arg: Float64Vector2D): Double = sqrt(arg.x.pow(2) + arg.y.pow(2))
override fun DoubleVector2D.unaryMinus(): DoubleVector2D = vector(-x, -y)
override fun Float64Vector2D.unaryMinus(): Float64Vector2D = vector(-x, -y)
override fun DoubleVector2D.distanceTo(other: DoubleVector2D): Double = norm(this - other)
override fun add(left: DoubleVector2D, right: DoubleVector2D): DoubleVector2D =
override fun Float64Vector2D.distanceTo(other: Float64Vector2D): Double = norm(this - other)
override fun add(left: Float64Vector2D, right: Float64Vector2D): Float64Vector2D =
vector(left.x + right.x, left.y + right.y)
override fun scale(a: DoubleVector2D, value: Double): DoubleVector2D = vector(a.x * value, a.y * value)
override fun DoubleVector2D.dot(other: DoubleVector2D): Double = x * other.x + y * other.y
override fun scale(a: Float64Vector2D, value: Double): Float64Vector2D = vector(a.x * value, a.y * value)
override fun Float64Vector2D.dot(other: Float64Vector2D): Double = x * other.x + y * other.y
public val xAxis: DoubleVector2D = vector(1.0, 0.0)
public val yAxis: DoubleVector2D = vector(0.0, 1.0)
public val xAxis: Float64Vector2D = vector(1.0, 0.0)
public val yAxis: Float64Vector2D = vector(0.0, 1.0)
override val defaultPrecision: Double = 1e-6
override val bufferFactory: MutableBufferFactory<Float64Vector2D> = MutableBufferFactory()
}
public fun Float64Vector2D(x: Number, y: Number): Float64Vector2D = Float64Space2D.vector(x, y)
public val Float64Vector2D.r: Float64 get() = Float64Space2D.norm(this)
public val Float64Field.euclidean2D: Float64Space2D get() = Float64Space2D

8
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean3d/Float32Space3D.kt
View File

@ -11,15 +11,19 @@ import kotlinx.serialization.Serializable
import kotlinx.serialization.descriptors.SerialDescriptor
import kotlinx.serialization.encoding.Decoder
import kotlinx.serialization.encoding.Encoder
import space.kscience.attributes.SafeType
import space.kscience.kmath.geometry.GeometrySpace
import space.kscience.kmath.geometry.Vector3D
import space.kscience.kmath.operations.Float32Field
import space.kscience.kmath.structures.Float32
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.math.pow
import kotlin.math.sqrt
@Serializable(Float32Space3D.VectorSerializer::class)
public interface Float32Vector3D : Vector3D<Float>
public interface Float32Vector3D : Vector3D<Float>{
override val type: SafeType<Float32> get() = Float32Field.type
}
public object Float32Space3D : GeometrySpace<Float32Vector3D, Float32> {
@ -101,6 +105,8 @@ public object Float32Space3D : GeometrySpace<Float32Vector3D, Float32> {
public val zAxis: Float32Vector3D = vector(0.0, 0.0, 1.0)
override val defaultPrecision: Float32 = 1e-3f
override val bufferFactory: MutableBufferFactory<Float32Vector3D> = MutableBufferFactory()
}
public fun Float32Vector3D(x: Number, y: Number, z: Number): Float32Vector3D = Float32Space3D.vector(x, y, z)

62
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean3d/Float64Space3D.kt
View File

@ -11,10 +11,13 @@ import kotlinx.serialization.Serializable
import kotlinx.serialization.descriptors.SerialDescriptor
import kotlinx.serialization.encoding.Decoder
import kotlinx.serialization.encoding.Encoder
import space.kscience.attributes.SafeType
import space.kscience.kmath.geometry.GeometrySpace
import space.kscience.kmath.geometry.Vector3D
import space.kscience.kmath.linear.Float64LinearSpace
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.math.pow
import kotlin.math.sqrt
@ -31,12 +34,16 @@ internal fun leviCivita(i: Int, j: Int, k: Int): Int = when {
else -> 0
}
public typealias DoubleVector3D = Vector3D<Double>
public typealias Float64Vector3D = Vector3D<Double>
@Serializable(Float64Space3D.VectorSerializer::class)
public interface Float64Vector3D : Vector3D<Float64> {
override val type: SafeType<Float64> get() = Float64Field.type
}
public val DoubleVector3D.r: Double get() = Float64Space3D.norm(this)
@Deprecated("Use Float64Vector3D", ReplaceWith("Float64Vector3D"))
public typealias DoubleVector3D = Float64Vector3D
public object Float64Space3D : GeometrySpace<DoubleVector3D, Double>{
public object Float64Space3D : GeometrySpace<Vector3D<Float64>, Double> {
public val linearSpace: Float64LinearSpace = Float64LinearSpace
@ -46,52 +53,52 @@ public object Float64Space3D : GeometrySpace<DoubleVector3D, Double>{
override val x: Double,
override val y: Double,
override val z: Double,
) : DoubleVector3D
) : Float64Vector3D
public object VectorSerializer : KSerializer<DoubleVector3D> {
public object VectorSerializer : KSerializer<Float64Vector3D> {
private val proxySerializer = Vector3DImpl.serializer()
override val descriptor: SerialDescriptor get() = proxySerializer.descriptor
override fun deserialize(decoder: Decoder): DoubleVector3D = decoder.decodeSerializableValue(proxySerializer)
override fun deserialize(decoder: Decoder): Float64Vector3D = decoder.decodeSerializableValue(proxySerializer)
override fun serialize(encoder: Encoder, value: DoubleVector3D) {
override fun serialize(encoder: Encoder, value: Float64Vector3D) {
val vector = value as? Vector3DImpl ?: Vector3DImpl(value.x, value.y, value.z)
encoder.encodeSerializableValue(proxySerializer, vector)
}
}
public fun vector(x: Double, y: Double, z: Double): DoubleVector3D =
public fun vector(x: Double, y: Double, z: Double): Float64Vector3D =
Vector3DImpl(x, y, z)
public fun vector(x: Number, y: Number, z: Number): DoubleVector3D =
public fun vector(x: Number, y: Number, z: Number): Float64Vector3D =
vector(x.toDouble(), y.toDouble(), z.toDouble())
override val zero: DoubleVector3D by lazy { vector(0.0, 0.0, 0.0) }
override val zero: Float64Vector3D by lazy { vector(0.0, 0.0, 0.0) }
override fun norm(arg: DoubleVector3D): Double = sqrt(arg.x.pow(2) + arg.y.pow(2) + arg.z.pow(2))
override fun norm(arg: Vector3D<Float64>): Double = sqrt(arg.x.pow(2) + arg.y.pow(2) + arg.z.pow(2))
public fun DoubleVector3D.norm(): Double = norm(this)
public fun Vector3D<Float64>.norm(): Double = norm(this)
override fun DoubleVector3D.unaryMinus(): DoubleVector3D = vector(-x, -y, -z)
override fun Vector3D<Float64>.unaryMinus(): Float64Vector3D = vector(-x, -y, -z)
override fun DoubleVector3D.distanceTo(other: DoubleVector3D): Double = (this - other).norm()
override fun Vector3D<Float64>.distanceTo(other: Vector3D<Float64>): Double = (this - other).norm()
override fun add(left: DoubleVector3D, right: DoubleVector3D): DoubleVector3D =
override fun add(left: Vector3D<Float64>, right: Vector3D<Float64>): Float64Vector3D =
vector(left.x + right.x, left.y + right.y, left.z + right.z)
override fun scale(a: DoubleVector3D, value: Double): DoubleVector3D =
override fun scale(a: Vector3D<Float64>, value: Double): Float64Vector3D =
vector(a.x * value, a.y * value, a.z * value)
override fun DoubleVector3D.dot(other: DoubleVector3D): Double =
override fun Vector3D<Float64>.dot(other: Vector3D<Float64>): Double =
x * other.x + y * other.y + z * other.z
/**
* Compute vector product of [first] and [second]. The basis is assumed to be right-handed.
*/
public fun vectorProduct(
first: DoubleVector3D,
second: DoubleVector3D,
): DoubleVector3D {
first: Vector3D<Float64>,
second: Vector3D<Float64>,
): Float64Vector3D {
var x = 0.0
var y = 0.0
var z = 0.0
@ -110,13 +117,18 @@ public object Float64Space3D : GeometrySpace<DoubleVector3D, Double>{
/**
* Vector product with the right basis
*/
public infix fun DoubleVector3D.cross(other: DoubleVector3D): Vector3D<Double> = vectorProduct(this, other)
public infix fun Vector3D<Float64>.cross(other: Vector3D<Float64>): Vector3D<Double> = vectorProduct(this, other)
public val xAxis: DoubleVector3D = vector(1.0, 0.0, 0.0)
public val yAxis: DoubleVector3D = vector(0.0, 1.0, 0.0)
public val zAxis: DoubleVector3D = vector(0.0, 0.0, 1.0)
public val xAxis: Float64Vector3D = vector(1.0, 0.0, 0.0)
public val yAxis: Float64Vector3D = vector(0.0, 1.0, 0.0)
public val zAxis: Float64Vector3D = vector(0.0, 0.0, 1.0)
override val defaultPrecision: Double = 1e-6
override val bufferFactory: MutableBufferFactory<Vector3D<Float64>> = MutableBufferFactory()
}
public val Float64Field.euclidean3D: Float64Space3D get() = Float64Space3D
public val Float64Vector3D.r: Double get() = Float64Space3D.norm(this)

20
kmath-geometry/src/commonMain/kotlin/space/kscience/kmath/geometry/euclidean3d/rotations3D.kt
View File

@ -5,6 +5,7 @@
package space.kscience.kmath.geometry.euclidean3d
import space.kscience.attributes.SafeType
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.complex.*
import space.kscience.kmath.geometry.*
@ -13,6 +14,7 @@ import space.kscience.kmath.linear.Matrix
import space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.linear.matrix
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Float64
import kotlin.math.*
public operator fun Quaternion.times(other: Quaternion): Quaternion = QuaternionAlgebra.multiply(this, other)
@ -35,7 +37,7 @@ public infix fun Quaternion.dot(other: Quaternion): Double = w * other.w + x * o
/**
* Represent a vector as quaternion with zero a rotation angle.
*/
internal fun DoubleVector3D.asQuaternion(): Quaternion = Quaternion(0.0, x, y, z)
internal fun Float64Vector3D.asQuaternion(): Quaternion = Quaternion(0.0, x, y, z)
/**
* Angle in radians denoted by this quaternion rotation
@ -45,7 +47,7 @@ public val Quaternion.theta: Radians get() = (kotlin.math.acos(normalized().w) *
/**
* Create a normalized Quaternion from rotation angle and rotation vector
*/
public fun Quaternion.Companion.fromRotation(theta: Angle, vector: DoubleVector3D): Quaternion {
public fun Quaternion.Companion.fromRotation(theta: Angle, vector: Float64Vector3D): Quaternion {
val s = sin(theta / 2)
val c = cos(theta / 2)
val norm = with(Float64Space3D) { vector.norm() }
@ -55,9 +57,9 @@ public fun Quaternion.Companion.fromRotation(theta: Angle, vector: DoubleVector3
/**
* An axis of quaternion rotation
*/
public val Quaternion.vector: DoubleVector3D
public val Quaternion.vector: Float64Vector3D
get() {
return object : DoubleVector3D {
return object : Float64Vector3D {
private val sint2 = sqrt(1 - w * w)
override val x: Double get() = this@vector.x / sint2
override val y: Double get() = this@vector.y / sint2
@ -69,7 +71,7 @@ public val Quaternion.vector: DoubleVector3D
/**
* Rotate a vector in a [Float64Space3D] with [quaternion]
*/
public fun Float64Space3D.rotate(vector: DoubleVector3D, quaternion: Quaternion): DoubleVector3D =
public fun Float64Space3D.rotate(vector: Float64Vector3D, quaternion: Quaternion): Float64Vector3D =
with(QuaternionAlgebra) {
val p = vector.asQuaternion()
(quaternion * p * quaternion.reciprocal).vector
@ -80,15 +82,15 @@ public fun Float64Space3D.rotate(vector: DoubleVector3D, quaternion: Quaternion)
*/
@UnstableKMathAPI
public fun Float64Space3D.rotate(
vector: DoubleVector3D,
vector: Float64Vector3D,
composition: QuaternionAlgebra.() -> Quaternion,
): DoubleVector3D =
): Float64Vector3D =
rotate(vector, QuaternionAlgebra.composition())
/**
* Rotate a [Float64] vector in 3D space with a rotation matrix
*/
public fun Float64Space3D.rotate(vector: DoubleVector3D, matrix: Matrix<Double>): DoubleVector3D {
public fun Float64Space3D.rotate(vector: Float64Vector3D, matrix: Matrix<Double>): Vector3D<Float64> {
require(matrix.colNum == 3 && matrix.rowNum == 3) { "Square 3x3 rotation matrix is required" }
return with(linearSpace) { (matrix dot vector).asVector3D() }
}
@ -242,6 +244,8 @@ public fun Quaternion.Companion.fromEuler(
* A vector consisting of angles
*/
public data class AngleVector(override val x: Angle, override val y: Angle, override val z: Angle) : Vector3D<Angle> {
override val type: SafeType<Angle> get() = Angle.type
public companion object
}

7
kmath-geometry/src/commonTest/kotlin/space/kscience/kmath/geometry/testUtils.kt
View File

@ -5,8 +5,7 @@
package space.kscience.kmath.geometry
import space.kscience.kmath.geometry.euclidean2d.DoubleVector2D
import space.kscience.kmath.geometry.euclidean3d.DoubleVector3D
import space.kscience.kmath.structures.Float64
import kotlin.math.abs
import kotlin.test.assertEquals
@ -27,12 +26,12 @@ fun grid(
return xs.flatMap { x -> ys.map { y -> x to y } }
}
fun assertVectorEquals(expected: DoubleVector2D, actual: DoubleVector2D, absoluteTolerance: Double = 1e-3) {
fun assertVectorEquals(expected: Vector2D<Float64>, actual: Vector2D<Float64>, absoluteTolerance: Double = 1e-3) {
assertEquals(expected.x, actual.x, absoluteTolerance)
assertEquals(expected.y, actual.y, absoluteTolerance)
}
fun assertVectorEquals(expected: DoubleVector3D, actual: DoubleVector3D, absoluteTolerance: Double = 1e-6) {
fun assertVectorEquals(expected: Vector3D<Float64>, actual: Vector3D<Float64>, absoluteTolerance: Double = 1e-6) {
assertEquals(expected.x, actual.x, absoluteTolerance)
assertEquals(expected.y, actual.y, absoluteTolerance)
assertEquals(expected.z, actual.z, absoluteTolerance)

3
kmath-histograms/build.gradle.kts
View File

@ -6,6 +6,8 @@ kscience{
jvm()
js()
native()
wasm()
useCoroutines()
}
//apply(plugin = "kotlinx-atomicfu")
@ -21,7 +23,6 @@ kotlin.sourceSets {
dependencies {
implementation(project(":kmath-for-real"))
implementation(projects.kmath.kmathStat)
implementation("org.jetbrains.kotlinx:kotlinx-coroutines-test:1.6.0")
}
}
}

6
kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/Histogram.kt
View File

@ -47,7 +47,7 @@ public interface Histogram<in T : Any, out V, out B : Bin<T, V>> {
}
}
public interface HistogramBuilder<in T : Any, V : Any> {
public interface HistogramBuilder<in T, V> {
/**
* The default value increment for a bin
@ -61,9 +61,9 @@ public interface HistogramBuilder<in T : Any, V : Any> {
}
public fun <T : Any> HistogramBuilder<T, *>.put(point: Point<out T>): Unit = putValue(point)
public fun <T> HistogramBuilder<T, *>.put(point: Point<T>): Unit = putValue(point)
public fun <T : Any> HistogramBuilder<T, *>.put(vararg point: T): Unit = put(point.asBuffer())
public inline fun <reified T> HistogramBuilder<T, *>.put(vararg point: T): Unit = put(point.asList().asBuffer())
public fun HistogramBuilder<Double, *>.put(vararg point: Number): Unit =
put(Float64Buffer(point.map { it.toDouble() }.toDoubleArray()))

3
kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/UniformHistogram1D.kt
View File

@ -12,6 +12,7 @@ import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.MutableBufferFactory
import kotlin.math.floor
@OptIn(UnstableKMathAPI::class)
@ -46,6 +47,8 @@ public class UniformHistogram1DGroup<V : Any, A>(
public val startPoint: Double = 0.0,
) : Group<Histogram1D<Double, V>>, ScaleOperations<Histogram1D<Double, V>> where A : Ring<V>, A : ScaleOperations<V> {
override val bufferFactory: MutableBufferFactory<Histogram1D<Double, V>> = MutableBufferFactory()
override val zero: UniformHistogram1D<V> = UniformHistogram1D(this, emptyMap())
/**

31
kmath-histograms/src/commonMain/kotlin/space/kscience/kmath/histogram/UniformHistogramGroupND.kt
View File

@ -37,6 +37,8 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
require(!lower.indices.any { upper[it] - lower[it] < 0 }) { "Range for one of axis is not strictly positive" }
}
override val bufferFactory: MutableBufferFactory<HistogramND<Double, HyperSquareDomain, V>> = MutableBufferFactory()
public val dimension: Int get() = lower.size
override val shape: ShapeND = ShapeND(IntArray(binNums.size) { binNums[it] + 2 })
@ -87,7 +89,7 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
builder: HistogramBuilder<Double, V>.() -> Unit,
): HistogramND<Double, HyperSquareDomain, V> {
val ndCounter: BufferND<ObjectCounter<V>> =
StructureND.buffered(shape) { Counter.of(valueAlgebraND.elementAlgebra) }
BufferND(shape) { Counter.of(valueAlgebraND.elementAlgebra) }
val hBuilder = object : HistogramBuilder<Double, V> {
override val defaultValue: V get() = valueAlgebraND.elementAlgebra.one
@ -97,7 +99,8 @@ public class UniformHistogramGroupND<V : Any, A : Field<V>>(
}
}
hBuilder.apply(builder)
val values: BufferND<V> = BufferND(ndCounter.indices, ndCounter.buffer.mapToBuffer(valueBufferFactory) { it.value })
val values: BufferND<V> =
BufferND(ndCounter.indices, ndCounter.buffer.mapToBuffer(valueBufferFactory) { it.value })
return HistogramND(this, values)
}
@ -128,8 +131,7 @@ public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
public fun Histogram.Companion.uniformDoubleNDFromRanges(
vararg ranges: ClosedFloatingPointRange<Double>,
): UniformHistogramGroupND<Double, Float64Field> =
uniformNDFromRanges(Floa64FieldOpsND, *ranges, bufferFactory = ::Float64Buffer)
): UniformHistogramGroupND<Double, Float64Field> = uniformNDFromRanges(Floa64FieldOpsND, *ranges)
/**
@ -147,21 +149,18 @@ public fun <V : Any, A : Field<V>> Histogram.Companion.uniformNDFromRanges(
bufferFactory: BufferFactory<V> = valueAlgebraND.elementAlgebra.bufferFactory,
): UniformHistogramGroupND<V, A> = UniformHistogramGroupND(
valueAlgebraND,
DoubleBuffer(
ranges
.map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
.map(ClosedFloatingPointRange<Double>::start)
),
DoubleBuffer(
ranges
.map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
.map(ClosedFloatingPointRange<Double>::endInclusive)
),
ranges
.map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
.map(ClosedFloatingPointRange<Double>::start)
.asBuffer(),
ranges
.map(Pair<ClosedFloatingPointRange<Double>, Int>::first)
.map(ClosedFloatingPointRange<Double>::endInclusive)
.asBuffer(),
ranges.map(Pair<ClosedFloatingPointRange<Double>, Int>::second).toIntArray(),
valueBufferFactory = bufferFactory
)
public fun Histogram.Companion.uniformDoubleNDFromRanges(
vararg ranges: Pair<ClosedFloatingPointRange<Double>, Int>,
): UniformHistogramGroupND<Double, Float64Field> =
uniformNDFromRanges(Floa64FieldOpsND, *ranges, bufferFactory = ::Float64Buffer)
): UniformHistogramGroupND<Double, Float64Field> = uniformNDFromRanges(Floa64FieldOpsND, *ranges)

7
kmath-histograms/src/jvmMain/kotlin/space/kscience/kmath/histogram/TreeHistogramGroup.kt
View File

@ -14,10 +14,7 @@ import space.kscience.kmath.misc.sorted
import space.kscience.kmath.operations.Group
import space.kscience.kmath.operations.Ring
import space.kscience.kmath.operations.ScaleOperations
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.first
import space.kscience.kmath.structures.indices
import space.kscience.kmath.structures.last
import space.kscience.kmath.structures.*
import java.util.*
private fun <B : ClosedRange<Double>> TreeMap<Double, B>.getBin(value: Double): B? {
@ -53,6 +50,8 @@ public class TreeHistogramGroup<V : Any, A>(
@PublishedApi internal val binFactory: (Double) -> DoubleDomain1D,
) : Group<TreeHistogram<V>>, ScaleOperations<TreeHistogram<V>> where A : Ring<V>, A : ScaleOperations<V> {
override val bufferFactory: MutableBufferFactory<TreeHistogram<V>> = MutableBufferFactory()
internal inner class DomainCounter(val domain: DoubleDomain1D, val counter: Counter<V> = Counter.of(valueAlgebra)) :
ClosedRange<Double> by domain.range

3
kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt
View File

@ -7,6 +7,7 @@ package space.kscience.kmath.kotlingrad
import ai.hypergraph.kotlingrad.api.SFun
import ai.hypergraph.kotlingrad.api.SVar
import space.kscience.attributes.SafeType
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.NumericAlgebra
@ -25,6 +26,8 @@ public class KotlingradExpression<T : Number, A : NumericAlgebra<T>>(
public val algebra: A,
public val mst: MST,
) : SpecialDifferentiableExpression<T, KotlingradExpression<T, A>> {
override val type: SafeType<T> = algebra.type
override fun invoke(arguments: Map<Symbol, T>): T = mst.interpret(algebra, arguments)
override fun derivativeOrNull(

2
kmath-nd4j/build.gradle.kts
View File

@ -12,7 +12,7 @@ dependencies {
}
readme {
maturity = space.kscience.gradle.Maturity.EXPERIMENTAL
maturity = space.kscience.gradle.Maturity.DEPRECATED
propertyByTemplate("artifact", rootProject.file("docs/templates/ARTIFACT-TEMPLATE.md"))
feature(id = "nd4jarraystructure") { "NDStructure wrapper for INDArray" }
feature(id = "nd4jarrayrings") { "Rings over Nd4jArrayStructure of Int and Long" }

11
kmath-nd4j/src/main/kotlin/space/kscience/kmath/nd4j/Nd4jArrayStructure.kt
View File

@ -6,8 +6,10 @@
package space.kscience.kmath.nd4j
import org.nd4j.linalg.api.ndarray.INDArray
import space.kscience.attributes.SafeType
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.nd.*
import space.kscience.kmath.operations.Float32Field
/**
* Represents a [StructureND] wrapping an [INDArray] object.
@ -31,6 +33,7 @@ public sealed class Nd4jArrayStructure<T> : MutableStructureND<T> {
}
public data class Nd4jArrayIntStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Int>(), StructureNDOfInt {
override fun elementsIterator(): Iterator<Pair<IntArray, Int>> = ndArray.intIterator()
@OptIn(PerformancePitfall::class)
@ -47,8 +50,10 @@ public data class Nd4jArrayIntStructure(override val ndArray: INDArray) : Nd4jAr
*/
public fun INDArray.asIntStructure(): Nd4jArrayIntStructure = Nd4jArrayIntStructure(this)
public data class Nd4jArrayDoubleStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Double>(), StructureNDOfDouble {
public data class Nd4jArrayDoubleStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Double>(),
StructureNDOfDouble {
override fun elementsIterator(): Iterator<Pair<IntArray, Double>> = ndArray.realIterator()
@OptIn(PerformancePitfall::class)
override fun get(index: IntArray): Double = ndArray.getDouble(*index)
@ -64,7 +69,11 @@ public data class Nd4jArrayDoubleStructure(override val ndArray: INDArray) : Nd4
public fun INDArray.asDoubleStructure(): Nd4jArrayStructure<Double> = Nd4jArrayDoubleStructure(this)
public data class Nd4jArrayFloatStructure(override val ndArray: INDArray) : Nd4jArrayStructure<Float>() {
override val type: SafeType<Float> get() = Float32Field.type
override fun elementsIterator(): Iterator<Pair<IntArray, Float>> = ndArray.floatIterator()
@PerformancePitfall
override fun get(index: IntArray): Float = ndArray.getFloat(*index)

1
kmath-optimization/build.gradle.kts
View File

@ -6,6 +6,7 @@ kscience{
jvm()
js()
native()
wasm()
}
kotlin.sourceSets {

4
kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/FunctionOptimization.kt
View File

@ -11,6 +11,10 @@ import space.kscience.kmath.expressions.Symbol
public class OptimizationValue<V>(type: SafeType<V>) : PolymorphicAttribute<V>(type)
public inline fun <reified T> AttributesBuilder<FunctionOptimization<T>>.value(value: T) {
set(OptimizationValue(safeTypeOf<T>()), value)
}
public enum class OptimizationDirection {
MAXIMIZE,
MINIMIZE

8
kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationProblem.kt
View File

@ -34,10 +34,18 @@ public fun <T> AttributesBuilder<OptimizationProblem<T>>.startAt(startingPoint:
public class OptimizationCovariance<T> : OptimizationAttribute<NamedMatrix<T>>,
PolymorphicAttribute<NamedMatrix<T>>(safeTypeOf())
public fun <T> AttributesBuilder<OptimizationProblem<T>>.covariance(covariance: NamedMatrix<T>) {
set(OptimizationCovariance(),covariance)
}
public class OptimizationResult<T>() : OptimizationAttribute<Map<Symbol, T>>,
PolymorphicAttribute<Map<Symbol, T>>(safeTypeOf())
public fun <T> AttributesBuilder<OptimizationProblem<T>>.result(result: Map<Symbol, T>) {
set(OptimizationResult(), result)
}
public val <T> OptimizationProblem<T>.resultOrNull: Map<Symbol, T>? get() = attributes[OptimizationResult()]
public val <T> OptimizationProblem<T>.result: Map<Symbol, T>

6
kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/QowOptimizer.kt
View File

@ -264,10 +264,10 @@ public object QowOptimizer : Optimizer<Double, XYFit> {
qow = QoWeight(problem, res.freeParameters)
res = qow.newtonianRun(maxSteps = iterations)
}
val covariance = res.covariance()
return res.problem.withAttributes {
set(OptimizationResult(), res.freeParameters)
set(OptimizationCovariance(), covariance)
result(res.freeParameters)
covariance(res.covariance())
}
}
}

1
kmath-stat/build.gradle.kts
View File

@ -6,6 +6,7 @@ kscience{
jvm()
js()
native()
wasm()
}
kotlin.sourceSets {

2
kmath-stat/src/jvmTest/kotlin/space/kscience/kmath/stat/MCScopeTest.kt
View File

@ -68,7 +68,7 @@ internal class MCScopeTest {
}
@OptIn(DelicateCoroutinesApi::class)
@OptIn(DelicateCoroutinesApi::class, ExperimentalCoroutinesApi::class)
fun compareResult(test: ATest) {
val res1 = runBlocking(Dispatchers.Default) { test() }
val res2 = runBlocking(newSingleThreadContext("test")) { test() }

5
kmath-symja/src/main/kotlin/space/kscience/kmath/symja/SymjaExpression.kt
View File

@ -7,8 +7,9 @@ package space.kscience.kmath.symja
import org.matheclipse.core.eval.ExprEvaluator
import org.matheclipse.core.expression.F
import space.kscience.kmath.expressions.SpecialDifferentiableExpression
import space.kscience.attributes.SafeType
import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.SpecialDifferentiableExpression
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.expressions.interpret
import space.kscience.kmath.operations.NumericAlgebra
@ -30,6 +31,8 @@ public class SymjaExpression<T : Number, A : NumericAlgebra<T>>(
public val mst: MST,
public val evaluator: ExprEvaluator = DEFAULT_EVALUATOR,
) : SpecialDifferentiableExpression<T, SymjaExpression<T, A>> {
override val type: SafeType<T> get() = algebra.type
override fun invoke(arguments: Map<Symbol, T>): T = mst.interpret(algebra, arguments)
override fun derivativeOrNull(symbols: List<Symbol>): SymjaExpression<T, A> = SymjaExpression(

3
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/DoubleTensorFlowAlgebra.kt
View File

@ -10,6 +10,7 @@ import org.tensorflow.Output
import org.tensorflow.ndarray.NdArray
import org.tensorflow.op.core.Constant
import org.tensorflow.types.TFloat64
import space.kscience.attributes.SafeType
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.UnstableKMathAPI
import space.kscience.kmath.expressions.Symbol
@ -25,9 +26,9 @@ public class DoubleTensorFlowOutput(
graph: Graph,
output: Output<TFloat64>,
) : TensorFlowOutput<Double, TFloat64>(graph, output) {
override val type: SafeType<Double> get() = Float64Field.type
override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Double> = this as TFloat64
}
internal fun ShapeND.toLongArray(): LongArray = LongArray(size) { get(it).toLong() }

10
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/IntTensorFlowAlgebra.kt
View File

@ -10,11 +10,19 @@ import org.tensorflow.Output
import org.tensorflow.ndarray.NdArray
import org.tensorflow.types.TInt32
import org.tensorflow.types.TInt64
import space.kscience.attributes.SafeType
import space.kscience.kmath.operations.Int32Ring
import space.kscience.kmath.operations.Int64Ring
import space.kscience.kmath.structures.Int32
import space.kscience.kmath.structures.Int64
public class IntTensorFlowOutput(
graph: Graph,
output: Output<TInt32>,
) : TensorFlowOutput<Int, TInt32>(graph, output) {
override val type: SafeType<Int32> get() = Int32Ring.type
override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Int> = this as TInt32
}
@ -22,5 +30,7 @@ public class LongTensorFlowOutput(
graph: Graph,
output: Output<TInt64>,
) : TensorFlowOutput<Long, TInt64>(graph, output) {
override val type: SafeType<Int64> get() = Int64Ring.type
override fun org.tensorflow.Tensor.actualizeTensor(): NdArray<Long> = this as TInt64
}

7
kmath-tensorflow/src/main/kotlin/space/kscience/kmath/tensorflow/TensorFlowAlgebra.kt
View File

@ -17,6 +17,7 @@ import org.tensorflow.op.core.*
import org.tensorflow.types.TInt32
import org.tensorflow.types.family.TNumber
import org.tensorflow.types.family.TType
import space.kscience.attributes.SafeType
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.UnsafeKMathAPI
import space.kscience.kmath.UnstableKMathAPI
@ -39,8 +40,8 @@ public sealed interface TensorFlowTensor<T> : Tensor<T>
/**
* Static (eager) in-memory TensorFlow tensor
*/
@JvmInline
public value class TensorFlowArray<T>(public val tensor: NdArray<T>) : Tensor<T> {
public class TensorFlowArray<T>(override val type: SafeType<T>, public val tensor: NdArray<T>) : Tensor<T> {
override val shape: ShapeND get() = ShapeND(tensor.shape().asArray().toIntArray())
@PerformancePitfall
@ -73,7 +74,7 @@ public abstract class TensorFlowOutput<T, TT : TType>(
internal val actualTensor by lazy {
Session(graph).use { session ->
TensorFlowArray(session.runner().fetch(output).run().first().actualizeTensor())
TensorFlowArray(type, session.runner().fetch(output).run().first().actualizeTensor())
}
}

1
kmath-tensors/build.gradle.kts
View File

@ -12,6 +12,7 @@ kscience{
}
}
native()
wasm()
dependencies {
api(projects.kmathCore)

10
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/Tensor.kt
View File

@ -1,10 +0,0 @@
/*
* 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.tensors.api
import space.kscience.kmath.nd.MutableStructureND
public typealias Tensor<T> = MutableStructureND<T>

3
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/TensorAlgebra.kt
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@ -5,11 +5,14 @@
package space.kscience.kmath.tensors.api
import space.kscience.kmath.nd.MutableStructureND
import space.kscience.kmath.nd.RingOpsND
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.nd.StructureND
import space.kscience.kmath.operations.Ring
public typealias Tensor<T> = MutableStructureND<T>
/**
* Algebra over a ring on [Tensor].
* For more information: https://proofwiki.org/wiki/Definition:Algebra_over_Ring

9
kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/LevenbergMarquardtAlgorithm.kt
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@ -6,7 +6,6 @@
package space.kscience.kmath.tensors.core
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.linear.transposed
import space.kscience.kmath.nd.*
import kotlin.math.abs
import kotlin.math.max
@ -139,7 +138,7 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
if (inputData.nargin < 5) {
weight = fromArray(
ShapeND(intArrayOf(1, 1)),
doubleArrayOf((inputData.realValues.transposed dot inputData.realValues).as1D()[0])
doubleArrayOf((inputData.realValues.transposed() dot inputData.realValues).as1D()[0])
).as2D()
}
@ -266,12 +265,12 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
settings.funcCalls += 1
// val tmp = deltaY.times(weight)
var X2Try = deltaY.as2D().transposed dot deltaY.times(weight) // Chi-squared error criteria
var X2Try = deltaY.as2D().transposed() dot deltaY.times(weight) // Chi-squared error criteria
val alpha = 1.0
if (updateType == 2) { // Quadratic
// One step of quadratic line update in the h direction for minimum X2
val alphaTensor = (jtWdy.transposed dot h) / ((X2Try - x2) / 2.0 + 2 * (jtWdy.transposed dot h))
val alphaTensor = (jtWdy.transposed() dot h) / ((X2Try - x2) / 2.0 + 2 * (jtWdy.transposed() dot h))
h = h dot alphaTensor
pTry = (p + h).as2D() // update only [idx] elements
pTry = smallestElementComparison(
@ -289,7 +288,7 @@ public fun DoubleTensorAlgebra.levenbergMarquardt(inputData: LMInput): LMResultI
) // residual error using p_try
settings.funcCalls += 1
X2Try = deltaY.as2D().transposed dot deltaY * weight // Chi-squared error criteria
X2Try = deltaY.as2D().transposed() dot deltaY * weight // Chi-squared error criteria
}
val rho = when (updateType) { // Nielsen

4
kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorBuffer.kt
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@ -6,12 +6,16 @@
package space.kscience.kmath.viktor
import org.jetbrains.bio.viktor.F64FlatArray
import space.kscience.attributes.SafeType
import space.kscience.kmath.operations.Float64Field
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.MutableBuffer
@Suppress("NOTHING_TO_INLINE", "OVERRIDE_BY_INLINE")
@JvmInline
public value class ViktorBuffer(public val flatArray: F64FlatArray) : MutableBuffer<Double> {
override val type: SafeType<Double> get() = Float64Field.type
override val size: Int
get() = flatArray.length

4
kmath-viktor/src/main/kotlin/space/kscience/kmath/viktor/ViktorStructureND.kt
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@ -6,13 +6,17 @@
package space.kscience.kmath.viktor
import org.jetbrains.bio.viktor.F64Array
import space.kscience.attributes.SafeType
import space.kscience.kmath.PerformancePitfall
import space.kscience.kmath.nd.ColumnStrides
import space.kscience.kmath.nd.MutableStructureND
import space.kscience.kmath.nd.ShapeND
import space.kscience.kmath.operations.Float64Field
@Suppress("OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
public class ViktorStructureND(public val f64Buffer: F64Array) : MutableStructureND<Double> {
override val type: SafeType<Double> get() = Float64Field.type
override val shape: ShapeND get() = ShapeND(f64Buffer.shape)
@OptIn(PerformancePitfall::class)